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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2008.02355
|
Prasanna Date
|
Prasanna Date, Thomas Potok
|
Adiabatic Quantum Linear Regression
| null | null |
10.1038/s41598-021-01445-6
| null |
cs.LG physics.data-an stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A major challenge in machine learning is the computational expense of
training these models. Model training can be viewed as a form of optimization
used to fit a machine learning model to a set of data, which can take up
significant amount of time on classical computers. Adiabatic quantum computers
have been shown to excel at solving optimization problems, and therefore, we
believe, present a promising alternative to improve machine learning training
times. In this paper, we present an adiabatic quantum computing approach for
training a linear regression model. In order to do this, we formulate the
regression problem as a quadratic unconstrained binary optimization (QUBO)
problem. We analyze our quantum approach theoretically, test it on the D-Wave
2000Q adiabatic quantum computer and compare its performance to a classical
approach that uses the Scikit-learn library in Python. Our analysis shows that
the quantum approach attains up to 2.8x speedup over the classical approach on
larger datasets, and performs at par with the classical approach on the
regression error metric.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711625 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.02369
|
Prasanna Date
|
Prasanna Date, Davis Arthur, Lauren Pusey-Nazzaro
|
QUBO Formulations for Training Machine Learning Models
| null | null |
10.1038/s41598-021-89461-4
| null |
cs.LG physics.data-an stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Training machine learning models on classical computers is usually a time and
compute intensive process. With Moore's law coming to an end and ever
increasing demand for large-scale data analysis using machine learning, we must
leverage non-conventional computing paradigms like quantum computing to train
machine learning models efficiently. Adiabatic quantum computers like the
D-Wave 2000Q can approximately solve NP-hard optimization problems, such as the
quadratic unconstrained binary optimization (QUBO), faster than classical
computers. Since many machine learning problems are also NP-hard, we believe
adiabatic quantum computers might be instrumental in training machine learning
models efficiently in the post Moore's law era. In order to solve a problem on
adiabatic quantum computers, it must be formulated as a QUBO problem, which is
a challenging task in itself. In this paper, we formulate the training problems
of three machine learning models---linear regression, support vector machine
(SVM) and equal-sized k-means clustering---as QUBO problems so that they can be
trained on adiabatic quantum computers efficiently. We also analyze the time
and space complexities of our formulations and compare them to the
state-of-the-art classical algorithms for training these machine learning
models. We show that the time and space complexities of our formulations are
better (in the case of SVM and equal-sized k-means clustering) or equivalent
(in case of linear regression) to their classical counterparts.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712807 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.03692
|
Johan Koskinen
|
Johan Koskinen and Pete Jones and Darkhan Medeuov and Artem Antonyuk
and Kseniia Puzyreva and Nikita Basov
|
Analysing Networks of Networks
| null | null | null | null |
cs.SI physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider data with multiple observations or reports on a network in the
case when these networks themselves are connected through some form of network
ties. We could take the example of a cognitive social structure where there is
another type of tie connecting the actors that provide the reports; or the
study of interpersonal spillover effects from one cultural domain to another
facilitated by the social ties. Another example is when the individual semantic
structures are represented as semantic networks of a group of actors and
connected through these actors' social ties to constitute knowledge of a social
group. How to jointly represent the two types of networks is not trivial as the
layers and not the nodes of the layers of the reported networks are coupled
through a network on the reports. We propose to transform the different
multiple networks using line graphs, where actors are affiliated with ties
represented as nodes, and represent the totality of the different types of ties
as a multilevel network. This affords studying the associations between the
social network and the reports as well as the alignment of the reports to a
criterion graph. We illustrate how the procedure can be applied to studying the
social construction of knowledge in local flood management groups. Here we use
multilevel exponential random graph models but the representation also lends
itself to stochastic actor-oriented models, multilevel blockmodels, and any
model capable of handling multilevel networks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707803 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.04024
|
Xin Zhang
|
Xin Zhang, Liangxiu Han, Wenyong Zhu, Liang Sun, Daoqiang Zhang
|
An Explainable 3D Residual Self-Attention Deep Neural Network FOR Joint
Atrophy Localization and Alzheimer's Disease Diagnosis using Structural MRI
|
IEEE Journal of Biomedical and Health Informatics (2021)
| null |
10.1109/JBHI.2021.3066832
| null |
eess.IV cs.CV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Computer-aided early diagnosis of Alzheimer's disease (AD) and its prodromal
form mild cognitive impairment (MCI) based on structure Magnetic Resonance
Imaging (sMRI) has provided a cost-effective and objective way for early
prevention and treatment of disease progression, leading to improved patient
care. In this work, we have proposed a novel computer-aided approach for early
diagnosis of AD by introducing an explainable 3D Residual Attention Deep Neural
Network (3D ResAttNet) for end-to-end learning from sMRI scans. Different from
the existing approaches, the novelty of our approach is three-fold: 1) A
Residual Self-Attention Deep Neural Network has been proposed to capture local,
global and spatial information of MR images to improve diagnostic performance;
2) An explanation method using Gradient-based Localization Class Activation
mapping (Grad-CAM) has been introduced to improve the explainable of the
proposed method; 3) This work has provided a full end-to-end learning solution
for automated disease diagnosis. Our proposed 3D ResAttNet method has been
evaluated on a large cohort of subjects from real datasets for two changeling
classification tasks (i.e., Alzheimer's disease (AD) vs. Normal cohort (NC) and
progressive MCI (pMCI) vs. stable MCI (sMCI)). The experimental results show
that the proposed approach has a competitive advantage over the
state-of-the-art models in terms of accuracy performance and generalizability.
The explainable mechanism in our approach is able to identify and highlight the
contribution of the important brain parts (e.g., hippocampus, lateral ventricle
and most parts of the cortex) for transparent decisions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710377 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.04464
|
Mostafa Nouh
|
R. Adlakha, M. Moghaddaszadeh, M. A. Attarzadeh, A. Aref, and M. Nouh
|
Frequency Selective Wave Beaming in Nonreciprocal Acoustic Phased Arrays
| null |
Scientific Reports 10, 21339 (2020)
|
10.1038/s41598-020-77489-x
| null |
physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Acoustic phased arrays are capable of steering and focusing a beam of sound
via selective coordination of the spatial distribution of phase angles between
multiple sound emitters. Constrained by the principle of reciprocity,
conventional phased arrays exhibit identical transmission and reception
patterns which limit the scope of their operation. This work presents a
controllable space-time acoustic phased array which breaks time-reversal
symmetry, and enables phononic transition in both momentum and energy spaces.
By leveraging a dynamic phase modulation, the proposed linear phased array is
no longer bound by the acoustic reciprocity, and supports asymmetric
transmission and reception patterns that can be tuned independently at multiple
channels. A foundational framework is developed to characterize and interpret
the emergent nonreciprocal phenomena and is later validated against benchmark
numerical experiments. The new phased array selectively alters the directional
and frequency content of the incident signal and the frequency conversion
between the different wave fields is analyzed as a function of the imposed
modulation. The space-time acoustic phased array enables unprecedented control
over sound waves in a variety of applications ranging from ultrasonic imaging
to non-destructive testing and underwater SONAR telecommunication.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712188 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.05505
|
Tiffany D. Do
|
Tiffany D. Do, Joseph J. LaViola Jr., Ryan P. McMahan
|
The Effects of Object Shape, Fidelity, Color, and Luminance on Depth
Perception in Handheld Mobile Augmented Reality
|
9 pages, In proceedings of IEEE International Symposium on Mixed and
Augmented Reality (ISMAR) 2020
|
2020 IEEE International Symposium on Mixed and Augmented Reality
(ISMAR)
|
10.1109/ISMAR50242.2020.00026
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Depth perception of objects can greatly affect a user's experience of an
augmented reality (AR) application. Many AR applications require depth matching
of real and virtual objects and have the possibility to be influenced by depth
cues. Color and luminance are depth cues that have been traditionally studied
in two-dimensional (2D) objects. However, there is little research
investigating how the properties of three-dimensional (3D) virtual objects
interact with color and luminance to affect depth perception, despite the
substantial use of 3D objects in visual applications. In this paper, we present
the results of a paired comparison experiment that investigates the effects of
object shape, fidelity, color, and luminance on depth perception of 3D objects
in handheld mobile AR. The results of our study indicate that bright colors are
perceived as nearer than dark colors for a high-fidelity, simple 3D object,
regardless of hue. Additionally, bright red is perceived as nearer than any
other color. These effects were not observed for a low-fidelity version of the
simple object or for a more-complex 3D object. High-fidelity objects had more
perceptual differences than low-fidelity objects, indicating that fidelity
interacts with color and luminance to affect depth perception. These findings
reveal how the properties of 3D models influence the effects of color and
luminance on depth perception in handheld mobile AR and can help developers
select colors for their applications.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.693304 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.08883
|
Kris Nikov
|
Kris Nikov (1), Mohammad Hosseinabady (1), Rafael Asenjo (2), Andr\'es
Rodr\'iguezz (2), Angeles Navarro (2) and Jose Nunez-Yanez (1) ((1)
University of Bristol, UK, (2) Universidad de M\'alaga, Spain)
|
High-Performance Simultaneous Multiprocessing for Heterogeneous
System-on-Chip
|
7 pages, 5 figures, 1 table Presented at the 13th International
Workshop on Programmability and Architectures for Heterogeneous Multicores,
2020 (arXiv:2005.07619)
| null | null |
MULTIPROG/2020/4
|
cs.DC cs.AR cs.PF
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
This paper presents a methodology for simultaneous heterogeneous computing,
named ENEAC, where a quad core ARM Cortex-A53 CPU works in tandem with a
preprogrammed on-board FPGA accelerator. A heterogeneous scheduler distributes
the tasks optimally among all the resources and all compute units run
asynchronously, which allows for improved performance for irregular workloads.
ENEAC achieves up to 17\% performance improvement \ignore{and 14\% energy usage
reduction,} when using all platform resources compared to just using the FPGA
accelerators and up to 865\% performance increase \ignore{and up to 89\% energy
usage decrease} when using just the CPU. The workflow uses existing commercial
tools and C/C++ as a single programming language for both accelerator design
and CPU programming for improved productivity and ease of verification.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711638 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2008.10846
|
Ahmet M. Elbir
|
Ahmet M. Elbir and Sinem Coleri
|
Federated Learning for Channel Estimation in Conventional and
RIS-Assisted Massive MIMO
|
Accepted paper in IEEE Transactions on Wireless Communications
| null | null | null |
eess.SP cs.IT cs.LG math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine learning (ML) has attracted a great research interest for physical
layer design problems, such as channel estimation, thanks to its low complexity
and robustness. Channel estimation via ML requires model training on a dataset,
which usually includes the received pilot signals as input and channel data as
output. In previous works, model training is mostly done via centralized
learning (CL), where the whole training dataset is collected from the users at
the base station (BS). This approach introduces huge communication overhead for
data collection. In this paper, to address this challenge, we propose a
federated learning (FL) framework for channel estimation. We design a
convolutional neural network (CNN) trained on the local datasets of the users
without sending them to the BS. We develop FL-based channel estimation schemes
for both conventional and RIS (intelligent reflecting surface) assisted massive
MIMO (multiple-input multiple-output) systems, where a single CNN is trained
for two different datasets for both scenarios. We evaluate the performance for
noisy and quantized model transmission and show that the proposed approach
provides approximately 16 times lower overhead than CL, while maintaining
satisfactory performance close to CL. Furthermore, the proposed architecture
exhibits lower estimation error than the state-of-the-art ML-based schemes.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711268 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.00540
|
Prasanna Date
|
Prasanna Date, Christopher D. Carothers, John E. Mitchell, James A.
Hendler, Malik Magdon-Ismail
|
Training Deep Neural Networks with Constrained Learning Parameters
| null | null |
10.1109/ICRC2020.2020.00018
| null |
cs.LG cs.CV stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Today's deep learning models are primarily trained on CPUs and GPUs. Although
these models tend to have low error, they consume high power and utilize large
amount of memory owing to double precision floating point learning parameters.
Beyond the Moore's law, a significant portion of deep learning tasks would run
on edge computing systems, which will form an indispensable part of the entire
computation fabric. Subsequently, training deep learning models for such
systems will have to be tailored and adopted to generate models that have the
following desirable characteristics: low error, low memory, and low power. We
believe that deep neural networks (DNNs), where learning parameters are
constrained to have a set of finite discrete values, running on neuromorphic
computing systems would be instrumental for intelligent edge computing systems
having these desirable characteristics. To this extent, we propose the
Combinatorial Neural Network Training Algorithm (CoNNTrA), that leverages a
coordinate gradient descent-based approach for training deep learning models
with finite discrete learning parameters. Next, we elaborate on the theoretical
underpinnings and evaluate the computational complexity of CoNNTrA. As a proof
of concept, we use CoNNTrA to train deep learning models with ternary learning
parameters on the MNIST, Iris and ImageNet data sets and compare their
performance to the same models trained using Backpropagation. We use following
performance metrics for the comparison: (i) Training error; (ii) Validation
error; (iii) Memory usage; and (iv) Training time. Our results indicate that
CoNNTrA models use 32x less memory and have errors at par with the
Backpropagation models.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.02961
|
Cemre Zor
|
Sara Atito Ali Ahmed, Cemre Zor, Berrin Yanikoglu, Muhammad Awais,
Josef Kittler
|
Deep Convolutional Neural Network Ensembles using ECOC
|
13 pages double column IEEE transactions style
| null |
10.1109/ACCESS.2021.3088717
| null |
cs.LG stat.ML
|
http://creativecommons.org/licenses/by/4.0/
|
Deep neural networks have enhanced the performance of decision making systems
in many applications including image understanding, and further gains can be
achieved by constructing ensembles. However, designing an ensemble of deep
networks is often not very beneficial since the time needed to train the
networks is very high or the performance gain obtained is not very significant.
In this paper, we analyse error correcting output coding (ECOC) framework to be
used as an ensemble technique for deep networks and propose different design
strategies to address the accuracy-complexity trade-off. We carry out an
extensive comparative study between the introduced ECOC designs and the
state-of-the-art ensemble techniques such as ensemble averaging and gradient
boosting decision trees. Furthermore, we propose a combinatory technique which
is shown to achieve the highest classification performance amongst all.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710635 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.05949
|
Fangke Ye
|
Fangke Ye, Jisheng Zhao, Vivek Sarkar
|
Advanced Graph-Based Deep Learning for Probabilistic Type Inference
| null | null | null | null |
cs.PL cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Dynamically typed languages such as JavaScript and Python have emerged as the
most popular programming languages in use. Important benefits can accrue from
including type annotations in dynamically typed programs. This approach to
gradual typing is exemplified by the TypeScript programming system which allows
programmers to specify partially typed programs, and then uses static analysis
to infer the remaining types. However, in general, the effectiveness of static
type inference is limited and depends on the complexity of the program's
structure and the initial type annotations. As a result, there is a strong
motivation for new approaches that can advance the state of the art in
statically predicting types in dynamically typed programs, and that do so with
acceptable performance for use in interactive programming environments.
