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2106.03904
|
Harshavardhan Kamarthi
|
Harshavardhan Kamarthi, Lingkai Kong, Alexander Rodr\'iguez, Chao
Zhang, B. Aditya Prakash
|
When in Doubt: Neural Non-Parametric Uncertainty Quantification for
Epidemic Forecasting
|
Accepted at NeurIPS 2021
| null | null | null |
cs.LG cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Accurate and trustworthy epidemic forecasting is an important problem that
has impact on public health planning and disease mitigation. Most existing
epidemic forecasting models disregard uncertainty quantification, resulting in
mis-calibrated predictions. Recent works in deep neural models for
uncertainty-aware time-series forecasting also have several limitations; e.g.
it is difficult to specify meaningful priors in Bayesian NNs, while methods
like deep ensembling are computationally expensive in practice. In this paper,
we fill this important gap. We model the forecasting task as a probabilistic
generative process and propose a functional neural process model called EPIFNP,
which directly models the probability density of the forecast value. EPIFNP
leverages a dynamic stochastic correlation graph to model the correlations
between sequences in a non-parametric way, and designs different stochastic
latent variables to capture functional uncertainty from different perspectives.
Our extensive experiments in a real-time flu forecasting setting show that
EPIFNP significantly outperforms previous state-of-the-art models in both
accuracy and calibration metrics, up to 2.5x in accuracy and 2.4x in
calibration. Additionally, due to properties of its generative process,EPIFNP
learns the relations between the current season and similar patterns of
historical seasons,enabling interpretable forecasts. Beyond epidemic
forecasting, the EPIFNP can be of independent interest for advancing principled
uncertainty quantification in deep sequential models for predictive analytics
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709252 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.03948
|
James P. Crutchfield
|
Samuel P. Loomis and Mark Cooper and James P. Crutchfield
|
Nonequilibrium Thermodynamics in Measuring Carbon Footprints:
Disentangling Structure and Artifact in Input-Output Accounting
|
14 pages, 5 figures; 1 appendix;
http://csc.ucdavis.edu/~cmg/compmech/pubs/netacam.htm
| null | null | null |
physics.soc-ph cond-mat.stat-mech cs.IT math.IT quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multiregional input-output (MRIO) tables, in conjunction with Leontief
analysis, are widely-used to assess the geographical distribution of carbon
emissions and the economic activities that cause them. Majorization, a tool
originating in economics that has found utility in statistical mechanics, can
provide insight into how Leontief analysis links disparities in emissions with
global income inequality. We examine Leontief analysis as a model, drawing out
similarities with modern nonequilibrium statistical mechanics. Paralleling the
physical concept of thermo-majorization, we define the concept of
eco-majorization and show it is a sufficient condition to determine the
directionality of embodied emission flows. Surprisingly, relatively small trade
deficits and a geographically heterogeneous emissions-per-dollar ratio greatly
increases the appearance of eco-majorization, regardless of any further content
in the MRIO tables used. Our results are bolstered by a statistical analysis of
null models of MRIO tables, based on data provided by the Global Trade
Aggregation Project9
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708855 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.04025
|
Taehun Kim
|
Taehun Kim, Jinseong Kim, Daijin Kim
|
SpaceMeshLab: Spatial Context Memoization and Meshgrid Atrous
Convolution Consensus for Semantic Segmentation
|
5 pages, 3 figures, 4 tables. To appear in the proceedings of the
28th IEEE International Conference on Image Processing (IEEE - ICIP),
September 19-22, 2021, Anchorage, Alaska, USA
| null |
10.1109/ICIP42928.2021.9506531
| null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Semantic segmentation networks adopt transfer learning from image
classification networks which occurs a shortage of spatial context information.
For this reason, we propose Spatial Context Memoization (SpaM), a bypassing
branch for spatial context by retaining the input dimension and constantly
communicating its spatial context and rich semantic information mutually with
the backbone network. Multi-scale context information for semantic segmentation
is crucial for dealing with diverse sizes and shapes of target objects in the
given scene. Conventional multi-scale context scheme adopts multiple effective
receptive fields by multiple dilation rates or pooling operations, but often
suffer from misalignment problem with respect to the target pixel. To this end,
we propose Meshgrid Atrous Convolution Consensus (MetroCon^2) which brings
multi-scale scheme into fine-grained multi-scale object context using
convolutions with meshgrid-like scattered dilation rates. SpaceMeshLab
(ResNet-101 + SpaM + MetroCon^2) achieves 82.0% mIoU in Cityscapes test and
53.5% mIoU on Pascal-Context validation set.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711819 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.04582
|
Emil J. Bergholtz
|
Marcus St{\aa}lhammar, Emil J. Bergholtz
|
Classification of Exceptional Nodal Topologies Protected by
$\mathcal{PT}$ Symmetry
|
Including supplementary material
|
Phys. Rev. B 104, L201104 (2021)
|
10.1103/PhysRevB.104.L201104
| null |
cond-mat.mes-hall physics.optics quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Exceptional degeneracies, at which both eigenvalues and eigenvectors
coalesce, and parity-time ($\mathcal{PT}$) symmetry, reflecting balanced gain
and loss in photonic systems, are paramount concepts in non-Hermitian systems.
We here complete the topological classification of exceptional nodal
degeneracies protected by $\mathcal{PT}$ symmetry in up to three dimensions and
provide simple example models whose exceptional nodal topologies include
previously overlooked possibilities such as second-order knotted surfaces of
arbitrary genus, third-order knots and fourth-order points.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711067 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.04633
|
John Michael Goddard Kallaugher
|
John Kallaugher
|
A Quantum Advantage for a Natural Streaming Problem
| null | null | null | null |
quant-ph cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data streaming, in which a large dataset is received as a "stream" of
updates, is an important model in the study of space-bounded computation.
Starting with the work of Le Gall [SPAA `06], it has been known that quantum
streaming algorithms can use asymptotically less space than their classical
counterparts for certain problems. However, so far, all known examples of
quantum advantages in streaming are for problems that are either specially
constructed for that purpose, or require many streaming passes over the input.
We give a one-pass quantum streaming algorithm for one of the best studied
problems in classical graph streaming - the triangle counting problem.
Almost-tight parametrized upper and lower bounds are known for this problem in
the classical setting; our algorithm uses polynomially less space in certain
regions of the parameter space, resolving a question posed by Jain and Nayak in
2014 on achieving quantum advantages for natural streaming problems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.04986
|
Tai-Yu Ma
|
Tai-Yu Ma and S\'ebastien Faye
|
Multistep Electric Vehicle Charging Station Occupancy Prediction using
Hybrid LSTM Neural Networks
| null | null | null | null |
cs.LG cs.NE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Public charging station occupancy prediction plays key importance in
developing a smart charging strategy to reduce electric vehicle (EV) operator
and user inconvenience. However, existing studies are mainly based on
conventional econometric or time series methodologies with limited accuracy. We
propose a new mixed long short-term memory neural network incorporating both
historical charging state sequences and time-related features for multistep
discrete charging occupancy state prediction. Unlike the existing LSTM
networks, the proposed model separates different types of features and handles
them differently with mixed neural network architecture. The model is compared
to a number of state-of-the-art machine learning and deep learning approaches
based on the EV charging data obtained from the open data portal of the city of
Dundee, UK. The results show that the proposed method produces very accurate
predictions (99.99% and 81.87% for 1 step (10 minutes) and 6 steps (1 hour)
ahead, respectively, and outperforms the benchmark approaches significantly
(+22.4% for one-step-ahead prediction and +6.2% for 6 steps ahead). A
sensitivity analysis is conducted to evaluate the impact of the model
parameters on prediction accuracy.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710998 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.05815
|
Fabio Saracco
|
Mattia Mattei, Guido Caldarelli, Tiziano Squartini and Fabio Saracco
|
Italian Twitter semantic network during the Covid-19 epidemic
|
29 pages, 11 figures
|
EPJ Data Science 10 (47) (2021)
|
10.1140/epjds/s13688-021-00301-x
| null |
cs.SI physics.data-an
|
http://creativecommons.org/licenses/by/4.0/
|
The Covid-19 pandemic has had a deep impact on the lives of the entire world
population, inducing a participated societal debate. As in other contexts, the
debate has been the subject of several d/misinformation campaigns; in a quite
unprecedented fashion, however, the presence of false information has seriously
put at risk the public health. In this sense, detecting the presence of
malicious narratives and identifying the kinds of users that are more prone to
spread them represent the first step to limit the persistence of the former
ones. In the present paper we analyse the semantic network observed on Twitter
during the first Italian lockdown (induced by the hashtags contained in
approximately 1.5 millions tweets published between the 23rd of March 2020 and
the 23rd of April 2020) and study the extent to which various discursive
communities are exposed to d/misinformation arguments. As observed in other
studies, the recovered discursive communities largely overlap with traditional
political parties, even if the debated topics concern different facets of the
management of the pandemic. Although the themes directly related to
d/misinformation are a minority of those discussed within our semantic
networks, their popularity is unevenly distributed among the various discursive
communities.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709177 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.06022
|
Martin Bauer
|
Martin Bauer
|
IoT Virtualization with ML-based Information Extraction
| null |
IEEE 7th World Forum on Internet of Things (WF-IoT), 2021, pp.
915-920
|
10.1109/WF-IoT51360.2021.9595119
| null |
cs.DC cs.IR
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
For IoT to reach its full potential, the sharing and reuse of information in
different applications and across verticals is of paramount importance.
However, there are a plethora of IoT platforms using different representations,
protocols and interaction patterns. To address this issue, the Fed4IoT project
has developed an IoT virtualization platform that, on the one hand, integrates
information from many different source platforms and, on the other hand, makes
the information required by the respective users available in the target
platform of choice. To enable this, information is translated into a common,
neutral exchange format. The format of choice is NGSI-LD, which is being
standardized by the ETSI Industry Specification Group on Context Information
Management (ETSI ISG CIM). Thing Visors are the components that translate the
source information to NGSI-LD, which is then delivered to the target platform
and translated into the target format. ThingVisors can be implemented by hand,
but this requires significant human effort, especially considering the
heterogeneity of low level information produced by a multitude of sensors.
Thus, supporting the human developer and, ideally, fully automating the process
of extracting and enriching data and translating it to NGSI-LD is a crucial
step. Machine learning is a promising approach for this, but it typically
requires large amounts of hand-labelled data for training, an effort that makes
it unrealistic in many IoT scenarios. A programmatic labelling approach called
knowledge infusion that encodes expert knowledge is used for matching a schema
or ontology extracted from the data with a target schema or ontology, providing
the basis for annotating the data and facilitating the translation to NGSI-LD.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709453 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.06026
|
Mina Dalirrooyfard
|
Mina Dalirrooyfard, Ray Li, Virginia Vassilevska Williams
|
Hardness of Approximate Diameter: Now for Undirected Graphs
| null | null | null | null |
cs.CC
|
http://creativecommons.org/licenses/by/4.0/
|
Approximating the graph diameter is a basic task of both theoretical and
practical interest. A simple folklore algorithm can output a 2-approximation to
the diameter in linear time by running BFS from an arbitrary vertex. It has
been open whether a better approximation is possible in near-linear time. A
series of papers on fine-grained complexity have led to strong hardness results
for diameter in directed graphs, culminating in a recent tradeoff curve
independently discovered by [Li, STOC'21] and [Dalirrooyfard and Wein,
STOC'21], showing that under the Strong Exponential Time Hypothesis (SETH), for
any integer $k\ge 2$ and $\delta>0$, a $2-\frac{1}{k}-\delta$ approximation for
diameter in directed $m$-edge graphs requires $mn^{1+1/(k-1)-o(1)}$ time. In
particular, the simple linear time $2$-approximation algorithm is optimal for
directed graphs.
In this paper we prove that the same tradeoff lower bound curve is possible
for undirected graphs as well, extending results of [Roditty and Vassilevska
W., STOC'13], [Li'20] and [Bonnet, ICALP'21] who proved the first few cases of
the curve, $k=2,3$ and $4$, respectively. Our result shows in particular that
the simple linear time $2$-approximation algorithm is also optimal for
undirected graphs. To obtain our result we develop new tools for fine-grained
reductions that could be useful for proving SETH-based hardness for other
problems in undirected graphs related to distance computation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708629 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.06960
|
Mengmeng Cui
|
Mengmeng Cui, Wei Wang, Jinjin Zhang, Liang Wang
|
Representation and Correlation Enhanced Encoder-Decoder Framework for
Scene Text Recognition
|
15 pages, 5 figures, 3 tables
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Attention-based encoder-decoder framework is widely used in the scene text
recognition task. However, for the current state-of-the-art(SOTA) methods,
there is room for improvement in terms of the efficient usage of local visual
and global context information of the input text image, as well as the robust
correlation between the scene processing module(encoder) and the text
processing module(decoder). In this paper, we propose a Representation and
Correlation Enhanced Encoder-Decoder Framework(RCEED) to address these
deficiencies and break performance bottleneck. In the encoder module, local
visual feature, global context feature, and position information are aligned
and fused to generate a small-size comprehensive feature map. In the decoder
module, two methods are utilized to enhance the correlation between scene and
text feature space. 1) The decoder initialization is guided by the holistic
feature and global glimpse vector exported from the encoder. 2) The feature
enriched glimpse vector produced by the Multi-Head General Attention is used to
assist the RNN iteration and the character prediction at each time step.
