id
large_stringlengths 9
16
| submitter
large_stringlengths 1
64
⌀ | authors
large_stringlengths 4
58.5k
| title
large_stringlengths 1
381
| comments
large_stringlengths 1
827
⌀ | journal-ref
large_stringlengths 1
557
⌀ | doi
large_stringlengths 8
153
⌀ | report-no
large_stringlengths 2
509
⌀ | categories
large_stringlengths 5
125
| license
large_stringclasses 9
values | abstract
large_stringlengths 6
5.09k
| update_date
timestamp[ms]date 2007-05-23 00:00:00
2025-08-15 00:00:00
| classification_label
stringclasses 2
values | is_new_dataset
bool 2
classes | confidence_score
float64 0.5
0.72
| classification_date
stringdate 2025-08-19 16:18:03
2025-08-19 16:18:03
| model_version
stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2111.07200
|
Marco Polin
|
Lewis Scott Mosby and Anne Straube and Marco Polin
|
Predicting the Directional Transport of Multivalent Cargo from Position
Dependent Binding and Unbinding Rates
|
13 pages, 5 figures (plus Supplementary Material)
| null | null | null |
q-bio.SC physics.bio-ph q-bio.QM
|
http://creativecommons.org/licenses/by/4.0/
|
Multivalent cargo that can interact with substrates via multiple interaction
sites exhibit shared characteristics despite being found in different systems
at different length-scales. Here, a general analytical model has been developed
that can describe the motion of multivalent cargo as a response to position
dependence in the binding and unbinding rates of their interaction sites. Cargo
exhibit both an effective diffusivity and velocity, which acts in the direction
of increasing cargo-substrate binding rate and decreasing cargo-substrate
unbinding rate. This model can reproduce previously published experimental
findings using only the binding and unbinding rate distributions of cargo
interaction sites, and without any further parameter fitting. Extension of the
cargo binding model to two dimensions reveals an effective velocity with the
same properties as that derived for the $1$D case.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712651 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07217
|
Paul Liu
|
Paul Liu, Aviad Rubinstein, Jan Vondrak, Junyao Zhao
|
Cardinality constrained submodular maximization for random streams
|
To appear in NeurIPS 2021
| null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
We consider the problem of maximizing submodular functions in single-pass
streaming and secretaries-with-shortlists models, both with random arrival
order. For cardinality constrained monotone functions, Agrawal, Shadravan, and
Stein gave a single-pass $(1-1/e-\varepsilon)$-approximation algorithm using
only linear memory, but their exponential dependence on $\varepsilon$ makes it
impractical even for $\varepsilon=0.1$. We simplify both the algorithm and the
analysis, obtaining an exponential improvement in the $\varepsilon$-dependence
(in particular, $O(k/\varepsilon)$ memory). Extending these techniques, we also
give a simple $(1/e-\varepsilon)$-approximation for non-monotone functions in
$O(k/\varepsilon)$ memory. For the monotone case, we also give a corresponding
unconditional hardness barrier of $1-1/e+\varepsilon$ for single-pass
algorithms in randomly ordered streams, even assuming unlimited computation.
Finally, we show that the algorithms are simple to implement and work well on
real world datasets.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710597 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07218
|
Songxiang Liu
|
Songxiang Liu, Dan Su, Dong Yu
|
Meta-Voice: Fast few-shot style transfer for expressive voice cloning
using meta learning
|
Pre-print technical report, 6 pages, 6 figures
| null | null | null |
eess.AS cs.CL cs.SD
|
http://creativecommons.org/licenses/by/4.0/
|
The task of few-shot style transfer for voice cloning in text-to-speech (TTS)
synthesis aims at transferring speaking styles of an arbitrary source speaker
to a target speaker's voice using very limited amount of neutral data. This is
a very challenging task since the learning algorithm needs to deal with
few-shot voice cloning and speaker-prosody disentanglement at the same time.
Accelerating the adaptation process for a new target speaker is of importance
in real-world applications, but even more challenging. In this paper, we
approach to the hard fast few-shot style transfer for voice cloning task using
meta learning. We investigate the model-agnostic meta-learning (MAML) algorithm
and meta-transfer a pre-trained multi-speaker and multi-prosody base TTS model
to be highly sensitive for adaptation with few samples. Domain adversarial
training mechanism and orthogonal constraint are adopted to disentangle speaker
and prosody representations for effective cross-speaker style transfer.
Experimental results show that the proposed approach is able to conduct fast
voice cloning using only 5 samples (around 12 second speech data) from a target
speaker, with only 100 adaptation steps. Audio samples are available online.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711017 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07220
|
Qiyuan Tian Dr.
|
Qiyuan Tian, Ziyu Li, Qiuyun Fan, Jonathan R. Polimeni, Berkin Bilgic,
David H. Salat, Susie Y. Huang
|
SDnDTI: Self-supervised deep learning-based denoising for diffusion
tensor MRI
| null | null | null | null |
eess.IV cs.LG physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The noise in diffusion-weighted images (DWIs) decreases the accuracy and
precision of diffusion tensor magnetic resonance imaging (DTI) derived
microstructural parameters and leads to prolonged acquisition time for
achieving improved signal-to-noise ratio (SNR). Deep learning-based image
denoising using convolutional neural networks (CNNs) has superior performance
but often requires additional high-SNR data for supervising the training of
CNNs, which reduces the practical feasibility. We develop a self-supervised
deep learning-based method entitled "SDnDTI" for denoising DTI data, which does
not require additional high-SNR data for training. Specifically, SDnDTI divides
multi-directional DTI data into many subsets, each consisting of six DWI
volumes along optimally chosen diffusion-encoding directions that are robust to
noise for the tensor fitting, and then synthesizes DWI volumes along all
acquired directions from the diffusion tensors fitted using each subset of the
data as the input data of CNNs. On the other hand, SDnDTI synthesizes DWI
volumes along acquired diffusion-encoding directions with higher SNR from the
diffusion tensors fitted using all acquired data as the training target. SDnDTI
removes noise from each subset of synthesized DWI volumes using a deep
3-dimensional CNN to match the quality of the cleaner target DWI volumes and
achieves even higher SNR by averaging all subsets of denoised data. The
denoising efficacy of SDnDTI is demonstrated on two datasets provided by the
Human Connectome Project (HCP) and the Lifespan HCP in Aging. The SDnDTI
results preserve image sharpness and textural details and substantially improve
upon those from the raw data. The results of SDnDTI are comparable to those
from supervised learning-based denoising and outperform those from
state-of-the-art conventional denoising algorithms including BM4D, AONLM and
MPPCA.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712263 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07222
|
William Kuszmaul
|
William Kuszmaul, Shyam Narayanan
|
Stochastic and Worst-Case Generalized Sorting Revisited
| null |
FOCS 2021
| null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The \emph{generalized sorting problem} is a restricted version of standard
comparison sorting where we wish to sort $n$ elements but only a subset of
pairs are allowed to be compared. Formally, there is some known graph $G = (V,
E)$ on the $n$ elements $v_1, \dots, v_n$, and the goal is to determine the
true order of the elements using as few comparisons as possible, where all
comparisons $(v_i, v_j)$ must be edges in $E$. We are promised that if the true
ordering is $x_1 < x_2 < \cdots < x_n$ for $\{x_i\}$ an unknown permutation of
the vertices $\{v_i\}$, then $(x_i, x_{i+1}) \in E$ for all $i$: this
Hamiltonian path ensures that sorting is actually possible.
In this work, we improve the bounds for generalized sorting on both random
graphs and worst-case graphs. For Erd\H{o}s-Renyi random graphs $G(n, p)$ (with
the promised Hamiltonian path added to ensure sorting is possible), we provide
an algorithm for generalized sorting with an expected $O(n \log (np))$
comparisons, which we prove to be optimal for query complexity. This strongly
improves over the best known algorithm of Huang, Kannan, and Khanna (FOCS
2011), which uses $\tilde{O}(\min(n \sqrt{np}, n/p^2))$ comparisons. For
arbitrary graphs $G$ with $n$ vertices and $m$ edges (again with the promised
Hamiltonian path), we provide an algorithm for generalized sorting with
$\tilde{O}(\sqrt{mn})$ comparisons. This improves over the best known algorithm
of Huang et al., which uses $\min(m, \tilde{O}(n^{3/2}))$ comparisons.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709384 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07226
|
Kostis Kaffes
|
Kostis Kaffes and Neeraja J. Yadwadkar and Christos Kozyrakis
|
Practical Scheduling for Real-World Serverless Computing
| null | null | null | null |
cs.DC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Serverless computing has seen rapid growth due to the ease-of-use and
cost-efficiency it provides. However, function scheduling, a critical component
of serverless systems, has been overlooked. In this paper, we take a
first-principles approach toward designing a scheduler that caters to the
unique characteristics of serverless functions as seen in real-world
deployments. We first create a taxonomy of scheduling policies along three
dimensions. Next, we use simulation to explore the scheduling policy space for
the function characteristics in a 14-day trace of Azure functions and conclude
that frequently used features such as late binding and random load balancing
are sub-optimal for common execution time distributions and load ranges. We use
these insights to design Hermes, a scheduler for serverless functions with
three key characteristics. First, to avoid head-of-line blocking due to high
function execution time variability, Hermes uses a combination of early binding
and processor sharing for scheduling at individual worker machines. Second,
Hermes uses a hybrid load balancing approach that improves consolidation at low
load while employing least-loaded balancing at high load to retain high
performance. Third, Hermes is both load and locality-aware, reducing the number
of cold starts compared to pure load-based policies. We implement Hermes for
Apache OpenWhisk and demonstrate that, for the case of the function patterns
observed both in the Azure and in other real-world traces, it achieves up to
85% lower function slowdown and 60% higher throughput compared to existing
policies.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710879 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07228
|
Jiwen Zhang
|
Jiwen Zhang, Zhongyu Wei, Jianqing Fan, Jiajie Peng
|
Curriculum Learning for Vision-and-Language Navigation
|
Accepted by NeurIPS 2021
| null | null | null |
cs.LG cs.AI cs.CL cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Vision-and-Language Navigation (VLN) is a task where an agent navigates in an
embodied indoor environment under human instructions. Previous works ignore the
distribution of sample difficulty and we argue that this potentially degrade
their agent performance. To tackle this issue, we propose a novel
curriculum-based training paradigm for VLN tasks that can balance human prior
knowledge and agent learning progress about training samples. We develop the
principle of curriculum design and re-arrange the benchmark Room-to-Room (R2R)
dataset to make it suitable for curriculum training. Experiments show that our
method is model-agnostic and can significantly improve the performance, the
generalizability, and the training efficiency of current state-of-the-art
navigation agents without increasing model complexity.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712657 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07229
|
Bin Pan
|
Bin Pan
|
Video Streaming in Cooperative Vehicular Networks
| null | null | null | null |
cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Video services in vehicular networks play a significant role in our daily
traveling. In this paper, we propose a cooperative communication scheme to
facilitate video data transmission, utilizing the mobility of vehicles and the
cooperation among infrastructure and vehicles. To improve the video quality of
experience (QoE), i.e., reduce the interruption ratio, quality variation and
improve the playback quality, we design a Back Compensation (BC) video
transmission strategy with the knowledge of vehicle status information. In
addition, we analyze the throughput with one-hop and target-cluster-based
cooperation schemes and obtain their closed-form expressions, respectively,
which is useful for video encoding design in the central server. Simulation
results demonstrate that the proposed approach can improve the video
performance significantly and verify the accuracy of our analytical results.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07232
|
Tao He
|
Tao He (1, 2 and 3), Tong Liu (4), Shiyi Xiao (5), Zeyong Wei (1 and
3), Zhanshan Wang (1, 2 and 3), Lei Zhou (4), Xinbin Cheng (1, 2 and 3) ((1)
MOE Key Laboratory of Advanced Micro-Structured Materials, Shanghai China,
(2) Institute of Precision Optical Engineering, School of Physics Science and
Engineering, Tongji University, Shanghai China, (3) Shanghai Institute of
Intelligent Science and Technology, Tongji University, Shanghai China, (4)
State Key Laboratory of Surface Physics, Key Laboratory of Micro and Nano
Photonic Structures (Ministry of Education), and Department of Physics, Fudan
University, Shanghai China, (5) Key Laboratory of Specialty Fiber Optics and
Optical Access Networks, Joint International Research Laboratory of Specialty
Fiber Optics and Advanced Communication, Shanghai University, Shanghai China)
|
Perfect anomalous reflectors at optical frequencies
|
29 pages, 5 figures
| null | null | null |
physics.optics physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Reflecting light to a pre-determined non-specular direction is an important
ability of metasurfaces, which is the basis for a wide range of applications
(e.g., beam steering/splitting and imaging). However, anomalous reflection with
100% efficiency has not been achieved at optical frequencies in conventional
metasurfaces, due to losses and/or insufficient nonlocal control of light
waves. Here, we propose a new type of all-dielectric quasi-three-dimensional
subwavelength structures, consisting of multilayer films and specifically
designed meta-gratings, to achieve perfect anomalous reflections at optical
frequencies. A complex multiple scattering process was stimulated by
effectively coupling different Bloch waves and propagating waves in the
proposed meta-system, thus offering the whole meta-system the desired nonlocal
control on light waves required to achieve perfect anomalous reflections. Two
perfect anomalous reflectors were designed to reflect normally incident 1550 nm
light to the 40{\deg} and 75{\deg} directions with absolute efficiencies higher
than 99%, and were subsequently fabricated and experimentally demonstrated to
exhibit efficiencies 98% and 88%, respectively. Our results pave the way
towards realizing optical meta-devices with desired high efficiencies in
realistic applications.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712826 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07234
|
Fatemeh Daneshfar
|
Fatemeh Daneshfar, Seyed Jahanshah Kabudian
|
Speech Emotion Recognition System by Quaternion Nonlinear Echo State
Network
| null | null | null | null |
cs.SD eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
The echo state network (ESN) is a powerful and efficient tool for displaying
dynamic data. However, many existing ESNs have limitations for properly
modeling high-dimensional data. The most important limitation of these networks
is the high memory consumption due to their reservoir structure, which has
prevented the increase of reservoir units and the maximum use of special
capabilities of this type of network. One way to solve this problem is to use
quaternion algebra. Because quaternions have four different dimensions,
high-dimensional data are easily represented and, using Hamilton
multiplication, with fewer parameters than real numbers, make external
relations between the multidimensional features easier. In addition to the
memory problem in the ESN network, the linear output of the ESN network poses
an indescribable limit to its processing capacity, as it cannot effectively
utilize higher-order statistics of features provided by the nonlinear dynamics
of reservoir neurons. In this research, a new structure based on ESN is
presented, in which quaternion algebra is used to compress the network data
with the simple split function, and the output linear combiner is replaced by a
multidimensional bilinear filter. This filter will be used for nonlinear
calculations of the output layer of the ESN. In addition, the two-dimensional
principal component analysis technique is used to reduce the number of data
transferred to the bilinear filter. In this study, the coefficients and the
weights of the quaternion nonlinear ESN (QNESN) are optimized using the genetic
algorithm. In order to prove the effectiveness of the proposed model compared
to the previous methods, experiments for speech emotion recognition have been
performed on EMODB, SAVEE, and IEMOCAP speech emotional datasets. Comparisons
show that the proposed QNESN network performs better than the ESN and most
currently SER systems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07235
|
Yasushi Kawase
|
Yasushi Kawase, Hanna Sumita
|
Online Max-min Fair Allocation
| null | null | null | null |
cs.GT cs.DS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We study an online version of the max-min fair allocation problem for
indivisible items. In this problem, items arrive one by one, and each item must
be allocated irrevocably on arrival to one of $n$ agents, who have additive
valuations for the items. Our goal is to make the least happy agent as happy as
possible. In research on the topic of online allocation, this is a fundamental
and natural problem. Our main result is to reveal the asymptotic competitive
ratios of the problem for both the adversarial and i.i.d. input models. We
design a polynomial-time deterministic algorithm that is asymptotically
$1/n$-competitive for the adversarial model, and we show that this guarantee is
optimal. To this end, we present a randomized algorithm with the same
competitive ratio first and then derandomize it. A natural derandomization
fails to achieve the competitive ratio of $1/n$. We instead build the algorithm
by introducing a novel technique. When the items are drawn from an unknown
identical and independent distribution, we construct a simple polynomial-time
deterministic algorithm that outputs a nearly optimal allocation. We analyze
the strict competitive ratio and show almost tight bounds for the solution. We
further mention some implications of our results on variants of the problem.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710823 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07238
|
Kien Luong
|
Kien Luong, Mohammad Hadi, Ferdian Thung, Fatemeh Fard, and David Lo
|
FACOS: Finding API Relevant Contents on Stack Overflow with Semantic and
Syntactic Analysis
| null | null | null | null |
cs.SE cs.AI cs.PL
|
http://creativecommons.org/licenses/by/4.0/
|
Collecting API examples, usages, and mentions relevant to a specific API
method over discussions on venues such as Stack Overflow is not a trivial
problem. It requires efforts to correctly recognize whether the discussion
refers to the API method that developers/tools are searching for. The content
of the thread, which consists of both text paragraphs describing the
involvement of the API method in the discussion and the code snippets
containing the API invocation, may refer to the given API method. Leveraging
this observation, we develop FACOS, a context-specific algorithm to capture the
semantic and syntactic information of the paragraphs and code snippets in a
discussion. FACOS combines a syntactic word-based score with a score from a
predictive model fine-tuned from CodeBERT. FACOS beats the state-of-the-art
approach by 13.9% in terms of F1-score.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07244
|
Sharat Ibrahimpur
|
Sharat Ibrahimpur and Chaitanya Swamy
|
A Simple Approximation Algorithm for Vector Scheduling and Applications
to Stochastic Min-Norm Load Balancing
|
An extended abstract is to appear in the Proceedings of the 5th SOSA,
2022
| null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
We consider the Vector Scheduling problem on identical machines: we have m
machines, and a set J of n jobs, where each job j has a processing-time vector
$p_j\in \mathbb{R}^d_{\geq 0}$. The goal is to find an assignment $\sigma:J\to
[m]$ of jobs to machines so as to minimize the makespan $\max_{i\in
[m]}\max_{r\in [d]}( \sum_{j:\sigma(j)=i}p_{j,r})$. A natural lower bound on
the optimal makespan is lb $:=\max\{\max_{j\in J,r\in [d]}p_{j,r},\max_{r\in
[d]}(\sum_{j\in J}p_{j,r}/m)\}$. Our main result is a very simple O(log
d)-approximation algorithm for vector scheduling with respect to the lower
bound lb: we devise an algorithm that returns an assignment whose makespan is
at most O(log d)*lb.