Previous work has demonstrated the promise of probabilistic type inference
using deep learning. In this paper, we advance past work by introducing a range
of graph neural network (GNN) models that operate on a novel type flow graph
(TFG) representation. The TFG represents an input program's elements as graph
nodes connected with syntax edges and data flow edges, and our GNN models are
trained to predict the type labels in the TFG for a given input program. We
study different design choices for our GNN models for the 100 most common types
in our evaluation dataset, and show that our best two GNN configurations for
accuracy achieve a top-1 accuracy of 87.76% and 86.89% respectively,
outperforming the two most closely related deep learning type inference
approaches from past work -- DeepTyper with a top-1 accuracy of 84.62% and
LambdaNet with a top-1 accuracy of 79.45%. Further, the average inference
throughputs of those two configurations are 353.8 and 1,303.9 files/second,
compared to 186.7 files/second for DeepTyper and 1,050.3 files/second for
LambdaNet.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.568116 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.07717
|
Sara Ahmed
|
Sara Atito Ali Ahmed, Berrin Yanikoglu
|
Relative Attribute Classification with Deep Rank SVM
| null | null |
10.1007/978-3-030-68790-8_51
| null |
cs.CV cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Relative attributes indicate the strength of a particular attribute between
image pairs. We introduce a deep Siamese network with rank SVM loss function,
called Deep Rank SVM (DRSVM), in order to decide which one of a pair of images
has a stronger presence of a specific attribute. The network is trained in an
end-to-end fashion to jointly learn the visual features and the ranking
function. We demonstrate the effectiveness of our approach against the
state-of-the-art methods on four image benchmark datasets: LFW-10, PubFig,
UTZap50K-lexi and UTZap50K-2 datasets. DRSVM surpasses state-of-art in terms of
the average accuracy across attributes, on three of the four image benchmark
datasets.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713013 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2009.11128
|
Konstantinos Nikolaidis
|
Konstantinos Nikolaidis, Thomas Plagemann, Stein Kristiansen, Vera
Goebel, Mohan Kankanhalli
|
Using Under-trained Deep Ensembles to Learn Under Extreme Label Noise
| null | null | null | null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Improper or erroneous labelling can pose a hindrance to reliable
generalization for supervised learning. This can have negative consequences,
especially for critical fields such as healthcare. We propose an effective new
approach for learning under extreme label noise, based on under-trained deep
ensembles. Each ensemble member is trained with a subset of the training data,
to acquire a general overview of the decision boundary separation, without
focusing on potentially erroneous details. The accumulated knowledge of the
ensemble is combined to form new labels, that determine a better class
separation than the original labels. A new model is trained with these labels
to generalize reliably despite the label noise. We focus on a healthcare
setting and extensively evaluate our approach on the task of sleep apnea
detection. For comparison with related work, we additionally evaluate on the
task of digit recognition. In our experiments, we observed performance
improvement in accuracy from 6.7\% up-to 49.3\% for the task of digit
classification and in kappa from 0.02 up-to 0.55 for the task of sleep apnea
detection.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7118 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.08348
|
Stephan Radonic
|
Stephan Radonic, J\"urgen Besserer, Valeria Meier, Carla Rohrer Bley,
Uwe Schneider
|
A novel analytical population TCP model includes cell density and volume
variations: application to canine brain tumor
| null |
International Journal of Radiation Oncology, Biology, Physics,
(2021), Volume 110, Issue 5, 1530 - 1537
|
10.1016/j.ijrobp.2021.03.021
| null |
physics.med-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
TCP models based on Poisson statistics are characterizing the distribution of
the surviving clonogens. It enables the calculation of TCP for individuals. In
order to describe clinically observed survival data of patient cohorts it is
necessary to extend the model. This is typically done by either incorporating
variations of various model parameters, or by using an empirical logistic
model. The purpose of this work is the development of an analytical population
TCP model by mechanistic extension of the Poisson model.The frequency
distribution of GTVs is used to incorporate tumor volume variations into the
TCP model. Additionally the tumor cell density variation is incorporated. Both
versions of the population TCP model were fitted to clinical data and compared
to literature. It was shown that clinically observed brain tumor volumes of
dogs undergoing radiotherapy are exponentially distributed. The average GTV
size was 3.37 cm$^3$. Fitting the population TCP model including the volume
variation LQ and track-event model yielded $\alpha=0.36 ~Gy^{-1}$,
$\beta=0.045~Gy^{-2}$, $a=0.9$, $T_D=5.0~d$ and $p = 0.36~Gy^{-1}$,
$q=0.48~Gy^{-1}$, $a=0.80$, $T_D = 3.0~d$, respectively. Fitting the population
TCP model including both the volume and cell density variation yields
$\alpha=0.43~Gy^{-1}$, $\beta=0.0537~Gy^{-2}$, $a=2.0$, $T_D=3.0~d$,
$\sigma=2.5$ and $p=0.43~ Gy^{-1}$, $q=0.55~Gy^{-1}$, $a=2.0$, $T_D=2.0~d$,
$\sigma=3.0$ respectively. Two sets of radiobiological parameters were obtained
which can be used for quantifying the TCP for radiation therapy of dog brain
tumors. We established a mechanistic link between the poisson statistics based
individual TCP model and the logistic TCP model. This link can be used to
determine the radiobiological parameters of patient specific TCP models from
published fits of logistic models to cohorts of patients.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.11585
|
Andre Romano Alho Dr
|
Andre Alho, Takanori Sakai, Simon Oh, Cheng Cheng, Ravi Seshadri, Wen
Han Chong, Yusuke Hara, Julia Caravias, Lynette Cheah, Moshe Ben-Akiva
|
A simulation-based evaluation of a Cargo-Hitching service for E-commerce
using mobility-on-demand vehicles
|
19 pages, 4 tables, 7 figures. Submitted to Transportation (Springer)
|
Future Transp. 2021, 1, 639-656
|
10.3390/futuretransp1030034
| null |
cs.MA cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Time-sensitive parcel deliveries, shipments requested for delivery in a day
or less, are an increasingly important research subject. It is challenging to
deal with these deliveries from a carrier perspective since it entails
additional planning constraints, preventing an efficient consolidation of
deliveries which is possible when demand is well known in advance. Furthermore,
such time-sensitive deliveries are requested to a wider spatial scope than
retail centers, including homes and offices. Therefore, an increase in such
deliveries is considered to exacerbate negative externalities such as
congestion and emissions. One of the solutions is to leverage spare capacity in
passenger transport modes. This concept is often denominated as cargo-hitching.
While there are various possible system designs, it is crucial that such
solution does not deteriorate the quality of service of passenger trips. This
research aims to evaluate the use of Mobility-On-Demand services to perform
same-day parcel deliveries. For this purpose, we use SimMobility, a
high-resolution agent-based simulation platform of passenger and freight flows,
applied in Singapore. E-commerce demand carrier data are used to characterize
simulated parcel delivery demand. Operational scenarios that aim to minimize
the adverse effect of fulfilling deliveries with Mobility-On-Demand vehicles on
Mobility-On-Demand passenger flows (fulfillment, wait and travel times) are
explored. Results indicate that the Mobility-On-Demand services have potential
to fulfill a considerable amount of parcel deliveries and decrease freight
vehicle traffic and total vehicle-kilometers-travelled without compromising the
quality of Mobility On-Demand for passenger travel.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708408 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.12827
|
Amane Sugiyama
|
Amane Sugiyama and Naoki Yoshinaga
|
Context-aware Decoder for Neural Machine Translation using a Target-side
Document-Level Language Model
| null | null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Although many context-aware neural machine translation models have been
proposed to incorporate contexts in translation, most of those models are
trained end-to-end on parallel documents aligned in sentence-level. Because
only a few domains (and language pairs) have such document-level parallel data,
we cannot perform accurate context-aware translation in most domains. We
therefore present a simple method to turn a sentence-level translation model
into a context-aware model by incorporating a document-level language model
into the decoder. Our context-aware decoder is built upon only a sentence-level
parallel corpora and monolingual corpora; thus no document-level parallel data
is needed. In a theoretical viewpoint, the core part of this work is the novel
representation of contextual information using point-wise mutual information
between context and the current sentence. We show the effectiveness of our
approach in three language pairs, English to French, English to Russian, and
Japanese to English, by evaluation in \textsc{bleu} and contrastive tests for
context-aware translation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2010.16100
|
Michal Yemini
|
Michal Yemini, Elza Erkip and Andrea J. Goldsmith
|
Interference Reduction in Virtual Cell Optimization
| null | null | null | null |
eess.SP cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Virtual cell optimization clusters cells into neighborhoods and performs
optimized resource allocation over each neighborhood. In prior works we
proposed resource allocation schemes to mitigate the interference caused by
transmissions in the same virtual cell. This work aims at mitigating both the
interference caused by the transmissions of users in the same virtual cell and
the interference between transmissions in different virtual cells. We propose a
resource allocation technique that reduces the number of users that cannot
achieve their constant guaranteed bit rate, i.e., the "unsatisfied users", in
an uplink virtual cell system with cooperative decoding. The proposed scheme
requires only the knowledge of the number of users each base station serves and
relies on creating the interference graph between base stations at the edges of
virtual cells. Allocation of frequency bands to users is based on the number of
users each base station would serve in a non cooperative setup. We evaluate the
performance of our scheme for a mmWave system. Our numerical results show that
our scheme decreases the number of users in the system whose rate falls below
the guaranteed rate, set to $128$kbps, $256$kbps or $512$kbps, when compared
with our previously proposed optimization methods.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.00733
|
Sergey Alyaev
|
Sergey Alyaev, Reidar Brumer Bratvold, Sofija Ivanova, Andrew
Holsaeter, Morten Bendiksen
|
An interactive sequential-decision benchmark from geosteering
|
arXiv admin note: substantial text overlap with arXiv:2005.08916
|
Applied Computing and Geosciences Volume 12, December 2021, 100072
|
10.1016/j.acags.2021.100072
| null |
cs.HC cs.SY eess.SY stat.AP
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Geosteering workflows are increasingly based on the quantification of
subsurface uncertainties during real-time operations. As a consequence
operational decision making is becoming both better informed and more complex.
This paper presents an experimental web-based decision support system, which
can be used to both aid expert decisions under uncertainty or further develop
decision optimization algorithms in controlled environment. A user of the
system (either human or AI) controls the decisions to steer the well or stop
drilling. Whenever a user drills ahead, the system produces simulated
measurements along the selected well trajectory which are used to update the
uncertainty represented by model realizations using the ensemble Kalman filter.
To enable informed decisions the system is equipped with functionality to
evaluate the value of the selected trajectory under uncertainty with respect to
the objectives of the current experiment.
To illustrate the utility of the system as a benchmark, we present the
initial experiment, in which we compare the decision skills of geoscientists
with those of a recently published automatic decision support algorithm. The
experiment and the survey after it showed that most participants were able to
use the interface and complete the three test rounds. At the same time, the
automated algorithm outperformed 28 out of 29 qualified human participants.
Such an experiment is not sufficient to draw conclusions about practical
geosteering, but is nevertheless useful for geoscience. First, this
communication-by-doing made 76% of respondents more curious about and/or
confident in the presented technologies. Second, the system can be further used
as a benchmark for sequential decisions under uncertainty. This can accelerate
development of algorithms and improve the training for decision making.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.699088 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.03627
|
Markus Haltmeier
|
Stephan Antholzer, Markus Haltmeier
|
Discretization of learned NETT regularization for solving inverse
problems
| null | null | null | null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Deep learning based reconstruction methods deliver outstanding results for
solving inverse problems and are therefore becoming increasingly important. A
recently invented class of learning-based reconstruction methods is the
so-called NETT (for Network Tikhonov Regularization), which contains a trained
neural network as regularizer in generalized Tikhonov regularization. The
existing analysis of NETT considers fixed operator and fixed regularizer and
analyzes the convergence as the noise level in the data approaches zero. In
this paper, we extend the frameworks and analysis considerably to reflect
various practical aspects and take into account discretization of the data
space, the solution space, the forward operator and the neural network defining
the regularizer. We show the asymptotic convergence of the discretized NETT
approach for decreasing noise levels and discretization errors. Additionally,
we derive convergence rates and present numerical results for a limited data
problem in photoacoustic tomography.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710459 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.04834
|
Daniel Andr\'es D\'iaz-Pach\'on
|
Daniel Andr\'es D\'iaz-Pach\'on and Juan Pablo S\'aenz and J. Sunil
Rao
|
Hypothesis testing with active information
|
Typo changed in one of the names in the Metadata, and a reference to
an equation from the paper in the Supplement
|
Statistics and Probability Letters 161, June 2020, 108742
|
10.1016/j.spl.2020.108742
| null |
math.ST cs.IT math.IT stat.TH
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
We develop hypothesis testing for active information -the averaged quantity
in the Kullback-Liebler divergence. To our knowledge, this is the first paper
to derive exact probabilities of type-I errors for hypothesis testing in the
area.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71227 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.05146
|
David Paganin
|
David M. Paganin and Daniele Pelliccia
|
X-ray phase-contrast imaging: a broad overview of some fundamentals
|
Some minor corrections have been made to some of the equations in the
preceding version. To appear in Advances in Imaging and Electron Physics.
arXiv admin note: text overlap with arXiv:1902.00364
|
Advances in Imaging and Electron Physics, Volume 218, Pages 63-158
(2021)
|
10.1016/bs.aiep.2021.04.002
| null |
eess.IV physics.app-ph physics.med-ph physics.optics
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We outline some basics of imaging using both fully-coherent and
partially-coherent X-ray beams, with an emphasis on phase-contrast imaging. We
open with some of the basic notions of X-ray imaging, including the vacuum wave
equations and the physical meaning of the intensity and phase of complex scalar
fields. The projection approximation is introduced, together with the concepts
of attenuation contrast and phase contrast. We also outline the multi-slice
approach to X-ray propagation through thick samples or optical elements,
together with the Fresnel scaling theorem. Having introduced the fundamentals,
we then consider several aspects of the forward problem, of modelling the
formation of phase-contrast X-ray images. Several topics related to this
forward problem are considered, including the transport-of-intensity equation,
arbitrary linear imaging systems, shift-invariant linear imaging systems, the
transfer-function formalism, blurring induced by finite source size, the
space-frequency model for partially-coherent fields, and the Fokker-Planck
equation for paraxial X-ray imaging. Having considered these means for
modelling the formation of X-ray phase-contrast images, we then consider
aspects of the associated inverse problem of phase retrieval. This concerns how
one may decode phase-contrast images to gain information regarding the
sample-induced attenuation and phase shift.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712476 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.07209
|
Hsin-Yu Ko
|
Hsin-Yu Ko and Biswajit Santra and Robert A. DiStasio Jr
|
Enabling Large-Scale Condensed-Phase Hybrid Density Functional Theory
Based $Ab$ $Initio$ Molecular Dynamics II: Extensions to the
Isobaric-Isoenthalpic and Isobaric-Isothermal Ensembles
|
30 pages and 5 figures
| null |
10.1021/acs.jctc.0c01194
| null |
cond-mat.mtrl-sci cond-mat.stat-mech physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In the previous paper of this series [JCTC 2020, 16, 3757], we presented a
theoretical and algorithmic framework based on a localized representation of
the occupied space that exploits the inherent sparsity in the real-space
evaluation of the EXX interaction in finite-gap systems. This was accompanied
by a detailed description of exx, a massively parallel hybrid MPI/OpenMP
implementation of this approach in Quantum ESPRESSO that enables linear-scaling
hybrid DFT based AIMD in the NVE/NVT ensembles of condensed-phase systems
containing 500--1000 atoms (in fixed orthorhombic cells) with a wall time cost
comparable to semi-local DFT. In this work, we extend exx to enable hybrid DFT
based AIMD of large-scale condensed-phase systems with general and fluctuating
cells in the NpH/NpT ensembles. Our theoretical extension includes an
analytical derivation of the EXX contribution to the stress tensor for systems
in general cells with a computational complexity that scales linearly with
system size. The corresponding algorithmic extensions to exx include optimized
routines that: (i) handle static/fluctuating cells with non-orthogonal lattice
symmetries, (ii) solve Poisson's equation in general cells via an automated
selection of the auxiliary grid directions in the Natan-Kronik representation
of the discrete Laplacian operator, and (iii) evaluate the EXX contribution to
the stress tensor. We also critically assess the computational performance of
the extended exx module across several different HPC architectures via case
studies on ice Ih, II, and III as well as ambient liquid water. We find that
the extended exx can evaluate the EXX contribution to the stress tensor with
negligible cost (< 1%) and remains highly scalable, thereby bringing us another
step closer to routinely performing hybrid DFT based AIMD for large-scale
condensed-phase systems across a wide range of thermodynamic conditions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708042 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.08097
|
Calvin Beideman
|
Calvin Beideman, Karthekeyan Chandrasekaran, Sagnik Mukhopadhyay,
Danupon Nanongkai
|
Faster connectivity in low-rank hypergraphs via expander decomposition
|
Incorporated a new algorithm of Chekuri and Quanrud into our
algorithm and analysis. Fixed a bug in the analysis of the algorithm, and
edited exposition throughout for greater clarity
| null | null | null |
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
We design an algorithm for computing connectivity in hypergraphs which runs
in time $\hat O_r(p + \min\{\lambda^{\frac{r-3}{r-1}} n^2,
n^r/\lambda^{r/(r-1)}\})$ (the $\hat O_r(\cdot)$ hides the terms subpolynomial
in the main parameter and terms that depend only on $r$) where $p$ is the size,
$n$ is the number of vertices, and $r$ is the rank of the hypergraph. Our
algorithm is faster than existing algorithms when the the rank is constant and
the connectivity $\lambda$ is $\omega(1)$. At the heart of our algorithm is a
structural result regarding min-cuts in simple hypergraphs. We show a trade-off
between the number of hyperedges taking part in all min-cuts and the size of
the smaller side of the min-cut. This structural result can be viewed as a
generalization of a well-known structural theorem for simple graphs
[Kawarabayashi-Thorup, JACM 19]. We extend the framework of expander
decomposition to simple hypergraphs in order to prove this structural result.