Meanwhile, we also design a Layernorm-Dropout LSTM cell to improve model's
generalization towards changeable texts. Extensive experiments on the
benchmarks demonstrate the advantageous performance of RCEED in scene text
recognition tasks, especially the irregular ones.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708824 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.08810
|
Milton Kumar Kundu
|
S. M. S. Shahriyer, A. S. M. Badrudduza, S. Shabab, M. K. Kundu, H. Yu
|
Opportunistic Relay in Multicast Channels with Generalized Shadowed
Fading Effects: A Physical Layer Security Perspective
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Through ordinary transmissions over wireless multicast networks are greatly
hampered due to the simultaneous presence of fading and shadowing of wireless
channels, secure transmissions can be enhanced by properly exploiting random
attributes of the propagation medium. This study focuses on the utilization of
those attributes to enhance the physical layer security (PLS) performance of a
dual-hop wireless multicast network over kappa-mu shadow-fading channel under
the wiretapping attempts of multiple eavesdroppers. In order to improve the
secrecy level, the best relay selection strategy among multiple relays is
employed. Performance analysis is carried out based on the mathematical
modeling in terms of analytical expressions of non-zero secrecy capacity
probability, secure outage probability, and ergodic secrecy capacity over
multicast relay networks. Capitalizing on those expressions, the effects of
system parameters, i.e., fading, shadowing, the number of antennas, destination
receivers, eavesdroppers, and relays, on the secrecy performance are
investigated. Numerical results show that the detrimental impacts caused by
fading and shadowing can be remarkably mitigated using the well-known
opportunistic relaying technique. Moreover, the proposed model unifies secrecy
analysis of several classical models, thereby exhibiting enormous versatility
than the existing works.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711437 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.10034
|
Dimitrios Tyrovolas
|
Dimitrios Tyrovolas, Sotiris A. Tegos, Panagiotis D. Diamantoulakis
and George K. Karagiannidis
|
Synergetic UAV-RIS Communication with Highly Directional Transmission
|
5 pages, 5 figures
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
The effective integration of unmanned aerial vehicles (UAVs) in future
wireless communication systems depends on the conscious use of their limited
energy, which constrains their flight time. Reconfigurable intelligent surfaces
(RISs) can be used in combination with UAVs with the aim to improve the
communication performance without increasing complexity at the UAV side. In
this paper, we propose a synergetic UAV RIS communication system, utilizing a
UAV with a highly directional antenna aiming to the RIS. The proposed scenario
can be applied in all air-to-ground RIS-assisted networks and numerical results
illustrate that it is superior from the cases where the UAV utilizes either an
omnidirectional antenna or a highly directional antenna aiming towards the
ground node.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712063 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.10064
|
Guillaume Bellec
|
Guillaume Bellec, Shuqi Wang, Alireza Modirshanechi, Johanni Brea,
Wulfram Gerstner
|
Fitting summary statistics of neural data with a differentiable spiking
network simulator
| null | null | null | null |
stat.ML cs.LG q-bio.NC
|
http://creativecommons.org/licenses/by/4.0/
|
Fitting network models to neural activity is an important tool in
neuroscience. A popular approach is to model a brain area with a probabilistic
recurrent spiking network whose parameters maximize the likelihood of the
recorded activity. Although this is widely used, we show that the resulting
model does not produce realistic neural activity. To correct for this, we
suggest to augment the log-likelihood with terms that measure the dissimilarity
between simulated and recorded activity. This dissimilarity is defined via
summary statistics commonly used in neuroscience and the optimization is
efficient because it relies on back-propagation through the stochastically
simulated spike trains. We analyze this method theoretically and show
empirically that it generates more realistic activity statistics. We find that
it improves upon other fitting algorithms for spiking network models like GLMs
(Generalized Linear Models) which do not usually rely on back-propagation. This
new fitting algorithm also enables the consideration of hidden neurons which is
otherwise notoriously hard, and we show that it can be crucial when trying to
infer the network connectivity from spike recordings.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710603 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.11879
|
Yoel Drori
|
Alon Cohen, Amit Daniely, Yoel Drori, Tomer Koren, Mariano Schain
|
Asynchronous Stochastic Optimization Robust to Arbitrary Delays
| null | null | null | null |
math.OC cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We consider stochastic optimization with delayed gradients where, at each
time step $t$, the algorithm makes an update using a stale stochastic gradient
from step $t - d_t$ for some arbitrary delay $d_t$. This setting abstracts
asynchronous distributed optimization where a central server receives gradient
updates computed by worker machines. These machines can experience computation
and communication loads that might vary significantly over time. In the general
non-convex smooth optimization setting, we give a simple and efficient
algorithm that requires $O( \sigma^2/\epsilon^4 + \tau/\epsilon^2 )$ steps for
finding an $\epsilon$-stationary point $x$, where $\tau$ is the \emph{average}
delay $\smash{\frac{1}{T}\sum_{t=1}^T d_t}$ and $\sigma^2$ is the variance of
the stochastic gradients. This improves over previous work, which showed that
stochastic gradient decent achieves the same rate but with respect to the
\emph{maximal} delay $\max_{t} d_t$, that can be significantly larger than the
average delay especially in heterogeneous distributed systems. Our experiments
demonstrate the efficacy and robustness of our algorithm in cases where the
delay distribution is skewed or heavy-tailed.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709177 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.14483
|
Azmi Can \"Ozgen
|
Azmi Can \"Ozgen, Mahiye Uluya\u{g}mur \"Ozt\"urk, Umut Bayraktar
|
Cheating Detection Pipeline for Online Interviews and Exams
| null | null | null | null |
cs.CV cs.AI cs.HC cs.LG cs.MM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Remote examination and job interviews have gained popularity and become
indispensable because of both pandemics and the advantage of remote working
circumstances. Most companies and academic institutions utilize these systems
for their recruitment processes and also for online exams. However, one of the
critical problems of the remote examination systems is conducting the exams in
a reliable environment. In this work, we present a cheating analysis pipeline
for online interviews and exams. The system only requires a video of the
candidate, which is recorded during the exam. Then cheating detection pipeline
is employed to detect another person, electronic device usage, and candidate
absence status. The pipeline consists of face detection, face recognition,
object detection, and face tracking algorithms. To evaluate the performance of
the pipeline we collected a private video dataset. The video dataset includes
both cheating activities and clean videos. Ultimately, our pipeline presents an
efficient and fast guideline to detect and analyze cheating activities in an
online interview and exam video.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.714603 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.16002
|
Ziqin Chen
|
Ziqin Chen, Ji Ma, Shu Liang, Li Li
|
Distributed Nash Equilibrium Seeking under Quantization Communication
|
8 pages, 5 figures
| null | null | null |
cs.DC math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper investigates Nash equilibrium (NE) seeking problems for
noncooperative games over multi-players networks with finite bandwidth
communication. A distributed quantized algorithm is presented, which consists
of local gradient play, distributed decision estimating, and adaptive
quantization. Exponential convergence of the algorithm is established, and a
relationship between the convergence rate and the bandwidth is quantitatively
analyzed. Finally, a simulation of an energy consumption game is presented to
validate the proposed results.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.16070
|
M\'arton Karsai
|
Gergely \'Odor, Domonkos Czifra, J\'ulia Komj\'athy, L\'aszl\'o
Lov\'asz and M\'arton Karsai
|
Switchover phenomenon induced by epidemic seeding on geometric networks
|
29 pages, 6 figures
|
Proceedings of the National Academy of Sciences Oct 2021, 118 (41)
e2112607118
|
10.1073/pnas.2112607118
| null |
physics.soc-ph physics.app-ph stat.AP
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
It is a fundamental question in disease modelling how the initial seeding of
an epidemic, spreading over a network, determines its final outcome. Research
in this topic has primarily concentrated on finding the seed configuration
which infects the most individuals. Although these optimal configurations give
insight into how the initial state affects the outcome of an epidemic, they are
unlikely to occur in real life. In this paper we identify two important seeding
scenarios, both motivated by historical data, that reveal a new complex
phenomenon. In one scenario, the seeds are concentrated on the central nodes of
a network, while in the second, they are spread uniformly in the population.
Comparing the final size of the epidemic started from these two initial
conditions through data-driven and synthetic simulations on real and modelled
geometric metapopulation networks, we find evidence for a switchover
phenomenon: When the basic reproduction number $R_0$ is close to its critical
value, more individuals become infected in the first seeding scenario, but for
larger values of $R_0$, the second scenario is more dangerous. We find that the
switchover phenomenon is amplified by the geometric nature of the underlying
network, and confirm our results via mathematically rigorous proofs, by mapping
the network epidemic processes to bond percolation. Our results expand on the
previous finding that in case of a single seed, the first scenario is always
more dangerous, and further our understanding why the sizes of consecutive
waves can differ even if their epidemic characters are similar.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71365 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.01740
|
Seyedmohammad Yusofsani
|
Seyedmohammad Yusofsani, Miroslav Kolesik
|
Beyond Fowler-Nordheim model: Harmonic generation from metallic
nano-structures
|
Eur. Phys. J. Spec. Top. (2021)
|
Eur. Phys. J. Spec. Top. (2021)
|
10.1140/epjs/s11734-021-00189-8
| null |
physics.atom-ph quant-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Metallic structures interacting with electromagnetic fields are known to
exhibit properties similar to those found in atoms and molecules, such as
multi-photon and tunnel ionization. Developing this similarity beyond the
electron emission current, we generalize the wellknown Fowler-Nordheim model,
and predict heretofore unrecognized source of nonlinear optical response from
nano-structures exposed to illumination with intense optical pulses.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.714784 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.02369
|
Jacinta Yap
|
J. S. L. Yap, N. J. S. Bal, A. Kacperek, J. Resta L\'opez, C. P.
Welsch
|
Medipix3 for dosimetry and real-time beam monitoring: first tests at a
60 MeV proton therapy facility
|
Revised. Prepared for submission to JINST as a Tech Report, 22 pages,
12 figures
|
JINST 2021 16 T11001
|
10.1088/1748-0221/16/11/T11001
|
T11001
|
physics.ins-det physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Charged particle therapy (CPT) is an advanced modality of radiation therapy
which has grown rapidly worldwide, driven by recent developments in technology
and methods of delivery. To ensure safe and high quality treatments, various
instruments are used for a range of different measurements such as for quality
assurance, monitoring and dosimetry purposes. With the emergence of new and
enhanced delivery techniques, systems with improved capabilities are needed to
exceed existing performance limitations of conventional tools. The Medipix3 is
a hybrid pixel detector able to count individual protons with millisecond time
resolution at clinical flux with near instant readout and count rate linearity.
The system has previously demonstrated use in medical and other applications,
showing wide versatility and potential for particle therapy. In this work we
present measurements of the Medipix3 detector in the 60 MeV ocular proton
therapy beamline at the Clatterbridge Cancer Centre, UK. The beam current and
lateral beam profiles were evaluated at multiple positions in the treatment
line and compared with EBT3 Gafchromic film. The recorded count rate linearity
and temporal analysis of the beam structure was measured with Medipix3 across
the full range of available beam intensities, up to $3.12 \times 10^{10}$
protons/s. We explore the capacity of Medipix3 to provide non-reference
measurements and its applicability as a tool for dosimetry and beam monitoring
for CPT. This is the first known time the performance of the Medipix3 detector
technology has been tested within a clinical, high proton flux environment.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.706589 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.02748
|
Shyan Akmal
|
Shyan Akmal and Ryan Williams
|
MAJORITY-3SAT (and Related Problems) in Polynomial Time
|
Abstract shortened to fit arXiv requirements
| null | null | null |
cs.CC cs.AI cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
Majority-SAT is the problem of determining whether an input $n$-variable
formula in conjunctive normal form (CNF) has at least $2^{n-1}$ satisfying
assignments. Majority-SAT and related problems have been studied extensively in
various AI communities interested in the complexity of probabilistic planning
and inference. Although Majority-SAT has been known to be PP-complete for over
40 years, the complexity of a natural variant has remained open:
Majority-$k$SAT, where the input CNF formula is restricted to have clause width
at most $k$.
We prove that for every $k$, Majority-$k$SAT is in P. In fact, for any
positive integer $k$ and rational $\rho \in (0,1)$ with bounded denominator, we
give an algorithm that can determine whether a given $k$-CNF has at least $\rho
\cdot 2^n$ satisfying assignments, in deterministic linear time (whereas the
previous best-known algorithm ran in exponential time). Our algorithms have
interesting positive implications for counting complexity and the complexity of
inference, significantly reducing the known complexities of related problems
such as E-MAJ-$k$SAT and MAJ-MAJ-$k$SAT. At the heart of our approach is an
efficient method for solving threshold counting problems by extracting
sunflowers found in the corresponding set system of a $k$-CNF.
We also show that the tractability of Majority-$k$SAT is somewhat fragile.
For the closely related GtMajority-SAT problem (where we ask whether a given
formula has greater than $2^{n-1}$ satisfying assignments) which is known to be
PP-complete, we show that GtMajority-$k$SAT is in P for $k\le 3$, but becomes
NP-complete for $k\geq 4$. These results are counterintuitive, because the
``natural'' classifications of these problems would have been PP-completeness,
and because there is a stark difference in the complexity of GtMajority-$k$SAT
and Majority-$k$SAT for all $k\ge 4$.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709422 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.02980
|
Yuanxin Zhong
|
Yuanxin Zhong, Minghan Zhu, Huei Peng
|
VIN: Voxel-based Implicit Network for Joint 3D Object Detection and
Segmentation for Lidars
|
To be presented at BMVC 2021
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A unified neural network structure is presented for joint 3D object detection
and point cloud segmentation in this paper. We leverage rich supervision from
both detection and segmentation labels rather than using just one of them. In
addition, an extension based on single-stage object detectors is proposed based
on the implicit function widely used in 3D scene and object understanding. The
extension branch takes the final feature map from the object detection module
as input, and produces an implicit function that generates semantic
distribution for each point for its corresponding voxel center. We demonstrated
the performance of our structure on nuScenes-lidarseg, a large-scale outdoor
dataset. Our solution achieves competitive results against state-of-the-art
methods in both 3D object detection and point cloud segmentation with little
additional computation load compared with object detection solutions. The
capability of efficient weakly supervision semantic segmentation of the
proposed method is also validated by experiments.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7114 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.05252
|
Jiacheng Liang
|
Jiacheng Liang, Songze Li, Bochuan Cao, Wensi Jiang, Chaoyang He
|
OmniLytics: A Blockchain-based Secure Data Market for Decentralized
Machine Learning
|
An initial version of the article has been published in International
Workshop on Federated Learning for User Privacy and Data Confidentiality in
Conjunction with ICML 2021(http://federated-learning.org/fl-icml-2021/). This
version has been submmited to AAAI'22
| null | null | null |
cs.CR cs.DC cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We propose OmniLytics, a blockchain-based secure data trading marketplace for
machine learning applications. Utilizing OmniLytics, many distributed data
owners can contribute their private data to collectively train an ML model
requested by some model owners, and receive compensation for data contribution.
OmniLytics enables such model training while simultaneously providing 1) model
security against curious data owners; 2) data security against the curious
model and data owners; 3) resilience to malicious data owners who provide
faulty results to poison model training; and 4) resilience to malicious model
owners who intend to evade payment. OmniLytics is implemented as a blockchain
smart contract to guarantee the atomicity of payment. In OmniLytics, a model
owner splits its model into the private and public parts and publishes the
public part on the contract. Through the execution of the contract, the
participating data owners securely aggregate their locally trained models to
update the model owner's public model and receive reimbursement through the
contract. We implement a working prototype of OmniLytics on Ethereum blockchain
and perform extensive experiments to measure its gas cost, execution time, and
model quality under various parameter combinations. For training a CNN on the
MNIST dataset, the MO is able to boost its model accuracy from 62% to 83%
within 500ms in blockchain processing time.This demonstrates the effectiveness
of OmniLytics for practical deployment.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713419 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.05838
|
Hang-Hyun Jo
|
Hang-Hyun Jo, Eun Lee, Young-Ho Eom
|
Analytical approach to the generalized friendship paradox in networks
with correlated attributes
|
10 pages, 5 figures
|
Physical Review E 104, 054301 (2021)
|
10.1103/PhysRevE.104.054301
| null |
physics.soc-ph cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the interesting phenomena due to the topological heterogeneities in
complex networks is the friendship paradox, stating that your friends have on
average more friends than you do. Recently, this paradox has been generalized
for arbitrary nodal attributes, called a generalized friendship paradox (GFP).
In this paper, we analyze the GFP for the networks in which the attributes of
neighboring nodes are correlated with each other. The correlation structure
between attributes of neighboring nodes is modeled by the
Farlie-Gumbel-Morgenstern copula, enabling us to derive approximate analytical
solutions of the GFP for three kinds of methods summarizing the neighborhood of
the focal node, i.e., mean-based, median-based, and fraction-based methods. The
analytical solutions are comparable to simulation results, while some
systematic deviations between them might be attributed to the higher-order
correlations between nodal attributes. These results help us get deeper insight
into how various summarization methods as well as the correlation structure of
nodal attributes affect the GFP behavior, hence better understand various
related phenomena in complex networks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711819 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.05950
|
David Wilkowski Dr
|
Athanasios Laliotis, Bing-Sui Lu, Martial Ducloy, David Wilkowski
|
Atom-surface physics: A review
|
Review paper 43 pages, 6 figures
|
AVS Quantum Sci. 3, 043501 (2021)
|
10.1116/5.0063701
| null |
physics.atom-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An atom in front of a surface is one of the simplest and fundamental problem
in physics. Yet, it allows testing quantum electrodynamics, while providing
potential platforms and interfaces for quantum technologies. Despite, its
simplicity, combined with strong scientific and technological interests,
atom-surface physics, at its fundamental level, remains largely unexplored
mainly because of challenges associated with precise control of the
atom-surface distance. Nevertheless, substantial breakthroughs have been made
over the last two decades. With the development of cold and quantum atomic
gases, one has gained further control on atom-surface position, naturally
leading to improved precision in the Casimir-Polder interaction measurement.
Advances have also been reported in finding experimental knobs to tune and even
reverse the Casimir-Polder interaction strength. So far, this has only been
achieved for atoms in short-lived excited states, however, the rapid progresses
in material sciences, e.g. metamaterials and topological materials have
inspired new ideas for controlling the atom-surface interaction in long-lived
states. In addition, combining nano-photonic and atom-surface physics is now
envisioned for applications in quantum information processing. The first
purpose of this review is to give a general overview on the latest experimental
developments in atom-surface physics. The second main objective is to sketch a
vision of the future of the field, mainly inspired by the abundant theoretical
works and proposals available now in the literature.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711606 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.06071
|
Dorien Herremans
|
Dorien Herremans
|
aiSTROM -- A roadmap for developing a successful AI strategy
| null |
IEEE Access, 2021
|
10.1109/ACCESS.2021.3127548
| null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
A total of 34% of AI research and development projects fails or are
abandoned, according to a recent survey by Rackspace Technology of 1,870
companies. We propose a new strategic framework, aiSTROM, that empowers
managers to create a successful AI strategy based on a thorough literature
review. This provides a unique and integrated approach that guides managers and
lead developers through the various challenges in the implementation process.