As an application, we show that the above guarantee leads to an O(log log
m)-approximation for Stochastic Minimum-Norm Load Balancing (StochNormLB). In
StochNormLB, we have m identical machines, a set J of n independent stochastic
jobs whose processing times are nonnegative random variables, and a monotone,
symmetric norm $f:\mathbb{R}^m \to \mathbb{R}_{\geq 0}$. The goal is to find an
assignment $\sigma:J\to [m]$ that minimizes the expected $f$-norm of the
induced machine-load vector, where the load on machine i is the (random) total
processing time assigned to it. Our O(log log m)-approximation guarantee is in
fact much stronger: we obtain an assignment that is simultaneously an O(log log
m)-approximation for StochNormLB with all monotone, symmetric norms. Next, this
approximation factor significantly improves upon the O(log m/log log
m)-approximation in (Ibrahimpur and Swamy, FOCS 2020) for StochNormLB, and is a
consequence of a more-general black-box reduction that we present, showing that
a $\gamma(d)$-approximation for d-dimensional vector scheduling with respect to
the lower bound lb yields a simultaneous $\gamma(\log m)$-approximation for
StochNormLB with all monotone, symmetric norms.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707834 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07249
|
Qi Yu
|
Qi Yu, Shota Yokoyama, Daoyi Dong, David McManus and Hidehiro Yonezawa
|
Simultaneous estimation of parameters and the state of an optical
parametric oscillator system
|
8 pages, 5 figures
| null | null | null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we consider the filtering problem of an optical parametric
oscillator (OPO). The OPO pump power may fluctuate due to environmental
disturbances, resulting in uncertainty in the system modeling. Thus, both the
state and the unknown parameter may need to be estimated simultaneously. We
formulate this problem using a state-space representation of the OPO dynamics.
Under the assumption of Gaussianity and proper constraints, the dual Kalman
filter method and the joint extended Kalman filter method are employed to
simultaneously estimate the system state and the pump power. Numerical examples
demonstrate the effectiveness of the proposed algorithms.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711875 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07254
|
George Papakostas Prof.
|
T. Kalampokas and G.A. Papakostas
|
Moment Transform-Based Compressive Sensing in Image Processing
|
12 pages, 13 figures
| null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Over the last decades, images have become an important source of information
in many domains, thus their high quality has become necessary to acquire better
information. One of the important issues that arise is image denoising, which
means recovering a signal from inaccurately and/or partially measured samples.
This interpretation is highly correlated to the compressive sensing theory,
which is a revolutionary technology and implies that if a signal is sparse then
the original signal can be obtained from a few measured values, which are much
less, than the ones suggested by other used theories like Shannon's sampling
theories. A strong factor in Compressive Sensing (CS) theory to achieve the
sparsest solution and the noise removal from the corrupted image is the
selection of the basis dictionary. In this paper, Discrete Cosine Transform
(DCT) and moment transform (Tchebichef, Krawtchouk) are compared in order to
achieve image denoising of Gaussian additive white noise based on compressive
sensing and sparse approximation theory. The experimental results revealed that
the basis dictionaries constructed by the moment transform perform
competitively to the traditional DCT. The latter transform shows a higher PSNR
of 30.82 dB and the same 0.91 SSIM value as the Tchebichef transform. Moreover,
from the sparsity point of view, Krawtchouk moments provide approximately
20-30% more sparse results than DCT.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71027 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07256
|
Elena Mikhalkova
|
Elena Mikhalkova, Timofei Protasov, Anastasiia Drozdova, Anastasiia
Bashmakova, Polina Gavin
|
Towards annotation of text worlds in a literary work
|
Conference: Computational Linguistics and Intellectual Technologies.
Papers from the Annual International Conference Dialogue At: Moscow, Russia
Volume: Issue 18. Supplementary volume
| null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
Literary texts are usually rich in meanings and their interpretation
complicates corpus studies and automatic processing. There have been several
attempts to create collections of literary texts with annotation of literary
elements like the author's speech, characters, events, scenes etc. However,
they resulted in small collections and standalone rules for annotation. The
present article describes an experiment on lexical annotation of text worlds in
a literary work and quantitative methods of their comparison. The experiment
shows that for a well-agreed tag assignment annotation rules should be set much
more strictly. However, if borders between text worlds and other elements are
the result of a subjective interpretation, they should be modeled as fuzzy
entities.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.694056 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07258
|
Jichao Kan
|
Jichao Kan, Kun Hu, Markus Hagenbuchner, Ah Chung Tsoi, Mohammed
Bennamounm, Zhiyong Wang
|
Sign Language Translation with Hierarchical Spatio-TemporalGraph Neural
Network
| null | null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Sign language translation (SLT), which generates text in a spoken language
from visual content in a sign language, is important to assist the
hard-of-hearing community for their communications. Inspired by neural machine
translation (NMT), most existing SLT studies adopted a general sequence to
sequence learning strategy. However, SLT is significantly different from
general NMT tasks since sign languages convey messages through multiple
visual-manual aspects. Therefore, in this paper, these unique characteristics
of sign languages are formulated as hierarchical spatio-temporal graph
representations, including high-level and fine-level graphs of which a vertex
characterizes a specified body part and an edge represents their interactions.
Particularly, high-level graphs represent the patterns in the regions such as
hands and face, and fine-level graphs consider the joints of hands and
landmarks of facial regions. To learn these graph patterns, a novel deep
learning architecture, namely hierarchical spatio-temporal graph neural network
(HST-GNN), is proposed. Graph convolutions and graph self-attentions with
neighborhood context are proposed to characterize both the local and the global
graph properties. Experimental results on benchmark datasets demonstrated the
effectiveness of the proposed method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71123 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07260
|
Humeyra Caglayan
|
Rakesh Dhama, Ali Panahpour, Tuomas Pihlava, Dipa Ghindani and Humeyra
Caglayan
|
All-optical switching via coherent control of plasmon resonances
| null | null | null | null |
physics.optics
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A novel ultrafast all-optical switching mechanism is demonstrated
theoretically and experimentally based on a plasmonic analog of the effect of
\textit{Enhancement of Index of Refraction}(EIR) in quantum optics. In the
quantum optical EIR the atomic systems are rendered by coherence and quantum
interference to exhibit orders of magnitude higher index of refraction with
vanishing or even negative absorption near their resonances. Similarly, in the
plasmon-induced EIR, a probe signal can experience positive, zero or negative
extinction while strongly interacting with a metallic nanorod in a metamolecule
that is coherently excited by a control beam. The same mechanism is observed in
the collective response of a square array of such metamolecules in the form of
a metasurface to modulate the amplitude of a signal by coherent control of
absorption from positive to negative values without implementing gain materials
or nonlinear processes. This novel approach can be used for challenging the
control of light by light at the extreme levels of space, time, and intensity
by applying ultra-short pulses interacting with ultrafast surface plasmons or
extremely low-intensity pulses at the level of single photon to a nanoscale
single plasmonic metamolecule. The scheme also introduces an effective tool for
improving the modulation strength of optical modulators and switches through
the amplification of the input signal.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712826 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07263
|
Tenzin Jinpa
|
Tenzin Jinpa and Yong Gao
|
Code Representation Learning with Pr\"ufer Sequences
|
Paper has been accepted in AAAI-22 Student Abstract and Poster
Program (SA-22)
| null | null | null |
cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An effective and efficient encoding of the source code of a computer program
is critical to the success of sequence-to-sequence deep neural network models
for tasks in computer program comprehension, such as automated code
summarization and documentation. A significant challenge is to find a
sequential representation that captures the structural/syntactic information in
a computer program and facilitates the training of the learning models.
In this paper, we propose to use the Pr\"ufer sequence of the Abstract Syntax
Tree (AST) of a computer program to design a sequential representation scheme
that preserves the structural information in an AST. Our representation makes
it possible to develop deep-learning models in which signals carried by lexical
tokens in the training examples can be exploited automatically and selectively
based on their syntactic role and importance. Unlike other recently-proposed
approaches, our representation is concise and lossless in terms of the
structural information of the AST. Empirical studies on real-world benchmark
datasets, using a sequence-to-sequence learning model we designed for code
summarization, show that our Pr\"ufer-sequence-based representation is indeed
highly effective and efficient, outperforming significantly all the
recently-proposed deep-learning models we used as the baseline models.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711663 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07271
|
Auriol Degbelo
|
Lucas Braun, Auriol Degbelo, Christian Kray
|
Geofreebie: A Location-Based Freecycling App to Support Forced Migrant
Resettlement
|
Article accepted for publication in the Journal of Location-based
Services
| null |
10.1080/17489725.2021.1874553
| null |
cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
Germany has witnessed an influx of forced migrants in recent years. Promoting
social interaction with the local community is key to supporting the
resettlement of these newcomers. Location-based freecycling services present
important benefits due to freecycling's potential to bolster social engagement
and location-based services' ability to adapt to the user's context. Yet, their
potential to support forced migrants' resettlement is yet to be examined. We
conducted needs assessment interviews with 11 participants in Muenster,
Germany. We analyzed the interview results to develop user requirements for
location-based freecycling services. We then implemented a subset of the user
requirements as a prototype mobile app called Geofreebie. The evaluation of the
app with 22 participants showed that Geofreebie offered two key advantages for
forced migrants' resettlement: it increased the size of their social network,
and created a sense of community on their side. These findings can benefit
researchers and developers of location-based services to support forced migrant
resettlement.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.690533 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07272
|
Xiaoxu Li
|
Xiaoxu Li, Huajie Chen and Xingyu Gao
|
Numerical Analysis of the Multiple Scattering Theory for Electronic
Structure Calculations
|
33 pages, 7 figures
| null | null | null |
math.NA cs.NA physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The multiple scattering theory (MST) is one of the most widely used methods
in electronic structure calculations. It features a perfect separation between
the atomic configurations and site potentials, and hence provides an efficient
way to simulate defected and disordered systems. This work studies the MST
methods from a numerical point of view and shows the convergence with respect
to the truncation of the angular momentum summations, which is a fundamental
approximation parameter for all MST methods. We provide both rigorous analysis
and numerical experiments to illustrate the efficiency of the MST methods
within the angular momentum representations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710622 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07273
|
Auriol Degbelo
|
Auriol Degbelo
|
FAIR Geovisualizations: Definitions, Challenges, and the Road Ahead
|
Article accepted for publication in the International Journal of
Geographical Information Science
| null |
10.1080/13658816.2021.1983579
| null |
cs.IR cs.HC
|
http://creativecommons.org/licenses/by/4.0/
|
The availability of open data and of tools to create visualizations on top of
these open datasets have led to an ever-growing amount of geovisualizations on
the Web. There is thus an increasing need for techniques to make
geovisualizations FAIR - Findable, Accessible, Interoperable, and Reusable.