We also make the proof of the structural result constructive to obtain our
faster hypergraph connectivity algorithm.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709787 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.11236
|
Rahul Singh
|
Rahul Singh, Qinsheng Zhang, Yongxin Chen
|
Learning Hidden Markov Models from Aggregate Observations
| null | null | null | null |
cs.LG cs.SY eess.IV eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this paper, we propose an algorithm for estimating the parameters of a
time-homogeneous hidden Markov model from aggregate observations. This problem
arises when only the population level counts of the number of individuals at
each time step are available, from which one seeks to learn the individual
hidden Markov model. Our algorithm is built upon expectation-maximization and
the recently proposed aggregate inference algorithm, the Sinkhorn belief
propagation. As compared with existing methods such as expectation-maximization
with non-linear belief propagation, our algorithm exhibits convergence
guarantees. Moreover, our learning framework naturally reduces to the standard
Baum-Welch learning algorithm when observations corresponding to a single
individual are recorded. We further extend our learning algorithm to handle
HMMs with continuous observations. The efficacy of our algorithm is
demonstrated on a variety of datasets.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711481 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.14371
|
Arnav Kumar Jain
|
Hadia Mohmmed Osman Ahmed Samil, Annabelle Martin, Arnav Kumar Jain,
Susan Amin and Samira Ebrahimi Kahou
|
Predicting Regional Locust Swarm Distribution with Recurrent Neural
Networks
| null | null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Locust infestation of some regions in the world, including Africa, Asia and
Middle East has become a concerning issue that can affect the health and the
lives of millions of people. In this respect, there have been attempts to
resolve or reduce the severity of this problem via detection and monitoring of
locust breeding areas using satellites and sensors, or the use of chemicals to
prevent the formation of swarms. However, such methods have not been able to
suppress the emergence and the collective behaviour of locusts. The ability to
predict the location of the locust swarms prior to their formation, on the
other hand, can help people get prepared and tackle the infestation issue more
effectively. Here, we use machine learning to predict the location of locust
swarms using the available data published by the Food and Agriculture
Organization of the United Nations. The data includes the location of the
observed swarms as well as environmental information, including soil moisture
and the density of vegetation. The obtained results show that our proposed
model can successfully, and with reasonable precision, predict the location of
locust swarms, as well as their likely level of damage using a notion of
density.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71365 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2011.14958
|
Mohammad Reza Jafari Harandi
|
M. Reza J. Harandi and Hamid D. Taghirad
|
On the Matching Equations of Kinetic Energy Shaping in IDA-PBC
| null | null |
10.1016/j.jfranklin.2021.08.034
| null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Interconnection and damping assignment passivity-based control scheme has
been used to stabilize many physical systems such as underactuated mechanical
systems through total energy shaping. In this method, some partial differential
equations (PDEs) arisen by kinetic and potential energy shaping, shall be
solved analytically. Finding a suitable desired inertia matrix as the solution
of nonlinear PDEs related to kinetic energy shaping is a challenging problem.
In this paper, a systematic approach to solve this matching equation for
systems with one degree of underactuation is proposed. A special structure for
desired inertia matrix is proposed to simplify the solution of the
corresponding PDE. It is shown that the proposed method is more general than
that of some reported methods in the literature. In order to derive a suitable
desired inertia matrix, a necessary condition is also derived. The proposed
method is applied to three examples, including VTOL aircraft, pendubot and 2D
SpiderCrane system.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710459 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.01734
|
Jiazhong Hu
|
Qi Huang, Ruixiao Yao, Libo Liang, Shuai Wang, Qinpei Zheng, Dingping
Li, Wei Xiong, Xiaoji Zhou, Wenlan Chen, Xuzong Chen, Jiazhong Hu
|
Observation of many-body quantum phase transitions beyond the
Kibble-Zurek mechanism
|
6 pages, 4 figures for main text
|
Phys. Rev. Lett. 27, 200601 (2021)
|
10.1103/PhysRevLett.127.200601
| null |
quant-ph cond-mat.quant-gas physics.atom-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Quantum critical behavior of many-body phase transitions is one of the most
fascinating yet challenging questions in quantum physics. Here, we improved the
band-mapping method to investigate the quantum phase transition from superfluid
to Mott insulators, and we observed the critical behaviors of quantum phase
transitions in both dynamical steady-state-relaxation region and
phase-oscillation region. Based on various observables, two different values
for the same quantum critical parameter are observed. This result is beyond a
universal-scaling-law description of quantum phase transitions known as the
Kibble-Zurek mechanism, and suggests that multiple quantum critical mechanisms
are competing in many-body quantum phase transition experiments in
inhomogeneous systems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710653 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.01829
|
Fengchao Xiong
|
Fengchao Xiong, Shuyin Tao, Jun Zhou, Jianfeng Lu, Jiantao Zhou, and
Yuntao Qian
|
SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image
Denoising
|
The experimental settings have been updated
| null | null | null |
eess.IV cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
Deep learning (DL) based hyperspectral images (HSIs) denoising approaches
directly learn the nonlinear mapping between observed noisy images and
underlying clean images. They normally do not consider the physical
characteristics of HSIs, therefore making them lack of interpretability that is
key to understand their denoising mechanism.. In order to tackle this problem,
we introduce a novel model guided interpretable network for HSI denoising.
Specifically, fully considering the spatial redundancy, spectral low-rankness
and spectral-spatial properties of HSIs, we first establish a subspace based
multi-dimensional sparse model. This model first projects the observed HSIs
into a low-dimensional orthogonal subspace, and then represents the projected
image with a multidimensional dictionary. After that, the model is unfolded
into an end-to-end network named SMDS-Net whose fundamental modules are
seamlessly connected with the denoising procedure and optimization of the
model. This makes SMDS-Net convey clear physical meanings, i.e., learning the
low-rankness and sparsity of HSIs. Finally, all key variables including
dictionaries and thresholding parameters are obtained by the end-to-end
training. Extensive experiments and comprehensive analysis confirm the
denoising ability and interpretability of our method against the
state-of-the-art HSI denoising methods.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709787 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.02154
|
Kartik Singhal
|
Kartik Singhal
|
Quantum Hoare Type Theory
|
UChicago CS master's paper. 34 pages, 12 code listings. Preliminary
version accepted at QPL'20: arXiv:2109.02198
| null | null | null |
cs.PL cs.ET cs.LO quant-ph
|
http://creativecommons.org/licenses/by/4.0/
|
As quantum computers become real, it is high time we come up with effective
techniques that help programmers write correct quantum programs. Inspired by
Hoare Type Theory in classical computing, we propose Quantum Hoare Type Theory
(QHTT), in which precise specifications about the modification to the quantum
state can be provided within the type of computation. These specifications
within a Hoare type are given in the form of Hoare-logic style pre- and
postconditions following the propositions-as-types principle. The type-checking
process verifies that the implementation conforms to the provided
specification. QHTT has the potential to be a unified system for programming,
specifying, and reasoning about quantum programs.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.05345
|
Amandalynne Paullada
|
Amandalynne Paullada, Inioluwa Deborah Raji, Emily M. Bender, Emily
Denton, Alex Hanna
|
Data and its (dis)contents: A survey of dataset development and use in
machine learning research
| null |
Patterns, Volume 2, Issue 11, 100336. 2021
|
10.1016/j.patter.2021.100336
| null |
cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
Datasets have played a foundational role in the advancement of machine
learning research. They form the basis for the models we design and deploy, as
well as our primary medium for benchmarking and evaluation. Furthermore, the
ways in which we collect, construct and share these datasets inform the kinds
of problems the field pursues and the methods explored in algorithm
development. However, recent work from a breadth of perspectives has revealed
the limitations of predominant practices in dataset collection and use. In this
paper, we survey the many concerns raised about the way we collect and use data
in machine learning and advocate that a more cautious and thorough
understanding of data is necessary to address several of the practical and
ethical issues of the field.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711055 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.05398
|
Jason Altschuler
|
Jason M. Altschuler and Enric Boix-Adsera
|
Hardness results for Multimarginal Optimal Transport problems
|
For expository purposes, some of these results were moved from v1 of
arXiv 2008.03006. The current drafts of these papers have no overlapping
results. arXiv admin note: text overlap with arXiv:2008.03006
|
Discrete Optimization, 42, 100669, 2021. (21 pages)
|
10.1016/j.disopt.2021.100669
| null |
math.OC cs.CC cs.DS cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Multimarginal Optimal Transport (MOT) is the problem of linear programming
over joint probability distributions with fixed marginals. A key issue in many
applications is the complexity of solving MOT: the linear program has
exponential size in the number of marginals k and their support sizes n. A
recent line of work has shown that MOT is poly(n,k)-time solvable for certain
families of costs that have poly(n,k)-size implicit representations. However,
it is unclear what further families of costs this line of algorithmic research
can encompass. In order to understand these fundamental limitations, this paper
initiates the study of intractability results for MOT.
Our main technical contribution is developing a toolkit for proving
NP-hardness and inapproximability results for MOT problems. We demonstrate this
toolkit by using it to establish the intractability of a number of MOT problems
studied in the literature that have resisted previous algorithmic efforts. For
instance, we provide evidence that repulsive costs make MOT intractable by
showing that several such problems of interest are NP-hard to solve--even
approximately.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708187 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.05549
|
Laurent Bonnasse-Gahot
|
Laurent Bonnasse-Gahot and Jean-Pierre Nadal
|
Categorical Perception: A Groundwork for Deep Learning
| null |
Neural Computation 2021
|
10.1162/neco_a_01454
| null |
cs.LG cs.IT math.IT q-bio.NC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A well-known perceptual consequence of categorization in humans and other
animals, called categorical perception, is notably characterized by a
within-category compression and a between-category separation: two items, close
in input space, are perceived closer if they belong to the same category than
if they belong to different categories. Elaborating on experimental and
theoretical results in cognitive science, here we study categorical effects in
artificial neural networks. We combine a theoretical analysis that makes use of
mutual and Fisher information quantities, and a series of numerical simulations
on networks of increasing complexity. These formal and numerical analyses
provide insights into the geometry of the neural representation in deep layers,
with expansion of space near category boundaries and contraction far from
category boundaries. We investigate categorical representation by using two
complementary approaches: one mimics experiments in psychophysics and cognitive
neuroscience by means of morphed continua between stimuli of different
categories, while the other introduces a categoricality index that, for each
layer in the network, quantifies the separability of the categories at the
neural population level. We show on both shallow and deep neural networks that
category learning automatically induces categorical perception. We further show
that the deeper a layer, the stronger the categorical effects. As an outcome of
our study, we propose a coherent view of the efficacy of different heuristic
practices of the dropout regularization technique. More generally, our view,
which finds echoes in the neuroscience literature, insists on the differential
impact of noise in any given layer depending on the geometry of the neural
representation that is being learned, i.e. on how this geometry reflects the
structure of the categories.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711469 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.07012
|
Timur Gureyev
|
T.E. Gureyev, H.M. Quiney, A. Kozlov, D.M. Paganin, G. Schmalz and
L.J. Allen
|
Relative roles of multiple scattering and Fresnel diffraction in the
imaging of small molecules using electrons, Part II: Differential Holographic
Tomography
|
32 pages, 8 figures, version 5c (a few typos have been found in the
previous version and fixed in the current version)
|
Ultramicroscopy Volume 230, 113311 (2021)
|
10.1016/j.ultramic.2021.113311
| null |
physics.optics cond-mat.mtrl-sci
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
It has been argued that in atomic-resolution transmission electron microscopy
(TEM) of sparse weakly scattering structures, such as small biological
molecules, multiple electron scattering usually has only a small effect, while
the in-molecule Fresnel diffraction can be significant due to the intrinsically
shallow depth of focus. These facts suggest that the three-dimensional
reconstruction of such structures from defocus image series collected at
multiple rotational orientations of a molecule can be effectively performed for
each atom separately, using the incoherent first Born approximation. The
corresponding reconstruction method, termed here Differential Holographic
Tomography, is developed theoretically and demonstrated computationally on
several numerical models of biological molecules. It is shown that the method
is capable of accurate reconstruction of the locations of atoms in a molecule
from TEM data collected at a small number of random orientations of the
molecule, with one or more defocus images per orientation. Possible
applications to cryogenic electron microscopy and other areas are briefly
discussed.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711462 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.07416
|
Yuyue Yan
|
Yuyue Yan and Tomohisa Hayakawa
|
Stability Analysis of Nash Equilibrium for 2-Agent Loss-Aversion-Based
Noncooperative Switched Systems
|
8 pages, 14 figures. Accepted by IEEE Transactions on Automatic
Control
| null |
10.1109/TAC.2021.3079276
| null |
eess.SY cs.SY math.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The stability property of the loss-aversion-based noncooperative switched
systems with quadratic payoffs is investigated. In this system, each agent
adopts the lower sensitivity parameter in the myopic pseudo-gradient dynamics
for the case of losing utility than gaining utility, and both systems' dynamics
and switching events (conditions) are depending on agents' payoff functions.
Sufficient conditions under which agents' state converges towards the Nash
equilibrium are derived in accordance with the location of the Nash
equilibrium. In the analysis, the mode transition sequence and interesting
phenomena which we call flash switching instants are characterized. Finally, we
present several numerical examples to illustrate the properties of our results.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709019 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.07458
|
Sophie Gruenbacher
|
Sophie Gruenbacher, Jacek Cyranka, Mathias Lechner, Md. Ariful Islam,
Scott A. Smolka and Radu Grosu
|
Lagrangian Reachtubes: The Next Generation
|
12 pages, 14 figures
|
Proceedings of the 59th IEEE Conference on Decision and Control
(CDC), 2020, pages 1556-1563
|
10.1109/CDC42340.2020.9304042
| null |
eess.SY cs.LG cs.NE cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce LRT-NG, a set of techniques and an associated toolset that
computes a reachtube (an over-approximation of the set of reachable states over
a given time horizon) of a nonlinear dynamical system. LRT-NG significantly
advances the state-of-the-art Langrangian Reachability and its associated tool
LRT. From a theoretical perspective, LRT-NG is superior to LRT in three ways.
First, it uses for the first time an analytically computed metric for the
propagated ball which is proven to minimize the ball's volume. We emphasize
that the metric computation is the centerpiece of all bloating-based
techniques. Secondly, it computes the next reachset as the intersection of two
balls: one based on the Cartesian metric and the other on the new metric. While
the two metrics were previously considered opposing approaches, their joint use
considerably tightens the reachtubes. Thirdly, it avoids the "wrapping effect"
associated with the validated integration of the center of the reachset, by
optimally absorbing the interval approximation in the radius of the next ball.
From a tool-development perspective, LRT-NG is superior to LRT in two ways.
First, it is a standalone tool that no longer relies on CAPD. This required the
implementation of the Lohner method and a Runge-Kutta time-propagation method.