In the aiSTROM framework, we start by identifying the top n potential projects
(typically 3-5). For each of those, seven areas of focus are thoroughly
analysed. These areas include creating a data strategy that takes into account
unique cross-departmental machine learning data requirements, security, and
legal requirements. aiSTROM then guides managers to think about how to put
together an interdisciplinary artificial intelligence (AI) implementation team
given the scarcity of AI talent. Once an AI team strategy has been established,
it needs to be positioned within the organization, either cross-departmental or
as a separate division. Other considerations include AI as a service (AIaas),
or outsourcing development. Looking at new technologies, we have to consider
challenges such as bias, legality of black-box-models, and keeping humans in
the loop. Next, like any project, we need value-based key performance
indicators (KPIs) to track and validate the progress. Depending on the
company's risk-strategy, a SWOT analysis (strengths, weaknesses, opportunities,
and threats) can help further classify the shortlisted projects. Finally, we
should make sure that our strategy includes continuous education of employees
to enable a culture of adoption. This unique and comprehensive framework offers
a valuable, literature supported, tool for managers and lead developers.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709403 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.06200
|
Erin Carson
|
Eda Oktay and Erin Carson
|
Multistage Mixed Precision Iterative Refinement
|
30 pages
| null | null | null |
math.NA cs.NA
|
http://creativecommons.org/licenses/by/4.0/
|
Low precision arithmetic, in particular half precision floating point
arithmetic, is now available in commercial hardware. Using lower precision can
offer significant savings in computation and communication costs with
proportional savings in energy. Motivated by this, there has been a renewed
interest in mixed precision iterative refinement for solving linear systems
$Ax=b$, and new variants of GMRES-based iterative refinement have been
developed. Each particular variant with a given combination of precisions leads
to different condition number-based constraints for convergence of the backward
and forward errors, and each has different performance costs. The constraints
for convergence given in the literature are, as an artifact of the analyses,
often overly strict in practice, and thus could lead a user to select a more
expensive variant when a less expensive one would have sufficed.
In this work, we develop a multistage mixed precision iterative refinement
solver which aims to combine existing mixed precision approaches to balance
performance and accuracy and improve usability. For an initial combination of
precisions, the algorithm begins with the least expensive approach and
convergence is monitored via inexpensive computations with quantities produced
during the iteration. If slow convergence or divergence is detected using
particular stopping criteria, the algorithm switches to use a more expensive,
but more reliable variant. A novel aspect of our approach is that, unlike
existing implementations, our algorithm first attempts to use ``stronger''
solvers for the solution update before resorting to increasing the
precision(s). In some scenarios, this can avoid the need to refactorize the
matrix in higher precision. We perform extensive numerical experiments on
random dense problems and problems from real applications which confirm the
benefits of the multistage approach.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710446 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.06507
|
Hang-Hyun Jo
|
Yohsuke Murase, Hang-Hyun Jo, J\'anos T\"or\"ok, J\'anos Kert\'esz,
Kimmo Kaski
|
Deep learning based parameter search for an agent based social network
model
|
12 pages, 4 figures, 3 tables, 1 pseudocode
|
Frontiers in Big Data 4, 739081 (2021)
|
10.3389/fdata.2021.739081
| null |
physics.soc-ph cs.SI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Interactions between humans give rise to complex social networks that are
characterized by heterogeneous degree distribution, weight-topology relation,
overlapping community structure, and dynamics of links. Understanding such
networks is a primary goal of science due to serving as the scaffold for many
emergent social phenomena from disease spreading to political movements. An
appropriate tool for studying them is agent-based modeling, in which nodes,
representing persons, make decisions about creating and deleting links, thus
yielding various macroscopic behavioral patterns. Here we focus on studying a
generalization of the weighted social network model, being one of the most
fundamental agent-based models for describing the formation of social ties and
social networks. This Generalized Weighted Social Network (GWSN) model
incorporates triadic closure, homophilic interactions, and various link
termination mechanisms, which have been studied separately in the previous
works. Accordingly, the GWSN model has an increased number of input parameters
and the model behavior gets excessively complex, making it challenging to
clarify the model behavior. We have executed massive simulations with a
supercomputer and using the results as the training data for deep neural
networks to conduct regression analysis for predicting the properties of the
generated networks from the input parameters. The obtained regression model was
also used for global sensitivity analysis to identify which parameters are
influential or insignificant. We believe that this methodology is applicable
for a large class of complex network models, thus opening the way for more
realistic quantitative agent-based modeling.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711036 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.07851
|
Antonyo Musabini
|
Antonyo Musabini, Evin Bozbayir, Herv\'e Marcasuzaa, Omar Adair Islas
Ram\'irez
|
Park4U Mate: Context-Aware Digital Assistant for Personalized Autonomous
Parking
|
Accepted at 2021 IEEE Intelligent Vehicles Symposium - IV (matching
camera-ready version)
| null |
10.1109/iv48863.2021.9575453
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
People park their vehicle depending on interior and exterior contexts. They
do it naturally, even unconsciously. For instance, with a baby seat on the
rear, the driver might leave more space on one side to be able to get the baby
out easily; or when grocery shopping, s/he may position the vehicle to remain
the trunk accessible. Autonomous vehicles are becoming technically effective at
driving from A to B and parking in a proper spot, with a default way. However,
in order to satisfy users' expectations and to become trustworthy, they will
also need to park or make a temporary stop, appropriate to the given situation.
In addition, users want to understand better the capabilities of their driving
assistance features, such as automated parking systems. A voice-based interface
can help with this and even ease the adoption of these features. Therefore, we
developed a voice-based in-car assistant (Park4U Mate), that is aware of
interior and exterior contexts (thanks to a variety of sensors), and that is
able to park autonomously in a smart way (with a constraints minimization
strategy). The solution was demonstrated to thirty-five users in test-drives
and their feedback was collected on the system's decision-making capability as
well as on the human-machine-interaction. The results show that: (1) the
proposed optimization algorithm is efficient at deciding the best parking
strategy; hence, autonomous vehicles can adopt it; (2) a voice-based digital
assistant for autonomous parking is perceived as a clear and effective
interaction method. However, the interaction speed remained the most important
criterion for users. In addition, they clearly wish not to be limited on only
voice-interaction, to use the automated parking function and rather appreciate
a multi-modal interaction.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.68338 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.09113
|
Nicola Fabiano
|
Nicola Fabiano
|
The approach with the Data Protection and Privacy Relationships Model
(DAPPREMO)
| null |
The Journal on Systemics, Cybernetics and Informatics: JSCI -
Volume 19 - Number 7 - Year 2021, pp. 1-19 - ISSN: 1690-4524
| null |
ISSN: 1690-4524
|
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We describe the Data Protection and Privacy Relationships Model (DAPPREMO),
which is based on the set theory, considering that both the data protection and
privacy regulation and Ethics principles in those domains belong to a set.
DAPPREMO is a new and innovative solution to adopt a model in any data
protection and privacy activities. We theorise that DAPPREMO is an innovative
approach to have a broad overview of all the objects related to a specific case
or more cases from data protection and privacy perspective. We describe
DAPPREMO as a solution for a multidisciplinary approach to address any data
protection and privacy issue.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710848 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.10396
|
Yuyue Yan
|
Fengqiang Gao, Yuyue Yan, Zhihao Chen, Linxiao Zheng, Huan Ren
|
Effect of density control in partially observable asymmetric-exit
evacuation under guidance: Strategic suggestion under time delay
|
15 pages, 11 figures
| null | null | null |
physics.soc-ph
|
http://creativecommons.org/licenses/by/4.0/
|
To enhance the evacuation efficiency in partially observable asymmetric-exit
evacuation under guidance, a general framework of the dynamic guiding assistant
system is presented to investigate the effect of density control. In this
framework, several evacuation assistants are established to observe the partial
information of pedestrians' location and adjust the guiding signals of the
dynamic guiding assistant systems. A simple on-off-based density control
algorithm is proposed for the evacuation assistants according to the delayed
data of the observed information (i.e., pedestrian densities in the observed
regions near the corresponding exits). This paper provides strategic
suggestions on how to set the observed region and the target density by
involving a force-driven cellular automaton model. It is observed that the
proposed density control algorithm can control (positively affect) the global
distribution of the pedestrians' locations and suppress arching phenomena in
the evacuation process even using the observed partial information under time
delays. By imposing a moderate target density, the dynamic guiding assistant
system also suppresses the triggers of collisions around the exits and avoids
inefficiently separating the pedestrians. To enhance evacuation efficiency, we
reveal an interesting fact without loss of generality that we only need to
observe the pedestrians' location from a small region near the exit instead of
a large region when the time delay of the observed information is slight
enough. Our numerical findings are expected to provide new insights into
designing computer-aided guiding strategies in real evacuations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710622 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.12193
|
Muhammad Bilal
|
Muhammad Basit Umair, Zeshan Iqbal, Muhammad Bilal, Tarik Adnan
Almohamad, Jamel Nebhen, Raja Majid Mehmood
|
An Efficient Internet Traffic Classification System Using Deep Learning
for IoT
|
14 pages, 4 figures, 11 tables, Accepted for publication in
CMC-Computers, Materials & Continua
|
CMC-Computers, Materials and Continua, 71(1), 407-422, 2022
|
10.32604/cmc.2022.020727
| null |
cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Internet of Things (IoT) defines a network of devices connected to the
internet and sharing a massive amount of data between each other and a central
location. These IoT devices are connected to a network therefore prone to
attacks. Various management tasks and network operations such as security,
intrusion detection, Quality-of-Service provisioning, performance monitoring,
resource provisioning, and traffic engineering require traffic classification.
Due to the ineffectiveness of traditional classification schemes, such as
port-based and payload-based methods, researchers proposed machine
learning-based traffic classification systems based on shallow neural networks.
Furthermore, machine learning-based models incline to misclassify internet
traffic due to improper feature selection. In this research, an efficient
multilayer deep learning based classification system is presented to overcome
these challenges that can classify internet traffic. To examine the performance
of the proposed technique, Moore-dataset is used for training the classifier.
The proposed scheme takes the pre-processed data and extracts the flow features
using a deep neural network (DNN). In particular, the maximum entropy
classifier is used to classify the internet traffic. The experimental results
show that the proposed hybrid deep learning algorithm is effective and achieved
high accuracy for internet traffic classification, i.e., 99.23%. Furthermore,
the proposed algorithm achieved the highest accuracy compared to the support
vector machine (SVM) based classification technique and k-nearest neighbours
(KNNs) based classification technique.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711994 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2107.13230
|
Humeyra Caglayan
|
Ibrahim Issah, Mohsin Habib and Humeyra Caglayan
|
Rolled-up Epsilon-near-zero Waveguide reservoir for long-range qubit
entanglement
| null | null |
10.1515/nanoph-2021-0453
| null |
quant-ph physics.optics
|
http://creativecommons.org/licenses/by/4.0/
|
Preservation of the entangled state of a quantum system is relevant in
quantum applications. However, the preservation of entangled states is
constrained due to the energy dissipation of the quantum system arising from
the environment. As a result, the design of the environment seen by quantum
bits is relevant due to its relation to the final state of the quantum system.
This work presents the concurrence measure of entanglement between two qubits
coupled to a rolled-up epsilon-near-zero (ENZ) waveguide reservoir consisting
of an alternating layer of metal and dielectric. Our numerical calculations
demonstrate that the proposed rolled-up ENZ waveguide reservoir can preserve
the entanglement of two qubits at the cutoff wavelength of the reservoir via
enhanced energy transfer. This proposed rolled-up ENZ waveguide can serve as a
unique reservoir for various quantum technologies such as quantum
communication, quantum information processing, and single-photon generation. As
a proof of concept, we also demonstrate that this novel structure can be
fabricated using cost-effective self-rolling techniques.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710051 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.00704
|
Wei Ren
|
Wei Ren, Julien Calbert and Raphael Jungers
|
Zonotope-based Controller Synthesis for LTL Specifications
|
6 pages, 5 figures, CDC2021
| null | null | null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper studies the controller synthesis problem for Linear Temporal Logic
(LTL) specifications using (constrained) zonotope techniques. First, we
implement (constrained) zonotope techniques to partition the state space and
further to verify whether the LTL specification can be satisfied. Once the LTL
specification can be satisfied, the next step is to design a controller to
guarantee the satisfaction of the LTL specification for dynamic systems. Based
on the verification of the LTL specification, an abstraction-based control
design approach is proposed in this paper: a novel abstraction construction is
developed first, then finite local abstract controllers are designed to achieve
the LTL specification, and finally the designed abstract controllers are
combined and refined as the controller for the original system. The proposed
control strategy is illustrated via a numerical example from autonomous robots.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709585 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.02221
|
Riccardo Finotello
|
Harold Erbin, Riccardo Finotello, Robin Schneider and Mohamed
Tamaazousti
|
Deep multi-task mining Calabi-Yau four-folds
|
15 pages; additional details, references updated
|
Mach. Learn.: Sci. Technol. (2021)
|
10.1088/2632-2153/ac37f7
|
MIT-CTP/5319, UUITP-36/21
|
hep-th cs.LG math.AG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We continue earlier efforts in computing the dimensions of tangent space
cohomologies of Calabi-Yau manifolds using deep learning. In this paper, we
consider the dataset of all Calabi-Yau four-folds constructed as complete
intersections in products of projective spaces. Employing neural networks
inspired by state-of-the-art computer vision architectures, we improve earlier
benchmarks and demonstrate that all four non-trivial Hodge numbers can be
learned at the same time using a multi-task architecture. With 30% (80%)
training ratio, we reach an accuracy of 100% for $h^{(1,1)}$ and 97% for
$h^{(2,1)}$ (100% for both), 81% (96%) for $h^{(3,1)}$, and 49% (83%) for
$h^{(2,2)}$. Assuming that the Euler number is known, as it is easy to compute,
and taking into account the linear constraint arising from index computations,
we get 100% total accuracy.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707436 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.04121
|
Andreas Waldvogel
|
Vanessa Tietz, Julian Schoepf, Andreas Waldvogel, Bjoern Annighoefer
|
A Concept for a Qualifiable (Meta)-Modeling Framework Deployable in
Systems and Tools of Safety-critical and Cyber-physical Environments
|
7 pages, 2 figures
|
2021 ACM/IEEE 24th International Conference on Model Driven
Engineering Languages and Systems (MODELS)
|
10.1109/MODELS50736.2021.00025
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The development of cyber-physical systems can significantly benefit from
domain-specific modeling and requires adequate (meta)-modeling frameworks. If
such systems are designed for the safety-critical area, the systems must
undergo qualification processes defined and monitored by a certification
authority. To use the resulting artifacts of modeling tools without further
qualification activities, the modeling tool must be additionally qualified.
Tool qualification has to be conducted by the tool user and can be assisted by
the tool developer by providing qualification artifacts. However,
state-of-the-art domain-specific modeling frameworks barely support the user in
the qualification process, which results in an extensive manual effort. To
reduce this effort and to avoid modeling constructs that can hardly be
implemented in a qualifiable way, we propose the development of an open source
(meta)-modeling framework that inherently considers qualification issues. Based
on the functionality of existing frameworks, we have identified components that
necessarily need to be rethought or changed. This leads to the consideration of
the following six cornerstones for our framework: (1) an essential
meta-language, (2) a minimal runtime, (3) deterministic transformations, (4) a
qualification artifact generation, (5) a sophisticated visualization, and (6) a
decoupled interaction of framework components. All these cornerstones consider
the aspect of a safety-critical (meta)-modeling framework in their own manner.