This article explores what it would mean for a geovisualization to be FAIR,
presents relevant approaches to FAIR geovisualizations and lists open research
questions on the road towards FAIR geovisualizations. The discussion is done
using three complementary perspectives: the computer, which stores
geovisualizations digitally; the analyst, who uses them for sensemaking; and
the developer, who creates them. The framework for FAIR geovisualizations
proposed, and the open questions identified are relevant to researchers working
on findable, accessible, interoperable, and reusable online visualizations of
geographic information.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7138 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07280
|
Alex James Dr
|
R. Chithra, A.R. Aswani, A.P. James
|
TMS-Crossbars with Tactile Sensing
|
5 pages, 7 figures, TCAS2
|
IEEE Transactions on Circuits and Systems--II: Express Briefs,
2021
|
10.1109/TCSII.2021.3128376
| null |
cs.ET cs.NE
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
The first stage of tactile sensing is data acquisition using tactile sensors
and the sensed data is transmitted to the central unit for neuromorphic
computing. The memristive crossbars were proposed to use as synapses in
neuromorphic computing but device intelligence at the sensor level are not
investigated in literature. We propose the concept of Transistor Memristor
Sensor (TMS)-crossbar by including sensor to memristor crossbar array
configuration in the input layer of the neural network architecture. 2 possible
cell configurations of TMS crossbar arrays: 1 Transistor 1 Memristor 1 Sensor
(1T1M1S) and 2 Transistor 1 Memristor 1 Sensor (2T1M1S) are presented. We
verified the proposed TMS-crossbar in the practical design of analog neural
networks based Braille character recognition system. The proposed design is
verified with SPICE simulations using circuit equivalent of FLX-A501 force
sensor, TiO$_2$ memristors and low power 22nm high-k CMOS transistors. The
proposed analog neuromorphic computing system presents a scalable solution and
is possible to encode 125 symbols with good accuracy in comparison with other
Braille character recognition systems in the literature. The benefits of analog
implementation of the TMS crossbar arrays is substantiated with results of
accuracy, area and power requirements in comparison with the binary
counterparts.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07291
|
Ruba Alkadi Mrs
|
Abdulhadi, Shoufan, Ruba, Alkadi
|
Integrating Counter-UAS Systems into the UTM System for Reliable
Decision Making
|
This work is submitted to an IEEE Journal
| null | null | null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Despite significant progress, the deployment of UAV technology in commercial
and civil applications is still lagging. This is essentially due to the risks
associated with drone flights and the lack of coordinated technologies that
would mitigate these risks. While Unmanned Aircraft System Traffic Management
systems (UTM) are being developed worldwide to enable safe operation, the
counter-drone technology operates on an all-enemy basis and regards any sighted
drone as a threat. This situation is essentially caused by the lack of
information exchange between stakeholders. Without the exchange of relevant
information, a counter-drone system can misclassify drones and initiate
erroneous interdiction procedures. This paper proposes a system that integrates
counter-drone technology into the UTM system for information exchange and
coordination using a set of clarification protocols towards accountable
response to sighted drones. The system functionality and performance were
evaluated by simulation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711875 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07300
|
Jayashrita Debnath
|
Jayashrita Debnath, Michele Parrinello
|
Computing rates and understanding unbinding mechanism in host-guest
systems
|
11 pages, including Supplementary Information, 5 figures
| null | null | null |
physics.bio-ph cond-mat.stat-mech physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The long timescale associated with ligand residence times renders their
computation challenging. Therefore, the influence of factors like solvation and
steric hindrance on residence times are not fully understood. Here, we
demonstrate in a set of model host-guest systems that the recently developed
Gaussian Mixture Based Enhanced Sampling allows residence times to be computed
and enables understanding their unbinding mechanism. We observe that guest
unbinding often proceeds via a series of intermediate states that can be
labelled by the number of water molecules present in the binding cavity. And in
several cases the residence time is correlated to the water trapping times in
the cavity.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711469 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07304
|
Sandor P. Fekete
|
Erik D. Demaine and S\'andor P. Fekete and Phillip Keldenich and
Dominik Krupke and Joseph S. B. Mitchell
|
Area-Optimal Simple Polygonalizations: The CG Challenge 2019
|
12 pages, 9 Futures, 1 table
| null | null | null |
cs.CG cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We give an overview of theoretical and practical aspects of finding a simple
polygon of minimum (Min-Area) or maximum (Max-Area) possible area for a given
set of n points in the plane. Both problems are known to be NP-hard and were
the subject of the 2019 Computational Geometry Challenge, which presented the
quest of finding good solutions to more than 200 instances, ranging from n = 10
all the way to n = 1, 000, 000.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710879 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07307
|
Elie Azeraf
|
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
|
Improving usual Naive Bayes classifier performances with Neural Naive
Bayes based models
|
10 pages, 3 figures, 3 tables
| null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Naive Bayes is a popular probabilistic model appreciated for its simplicity
and interpretability. However, the usual form of the related classifier suffers
from two major problems. First, as caring about the observations' law, it
cannot consider complex features. Moreover, it considers the conditional
independence of the observations given the hidden variable. This paper
introduces the original Neural Naive Bayes, modeling the parameters of the
classifier induced from the Naive Bayes with neural network functions. This
allows to correct the first problem. We also introduce new Neural Pooled Markov
Chain models, alleviating the independence condition. We empirically study the
benefits of these models for Sentiment Analysis, dividing the error rate of the
usual classifier by 4.5 on the IMDB dataset with the FastText embedding.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710635 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07308
|
Ariel Rosenfeld
|
Ariel Rosenfeld, Nimrod Talmon
|
What Should We Optimize in Participatory Budgeting? An Experimental
Study
|
Currently under review
| null | null | null |
cs.MA cs.AI cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
Participatory Budgeting (PB) is a process in which voters decide how to
allocate a common budget; most commonly it is done by ordinary people -- in
particular, residents of some municipality -- to decide on a fraction of the
municipal budget. From a social choice perspective, existing research on PB
focuses almost exclusively on designing computationally-efficient aggregation
methods that satisfy certain axiomatic properties deemed "desirable" by the
research community. Our work complements this line of research through a user
study (N = 215) involving several experiments aimed at identifying what
potential voters (i.e., non-experts) deem fair or desirable in simple PB
settings. Our results show that some modern PB aggregation techniques greatly
differ from users' expectations, while other, more standard approaches, provide
more aligned results. We also identify a few possible discrepancies between
what non-experts consider \say{desirable} and how they perceive the notion of
"fairness" in the PB context. Taken jointly, our results can be used to help
the research community identify appropriate PB aggregation methods to use in
practice.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.704745 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07309
|
Yuqing Cheng
|
Yuqing Cheng and Mengtao Sun
|
Understanding photoluminescence of coupled metallic nanostructures based
on a coupling classic harmonic oscillator model
|
7 pages, 6 figures, 25 refs
| null | null | null |
physics.optics
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Photoluminescence (PL) phenomenon from metallic nanostructures has been
explained and understood by several point of views. One of them is based on the
classic harmonic oscillator model, which describes PL of single mode. In this
study, we continue to expand this classic model to a coupling case, which
involves two oscillators that interact with each other together with the
excitation electric field. The new generated modes due to the coupling are
carefully analyzed, including their behaviors varying with the coupling
coefficients in different cases. Furthermore, for practical purpose, PL spectra
and white light scattering spectra of two individual metallic nanostuctures are
calculated as examples employing the model to verify its validity. This work
would give a deeper understanding on coupling PL phenomena and is helpful to
relative applications.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712476 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07325
|
Chunhua Dong
|
Cheng-Zhe Chai, Zhen Shen, Yan-Lei Zhang, Hao-Qi Zhao, Guang-Can Guo,
Chang-Ling Zou and Chun-Hua Dong
|
Single-sideband microwave-to-optical conversion in high-Q ferrimagnetic
microspheres
|
6 pages, 4 figures
| null | null | null |
physics.optics physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Coherent conversion of microwave and optical photons can significantly expand
the ability to control the information processing and communication systems.
Here, we experimentally demonstrate the microwave-to-optical frequency
conversion in a magneto-optical whispering gallery mode microcavity. By
applying a magnetic field parallel to the microsphere equator, the intra-cavity
optical field will be modulated when the magnon is excited by the microwave
drive, leading to microwave-to-optical conversion via the magnetic Stokes and
anti-Stokes scattering processes. The observed single sideband conversion
phenomenon indicates a non-trivial optical photon-magnon interaction mechanism,
which is derived from the magnon induced both the frequency shift and modulated
coupling rate of optical modes. In addition, we demonstrate the single-sideband
frequency conversion with an ultrawide tuning range up to 2.5GHz, showing its
great potential in microwave-to-optical conversion.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07327
|
Pawan Kumar Pandey
|
Pawan Kumar Pandey, Malay Kumar Das
|
Quantifying the Consequences of Catheter Steerability Limitations on
Targeted Drug Delivery
|
39 pages, 13 figures + 1 graphical abstract
| null | null | null |
physics.med-ph
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we virtually study the intra-arterial targeted drug delivery.
Specifically, this work models and quantifies the uncertainties associated with
catheter steerability limitations. We classify catheter's limited steerability
into two types, i.e., zero steerability, and wall pressing steerability.
Further, we investigate the effects of steerability limitations on uncertainty
of causing systemic toxicity levels, i.e., percentage of drug particles missing
target. Proposed method quantifies the uncertainty of causing systemic toxicity
in terms of probability. With this calculation approach, we look at the effects
of upstream vasculature and catheter tip size. Results indicate the existence
of a 'transition toxicity' level. Beyond transition toxicity level, larger
catheters should be preferred over smaller catheters. Furthermore, we found
that it is relatively easier to decide preferrable catheter size in
zero-steerability than wall-pressing steerability conditions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710459 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07334
|
Yuzi Yan
|
Yuzi Yan, Xiaoxiang Li, Xinyou Qiu, Jiantao Qiu, Jian Wang, Yu Wang,
Yuan Shen
|
Relative Distributed Formation and Obstacle Avoidance with Multi-agent
Reinforcement Learning
| null | null | null | null |
eess.SY cs.AI cs.LG cs.MA cs.RO cs.SY
|
http://creativecommons.org/licenses/by/4.0/
|
Multi-agent formation as well as obstacle avoidance is one of the most
actively studied topics in the field of multi-agent systems. Although some
classic controllers like model predictive control (MPC) and fuzzy control
achieve a certain measure of success, most of them require precise global
information which is not accessible in harsh environments. On the other hand,
some reinforcement learning (RL) based approaches adopt the leader-follower
structure to organize different agents' behaviors, which sacrifices the
collaboration between agents thus suffering from bottlenecks in maneuverability
and robustness. In this paper, we propose a distributed formation and obstacle
avoidance method based on multi-agent reinforcement learning (MARL). Agents in
our system only utilize local and relative information to make decisions and
control themselves distributively. Agent in the multi-agent system will
reorganize themselves into a new topology quickly in case that any of them is
disconnected. Our method achieves better performance regarding formation error,
formation convergence rate and on-par success rate of obstacle avoidance
compared with baselines (both classic control methods and another RL-based
method). The feasibility of our method is verified by both simulation and
hardware implementation with Ackermann-steering vehicles.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07341
|
Jaafar Elmirghani
|
Khulood Alazwary, Ahmad Adnan Qidan, Taisir El-Gorashi and Jaafar M.
H. Elmirghani
|
On Optimizing Rate Splitting in Laser-based Optical Wireless Networks
| null | null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Optical wireless communication (OWC) is a promising technology that has the
potential to provide Tb/s aggregate rates. In this paper, interference
management is studied in a Laser-based optical wireless network where
vertical-cavity surface-emitting (VCSEL) lasers are used for data transmission.
In particular, rate splitting (RS) and hierarchical rate splitting (HRS) are
proposed to align multi-user interference, while maximizing the multiplexing
gain of the network. Basically, RS serves multiple users simultaneously by
splitting a message of a user into common and private messages, each message
with a certain level of power, while on the other side users decode their
messages following a specific methodology. The performance of the conventional
RS scheme is limited in high density wireless networks. Therefore, the HRS
scheme is developed aiming to achieve high rates where users are divided into
multiple groups, and a new message called outer common message is used for
managing inter-group interference. We formulate an optimization problem that
addresses power allocation among the messages of the HRS scheme to further
enhance the performance of the network. The results show that the proposed
approach provides high achievable rates compared with the conventional RS and
HRS schemes in different scenarios.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711625 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07344
|
Jimiama Mafeni Mase
|
Jimiama M. Mase, Natalie Leesakul, Fan Yang, Grazziela P. Figueredo,
Mercedes Torres Torres
|
Towards Privacy-Preserving Affect Recognition: A Two-Level Deep Learning
Architecture
|
8 pages, 6 figures, 4 tables
| null | null | null |
cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Automatically understanding and recognising human affective states using
images and computer vision can improve human-computer and human-robot
interaction. However, privacy has become an issue of great concern, as the
identities of people used to train affective models can be exposed in the
process. For instance, malicious individuals could exploit images from users
and assume their identities. In addition, affect recognition using images can
lead to discriminatory and algorithmic bias, as certain information such as
race, gender, and age could be assumed based on facial features. Possible
solutions to protect the privacy of users and avoid misuse of their identities
are to: (1) extract anonymised facial features, namely action units (AU) from a
database of images, discard the images and use AUs for processing and training,
and (2) federated learning (FL) i.e. process raw images in users' local
machines (local processing) and send the locally trained models to the main
processing machine for aggregation (central processing). In this paper, we
propose a two-level deep learning architecture for affect recognition that uses
AUs in level 1 and FL in level 2 to protect users' identities. The architecture
consists of recurrent neural networks to capture the temporal relationships
amongst the features and predict valence and arousal affective states. In our
experiments, we evaluate the performance of our privacy-preserving architecture
using different variations of recurrent neural networks on RECOLA, a
comprehensive multimodal affective database. Our results show state-of-the-art
performance of $0.426$ for valence and $0.401$ for arousal using the
Concordance Correlation Coefficient evaluation metric, demonstrating the
feasibility of developing models for affect recognition that are both accurate
and ensure privacy.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710245 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07346
|
Yonghyun Kim
|
Yonghyun Kim
|
A Study on the Efficient Product Search Service for the Damaged Image
Information
|
5 pages, 8 figures
| null | null | null |
cs.IR cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
With the development of Information and Communication Technologies and the
dissemination of smartphones, especially now that image search is possible
through the internet, e-commerce markets are more activating purchasing
services for a wide variety of products. However, it often happens that the
image of the desired product is impaired and that the search engine does not
recognize it properly. The idea of this study is to help search for products
through image restoration using an image pre-processing and image inpainting
algorithm for damaged images. It helps users easily purchase the items they
want by providing a more accurate image search system. Besides, the system has
the advantage of efficiently showing information by category, so that enables
efficient sales of registered information.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708843 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07363
|
Jean Carlo Moraes
|
Jean Carlo Moraes
|
A Note on the Pure Nash Equilibria for Evolutionary Games on Networks
| null | null | null | null |
cs.GT
|
http://creativecommons.org/licenses/by/4.0/
|
Recently, a new model extending the standard replicator equation to a finite
set of players connected on an arbitrary graph was developed in evolutionary
game dynamics. The players are interpreted as subpopulations of
multipopulations dynamical game and represented as vertices of the graph, and
an edge constitutes the relation among the subpopulations. At each instant,
members of connected vertices of the graph play a 2-player game and collect a
payoff that determines if the chosen strategies will vanish or flourish. The
model describes the game dynamics of a finite set of players connected by a
graph emulating the replicator dynamics. It was proved a relation between the
stability of the mixed equilibrium with the topology of the network. More
specifically, the eigenvalues of the Jacobian matrix of the system evaluated at
the mixed steady state are the eigenvalues of the graph's adjacency matrix
multiplied by a scalar. This paper studies the pure (strict) Nash equilibria of
these games and how it connects to the network. We present necessary and
sufficient conditions for a pure steady-state in coordination or
anti-coordination game to be a (strict) Nash Equilibrium.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708855 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07369
|
Fatemeh Shahbazi
|
Ata Jodeiri, Hadi Seyedarabi, Fatemeh Shahbazi, Seyed Mohammad Mahdi
Hashemi, Seyyedhossein Shafiei
|
Estimation of Acetabular Version from Anteroposterior Pelvic Radiograph
Employing Deep Learning
|
12 pages, 8 figures
| null | null | null |
eess.IV cs.AI cs.CV cs.LG physics.med-ph
|
http://creativecommons.org/licenses/by/4.0/
|
Background and Objective: The Acetabular version, an essential factor in
total hip arthroplasty, is measured by CT scan as the gold standard. The dose
of radiation and expensiveness of CT make anterior-posterior pelvic radiograph
an appropriate alternative procedure. In this study, we applied a deep learning
approach on anteroposterior pelvic X-rays to measure anatomical version,
eliminating the necessity of using Computed tomography scan. Methods: The right
and left acetabular version angles of the hips of 300 patients are computed
using their CT images. The proposed deep learning model, Attention on
Pretrained-VGG16 for Bone Age, is applied to the AP images of the included
population. The age and gender of these people are added as two other inputs to
the last fully connected layer of attention mechanism. As the output, the
angles of both hips are predicted. Results: The angles of hips computed on CT
increase as people get older with the mean values of 16.54 and 16.11 (right and
left angles) for men and 20.61 and 19.55 for women in our dataset. The
predicted errors in the estimation of right and left angles using the proposed
method of deep learning are in the accurate region of error (<=3 degrees) which
shows the ability of the proposed method in measuring anatomical version based
on AP images. Conclusion: The suggested algorithm, applying pre-trained vgg16
on the AP images of the pelvis of patients followed by an attention model
considering age and gender of patients, can assess version accurately using
only AP radiographs while obviating the need for CT scan. The applied technique
of estimation of anatomical acetabular version based on AP pelvic images using
DL approaches, to the best of authors' knowledge, has not been published yet.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.706634 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07372
|
Shahrzad Haddadan
|
Shahrzad Haddadan, Yue Zhuang, Cyrus Cousins, Eli Upfal
|
Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with
Weak Mixing Time Bounds
|
A short version of this paper will appear inthe 35th Conference on
NeuralInformation Processing Systems, NeurIPS 2021
| null | null | null |
stat.ML cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
We present a novel method for reducing the computational complexity of
rigorously estimating the partition functions (normalizing constants) of Gibbs
(Boltzmann) distributions, which arise ubiquitously in probabilistic graphical
models. A major obstacle to practical applications of Gibbs distributions is
the need to estimate their partition functions. The state of the art in
addressing this problem is multi-stage algorithms, which consist of a cooling
schedule, and a mean estimator in each step of the schedule. While the cooling
schedule in these algorithms is adaptive, the mean estimation computations use
MCMC as a black-box to draw approximate samples. We develop a doubly adaptive
approach, combining the adaptive cooling schedule with an adaptive MCMC mean
estimator, whose number of Markov chain steps adapts dynamically to the
underlying chain. Through rigorous theoretical analysis, we prove that our
method outperforms the state of the art algorithms in several factors: (1) The
computational complexity of our method is smaller; (2) Our method is less
sensitive to loose bounds on mixing times, an inherent component in these
algorithms; and (3) The improvement obtained by our method is particularly
significant in the most challenging regime of high-precision estimation. We
demonstrate the advantage of our method in experiments run on classic factor
graphs, such as voting models and Ising models.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711067 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07376
|
Elie Azeraf
|
Elie Azeraf, Emmanuel Monfrini, Wojciech Pieczynski
|
On equivalence between linear-chain conditional random fields and hidden
Markov chains
|
5 pages
| null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Practitioners successfully use hidden Markov chains (HMCs) in different
problems for about sixty years. HMCs belong to the family of generative models
and they are often compared to discriminative models, like conditional random
fields (CRFs). Authors usually consider CRFs as quite different from HMCs, and
CRFs are often presented as interesting alternative to HMCs. In some areas,
like natural language processing (NLP), discriminative models have completely
supplanted generative models. However, some recent results show that both
families of models are not so different, and both of them can lead to identical
processing power. In this paper we compare the simple linear-chain CRFs to the
basic HMCs. We show that HMCs are identical to CRFs in that for each CRF we
explicitly construct an HMC having the same posterior distribution. Therefore,
HMCs and linear-chain CRFs are not different but just differently parametrized
models.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712182 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07379
|
Vadim Borisov
|
Vadim Borisov, Johannes Meier, Johan van den Heuvel, Hamed Jalali,
Gjergji Kasneci
|
A Robust Unsupervised Ensemble of Feature-Based Explanations using
Restricted Boltzmann Machines
| null | null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Understanding the results of deep neural networks is an essential step
towards wider acceptance of deep learning algorithms. Many approaches address
the issue of interpreting artificial neural networks, but often provide
divergent explanations. Moreover, different hyperparameters of an explanatory
method can lead to conflicting interpretations. In this paper, we propose a
technique for aggregating the feature attributions of different explanatory
algorithms using Restricted Boltzmann Machines (RBMs) to achieve a more
reliable and robust interpretation of deep neural networks. Several challenging
experiments on real-world datasets show that the proposed RBM method
outperforms popular feature attribution methods and basic ensemble techniques.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709837 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07382
|
Ohad Volk
|
Ohad Volk, Gonen Singer
|
Adaptive Cost-Sensitive Learning in Neural Networks for
Misclassification Cost Problems
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We design a new adaptive learning algorithm for misclassification cost
problems that attempt to reduce the cost of misclassified instances derived
from the consequences of various errors. Our algorithm (adaptive cost sensitive
learning - AdaCSL) adaptively adjusts the loss function such that the
classifier bridges the difference between the class distributions between
subgroups of samples in the training and test data sets with similar predicted
probabilities (i.e., local training-test class distribution mismatch). We
provide some theoretical performance guarantees on the proposed algorithm and
present empirical evidence that a deep neural network used with the proposed
AdaCSL algorithm yields better cost results on several binary classification
data sets that have class-imbalanced and class-balanced distributions compared
to other alternative approaches.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07383
|
Jiehong Lin
|
Jiehong Lin, Hongyang Li, Ke Chen, Jiangbo Lu, Kui Jia
|
Sparse Steerable Convolutions: An Efficient Learning of
SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D
Space
|
Accepted by NeurIPS 2021
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As a basic component of SE(3)-equivariant deep feature learning, steerable
convolution has recently demonstrated its advantages for 3D semantic analysis.