Secondly, it has an improved interface, allowing the input model and initial
conditions to be provided as external input files. Our experiments on a
comprehensive set of benchmarks, including two Neural ODEs, demonstrates
LRT-NG's superior performance compared to LRT, CAPD, and Flow*.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708635 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.08265
|
Ertan Kaz{\i}kl{\i}
|
Ertan Kaz{\i}kl{\i}, Serkan Sar{\i}ta\c{s}, Sinan Gezici, Tam\'as
Linder, Serdar Y\"uksel
|
Signaling Games for Log-Concave Distributions: Number of Bins and
Properties of Equilibria
|
27 pages and 1 figure. arXiv admin note: text overlap with
arXiv:1901.06738
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We investigate the equilibrium behavior for the decentralized cheap talk
problem for real random variables and quadratic cost criteria in which an
encoder and a decoder have misaligned objective functions. In prior work, it
has been shown that the number of bins in any equilibrium has to be countable,
generalizing a classical result due to Crawford and Sobel who considered
sources with density supported on $[0,1]$. In this paper, we first refine this
result in the context of log-concave sources. For sources with two-sided
unbounded support, we prove that, for any finite number of bins, there exists a
unique equilibrium. In contrast, for sources with semi-unbounded support, there
may be a finite upper bound on the number of bins in equilibrium depending on
certain conditions stated explicitly. Moreover, we prove that for log-concave
sources, the expected costs of the encoder and the decoder in equilibrium
decrease as the number of bins increases. Furthermore, for strictly log-concave
sources with two-sided unbounded support, we prove convergence to the unique
equilibrium under best response dynamics which starts with a given number of
bins, making a connection with the classical theory of optimal quantization and
convergence results of Lloyd's method. In addition, we consider more general
sources which satisfy certain assumptions on the tail(s) of the distribution
and we show that there exist equilibria with infinitely many bins for sources
with two-sided unbounded support. Further explicit characterizations are
provided for sources with exponential, Gaussian, and compactly-supported
probability distributions.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.7114 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.08863
|
Sophie Gruenbacher
|
Sophie Gruenbacher, Ramin Hasani, Mathias Lechner, Jacek Cyranka,
Scott A. Smolka, Radu Grosu
|
On The Verification of Neural ODEs with Stochastic Guarantees
|
12 pages, 2 figures
|
Proceedings of the AAAI Conference on Artificial Intelligence,
35(13), 2021, pages 11525-11535
| null | null |
cs.LG cs.NE cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We show that Neural ODEs, an emerging class of time-continuous neural
networks, can be verified by solving a set of global-optimization problems. For
this purpose, we introduce Stochastic Lagrangian Reachability (SLR), an
abstraction-based technique for constructing a tight Reachtube (an
over-approximation of the set of reachable states over a given time-horizon),
and provide stochastic guarantees in the form of confidence intervals for the
Reachtube bounds. SLR inherently avoids the infamous wrapping effect
(accumulation of over-approximation errors) by performing local optimization
steps to expand safe regions instead of repeatedly forward-propagating them as
is done by deterministic reachability methods. To enable fast local
optimizations, we introduce a novel forward-mode adjoint sensitivity method to
compute gradients without the need for backpropagation. Finally, we establish
asymptotic and non-asymptotic convergence rates for SLR.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708792 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.11075
|
Zhaoli Guo
|
Zhaoli Guo
|
Well-balanced lattice Boltzmann equation for two-phase flows
|
9 papes, 5 figures; presented at the 11th Conference on Fluid
Dynamics of China (Shen Zheng, Dec. 2-7, 2020)
|
Phys. Fluids 33, 031709 (2021)
|
10.1063/5.0041446
| null |
physics.flu-dyn cs.NA math.NA physics.comp-ph
|
http://creativecommons.org/licenses/by/4.0/
|
The standard lattice Boltzmann equation (LBE) method usually fails to capture
the physical equilibrium state of a two-phase fluid system, i.e., zero velocity
and constant chemical potential. Consequently, spurious velocities and
inconsistent thermodynamic density properties are frequently encountered in LBE
simulations. In this work, based on a rigorous analysis of the discrete balance
equation of LBE, we identify the structure of the force imbalance due to
discretization errors from different parts. Then a well-balanced LBE is
proposed which can achieve the discrete equilibrium state. The well-balanced
properties of the LBE are confirmed by some numerical tests of a flat interface
problem and a droplet system.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711212 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.12347
|
Kevin Thompson
|
Ojas Parekh and Kevin Thompson
|
Beating Random Assignment for Approximating Quantum 2-Local Hamiltonian
Problems
| null |
Proceedings of the European Symposium on Algorithms (ESA), 2021
|
10.4230/LIPIcs.ESA.2021.74
| null |
quant-ph cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The quantum k-Local Hamiltonian problem is a natural generalization of
classical constraint satisfaction problems (k-CSP) and is complete for QMA, a
quantum analog of NP. Although the complexity of k-Local Hamiltonian problems
has been well studied, only a handful of approximation results are known. For
Max 2-Local Hamiltonian where each term is a rank 3 projector, a natural
quantum generalization of classical Max 2-SAT, the best known approximation
algorithm was the trivial random assignment, yielding a 0.75-approximation. We
present the first approximation algorithm beating this bound, a classical
polynomial-time 0.764-approximation. For strictly quadratic instances, which
are maximally entangled instances, we provide a 0.801 approximation algorithm,
and numerically demonstrate that our algorithm is likely a 0.821-approximation.
We conjecture these are the hardest instances to approximate. We also give
improved approximations for quantum generalizations of other related classical
2-CSPs. Finally, we exploit quantum connections to a generalization of the
Grothendieck problem to obtain a classical constant-factor approximation for
the physically relevant special case of strictly quadratic traceless 2-Local
Hamiltonians on bipartite interaction graphs, where a inverse logarithmic
approximation was the best previously known (for general interaction graphs).
Our work employs recently developed techniques for analyzing classical
approximations of CSPs and is intended to be accessible to both quantum
information scientists and classical computer scientists.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.12369
|
Daniel Lemire
|
Daniel Lemire, Colin Bartlett, Owen Kaser
|
Integer Division by Constants: Optimal Bounds
| null |
Heliyon 7 (6), 2021
|
10.1016/j.heliyon.2021.e07442
|
TR-20-001, Dept of CS, UNB Saint John
|
cs.DS
|
http://creativecommons.org/licenses/by/4.0/
|
The integer division of a numerator n by a divisor d gives a quotient q and a
remainder r. Optimizing compilers accelerate software by replacing the division
of n by d with the division of c * n (or c * n + c) by m for convenient
integers c and m chosen so that they approximate the reciprocal: c/m ~= 1/d.
Such techniques are especially advantageous when m is chosen to be a power of
two and when d is a constant so that c and m can be precomputed. The literature
contains many bounds on the distance between c/m and the divisor d. Some of
these bounds are optimally tight, while others are not. We present optimally
tight bounds for quotient and remainder computations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709189 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.13337
|
Sina Rezaei Aghdam
|
Sina Rezaei Aghdam, Sven Jacobsson, Ulf Gustavsson, Giuseppe Durisi,
Christoph Studer, Thomas Eriksson
|
Distortion-Aware Linear Precoding for Massive MIMO Downlink Systems with
Nonlinear Power Amplifiers
|
30 pages, 10 figures
| null | null | null |
cs.IT eess.SP math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
We introduce a framework for linear precoder design over a massive
multiple-input multiple-output downlink system in the presence of nonlinear
power amplifiers (PAs). By studying the spatial characteristics of the
distortion, we demonstrate that conventional linear precoding techniques steer
nonlinear distortions towards the users. We show that, by taking into account
PA nonlinearity, one can design linear precoders that reduce, and in
single-user scenarios, even completely remove the distortion transmitted in the
direction of the users. This, however, is achieved at the price of a reduced
array gain. To address this issue, we present precoder optimization algorithms
that simultaneously take into account the effects of array gain, distortion,
multiuser interference, and receiver noise. Specifically, we derive an
expression for the achievable sum rate and propose an iterative algorithm that
attempts to find the precoding matrix which maximizes this expression.
Moreover, using a model for PA power consumption, we propose an algorithm that
attempts to find the precoding matrix that minimizes the consumed power for a
given minimum achievable sum rate. Our numerical results demonstrate that the
proposed distortion-aware precoding techniques provide significant improvements
in spectral and energy efficiency compared to conventional linear precoders.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708219 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.14067
|
Cristhian Garay
|
Ethan Cotterill, Cristhian Garay, Johana Luviano
|
Exploring tropical differential equations
|
28 pages, 3 figures. Some proofs were corrected
| null | null | null |
math.AG cs.SC
|
http://creativecommons.org/licenses/by/4.0/
|
The purpose of this paper is fourfold. The first is to develop the theory of
tropical differential algebraic geometry from scratch; the second is to present
the tropical fundamental theorem for differential algebraic geometry, and show
how it may be used to extract combinatorial information about the set of power
series solutions to a given system of differential equations, both in the
archimedean (complex analytic) and in the non-archimedean (e.g., $p$-adic)
settings. A third and subsidiary aim is to show how tropical differential
algebraic geometry is a natural application of semiring theory, and in so
doing, contribute to the valuative study of differential algebraic geometry. We
use this formalism to extend the fundamental theorem of partial differential
algebraic geometry to the differential fraction field of the ring of formal
power series in arbitrarily (finitely) many variables; in doing so we produce
new examples of non-Krull valuations that merit further study in their own
right.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2012.14172
|
Joe Kileel
|
Joe Kileel, Amit Moscovich, Nathan Zelesko, Amit Singer
|
Manifold learning with arbitrary norms
|
53 pages, 8 figures, 3 tables, to appear in Journal of Fourier
Analysis and Applications
|
Journal of Fourier Analysis and Applications 27, 82 (2021)
|
10.1007/s00041-021-09879-2
| null |
cs.LG stat.ML
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Manifold learning methods play a prominent role in nonlinear dimensionality
reduction and other tasks involving high-dimensional data sets with low
intrinsic dimensionality. Many of these methods are graph-based: they associate
a vertex with each data point and a weighted edge with each pair. Existing
theory shows that the Laplacian matrix of the graph converges to the
Laplace-Beltrami operator of the data manifold, under the assumption that the
pairwise affinities are based on the Euclidean norm. In this paper, we
determine the limiting differential operator for graph Laplacians constructed
using $\textit{any}$ norm. Our proof involves an interplay between the second
fundamental form of the manifold and the convex geometry of the given norm's
unit ball. To demonstrate the potential benefits of non-Euclidean norms in
manifold learning, we consider the task of mapping the motion of large
molecules with continuous variability. In a numerical simulation we show that a
modified Laplacian eigenmaps algorithm, based on the Earthmover's distance,
outperforms the classic Euclidean Laplacian eigenmaps, both in terms of
computational cost and the sample size needed to recover the intrinsic
geometry.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711005 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.00124
|
I-Hung Hsu
|
I-Hung Hsu, Xiao Guo, Premkumar Natarajan, Nanyun Peng
|
Discourse-level Relation Extraction via Graph Pooling
|
12 pages, page 10-12 appendix
| null | null | null |
cs.CL cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The ability to capture complex linguistic structures and long-term
dependencies among words in the passage is essential for discourse-level
relation extraction (DRE) tasks. Graph neural networks (GNNs), one of the
methods to encode dependency graphs, have been shown effective in prior works
for DRE. However, relatively little attention has been paid to receptive fields
of GNNs, which can be crucial for cases with extremely long text that requires
discourse understanding. In this work, we leverage the idea of graph pooling
and propose to use pooling-unpooling framework on DRE tasks. The pooling branch
reduces the graph size and enables the GNNs to obtain larger receptive fields
within fewer layers; the unpooling branch restores the pooled graph to its
original resolution so that representations for entity mention can be
extracted. We propose Clause Matching (CM), a novel linguistically inspired
graph pooling method for NLP tasks. Experiments on two DRE datasets demonstrate
that our models significantly improve over baselines when modeling long-term
dependencies is required, which shows the effectiveness of the
pooling-unpooling framework and our CM pooling method.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712057 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.00169
|
Daniel Szelogowski
|
Daniel Szelogowski
|
Generative Deep Learning for Virtuosic Classical Music: Generative
Adversarial Networks as Renowned Composers
|
13 pages, 6 figures Update: Revised format to align closer to IEEE
standards
| null | null | null |
cs.SD cs.LG cs.NE eess.AS
|
http://creativecommons.org/licenses/by/4.0/
|
Current AI-generated music lacks fundamental principles of good compositional
techniques. By narrowing down implementation issues both programmatically and
musically, we can create a better understanding of what parameters are
necessary for a generated composition nearly indistinguishable from that of a
master composer.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707626 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.01453
|
Alexander Bilmes
|
Alexander Bilmes, Alexander K. H\"andel, Serhii Volosheniuk, Alexey V.
Ustinov, and J\"urgen Lisenfeld
|
In-situ bandaged Josephson junctions for superconducting quantum
processors
| null | null |
10.1088/1361-6668/ac2a6d
| null |
quant-ph physics.app-ph
|
http://creativecommons.org/licenses/by-nc-sa/4.0/
|
Shadow evaporation is commonly used to micro-fabricate the key element of
superconducting qubits - the Josephson junction. However, in conventional
two-angle deposition circuit topology, unwanted stray Josephson junctions are
created which contribute to dielectric loss. So far, this could be avoided by
shorting the stray junctions with a so-called bandage layer deposited in an
additional lithography step, which may further contaminate the chip surface.
Here, we present an improved shadow evaporation technique allowing one to
fabricate sub-micrometer-sized Josephson junctions together with bandage layers
in a single lithography step. We also show that junction aging is significantly
reduced when junction electrodes are oxidized in an oxygen atmosphere directly
after deposition.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709453 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.07241
|
Haoyu Xiong
|
Haoyu Xiong, Quanzhou Li, Yun-Chun Chen, Homanga Bharadhwaj, Samarth
Sinha, Animesh Garg
|
Learning by Watching: Physical Imitation of Manipulation Skills from
Human Videos
|
Project Website: https://www.pair.toronto.edu/lbw-kp/
|
IROS 2021
| null | null |
cs.RO cs.CV cs.LG
|
http://creativecommons.org/publicdomain/zero/1.0/
|
Learning from visual data opens the potential to accrue a large range of
manipulation behaviors by leveraging human demonstrations without specifying
each of them mathematically, but rather through natural task specification. In
this paper, we present Learning by Watching (LbW), an algorithmic framework for
policy learning through imitation from a single video specifying the task. The
key insights of our method are two-fold. First, since the human arms may not
have the same morphology as robot arms, our framework learns unsupervised human
to robot translation to overcome the morphology mismatch issue. Second, to
capture the details in salient regions that are crucial for learning state
representations, our model performs unsupervised keypoint detection on the
translated robot videos. The detected keypoints form a structured
representation that contains semantically meaningful information and can be
used directly for computing reward and policy learning. We evaluate the
effectiveness of our LbW framework on five robot manipulation tasks, including
reaching, pushing, sliding, coffee making, and drawer closing. Extensive
experimental evaluations demonstrate that our method performs favorably against
the state-of-the-art approaches.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709806 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.09705
|
Brenda Vilas Boas
|
Brenda Vilas Boas, Wolfgang Zirwas and Martin Haardt
|
Two-step Machine Learning Approach for Channel Estimation with Mixed
Resolution RF Chains
|
to be published
| null |
10.1109/ICCWorkshops50388.2021.9473491
| null |
eess.SP cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Massive MIMO is one of the main features of 5G mobile radio systems. However,
it often leads to high cost, size and power consumption. To overcome these
issues, the use of constrained radio frequency (RF) frontends has been
proposed, as well as novel precoders, e.g., a multi-antenna, greedy, iterative
and quantized precoding algorithm (MAGIQ). Nevertheless, the best performance
of MAGIQ assumes accurate channel knowledge per antenna element, for example,
from uplink sounding reference signals. In this context, we propose an
efficient uplink channel estimator by applying machine learning (ML)
algorithms. In a first step a conditional generative adversarial network (cGAN)
predicts the radio channels from a limited set of full resolution RF chains to
the rest of the low resolution RF chain antenna elements. A long-short term
memory (LSTM) neural network extracts further phase information from the low
resolution RF chain antenna elements. Our results indicate that our proposed
approach is competitive with traditional Unitary tensor-ESPRIT in scenarios
with various closely spaced multipath components (MPCs).
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710478 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.09839
|
Aniruddhe Pradhan
|
Aniruddhe Pradhan, Karthik Duraisamy
|
Variational Multi-scale Super-resolution : A data-driven approach for
reconstruction and predictive modeling of unresolved physics
|
38 pages, 21 figures
| null | null | null |
physics.comp-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The variational multiscale (VMS) formulation formally segregates the
evolution of the coarse-scales from the fine-scales. VMS modeling requires the
approximation of the impact of the fine scales in terms of the coarse scales.
In linear problems, our formulation reduces the problem of learning the
sub-scales to learning the projected element Green's function basis
coefficients. For the purpose of this approximation, a special neural-network
structure - the variational super-resolution N-N (VSRNN) - is proposed. The
VSRNN constructs a super-resolved model of the unresolved scales as a sum of
the products of individual functions of coarse scales and physics-informed
parameters. Combined with a set of locally non-dimensional features obtained by
normalizing the input coarse-scale and output sub-scale basis coefficients, the
VSRNN provides a general framework for the discovery of closures for both the
continuous and the discontinuous Galerkin discretizations. By training this
model on a sequence of $L_2-$projected data and using the subscale to compute
the continuous Galerkin subgrid terms, and the super-resolved state to compute
the discontinuous Galerkin fluxes, we improve the optimality and the accuracy
of these methods for the convection-diffusion problem, linear advection and
turbulent channel flow. Finally, we demonstrate that - in the investigated
examples - the present model allows generalization to out-of-sample initial
conditions and Reynolds numbers. Perspectives are provided on data-driven
closure modeling, limitations of the present approach, and opportunities for
improvement.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.707613 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.10955
|
Zhengzhong Tu
|
Zhengzhong Tu, Xiangxu Yu, Yilin Wang, Neil Birkbeck, Balu Adsumilli,
and Alan C. Bovik
|
RAPIQUE: Rapid and Accurate Video Quality Prediction of User Generated
Content
|
IEEE Open Journal of Signal Processing 2021
| null |
10.1109/OJSP.2021.3090333
| null |
cs.CV cs.MM eess.IV
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Blind or no-reference video quality assessment of user-generated content
(UGC) has become a trending, challenging, heretofore unsolved problem. Accurate
and efficient video quality predictors suitable for this content are thus in
great demand to achieve more intelligent analysis and processing of UGC videos.