This combination leads to a holistic framework usable in the safety-critical
system development domain.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709435 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.04374
|
Martin Byrenheid
|
Martin Byrenheid, Stefanie Roos, Thorsten Strufe
|
Topology Inference of Networks utilizing Rooted Spanning Tree Embeddings
|
11 pages, 6 figures, Extended version of paper published at ICDCN
2022
| null | null | null |
cs.CR
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Due to its high efficiency, routing based on greedy embeddings of rooted
spanning trees is a promising approach for dynamic, large-scale networks with
restricted topologies. Friend-to-friend (F2F) overlays, one key application of
embedding-based routing, aim to prevent disclosure of their participants to
malicious members by restricting exchange of messages to mutually trusted
nodes. Since embeddings assign a unique integer vector to each node that
encodes its position in a spanning tree of the overlay, attackers can infer
network structure from knowledge about assigned vectors. As this information
can be used to identify participants, an evaluation of the scale of leakage is
needed. In this work, we analyze in detail which information malicious
participants can infer from knowledge about assigned vectors. Also, we show
that by monitoring packet trajectories, malicious participants cannot
unambiguously infer links between nodes of unidentified participants. Using
simulation, we find that the vector assignment procedure has a strong impact on
the feasibility of inference. In F2F overlay networks, using vectors of
randomly chosen numbers for routing decreases the mean number of discovered
individuals by one order of magnitude compared to the popular approach of using
child enumeration indexes as vector elements.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.04652
|
Carlos Diego Nascimento Damasceno
|
Carlos Diego Nascimento Damasceno, Daniel Str\"uber
|
Quality Guidelines for Research Artifacts in Model-Driven Engineering
|
12 pages, 5 figures, 7 tables, accepted for publication at the
ACM/IEEE 24th International Conference on Model Driven Engineering Languages
and Systems (MODELS 2021), Foundations Track - Technical Papers
| null |
10.1109/MODELS50736.2021.00036
| null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sharing research artifacts is known to help people to build upon existing
knowledge, adopt novel contributions in practice, and increase the chances of
papers receiving attention. In Model-Driven Engineering (MDE), openly providing
research artifacts plays a key role, even more so as the community targets a
broader use of AI techniques, which can only become feasible if large open
datasets and confidence measures for their quality are available. However, the
current lack of common discipline-specific guidelines for research data sharing
opens the opportunity for misunderstandings about the true potential of
research artifacts and subjective expectations regarding artifact quality. To
address this issue, we introduce a set of guidelines for artifact sharing
specifically tailored to MDE research. To design this guidelines set, we
systematically analyzed general-purpose artifact sharing practices of major
computer science venues and tailored them to the MDE domain. Subsequently, we
conducted an online survey with 90 researchers and practitioners with expertise
in MDE. We investigated our participants' experiences in developing and sharing
artifacts in MDE research and the challenges encountered while doing so. We
then asked them to prioritize each of our guidelines as essential, desirable,
or unnecessary. Finally, we asked them to evaluate our guidelines with respect
to clarity, completeness, and relevance. In each of these dimensions, our
guidelines were assessed positively by more than 92\% of the participants. To
foster the reproducibility and reusability of our results, we make the full set
of generated artifacts available in an open repository at
\texttt{\url{https://mdeartifacts.github.io/}}.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.05505
|
Peihan Zhang
|
Peihan Zhang, Gang Chen, Yuzhu Li, Wei Dong
|
Agile Formation Control of Drone Flocking Enhanced with Active
Vision-based Relative Localization
| null | null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The vision-based relative localization can provide effective feedback for the
cooperation of aerial swarm and has been widely investigated in previous works.
However, the limited field of view (FOV) inherently restricts its performance.
To cope with this issue, this letter proposes a novel distributed active
vision-based relative localization framework and apply it to formation control
in aerial swarms. Inspired by bird flocks in nature, we devise graph-based
attention planning (GAP) to improve the observation quality of the active
vision in the swarm. Then active detection results are fused with onboard
measurements from Ultra-WideBand (UWB) and visual-inertial odometry (VIO) to
obtain real-time relative positions, which further improve the formation
control performance of the swarm. Simulations and experiments demonstrate that
the proposed active vision system outperforms the fixed vision system in terms
of estimation and formation accuracy.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71145 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.06705
|
Mojtaba Shahin
|
Mojtaba Shahin, Ali Rezaei Nasab, Muhammad Ali Babar
|
A Qualitative Study of Architectural Design Issues in DevOps
|
Preprint accepted for publication in Journal of Software: Evolution
and Process, 2021. 38 Pages, 6 Tables, 11 Figures. This article is an
extended version of the ICSSP2020 paper (the preprint is available at
arXiv:2003.06108). arXiv admin note: text overlap with arXiv:2003.06108
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Software architecture is critical in succeeding with DevOps. However,
designing software architectures that enable and support DevOps (DevOps-driven
software architectures) is a challenge for organizations. We assert that one of
the essential steps towards characterizing DevOps-driven architectures is to
understand architectural design issues raised in DevOps. At the same time, some
of the architectural issues that emerge in the DevOps context (and their
corresponding architectural practices or tactics) may stem from the context
(i.e., domain) and characteristics of software organizations. To this end, we
conducted a mixed-methods study that consists of a qualitative case study of
two teams in a company during their DevOps transformation and a content
analysis of Stack Overflow and DevOps Stack Exchange posts to understand
architectural design issues in DevOps. Our study found eight specific and
contextual architectural design issues faced by the two teams and classified
architectural design issues discussed in Stack Overflow and DevOps Stack
Exchange into 11 groups. Our aggregated results reveal that the main
characteristics of DevOps-driven architectures are: being loosely coupled and
prioritizing deployability, testability, supportability, and modifiability over
other quality attributes. Finally, we discuss some concrete implications for
research and practice.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.698445 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.07162
|
Giovanni Modanese
|
F. Minotti, G. Modanese
|
Quantum uncertainty and energy flux in extended electrodynamics
|
27 pages
|
Quantum Rep. 2021, 3(4), 703-723
|
10.3390/quantum3040044
| null |
physics.gen-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In quantum theory, for a system with macroscopic wavefunction, the charge
density and current density are represented by non-commuting operators. It
follows that the anomaly $I=\partial_t \rho + \nabla \cdot \mathbf{j}$, being
essentially a linear combination of these two operators in the
frequency-momentum domain, does not admit eigenstates and has a minimum
uncertainty fixed by the Heisenberg relation $\Delta N \Delta \phi \simeq 1$
which involves the occupation number and the phase of the wavefunction. We give
an estimate of the minimum uncertainty in the case of a tunnel Josephson
junction made of Nb. Due to this violation of the local conservation of charge,
for the evaluation of the e.m. field generated by the system it is necessary to
use the extended Aharonov-Bohm electrodynamics. After recalling its field
equations, we compute in general form the energy-momentum tensor and the
radiation power flux generated by a localized oscillating source. The physical
requirements that the total flux be positive, negative or zero yield some
conditions on the dipole moment of the anomaly $I$.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707803 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.07318
|
Daniel Katz
|
Daniel J. Katz and Courtney M. van der Linden
|
Peak Sidelobe Level and Peak Crosscorrelation of Golay-Rudin-Shapiro
Sequences
|
39 pages
| null | null | null |
cs.IT cs.DM eess.SP math.CO math.IT math.NT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Sequences with low aperiodic autocorrelation and crosscorrelation are used in
communications and remote sensing. Golay and Shapiro independently devised a
recursive construction that produces families of complementary pairs of binary
sequences. In the simplest case, the construction produces the Rudin-Shapiro
sequences, and in general it produces what we call Golay-Rudin-Shapiro
sequences. Calculations by Littlewood show that the Rudin-Shapiro sequences
have low mean square autocorrelation. A sequence's peak sidelobe level is its
largest magnitude of autocorrelation over all nonzero shifts. H{\o}holdt,
Jensen, and Justesen showed that there is some undetermined positive constant
$A$ such that the peak sidelobe level of a Rudin-Shapiro sequence of length
$2^n$ is bounded above by $A(1.842626\ldots)^n$, where $1.842626\ldots$ is the
positive real root of $X^4-3 X-6$. We show that the peak sidelobe level is
bounded above by $5(1.658967\ldots)^{n-4}$, where $1.658967\ldots$ is the real
root of $X^3+X^2-2 X-4$. Any exponential bound with lower base will fail to be
true for almost all $n$, and any bound with the same base but a lower constant
prefactor will fail to be true for at least one $n$. We provide a similar bound
on the peak crosscorrelation (largest magnitude of crosscorrelation over all
shifts) between the sequences in each Rudin-Shapiro pair. The methods that we
use generalize to all families of complementary pairs produced by the
Golay-Rudin-Shapiro recursion, for which we obtain bounds on the peak sidelobe
level and peak crosscorrelation with the same exponential growth rate as we
obtain for the original Rudin-Shapiro sequences.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712401 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.08339
|
Mohammad Sabik Irbaz
|
Alif Ashrafee, Akib Mohammed Khan, Mohammad Sabik Irbaz, MD Abdullah
Al Nasim
|
Real-time Bangla License Plate Recognition System for Low Resource
Video-based Applications
|
Accepted in IEEE/CVF Winter Conference on Applications of Computer
Vision - Real-World Surveillance 2022 (IEEE/CVF WACV RWS 2022)
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Automatic License Plate Recognition systems aim to provide a solution for
detecting, localizing, and recognizing license plate characters from vehicles
appearing in video frames. However, deploying such systems in the real world
requires real-time performance in low-resource environments. In our paper, we
propose a two-stage detection pipeline paired with Vision API that provides
real-time inference speed along with consistently accurate detection and
recognition performance. We used a haar-cascade classifier as a filter on top
of our backbone MobileNet SSDv2 detection model. This reduces inference time by
only focusing on high confidence detections and using them for recognition. We
also impose a temporal frame separation strategy to distinguish between
multiple vehicle license plates in the same clip. Furthermore, there are no
publicly available Bangla license plate datasets, for which we created an image
dataset and a video dataset containing license plates in the wild. We trained
our models on the image dataset and achieved an AP(0.5) score of 86% and tested
our pipeline on the video dataset and observed reasonable detection and
recognition performance (82.7% detection rate, and 60.8% OCR F1 score) with
real-time processing speed (27.2 frames per second).
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.712245 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.09322
|
Jiawei Chen
|
Jiawei Chen, Chiu Man Ho
|
MM-ViT: Multi-Modal Video Transformer for Compressed Video Action
Recognition
|
Winter Conference on Applications of Computer Vision (WACV) 2022
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper presents a pure transformer-based approach, dubbed the Multi-Modal
Video Transformer (MM-ViT), for video action recognition. Different from other
schemes which solely utilize the decoded RGB frames, MM-ViT operates
exclusively in the compressed video domain and exploits all readily available
modalities, i.e., I-frames, motion vectors, residuals and audio waveform. In
order to handle the large number of spatiotemporal tokens extracted from
multiple modalities, we develop several scalable model variants which factorize
self-attention across the space, time and modality dimensions. In addition, to
further explore the rich inter-modal interactions and their effects, we develop
and compare three distinct cross-modal attention mechanisms that can be
seamlessly integrated into the transformer building block. Extensive
experiments on three public action recognition benchmarks (UCF-101,
Something-Something-v2, Kinetics-600) demonstrate that MM-ViT outperforms the
state-of-the-art video transformers in both efficiency and accuracy, and
performs better or equally well to the state-of-the-art CNN counterparts with
computationally-heavy optical flow.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710478 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.09484
|
Lifeng Han
|
Lifeng Han, Irina Sorokina, Gleb Erofeev, Serge Gladkoff
|
cushLEPOR: customising hLEPOR metric using Optuna for higher agreement
with human judgments or pre-trained language model LaBSE
|
Forthcoming: in Proceedings of Six Conference on Machine Translation
(WMT2021)
| null | null | null |
cs.CL cs.AI cs.LG
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Human evaluation has always been expensive while researchers struggle to
trust the automatic metrics. To address this, we propose to customise
traditional metrics by taking advantages of the pre-trained language models
(PLMs) and the limited available human labelled scores. We first re-introduce
the hLEPOR metric factors, followed by the Python version we developed (ported)
which achieved the automatic tuning of the weighting parameters in hLEPOR
metric. Then we present the customised hLEPOR (cushLEPOR) which uses Optuna
hyper-parameter optimisation framework to fine-tune hLEPOR weighting parameters
towards better agreement to pre-trained language models (using LaBSE) regarding
the exact MT language pairs that cushLEPOR is deployed to. We also optimise
cushLEPOR towards professional human evaluation data based on MQM and pSQM
framework on English-German and Chinese-English language pairs. The
experimental investigations show cushLEPOR boosts hLEPOR performances towards
better agreements to PLMs like LaBSE with much lower cost, and better
agreements to human evaluations including MQM and pSQM scores, and yields much
better performances than BLEU (data available at
\url{https://github.com/poethan/cushLEPOR}). Official results show that our
submissions win three language pairs including \textbf{English-German} and
\textbf{Chinese-English} on \textit{News} domain via cushLEPOR(LM) and
\textbf{English-Russian} on \textit{TED} domain via hLEPOR.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70866 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.09873
|
Mona Zehni
|
Mona Zehni, Zhizhen Zhao
|
An Adversarial Learning Based Approach for Unknown View Tomographic
Reconstruction
| null | null | null | null |
eess.IV cs.LG eess.SP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The goal of 2D tomographic reconstruction is to recover an image given its
projections from various views. It is often presumed that projection angles
associated with the projections are known in advance. Under certain situations,
however, these angles are known only approximately or are completely unknown.
It becomes more challenging to reconstruct the image from a collection of
random projections. We propose an adversarial learning based approach to
recover the image and the projection angle distribution by matching the
empirical distribution of the measurements with the generated data. Fitting the
distributions is achieved through solving a min-max game between a generator
and a critic based on Wasserstein generative adversarial network structure. To
accommodate the update of the projection angle distribution through gradient
back propagation, we approximate the loss using the Gumbel-Softmax
reparameterization of samples from discrete distributions. Our theoretical
analysis verifies the unique recovery of the image and the projection
distribution up to a rotation and reflection upon convergence. Our extensive
numerical experiments showcase the potential of our method to accurately
recover the image and the projection angle distribution under noise
contamination.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71103 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.10536
|
Chuang Liu
|
Chuang Liu, Hua Yang, Qin Zhou and Shibao Zheng
|
Making Person Search Enjoy the Merits of Person Re-identification
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Person search is an extended task of person re-identification (Re-ID).
However, most existing one-step person search works have not studied how to
employ existing advanced Re-ID models to boost the one-step person search
performance due to the integration of person detection and Re-ID. To address
this issue, we propose a faster and stronger one-step person search framework,
the Teacher-guided Disentangling Networks (TDN), to make the one-step person
search enjoy the merits of the existing Re-ID researches. The proposed TDN can
significantly boost the person search performance by transferring the advanced
person Re-ID knowledge to the person search model. In the proposed TDN, for
better knowledge transfer from the Re-ID teacher model to the one-step person
search model, we design a strong one-step person search base framework by
partially disentangling the two subtasks. Besides, we propose a Knowledge
Transfer Bridge module to bridge the scale gap caused by different input
formats between the Re-ID model and one-step person search model. During
testing, we further propose the Ranking with Context Persons strategy to
exploit the context information in panoramic images for better retrieval.
Experiments on two public person search datasets demonstrate the favorable
performance of the proposed method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.10642
|
Norbert Weber
|
Kashif Mushtaq, Ji Zhao, Norbert Weber, Adelio Mendes, Donald R.
Sadoway
|
Self-discharge mitigation in a liquid metal displacement battery
| null |
Journal of Energy Chemistry 66 (2022) 390-396
|
10.1016/j.jechem.2021.08.015
| null |
physics.chem-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Recently, a disruptive idea was reported about the discovery of a new type of
battery named Liquid Displacement Battery (LDB) comprising liquid metal
electrodes and molten salt electrolyte. This cell featured a novel concept of a
porous electronically conductive faradaic membrane instead of the traditional
ion-selective ceramic membrane. LDBs are attractive for stationary storage
applications but need mitigation against self-discharge. In the instant battery
chemistry, Li|LiCl-PbCl$_2$|Pb, reducing the diffusion coefficient of lead ions
can be a way forward and a solution can be the addition of PbO to the
electrolyte. The latter acts as a supplementary barrier and complements the
function of the faradaic membrane. The remedial actions improved the cell's
coulombic efficiency from 92% to 97% without affecting the voltage efficiency.
In addition, the limiting current density of a 500 mAh cell increased from 575
to 831 mA cm$^{-2}$ and the limiting power from 2.53 to 3.66 W. Finally, the
effect of PbO on the impedance and polarization of the cell was also studied.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.69632 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.11838
|
Barathi Ganesh H B
|
N S Kamal, Barathi Ganesh HB, Sajith Variyar VV, Sowmya V, Soman KP
|
Geometry Based Machining Feature Retrieval with Inductive Transfer
Learning
|
Submitted to 9th International Conference on Frontiers of Intelligent
Computing: Theory and Applications (FICTA 2021)
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Manufacturing industries have widely adopted the reuse of machine parts as a
method to reduce costs and as a sustainable manufacturing practice.
Identification of reusable features from the design of the parts and finding
their similar features from the database is an important part of this process.
In this project, with the help of fully convolutional geometric features, we
are able to extract and learn the high level semantic features from CAD models
with inductive transfer learning. The extracted features are then compared with
that of other CAD models from the database using Frobenius norm and identical
features are retrieved. Later we passed the extracted features to a deep
convolutional neural network with a spatial pyramid pooling layer and the
performance of the feature retrieval increased significantly. It was evident
from the results that the model could effectively capture the geometrical
elements from machining features.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710258 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.12066
|
Chang-Chun Chen
|
Chang-Chun Chen, Patrick H. Diamond, Steven M. Tobias
|
Ion Heat and Parallel Momentum Transport by Stochastic Magnetic Fields
and Turbulence
|
16 pages, 5 figures, PPCF invited paper
| null |
10.1088/1361-6587/ac38b2
| null |
physics.plasm-ph nucl-th physics.flu-dyn
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The theory of turbulent transport of parallel momentum and ion heat by the
interaction of stochastic magnetic fields and turbulence is presented.