The advantages are, however, brought by expensive computations on dense,
volumetric data, which prevent its practical use for efficient processing of 3D
data that are inherently sparse. In this paper, we propose a novel design of
Sparse Steerable Convolution (SS-Conv) to address the shortcoming; SS-Conv
greatly accelerates steerable convolution with sparse tensors, while strictly
preserving the property of SE(3)-equivariance. Based on SS-Conv, we propose a
general pipeline for precise estimation of object poses, wherein a key design
is a Feature-Steering module that takes the full advantage of
SE(3)-equivariance and is able to conduct an efficient pose refinement. To
verify our designs, we conduct thorough experiments on three tasks of 3D object
semantic analysis, including instance-level 6D pose estimation, category-level
6D pose and size estimation, and category-level 6D pose tracking. Our proposed
pipeline based on SS-Conv outperforms existing methods on almost all the
metrics evaluated by the three tasks. Ablation studies also show the
superiority of our SS-Conv over alternative convolutions in terms of both
accuracy and efficiency. Our code is released publicly at
https://github.com/Gorilla-Lab-SCUT/SS-Conv.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709069 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07387
|
David Cohen
|
Charles-Edouard Br\'ehier, David Cohen, Tobias Jahnke
|
Splitting integrators for stochastic Lie--Poisson systems
| null | null | null | null |
math.NA cs.NA math.PR
|
http://creativecommons.org/licenses/by/4.0/
|
We study stochastic Poisson integrators for a class of stochastic Poisson
systems driven by Stratonovich noise. Such geometric integrators preserve
Casimir functions and the Poisson map property. For this purpose, we propose
explicit stochastic Poisson integrators based on a splitting strategy, and
analyse their qualitative and quantitative properties: preservation of Casimir
functions, existence of almost sure or moment bounds, asymptotic preserving
property, and strong and weak rates of convergence. The construction of the
schemes and the theoretical results are illustrated through extensive numerical
experiments for three examples of stochastic Lie--Poisson systems, namely:
stochastically perturbed Maxwell--Bloch, rigid body and sine--Euler equations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712426 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07393
|
Junjie Hu
|
Junjie Hu, Hiroaki Hayashi, Kyunghyun Cho, Graham Neubig
|
DEEP: DEnoising Entity Pre-training for Neural Machine Translation
|
13 pages
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It has been shown that machine translation models usually generate poor
translations for named entities that are infrequent in the training corpus.
Earlier named entity translation methods mainly focus on phonetic
transliteration, which ignores the sentence context for translation and is
limited in domain and language coverage. To address this limitation, we propose
DEEP, a DEnoising Entity Pre-training method that leverages large amounts of
monolingual data and a knowledge base to improve named entity translation
accuracy within sentences. Besides, we investigate a multi-task learning
strategy that finetunes a pre-trained neural machine translation model on both
entity-augmented monolingual data and parallel data to further improve entity
translation. Experimental results on three language pairs demonstrate that
\method results in significant improvements over strong denoising auto-encoding
baselines, with a gain of up to 1.3 BLEU and up to 9.2 entity accuracy points
for English-Russian translation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712182 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07407
|
Jingshu Liu
|
Jingshu Liu, Patricia J Allen, Luke Benz, Daniel Blickstein, Evon
Okidi, Xiao Shi
|
A Machine Learning Approach for Recruitment Prediction in Clinical Trial
Design
|
Machine Learning for Health (ML4H) - Extended Abstract
| null | null | null |
cs.LG stat.AP stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Significant advancements have been made in recent years to optimize patient
recruitment for clinical trials, however, improved methods for patient
recruitment prediction are needed to support trial site selection and to
estimate appropriate enrollment timelines in the trial design stage. In this
paper, using data from thousands of historical clinical trials, we explore
machine learning methods to predict the number of patients enrolled per month
at a clinical trial site over the course of a trial's enrollment duration. We
show that these methods can reduce the error that is observed with current
industry standards and propose opportunities for further improvement.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712245 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07414
|
Zachary Friggstad
|
Sina Dezfuli, Zachary Friggstad, Ian Post, Chaitanya Swamy
|
Combinatorial Algorithms for Rooted Prize-Collecting Walks and
Applications to Orienteering and Minimum-Latency Problems
| null | null | null | null |
cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the rooted prize-collecting walks (PCW) problem, wherein we seek
a collection $C$ of rooted walks having minimum prize-collecting cost, which is
the (total cost of walks in $C$) + (total node-reward of nodes not visited by
any walk in $C$). This problem arises naturally as the Lagrangian relaxation of
both orienteering, where we seek a length-bounded walk of maximum reward, and
the $\ell$-stroll problem, where we seek a minimum-length walk covering at
least $\ell$ nodes. Our main contribution is to devise a simple, combinatorial
algorithm for the PCW problem in directed graphs that returns a rooted tree
whose prize-collecting cost is at most the optimum value of the
prize-collecting walks problem.
We utilize our algorithm to develop combinatorial approximation algorithms
for two fundamental vehicle-routing problems (VRPs): (1) orienteering; and (2)
$k$-minimum-latency problem ($k$-MLP), wherein we seek to cover all nodes using
$k$ paths starting at a prescribed root node, so as to minimize the sum of the
node visiting times. Our combinatorial algorithm allows us to sidestep the part
where we solve a preflow-based LP in the LP-rounding algorithms of Friggstand
and Swamy (2017) for orienteering, and in the state-of-the-art
$7.183$-approximation algorithm for $k$-MP in Post and Swamy (2015).
Consequently, we obtain combinatorial implementations of these algorithms with
substantially improved running times compared with the current-best
approximation factors.
We report computational results for our resulting (combinatorial
implementations of) orienteering algorithms, which show that the algorithms
perform quite well in practice, both in terms of the quality of the solution
they return, as also the upper bound they yield on the orienteering optimum
(which is obtained by leveraging the workings of our PCW algorithm).
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707196 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07418
|
Nan Yang
|
Lukas Koestler, Nan Yang, Niclas Zeller, Daniel Cremers
|
TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view
Stereo
|
CoRL 2021. The manuscript contains the main paper and the
supplementary materials. Project page: https://go.vision.in.tum.de/tandem
| null | null | null |
cs.CV cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we present TANDEM a real-time monocular tracking and dense
mapping framework. For pose estimation, TANDEM performs photometric bundle
adjustment based on a sliding window of keyframes. To increase the robustness,
we propose a novel tracking front-end that performs dense direct image
alignment using depth maps rendered from a global model that is built
incrementally from dense depth predictions. To predict the dense depth maps, we
propose Cascade View-Aggregation MVSNet (CVA-MVSNet) that utilizes the entire
active keyframe window by hierarchically constructing 3D cost volumes with
adaptive view aggregation to balance the different stereo baselines between the
keyframes. Finally, the predicted depth maps are fused into a consistent global
map represented as a truncated signed distance function (TSDF) voxel grid. Our
experimental results show that TANDEM outperforms other state-of-the-art
traditional and learning-based monocular visual odometry (VO) methods in terms
of camera tracking. Moreover, TANDEM shows state-of-the-art real-time 3D
reconstruction performance.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.706836 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07419
|
Sharmita Dey
|
Sharmita Dey, Sabri Boughorbel, Arndt F. Schilling
|
Learning a Shared Model for Motorized Prosthetic Joints to Predict
Ankle-Joint Motion
|
NeurIPS 2021 Workshop Spotlight presentation, Machine Learning for
Health (ML4H) 2021 - Extended Abstract
| null | null | null |
cs.RO cs.LG stat.AP
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Control strategies for active prostheses or orthoses use sensor inputs to
recognize the user's locomotive intention and generate corresponding control
commands for producing the desired locomotion. In this paper, we propose a
learning-based shared model for predicting ankle-joint motion for different
locomotion modes like level-ground walking, stair ascent, stair descent, slope
ascent, and slope descent without the need to classify between them. Features
extracted from hip and knee joint angular motion are used to continuously
predict the ankle angles and moments using a Feed-Forward Neural Network-based
shared model. We show that the shared model is adequate for predicting the
ankle angles and moments for different locomotion modes without explicitly
classifying between the modes. The proposed strategy shows the potential for
devising a high-level controller for an intelligent prosthetic ankle that can
adapt to different locomotion modes.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707803 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07430
|
Dileep Kalathil
|
Sapana Chaudhary and Dileep Kalathil
|
Safe Online Convex Optimization with Unknown Linear Safety Constraints
|
18 pages
| null | null | null |
cs.LG math.OC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We study the problem of safe online convex optimization, where the action at
each time step must satisfy a set of linear safety constraints. The goal is to
select a sequence of actions to minimize the regret without violating the
safety constraints at any time step (with high probability). The parameters
that specify the linear safety constraints are unknown to the algorithm. The
algorithm has access to only the noisy observations of constraints for the
chosen actions. We propose an algorithm, called the {Safe Online Projected
Gradient Descent} (SO-PGD) algorithm, to address this problem. We show that,
under the assumption of the availability of a safe baseline action, the SO-PGD
algorithm achieves a regret $O(T^{2/3})$. While there are many algorithms for
online convex optimization (OCO) problems with safety constraints available in
the literature, they allow constraint violations during learning/optimization,
and the focus has been on characterizing the cumulative constraint violations.
To the best of our knowledge, ours is the first work that provides an algorithm
with provable guarantees on the regret, without violating the linear safety
constraints (with high probability) at any time step.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711387 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07432
|
Fernando Alonso-Fernandez
|
Fernando Alonso-Fernandez, Julian Fierrez, Javier Ortega-Garcia,
Joaquin Gonzalez-Rodriguez, Hartwig Fronthaler, Klaus Kollreider, Josef Bigun
|
A Comparative Study of Fingerprint Image-Quality Estimation Methods
|
Published at IEEE Transactions on Information Forensics and Security
| null |
10.1109/TIFS.2007.908228
| null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
One of the open issues in fingerprint verification is the lack of robustness
against image-quality degradation. Poor-quality images result in spurious and
missing features, thus degrading the performance of the overall system.
Therefore, it is important for a fingerprint recognition system to estimate the
quality and validity of the captured fingerprint images. In this work, we
review existing approaches for fingerprint image-quality estimation, including
the rationale behind the published measures and visual examples showing their
behavior under different quality conditions. We have also tested a selection of
fingerprint image-quality estimation algorithms. For the experiments, we employ
the BioSec multimodal baseline corpus, which includes 19200 fingerprint images
from 200 individuals acquired in two sessions with three different sensors. The
behavior of the selected quality measures is compared, showing high correlation
between them in most cases. The effect of low-quality samples in the
verification performance is also studied for a widely available minutiae-based
fingerprint matching system.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709435 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07434
|
Hygor Piaget Melo Dr.
|
Hygor P. M. Melo, Diogo P. Mota, Jos\'e S. Andrade Jr., Nuno A. M.