Previous studies have shown that natural scene statistics and deep learning
features are both sufficient to capture spatial distortions, which contribute
to a significant aspect of UGC video quality issues. However, these models are
either incapable or inefficient for predicting the quality of complex and
diverse UGC videos in practical applications. Here we introduce an effective
and efficient video quality model for UGC content, which we dub the Rapid and
Accurate Video Quality Evaluator (RAPIQUE), which we show performs comparably
to state-of-the-art (SOTA) models but with orders-of-magnitude faster runtime.
RAPIQUE combines and leverages the advantages of both quality-aware scene
statistics features and semantics-aware deep convolutional features, allowing
us to design the first general and efficient spatial and temporal (space-time)
bandpass statistics model for video quality modeling. Our experimental results
on recent large-scale UGC video quality databases show that RAPIQUE delivers
top performances on all the datasets at a considerably lower computational
expense. We hope this work promotes and inspires further efforts towards
practical modeling of video quality problems for potential real-time and
low-latency applications. To promote public usage, an implementation of RAPIQUE
has been made freely available online: \url{https://github.com/vztu/RAPIQUE}.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71086 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.11745
|
Jorge Cipri\'an S\'anchez
|
J. F. Cipri\'an-S\'anchez and G. Ochoa-Ruiz and M. Gonzalez-Mendoza
and L. Rossi
|
FIRe-GAN: A novel Deep Learning-based infrared-visible fusion method for
wildfire imagery
|
16 pages, 10 figures. Submitted to the Special Issue (SI) in the
Neural Computing and Applications Journal
| null |
10.1007/s00521-021-06691-3
| null |
cs.CV cs.LG eess.IV
|
http://creativecommons.org/licenses/by/4.0/
|
Early wildfire detection is of paramount importance to avoid as much damage
as possible to the environment, properties, and lives. Deep Learning (DL)
models that can leverage both visible and infrared information have the
potential to display state-of-the-art performance, with lower false-positive
rates than existing techniques. However, most DL-based image fusion methods
have not been evaluated in the domain of fire imagery. Additionally, to the
best of our knowledge, no publicly available dataset contains visible-infrared
fused fire images. There is a growing interest in DL-based image fusion
techniques due to their reduced complexity. Due to the latter, we select three
state-of-the-art, DL-based image fusion techniques and evaluate them for the
specific task of fire image fusion. We compare the performance of these methods
on selected metrics. Finally, we also present an extension to one of the said
methods, that we called FIRe-GAN, that improves the generation of artificial
infrared images and fused ones on selected metrics.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711049 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.11871
|
Pengwei Zhan
|
Pengwei Zhan, Liming Wang, Yi Tang
|
Website fingerprinting on early QUIC traffic
|
This work has been accepted by Elsevier Computer Networks for
publication
|
Computer Networks 200 (2021) 108538
|
10.1016/j.comnet.2021.108538
| null |
cs.CR cs.LG cs.NI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Cryptographic protocols have been widely used to protect the user's privacy
and avoid exposing private information. QUIC (Quick UDP Internet Connections),
including the version originally designed by Google (GQUIC) and the version
standardized by IETF (IQUIC), as alternatives to the traditional HTTP,
demonstrate their unique transmission characteristics: based on UDP for
encrypted resource transmitting, accelerating web page rendering. However,
existing encrypted transmission schemes based on TCP are vulnerable to website
fingerprinting (WFP) attacks, allowing adversaries to infer the users' visited
websites by eavesdropping on the transmission channel. Whether GQUIC and IQUIC
can effectively resist such attacks is worth investigating. In this paper, we
study the vulnerabilities of GQUIC, IQUIC, and HTTPS to WFP attacks from the
perspective of traffic analysis. Extensive experiments show that, in the early
traffic scenario, GQUIC is the most vulnerable to WFP attacks among GQUIC,
IQUIC, and HTTPS, while IQUIC is more vulnerable than HTTPS, but the
vulnerability of the three protocols is similar in the normal full traffic
scenario. Features transferring analysis shows that most features are
transferable between protocols when on normal full traffic scenario. However,
combining with the qualitative analysis of latent feature representation, we
find that the transferring is inefficient when on early traffic, as GQUIC,
IQUIC, and HTTPS show the significantly different magnitude of variation in the
traffic distribution on early traffic. By upgrading the one-time WFP attacks to
multiple WFP Top-a attacks, we find that the attack accuracy on GQUIC and IQUIC
reach 95.4% and 95.5%, respectively, with only 40 packets and just using simple
features, whereas reach only 60.7% when on HTTPS. We also demonstrate that the
vulnerability of IQUIC is only slightly dependent on the network environment.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708616 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2101.12164
|
Hussam Al Daas
|
Hussam Al Daas, Tyrone Rees and Jennifer Scott
|
Two-level Nystr\"om--Schur preconditioner for sparse symmetric positive
definite matrices
| null | null |
10.1137/21M139548X
| null |
math.NA cs.NA
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Randomized methods are becoming increasingly popular in numerical linear
algebra. However, few attempts have been made to use them in developing
preconditioners. Our interest lies in solving large-scale sparse symmetric
positive definite linear systems of equations where the system matrix is
preordered to doubly bordered block diagonal form (for example, using a nested
dissection ordering). We investigate the use of randomized methods to construct
high quality preconditioners. In particular, we propose a new and efficient
approach that employs Nystr\"om's method for computing low rank approximations
to develop robust algebraic two-level preconditioners. Construction of the new
preconditioners involves iteratively solving a smaller but denser symmetric
positive definite Schur complement system with multiple right-hand sides.
Numerical experiments on problems coming from a range of application areas
demonstrate that this inner system can be solved cheaply using block conjugate
gradients and that using a large convergence tolerance to limit the cost does
not adversely affect the quality of the resulting Nystr\"om--Schur two-level
preconditioner.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709604 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.02118
|
Zhongchang Liu
|
Zhongchang Liu and Wing Shing Wong
|
Group Consensus of Linear Multi-agent Systems under Nonnegative Directed
Graphs
|
to be published in IEEE Transactions on Automatic Control
| null |
10.1109/TAC.2021.3124985
| null |
eess.SY cs.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Group consensus implies reaching multiple groups where agents belonging to
the same cluster reach state consensus. This paper focuses on linear
multi-agent systems under nonnegative directed graphs. A new necessary and
sufficient condition for ensuring group consensus is derived, which requires
the spanning forest of the underlying directed graph and that of its quotient
graph induced with respect to a clustering partition to contain equal minimum
number of directed trees. This condition is further shown to be equivalent to
containing cluster spanning trees, a commonly used topology for the underlying
graph in the literature. Under a designed controller gain, lower bound of the
overall coupling strength for achieving group consensus is specified. Moreover,
the pattern of the multiple consensus states formed by all clusters is
characterized when the overall coupling strength is large enough.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710666 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.02727
|
Lorenz Welter
|
Lorenz Welter, Rawad Bitar, Antonia Wachter-Zeh, Eitan Yaakobi
|
Multiple Criss-Cross Insertion and Deletion Correcting Codes
| null | null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
This paper investigates the problem of correcting multiple criss-cross
insertions and deletions in arrays. More precisely, we study the unique
recovery of $n \times n$ arrays affected by $t$-criss-cross deletions defined
as any combination of $t_r$ row and $t_c$ column deletions such that $t_r + t_c
= t$ for a given $t$. We show an equivalence between correcting $t$-criss-cross
deletions and $t$-criss-cross insertions and show that a code correcting
$t$-criss-cross insertions/deletions has redundancy at least $tn + t \log n -
\log(t!)$. Then, we present an existential construction of $t$-criss-cross
insertion/deletion correcting code with redundancy bounded from above by $tn +
\mathcal{O}(t^2 \log^2 n)$. The main ingredients of the presented code
construction are systematic binary $t$-deletion correcting codes and Gabidulin
codes. The first ingredient helps locating the indices of the inserted/deleted
rows and columns, thus transforming the insertion/deletion-correction problem
into a row/column erasure-correction problem which is then solved using the
second ingredient.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.704757 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.05198
|
Tesi Xiao
|
Yanhao Jin, Tesi Xiao, Krishnakumar Balasubramanian
|
Statistical Inference for Polyak-Ruppert Averaged Zeroth-order
Stochastic Gradient Algorithm
| null | null | null | null |
stat.ML cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Statistical machine learning models trained with stochastic gradient
algorithms are increasingly being deployed in critical scientific applications.
However, computing the stochastic gradient in several such applications is
highly expensive or even impossible at times. In such cases, derivative-free or
zeroth-order algorithms are used. An important question which has thus far not
been addressed sufficiently in the statistical machine learning literature is
that of equipping stochastic zeroth-order algorithms with practical yet
rigorous inferential capabilities so that we not only have point estimates or
predictions but also quantify the associated uncertainty via confidence
intervals or sets. Towards this, in this work, we first establish a central
limit theorem for Polyak-Ruppert averaged stochastic zeroth-order gradient
algorithm. We then provide online estimators of the asymptotic covariance
matrix appearing in the central limit theorem, thereby providing a practical
procedure for constructing asymptotically valid confidence sets (or intervals)
for parameter estimation (or prediction) in the zeroth-order setting.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711813 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.06863
|
Ziyang Huang
|
Ziyang Huang and Guang Lin and Arezoo M. Ardekani
|
A consistent and conservative Phase-Field model for
thermo-gas-liquid-solid flows including liquid-solid phase change
|
This is an accepted manuscript
|
Journal of Computational Physics 449 (2022) 110795
|
10.1016/j.jcp.2021.110795
| null |
physics.comp-ph cs.NA math.NA physics.flu-dyn
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
In the present study, a consistent and conservative Phase-Field model is
developed to study thermo-gas-liquid-solid flows with liquid-solid phase
change. The proposed model is derived with the help of the consistency
conditions and exactly reduces to the consistent and conservative Phase-Field
method for incompressible two-phase flows, the fictitious domain Brinkman
penalization (FD/BP) method for fluid-structure interactions, and the
Phase-Field model of solidification of pure material. It honors the mass
conservation, defines the volume fractions of individual phases unambiguously,
and therefore captures the volume change due to phase change. The momentum is
conserved when the solid phase is absent, but it changes when the solid phase
appears due to the no-slip condition at the solid boundary. The proposed model
also conserves the energy, preserves the temperature equilibrium, and is
Galilean invariant. A novel continuous surface tension force to confine its
contribution at the gas-liquid interface and a drag force modified from the
Carman-Kozeny equation to reduce solid velocity to zero are proposed. The issue
of initiating phase change in the original Phase-Field model of solidification
is addressed by physically modifying the interpolation function. The
corresponding consistent scheme is developed to solve the model, and the
numerical results agree well with the analytical solutions and the existing
experimental and numerical data. Two challenging problems having a wide range
of material properties and complex dynamics are conducted to demonstrate the
capability of the proposed model.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712632 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.07505
|
Johannes Nguyen
|
Johannes Nguyen, Simon T. Powers, Neil Urquhart, Thomas Farrenkopf,
Michael Guckert
|
An Overview of Agent-based Traffic Simulators
|
The final publication is available at Elsevier Transportation
Research Interdisciplinary Perspectives via
https://doi.org/10.1016/j.trip.2021.100486
| null |
10.1016/j.trip.2021.100486
| null |
cs.MA cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Individual traffic significantly contributes to climate change and
environmental degradation. Therefore, innovation in sustainable mobility is
gaining importance as it helps to reduce environmental pollution. However,
effects of new ideas in mobility are difficult to estimate in advance and
strongly depend on the individual traffic participants. The application of
agent technology is particularly promising as it focuses on modelling
heterogeneous individual preferences and behaviours. In this paper, we show how
agent-based models are particularly suitable to address three pressing research
topics in mobility: 1. Social dilemmas in resource utilisation; 2. Digital
connectivity; and 3. New forms of mobility. We then explain how the features of
several agent-based simulators are suitable for addressing these topics. We
assess the capability of simulators to model individual travel behaviour,
discussing implemented features and identifying gaps in functionality that we
consider important.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709208 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.07969
|
Yuantian Miao
|
Yuantian Miao, Chao Chen, Lei Pan, Qing-Long Han, Jun Zhang, Yang
Xiang
|
Machine Learning Based Cyber Attacks Targeting on Controlled
Information: A Survey
|
Published in ACM Computing Surveys
|
ACM Comput. Surv. 54, 7, Article 139 (September 2022), 36 pages
|
10.1145/3465171
| null |
cs.CR cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Stealing attack against controlled information, along with the increasing
number of information leakage incidents, has become an emerging cyber security
threat in recent years. Due to the booming development and deployment of
advanced analytics solutions, novel stealing attacks utilize machine learning
(ML) algorithms to achieve high success rate and cause a lot of damage.
Detecting and defending against such attacks is challenging and urgent so that
governments, organizations, and individuals should attach great importance to
the ML-based stealing attacks. This survey presents the recent advances in this
new type of attack and corresponding countermeasures. The ML-based stealing
attack is reviewed in perspectives of three categories of targeted controlled
information, including controlled user activities, controlled ML model-related
information, and controlled authentication information. Recent publications are
summarized to generalize an overarching attack methodology and to derive the
limitations and future directions of ML-based stealing attacks. Furthermore,
countermeasures are proposed towards developing effective protections from
three aspects -- detection, disruption, and isolation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710239 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.10666
|
Michele Borgese
|
Filippo Costa and Michele Borgese
|
Electromagnetic Model of Reflective Intelligent Surfaces
|
11 pages, 13 figures
|
in IEEE Open Journal of the Communications Society, vol. 2, pp.