Attention is focused on determining the kinetic stress and the compressive
energy flux. A critical parameter is identified as the ratio of the turbulent
scattering rate to the rate of parallel acoustic dispersion. For the parameter
large, the kinetic stress takes the form of a viscous stress. For the parameter
small, the quasilinear residual stress is recovered. In practice, the viscous
stress is the relevant form, and the quasilinear limit is not observable. This
is the principal prediction of this paper. A simple physical picture is
developed and shown to recover the results of the detailed analysis.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711625 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.12172
|
Yassine Hamoudi
|
Yassine Hamoudi
|
Quantum Sub-Gaussian Mean Estimator
|
20 pages
|
Proceedings of the 29th European Symposium on Algorithms (ESA),
volume 204 of LIPIcs, pages 50:1--50:17, 2021
|
10.4230/LIPIcs.ESA.2021.50
| null |
quant-ph cs.CC cs.DS math.ST stat.ML stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We present a new quantum algorithm for estimating the mean of a real-valued
random variable obtained as the output of a quantum computation. Our estimator
achieves a nearly-optimal quadratic speedup over the number of classical i.i.d.
samples needed to estimate the mean of a heavy-tailed distribution with a
sub-Gaussian error rate. This result subsumes (up to logarithmic factors)
earlier works on the mean estimation problem that were not optimal for
heavy-tailed distributions [BHMT02,BDGT11], or that require prior information
on the variance [Hein02,Mon15,HM19]. As an application, we obtain new quantum
algorithms for the $(\epsilon,\delta)$-approximation problem with an optimal
dependence on the coefficient of variation of the input random variable.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709403 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.12618
|
Marina Haikin
|
Marina Haikin, Matan Gavish, Dustin G. Mixon, Ram Zamir
|
Asymptotic Frame Theory for Analog Coding
| null | null | null | null |
cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Over-complete systems of vectors, or in short, frames, play the role of
analog codes in many areas of communication and signal processing. To name a
few, spreading sequences for code-division multiple access (CDMA),
over-complete representations for multiple-description (MD) source coding,
space-time codes, sensing matrices for compressed sensing (CS), and more
recently, codes for unreliable distributed computation. In this survey paper we
observe an information-theoretic random-like behavior of frame subsets. Such
sub-frames arise in setups involving erasures (communication), random user
activity (multiple access), or sparsity (signal processing), in addition to
channel or quantization noise. The goodness of a frame as an analog code is a
function of the eigenvalues of a sub-frame, averaged over all sub-frames.
Within the highly symmetric class of Equiangular Tight Frames (ETF), as well
as other "near ETF" families, we show a universal behavior of the empirical
eigenvalue distribution (ESD) of a randomly-selected sub-frame: (i) the ESD is
asymptotically indistinguishable from Wachter's MANOVA distribution; and (ii)
it exhibits a convergence rate to this limit that is indistinguishable from
that of a matrix sequence drawn from MANOVA (Jacobi) ensembles of corresponding
dimensions. Some of these results follow from careful statistical analysis of
empirical evidence, and some are proved analytically using random matrix theory
arguments of independent interest. The goodness measures of the MANOVA limit
distribution are better, in a concrete formal sense, than those of the
Marchenko-Pastur distribution at the same aspect ratio, implying that
deterministic analog codes are better than random (i.i.d.) analog codes. We
further give evidence that the ETF (and near ETF) family is in fact superior to
any other frame family in terms of its typical sub-frame goodness.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709648 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2108.13910
|
Viktoria Schuster
|
Viktoria Schuster and Anders Krogh
|
A manifold learning perspective on representation learning: Learning
decoder and representations without an encoder
| null |
Entropy 23 (2021) 1403
|
10.3390/e23111403
| null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Autoencoders are commonly used in representation learning. They consist of an
encoder and a decoder, which provide a straightforward way to map n-dimensional
data in input space to a lower m-dimensional representation space and back. The
decoder itself defines an m-dimensional manifold in input space. Inspired by
manifold learning, we show that the decoder can be trained on its own by
learning the representations of the training samples along with the decoder
weights using gradient descent. A sum-of-squares loss then corresponds to
optimizing the manifold to have the smallest Euclidean distance to the training
samples, and similarly for other loss functions. We derive expressions for the
number of samples needed to specify the encoder and decoder and show that the
decoder generally requires much less training samples to be well-specified
compared to the encoder. We discuss training of autoencoders in this
perspective and relate to previous work in the field that use noisy training
examples and other types of regularization. On the natural image data sets
MNIST and CIFAR10, we demonstrate that the decoder is much better suited to
learn a low-dimensional representation, especially when trained on small data
sets. Using simulated gene regulatory data, we further show that the decoder
alone leads to better generalization and meaningful representations. Our
approach of training the decoder alone facilitates representation learning even
on small data sets and can lead to improved training of autoencoders. We hope
that the simple analyses presented will also contribute to an improved
conceptual understanding of representation learning.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710258 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.02538
|
Fr\'ed\'eric Ouimet
|
Eric Bax and Fr\'ed\'eric Ouimet
|
Bounding Means of Discrete Distributions
|
9 pages, 8 figures
|
IEEE International Conference on Big Data, December 15-18, 2021
| null | null |
math.ST cs.IT math.IT math.PR stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce methods to bound the mean of a discrete distribution (or finite
population) based on sample data, for random variables with a known set of
possible values. In particular, the methods can be applied to categorical data
with known category-based values. For small sample sizes, we show how to
leverage the knowledge of the set of possible values to compute bounds that are
stronger than for general random variables such as standard concentration
inequalities.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711619 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.03044
|
Giovanni Modanese
|
M.L. Bertotti, G. Modanese
|
Diagonal degree correlations vs. epidemic threshold in scale-free
networks
|
18 pages, 6 figures
|
Complexity - Volume 2021, Article ID 7704586
|
10.1155/2021/7704586
| null |
physics.soc-ph cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
We prove that the presence of a diagonal assortative degree correlation, even
if small, has the effect of dramatically lowering the epidemic threshold of
large scale-free networks. The correlation matrix considered is
$P(h|k)=(1-r)P^U_{hk}+r\delta_{hk}$, where $P^U$ is uncorrelated and $r$ (the
Newman assortativity coefficient) can be very small. The effect is uniform in
the scale exponent $\gamma$, if the network size is measured by the largest
degree $n$. We also prove that it is possible to construct, via the Porto-Weber
method, correlation matrices which have the same $k_{nn}$ as the $P(h|k)$
above, but very different elements and spectrum, and thus lead to different
epidemic diffusion and threshold. Moreover, we study a subset of the admissible
transformations of the form $P(h|k) \to P(h|k)+\Phi(h,k)$ with $\Phi(h,k)$
depending on a parameter which leave $k_{nn}$ invariant. Such transformations
affect in general the epidemic threshold. We find however that this does not
happen when they act between networks with constant $k_{nn}$, i.e. networks in
which the average neighbor degree is independent from the degree itself (a
wider class than that of strictly uncorrelated networks).
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709818 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.03973
|
Zachary Charles
|
Zachary Charles, Keith Rush
|
Iterated Vector Fields and Conservatism, with Applications to Federated
Learning
| null | null | null | null |
math.OC cs.DC cs.LG math.CA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study whether iterated vector fields (vector fields composed with
themselves) are conservative. We give explicit examples of vector fields for
which this self-composition preserves conservatism. Notably, this includes
gradient vector fields of loss functions associated with some generalized
linear models. As we show, characterizing the set of vector fields satisfying
this condition leads to non-trivial geometric questions. In the context of
federated learning, we show that when clients have loss functions whose
gradients satisfy this condition, federated averaging is equivalent to gradient
descent on a surrogate loss function. We leverage this to derive novel
convergence results for federated learning. By contrast, we demonstrate that
when the client losses violate this property, federated averaging can yield
behavior which is fundamentally distinct from centralized optimization.
Finally, we discuss theoretical and practical questions our analytical
framework raises for federated learning.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709604 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.04138
|
Hareesh Mandalapu
|
Hareesh Mandalapu, Aravinda Reddy P N, Raghavendra Ramachandra, K
Sreenivasa Rao, Pabitra Mitra, S R Mahadeva Prasanna, Christoph Busch
|
Multilingual Audio-Visual Smartphone Dataset And Evaluation
| null | null | null | null |
cs.CR cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Smartphones have been employed with biometric-based verification systems to
provide security in highly sensitive applications. Audio-visual biometrics are
getting popular due to their usability, and also it will be challenging to
spoof because of their multimodal nature. In this work, we present an
audio-visual smartphone dataset captured in five different recent smartphones.
This new dataset contains 103 subjects captured in three different sessions
considering the different real-world scenarios. Three different languages are
acquired in this dataset to include the problem of language dependency of the
speaker recognition systems. These unique characteristics of this dataset will
pave the way to implement novel state-of-the-art unimodal or audio-visual
speaker recognition systems. We also report the performance of the bench-marked
biometric verification systems on our dataset. The robustness of biometric
algorithms is evaluated towards multiple dependencies like signal noise,
device, language and presentation attacks like replay and synthesized signals
with extensive experiments. The obtained results raised many concerns about the
generalization properties of state-of-the-art biometrics methods in
smartphones.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.713007 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.04212
|
Junxian He
|
Junxian He, Graham Neubig, Taylor Berg-Kirkpatrick
|
Efficient Nearest Neighbor Language Models
|
EMNLP 2021. Update to fix typos. Code is at
https://github.com/jxhe/efficient-knnlm
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Non-parametric neural language models (NLMs) learn predictive distributions
of text utilizing an external datastore, which allows them to learn through
explicitly memorizing the training datapoints. While effective, these models
often require retrieval from a large datastore at test time, significantly
increasing the inference overhead and thus limiting the deployment of
non-parametric NLMs in practical applications. In this paper, we take the
recently proposed $k$-nearest neighbors language model (Khandelwal et al.,
2020) as an example, exploring methods to improve its efficiency along various
dimensions. Experiments on the standard WikiText-103 benchmark and
domain-adaptation datasets show that our methods are able to achieve up to a 6x
speed-up in inference speed while retaining comparable performance. The
empirical analysis we present may provide guidelines for future research
seeking to develop or deploy more efficient non-parametric NLMs.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.05019
|
Sarwan Ali
|
Sarwan Ali, Murray Patterson
|
Spike2Vec: An Efficient and Scalable Embedding Approach for COVID-19
Spike Sequences
|
Accepted at IEEE International Conference on Big Data (IEEE Big Data)
| null | null | null |
q-bio.GN cs.LG
|
http://creativecommons.org/publicdomain/zero/1.0/
|
With the rapid global spread of COVID-19, more and more data related to this
virus is becoming available, including genomic sequence data. The total number
of genomic sequences that are publicly available on platforms such as GISAID is
currently several million, and is increasing with every day. The availability
of such \emph{Big Data} creates a new opportunity for researchers to study this
virus in detail. This is particularly important with all of the dynamics of the
COVID-19 variants which emerge and circulate. This rich data source will give
us insights on the best ways to perform genomic surveillance for this and
future pandemic threats, with the ultimate goal of mitigating or eliminating
such threats. Analyzing and processing the several million genomic sequences is
a challenging task. Although traditional methods for sequence classification
are proven to be effective, they are not designed to deal with these specific
types of genomic sequences. Moreover, most of the existing methods also face
the issue of scalability. Previous studies which were tailored to coronavirus
genomic data proposed to use spike sequences (corresponding to a subsequence of
the genome), rather than using the complete genomic sequence, to perform
different machine learning (ML) tasks such as classification and clustering.
However, those methods suffer from scalability issues. In this paper, we
propose an approach called Spike2Vec, an efficient and scalable feature vector
representation for each spike sequence that can be used for downstream ML
tasks. Through experiments, we show that Spike2Vec is not only scalable on
several million spike sequences, but also outperforms the baseline models in
terms of prediction accuracy, F1 score, etc.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712182 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.06631
|
Derek Wang
|
Derek S Wang and Tom\'a\v{s} Neuman and Susanne F Yelin and Johannes
Flick
|
Cavity-modified unimolecular dissociation reactions via intramolecular
vibrational energy redistribution
|
13 pages, 9 figures
| null | null | null |
physics.chem-ph nlin.CD quant-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
While the emerging field of vibrational polariton chemistry has the potential
to overcome traditional limitations of synthetic chemistry, the underlying
mechanism is not yet well understood. Here, we explore how the dynamics of
unimolecular dissociation reactions that are rate-limited by intramolecular
vibrational energy redistribution (IVR) can be modified inside an infrared
optical cavity. We study a classical model of a bent triatomic molecule, where
the two outer atoms are bound by anharmonic Morse potentials to the center atom
coupled to a harmonic bending mode. We show that an optical cavity resonantly
coupled to particular anharmonic vibrational modes can interfere with IVR and
alter unimolecular dissociation reaction rates when the cavity mode acts as a
reservoir for vibrational energy. We find a strong dependence on the initial
state of the cavity and molecule. In particular, when the cavity is initially
empty, the dissociation rate decreases, while when the cavity is initially
hotter than the molecule, the cavity can instead accelerate the reaction rate.
These results lay the foundation for further theoretical work toward
understanding the intriguing experimental results of vibrational polaritonic
chemistry within the context of IVR.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712414 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.07399
|
Christian Jacobsen
|
Christian Jacobsen and Karthik Duraisamy
|
Disentangling Generative Factors of Physical Fields Using Variational
Autoencoders
| null | null | null | null |
physics.comp-ph cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to extract generative parameters from high-dimensional fields of
data in an unsupervised manner is a highly desirable yet unrealized goal in
computational physics. This work explores the use of variational autoencoders
(VAEs) for non-linear dimension reduction with the specific aim of {\em
disentangling} the low-dimensional latent variables to identify independent
physical parameters that generated the data. A disentangled decomposition is
interpretable, and can be transferred to a variety of tasks including
generative modeling, design optimization, and probabilistic reduced order
modelling. A major emphasis of this work is to characterize disentanglement
using VAEs while minimally modifying the classic VAE loss function (i.e. the
Evidence Lower Bound) to maintain high reconstruction accuracy. The loss
landscape is characterized by over-regularized local minima which surround
desirable solutions. We illustrate comparisons between disentangled and
entangled representations by juxtaposing learned latent distributions and the
true generative factors in a model porous flow problem. Hierarchical priors are
shown to facilitate the learning of disentangled representations. The
regularization loss is unaffected by latent rotation when training with
rotationally-invariant priors, and thus learning non-rotationally-invariant
priors aids in capturing the properties of generative factors, improving
disentanglement. Finally, it is shown that semi-supervised learning -
accomplished by labeling a small number of samples ($O(1\%)$) - results in
accurate disentangled latent representations that can be consistently learned.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709069 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.09286
|
Mojtaba Shahin
|
Benjamin Koh, Mojtaba Shahin, Annette Ong, Soo Ying Yeap, Priyanka
Saxena, Manvendra Singh, Chunyang Chen
|
Pandemic Software Development: The Student Experiences from Developing a
COVID-19 Information Dashboard
|
11 Pages. Accepted for publication in 28th Asia-Pacific Software
Engineering Conference (APSEC 2021), IEEE, 2021 (Preprint)
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The COVID-19 pandemic has birthed a wealth of information through many
publicly accessible sources, such as news outlets and social media. However,
gathering and understanding the content can be difficult due to inaccuracies or
inconsistencies between the different sources. To alleviate this challenge in
Australia, a team of 48 student volunteers developed an open-source COVID-19
information dashboard to provide accurate, reliable, and real-time COVID-19
information for Australians. The students developed this software while working
under legislative restrictions that required social isolation. The goal of this
study is to characterize the experiences of the students throughout the
project. We conducted an online survey completed by 39 of the volunteering
students contributing to the COVID-19 dashboard project. Our results indicate
that playing a positive role in the COVID-19 crisis and learning new skills and
technologies were the most cited motivating factors for the students to
participate in the project. While working on the project, some students
struggled to maintain a work-life balance due to working from home. However,
the students generally did not express strong sentiment towards general project
challenges. The students expressed more strongly that data collection was a
significant challenge as it was difficult to collect reliable, accurate, and
up-to-date data from various government sources. The students have been able to
mitigate these challenges by establishing a systematic data collection process
in the team, leveraging frequent and clear communication through text, and
appreciating and encouraging each other's efforts. By participating in the
project, the students boosted their technical (e.g., front-end development) and
non-technical (e.g., task prioritization) skills. Our study discusses several
implications for students, educators, and policymakers.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.698689 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.09463
|
Mathieu Godbout
|
M. Godbout, A. Lachance, F. Antaki, A. Dirani, A. Durand
|
Predicting Visual Improvement after Macular Hole Surgery: a Cautionary
Tale on Deep Learning with Very Limited Data
|
Machine Learning for Health (ML4H) - Extended Abstract
| null | null | null |
eess.IV cs.CV cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
We investigate the potential of machine learning models for the prediction of
visual improvement after macular hole surgery from preoperative data (retinal
images and clinical features). Collecting our own data for the task, we end up
with only 121 total samples, putting our work in the very limited data regime.