Ara\'ujo
|
The impact of one-way streets on the asymmetry of the shortest commuting
routes
|
6 pages, 4 figures
| null | null | null |
physics.soc-ph cond-mat.stat-mech
|
http://creativecommons.org/licenses/by/4.0/
|
On a daily commute, the shortest route from home to work rarely overlaps
completely the shortest way back. We analyze this asymmetry for several cities
and show that it exists even without traffic, due to a non-negligible fraction
of one-way streets. For different pairs of origin-destination ($\rm OD$), we
compute the log-ratio $r=\ln(\ell_{\rm D}/\ell_{\rm O})$, where $\ell_{\rm O}$
and $\ell_{\rm D}$ are the lengths of the shortest routes from $\rm O$ to $\rm
D$ and from $\rm D$ to $\rm O$, respectively. While its average is zero, the
amplitude of the fluctuations decays as a power law of the $\rm OD$ shortest
path length, $r\sim \ell_{\rm O}^{-\beta}$. Similarly, the fraction of one-way
streets in a shortest route also decays as $\ell_{\rm O}^{-\alpha}$. Based on
semi-analytic arguments, we show that $\beta=(1+\alpha)/2$. Thus, the value of
the exponent $\beta$ is related to correlations in the structure of the
underlying street network.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.704973 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07435
|
M\'aria Luk\'a\v{c}ov\'a-Medvid'ov\'a
|
Eduard Feireisl and M\'aria Luk\'a\v{c}ov\'a-Medvid'ov\'a
|
Convergence of a stochastic collocation finite volume method for the
compressible Navier-Stokes system
| null | null | null | null |
math.NA cs.NA math.AP math.PR
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We propose a stochastic collocation method based on the piecewise constant
interpolation on the probability space combined with a finite volume method to
solve the compressible Navier-Stokes system at the nodal points. We show
convergence of numerical solutions to a statistical solution of the
Navier-Stokes system on condition that the numerical solutions are bounded in
probability. The analysis uses the stochastic compactness method based on the
Skorokhod/Jakubowski representation theorem and the criterion of convergence in
probability due to Gy\"ongy and Krylov.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709849 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07436
|
Nicole Riemer
|
Matthew L. Dawson, Christian Guzman, Jeffrey H. Curtis, Mario Acosta,
Shupeng Zhu, Donald Dabdub, Andrew Conley, Matthew West, Nicole Riemer, Oriol
Jorba
|
Chemistry Across Multiple Phases (CAMP) version 1.0: An integrated
multi-phase chemistry model
| null | null | null | null |
cs.CE physics.ao-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A flexible treatment for gas- and aerosol-phase chemical processes has been
developed for models of diverse scale, from box models up to global models. At
the core of this novel framework is an "abstracted aerosol representation" that
allows a given chemical mechanism to be solved in atmospheric models with
different aerosol representations (e.g., sectional, modal, or
particle-resolved). This is accomplished by treating aerosols as a collection
of condensed phases that are implemented according to the aerosol
representation of the host model. The framework also allows multiple chemical
processes (e.g., gas- and aerosol-phase chemical reactions, emissions,
deposition, photolysis, and mass-transfer) to be solved simultaneously as a
single system. The flexibility of the model is achieved by (1) using an
object-oriented design that facilitates extensibility to new types of chemical
processes and to new ways of representing aerosol systems; (2) runtime model
configuration using JSON input files that permits making changes to any part of
the chemical mechanism without recompiling the model; this widely used,
human-readable format allows entire gas- and aerosol-phase chemical mechanisms
to be described with as much complexity as necessary; and (3) automated
comprehensive testing that ensures stability of the code as new functionality
is introduced. Together, these design choices enable users to build a
customized multiphase mechanism, without having to handle pre-processors,
solvers or compilers. This new treatment compiles as a stand-alone library and
has been deployed in the particle-resolved PartMC model and in the MONARCH
chemical weather prediction system for use at regional and global scales.
Results from the initial deployment will be discussed, along with future
extension to more complex gas-aerosol systems, and the integration of GPU-based
solvers.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710622 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07447
|
Norman Di Palo
|
Norman Di Palo and Edward Johns
|
Learning Multi-Stage Tasks with One Demonstration via Self-Replay
|
Published at the 5th Conference on Robot Learning (CoRL) 2021
| null | null | null |
cs.RO cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
In this work, we introduce a novel method to learn everyday-like multi-stage
tasks from a single human demonstration, without requiring any prior object
knowledge. Inspired by the recent Coarse-to-Fine Imitation Learning method, we
model imitation learning as a learned object reaching phase followed by an
open-loop replay of the demonstrator's actions. We build upon this for
multi-stage tasks where, following the human demonstration, the robot can
autonomously collect image data for the entire multi-stage task, by reaching
the next object in the sequence and then replaying the demonstration, and then
repeating in a loop for all stages of the task. We evaluate with real-world
experiments on a set of everyday-like multi-stage tasks, which we show that our
method can solve from a single demonstration. Videos and supplementary material
can be found at https://www.robot-learning.uk/self-replay.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710434 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07448
|
Jared Mowery
|
Jared Mowery
|
Contrastive Clustering: Toward Unsupervised Bias Reduction for Emotion
and Sentiment Classification
|
19 pages, 3 figures, 5 tables
| null | null | null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Background: When neural network emotion and sentiment classifiers are used in
public health informatics studies, biases present in the classifiers could
produce inadvertently misleading results.
Objective: This study assesses the impact of bias on COVID-19 topics, and
demonstrates an automatic algorithm for reducing bias when applied to COVID-19
social media texts. This could help public health informatics studies produce
more timely results during crises, with a reduced risk of misleading results.
Methods: Emotion and sentiment classifiers were applied to COVID-19 data
before and after debiasing the classifiers using unsupervised contrastive
clustering. Contrastive clustering approximates the degree to which tokens
exhibit a causal versus correlational relationship with emotion or sentiment,
by contrasting the tokens' relative salience to topics versus emotions or
sentiments.
Results: Contrastive clustering distinguishes correlation from causation for
tokens with an F1 score of 0.753. Masking bias prone tokens from the classifier
input decreases the classifier's overall F1 score by 0.02 (anger) and 0.033
(negative sentiment), but improves the F1 score for sentences annotated as bias
prone by 0.155 (anger) and 0.103 (negative sentiment). Averaging across topics,
debiasing reduces anger estimates by 14.4% and negative sentiment estimates by
8.0%.
Conclusions: Contrastive clustering reduces algorithmic bias in emotion and
sentiment classification for social media text pertaining to the COVID-19
pandemic. Public health informatics studies should account for bias, due to its
prevalence across a range of topics. Further research is needed to improve bias
reduction techniques and to explore the adverse impact of bias on public health
informatics analyses.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710867 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07454
|
Xiaohui Liang
|
Youxiang Zhu, Bang Tran, Xiaohui Liang, John A. Batsis, Robert M. Roth
|
Towards Interpretability of Speech Pause in Dementia Detection using
Adversarial Learning
| null | null | null | null |
cs.CL cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Speech pause is an effective biomarker in dementia detection. Recent deep
learning models have exploited speech pauses to achieve highly accurate
dementia detection, but have not exploited the interpretability of speech
pauses, i.e., what and how positions and lengths of speech pauses affect the
result of dementia detection. In this paper, we will study the positions and
lengths of dementia-sensitive pauses using adversarial learning approaches.
Specifically, we first utilize an adversarial attack approach by adding the
perturbation to the speech pauses of the testing samples, aiming to reduce the
confidence levels of the detection model. Then, we apply an adversarial
training approach to evaluate the impact of the perturbation in training
samples on the detection model. We examine the interpretability from the
perspectives of model accuracy, pause context, and pause length. We found that
some pauses are more sensitive to dementia than other pauses from the model's
perspective, e.g., speech pauses near to the verb "is". Increasing lengths of
sensitive pauses or adding sensitive pauses leads the model inference to
Alzheimer's Disease, while decreasing the lengths of sensitive pauses or
deleting sensitive pauses leads to non-AD.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07455
|
Mehdi Rahim
|
Quentin Blampey and Mehdi Rahim
|
HAD-Net: Hybrid Attention-based Diffusion Network for Glucose Level
Forecast
|
Machine Learning for Health (ML4H) - Extended Abstract
| null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Data-driven models for glucose level forecast often do not provide meaningful
insights despite accurate predictions. Yet, context understanding in medicine
is crucial, in particular for diabetes management. In this paper, we introduce
HAD-Net: a hybrid model that distills knowledge into a deep neural network from
physiological models. It models glucose, insulin and carbohydrates diffusion
through a biologically inspired deep learning architecture tailored with a
recurrent attention network constrained by ODE expert models. We apply HAD-Net
for glucose level forecast of patients with type-2 diabetes. It achieves
competitive performances while providing plausible measurements of insulin and
carbohydrates diffusion over time.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7116 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07457
|
Amir Hossein Estiri
|
Amir Hossein Estiri, Muthucumaru Maheswaran
|
Attentive Federated Learning for Concept Drift in Distributed 5G Edge
Networks
|
6 pages, 7 figures, IEEE International Conference on Communications
(ICCC) 2022
| null | null | null |
cs.LG cs.DC cs.NI
|
http://creativecommons.org/licenses/by/4.0/
|
Machine learning (ML) is expected to play a major role in 5G edge computing.
Various studies have demonstrated that ML is highly suitable for optimizing
edge computing systems as rapid mobility and application-induced changes occur
at the edge. For ML to provide the best solutions, it is important to
continually train the ML models to include the changing scenarios. The sudden
changes in data distributions caused by changing scenarios (e.g., 5G base
station failures) is referred to as concept drift and is a major challenge to
continual learning. The ML models can present high error rates while the drifts
take place and the errors decrease only after the model learns the
distributions. This problem is more pronounced in a distributed setting where
multiple ML models are being used for different heterogeneous datasets and the
final model needs to capture all concept drifts. In this paper, we show that
using Attention in Federated Learning (FL) is an efficient way of handling
concept drifts. We use a 5G network traffic dataset to simulate concept drift
and test various scenarios. The results indicate that Attention can
significantly improve the concept drift handling capability of FL.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709265 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07458
|
Arpan Mukherjee
|
Arpan Mukherjee, Ali Tajer, Pin-Yu Chen and Payel Das
|
Mean-based Best Arm Identification in Stochastic Bandits under Reward
Contamination
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper investigates the problem of best arm identification in
$\textit{contaminated}$ stochastic multi-arm bandits. In this setting, the
rewards obtained from any arm are replaced by samples from an adversarial model
with probability $\varepsilon$. A fixed confidence (infinite-horizon) setting
is considered, where the goal of the learner is to identify the arm with the
largest mean. Owing to the adversarial contamination of the rewards, each arm's
mean is only partially identifiable. This paper proposes two algorithms, a
gap-based algorithm and one based on the successive elimination, for best arm
identification in sub-Gaussian bandits. These algorithms involve mean estimates
that achieve the optimal error guarantee on the deviation of the true mean from
the estimate asymptotically. Furthermore, these algorithms asymptotically
achieve the optimal sample complexity. Specifically, for the gap-based
algorithm, the sample complexity is asymptotically optimal up to constant
factors, while for the successive elimination-based algorithm, it is optimal up
to logarithmic factors. Finally, numerical experiments are provided to
illustrate the gains of the algorithms compared to the existing baselines.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71123 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07459
|
Dipanjan Ghosh
|
Dipanjan Ghosh and Xiang Cheng
|
To cross or not to cross: collective swimming of Escherichia coli under
two-dimensional confinement
|
11 pages, 5 figures
| null | null | null |
cond-mat.soft physics.bio-ph physics.flu-dyn
|
http://creativecommons.org/licenses/by/4.0/
|
Bacteria in bulk fluids swim collectively and display fascinating emergent
dynamics. Although bacterial collective swimming in three-dimensional (3D)
geometries has been well studied, its counterpart in confined two-dimensional
(2D) geometries relevant to natural habitats of bacteria is still poorly
understood. Here, through carefully designed experiments on Escherichia coli in
Hele-Shaw chambers, we show that a small change in the degree of confinement
leads to a drastic change in bacterial collective swimming. While long-range
nematic order emerges for bacteria that can cross during encounters, a slight
decrease of the chamber height prevents the crossing, leading to the formation
of bacterial clusters with short-range polar order. By tracking the swimming
kinetics of individual bacteria, we reveal the microscopic origins of the two
collective phases. Our study provides important insights into bacterial
collective swimming under confinement and demonstrates a convenient way to
control the emergent symmetry of collective phases.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711049 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07462
|
Nastaran Gholizadeh
|
Nastaran Gholizadeh, Petr Musilek
|
Federated Learning with Hyperparameter-based Clustering for Electrical
Load Forecasting
|
Accepted in Internet of Things; Engineering Cyber Physical Human
Systems
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Electrical load prediction has become an integral part of power system
operation. Deep learning models have found popularity for this purpose.
However, to achieve a desired prediction accuracy, they require huge amounts of
data for training. Sharing electricity consumption data of individual
households for load prediction may compromise user privacy and can be expensive
in terms of communication resources. Therefore, edge computing methods, such as
federated learning, are gaining more importance for this purpose. These methods
can take advantage of the data without centrally storing it. This paper
evaluates the performance of federated learning for short-term forecasting of
individual house loads as well as the aggregate load. It discusses the
advantages and disadvantages of this method by comparing it to centralized and
local learning schemes. Moreover, a new client clustering method is proposed to
reduce the convergence time of federated learning. The results show that
federated learning has a good performance with a minimum root mean squared
error (RMSE) of 0.117kWh for individual load forecasting.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709044 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07463
|
Abbas Khalili
|
Abbas Khalili, Alexei Ashikhmin, Hong Yang
|
Cell-Free Massive MIMO with Low-Complexity Hybrid Beamforming
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cell-Free Massive Multiple-input Multiple-output (mMIMO) consists of many
access points (APs) in a coverage area that jointly serve the users. These
systems can significantly reduce the interference among the users compared to
conventional MIMO networks and so enable higher data rates and a larger
coverage area. However, Cell-Free mMIMO systems face multiple practical
challenges such as the high complexity and power consumption of the APs' analog
front-ends. Motivated by prior works, we address these issues by considering a
low complexity hybrid beamforming framework at the APs in which each AP has a
limited number of RF-chains to reduce power consumption, and the analog
combiner is designed only using the large-scale statistics of the channel to
reduce the system's complexity. We provide closed-form expressions for the
signal to interference and noise ratio (SINR) of both uplink and downlink data
transmission with accurate random matrix approximations. Also, based on the
existing literature, we provide a power optimization algorithm that maximizes
the minimum SINR of the users for uplink scenario. Through several simulations,
we investigate the accuracy of the derived random matrix approximations,
trade-off between the 95% outage data rate and the number of RF-chains, and the
impact of power optimization. We observe that the derived approximations
accurately follow the exact simulations and that in uplink scenario while using
MMSE combiner, power optimization does not improve the performance much.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70978 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07468
|
Yuhang Lu
|
Yuhang Lu, Evgeniy Upenik, Touradj Ebrahimi
|
Impact of Benign Modifications on Discriminative Performance of Deepfake
Detectors
| null | null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deepfakes are becoming increasingly popular in both good faith applications
such as in entertainment and maliciously intended manipulations such as in
image and video forgery. Primarily motivated by the latter, a large number of
deepfake detectors have been proposed recently in order to identify such
content. While the performance of such detectors still need further
improvements, they are often assessed in simple if not trivial scenarios. In
particular, the impact of benign processing operations such as transcoding,
denoising, resizing and enhancement are not sufficiently studied. This paper
proposes a more rigorous and systematic framework to assess the performance of
deepfake detectors in more realistic situations. It quantitatively measures how
and to which extent each benign processing approach impacts a state-of-the-art
deepfake detection method. By illustrating it in a popular deepfake detector,
our benchmark proposes a framework to assess robustness of detectors and
provides valuable insights to design more efficient deepfake detectors.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711406 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07470
|
Nal Kalchbrenner
|
Lasse Espeholt, Shreya Agrawal, Casper S{\o}nderby, Manoj Kumar,
Jonathan Heek, Carla Bromberg, Cenk Gazen, Jason Hickey, Aaron Bell, Nal
Kalchbrenner
|
Skillful Twelve Hour Precipitation Forecasts using Large Context Neural
Networks
|
34 pages
| null | null | null |
cs.LG physics.ao-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The problem of forecasting weather has been scientifically studied for
centuries due to its high impact on human lives, transportation, food
production and energy management, among others. Current operational forecasting
models are based on physics and use supercomputers to simulate the atmosphere
to make forecasts hours and days in advance. Better physics-based forecasts
require improvements in the models themselves, which can be a substantial
scientific challenge, as well as improvements in the underlying resolution,
which can be computationally prohibitive. An emerging class of weather models
based on neural networks represents a paradigm shift in weather forecasting:
the models learn the required transformations from data instead of relying on
hand-coded physics and are computationally efficient. For neural models,
however, each additional hour of lead time poses a substantial challenge as it
requires capturing ever larger spatial contexts and increases the uncertainty
of the prediction. In this work, we present a neural network that is capable of
large-scale precipitation forecasting up to twelve hours ahead and, starting
from the same atmospheric state, the model achieves greater skill than the
state-of-the-art physics-based models HRRR and HREF that currently operate in
the Continental United States. Interpretability analyses reinforce the
observation that the model learns to emulate advanced physics principles. These
results represent a substantial step towards establishing a new paradigm of
efficient forecasting with neural networks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710597 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07474
|
Deeparnab Chakrabarty
|
Deeparnab Chakrabarty and Yu Chen and Sanjeev Khanna
|
A Polynomial Lower Bound on the Number of Rounds for Parallel Submodular
Function Minimization and Matroid Intersection
|
An extended abstract will appear in the Proceedings of IEEE FOCS 2021
| null | null | null |
cs.DS cs.DM
|
http://creativecommons.org/licenses/by/4.0/
|
Submodular function minimization (SFM) and matroid intersection are
fundamental discrete optimization problems with applications in many fields. It
is well known that both of these can be solved making $\mathrm{poly}(N)$
queries to a relevant oracle (evaluation oracle for SFM and rank oracle for
matroid intersection), where $N$ denotes the universe size. However, all known
polynomial query algorithms are highly adaptive, requiring at least $N$ rounds
of querying the oracle. A natural question is whether these can be efficiently
solved in a highly parallel manner, namely, with $\mathrm{poly}(N)$ queries
using only poly-logarithmic rounds of adaptivity.
