June 2020 arXiv papers — page 41

Feng Zhou, Yixuan Zhang, Jun Zhu

Hawkes process provides an effective statistical framework for analyzing the time-dependent interaction of neuronal spiking activities. Although utilized in many real applications, the classic Hawkes process is incapable of modelling inhibitory interactions among neurons. Instead, the nonlinear Hawkes process allows for a more flexible influence pattern with

David Fajman, Gernot Heißel, Jin Woo Jang

Motivated by recent problems in mathematical cosmology, in which temporal averaging methods are applied in order to analyze the future asymptotics of models which exhibit oscillatory behavior, we provide a theorem concerning the large-time behavior for solutions of a general class of systems. We thus propose our result to be applicable to a wide range of pro

Louis Filstroff, Olivier Gouvert, Cédric Févotte, Olivier Cappé

Non-negative matrix factorization (NMF) has become a well-established class of methods for the analysis of non-negative data. In particular, a lot of effort has been devoted to probabilistic NMF, namely estimation or inference tasks in probabilistic models describing the data, based for example on Poisson or exponential likelihoods. When dealing with time se

Haotian Liu, Wenchuan Wu

The distributed Volt/Var control (VVC) methods have been widely studied for active distribution networks(ADNs), which is based on perfect model and real-time P2P communication. However, the model is always incomplete with significant parameter errors and such P2P communication system is hard to maintain. In this paper, we propose an online multi-agent reinfo

Pascal Bianchi, Kevin Elgui, François Portier

This paper introduces the \textit{weighted partial copula} function for testing conditional independence. The proposed test procedure results from these two ingredients: (i) the test statistic is an explicit Cramer-von Mises transformation of the \textit{weighted partial copula}, (ii) the regions of rejection are computed using a bootstrap procedure which mi

Xingwen Zheng, Minglei Xiong, Junzheng Zheng, Manyi Wang

Dynamic modeling has been capturing attention for its fundamentality in precise locomotion analyses and control of underwater robots. However, the existing researches have mainly focused on investigating two-dimensional motion of underwater robots, and little attention has been paid to three-dimensional dynamic modeling, which is just what we focus on. In th

Moritz Lehmann, Stephan Gekle

The plane-cube intersection problem has been around in literature since 1984 and iterative solutions to it have been used as part of piecewise linear interface construction (PLIC) in computational fluid dynamics simulation codes ever since. In many cases, PLIC is the bottleneck of these simulations regarding compute time, so a faster, analytic solution to th

Roberto Molinari, Gaetan Bakalli, Stéphane Guerrier, Cesare Miglioli

The majority of machine learning methods and algorithms give high priority to prediction performance which may not always correspond to the priority of the users. In many cases, practitioners and researchers in different fields, going from engineering to genetics, require interpretability and replicability of the results especially in settings where, for exa

R. L. Bailey, M. A. Reiss, C. N. Arge, C. Möstl

Studying the ambient solar wind, a continuous pressure-driven plasma flow emanating from our Sun, is an important component of space weather research. The ambient solar wind flows in interplanetary space determine how solar storms evolve through the heliosphere before reaching Earth, and especially during solar minimum are themselves a driver of activity in

Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion

We propose a versatile framework based on random search, Sparse-RS, for score-based sparse targeted and untargeted attacks in the black-box setting. Sparse-RS does not rely on substitute models and achieves state-of-the-art success rate and query efficiency for multiple sparse attack models: $l_0$-bounded perturbations, adversarial patches, and adversarial f

Andrzej Zdziarski, Barbara De Marco, Michal Szanecki, Andrzej Niedzwiecki

The accreting black-hole binary XTE J1752--223 was observed in a stable hard state for 25 d by RXTE, yielding a 3--140 keV spectrum of unprecedented statistical quality. Its published model required a single Comptonization spectrum reflecting from a disk close to the innermost stable circular orbit. We studied that model as well as a number of other single-C

Igor Yatsuta, Boris Malomed, Alexander Yakimenko

We propose emulation of Hawking radiation (HR) by means of acoustic excitations propagating on top of persistent current in an atomic Bose-Einstein condensate (BEC) loaded in an annular confining potential. The setting is initially created as a spatially uniform one, and then switches into a nonuniform configuration, while maintaining uniform BEC density. Th

Martti Pekkanen

There are currently two main, continuum models of entropy: a 'reversible', Clausius entropy model and an 'irreversible', Onsager-Prigogine entropy model. It is shown that the equations of the 'reversible' and the 'irreversible' entropy models are equivalent with respect to entropy accumulation, which entails same values of ent