We explore a variety of deep learning methods for limited data to train deep
computer vision models, finding that all tested deep vision models are
outperformed by a simple regression model on the clinical features. We believe
this is compelling evidence of the extreme difficulty of using deep learning on
very limited data.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.704999 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.12205
|
Ninad Jadhav
|
Ninad Jadhav, Weiying Wang, Diana Zhang, Swarun Kumar and Stephanie
Gil
|
Toolbox Release: A WiFi-Based Relative Bearing Sensor for Robotics
|
7 pages
| null | null | null |
cs.RO
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper presents the WiFi-Sensor-for-Robotics (WSR) toolbox, an open
source C++ framework. It enables robots in a team to obtain relative bearing to
each other, even in non-line-of-sight (NLOS) settings which is a very
challenging problem in robotics. It does so by analyzing the phase of their
communicated WiFi signals as the robots traverse the environment. This
capability, based on the theory developed in our prior works, is made available
for the first time as an opensource tool. It is motivated by the lack of easily
deployable solutions that use robots' local resources (e.g WiFi) for sensing in
NLOS. This has implications for localization, ad-hoc robot networks, and
security in multi-robot teams, amongst others. The toolbox is designed for
distributed and online deployment on robot platforms using commodity hardware
and on-board sensors. We also release datasets demonstrating its performance in
NLOS and line-of-sight (LOS) settings for a multi-robot localization usecase.
Empirical results show that the bearing estimation from our toolbox achieves
mean accuracy of 5.10 degrees. This leads to a median error of 0.5m and 0.9m
for localization in LOS and NLOS settings respectively, in a hardware
deployment in an indoor office environment.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.700255 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.14144
|
Ting-Rui Chiang
|
Ting-Rui Chiang, Yi-Ting Yeh
|
Improving Dialogue State Tracking by Joint Slot Modeling
|
Accepted to the 3rd Workshop on NLP for ConvAI in EMNLP 2021
| null | null | null |
cs.CL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dialogue state tracking models play an important role in a task-oriented
dialogue system. However, most of them model the slot types conditionally
independently given the input. We discover that it may cause the model to be
confused by slot types that share the same data type. To mitigate this issue,
we propose TripPy-MRF and TripPy-LSTM that models the slots jointly. Our
results show that they are able to alleviate the confusion mentioned above, and
they push the state-of-the-art on dataset MultiWoZ 2.1 from 58.7 to 61.3. Our
implementation is available at https://github.com/CTinRay/Trippy-Joint.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711268 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.14433
|
Sivaramakrishnan Rajaraman
|
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Sameer Antani
|
Multi-loss ensemble deep learning for chest X-ray classification
|
27 pages, 6 figures, 5 tables
| null | null | null |
eess.IV cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Medical images commonly exhibit multiple abnormalities. Predicting them
requires multi-class classifiers whose training and desired reliable
performance can be affected by a combination of factors, such as, dataset size,
data source, distribution, and the loss function used to train the deep neural
networks. Currently, the cross-entropy loss remains the de-facto loss function
for training deep learning classifiers. This loss function, however, asserts
equal learning from all classes, leading to a bias toward the majority class.
In this work, we benchmark various state-of-the-art loss functions that are
suitable for multi-class classification, critically analyze model performance,
and propose improved loss functions. We select a pediatric chest X-ray (CXR)
dataset that includes images with no abnormality (normal), and those exhibiting
manifestations consistent with bacterial and viral pneumonia. We construct
prediction-level and model-level ensembles, respectively, to improve
classification performance. Our results show that compared to the individual
models and the state-of-the-art literature, the weighted averaging of the
predictions for top-3 and top-5 model-level ensembles delivered significantly
superior classification performance (p < 0.05) in terms of MCC (0.9068, 95%
confidence interval (0.8839, 0.9297)) metric. Finally, we performed
localization studies to interpret model behaviors to visualize and confirm that
the individual models and ensembles learned meaningful features and highlighted
disease manifestations.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.69473 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2109.15193
|
Zhuoyue Lyu
|
Zhuoyue Lyu, Jiannan Li, Bryan Wang
|
AIive: Interactive Visualization and Sonification of Neural Networks in
Virtual Reality
|
3 pages, 3 figures, 2021 IEEE International Conference on Artificial
Intelligence and Virtual Reality (AIVR)
| null |
10.1109/AIVR52153.2021.00057
| null |
cs.HC
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Artificial Intelligence (AI), especially Neural Networks (NNs), has become
increasingly popular. However, people usually treat AI as a tool, focusing on
improving outcome, accuracy, and performance while paying less attention to the
representation of AI itself. We present AIive, an interactive visualization of
AI in Virtual Reality (VR) that brings AI "alive". AIive enables users to
manipulate the parameters of NNs with virtual hands and provides auditory
feedback for the real-time values of loss, accuracy, and hyperparameters. Thus,
AIive contributes an artistic and intuitive way to represent AI by integrating
visualization, sonification, and direct manipulation in VR, potentially
targeting a wide range of audiences.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710867 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.00188
|
Jianhao Wang
|
Jianhao Wang, Wenzhe Li, Haozhe Jiang, Guangxiang Zhu, Siyuan Li,
Chongjie Zhang
|
Offline Reinforcement Learning with Reverse Model-based Imagination
| null | null | null | null |
cs.LG cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In offline reinforcement learning (offline RL), one of the main challenges is
to deal with the distributional shift between the learning policy and the given
dataset. To address this problem, recent offline RL methods attempt to
introduce conservatism bias to encourage learning in high-confidence areas.
Model-free approaches directly encode such bias into policy or value function
learning using conservative regularizations or special network structures, but
their constrained policy search limits the generalization beyond the offline
dataset. Model-based approaches learn forward dynamics models with conservatism
quantifications and then generate imaginary trajectories to extend the offline
datasets. However, due to limited samples in offline datasets, conservatism
quantifications often suffer from overgeneralization in out-of-support regions.
The unreliable conservative measures will mislead forward model-based
imaginations to undesired areas, leading to overaggressive behaviors. To
encourage more conservatism, we propose a novel model-based offline RL
framework, called Reverse Offline Model-based Imagination (ROMI). We learn a
reverse dynamics model in conjunction with a novel reverse policy, which can
generate rollouts leading to the target goal states within the offline dataset.
These reverse imaginations provide informed data augmentation for model-free
policy learning and enable conservative generalization beyond the offline
dataset. ROMI can effectively combine with off-the-shelf model-free algorithms
to enable model-based generalization with proper conservatism. Empirical
results show that our method can generate more conservative behaviors and
achieve state-of-the-art performance on offline RL benchmark tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709831 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.02331
|
Bowen Weng
|
Bowen Weng, Linda Capito, Umit Ozguner, Keith Redmill
|
A Formal Characterization of Black-Box System Safety Performance with
Scenario Sampling
|
A shorter version of this manuscript has been accepted to be
published at IEEE Robotics and Automation Letters (RA-L)
|
IEEE Robotics and Automation Letters, vol. 7, no. 1, pp. 199-206,
Jan. 2022
|
10.1109/LRA.2021.3122517
| null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A typical scenario-based evaluation framework seeks to characterize a
black-box system's safety performance (e.g., failure rate) through repeatedly
sampling initialization configurations (scenario sampling) and executing a
certain test policy for scenario propagation (scenario testing) with the
black-box system involved as the test subject. In this letter, we first present
a novel safety evaluation criterion that seeks to characterize the actual
operational domain within which the test subject would remain safe indefinitely
with high probability. By formulating the black-box testing scenario as a
dynamic system, we show that the presented problem is equivalent to finding a
certain "almost" robustly forward invariant set for the given system. Second,
for an arbitrary scenario testing strategy, we propose a scenario sampling
algorithm that is provably asymptotically optimal in obtaining the safe
invariant set with arbitrarily high accuracy. Moreover, as one considers
different testing strategies (e.g., biased sampling of safety-critical cases),
we show that the proposed algorithm still converges to the unbiased
approximation of the safety characterization outcome if the scenario testing
satisfies a certain condition. Finally, the effectiveness of the presented
scenario sampling algorithms and various theoretical properties are
demonstrated in a case study of the safety evaluation of a control barrier
function-based mobile robot collision avoidance system.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708584 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.02376
|
Bernardo Anibal Subercaseaux Roa
|
Marcelo Arenas, Daniel Baez, Pablo Barcel\'o, Jorge P\'erez and
Bernardo Subercaseaux
|
Foundations of Symbolic Languages for Model Interpretability
|
Accepted as Spotlight for NeurIPS'2021
| null | null | null |
cs.AI cs.LO
|
http://creativecommons.org/licenses/by/4.0/
|
Several queries and scores have recently been proposed to explain individual
predictions over ML models. Given the need for flexible, reliable, and
easy-to-apply interpretability methods for ML models, we foresee the need for
developing declarative languages to naturally specify different explainability
queries. We do this in a principled way by rooting such a language in a logic,
called FOIL, that allows for expressing many simple but important
explainability queries, and might serve as a core for more expressive
interpretability languages. We study the computational complexity of FOIL
queries over two classes of ML models often deemed to be easily interpretable:
decision trees and OBDDs. Since the number of possible inputs for an ML model
is exponential in its dimension, the tractability of the FOIL evaluation
problem is delicate but can be achieved by either restricting the structure of
the models or the fragment of FOIL being evaluated. We also present a prototype
implementation of FOIL wrapped in a high-level declarative language and perform
experiments showing that such a language can be used in practice.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707771 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.04522
|
Lin Hongzhan
|
Hongzhan Lin, Jing Ma, Mingfei Cheng, Zhiwei Yang, Liangliang Chen and
Guang Chen
|
Rumor Detection on Twitter with Claim-Guided Hierarchical Graph
Attention Networks
|
Accepted to the main conference of EMNLP2021
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Rumors are rampant in the era of social media. Conversation structures
provide valuable clues to differentiate between real and fake claims. However,
existing rumor detection methods are either limited to the strict relation of
user responses or oversimplify the conversation structure. In this study, to
substantially reinforces the interaction of user opinions while alleviating the
negative impact imposed by irrelevant posts, we first represent the
conversation thread as an undirected interaction graph. We then present a
Claim-guided Hierarchical Graph Attention Network for rumor classification,
which enhances the representation learning for responsive posts considering the
entire social contexts and attends over the posts that can semantically infer
the target claim. Extensive experiments on three Twitter datasets demonstrate
that our rumor detection method achieves much better performance than
state-of-the-art methods and exhibits a superior capacity for detecting rumors
at early stages.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708975 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.06897
|
Yiping Lu
|
Yiping Lu, Haoxuan Chen, Jianfeng Lu, Lexing Ying, Jose Blanchet
|
Machine Learning For Elliptic PDEs: Fast Rate Generalization Bound,
Neural Scaling Law and Minimax Optimality
|
add a proof Proof Sketch in section 4.1
| null | null | null |
math.NA cs.LG cs.NA math.ST physics.comp-ph stat.ML stat.TH
|
http://creativecommons.org/licenses/by/4.0/
|
In this paper, we study the statistical limits of deep learning techniques
for solving elliptic partial differential equations (PDEs) from random samples
using the Deep Ritz Method (DRM) and Physics-Informed Neural Networks (PINNs).
To simplify the problem, we focus on a prototype elliptic PDE: the
Schr\"odinger equation on a hypercube with zero Dirichlet boundary condition,
which has wide application in the quantum-mechanical systems. We establish
upper and lower bounds for both methods, which improves upon concurrently
developed upper bounds for this problem via a fast rate generalization bound.
We discover that the current Deep Ritz Methods is sub-optimal and propose a
modified version of it. We also prove that PINN and the modified version of DRM
can achieve minimax optimal bounds over Sobolev spaces. Empirically, following
recent work which has shown that the deep model accuracy will improve with
growing training sets according to a power law, we supply computational
experiments to show a similar behavior of dimension dependent power law for
deep PDE solvers.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710635 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.08944
|
Jashandeep Singh
|
Jashandeep Singh, Arashdeep Singh, Ariba Khan, and Amar Gupta
|
Developing a novel fair-loan-predictor through a multi-sensitive
debiasing pipeline: DualFair
|
10 pages, 2 figures, 3 tables, 1 pseudocode
| null | null | null |
cs.LG cs.AI cs.CY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine learning (ML) models are increasingly used for high-stake
applications that can greatly impact people's lives. Despite their use, these
models have the potential to be biased towards certain social groups on the
basis of race, gender, or ethnicity. Many prior works have attempted to
mitigate this "model discrimination" by updating the training data
(pre-processing), altering the model learning process (in-processing), or
manipulating model output (post-processing). However, these works have not yet
been extended to the realm of multi-sensitive parameters and sensitive options
(MSPSO), where sensitive parameters are attributes that can be discriminated
against (e.g race) and sensitive options are options within sensitive
parameters (e.g black or white), thus giving them limited real-world usability.
Prior work in fairness has also suffered from an accuracy-fairness tradeoff
that prevents both the accuracy and fairness from being high. Moreover,
previous literature has failed to provide holistic fairness metrics that work
with MSPSO. In this paper, we solve all three of these problems by (a) creating
a novel bias mitigation technique called DualFair and (b) developing a new
fairness metric (i.e. AWI) that can handle MSPSO. Lastly, we test our novel
mitigation method using a comprehensive U.S mortgage lending dataset and show
that our classifier, or fair loan predictor, obtains better fairness and
accuracy metrics than current state-of-the-art models.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710189 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.13101
|
Artem Lenskiy
|
Muhammad S. Battikh, Artem A. Lenskiy
|
Latent-Insensitive autoencoders for Anomaly Detection
|
19 pages
| null | null | null |
cs.LG cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reconstruction-based approaches to anomaly detection tend to fall short when
applied to complex datasets with target classes that possess high inter-class
variance. Similar to the idea of self-taught learning used in transfer
learning, many domains are rich with similar unlabelled datasets that could be
leveraged as a proxy for out-of-distribution samples. In this paper we
introduce Latent-Insensitive autoencoder (LIS-AE) where unlabeled data from a
similar domain is utilized as negative examples to shape the latent layer
(bottleneck) of a regular autoencoder such that it is only capable of
reconstructing one task. We provide theoretical justification for the proposed
training process and loss functions along with an extensive ablation study
highlighting important aspects of our model. We test our model in multiple
anomaly detection settings presenting quantitative and qualitative analysis
showcasing the significant performance improvement of our model for anomaly
detection tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709655 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.13148
|
Emanuele Dalsasso
|
Emanuele Dalsasso, Lo\"ic Denis, Florence Tupin
|
As if by magic: self-supervised training of deep despeckling networks
with MERLIN
|
To appear on IEEE Transactions on Geoscience and Remote Sensing
| null |
10.1109/TGRS.2021.3128621
| null |
cs.CV eess.IV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Speckle fluctuations seriously limit the interpretability of synthetic
aperture radar (SAR) images. Speckle reduction has thus been the subject of
numerous works spanning at least four decades. Techniques based on deep neural
networks have recently achieved a new level of performance in terms of SAR
image restoration quality. Beyond the design of suitable network architectures
or the selection of adequate loss functions, the construction of training sets
is of uttermost importance. So far, most approaches have considered a
supervised training strategy: the networks are trained to produce outputs as
close as possible to speckle-free reference images. Speckle-free images are
generally not available, which requires resorting to natural or optical images
or the selection of stable areas in long time series to circumvent the lack of
ground truth. Self-supervision, on the other hand, avoids the use of
speckle-free images. We introduce a self-supervised strategy based on the
separation of the real and imaginary parts of single-look complex SAR images,
called MERLIN (coMplex sElf-supeRvised despeckLINg), and show that it offers a
straightforward way to train all kinds of deep despeckling networks. Networks
trained with MERLIN take into account the spatial correlations due to the SAR
transfer function specific to a given sensor and imaging mode. By requiring
only a single image, and possibly exploiting large archives, MERLIN opens the
door to hassle-free as well as large-scale training of despeckling networks.