An important step towards understanding the adaptivity needed for efficient
parallel SFM was taken recently in the work of Balkanski and Singer who showed
that any SFM algorithm making $\mathrm{poly}(N)$ queries necessarily requires
$\Omega(\log N/\log \log N)$ rounds. This left open the possibility of
efficient SFM algorithms in poly-logarithmic rounds. For matroid intersection,
even the possibility of a constant round, $\mathrm{poly}(N)$ query algorithm
was not hitherto ruled out.
In this work, we prove that any, possibly randomized, algorithm for
submodular function minimization or matroid intersection making
$\mathrm{poly}(N)$ queries requires $\tilde{\Omega}\left(N^{1/3}\right)$ rounds
of adaptivity. In fact, we show a polynomial lower bound on the number of
rounds of adaptivity even for algorithms that make at most $2^{N^{1-\delta}}$
queries, for any constant $\delta> 0$. Therefore, even though SFM and matroid
intersection are efficiently solvable, they are not highly parallelizable in
the oracle model.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07477
|
Zheyi Han
|
Zheyi Han, Shane Colburn, Arka Majumdar, and Karl F. Bohringer
|
Millimeter-scale focal length tuning with MEMS-integrated meta-optics
employing high-throughput fabrication
|
13 pages, 4 figures
| null | null | null |
physics.optics physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Miniature varifocal lenses are crucial for many applications requiring
compact optical systems. Here, utilizing electro-mechanically actuated 0.5-mm
aperture infrared Alvarez meta-optics, we demonstrate 3.1 mm (200 diopters)
focal length tuning with an actuation voltage below 40 V. This constitutes the
largest focal length tuning in any low-power electro-mechanically actuated
meta-optic, enabled by the high energy density in comb-drive actuators
producing large displacements at relatively low voltage. The demonstrated
device is produced by a novel nanofabrication process that accommodates
meta-optics with a larger aperture and has improved alignment between
meta-optics via flip-chip bonding. The whole fabrication process is CMOS
compatible and amenable to high-throughput manufacturing.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713382 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07481
|
Joseph Cheriyan
|
Logan Grout, Joseph Cheriyan, Bundit Laekhanukit
|
On a Partition LP Relaxation for Min-Cost 2-Node Connected Spanning
Subgraphs
| null | null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Our motivation is to improve on the best approximation guarantee known for
the problem of finding a minimum-cost 2-node connected spanning subgraph of a
given undirected graph with nonnegative edge costs. We present an LP (Linear
Programming) relaxation based on partition constraints.
The special case where the input contains a spanning tree of zero cost is
called 2NC-TAP. We present a greedy algorithm for 2NC-TAP, and we analyze it
via dual-fitting for our partition LP relaxation.
Keywords: 2-node connected graphs, approximation algorithms, connectivity
augmentation, greedy algorithm, network design, partition relaxation
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708395 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07489
|
Seongjin Choi
|
Seongjin Choi
|
Deep Learning based Urban Vehicle Trajectory Analytics
|
110 pages, PhD dissertation
| null | null | null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
A `trajectory' refers to a trace generated by a moving object in geographical
spaces, usually represented by of a series of chronologically ordered points,
where each point consists of a geo-spatial coordinate set and a timestamp.
Rapid advancements in location sensing and wireless communication technology
enabled us to collect and store a massive amount of trajectory data. As a
result, many researchers use trajectory data to analyze mobility of various
moving objects. In this dissertation, we focus on the `urban vehicle
trajectory,' which refers to trajectories of vehicles in urban traffic
networks, and we focus on `urban vehicle trajectory analytics.' The urban
vehicle trajectory analytics offers unprecedented opportunities to understand
vehicle movement patterns in urban traffic networks including both user-centric
travel experiences and system-wide spatiotemporal patterns. The spatiotemporal
features of urban vehicle trajectory data are structurally correlated with each
other, and consequently, many previous researchers used various methods to
understand this structure. Especially, deep-learning models are getting
attentions of many researchers due to its powerful function approximation and
feature representation abilities. As a result, the objective of this
dissertation is to develop deep-learning based models for urban vehicle
trajectory analytics to better understand the mobility patterns of urban
traffic networks. Particularly, this dissertation focuses on two research
topics, which has high necessity, importance and applicability: Next Location
Prediction, and Synthetic Trajectory Generation. In this study, we propose
various novel models for urban vehicle trajectory analytics using deep
learning.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710804 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07490
|
Kyubo Noh Dr.
|
Kyubo Noh, David Pardo, and Carlos Torres-Verdin
|
Deep-Learning Inversion Method for the Interpretation of Noisy
Logging-While-Drilling Resistivity Measurements
|
9 pages, 10 figures, 6 tables, a pre-print version of a paper under
revision for IEEE TGRS
| null | null | null |
physics.geo-ph cs.LG
|
http://creativecommons.org/licenses/by-sa/4.0/
|
Deep Learning (DL) inversion is a promising method for real time
interpretation of logging while drilling (LWD) resistivity measurements for
well navigation applications. In this context, measurement noise may
significantly affect inversion results. Existing publications examining the
effects of measurement noise on DL inversion results are scarce. We develop a
method to generate training data sets and construct DL architectures that
enhance the robustness of DL inversion methods in the presence of noisy LWD
resistivity measurements. We use two synthetic resistivity models to test three
approaches that explicitly consider the presence of noise: (1) adding noise to
the measurements in the training set, (2) augmenting the training set by
replicating it and adding varying noise realizations, and (3) adding a noise
layer in the DL architecture. Numerical results confirm that the three
approaches produce a denoising effect, yielding better inversion results in
both predicted earth model and measurements compared not only to the basic DL
inversion but also to traditional gradient based inversion results. A
combination of the second and third approaches delivers the best results. The
proposed methods can be readily generalized to multi dimensional DL inversion.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711663 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07503
|
Feras Batarseh
|
Chih-Hao Huang, Feras A. Batarseh, Adel Boueiz, Ajay Kulkarni,
Po-Hsuan Su, Jahan Aman
|
Measuring Outcomes in Healthcare Economics using Artificial
Intelligence: with Application to Resource Management
|
This paper is published at Cambridge University Press Journal of Data
& Policy
|
Data & Policy, 3, E30
|
10.1017/dap.2021.29
| null |
cs.AI cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
The quality of service in healthcare is constantly challenged by outlier
events such as pandemics (i.e. Covid-19) and natural disasters (such as
hurricanes and earthquakes). In most cases, such events lead to critical
uncertainties in decision making, as well as in multiple medical and economic
aspects at a hospital. External (geographic) or internal factors (medical and
managerial), lead to shifts in planning and budgeting, but most importantly,
reduces confidence in conventional processes. In some cases, support from other
hospitals proves necessary, which exacerbates the planning aspect. This
manuscript presents three data-driven methods that provide data-driven
indicators to help healthcare managers organize their economics and identify
the most optimum plan for resources allocation and sharing. Conventional
decision-making methods fall short in recommending validated policies for
managers. Using reinforcement learning, genetic algorithms, traveling salesman,
and clustering, we experimented with different healthcare variables and
presented tools and outcomes that could be applied at health institutes.
Experiments are performed; the results are recorded, evaluated, and presented.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71103 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07505
|
Feras Batarseh
|
Feras A. Batarseh, and Laura Freeman
|
A Survey on AI Assurance
|
This paper is published at Springer's Journal of Big Data
|
J Big Data 8, 60 (2021)
|
10.1186/s40537-021-00445-7
| null |
cs.AI cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Artificial Intelligence (AI) algorithms are increasingly providing decision
making and operational support across multiple domains. AI includes a wide
library of algorithms for different problems. One important notion for the
adoption of AI algorithms into operational decision process is the concept of
assurance. The literature on assurance, unfortunately, conceals its outcomes
within a tangled landscape of conflicting approaches, driven by contradicting
motivations, assumptions, and intuitions. Accordingly, albeit a rising and
novel area, this manuscript provides a systematic review of research works that
are relevant to AI assurance, between years 1985 - 2021, and aims to provide a
structured alternative to the landscape. A new AI assurance definition is
adopted and presented and assurance methods are contrasted and tabulated.
Additionally, a ten-metric scoring system is developed and introduced to
evaluate and compare existing methods. Lastly, in this manuscript, we provide
foundational insights, discussions, future directions, a roadmap, and
applicable recommendations for the development and deployment of AI assurance.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708988 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07506
|
Ahmad Alsharoa Dr
|
Ahmad Alsharoa and Mohamed-Slim Alouini
|
Facilitating Satellite-Airborne-Balloon-Terrestrial Integration for
Dynamic and Infrastructure-less Networks
|
arXiv admin note: text overlap with arXiv:1912.03819
| null | null | null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This magazine investigates the potential enhancement of the data throughput
of ground users by integrating ground base stations (GBS) with air stations,
such as balloon, airborne, and satellite. The objective is to establish dynamic
bi-directional wireless services (i.e., uplink and downlink) for ground users
in congested and remote areas. The proposed integration involves satellite,
high-altitude platforms (HAPs), and tethered balloons (TBs) in the exosphere,
stratosphere, and troposphere, respectively, for better altitude reuse coupled
with emerging optical or other high-frequency directional transceivers. This
will lead to a significant enhancement in scarce spectrum aggregate efficiency.
However, the air stations deployment and resource managements in this
integrated system faces difficulties. This article tackles resource management
challenges by (i) providing wireless services to ground users in remote areas
and connecting them with metropolitan and rural areas and (ii) employing HAPs
equipped with free-space-optical communication modules as back-hauling
backbones. Finally, we illustrate some numerical results to show the benefit of
our proposed integrated system.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.706804 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07508
|
Feras Batarseh
|
Feras A. Batarseh, Munisamy Gopinath, Anderson Monken, Zhengrong Gu
|
Public Policymaking for International Agricultural Trade using
Association Rules and Ensemble Machine Learning
|
Paper published at Elsevier's Journal of Machine Learning with
Applications
https://www.sciencedirect.com/science/article/pii/S2666827021000232
|
Machine Learning with Applications, Volume 5, 2021, 100046, ISSN
2666-8270
|
10.1016/j.mlwa.2021.100046
| null |
cs.LG cs.AI econ.GN q-fin.EC
|
http://creativecommons.org/licenses/by/4.0/
|
International economics has a long history of improving our understanding of
factors causing trade, and the consequences of free flow of goods and services
across countries. The recent shocks to the free trade regime, especially trade
disputes among major economies, as well as black swan events, such as trade
wars and pandemics, raise the need for improved predictions to inform policy
decisions. AI methods are allowing economists to solve such prediction problems
in new ways. In this manuscript, we present novel methods that predict and
associate food and agricultural commodities traded internationally. Association
Rules (AR) analysis has been deployed successfully for economic scenarios at
the consumer or store level, such as for market basket analysis. In our work
however, we present analysis of imports and exports associations and their
effects on commodity trade flows. Moreover, Ensemble Machine Learning methods
are developed to provide improved agricultural trade predictions, outlier
events' implications, and quantitative pointers to policy makers.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709799 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07509
|
James Bremer
|
James Bremer
|
On the numerical evaluation of the prolate spheroidal wave functions of
order zero
| null | null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We describe a method for the numerical evaluation of the angular prolate
spheroidal wave functions of the first kind of order zero. It is based on the
observation that underlies the WKB method, namely that many second order
differential equations admit solutions whose logarithms can be represented much
more efficiently than the solutions themselves. However, rather than exploiting
this fact to construct asymptotic expansions of the prolate spheroidal wave
functions, our algorithm operates by numerically solving the Riccati equation
satisfied by their logarithms. Its running time grows much more slowly with
bandlimit and characteristic exponent than standard algorithms. We illustrate
this and other properties of our algorithm with numerical experiments.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711794 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07510
|
James Bremer
|
Rafeh Rehan and James Bremer
|
An $\mathcal{O}\left(1\right)$ algorithm for the numerical evaluation of
the Sturm-Liouville eigenvalues of the spheroidal wave functions of order
zero
| null | null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In addition to being the eigenfunctions of the restricted Fourier operator,
the angular spheroidal wave functions of the first kind of order zero and
nonnegative integer characteristic exponents are the solutions of a singular
self-adjoint Sturm-Liouville problem. The running time of the standard
algorithm for the numerical evaluation of their Sturm-Liouville eigenvalues
grows with both bandlimit and characteristic exponent. Here, we describe a new
approach whose running time is bounded independent of these parameters.
Although the Sturm-Liouville eigenvalues are of little interest themselves, our
algorithm is a component of a fast scheme for the numerical evaluation of the
prolate spheroidal wave functions developed by one of the authors. We
illustrate the performance of our method with numerical 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
|
2111.07511
|
Peng Bao
|
Peng Bao, Zonghai Chen, Jikai Wang, Deyun Dai, Hao Zhao
|
Lifelong Vehicle Trajectory Prediction Framework Based on Generative
Replay
|
12pages,7 figures
| null | null | null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Accurate trajectory prediction of vehicles is essential for reliable
autonomous driving. To maintain consistent performance as a vehicle driving
around different cities, it is crucial to adapt to changing traffic
circumstances and achieve lifelong trajectory prediction model. To realize it,
catastrophic forgetting is a main problem to be addressed. In this paper, a
divergence measurement method based on conditional Kullback-Leibler divergence
is proposed first to evaluate spatiotemporal dependency difference among varied
driving circumstances. Then based on generative replay, a novel lifelong
vehicle trajectory prediction framework is developed. The framework consists of
a conditional generation model and a vehicle trajectory prediction model. The
conditional generation model is a generative adversarial network conditioned on
position configuration of vehicles. After learning and merging trajectory
distribution of vehicles across different cities, the generation model replays
trajectories with prior samplings as inputs, which alleviates catastrophic
forgetting. The vehicle trajectory prediction model is trained by the replayed
trajectories and achieves consistent prediction performance on visited cities.