G. P. de Brito, M. G. Campos, L. P. R. Ospedal, K. P. B. Veiga

We investigate spin- and velocity-dependent contributions to the gravitational inter-particle potential. The methodology adopted here is based on the expansion of the effective action in terms of form factors encoding quantum corrections. Restricting ourselves to corrections up to the level of the graviton propagator, we compute, in terms of general form fac

Michiel Min, Chris W. Ormel, Katy Chubb, Christiane Helling

Aims: ARCiS, a novel code for the analysis of exoplanet transmission and emission spectra is presented. The aim of the modelling framework is to provide a tool able to link observations to physical models of exoplanet atmospheres. Methods: The modelling philosophy chosen in this paper is to use physical and chemical models to constrain certain parameters whi

Zhaokang Wang, Weiwei Hu, Chunfeng Yuan, Rong Gu

Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as network motif mining and fraud detection. The state-of-the-art distributed methods are inefficient in communication. They have to shuffle partial matching results during the distri

Mohammed Eunus Ali, Shadman Saqib Eusuf, Kazi Ashik Islam

In this paper, we study a novel contact tracing query (CTQ) that finds users who have been in $direct$ $contact$ with the query user or $in$ $contact$ $with$ $the$ $already$ $contacted$ $users$ in subsequent timestamps from a large spatio-temporal database. The CTQ is of paramount importance in the era of new COVID-19 pandemic world for finding possible list

Riccardo Ben Alì Zinati, Omar Zanusso

We discuss how a spin system, which is subject to quenched disorder, might exhibit multicritical behaviors at criticality if the distribution of the impurities is arbitrary. In order to provide realistic candidates for such multicritical behaviors, we discuss several generalizations of the standard randomly diluted Ising's universality class adopting the

Amir Rahimi, Thomas Mensink, Kartik Gupta, Thalaiyasingam Ajanthan

Calibration of neural networks is a critical aspect to consider when incorporating machine learning models in real-world decision-making systems where the confidence of decisions are equally important as the decisions themselves. In recent years, there is a surge of research on neural network calibration and the majority of the works can be categorized into

Zeeshan Ahmed, Atul Bhargav

Nanofluids are known to have significantly different thermal properties relative to the corresponding conventional fluids. Heat transfer at the solid-fluid interface affects the thermal properties of nanofluids. The current work helps in understanding the role of two nanoscale phenomena, namely ordering of fluid layer around the nanoparticle (nanolayer) and

Soaad Hossain, Syed Ishtiaque Ahmed

With the shortage of physicians and surgeons and increase in demand worldwide due to situations such as the COVID-19 pandemic, there is a growing interest in finding solutions to help address the problem. A solution to this problem would be to use neurotechnology to provide them augmented cognition, senses and action for optimal diagnosis and treatment. Cons

Ryan Marcus, Andreas Kipf, Alexander van Renen, Mihail Stoian

Recent advancements in learned index structures propose replacing existing index structures, like B-Trees, with approximate learned models. In this work, we present a unified benchmark that compares well-tuned implementations of three learned index structures against several state-of-the-art "traditional" baselines. Using four real-world datasets, we

Liudmila A. Zhukas, Peter Svihra, Andrei Nomerotski, Boris B. Blinov

Qubit state detection is an important part of a quantum computation. As number of qubits in a quantum register increases, it is necessary to maintain high fidelity detection to accurately measure the multi-qubit state. Here we present experimental demonstration of high-fidelity detection of a multi-qubit trapped ion register with average single qubit detecti

Kartik Gupta, Amir Rahimi, Thalaiyasingam Ajanthan, Thomas Mensink

Calibrating neural networks is of utmost importance when employing them in safety-critical applications where the downstream decision making depends on the predicted probabilities. Measuring calibration error amounts to comparing two empirical distributions. In this work, we introduce a binning-free calibration measure inspired by the classical Kolmogorov-Sm

Zhelun Shen, Yuchao Dai, Xibin Song, Zhibo Rao

Existing deep learning based stereo matching methods either focus on achieving optimal performances on the target dataset while with poor generalization for other datasets or focus on handling the cross-domain generalization by suppressing the domain sensitive features which results in a significant sacrifice on the performance. To tackle these problems, we

A. Singh, H. Y. Huang, Y. Y. Chu, C. Y. Hua

We report on the development of a high-resolution and highly efficient beamline for soft-X-ray resonant inelastic X-ray scattering (RIXS) located at Taiwan Photon Source. This beamline adopts an optical design that uses an active grating monochromator (AGM) and an active grating spectrometer (AGS) to implement the energy compensation principle of grating dis

Jinghui Chen, Quanquan Gu

Deep neural networks are vulnerable to adversarial attacks. Among different attack settings, the most challenging yet the most practical one is the hard-label setting where the attacker only has access to the hard-label output (prediction label) of the target model. Previous attempts are neither effective enough in terms of attack success rate nor efficient