The code of the trained models is made freely available at
https://gitlab.telecom-paris.fr/RING/MERLIN.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711017 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2110.14870
|
Francis Indaheng
|
Francis Indaheng, Edward Kim, Kesav Viswanadha, Jay Shenoy, Jinkyu
Kim, Daniel J. Fremont, Sanjit A. Seshia
|
A Scenario-Based Platform for Testing Autonomous Vehicle Behavior
Prediction Models in Simulation
|
Accepted to the NeurIPS 2021 Workshop on Machine Learning for
Autonomous Driving
| null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
Behavior prediction remains one of the most challenging tasks in the
autonomous vehicle (AV) software stack. Forecasting the future trajectories of
nearby agents plays a critical role in ensuring road safety, as it equips AVs
with the necessary information to plan safe routes of travel. However, these
prediction models are data-driven and trained on data collected in real life
that may not represent the full range of scenarios an AV can encounter. Hence,
it is important that these prediction models are extensively tested in various
test scenarios involving interactive behaviors prior to deployment. To support
this need, we present a simulation-based testing platform which supports (1)
intuitive scenario modeling with a probabilistic programming language called
Scenic, (2) specifying a multi-objective evaluation metric with a partial
priority ordering, (3) falsification of the provided metric, and (4)
parallelization of simulations for scalable testing. As a part of the platform,
we provide a library of 25 Scenic programs that model challenging test
scenarios involving interactive traffic participant behaviors. We demonstrate
the effectiveness and the scalability of our platform by testing a trained
behavior prediction model and searching for failure scenarios.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.701342 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.00931
|
Diankun Zhang
|
Diankun Zhang, Zhijie Zheng, Xueting Bi, Xiaojun Liu
|
Structure Information is the Key: Self-Attention RoI Feature Extractor
in 3D Object Detection
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Unlike 2D object detection where all RoI features come from grid pixels, the
RoI feature extraction of 3D point cloud object detection is more diverse. In
this paper, we first compare and analyze the differences in structure and
performance between the two state-of-the-art models PV-RCNN and Voxel-RCNN.
Then, we find that the performance gap between the two models does not come
from point information, but structural information. The voxel features contain
more structural information because they do quantization instead of
downsampling to point cloud so that they can contain basically the complete
information of the whole point cloud. The stronger structural information in
voxel features makes the detector have higher performance in our experiments
even if the voxel features don't have accurate location information. Then, we
propose that structural information is the key to 3D object detection. Based on
the above conclusion, we propose a Self-Attention RoI Feature Extractor (SARFE)
to enhance structural information of the feature extracted from 3D proposals.
SARFE is a plug-and-play module that can be easily used on existing 3D
detectors. Our SARFE is evaluated on both KITTI dataset and Waymo Open dataset.
With the newly introduced SARFE, we improve the performance of the
state-of-the-art 3D detectors by a large margin in cyclist on KITTI dataset
while keeping real-time capability.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711437 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.01222
|
Alexander Moreno
|
Alexander Moreno, Supriya Nagesh, Zhenke Wu, Walter Dempsey, James M.
Rehg
|
Kernel Deformed Exponential Families for Sparse Continuous Attention
| null | null | null | null |
cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Attention mechanisms take an expectation of a data representation with
respect to probability weights. This creates summary statistics that focus on
important features. Recently, (Martins et al. 2020, 2021) proposed continuous
attention mechanisms, focusing on unimodal attention densities from the
exponential and deformed exponential families: the latter has sparse support.
(Farinhas et al. 2021) extended this to use Gaussian mixture attention
densities, which are a flexible class with dense support. In this paper, we
extend this to two general flexible classes: kernel exponential families and
our new sparse counterpart kernel deformed exponential families. Theoretically,
we show new existence results for both kernel exponential and deformed
exponential families, and that the deformed case has similar approximation
capabilities to kernel exponential families. Experiments show that kernel
deformed exponential families can attend to multiple compact regions of the
data domain.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711462 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.01288
|
Behzad Ghanbarian
|
Elnaz Rezaei, Kamran Zeinalzadeh, Behzad Ghanbarian
|
Effects of particle shape and size distribution on hydraulic properties
of grain packs: An experimental study
| null |
Journal of Contaminant Hydrology Volume 243, December 2021, 103918
|
10.1016/j.jconhyd.2021.103918
| null |
physics.geo-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Uniform and multi-dispersed grain packs have been frequently used to
conceptually study flow in porous media. Numerical simulations were previously
used to address the effect of particle shape on characteristics, such as pore
space fractal dimension, moisture characteristic curve (MCC) and saturated
hydraulic conductivity (SHC) of grain packs. However, experimental observations
are still required since fractal-based approaches have been extensively
proposed to model various properties in porous media. In this study, 16 angular
sand and 16 spherical glass bead samples with different particle size
distributions (PSDs) from well- to poorly-sorted were packed. The MCC was
measured using the combination of sandbox and pressure plates methods. The pore
space fractal dimension (DMCC), calculated from the measured MCC, ranged from
0.80 to 2.86 in sand and from -0.18 to 2.81 in glass bead packs, which
indicated that DMCC may be negative in homogenous media (e.g., glass bead
packs) consistent with several studies in the literature. Results showed
greater DMCC for the sand packs than the glass bead packs with the same
geometric mean diameter values and PSDs. This clearly demonstrated the effect
of particle shape on DMCC in the studied packs. The critical path analysis
(CPA) approach was used to estimate the SHC measured using the constant-head
method. We found that the CPA estimated the SHC accurately, within a factor of
four of the measurements on average. Although the CPA is theoretically known to
be accurate in media with broad pore size distributions, we experimentally
found that it estimated the SHC in various types of grain packs reasonably
well.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710791 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.01408
|
Stefan Schippers
|
A. Perry-Sassmannshausen, T. Buhr, M. Martins, S. Reinwardt, F.
Trinter, A. M\"uller, S. Fritzsche, S. Schippers
|
Multiple photodetachment of silicon anions via K-shell excitation and
ionization
|
8 pages, 4 figures
|
Phys. Rev. A 104 (2021) 053107
|
10.1103/PhysRevA.104.053107
| null |
physics.atom-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Experimental cross sections for $m$-fold photodetachment ($m=3-6$) of silicon
anions via $K$-shell excitation and ionization were measured in the
photon-energy range of 1830-1900 eV using the photon-ion merged-beams technique
at a synchrotron light source. All cross sections exhibit a threshold behavior
that is masked by pre-threshold resonances associated with the excitation of a
$1s$ electron to higher, either partly occupied or unoccupied atomic subshells.
Results from multi-configuration Dirac-Fock (MCDF) calculations agree with the
experimentally derived cross sections for photo-absorption if small energy
shifts are applied to the calculated resonance positions and detachment
thresholds. Moreover, a systematic approach is applied for modeling the
deexcitation cascades that set in after the initial creation of a $K$-shell
hole. The resulting product charge-state distributions compare well with the
measured ones for direct $K$-shell detachment but less well for resonant
$K$-shell excitation. The present results are potentially useful for
identifying silicon anions in cold plasmas such as interstellar gas clouds.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708458 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.02947
|
Junya Chen
|
Junya Chen, Danni Lu, Zidi Xiu, Ke Bai, Lawrence Carin, Chenyang Tao
|
Variational Inference with Holder Bounds
| null | null | null | null |
cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
The recent introduction of thermodynamic integration techniques has provided
a new framework for understanding and improving variational inference (VI). In
this work, we present a careful analysis of the thermodynamic variational
objective (TVO), bridging the gap between existing variational objectives and
shedding new insights to advance the field. In particular, we elucidate how the
TVO naturally connects the three key variational schemes, namely the
importance-weighted VI, Renyi-VI, and MCMC-VI, which subsumes most VI
objectives employed in practice. To explain the performance gap between theory
and practice, we reveal how the pathological geometry of thermodynamic curves
negatively affects TVO. By generalizing the integration path from the geometric
mean to the weighted Holder mean, we extend the theory of TVO and identify new
opportunities for improving VI. This motivates our new VI objectives, named the
Holder bounds, which flatten the thermodynamic curves and promise to achieve a
one-step approximation of the exact marginal log-likelihood. A comprehensive
discussion on the choices of numerical estimators is provided. We present
strong empirical evidence on both synthetic and real-world datasets to support
our claims.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710672 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.02949
|
Junya Chen
|
Junya Chen, Sijia Wang, Lawrence Carin, Chenyang Tao
|
Finite-Time Consensus Learning for Decentralized Optimization with
Nonlinear Gossiping
| null | null | null | null |
cs.LG cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
Distributed learning has become an integral tool for scaling up machine
learning and addressing the growing need for data privacy. Although more robust
to the network topology, decentralized learning schemes have not gained the
same level of popularity as their centralized counterparts for being less
competitive performance-wise. In this work, we attribute this issue to the lack
of synchronization among decentralized learning workers, showing both
empirically and theoretically that the convergence rate is tied to the
synchronization level among the workers. Such motivated, we present a novel
decentralized learning framework based on nonlinear gossiping (NGO), that
enjoys an appealing finite-time consensus property to achieve better
synchronization. We provide a careful analysis of its convergence and discuss
its merits for modern distributed optimization applications, such as deep
neural networks. Our analysis on how communication delay and randomized chats
affect learning further enables the derivation of practical variants that
accommodate asynchronous and randomized communications. To validate the
effectiveness of our proposal, we benchmark NGO against competing solutions
through an extensive set of tests, with encouraging results reported.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709648 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.03353
|
Changhao Xu
|
Changhao Xu (1), Yu Zhang (1), Qianchi Feng (1), Rongda Liang (1),
Chuanshan Tian (1 and 2) ((1) State Key Laboratory of Surface Physics and Key
Laboratory of Micro- and Nano-Photonic Structures (MOE), Department of
Physics, Fudan University, Shanghai, China, (2) Collaborative Innovation
Center of Advanced Microstructures, Nanjing, China)
|
Self-suppression of the Giant CARS Background for Detection of Buried
Interface with Sub-monolayer Sensitivity
|
7 pages, 5 figures
| null | null | null |
physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The past decades have witnessed marked progresses on the research of
interfacial science in complex systems promoted by the advances in novel
experimental techniques. Despite its success in many fields, implementation of
coherent anti-Stokes Raman spectroscopy (CARS) for tackling the problems at
interfaces was hindered by the huge resonant and non-resonant background from
the bulk. Here we have developed a novel CARS scheme that is capable of probing
a buried interface via suppression of the non-resonant and resonant bulk
contribution by at least $10^5$ times. The method utilizes self-destructive
interference between the forward and backward CARS generated in the bulk near
the Brewster angle. As a result, we are able to resolve the vibrational
spectrum of sub-monolayer interfacial species immersed in the surrounding media
with huge CARS responses. We expect our approach not only opens up the
opportunity for interrogation of the interfaces that involve apolar molecules,
but also benefits other nonlinear optical spectroscopic techniques in promoting
signal-to-background noise ratio.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709982 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.03797
|
Beibei Wang
|
Jiahui Fan and Beibei Wang and Milo\v{s} Ha\v{s}an and Jian Yang and
Ling-Qi Yan
|
Neural BRDFs: Representation and Operations
| null | null | null | null |
cs.GR cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Bidirectional reflectance distribution functions (BRDFs) are pervasively used
in computer graphics to produce realistic physically-based appearance. In
recent years, several works explored using neural networks to represent BRDFs,
taking advantage of neural networks' high compression rate and their ability to
fit highly complex functions. However, once represented, the BRDFs will be
fixed and therefore lack flexibility to take part in follow-up operations. In
this paper, we present a form of "Neural BRDF algebra", and focus on both
representation and operations of BRDFs at the same time. We propose a
representation neural network to compress BRDFs into latent vectors, which is
able to represent BRDFs accurately. We further propose several operations that
can be applied solely in the latent space, such as layering and interpolation.
Spatial variation is straightforward to achieve by using textures of latent
vectors. Furthermore, our representation can be efficiently evaluated and
sampled, providing a competitive solution to more expensive Monte Carlo
layering approaches.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.03930
|
Peng Gao
|
Renrui Zhang, Rongyao Fang, Wei Zhang, Peng Gao, Kunchang Li, Jifeng
Dai, Yu Qiao, Hongsheng Li
|
Tip-Adapter: Training-free CLIP-Adapter for Better Vision-Language
Modeling
|
preprints
| null | null | null |
cs.CV cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Contrastive Vision-Language Pre-training, known as CLIP, has provided a new
paradigm for learning visual representations by using large-scale contrastive
image-text pairs. It shows impressive performance on zero-shot knowledge
transfer to downstream tasks. To further enhance CLIP's few-shot capability,
CLIP-Adapter proposed to fine-tune a lightweight residual feature adapter and
significantly improves the performance for few-shot classification. However,
such a process still needs extra training and computational resources. In this
paper, we propose \textbf{T}raining-Free CL\textbf{IP}-\textbf{Adapter}
(\textbf{Tip-Adapter}), which not only inherits CLIP's training-free advantage
but also performs comparably or even better than CLIP-Adapter. Tip-Adapter does
not require any back propagation for training the adapter, but creates the
weights by a key-value cache model constructed from the few-shot training set.
In this non-parametric manner, Tip-Adapter acquires well-performed adapter
weights without any training, which is both efficient and effective. Moreover,
the performance of Tip-Adapter can be further boosted by fine-tuning such
properly initialized adapter for only a few epochs with super-fast convergence
speed. We conduct extensive experiments of few-shot classification on ImageNet
and other 10 datasets to demonstrate the superiority of proposed Tip-Adapter.
The code will be released at \url{https://github.com/gaopengcuhk/Tip-Adapter}.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711418 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.03970
|
Srinath Lakshman
|
Srinath Lakshman, Vatsal Sanjay, Pierre Chantelot, Jacco H. Snoeijer
and Detlef Lohse
|
Non-wetting drop dynamics
|
The manuscript was prematurely submitted by me (first author,
Lakshman), without the consent of any of the other co-authors
| null | null | null |
physics.flu-dyn
|
http://creativecommons.org/licenses/by-sa/4.0/
|
The impact of droplets on solids or weakly-deformable surfaces can lead to
non-wetting outcomes, depending on the control parameters. Here, we perform
experiments and develop a simple model to understand the impact dynamics by
varying three important drop parameters: the Ohnesorge number $\mathcal{O}h$,
the Bond number $\mathcal{B}o$ and the Weber number $\mathcal{W}e$. The model
suggests that the droplet dynamics is captured by only two non-dimensional
groups, namely $\ \mathcal{O}h$ and $\xi = \mathcal{B}o/\sqrt{\mathcal{W}e}$.
For $\xi \ll 1$, the droplet dynamics is fully dominated by $\mathcal{O}h$, but
for $\xi \gg 1$, the dynamics depends both on $\mathcal{O}h$ and $\xi$. While
the model results show some discrepancies for small $\mathcal{O}h$, they are in
very good agreement with the experiments for moderately large $\mathcal{O}h$.
The work thereby offers an elaborate description of the droplet impact dynamics
in a non-wetting scenario.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709416 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04007
|
Nitika Saran
|
Sanjith Athlur, Nitika Saran, Muthian Sivathanu, Ramachandran Ramjee
and Nipun Kwatra
|
Varuna: Scalable, Low-cost Training of Massive Deep Learning Models
|
14 pages, 10 figures
| null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Systems for training massive deep learning models (billions of parameters)
today assume and require specialized "hyper-clusters": hundreds or thousands of
GPUs wired with specialized high-bandwidth interconnects such as NV-Link and
Infiniband. Besides being expensive, such dependence on hyper-clusters and
custom high-speed inter-connects limits the size of such clusters, creating (a)
scalability limits on job parallelism; (b) resource fragmentation across
hyper-clusters.
In this paper, we present Varuna, a new system that enables training massive
deep learning models on commodity networking. Varuna makes thrifty use of
networking resources and automatically configures the user's training job to
efficiently use any given set of resources. Therefore, Varuna is able to
leverage "low-priority" VMs that cost about 5x cheaper than dedicated GPUs,
thus significantly reducing the cost of training massive models. We demonstrate
the efficacy of Varuna by training massive models, including a 200 billion
parameter model, on 5x cheaper "spot VMs", while maintaining high training
throughput. Varuna improves end-to-end training time by up to 18x compared to
other model-parallel approaches and up to 26% compared to other pipeline
parallel approaches.