A lifelong experiment setup is established on four open datasets including five
tasks. Spatiotemporal dependency divergence is calculated for different tasks.
Even though these divergence, the proposed framework exhibits lifelong learning
ability and achieves consistent performance on all tasks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711606 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07512
|
Burak Varici
|
Burak Varici, Karthikeyan Shanmugam, Prasanna Sattigeri, Ali Tajer
|
Scalable Intervention Target Estimation in Linear Models
|
23 pages, 4 figures, NeurIPS 2021
| null | null | null |
stat.ME cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper considers the problem of estimating the unknown intervention
targets in a causal directed acyclic graph from observational and
interventional data. The focus is on soft interventions in linear structural
equation models (SEMs). Current approaches to causal structure learning either
work with known intervention targets or use hypothesis testing to discover the
unknown intervention targets even for linear SEMs. This severely limits their
scalability and sample complexity. This paper proposes a scalable and efficient
algorithm that consistently identifies all intervention targets. The pivotal
idea is to estimate the intervention sites from the difference between the
precision matrices associated with the observational and interventional
datasets. It involves repeatedly estimating such sites in different subsets of
variables. The proposed algorithm can be used to also update a given
observational Markov equivalence class into the interventional Markov
equivalence class. Consistency, Markov equivalency, and sample complexity are
established analytically. Finally, simulation results on both real and
synthetic data demonstrate the gains of the proposed approach for scalable
causal structure recovery. Implementation of the algorithm and the code to
reproduce the simulation results are available at
\url{https://github.com/bvarici/intervention-estimation}.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709384 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07525
|
Siyu Lei
|
Siyu Lei, Ruiying Yang, Chu-Ren Huang
|
Automatic Analysis of Linguistic Features in Journal Articles of
Different Academic Impacts with Feature Engineering Techniques
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
English research articles (RAs) are an essential genre in academia, so the
attempts to employ NLP to assist the development of academic writing ability
have received considerable attention in the last two decades. However, there
has been no study employing feature engineering techniques to investigate the
linguistic features of RAs of different academic impacts (i.e., the papers of
high/moderate citation times published in the journals of high/moderate impact
factors). This study attempts to extract micro-level linguistic features in
high- and moderate-impact journal RAs, using feature engineering methods. We
extracted 25 highly relevant features from the Corpus of English Journal
Articles through feature selection methods. All papers in the corpus deal with
COVID-19 medical empirical studies. The selected features were then validated
of the classification performance in terms of consistency and accuracy through
supervised machine learning methods. Results showed that 24 linguistic features
such as the overlapping of content words between adjacent sentences, the use of
third-person pronouns, auxiliary verbs, tense, emotional words provide
consistent and accurate predictions for journal articles with different
academic impacts. Lastly, the random forest model is shown to be the best model
to fit the relationship between these 24 features and journal articles with
high and moderate impacts. These findings can be used to inform academic
writing courses and lay the foundation for developing automatic evaluation
systems for L2 graduate students.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71081 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07528
|
Abdallah Lakhdari
|
Abdallah Lakhdari, Athman Bouguettaya, Sajib Mistry, andAzadeh Ghari
Neiat
|
Composing Energy Services in a Crowdsourced IoT Environment
|
15 pages, accepted and to be published in the IEEE Transactions on
Services Computing, 2020
| null |
10.1109/TSC.2020.2980258
| null |
cs.DC
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a novel framework for composing crowdsourced wireless energy
services to satisfy users' energy requirements in a crowdsourced Internet of
Things (IoT) environment. A new energy service model is designed to transform
the harvested energy from IoT devices into crowdsourced services. We propose a
new energy service composability model that considers the spatio-temporal
aspects and the usage patterns of the IoT devices. A multiple local
knapsack-based approach is developed to select an optimal set of partial energy
services based on the deliverable energy capacity of IoT devices. We propose a
heuristic-based composition approach using the temporal and energy capacity
distributions of services. Experimental results demonstrate the effectiveness
and efficiency of the proposed approach.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710427 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07534
|
Samrudhdhi Bharatkumar Rangrej
|
Samrudhdhi B. Rangrej, James J. Clark
|
A Probabilistic Hard Attention Model For Sequentially Observed Scenes
|
Accepted to BMVC 2021
| null | null | null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
A visual hard attention model actively selects and observes a sequence of
subregions in an image to make a prediction. The majority of hard attention
models determine the attention-worthy regions by first analyzing a complete
image. However, it may be the case that the entire image is not available
initially but instead sensed gradually through a series of partial
observations. In this paper, we design an efficient hard attention model for
classifying such sequentially observed scenes. The presented model never
observes an image completely. To select informative regions under partial
observability, the model uses Bayesian Optimal Experiment Design. First, it
synthesizes the features of the unobserved regions based on the already
observed regions. Then, it uses the predicted features to estimate the expected
information gain (EIG) attained, should various regions be attended. Finally,
the model attends to the actual content on the location where the EIG mentioned
above is maximum. The model uses a) a recurrent feature aggregator to maintain
a recurrent state, b) a linear classifier to predict the class label, c) a
Partial variational autoencoder to predict the features of unobserved regions.
We use normalizing flows in Partial VAE to handle multi-modality in the
feature-synthesis problem. We train our model using a differentiable objective
and test it on five datasets. Our model gains 2-10% higher accuracy than the
baseline models when both have seen only a couple of glimpses.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711625 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07535
|
Dong Yang
|
Dong Yang, Andriy Myronenko, Xiaosong Wang, Ziyue Xu, Holger R. Roth,
Daguang Xu
|
T-AutoML: Automated Machine Learning for Lesion Segmentation using
Transformers in 3D Medical Imaging
|
Accepted at ICCV 2021
| null | null | null |
eess.IV cs.CV cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Lesion segmentation in medical imaging has been an important topic in
clinical research. Researchers have proposed various detection and segmentation
algorithms to address this task. Recently, deep learning-based approaches have
significantly improved the performance over conventional methods. However, most
state-of-the-art deep learning methods require the manual design of multiple
network components and training strategies. In this paper, we propose a new
automated machine learning algorithm, T-AutoML, which not only searches for the
best neural architecture, but also finds the best combination of
hyper-parameters and data augmentation strategies simultaneously. The proposed
method utilizes the modern transformer model, which is introduced to adapt to
the dynamic length of the search space embedding and can significantly improve
the ability of the search. We validate T-AutoML on several large-scale public
lesion segmentation data-sets and achieve state-of-the-art performance.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711794 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07537
|
Changjian Xie
|
Yongyong Cai, Jingrun Chen, Cheng Wang and Changjian Xie
|
Convergence Analysis of A Second-order Accurate, Linear Numerical Scheme
for The Landau-Lifshitz Equation with Large Damping Parameters
| null | null | null | null |
math.NA cs.NA math-ph math.MP
|
http://creativecommons.org/licenses/by/4.0/
|
A second order accurate, linear numerical method is analyzed for the
Landau-Lifshitz equation with large damping parameters. This equation describes
the dynamics of magnetization, with a non-convexity constraint of unit length
of the magnetization. The numerical method is based on the second-order
backward differentiation formula in time, combined with an implicit treatment
of the linear diffusion term and explicit extrapolation for the nonlinear
terms. Afterward, a projection step is applied to normalize the numerical
solution at a point-wise level. This numerical scheme has shown extensive
advantages in the practical computations for the physical model with large
damping parameters, which comes from the fact that only a linear system with
constant coefficients (independent of both time and the updated magnetization)
needs to be solved at each time step, and has greatly improved the numerical
efficiency. Meanwhile, a theoretical analysis for this linear numerical scheme
has not been available. In this paper, we provide a rigorous error estimate of
the numerical scheme, in the discrete $\ell^{\infty}(0,T; \ell^2) \cap
\ell^2(0,T; H_h^1)$ norm, under suitable regularity assumptions and reasonable
ratio between the time step-size and the spatial mesh-size. In particular, the
projection operation is nonlinear, and a stability estimate for the projection
step turns out to be highly challenging. Such a stability estimate is derived
in details, which will play an essential role in the convergence analysis for
the numerical scheme, if the damping parameter is greater than 3.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708862 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07538
|
EPTCS
|
Mario Gleirscher (University of Bremen), Jaco van de Pol (Aarhus
University), Jim Woodcock (University of York)
|
Proceedings First Workshop on Applicable Formal Methods
| null |
EPTCS 349, 2021
|
10.4204/EPTCS.349
| null |
cs.SE
|
http://creativecommons.org/licenses/by/4.0/
|
This volume contains the proceedings of the 1st International Workshop on
Applicable Formal Methods (AppFM 2021), 23 November 2021, held online as part
of the 24th International Symposium on Formal Methods (FM). The aim of the
AppFM workshop is to bring together researchers who improve and evaluate
existing formal approaches and new variants in practical contexts and support
the transfer of these approaches to software engineering practice.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707436 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07545
|
G\'abor Erd\'elyi
|
G\'abor Erd\'elyi, Olivia J. Erd\'elyi, and Vladimir Estivill-Castro
|
Randomized Classifiers vs Human Decision-Makers: Trustworthy AI May Have
to Act Randomly and Society Seems to Accept This
|
46 pages
| null | null | null |
cs.CY cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
As \emph{artificial intelligence} (AI) systems are increasingly involved in
decisions affecting our lives, ensuring that automated decision-making is fair
and ethical has become a top priority. Intuitively, we feel that akin to human
decisions, judgments of artificial agents should necessarily be grounded in
some moral principles. Yet a decision-maker (whether human or artificial) can
only make truly ethical (based on any ethical theory) and fair (according to
any notion of fairness) decisions if full information on all the relevant
factors on which the decision is based are available at the time of
decision-making. This raises two problems: (1) In settings, where we rely on AI
systems that are using classifiers obtained with supervised learning, some
induction/generalization is present and some relevant attributes may not be
present even during learning. (2) Modeling such decisions as games reveals that
any -- however ethical -- pure strategy is inevitably susceptible to
exploitation.
Moreover, in many games, a Nash Equilibrium can only be obtained by using
mixed strategies, i.e., to achieve mathematically optimal outcomes, decisions
must be randomized. In this paper, we argue that in supervised learning
settings, there exist random classifiers that perform at least as well as
deterministic classifiers, and may hence be the optimal choice in many
circumstances. We support our theoretical results with an empirical study
indicating a positive societal attitude towards randomized artificial
decision-makers, and discuss some policy and implementation issues related to
the use of random classifiers that relate to and are relevant for current AI
policy and standardization initiatives.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71202 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07547
|
Huaijin Pi
|
Huaijin Pi, Huiyu Wang, Yingwei Li, Zizhang Li, Alan Yuille
|
Searching for TrioNet: Combining Convolution with Local and Global
Self-Attention
|
BMVC 2021
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recently, self-attention operators have shown superior performance as a
stand-alone building block for vision models. However, existing self-attention
models are often hand-designed, modified from CNNs, and obtained by stacking
one operator only. A wider range of architecture space which combines different
self-attention operators and convolution is rarely explored. In this paper, we
explore this novel architecture space with weight-sharing Neural Architecture
Search (NAS) algorithms. The result architecture is named TrioNet for combining
convolution, local self-attention, and global (axial) self-attention operators.
In order to effectively search in this huge architecture space, we propose
Hierarchical Sampling for better training of the supernet. In addition, we
propose a novel weight-sharing strategy, Multi-head Sharing, specifically for
multi-head self-attention operators. Our searched TrioNet that combines
self-attention and convolution outperforms all stand-alone models with fewer
FLOPs on ImageNet classification where self-attention performs better than
convolution. Furthermore, on various small datasets, we observe inferior
performance for self-attention models, but our TrioNet is still able to match
the best operator, convolution in this case. Our code is available at
https://github.com/phj128/TrioNet.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709868 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07549
|
Zhu Li
|
Zhu Li, Yuqing Zhang, Mengxi Nie, Ming Yan, Mengnan He, Ruixiong
Zhang, Caixia Gong
|
Improving Prosody for Unseen Texts in Speech Synthesis by Utilizing
Linguistic Information and Noisy Data
| null | null | null | null |
cs.CL cs.SD eess.AS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Recent advancements in end-to-end speech synthesis have made it possible to
generate highly natural speech. However, training these models typically
requires a large amount of high-fidelity speech data, and for unseen texts, the
prosody of synthesized speech is relatively unnatural. To address these issues,
we propose to combine a fine-tuned BERT-based front-end with a pre-trained
FastSpeech2-based acoustic model to improve prosody modeling. The pre-trained
BERT is fine-tuned on the polyphone disambiguation task, the joint Chinese word
segmentation (CWS) and part-of-speech (POS) tagging task, and the prosody
structure prediction (PSP) task in a multi-task learning framework. FastSpeech
2 is pre-trained on large-scale external data that are noisy but easier to
obtain. Experimental results show that both the fine-tuned BERT model and the
pre-trained FastSpeech 2 can improve prosody, especially for those structurally
complex sentences.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711249 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07552
|
Ritik Batra
|
Alice Agogino, Hae Young Jang, Vivek Rao, Ritik Batra, Felicity Liao,
Rohan Sood, Irving Fang, R. Lily Hu, Emerson Shoichet-Bartus, John Matranga
|
Dynamic Placement of Rapidly Deployable Mobile Sensor Robots Using
Machine Learning and Expected Value of Information
|
14 pages, 11 figures, IMECE2021
| null | null | null |
eess.SY cs.RO cs.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Although the Industrial Internet of Things has increased the number of
sensors permanently installed in industrial plants, there will be gaps in
coverage due to broken sensors or sparse density in very large plants, such as
in the petrochemical industry. Modern emergency response operations are
beginning to use Small Unmanned Aerial Systems (sUAS) that have the ability to
drop sensor robots to precise locations. sUAS can provide longer-term
persistent monitoring that aerial drones are unable to provide. Despite the
relatively low cost of these assets, the choice of which robotic sensing
systems to deploy to which part of an industrial process in a complex plant
environment during emergency response remains challenging.
This paper describes a framework for optimizing the deployment of emergency
sensors as a preliminary step towards realizing the responsiveness of robots in
disaster circumstances. AI techniques (Long short-term memory, 1-dimensional
convolutional neural network, logistic regression, and random forest) identify
regions where sensors would be most valued without requiring humans to enter
the potentially dangerous area. In the case study described, the cost function
for optimization considers costs of false-positive and false-negative errors.