Christoph Benzmüller, David Fuenmayor, Bertram Lomfeld

The logico-pluralist LogiKEy knowledge engineering methodology and framework is applied to the modelling of a theory of legal balancing in which legal knowledge (cases and laws) is encoded by utilising context-dependent value preferences. The theory obtained is then used to formalise, automatically evaluate, and reconstruct illustrative property law cases (i

Xin Liu, Hsiang-nan Li, Zhen-Jun Xiao

We derive the $k_T$ resummation for a transverse-momentum-dependent charmonium wave function, which involves the bottom quark mass $m_b$, the charm quark mass $m_c$, and the charm quark transverse momentum $k_T$, up to the next-to-leading-logarithm (NLL) accuracy under the hierarchy $m_b\gg m_c \gg k_T$. The resultant Sudakov factor is employed in the perutr

Alexander Ostermann, Frédéric Rousset, Katharina Schratz

In this paper, we propose a new scheme for the integration of the periodic nonlinear Schrödinger equation and rigorously prove convergence rates at low regularity. The new integrator has decisive advantages over standard schemes at low regularity. In particular, it is able to handle initial data in $H^s$ for $0 < s\le 1$. The key feature of the integrator is

Yuankun Zhu, Yueqiang Cheng, Husheng Zhou, Yantao Lu

Deep Neural Networks (DNNs) models become one of the most valuable enterprise assets due to their critical roles in all aspects of applications. With the trend of privatization deployment of DNN models, the data leakage of the DNN models is becoming increasingly serious and widespread. All existing model-extraction attacks can only leak parts of targeted DNN

Markus Garbiso, Takaaki Ishii, Keiju Murata

The AdS soliton is a nonsingular spacetime that has a flat conformal boundary with a compact $S^1$ direction. We find a horizonless cohomogeneity-1 metric that describes nonlinear gravitational oscillations of the AdS soliton in five dimensions. We call this spacetime the resonating AdS soliton. This solution is obtained as the nonlinear extension of normal

Himadri Shekhar Chakraborty, Tsuyoshi Miezaki

In this paper, we give a representation of the average of complete joint weight enumerators of two linear codes of length $n$ over $\mathbb{F}_{q}$ and $\mathbb{Z}_{k}$ in terms of the compositions of $n$ and their distributions in the codes. We also obtain a generalization of the representation for the average of $g$-fold complete joint weight enumerators o

Lucien Hennecart

We prove that for extended Dynkin quivers, simple perverse sheaves in Lusztig category are characterized by the nilpotency of their singular support. This proves a conjecture of Lusztig in the case of affine quivers. For cyclic quivers, we prove a similar result for a larger nilpotent variety and a larger class of perverse sheaves. We formulate conjectures f

Francesco Cicala, Luca Bortolussi

The effectiveness and performance of artificial neural networks, particularly for visual tasks, depends in crucial ways on the receptive field of neurons. The receptive field itself depends on the interplay between several architectural aspects, including sparsity, pooling, and activation functions. In recent literature there are several ad hoc proposals try

A. Tuan Nguyen, Hyewon Jeong, Eunho Yang, Sung Ju Hwang

Although recent multi-task learning methods have shown to be effective in improving the generalization of deep neural networks, they should be used with caution for safety-critical applications, such as clinical risk prediction. This is because even if they achieve improved task-average performance, they may still yield degraded performance on individual tas

Tanmoy Pal, Jonas Wessén, Suman Das, Hue Sun Chan

Polyampholyte field theory and explicit-chain molecular dynamics models of sequence-specific phase separation of a system with two intrinsically disordered protein (IDP) species indicate consistently that a substantial polymer excluded volume and a significant mismatch of the IDP sequence charge patterns can act in concert, but not in isolation, to demix the

Yanan Wang, Shengcai Liao, Ling Shao

Person re-identification has seen significant advancement in recent years. However, the ability of learned models to generalize to unknown target domains still remains limited. One possible reason for this is the lack of large-scale and diverse source training data, since manually labeling such a dataset is very expensive and privacy sensitive. To address th

Anh Viet Do, Frank Neumann

Many important problems can be regarded as maximizing submodular functions under some constraints. A simple multi-objective evolutionary algorithm called GSEMO has been shown to achieve good approximation for submodular functions efficiently. While there have been many studies on the subject, most of existing run-time analyses for GSEMO assume a single cardi

Faisal Etminan

We investigate the three-baryon system with charm +1 in a simple model. Charmed lambda $Λ_{c}$ is assumed to orbit an unperturbed deuteron in a charmed lambda-deuteron potential based on a separable charmed lambda-nucleon potential from chiral effective field theory techniques extrapolations of lattice QCD data for the $Λ_{c}N$ $^{1}S_{0}$ and $^{3}S_{1}$ pa