1577-1589, 2021
|
10.1109/OJCOMS.2021.3092217
| null |
eess.SP cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
An accurate and simple analytical model for the computation of the reflection
amplitude and phase of Reconfigurable Intelligent Surfaces is presented. The
model is based on a transmission-line circuit representation of the RIS which
takes into account the physics behind the structure including the effect of all
relevant geometrical and electrical parameters. The proposed representation of
the RIS allows to take into account the effect of incidence angle, mutual
coupling among elements and the effect of the interaction of the periodic
surface with the RIS ground plane. It is shown that the proposed approach
allows to design a physically realisable RIS without recurring to onerous
electromagnetic simulations. The proposed model aims at filling the gap between
RIS assisted communications algorithms and physical implementation issues which
determine realistic performance of these surfaces.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710603 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.11436
|
Alexander Robey
|
Alexander Robey and George J. Pappas and Hamed Hassani
|
Model-Based Domain Generalization
| null | null | null | null |
stat.ML cs.AI cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Despite remarkable success in a variety of applications, it is well-known
that deep learning can fail catastrophically when presented with
out-of-distribution data. Toward addressing this challenge, we consider the
domain generalization problem, wherein predictors are trained using data drawn
from a family of related training domains and then evaluated on a distinct and
unseen test domain. We show that under a natural model of data generation and a
concomitant invariance condition, the domain generalization problem is
equivalent to an infinite-dimensional constrained statistical learning problem;
this problem forms the basis of our approach, which we call Model-Based Domain
Generalization. Due to the inherent challenges in solving constrained
optimization problems in deep learning, we exploit nonconvex duality theory to
develop unconstrained relaxations of this statistical problem with tight bounds
on the duality gap. Based on this theoretical motivation, we propose a novel
domain generalization algorithm with convergence guarantees. In our
experiments, we report improvements of up to 30 percentage points over
state-of-the-art domain generalization baselines on several benchmarks
including ColoredMNIST, Camelyon17-WILDS, FMoW-WILDS, and PACS.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71123 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2102.12107
|
Mojtaba Shahin
|
Waqar Hussain, Mojtaba Shahin, Rashina Hoda, Jon Whittle, Harsha
Perera, Arif Nurwidyantoro, Rifat Ara Shams, Gillian Oliver
|
How Can Human Values Be Addressed in Agile Methods? A Case Study on SAFe
|
Preprint - Accepted to be published in IEEE Transactions on Software
Engineering (2021), 18 Pages, 5 Figures, 3 Tables
| null | null | null |
cs.SE
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Agile methods are predominantly focused on delivering business values. But
can Agile methods be adapted to effectively address and deliver human values
such as social justice, privacy, and sustainability in the software they
produce? Human values are what an individual or a society considers important
in life. Ignoring these human values in software can pose difficulties or risks
for all stakeholders (e.g., user dissatisfaction, reputation damage, financial
loss). To answer this question, we selected the Scaled Agile Framework (SAFe),
one of the most commonly used Agile methods in the industry, and conducted a
qualitative case study to identify possible intervention points within SAFe
that are the most natural to address and integrate human values in software. We
present five high-level empirically-justified sets of interventions in SAFe:
artefacts, roles, ceremonies, practices, and culture. We elaborate how some
current Agile artefacts (e.g., user story), roles (e.g., product owner),
ceremonies (e.g., stand-up meeting), and practices (e.g., business-facing
testing) in SAFe can be modified to support the inclusion of human values in
software. Further, our study suggests new and exclusive values-based artefacts
(e.g., legislative requirement), ceremonies (e.g., values conversation), roles
(e.g., values champion), and cultural practices (e.g., induction and hiring) to
be introduced in SAFe for this purpose. Guided by our findings, we argue that
existing Agile methods can account for human values in software delivery with
some evolutionary adaptations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710415 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.01089
|
Qingru Zhang
|
Qingru Zhang, David Wipf, Quan Gan and Le Song
|
A Biased Graph Neural Network Sampler with Near-Optimal Regret
|
35th Conference on Neural Information Processing Systems (NeurIPS
2021)
| null | null | null |
cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Graph neural networks (GNN) have recently emerged as a vehicle for applying
deep network architectures to graph and relational data. However, given the
increasing size of industrial datasets, in many practical situations the
message passing computations required for sharing information across GNN layers
are no longer scalable. Although various sampling methods have been introduced
to approximate full-graph training within a tractable budget, there remain
unresolved complications such as high variances and limited theoretical
guarantees. To address these issues, we build upon existing work and treat GNN
neighbor sampling as a multi-armed bandit problem but with a newly-designed
reward function that introduces some degree of bias designed to reduce variance
and avoid unstable, possibly-unbounded pay outs. And unlike prior bandit-GNN
use cases, the resulting policy leads to near-optimal regret while accounting
for the GNN training dynamics introduced by SGD. From a practical standpoint,
this translates into lower variance estimates and competitive or superior test
accuracy across several benchmarks.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709573 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.01758
|
Bo Liu
|
Bo Liu, Chong Ye, C. P. Sun, and Yong Li
|
Spatial enantioseparation of gaseous chiral molecules
|
8 pages, 4 figures
|
Phys. Rev. A 104, 013113 (2021)
|
10.1103/PhysRevA.104.013113
| null |
physics.atom-ph quant-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We explore the spatial enantioseparation of gaseous chiral molecules for the
cyclic three-level systems coupled with three electromagnetic fields. Due to
molecular rotations, the specific requirements of the polarization directions
of the three electromagnetic fields lead to the space-dependent part of the
overall phase of the coupling strengths. Thus, the overall phase of the
coupling strengths, which differs with $\pi$ for the enantiomers in the cyclic
three-level model of chiral molecules, varies intensely in the length scale of
the typical wavelength of the applied electromagnetic fields. Under the induced
gauge potentials resulting from the space-dependent part of the overall phase
and the space-dependent intensities of coupling strengths, we further show
spatial enantioseparation for typical parameters of gaseous chiral molecules.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713213 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.04304
|
Tomer Ezra
|
Moshe Babaioff, Tomer Ezra and Uriel Feige
|
Fair-Share Allocations for Agents with Arbitrary Entitlements
| null | null | null | null |
cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider the problem of fair allocation of indivisible goods to $n$
agents, with no transfers. When agents have equal entitlements, the well
established notion of the maximin share (MMS) serves as an attractive fairness
criterion, where to qualify as fair, an allocation needs to give every agent at
least a substantial fraction of her MMS.
In this paper we consider the case of arbitrary (unequal) entitlements. We
explain shortcomings in previous attempts that extend the MMS to unequal
entitlements. Our conceptual contribution is the introduction of a new notion
of a share, the AnyPrice share (APS), that is appropriate for settings with
arbitrary entitlements. Even for the equal entitlements case, this notion is
new, and satisfies $APS \ge MMS$, where the inequality is sometimes strict. We
present two equivalent definitions for the APS (one as a minimization problem,
the other as a maximization problem), and provide comparisons between the APS
and previous notions of fairness.
Our main result concerns additive valuations and arbitrary entitlements, for
which we provide a polynomial-time algorithm that gives every agent at least a
$\frac{3}{5}$-fraction of her APS. This algorithm can also be viewed as
providing strategies in a certain natural bidding game, and these strategies
secure each agent at least a $\frac{3}{5}$-fraction of her APS.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711844 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.05623
|
Giulio Cimini
|
Marco Bardoscia, Paolo Barucca, Stefano Battiston, Fabio Caccioli,
Giulio Cimini, Diego Garlaschelli, Fabio Saracco, Tiziano Squartini, Guido
Caldarelli
|
The Physics of Financial Networks
|
version submitted to Nature Reviews Physics
|
Nat. Rev. Phys. 3 (7), 490-507 (2021)
|
10.1038/s42254-021-00322-5
| null |
physics.soc-ph cond-mat.stat-mech cs.SI q-fin.RM
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The field of Financial Networks is a paramount example of the novel
applications of Statistical Physics that have made possible by the present data
revolution. As the total value of the global financial market has vastly
outgrown the value of the real economy, financial institutions on this planet
have created a web of interactions whose size and topology calls for a
quantitative analysis by means of Complex Networks. Financial Networks are not
only a playground for the use of basic tools of statistical physics as ensemble
representation and entropy maximization; rather, their particular dynamics and
evolution triggered theoretical advancements as the definition of DebtRank to
measure the impact and diffusion of shocks in the whole systems. In this review
we present the state of the art in this field, starting from the different
definitions of financial networks (based either on loans, on assets ownership,
on contracts involving several parties -- such as credit default swaps, to
multiplex representation when firms are introduced in the game and a link with
real economy is drawn) and then discussing the various dynamics of financial
contagion as well as applications in financial network inference and
validation. We believe that this analysis is particularly timely since
financial stability as well as recent innovations in climate finance, once
properly analysed and understood in terms of complex network theory, can play a
pivotal role in the transformation of our society towards a more sustainable
world.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708603 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.10011
|
Wenlong Wang
|
Wenlong Wang, Thomas Pfeiffer
|
Securities Based Decision Markets
|
To be published in The Third International Conference on Distributed
Artificial Intelligence
| null | null | null |
cs.GT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Decision markets are mechanisms for selecting one among a set of actions
based on forecasts about their consequences. Decision markets that are based on
scoring rules have been proven to offer incentive compatibility analogous to
properly incentivised prediction markets. However, in contrast to prediction
markets, it is unclear how to implement decision markets such that forecasting
is done through the trading of securities. We here propose such a securities
based implementation, and show that it offers the same expected payoff as the
corresponding scoring rules based decision market. The distribution of realised
payoffs, however, might differ. Our analysis expands the knowledge on
forecasting based decision making and provides novel insights for intuitive and
easy-to-use decision market implementations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71247 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.10614
|
Zhongyang Zhang
|
Zhongyang Zhang, Zhiyang Xu, Zia Ahmed, Asif Salekin, Tauhidur Rahman
|
Hyperspectral Image Super-Resolution in Arbitrary Input-Output Band
Settings
|
Accepted by WACV 2022 Workshop WACI(Workshop on Applications of
Computational Imaging)
| null | null | null |
cs.CV eess.IV
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Hyperspectral image (HSI) with narrow spectral bands can capture rich
spectral information, but it sacrifices its spatial resolution in the process.
Many machine-learning-based HSI super-resolution (SR) algorithms have been
proposed recently. However, one of the fundamental limitations of these
approaches is that they are highly dependent on image and camera settings and
can only learn to map an input HSI with one specific setting to an output HSI
with another. However, different cameras capture images with different spectral
response functions and bands numbers due to the diversity of HSI cameras.
Consequently, the existing machine-learning-based approaches fail to learn to
super-resolve HSIs for a wide variety of input-output band settings. We propose
a single Meta-Learning-Based Super-Resolution (MLSR) model, which can take in
HSI images at an arbitrary number of input bands' peak wavelengths and generate
SR HSIs with an arbitrary number of output bands' peak wavelengths. We leverage
NTIRE2020 and ICVL datasets to train and validate the performance of the MLSR
model. The results show that the single proposed model can successfully
generate super-resolved HSI bands at arbitrary input-output band settings. The
results are better or at least comparable to baselines that are separately
trained on a specific input-output band setting.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71202 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.11746
|
Michael Lones
|
Michael A. Lones
|
Evolving Continuous Optimisers from Scratch
|
arXiv admin note: text overlap with arXiv:1910.00945
|
Genetic Programming and Evolvable Machines, vol 22, pages 395-428,
December 2021 (Special Issue: Highlights of Genetic Programming 2020 Events)
|
10.1007/s10710-021-09414-8
| null |
cs.NE cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
This work uses genetic programming to explore the space of continuous
optimisers, with the goal of discovering novel ways of doing optimisation. In
order to keep the search space broad, the optimisers are evolved from scratch
using Push, a Turing-complete, general-purpose, language. The resulting
optimisers are found to be diverse, and explore their optimisation landscapes
using a variety of interesting, and sometimes unusual, strategies.
Significantly, when applied to problems that were not seen during training,
many of the evolved optimisers generalise well, and often outperform existing
optimisers. This supports the idea that novel and effective forms of
optimisation can be discovered in an automated manner. This paper also shows
that pools of evolved optimisers can be hybridised to further increase their
generality, leading to optimisers that perform robustly over a broad variety of
problem types and sizes.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71202 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2103.14060
|
Nathan P. Lawrence
|
Daniel G. McClement, Nathan P. Lawrence, Philip D. Loewen, Michael G.
Forbes, Johan U. Backstr\"om, R. Bhushan Gopaluni
|
A Meta-Reinforcement Learning Approach to Process Control
|
ADCHEM 2021; Keynote Paper
| null |
10.1016/j.ifacol.2021.08.321
| null |
cs.LG
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Meta-learning is a branch of machine learning which aims to quickly adapt
models, such as neural networks, to perform new tasks by learning an underlying
structure across related tasks. In essence, models are being trained to learn
new tasks effectively rather than master a single task. Meta-learning is
appealing for process control applications because the perturbations to a
process required to train an AI controller can be costly and unsafe.
Additionally, the dynamics and control objectives are similar across many
different processes, so it is feasible to create a generalizable controller
through meta-learning capable of quickly adapting to different systems. In this
work, we construct a deep reinforcement learning (DRL) based controller and
meta-train the controller using a latent context variable through a separate
embedding neural network. We test our meta-algorithm on its ability to adapt to
new process dynamics as well as different control objectives on the same
process. In both cases, our meta-learning algorithm adapts very quickly to new
tasks, outperforming a regular DRL controller trained from scratch.
Meta-learning appears to be a promising approach for constructing more
intelligent and sample-efficient controllers.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711619 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.00062
|
Chang Liu
|
Chang Liu and Dennice F. Gayme
|
Structured input-output analysis of transitional wall-bounded flows
|
26pages, 10 figures
|
J. Fluid Mech. (2021) 927, A25
|
10.1017/jfm.2021.762
| null |
physics.flu-dyn cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
Input-output analysis of transitional channel flows has proven to be a
valuable analytical tool for identifying important flow structures and
energetic motions. The traditional approach abstracts the nonlinear terms as
forcing that is unstructured, in the sense that this forcing is not directly
tied to the underlying nonlinearity in the dynamics. This paper instead employs
a structured singular value-based approach that preserves certain input-output
properties of the nonlinear forcing function in an effort to recover the larger
range of key flow features identified through nonlinear analysis, experiments,
and direct numerical simulation (DNS) of transitional channel flows.
Application of this method to transitional plane Couette and plane Poiseuille
flows leads to not only the identification of the streamwise coherent
structures predicted through traditional input-output approaches, but also the
characterization of the oblique flow structures as those requiring the least
energy to induce transition in agreement with DNS studies, and nonlinear
optimal perturbation analysis. The proposed approach also captures the recently
observed oblique turbulent bands that have been linked to transition in
experiments and DNS with very large channel size. The ability to identify the
larger amplification of the streamwise varying structures predicted from DNS
and nonlinear analysis in both flow regimes suggests that the structured
approach allows one to maintain the nonlinear effects associated with weakening
of the lift-up mechanism, which is known to dominate the linear operator.
Capturing this key nonlinear effect enables the prediction of the wider range
of known transitional flow structures within the analytical input-output
modeling paradigm.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712251 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.00077
|
Gokhan Alcan
|
Jiyo Palatti, Andrei Aksjonov, Gokhan Alcan, Ville Kyrki
|
Planning for Safe Abortable Overtaking Maneuvers in Autonomous Driving
|
Accepted to be presented in IEEE International Conference on
Intelligent Transportation Systems (ITSC 2021)
| null |
10.1109/ITSC48978.2021.9564499
| null |
cs.RO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Overtaking is one of the most challenging tasks in driving, and the current
solutions to autonomous overtaking are limited to simple and static scenarios.
In this paper, we present a method for behaviour and trajectory planning for
safe autonomous overtaking. The proposed method optimizes the trajectory by
simultaneously enforcing safety and minimizing intrusion onto the adjacent
lane. Furthermore, the method allows the overtaking to be aborted, enabling the
autonomous vehicle to merge back in the lane, if safety is compromised, because
of e.g. traffic in opposing direction appearing during the maneuver execution.
A finite state machine is used to select an appropriate maneuver at each time,
and a combination of safe and reachable sets is used to iteratively generate
intermediate reference targets based on the current maneuver. A nonlinear model
predictive controller then plans dynamically feasible and collision-free
trajectories to these intermediate reference targets. Simulation experiments
demonstrate that the combination of intermediate reference generation and model
predictive control is able to handle multiple behaviors, including following a
lead vehicle, overtaking and aborting the overtake, within a single framework.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708427 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.01958
|
Donald Ebeigbe
|
Donald Ebeigbe, Tyrus Berry, Michael M. Norton, Andrew J. Whalen, Dan
Simon, Timothy Sauer, Steven J. Schiff
|
A Generalized Unscented Transformation for Probability Distributions
|
15 pages, 4 figures
| null | null | null |
stat.ME cs.SY eess.SY
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
The unscented transform uses a weighted set of samples called sigma points to
propagate the means and covariances of nonlinear transformations of random
variables. However, unscented transforms developed using either the Gaussian
assumption or a minimum set of sigma points typically fall short when the
random variable is not Gaussian distributed and the nonlinearities are
substantial. In this paper, we develop the generalized unscented transform
(GenUT), which uses 2n+1 sigma points to accurately capture up to the diagonal
components of the skewness and kurtosis tensors of most probability
distributions. Constraints can be analytically enforced on the sigma points
while guaranteeing at least second-order accuracy. The GenUT uses the same
number of sigma points as the original unscented transform while also being
applicable to non-Gaussian distributions, including the assimilation of
observations in the modeling of infectious diseases such as coronavirus
(SARS-CoV-2) causing COVID-19.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711619 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.02337
|
Mohammad Reza Jafari Harandi
|
M. Reza J. Harandi, Amir Molaei, Hamid D. Taghirad and Jose Guadalupe
Romero
|
Bounded Inputs Total Energy Shaping for Mechanical Systems
| null | null |
10.1002/rnc.5765
| null |
eess.SY cs.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Designing control systems with bounded input is a practical consideration
since realizable physical systems are limited by the saturation of actuators.
The actuators' saturation degrades the performance of the control system, and
in extreme cases, the stability of the closed-loop system may be lost. However,
actuator saturation is typically neglected in the design of control systems,
with compensation being made in the form of over-designing the actuator or by
post-analyzing the resulting system to ensure acceptable performance. The
bounded input control of fully actuated systems has been investigated in
multiple studies, but it is not generalized for under actuated mechanical
systems. This article proposes a systematic framework for finding the upper
bound of control effort in underactuated systems, based on interconnection and
the damping assignment passivity based control (IDA-PBC) approach. The proposed
method also offers design variables for the control law to be tuned,
considering the actuator's limit. The major difficulty in finding the control
input upper bounds is the velocity dependent kinetic energy related terms.