The code for Varuna is available at https://github.com/microsoft/varuna.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04266
|
Xiang Li
|
Xiang Li, Shihao Ji
|
Generative Dynamic Patch Attack
|
Published as a conference paper at BMVC 2021
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Adversarial patch attack is a family of attack algorithms that perturb a part
of image to fool a deep neural network model. Existing patch attacks mostly
consider injecting adversarial patches at input-agnostic locations: either a
predefined location or a random location. This attack setup may be sufficient
for attack but has considerable limitations when using it for adversarial
training. Thus, robust models trained with existing patch attacks cannot
effectively defend other adversarial attacks. In this paper, we first propose
an end-to-end patch attack algorithm, Generative Dynamic Patch Attack (GDPA),
which generates both patch pattern and patch location adversarially for each
input image. We show that GDPA is a generic attack framework that can produce
dynamic/static and visible/invisible patches with a few configuration changes.
Secondly, GDPA can be readily integrated for adversarial training to improve
model robustness to various adversarial attacks. Extensive experiments on
VGGFace, Traffic Sign and ImageNet show that GDPA achieves higher attack
success rates than state-of-the-art patch attacks, while adversarially trained
model with GDPA demonstrates superior robustness to adversarial patch attacks
than competing methods. Our source code can be found at
https://github.com/lxuniverse/gdpa.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710848 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04318
|
Fenglin Liu
|
Fenglin Liu, Chenyu You, Xian Wu, Shen Ge, Sheng Wang, Xu Sun
|
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
| null | null | null | null |
cs.LG cs.AI cs.CL cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Medical report generation, which aims to automatically generate a long and
coherent report of a given medical image, has been receiving growing research
interests. Existing approaches mainly adopt a supervised manner and heavily
rely on coupled image-report pairs. However, in the medical domain, building a
large-scale image-report paired dataset is both time-consuming and expensive.
To relax the dependency on paired data, we propose an unsupervised model
Knowledge Graph Auto-Encoder (KGAE) which accepts independent sets of images
and reports in training. KGAE consists of a pre-constructed knowledge graph, a
knowledge-driven encoder and a knowledge-driven decoder. The knowledge graph
works as the shared latent space to bridge the visual and textual domains; The
knowledge-driven encoder projects medical images and reports to the
corresponding coordinates in this latent space and the knowledge-driven decoder
generates a medical report given a coordinate in this space. Since the
knowledge-driven encoder and decoder can be trained with independent sets of
images and reports, KGAE is unsupervised. The experiments show that the
unsupervised KGAE generates desirable medical reports without using any
image-report training pairs. Moreover, KGAE can also work in both
semi-supervised and supervised settings, and accept paired images and reports
in training. By further fine-tuning with image-report pairs, KGAE consistently
outperforms the current state-of-the-art models on two datasets.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70939 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04326
|
Felix Kramer
|
Felix Kramer, Carl D. Modes
|
On biological flow networks: Antagonism between hydrodynamic and
metabolic stimuli as driver of topological transitions
| null | null | null | null |
q-bio.TO nlin.AO physics.bio-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A plethora of computational models have been developed in recent decades to
account for the morphogenesis of complex biological fluid networks, such as
capillary beds. Contemporary adaptation models are based on optimization
schemes where networks react and adapt toward given flow patterns. Doing so, a
system reduces dissipation and network volume, thereby altering its final form.
Yet, recent numeric studies on network morphogenesis, incorporating uptake of
metabolites by the embedding tissue, have indicated the conventional approach
to be insufficient. Here, we systematically study a hybrid-model which combines
the network adaptation schemes intended to generate space-filling perfusion as
well as optimal filtration of metabolites. As a result, we find hydrodynamic
stimuli (wall-shear stress) and filtration based stimuli (uptake of
metabolites) to be antagonistic as hydrodynamically optimized systems have
suboptimal uptake qualities and vice versa. We show that a switch between
different optimization regimes is typically accompanied with a complex
transition between topologically redundant meshes and spanning trees. Depending
on the metabolite demand and uptake capabilities of the adaptating networks, we
are further able to demonstrate the existence of nullity re-entrant behavior
and the development of compromised phenotypes such as dangling non-perfused
vessels and bottlenecks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710672 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04359
|
Burhan Gulbahar
|
Burhan Gulbahar
|
K-sparse Pure State Tomography with Phase Estimation
|
19 pages, 5 figures, edited v2
| null | null | null |
quant-ph cs.CC physics.optics
|
http://creativecommons.org/licenses/by/4.0/
|
Quantum state tomography (QST) for reconstructing pure states requires
exponentially increasing resources and measurements with the number of qubits
by using state-of-the-art quantum compressive sensing (CS) methods. In this
article, QST reconstruction for any pure state composed of the superposition of
$K$ different computational basis states of $n$ qubits in a specific
measurement set-up, i.e., denoted as $K$-sparse, is achieved without any
initial knowledge and with quantum polynomial-time complexity of resources
based on the assumption of the existence of polynomial size quantum circuits
for implementing exponentially large powers of a specially designed unitary
operator. The algorithm includes $\mathcal{O}(2 \, / \, \vert c_{k}\vert^2)$
repetitions of conventional phase estimation algorithm depending on the
probability $\vert c_{k}\vert^2$ of the least possible basis state in the
superposition and $\mathcal{O}(d \, K \,(log K)^c)$ measurement settings with
conventional quantum CS algorithms independent from the number of qubits while
dependent on $K$ for constant $c$ and $d$. Quantum phase estimation algorithm
is exploited based on the favorable eigenstructure of the designed operator to
represent any pure state as a superposition of eigenvectors. Linear optical
set-up is presented for realizing the special unitary operator which includes
beam splitters and phase shifters where propagation paths of single photon are
tracked with which-path-detectors. Quantum circuit implementation is provided
by using only CNOT, phase shifter and $- \pi \, / \, 2$ rotation gates around
X-axis in Bloch sphere, i.e., $R_{X}(- \pi \, / \, 2)$, allowing to be realized
in NISQ devices. Open problems are discussed regarding the existence of the
unitary operator and its practical circuit implementation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707979 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04731
|
Shima Kamyab
|
Shima Kamyab, Zohreh Azimifar, Rasool Sabzi, Paul Fieguth
|
Survey of Deep Learning Methods for Inverse Problems
| null | null | null | null |
cs.CV cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper we investigate a variety of deep learning strategies for
solving inverse problems. We classify existing deep learning solutions for
inverse problems into three categories of Direct Mapping, Data Consistency
Optimizer, and Deep Regularizer. We choose a sample of each inverse problem
type, so as to compare the robustness of the three categories, and report a
statistical analysis of their differences. We perform extensive experiments on
the classic problem of linear regression and three well-known inverse problems
in computer vision, namely image denoising, 3D human face inverse rendering,
and object tracking, selected as representative prototypes for each class of
inverse problems. The overall results and the statistical analyses show that
the solution categories have a robustness behaviour dependent on the type of
inverse problem domain, and specifically dependent on whether or not the
problem includes measurement outliers. Based on our experimental results, we
conclude by proposing the most robust solution category for each inverse
problem class.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711656 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.04845
|
Safwen Naimi
|
Safwen Naimi, Rien van Leeuwen, Wided Souidene and Slim Ben Saoud
|
Hybrid BYOL-ViT: Efficient approach to deal with small datasets
| null | null | null | null |
cs.CV cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Supervised learning can learn large representational spaces, which are
crucial for handling difficult learning tasks. However, due to the design of
the model, classical image classification approaches struggle to generalize to
new problems and new situations when dealing with small datasets. In fact,
supervised learning can lose the location of image features which leads to
supervision collapse in very deep architectures. In this paper, we investigate
how self-supervision with strong and sufficient augmentation of unlabeled data
can train effectively the first layers of a neural network even better than
supervised learning, with no need for millions of labeled data. The main goal
is to disconnect pixel data from annotation by getting generic task-agnostic
low-level features. Furthermore, we look into Vision Transformers (ViT) and
show that the low-level features derived from a self-supervised architecture
can improve the robustness and the overall performance of this emergent
architecture. We evaluated our method on one of the smallest open-source
datasets STL-10 and we obtained a significant boost of performance from 41.66%
to 83.25% when inputting low-level features from a self-supervised learning
architecture to the ViT instead of the raw images.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71022 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05004
|
Fabrizio Sossan
|
Stefano Cassano and Fabrizio Sossan
|
Model Predictive Control for a Medium-head Hydropower Plant Hybridized
with Battery Energy Storage to Reduce Penstock Fatigue
|
Paper submitted for PSCC 2022
| null | null | null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
A hybrid hydropower power plant is a conventional HydroPower Plant (HPP)
augmented with a Battery Energy Storage System (BESS) to decrease the wear and
tear of sensitive mechanical components and improve the reliability and
regulation performance of the overall plant. A central task of controlling
hybrid power plants is determining how the total power set-point should be
split between the BESS and the hybridized unit (power set-point splitting) as a
function of the operational objectives. This paper describes a Model Predictive
Control (MPC) framework for hybrid medium- and high-head plants to determine
the power set-point of the hydropower unit and the BESS. The splitting policy
relies on an explicit formulation of the mechanical loads incurred by the HPP's
penstock, which can be damaged due to fatigue when providing regulation
services to the grid. By filtering out from the HPP's power set-point the
components conducive to excess penstock fatigue and properly controlling the
BESS, the proposed MPC is able to maintain the same level of regulation
performance while significantly decreasing damages to the hydraulic conduits. A
proof-of-concept by simulations is provided considering a 230 MW medium-head
hydropower plant.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708629 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05025
|
Ashot Gevorgyan
|
A.H. Gevorgyan
|
Dirac points in helically structured 1D photonic crystals
| null | null | null | null |
physics.optics
|
http://creativecommons.org/licenses/by-sa/4.0/
|
We reported about observation of Dirac points in a helically structured 1D
photonic crystals, moreover, both as in the presence of longitudinal magnetic
field as its absence. We obtained analytical formulas for Dirac points
frequencies and the analytical dispersion relations for wave vectors.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.714603 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05701
|
Chaobing Zheng
|
Yuwen Li, Chaobing Zheng, Shiqian Wu, Wangming Xu
|
Single image dehazing via combining the prior knowledge and CNNs
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Aiming at the existing single image haze removal algorithms, which are based
on prior knowledge and assumptions, subject to many limitations in practical
applications, and could suffer from noise and halo amplification. An end-to-end
system is proposed in this paper to reduce defects by combining the prior
knowledge and deep learning method. The haze image is decomposed into the base
layer and detail layers through a weighted guided image filter (WGIF) firstly,
and the airlight is estimated from the base layer. Then, the base layer image
is passed to the efficient deep convolutional network for estimating the
transmission map. To restore object close to the camera completely without
amplifying noise in sky or heavily hazy scene, an adaptive strategy is proposed
based on the value of the transmission map. If the transmission map of a pixel
is small, the base layer of the haze image is used to recover a haze-free image
via atmospheric scattering model, finally. Otherwise, the haze image is used.
Experiments show that the proposed method achieves superior performance over
existing methods.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712826 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.05721
|
Lakith Rambukkanage
|
Sahan Jayasinghe, Lakith Rambukkanage, Ashan Silva, Nisansa de Silva,
Amal Shehan Perera
|
Critical Sentence Identification in Legal Cases Using Multi-Class
Classification
| null | null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Inherently, the legal domain contains a vast amount of data in text format.
Therefore it requires the application of Natural Language Processing (NLP) to
cater to the analytically demanding needs of the domain. The advancement of NLP
is spreading through various domains, such as the legal domain, in forms of
practical applications and academic research. Identifying critical sentences,
facts and arguments in a legal case is a tedious task for legal professionals.
In this research we explore the usage of sentence embeddings for multi-class
classification to identify critical sentences in a legal case, in the
perspective of the main parties present in the case. In addition, a
task-specific loss function is defined in order to improve the accuracy
restricted by the straightforward use of categorical cross entropy loss.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711049 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06038
|
Chaobing Zheng
|
Chaobing Zheng, Zhengguo Li, and Shiqian Wu
|
Hybrid Saturation Restoration for LDR Images of HDR Scenes
|
arXiv admin note: text overlap with arXiv:2007.02042
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
There are shadow and highlight regions in a low dynamic range (LDR) image
which is captured from a high dynamic range (HDR) scene. It is an ill-posed
problem to restore the saturated regions of the LDR image. In this paper, the
saturated regions of the LDR image are restored by fusing model-based and
data-driven approaches. With such a neural augmentation, two synthetic LDR
images are first generated from the underlying LDR image via the model-based
approach. One is brighter than the input image to restore the shadow regions
and the other is darker than the input image to restore the high-light regions.
Both synthetic images are then refined via a novel exposedness aware saturation
restoration network (EASRN). Finally, the two synthetic images and the input
image are combined together via an HDR synthesis algorithm or a multi-scale
exposure fusion algorithm. The proposed algorithm can be embedded in any smart
phones or digital cameras to produce an information-enriched LDR image.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713257 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06362
|
Tanmay Inamdar
|
Tanmay Inamdar, Kasturi Varadarajan
|
Non-Uniform $k$-Center and Greedy Clustering
| null | null | null | null |
cs.DS cs.CG
|
http://creativecommons.org/licenses/by/4.0/
|
In the Non-Uniform $k$-Center problem, a generalization of the famous
$k$-center clustering problem, we want to cover the given set of points in a
metric space by finding a placement of balls with specified radii. In
$t$-NU$k$C Problem, we assume that the number of distinct radii is equal to
$t$, and we are allowed to use $k_i$ balls of radius $r_i$, for $1 \le i \le
t$. This problem was introduced by Chakrabarty et al. [ACM Trans. Alg.
16(4):46:1-46:19], who showed that a constant approximation for $t$-NU$k$C is
not possible if $t$ is unbounded. On the other hand, they gave a bicriteria
approximation that violates the number of allowed balls as well as the given
radii by a constant factor. They also conjectured that a constant approximation
for $t$-NU$k$C should be possible if $t$ is a fixed constant. Since then, there
has been steady progress towards resolving this conjecture -- currently, a
constant approximation for $3$-NU$k$C is known via the results of Chakrabarty
and Negahbani [IPCO 2021], and Jia et al. [To appear in SOSA 2022]. We push the
horizon by giving an $O(1)$-approximation for the Non-Uniform $k$-Center for
$4$ distinct types of radii. Our result is obtained via a novel combination of
tools and techniques from the $k$-center literature, which also demonstrates
that the different generalizations of $k$-center involving non-uniform radii,
and multiple coverage constraints (i.e., colorful $k$-center), are closely
interlinked with each other. We hope that our ideas will contribute towards a
deeper understanding of the $t$-NU$k$C problem, eventually bringing us closer
to the resolution of the CGK conjecture.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70866 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06370
|
Juan Llorca-Schenk
|
Juan Llorca-Schenk, Irene Sentana-Gadea, Miguel Sanchez-Lozano
|
Design of porthole aluminium extrusion dies through mathematical
formulation
| null | null |
10.1016/j.mtcomm.2021.102301
| null |
stat.AP physics.data-an
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A mathematical approach to solve the porthole die design problem is achieved
by statistical analysis of a large amount of geometric data of successful
porthole die designs. Linear and logarithmic regression are used to analyse
geometrical data of 596 different ports from 88 first trial dies.
Non-significant variables or high correlated variables are discarded according
to knowledge of the extrusion process and statistical criteria. This paper
focuses on a validation model for a typical case of porthole dies: four
cavities and four ports per cavity dies. This mathematical formulation is a way
of summarizing in a single expression the experience accumulated in a large
number of designs over time. A broad way of research is open to generalise this
model or extend it to other types of porthole dies.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710829 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.06583
|
Sang Hoon Lee
|
Sang Hoon Lee
|
Mesoscale properties of mutualistic networks in ecosystems
|
9 pages, 4 figures, in Korean
|
New Phys.: Sae Mulli 70, 1077 (2020)
|
10.3938/NPSM.70.1077
| null |
physics.soc-ph cond-mat.stat-mech q-bio.QM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Uncovering structural properties of ecological networks is a crucial starting
point of studying the system's stability in response to various types of
perturbations. We analyze pollination and seed disposal networks, which are
representative examples of mutualistic networks in ecosystems, in various
scales. In particular, we examine mesoscale properties such as the nested
structure, the core-periphery structure, and the community structure by
statistically investigating their interrelationships with real network data. As
a result of community detection in different scales, we find the absence of
meaningful hierarchy between networks, and the negative correlation between the
modularity and the two other structures (nestedness and core-periphery-ness),
which themselves are highly positively correlated. In addition, no
characteristic scale of communities is perceivable from the
community-inconsistency analysis. Therefore, the community structures, which
are most widely studied mesoscale structures of networks, are not in fact
adequate to characterize the mutualistic networks of this scale in ecosystems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710025 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
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