Decisions on mitigation include implementing repairs or shutting down the
plant. The Expected Value of Information (EVI) is used to identify the most
valuable type and location of physical sensors to be deployed to increase the
decision-analytic value of a sensor network. This method is applied to a case
study using the Tennessee Eastman process data set of a chemical plant, and we
discuss implications of our findings for operation, distribution, and
decision-making of sensors in plant emergency and resilience scenarios.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711844 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07555
|
Pascale Fung Prof.
|
Pascale Fung and Hubert Etienne
|
Confucius, Cyberpunk and Mr. Science: Comparing AI ethics between China
and the EU
|
This is a paper on AI ethics and governance
| null | null | null |
cs.AI cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
The exponential development and application of artificial intelligence
triggered an unprecedented global concern for potential social and ethical
issues. Stakeholders from different industries, international foundations,
governmental organisations and standards institutions quickly improvised and
created various codes of ethics attempting to regulate AI. A major concern is
the large homogeneity and presumed consensualism around these principles. While
it is true that some ethical doctrines, such as the famous Kantian deontology,
aspire to universalism, they are however not universal in practice. In fact,
ethical pluralism is more about differences in which relevant questions to ask
rather than different answers to a common question. When people abide by
different moral doctrines, they tend to disagree on the very approach to an
issue. Even when people from different cultures happen to agree on a set of
common principles, it does not necessarily mean that they share the same
understanding of these concepts and what they entail. In order to better
understand the philosophical roots and cultural context underlying ethical
principles in AI, we propose to analyse and compare the ethical principles
endorsed by the Chinese National New Generation Artificial Intelligence
Governance Professional Committee (CNNGAIGPC) and those elaborated by the
European High-level Expert Group on AI (HLEGAI). China and the EU have very
different political systems and diverge in their cultural heritages. In our
analysis, we wish to highlight that principles that seem similar a priori may
actually have different meanings, derived from different approaches and reflect
distinct goals.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07556
|
Hongwei Xu
|
Hongwei Xu and Leijia Dai and Jianxing Fu and Xiangyuan Wang and
Quanwei Wang
|
High-Quality Real Time Facial Capture Based on Single Camera
|
arXiv admin note: text overlap with arXiv:1609.06536 by other authors
| null | null | null |
cs.CV cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
We propose a real time deep learning framework for video-based facial
expression capture. Our process uses a high-end facial capture pipeline based
on FACEGOOD to capture facial expression. We train a convolutional neural
network to produce high-quality continuous blendshape weight output from video
training. Since this facial capture is fully automated, our system can
drastically reduce the amount of labor involved in the development of modern
narrative-driven video games or films involving realistic digital doubles of
actors and potentially hours of animated dialogue per character. We demonstrate
compelling animation inference in challenging areas such as eyes and lips.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07566
|
Yifei Zhang
|
Mingming Feng (1), Baoqing Zhang (1), Haotian Ling (1), Zihao Zhang
(1), Yiming Wang (1), Yilin Wang (1), Xijian Zhang (1), Pingrang Hua (2),
Qingpu Wang (1), Aimin Song (1 and 3), Yifei Zhang (1) ((1) Shandong
Technology Center of Nanodevices and Integration, School of Microelectronics,
Shandong University, Jinan, China (2) Department of Opto-electronics and
Information Engineering, School of Precision Instruments and Opto-electronics
Engineering, Tianjin University, Tianjin, China (3) School of Electrical and
Electronic Engineering, University of Manchester, Manchester, United Kingdom)
|
Sweeping Plasma Frequency of Terahertz Surface Plasmon Polaritons with
Graphene
|
19pages, 6 figures
| null | null | null |
physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Plasma frequency is the spectral boundary for low-loss propagation and
evanescent decay of surface plasmon polariton (SPP) waves, which corresponds to
a high cut-off phenomenon and is typically utilized for identifying SPPs. At
terahertz (THz) frequencies, a metal line with periodic metallic grooves can
mimic the conventional optical SPPs, which is referred to as designer SPPs.
Theoretically, the plasma frequency of THz SPPs decreases as the groove depth
increases. Here, by replacing the metallic grooves with graphene sheets,
dynamically sweeping SPP plasma frequency is demonstrated for the first time.
The metal-graphene hybrid structure comprises a metal line with periodic
graphene grooves, a thin-layer ion gel for gating graphene, and metallic tips
for uniforming gate field. As the chemical potential changes, the average
conductivity of graphene is modulated so that the effective depth of the
graphene grooves changes, which sweeps the plasma frequency of THz SPPs
consequently. Both simulated and experimental data demonstrate a red shift of
plasma frequency from 195 to 180 GHz at a low bias from -0.5 to 0.5 V. The
proposed structure reveals a novel approach to control the on/off status of SPP
propagation in the THz range.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.714466 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07568
|
Minghao Liu
|
Minghao Liu, Fuqi Jia, Pei Huang, Fan Zhang, Yuchen Sun, Shaowei Cai,
Feifei Ma, Jian Zhang
|
Can Graph Neural Networks Learn to Solve MaxSAT Problem?
| null | null | null | null |
cs.AI
|
http://creativecommons.org/licenses/by/4.0/
|
With the rapid development of deep learning techniques, various recent work
has tried to apply graph neural networks (GNNs) to solve NP-hard problems such
as Boolean Satisfiability (SAT), which shows the potential in bridging the gap
between machine learning and symbolic reasoning. However, the quality of
solutions predicted by GNNs has not been well investigated in the literature.
In this paper, we study the capability of GNNs in learning to solve Maximum
Satisfiability (MaxSAT) problem, both from theoretical and practical
perspectives. We build two kinds of GNN models to learn the solution of MaxSAT
instances from benchmarks, and show that GNNs have attractive potential to
solve MaxSAT problem through experimental evaluation. We also present a
theoretical explanation of the effect that GNNs can learn to solve MaxSAT
problem to some extent for the first time, based on the algorithmic alignment
theory.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710867 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07574
|
Ahmed Alkhateeb
|
Gouranga Charan, Tawfik Osman, Andrew Hredzak, Ngwe Thawdar, and Ahmed
Alkhateeb
|
Vision-Position Multi-Modal Beam Prediction Using Real Millimeter Wave
Datasets
|
Dataset and code files will be available on the DeepSense 6G website
http://deepsense6g.net/
| null | null | null |
eess.SP cs.IT math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Enabling highly-mobile millimeter wave (mmWave) and terahertz (THz) wireless
communication applications requires overcoming the critical challenges
associated with the large antenna arrays deployed at these systems. In
particular, adjusting the narrow beams of these antenna arrays typically incurs
high beam training overhead that scales with the number of antennas. To address
these challenges, this paper proposes a multi-modal machine learning based
approach that leverages positional and visual (camera) data collected from the
wireless communication environment for fast beam prediction. The developed
framework has been tested on a real-world vehicular dataset comprising
practical GPS, camera, and mmWave beam training data. The results show the
proposed approach achieves more than $\approx$ 75\% top-1 beam prediction
accuracy and close to 100\% top-3 beam prediction accuracy in realistic
communication scenarios.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.634572 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07577
|
Toshiyuki Tabata
|
Toshiyuki Tabata, Fabien Roz\'e, Pablo Acosta Alba, S\'ebastien Halty,
Pierre-Edouard Raynal, Imen Karmous, S\'ebastien Kerdil\`es, and Fulvio
Mazzamuto
|
Solid Phase Recrystallization and Dopant Activation in Arsenic
Ion-Implanted Silicon-On-Insulator by UV Laser Annealing
|
Accepted Paper for 20th International Workshop on Junction Technology
(IWJT2021)
| null | null | null |
physics.app-ph
|
http://creativecommons.org/licenses/by/4.0/
|
UV laser annealing (UV-LA) enables surface-localized high-temperature thermal
processing to form abrupt junctions in emerging monolithically stacked devices,
where applicable thermal budget is restricted. In this work, UV-LA is performed
to regrow a SOI layer partially amorphized by arsenic ion implantation and to
activate the dopants. In a microsecond scale (~10^-6 s to ~10^-5 s) UV-LA
process, monocrystalline solid phase recrystallization and dopant activation
without junction deepening is evidenced, thus opening various applications in
low thermal budget integration flows.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711425 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07579
|
Fayza Sewid Fa
|
F. A. Sewid, K. I. Annas, A. Dubavik, A. V. Veniaminov, V.G. Maslov,
A. O.Orlov
|
Chitosan Nanocomposites with CdSe/ZnS Quantum Dots and Porphyrin
| null | null | null | null |
physics.app-ph physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Water-soluble nanocomposites based on CdSe/ZnS quantum dots (QDs) and
hydrophobic tetraphenylporphyrin (TPP) molecules passivated by chitosan (CS)
have been formed. Magnetic circular dichroism (MCD) spectra evidence TPP
presence in both monomeric and agglomerated forms in the nanocomposites. The
nanocomposites demonstrate more pronounced singlet oxygen generation in
comparison with free TPP in CS at the same concentration due to intracomplex
Forster resonance energy transfer (FRET) with 45 % average efficiency.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.706836 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07580
|
Toshiyuki Tabata
|
Toshiyuki Tabata, Pierre-Edouard Raynal, Fabien Roz\'e, S\'ebastien
Halty, Louis Thuries, Fuccio Cristiano, Emmanuel Scheid, and Fulvio Mazzamuto
|
Copper Large-scale Grain Growth by UV Nanosecond Pulsed Laser Annealing
|
Accepted Paper for the IEEE International Interconnect Technology
Conference (IITC) 2021 Virtual Symposium
| null |
10.1109/IITC51362.2021.9537312
| null |
physics.app-ph
|
http://creativecommons.org/licenses/by/4.0/
|
UV nanosecond pulsed laser annealing (UV NLA) enables both surface-localized
heating and short timescale high temperature processing, which can be
advantageous to reduce metal line resistance by enlarging metal grains in lines
or in thin films, while maintaining the integrity and performance of
surrounding structures. In this work UV NLA is applied on a typical Cu thin
film, demonstrating a mean grain size of over 1 {\mu}m and 400 nm in a melt and
sub-melt regime, respectively. Along with such grain enlargement, film
resistivity is also reduced.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71408 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07583
|
Srikant Manas Kala
|
Srikant Manas Kala, Vanlin Sathya, Kunal Dahiya, Teruo Higashino, and
Hirozumi Yamaguchi
|
Optimizing Unlicensed Coexistence Network Performance Through Data
Learning
|
Accepted for publication in Mobiquitous 2021
| null | null | null |
cs.NI cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Unlicensed LTE-WiFi coexistence networks are undergoing consistent
densification to meet the rising mobile data demands. With the increase in
coexistence network complexity, it is important to study network feature
relationships (NFRs) and utilize them to optimize dense coexistence network
performance. This work studies NFRs in unlicensed LTE-WiFi (LTE-U and LTE-LAA)
networks through supervised learning of network data collected from real-world
experiments. Different 802.11 standards and varying channel bandwidths are
considered in the experiments and the learning model selection policy is
precisely outlined. Thereafter, a comparative analysis of different LTE-WiFi
network configurations is performed through learning model parameters such as
R-sq, residual error, outliers, choice of predictor, etc. Further, a Network
Feature Relationship based Optimization (NeFRO) framework is proposed. NeFRO
improves upon the conventional optimization formulations by utilizing the
feature-relationship equations learned from network data. It is demonstrated to
be highly suitable for time-critical dense coexistence networks through two
optimization objectives, viz., network capacity and signal strength. NeFRO is
validated against four recent works on network optimization. NeFRO is
successfully able to reduce optimization convergence time by as much as 24%
while maintaining accuracy as high as 97.16%, on average.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07584
|
Dongyun Kam
|
Dongyun Kam, Jung Gyu Min, Jongho Yoon, Sunmean Kim, Seokhyeong Kang
and Youngjoo Lee
|
Design and Evaluation Frameworks for Advanced RISC-based Ternary
Processor
|
Accepted to DATE 2022
| null | null | null |
cs.AR cs.ET
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we introduce the design and verification frameworks for
developing a fully-functional emerging ternary processor. Based on the existing
compiling environments for binary processors, for the given ternary
instructions, the software-level framework provides an efficient way to convert
the given programs to the ternary assembly codes. We also present a
hardware-level framework to rapidly evaluate the performance of a ternary
processor implemented in arbitrary design technology. As a case study, the
fully-functional 9-trit advanced RISC-based ternary (ART-9) core is newly
developed by using the proposed frameworks. Utilizing 24 custom ternary
instructions, the 5-stage ART-9 prototype architecture is successfully verified
by a number of test programs including dhrystone benchmark in a ternary domain,
achieving the processing efficiency of 57.8 DMIPS/W and 3.06 x 10^6 DMIPS/W in
the FPGA-level ternary-logic emulations and the emerging CNTFET ternary gates,
respectively.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708635 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07585
|
Xiaojuan Feng
|
Ke-Chen Ouyang, Zheng Wang, Li Xing, Xiao-Juan Feng, Jin-Tao Zhang,
Cheng Ren, Xing-Tuan Yang
|
Temperature dependence of nitrogen-vacancy center ensembles in diamond
based on an optical fiber
| null | null | null | null |
physics.app-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The nitrogen-vacancy (NV) centers in diamond sensing has been considered to
be a promising micro-nano scale thermometer due to its high stability, good
temperature resolution and integration. In this work, we fabricated the sensing
core by attaching a diamond plate containing NV centers to the section of a
cut-off multi-mode fiber. Then we measured the zero-field splitting parameter
(D) of NV center ensembles using continuous-wave optical detected magnetic
resonance (CW-ODMR) technique. A home-made thermostatic system and two
calibrated platinum resistance thermometers were applied for reference
temperature measurement. The effects from preparation time and count time in
the pulse sequence, laser power, microwave power, and microwave frequency step
were investigated. Moreover, the experimental D and T from 298.15 K to 383.15 K
was obtained with the standard uncertainty of u(D) = (3.62268~8.54464)x10^-5
GHz and u(T) = (0.013~ 0.311) K. The experimental results are well consistent
with the work of Toyli, et al. (Toyli, et al., 2012) using the similar diamond
sample. The extrapolation for D-T at 0 K and 700 K also agree with other
references, and meanwhile dD/dT varies with temperature. Finally, comparing the
D-T relationship measured by different research groups, we can know that the NV
concentration resulting in different electron density and manufacturing
procedure resulting in different thermal expansion would lead to different D-T
relationship. It is worthy to continue further comprehensive research
especially from the metrological point of view to develop NV center as a
practical and accurate micro-nano scale thermometry.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711212 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07592
|
Tanay Gummadi
|
Naveen Ram, Tanay Gummadi, Rahul Bhethanabotla, Richard J. Savery, Gil
Weinberg
|
Say What? Collaborative Pop Lyric Generation Using Multitask Transfer
Learning
|
HAI '21: Proceedings of the 9th International Conference on
Human-Agent Interaction
|
Proceedings of the 9th International Conference on Human-Agent
Interaction (2021) 165-173
|
10.1145/3472307.3484175
| null |
cs.CL cs.HC cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Lyric generation is a popular sub-field of natural language generation that
has seen growth in recent years. Pop lyrics are of unique interest due to the
genre's unique style and content, in addition to the high level of
collaboration that goes on behind the scenes in the professional pop
songwriting process. In this paper, we present a collaborative line-level lyric
generation system that utilizes transfer-learning via the T5 transformer model,
which, till date, has not been used to generate pop lyrics. By working and
communicating directly with professional songwriters, we develop a model that
is able to learn lyrical and stylistic tasks like rhyming, matching line beat
requirements, and ending lines with specific target words. Our approach
compares favorably to existing methods for multiple datasets and yields
positive results from our online studies and interviews with industry
songwriters.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712063 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2111.07593
|
Haotong Zhang
|
Haotong Zhang, Fuhai Chen, Angela Yao
|
Weakly-Supervised Dense Action Anticipation
|
BMVC 2021
| null | null | null |
cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dense anticipation aims to forecast future actions and their durations for
long horizons. Existing approaches rely on fully-labelled data, i.e. sequences
labelled with all future actions and their durations. We present a (semi-)
weakly supervised method using only a small number of fully-labelled sequences
and predominantly sequences in which only the (one) upcoming action is
labelled. To this end, we propose a framework that generates pseudo-labels for
future actions and their durations and adaptively refines them through a
refinement module. Given only the upcoming action label as input, these
pseudo-labels guide action/duration prediction for the future. We further
design an attention mechanism to predict context-aware durations. Experiments
on the Breakfast and 50Salads benchmarks verify our method's effectiveness; we
are competitive even when compared to fully supervised state-of-the-art models.
We will make our code available at:
https://github.com/zhanghaotong1/WSLVideoDenseAnticipation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712401 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.