Thus, the upper bound of velocity is computed using a suitable Lyapunov
candidate as a function of closed-loop system parameters. The validity and
application of the proposed method are investigated in detail through two
benchmark systems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709585 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.02550
|
Sergey Alyaev
|
Kristian Fossum, Sergey Alyaev, Jan Tveranger, Ahmed Elsheikh
|
Deep learning for prediction of complex geology ahead of drilling
|
Accepted to ICCS 2021
|
In: Paszynski M., Kranzlmuller D., Krzhizhanovskaya V.V., Dongarra
J.J., Sloot P.M.A. (eds) Computational Science +IBM- ICCS 2021. ICCS 2021.
Lecture Notes in Computer Science, vol 12743. Springer, Cham
|
10.1007/978-3-030-77964-1_36
| null |
stat.ML cs.LG
|
http://creativecommons.org/licenses/by/4.0/
|
During a geosteering operation the well path is intentionally adjusted in
response to the new data acquired while drilling. To achieve consistent
high-quality decisions, especially when drilling in complex environments,
decision support systems can help cope with high volumes of data and
interpretation complexities. They can assimilate the real-time measurements
into a probabilistic earth model and use the updated model for decision
recommendations.
Recently, machine learning (ML) techniques have enabled a wide range of
methods that redistribute computational cost from on-line to off-line
calculations. In this paper, we introduce two ML techniques into the
geosteering decision support framework. Firstly, a complex earth model
representation is generated using a Generative Adversarial Network (GAN).
Secondly, a commercial extra-deep electromagnetic simulator is represented
using a Forward Deep Neural Network (FDNN).
The numerical experiments demonstrate that the combination of the GAN and the
FDNN in an ensemble randomized maximum likelihood data assimilation scheme
provides real-time estimates of complex geological uncertainty. This yields
reduction of geological uncertainty ahead of the drill-bit from the
measurements gathered behind and around the well bore.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71039 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.02809
|
Thomas Pike
|
Thomas Pike, Samantha Golden, Daniel Lowdermilk, Brandon Luong,
Benjamin Rosado
|
Growing the Simulation Ecosystem: Introducing Mesa Data to Provide
Transparent, Accessible and Extensible Data Pipelines for Simulation
Development
|
14 Pages 5 figures, tied to GitHub Repo
https://github.com/projectmesadata
| null | null | null |
cs.CY cs.MA
|
http://creativecommons.org/licenses/by/4.0/
|
The Agent Based Model community has a rich and diverse ecosystem of
libraries, platforms, and applications to help modelers develop rigorous
simulations. Despite this robust and diverse ecosystem, the complexity of life
from microbial communities to the global ecosystem still presents substantial
challenges in making reusable code that can optimize the ability of the
knowledge-sharing and reproducibility. This research seeks to provide new tools
to mitigate some of these challenges by offering a vision of a more holistic
ecosystem that takes researchers and practitioners from the data collection
through validation, with transparent, accessible, and extensible subcomponents.
This proposed approach is demonstrated through two data pipelines (crop yield
and synthetic population) that take users from data download through the
cleaning and processing until users of have data that can be integrated into an
ABM. These pipelines are built to be transparent: by walking users step by step
through the process, accessible: by being skill scalable so users can leverage
them without code or with code, and extensible by being freely available on the
coding sharing repository GitHub to facilitate community development. Reusing
code that simulates complex phenomena is a significant challenge but one that
must be consistently addressed to help the community move forward. This
research seeks to aid that progress by offering potential new tools extended
from the already robust ecosystem to help the community collaborate more
effectively internally and across disciplines.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710038 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.05124
|
Cuauhtemoc Daniel Suarez-Ramirez
|
Cuauhtemoc Daniel Suarez-Ramirez, Miguel Gonzalez-Mendoza, Leonardo
Chang-Fernandez, Gilberto Ochoa-Ruiz, Mario Alberto Duran-Vega
|
A Bop and Beyond: A Second Order Optimizer for Binarized Neural Networks
|
9 pages, 12 figures, Preprint accepted to the LatinX in CV Research
Workshop at CVPR'21
| null |
10.1109/cvprw53098.2021.00140
| null |
cs.CV
|
http://creativecommons.org/licenses/by/4.0/
|
The optimization of Binary Neural Networks (BNNs) relies on approximating the
real-valued weights with their binarized representations. Current techniques
for weight-updating use the same approaches as traditional Neural Networks
(NNs) with the extra requirement of using an approximation to the derivative of
the sign function - as it is the Dirac-Delta function - for back-propagation;
thus, efforts are focused adapting full-precision techniques to work on BNNs.
In the literature, only one previous effort has tackled the problem of directly
training the BNNs with bit-flips by using the first raw moment estimate of the
gradients and comparing it against a threshold for deciding when to flip a
weight (Bop). In this paper, we take an approach parallel to Adam which also
uses the second raw moment estimate to normalize the first raw moment before
doing the comparison with the threshold, we call this method Bop2ndOrder. We
present two versions of the proposed optimizer: a biased one and a
bias-corrected one, each with its own applications. Also, we present a complete
ablation study of the hyperparameters space, as well as the effect of using
schedulers on each of them. For these studies, we tested the optimizer in
CIFAR10 using the BinaryNet architecture. Also, we tested it in ImageNet 2012
with the XnorNet and BiRealNet architectures for accuracy. In both datasets our
approach proved to converge faster, was robust to changes of the
hyperparameters, and achieved better accuracy values.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710409 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.07768
|
Matthew Tsao
|
Matthew Tsao, Kaidi Yang, Stephen Zoepf, Marco Pavone
|
Trust but Verify: Cryptographic Data Privacy for Mobility Management
| null | null | null | null |
cs.CR cs.SI
|
http://creativecommons.org/licenses/by/4.0/
|
The era of Big Data has brought with it a richer understanding of user
behavior through massive data sets, which can help organizations optimize the
quality of their services. In the context of transportation research, mobility
data can provide Municipal Authorities (MA) with insights on how to operate,
regulate, or improve the transportation network. Mobility data, however, may
contain sensitive information about end users and trade secrets of Mobility
Providers (MP). Due to this data privacy concern, MPs may be reluctant to
contribute their datasets to MA. Using ideas from cryptography, we propose an
interactive protocol between a MA and a MP in which MA obtains insights from
mobility data without MP having to reveal its trade secrets or sensitive data
of its users. This is accomplished in two steps: a commitment step, and a
computation step. In the first step, Merkle commitments and aggregated traffic
measurements are used to generate a cryptographic commitment. In the second
step, MP extracts insights from the data and sends them to MA. Using the
commitment and zero-knowledge proofs, MA can certify that the information
received from MP is accurate, without needing to directly inspect the mobility
data. We also present a differentially private version of the protocol that is
suitable for the large query regime. The protocol is verifiable for both MA and
MP in the sense that dishonesty from one party can be detected by the other.
The protocol can be readily extended to the more general setting with multiple
MPs via secure multi-party computation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.712201 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.09027
|
Mengyuan Lee
|
Mengyuan Lee, Guanding Yu, and Huaiyu Dai
|
Decentralized Inference with Graph Neural Networks in Wireless
Communication Systems
|
The paper was accpeted by TMC
| null | null | null |
cs.IT cs.LG math.IT
|
http://creativecommons.org/licenses/by/4.0/
|
Graph neural network (GNN) is an efficient neural network model for graph
data and is widely used in different fields, including wireless communications.
Different from other neural network models, GNN can be implemented in a
decentralized manner with information exchanges among neighbors, making it a
potentially powerful tool for decentralized control in wireless communication
systems. The main bottleneck, however, is wireless channel impairments that
deteriorate the prediction robustness of GNN. To overcome this obstacle, we
analyze and enhance the robustness of the decentralized GNN in different
wireless communication systems in this paper. Specifically, using a GNN binary
classifier as an example, we first develop a methodology to verify whether the
predictions are robust. Then, we analyze the performance of the decentralized
GNN binary classifier in both uncoded and coded wireless communication systems.
To remedy imperfect wireless transmission and enhance the prediction
robustness, we further propose novel retransmission mechanisms for the above
two communication systems, respectively. Through simulations on the synthetic
graph data, we validate our analysis, verify the effectiveness of the proposed
retransmission mechanisms, and provide some insights for practical
implementation.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710226 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.09595
|
Bowen Weng
|
Bowen Weng and Linda Capito and Umit Ozguner and Keith Redmill
|
Towards Guaranteed Safety Assurance of Automated Driving Systems with
Scenario Sampling: An Invariant Set Perspective (Extended Version)
|
A shorter version of this manuscript has been accepted by the IEEE
Transactions on Intelligent Vehicles
| null |
10.1109/TIV.2021.3117049
| null |
cs.RO cs.SY eess.SY
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
How many scenarios are sufficient to validate the safe Operational Design
Domain (ODD) of an Automated Driving System (ADS) equipped vehicle? Is a more
significant number of sampled scenarios guaranteeing a more accurate safety
assessment of the ADS? Despite the various empirical success of ADS safety
evaluation with scenario sampling in practice, some of the fundamental
properties are largely unknown. This paper seeks to remedy this gap by
formulating and tackling the scenario sampling safety assurance problem from a
set invariance perspective. First, a novel conceptual equivalence is drawn
between the scenario sampling safety assurance problem and the data-driven
robustly controlled forward invariant set validation and quantification
problem. This paper then provides a series of resolution complete and
probabilistic complete solutions with finite-sampling analyses for the safety
validation problem that authenticates a given ODD. On the other hand, the
quantification problem escalates the validation challenge and starts looking
for a safe sub-domain of a particular property. This inspires various
algorithms that are provably probabilistic incomplete, probabilistic complete
but sub-optimal, and asymptotically optimal. Finally, the proposed
asymptotically optimal scenario sampling safety quantification algorithm is
also empirically demonstrated through simulation experiments.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708408 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.09665
|
Allen Liu
|
Allen Liu, Ankur Moitra
|
Learning GMMs with Nearly Optimal Robustness Guarantees
| null | null | null | null |
cs.LG cs.DS math.ST stat.ML stat.TH
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
In this work we solve the problem of robustly learning a high-dimensional
Gaussian mixture model with $k$ components from $\epsilon$-corrupted samples up
to accuracy $\widetilde{O}(\epsilon)$ in total variation distance for any
constant $k$ and with mild assumptions on the mixture. This robustness
guarantee is optimal up to polylogarithmic factors. The main challenge is that
most earlier works rely on learning individual components in the mixture, but
this is impossible in our setting, at least for the types of strong robustness
guarantees we are aiming for. Instead we introduce a new framework which we
call {\em strong observability} that gives us a route to circumvent this
obstacle.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711268 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.12128
|
Dai Quoc Nguyen
|
Thanh Vu and Dai Quoc Nguyen
|
Automatic Post-Editing for Vietnamese
|
Accepted to ALTA 2021
| null | null | null |
cs.CL
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Automatic post-editing (APE) is an important remedy for reducing errors of
raw translated texts that are produced by machine translation (MT) systems or
software-aided translation. In this paper, we present a systematic approach to
tackle the APE task for Vietnamese. Specifically, we construct the first
large-scale dataset of 5M Vietnamese translated and corrected sentence pairs.
We then apply strong neural MT models to handle the APE task, using our
constructed dataset. Experimental results from both automatic and human
evaluations show the effectiveness of the neural MT models in handling the
Vietnamese APE task.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.713207 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.12457
|
Ksenia Briling
|
Ksenia R. Briling, Alberto Fabrizio, Clemence Corminboeuf
|
Impact of quantum-chemical metrics on the machine learning prediction of
electron density
|
9 pages + SI (11 pages)
|
J. Chem. Phys. 155, 024107 (2021)
|
10.1063/5.0055393
| null |
physics.chem-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Machine learning (ML) algorithms have undergone an explosive development
impacting every aspect of computational chemistry. To obtain reliable
predictions, one needs to maintain the proper balance between the black-box
nature of ML frameworks and the physics of the target properties. One of the
most appealing quantum-chemical properties for regression models is the
electron density, and some of us recently proposed a transferable and scalable
model based on the decomposition of the density onto an atom-centered basis
set. The decomposition, as well as the training of the model, is at its core a
minimization of some loss function, which can be arbitrarily chosen and may
lead to results of different quality. Well-studied in the context of density
fitting (DF), the impact of the metric on the performance of ML models has not
been analyzed yet. In this work, we compare predictions obtained using the
overlap and the Coulomb-repulsion metrics for both decomposition and training.
As expected, the Coulomb metric used as both the DF and ML loss functions leads
to the best results for the electrostatic potential and dipole moments. The
origin of this difference lies in the fact that the model is not constrained to
predict densities that integrate to the exact number of electrons $N$. Since an
\textit{a posteriori} correction for the number of electrons decreases the
errors, we proposed a modification of the model where $N$ is included directly
into the kernel function, which allowed to lower the errors on the test and
out-of-sample sets.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710176 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2104.12643
|
Jialin Yu
|
Jialin Yu, Laila Alrajhi, Anoushka Harit, Zhongtian Sun, Alexandra I.
Cristea, Lei Shi
|
Exploring Bayesian Deep Learning for Urgent Instructor Intervention Need
in MOOC Forums
| null | null |
10.1007/978-3-030-80421-3_10
| null |
cs.CL cs.AI
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Massive Open Online Courses (MOOCs) have become a popular choice for
e-learning thanks to their great flexibility. However, due to large numbers of
learners and their diverse backgrounds, it is taxing to offer real-time
support. Learners may post their feelings of confusion and struggle in the
respective MOOC forums, but with the large volume of posts and high workloads
for MOOC instructors, it is unlikely that the instructors can identify all
learners requiring intervention. This problem has been studied as a Natural
Language Processing (NLP) problem recently, and is known to be challenging, due
to the imbalance of the data and the complex nature of the task. In this paper,
we explore for the first time Bayesian deep learning on learner-based text
posts with two methods: Monte Carlo Dropout and Variational Inference, as a new
solution to assessing the need of instructor interventions for a learner's
post. We compare models based on our proposed methods with probabilistic
modelling to its baseline non-Bayesian models under similar circumstances, for
different cases of applying prediction. The results suggest that Bayesian deep
learning offers a critical uncertainty measure that is not supplied by
traditional neural networks. This adds more explainability, trust and
robustness to AI, which is crucial in education-based applications.
Additionally, it can achieve similar or better performance compared to
non-probabilistic neural networks, as well as grant lower variance.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.713188 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.02737
|
SImone Rodini
|
Simone Rodini
|
Analytical derivatives of Neural Networks
|
12 pages, 7 figures
|
Comput.Phys.Commun. 270 (2022) 108169
|
10.1016/j.cpc.2021.108169
| null |
physics.comp-ph hep-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We propose a simple recursive algorithm that allows the computation of the
first- and second-order derivatives with respect to the inputs of an arbitrary
deep feed forward neural network (DFNN). The algorithm naturally incorporates
the derivatives with respect to the network parameters. To test the algorithm,
we apply it to the study of the quantum mechanical variational problem for few
cases of simple potentials, modeling the ground-state wave function in terms of
a DFNN.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710804 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.05129
|
Mutlu Ahmetoglu
|
Mutlu Ahmetoglu, Orhan Tahir Yavascan, Elif Uysal
|
MiSTA: An Age-Optimized Slotted ALOHA Protocol
|
13 pages, 10 figures
| null | null | null |
cs.IT math.IT
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We introduce Mini Slotted Threshold ALOHA (MiSTA), a slotted ALOHA
modification designed to minimize the network-wide time average Age of
Information (AoI). In MiSTA, sources whose ages are below a certain threshold
stay silent. When a node with age above the threshold has data to send, it
becomes active in the next time frame with a certain probability. The active
node first transmits a short control sequence in a mini-slot ahead of actual
data transmission, and if collision is sensed, it backs off with a certain
probability. We derive the steady state distribution of the number of active
sources and analyze its limiting behaviour. We show that MiSTA
probabilistically converges to a "thinned" slotted ALOHA, where the number of
active users at steady state adjusts to optimize age. With an optimal selection
of parameters, MiSTA achieves an AoI scaling with the number of sources, n, as
0.9641n, which is an improvement over the Threshold ALOHA policy proposed
earlier (for which the lowest possible scaling is 1.4169n). While achieving
this reduction in age, MiSTA also increases achievable throughput to
approximately 53%, from the 37% achievable by Threshold ALOHA and regular
slotted ALOHA.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711437 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.05698
|
Kevin Thompson
|
Ojas Parekh and Kevin Thompson
|
Application of the Level-$2$ Quantum Lasserre Hierarchy in Quantum
Approximation Algorithms
| null |
Proceedings of the International Colloquium on Automata,
Languages, and Programming (ICALP), 2021
|
10.4230/LIPIcs.ICALP.2021.102
| null |
quant-ph cs.DS
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Lasserre Hierarchy is a set of semidefinite programs which yield
increasingly tight bounds on optimal solutions to many NP-hard optimization
problems. The hierarchy is parameterized by levels, with a higher level
corresponding to a more accurate relaxation. High level programs have proven to
be invaluable components of approximation algorithms for many NP-hard
optimization problems. There is a natural analogous quantum hierarchy, which is
also parameterized by level and provides a relaxation of many (QMA-hard)
quantum problems of interest. In contrast to the classical case, however, there
is only one approximation algorithm which makes use of higher levels of the
hierarchy. Here we provide the first ever use of the level-$2$ hierarchy in an
approximation algorithm for a particular QMA-complete problem, so-called
Quantum Max Cut. We obtain modest improvements on state-of-the-art
approximation factors for this problem, as well as demonstrate that the
level-$2$ hierarchy satisfies many physically-motivated constraints that the
level-$1$ does not satisfy. Indeed, this observation is at the heart of our
analysis and indicates that higher levels of the quantum Lasserre Hierarchy may
be very useful tools in the design of approximation algorithms for QMA-complete
problems.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711067 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.07502
|
Luis Anchordoqui
|
Luis A. Anchordoqui and Thomas J. Weiler
|
Neutrinos as a probe of the Universe
|
To be published in The Innovation Platform;
https://www.innovationnewsnetwork.com/studying-neutrinos-better-understand-universe/10867/
|
Innovation Platform 6 (2021) 67
| null | null |
physics.pop-ph astro-ph.HE hep-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A brief essay on how studying neutrinos can help us to better understand the
Universe.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711262 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.07748
|
Aloke Kumar
|
Sarath Chandra Varma, Aniruddha Saha and Aloke Kumar
|
Coalescence of polymeric sessile drops on a partially wettable substrate
| null | null |
10.1063/5.0073936
| null |
physics.flu-dyn cond-mat.soft
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Coalescence of sessile polymeric fluid drops on a partially wettable
substrate exhibits a transition from inertial to viscoelastic regime at
concentration ratio $c/c^* \sim 1$. Our findings unveil that the temporal
evolution of the growing bridge height follows a power-law behaviour $t^b$,
such that the coefficient $b$ continuously decreases from 2/3 in the inertial
regime ($c/c^*<1$) to an asymptotic value of 1/2 in the visco-elastic regime
($c/c^*>1$). To account for fluid elasticity and characteristic time-scale in
the viscoelastic regime, a modified thin film equation under lubrication
approximation has been proposed using the linear Phan-Thien- Tanner
constitutive equation. The temporal evolution of the droplet has been evaluated
by solving the modified one-dimensional thin-film equation using a marching
explicit scheme. The initial droplet shapes are obtained by re-sorting to
energy minimization. A good agreement between numerical and experimental
results is obtained.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710434 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.09226
|
Divyansh Singh
|
Divyansh Singh
|
Detection of Emotions in Hindi-English Code Mixed Text Data
| null | null | null | null |
cs.CL
|
http://creativecommons.org/licenses/by/4.0/
|
In recent times, we have seen an increased use of text chat for communication
on social networks and smartphones. This particularly involves the use of
Hindi-English code-mixed text which contains words which are not recognized in
English vocabulary. We have worked on detecting emotions in these mixed data
and classify the sentences in human emotions which are angry, fear, happy or
sad. We have used state of the art natural language processing models and
compared their performance on the dataset comprising sentences in this mixed
data. The dataset was collected and annotated from sources and then used to
train the models.
| 2021-11-16T00:00:00 |
new_dataset
| true | 0.675577 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.09457
|
Danula Hettiachchi
|
Danula Hettiachchi, Mike Schaekermann, Tristan McKinney and Matthew
Lease
|
The Challenge of Variable Effort Crowdsourcing and How Visible Gold Can
Help
|
25 pages, To appear in the Proceedings of the ACM on Human-Computer
Interaction, CSCW 2021
|
Proc. ACM Hum.-Comput. Interact., 5(CSCW2), 26 (2021)
|
10.1145/3476073
| null |
cs.HC
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
We consider a class of variable effort human annotation tasks in which the
number of labels required per item can greatly vary (e.g., finding all faces in
an image, named entities in a text, bird calls in an audio recording, etc.). In
such tasks, some items require far more effort than others to annotate.
Furthermore, the per-item annotation effort is not known until after each item
is annotated since determining the number of labels required is an implicit
part of the annotation task itself. On an image bounding-box task with
crowdsourced annotators, we show that annotator accuracy and recall
consistently drop as effort increases. We hypothesize reasons for this drop and
investigate a set of approaches to counteract it. Firstly, we benchmark on this
task a set of general best-practice methods for quality crowdsourcing. Notably,
only one of these methods actually improves quality: the use of visible gold
questions that provide periodic feedback to workers on their accuracy as they
work. Given these promising results, we then investigate and evaluate variants
of the visible gold approach, yielding further improvement. Final results show
a 7% improvement in bounding-box accuracy over the baseline. We discuss the
generality of the visible gold approach and promising directions for future
research.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70978 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.10215
|
Jaime Agudo-Canalejo
|
Jaime Agudo-Canalejo, Tunrayo Adeleke-Larodo, Pierre Illien, and Ramin
Golestanian
|
Synchronization and enhanced catalysis of mechanically coupled enzymes
| null |
Phys. Rev. Lett. 127, 208103 (2021)
|
10.1103/PhysRevLett.127.208103
| null |
cond-mat.stat-mech nlin.AO physics.bio-ph
|
http://creativecommons.org/licenses/by-nc-nd/4.0/
|
We examine the stochastic dynamics of two enzymes that are mechanically
coupled to each other, e.g., through an elastic substrate or a fluid medium.
The enzymes undergo conformational changes during their catalytic cycle, which
itself is driven by stochastic steps along a biased chemical free energy
landscape. We find conditions under which the enzymes can synchronize their
catalytic steps, and discover that the coupling can lead to a significant
enhancement in their overall catalytic rate. Both effects can be understood as
arising from a global bifurcation in the underlying dynamical system at
sufficiently strong coupling. Our findings suggest that, despite their
molecular scale, enzymes can be cooperative and improve their performance in
metabolic clusters.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711638 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.10693
|
David Paganin
|
Konstantin M. Pavlov, David M. Paganin, Kaye S. Morgan, Heyang
(Thomas) Li, Sebastien Berujon, Laur\`ene Qu\'enot and Emmanuel Brun
|
Directional dark-field implicit x-ray speckle tracking using an
anisotropic-diffusion Fokker-Planck equation
|
To be published in Physical Review A; accepted version with minor
revisions compared to the previous version
|
Phys. Rev. A 104, 053505 (2021)
|
10.1103/PhysRevA.104.053505
| null |
physics.med-ph physics.app-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
When a macroscopic-sized non-crystalline sample is illuminated using coherent
x-ray radiation, a bifurcation of photon energy flow may occur. The
coarse-grained complex refractive index of the sample may be considered to
attenuate and refract the incident coherent beam, leading to a coherent
component of the transmitted beam. Spatially-unresolved sample microstructure,
associated with the fine-grained components of the complex refractive index,
introduces a diffuse component to the transmitted beam. This diffuse
photon-scattering channel may be viewed in terms of position-dependent fans of
ultra-small-angle x-ray scatter. These position-dependent fans, at the exit
surface of the object, may under certain circumstances be approximated as
having a locally-elliptical shape. By using an anisotropic-diffusion
Fokker-Planck approach to model this bifurcated x-ray energy flow, we show how
all three components (attenuation, refraction and locally-elliptical diffuse
scatter) may be recovered. This is done via x-ray speckle tracking, in which
the sample is illuminated with spatially-random x-ray fields generated by
coherent illumination of a spatially-random membrane. The theory is developed,
and then successfully applied to experimental x-ray data.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.711205 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.11909
|
Panagiotis Kourtesis
|
Panagiotis Kourtesis and Sarah E. MacPherson
|
Immersive virtual reality methods in cognitive neuroscience and
neuropsychology: Meeting the criteria of the National Academy of
Neuropsychology and American Academy of Clinical Neuropsychology
|
30 Pages, 2 Tables, 4 Figures, under Review
| null |
10.1016/j.chbr.2021.100151
| null |
cs.HC cs.CY
|
http://creativecommons.org/licenses/by/4.0/
|
Clinical tools involving immersive virtual reality (VR) may bring several
advantages to cognitive neuroscience and neuropsychology. However, there are
some technical and methodological pitfalls. The American Academy of Clinical
Neuropsychology (AACN) and the National Academy of Neuropsychology (NAN) raised
8 key issues pertaining to Computerized Neuropsychological Assessment Devices.
These issues pertain to: (1) the safety and effectivity; (2) the identity of
the end-user; (3) the technical hardware and software features; (4) privacy and
data security; (5) the psychometric properties; (6) examinee issues; (7) the
use of reporting services; and (8) the reliability of the responses and
results. The VR Everyday Assessment Lab (VR-EAL) is the first immersive VR
neuropsychological battery with enhanced ecological validity for the assessment
of everyday cognitive functions by offering a pleasant testing experience
without inducing cybersickness. The VR-EAL meets the criteria of the NAN and
AACN, addresses the methodological pitfalls, and brings advantages for
neuropsychological testing. However, there are still shortcomings of the
VR-EAL, which should be addressed. Future iterations should strive to improve
the embodiment illusion in VR-EAL and the creation of an open access VR
software library should be attempted. The discussed studies demonstrate the
utility of VR methods in cognitive neuroscience and neuropsychology.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710051 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.12643
|
Vitaly Wirthl
|
Vitaly Wirthl, Cristian D. Panda, Paul W. Hess, Gerald Gabrielse
|
Simple Self-calibrating Polarimeter for Measuring the Stokes Parameters
of Light
| null |
OSA Continuum 4, 2949-2969 (2021)
|
10.1364/OSAC.444102
| null |
physics.optics physics.ins-det
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
A simple, self-calibrating, rotating-waveplate polarimeter is largely
insensitive to light intensity fluctuations and is shown to be useful for
determining the Stokes parameters of light. This study shows how to minimize
the in situ self-calibration time, the measurement time and the measurement
uncertainty. The suggested methods are applied to measurements of spatial
variations in the linear and circular polarizations of laser light passing
through glass plates with a laser intensity dependent birefringence. These are
crucial measurements for the ACME electron electric dipole measurements,
requiring accuracies in circular and linear polarization fraction of about 0.1%
and 0.4%, with laser intensities up to 100 $\text{mW/mm}^2$ incident into the
polarimeter.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.70978 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.14718
|
Hang-Hyun Jo
|
Hang-Hyun Jo
|
Numerical study on the deadline-concerning priority queuing model
|
5 pages, 4 figures; to appear in Journal of the Korean Physical
Society
|
Journal of the Korean Physical Society 79, 407-411 (2021)
|
10.1007/s40042-021-00219-7
| null |
physics.soc-ph
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
The Barab\'asi's priority queuing model [A.-L. Barab\'asi, Nature
\textbf{435}, 207 (2005)] and its variants have been extensively studied to
understand heavy-tailed distributions of the inter-event times and the response
times observed in various empirical analyses of human dynamics. In this paper,
we focus on the effects of deadlines assigned to the tasks in a queue of fixed
size on the response-time distributions. Here, the response time is defined as
the time interval between the arrival and the execution of the task. We propose
a deadline-concerning priority queuing model, in which as the deadline
approaches, the priority is adjusted using the inverse of the remaining time to
the deadline. By performing the numerical simulations, we find that the
power-law exponent characterizing the response-time distributions is less than
$1$ under the deterministic selection protocol while it has the value of $1$
under the nondeterministic selection protocol.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710069 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2105.14980
|
Xin Zhang
|
Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Pengjun Xie
|
Crowdsourcing Learning as Domain Adaptation: A Case Study on Named
Entity Recognition
|
ACL-IJCNLP 2021 main conf, long paper; corrected the wrong reference
for "argument retrieval" in first paragraph of Introduction
| null |
10.18653/v1/2021.acl-long.432
| null |
cs.CL cs.HC cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Crowdsourcing is regarded as one prospective solution for effective
supervised learning, aiming to build large-scale annotated training data by
crowd workers. Previous studies focus on reducing the influences from the
noises of the crowdsourced annotations for supervised models. We take a
different point in this work, regarding all crowdsourced annotations as
gold-standard with respect to the individual annotators. In this way, we find
that crowdsourcing could be highly similar to domain adaptation, and then the
recent advances of cross-domain methods can be almost directly applied to
crowdsourcing. Here we take named entity recognition (NER) as a study case,
suggesting an annotator-aware representation learning model that inspired by
the domain adaptation methods which attempt to capture effective domain-aware
features. We investigate both unsupervised and supervised crowdsourcing
learning, assuming that no or only small-scale expert annotations are
available. Experimental results on a benchmark crowdsourced NER dataset show
that our method is highly effective, leading to a new state-of-the-art
performance. In addition, under the supervised setting, we can achieve
impressive performance gains with only a very small scale of expert
annotations.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.709239 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.00801
|
Ver\'onica Becher
|
Ver\'onica Becher
|
Insertion in constructed normal numbers
| null | null | null | null |
math.NT cs.DM math.CO
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Defined by Borel, a real number is normal to an integer base $b$, greater
than or equal to $2$, if in its base-$b$ expansion every block of digits occurs
with the same limiting frequency as every other block of the same length. We
consider the problem of insertion in constructed base-$b$ normal expansions to
obtain normality to base $(b+1)$.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.710427 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.03034
|
Qi Deng
|
Qi Deng and Wenzhi Gao
|
Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex
Optimization
|
39 pages, 9 figures
| null | null | null |
math.OC cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Stochastic model-based methods have received increasing attention lately due
to their appealing robustness to the stepsize selection and provable efficiency
guarantee. We make two important extensions for improving model-based methods
on stochastic weakly convex optimization. First, we propose new minibatch
model-based methods by involving a set of samples to approximate the model
function in each iteration. For the first time, we show that stochastic
algorithms achieve linear speedup over the batch size even for non-smooth and
non-convex (particularly, weakly convex) problems. To this end, we develop a
novel sensitivity analysis of the proximal mapping involved in each algorithm
iteration. Our analysis appears to be of independent interests in more general
settings. Second, motivated by the success of momentum stochastic gradient
descent, we propose a new stochastic extrapolated model-based method, greatly
extending the classic Polyak momentum technique to a wider class of stochastic
algorithms for weakly convex optimization. The rate of convergence to some
natural stationarity condition is established over a fairly flexible range of
extrapolation terms.
While mainly focusing on weakly convex optimization, we also extend our work
to convex optimization. We apply the minibatch and extrapolated model-based
methods to stochastic convex optimization, for which we provide a new
complexity bound and promising linear speedup in batch size. Moreover, an
accelerated model-based method based on Nesterov's momentum is presented, for
which we establish an optimal complexity bound for reaching optimality.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.708603 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
2106.03746
|
Marco De Nadai
|
Yahui Liu, Enver Sangineto, Wei Bi, Nicu Sebe, Bruno Lepri and Marco
De Nadai
|
Efficient Training of Visual Transformers with Small Datasets
| null |
Proceedings of the 35th Conference on Neural Information
Processing Systems (NeurIPS) 2021
| null | null |
cs.CV cs.LG
|
http://arxiv.org/licenses/nonexclusive-distrib/1.0/
|
Visual Transformers (VTs) are emerging as an architectural paradigm
alternative to Convolutional networks (CNNs). Differently from CNNs, VTs can
capture global relations between image elements and they potentially have a
larger representation capacity. However, the lack of the typical convolutional
inductive bias makes these models more data-hungry than common CNNs. In fact,
some local properties of the visual domain which are embedded in the CNN
architectural design, in VTs should be learned from samples. In this paper, we
empirically analyse different VTs, comparing their robustness in a small
training-set regime, and we show that, despite having a comparable accuracy
when trained on ImageNet, their performance on smaller datasets can be largely
different. Moreover, we propose a self-supervised task which can extract
additional information from images with only a negligible computational
overhead. This task encourages the VTs to learn spatial relations within an
image and makes the VT training much more robust when training data are scarce.
Our task is used jointly with the standard (supervised) training and it does
not depend on specific architectural choices, thus it can be easily plugged in
the existing VTs. Using an extensive evaluation with different VTs and
datasets, we show that our method can improve (sometimes dramatically) the
final accuracy of the VTs. Our code is available at:
https://github.com/yhlleo/VTs-Drloc.
| 2021-11-16T00:00:00 |
no_new_dataset
| false | 0.71247 |
2025-08-19T16:18:03.910982
|
davanstrien/ModernBERT-base-is-new-arxiv-dataset
|
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