Jing Wang, Jiahong Chen, Jianzhe Lin, Leonid Sigal

In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize well to an unlabeled data domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment. Previous work has attempted to directly align la

Aleš Černý, Johannes Ruf

The paper develops multiplicative compensation for complex-valued semimartingales and studies some of its consequences. It is shown that the stochastic exponential of any complex-valued semimartingale with independent increments becomes a true martingale after multiplicative compensation when such compensation is meaningful. This generalization of the Lévy--

Diyi Cheng, Thomas Andrew Wynn, Xuefeng Wang, Shen Wang

The solid electrolyte interphase (SEI) is regarded as the most complex but the least understood constituent in secondary batteries using liquid and solid electrolytes. The nanostructures of SEIs were recently reported to be equally important to the chemistry of SEIs for stabilizing Li metal in liquid electrolyte. However, the dearth of such knowledge in all-

Hidenori Ogata

In this paper, we propose a method of fundamental solutions for the problem of two-dimensional potential flow in a doubly-periodic domain. The solution involves a doubly-periodic function, to which it is difficult to give an approximation by the conventional method of fundamental solutions. We propose to approximate it by a linear combination of the periodic

Balthazar Paixão, Lais Baroni, Rebecca Salles, Luciana Escobar

Due to its impact, COVID-19 has been stressing the academy to search for curing, mitigating, or controlling it. However, when it comes to controlling, there are still few studies focused on under-reporting estimates. It is believed that under-reporting is a relevant factor in determining the actual mortality rate and, if not considered, can cause significant

Takashi Kodama, Nobuhiro Shinohara, Shih-Wei Hung, Xu Bin

Since solid-state heat transport in a highly porous nanocomposite strongly depends on the thermal boundary conductance (TBC) between constituent nanomaterials, further suppression of the TBC is important for improving performance of thermal insulators. Here, targeting a nanocomposite fabricated by stamping fumed silica nanoparticles, we perform a wide variet

Zacharias Roupas

Recent theoretical and numerical developments supported by observational evidence strongly suggest that many globular clusters host a black hole (BH) population in their centers. This stands in contrast to the prior long-standing belief that a BH subcluster would evaporate after undergoing core collapse and decoupling from the cluster. In this work, we propo

C. S Lopez-Monsalvo, F. Nettel, V. Pineda-Reyes, L. F. Escamilla-Herrera

In this work we show that a Legendre transformation is nothing but a mere change of contact polarization from the point of view of contact geometry. Then, we construct a set of Riemannian and pseudo-Riemannian metrics on a contact manifold by introducing almost contact and para-contact structures and we analyze their isometries. We show that it is not possib

Yixin Wang, Yao Zhang, Yang Liu, Jiang Tian

Coronavirus disease 2019 (COVID-19) is a highly contagious virus spreading all around the world. Deep learning has been adopted as an effective technique to aid COVID-19 detection and segmentation from computed tomography (CT) images. The major challenge lies in the inadequate public COVID-19 datasets. Recently, transfer learning has become a widely used tec

Zhiqiang Wang, Qingyun She, PengTao Zhang, Junlin Zhang

Normalization has become one of the most fundamental components in many deep neural networks for machine learning tasks while deep neural network has also been widely used in CTR estimation field. Among most of the proposed deep neural network models, few model utilize normalization approaches. Though some works such as Deep & Cross Network (DCN) and Neural

Tong Han, Yue Song, David J. Hill

Network connectedness is indispensable for the normal operation of transmission networks. However, there still remains a lack of efficient constraints that can be directly added to the problem formulation of optimal transmission switching (OTS) to ensure network connectedness strictly. To fill this gap, this paper proposes a set of linear connectedness const

Agniva Chowdhury, Petros Drineas, David P. Woodruff, Samson Zhou

Principal component analysis (PCA) is a widely used dimension reduction technique in machine learning and multivariate statistics. To improve the interpretability of PCA, various approaches to obtain sparse principal direction loadings have been proposed, which are termed Sparse Principal Component Analysis (SPCA). In this paper, we present thresholding as a

Shuqi Yang, Xingzhe He, Bo Zhu

We propose a new family of neural networks to predict the behaviors of physical systems by learning their underpinning constraints. A neural projection operator lies at the heart of our approach, composed of a lightweight network with an embedded recursive architecture that interactively enforces learned underpinning constraints and predicts the various gove

Kaize Ding, Jianling Wang, Jundong Li, Kai Shu

Attributed networks nowadays are ubiquitous in a myriad of high-impact applications, such as social network analysis, financial fraud detection, and drug discovery. As a central analytical task on attributed networks, node classification has received much attention in the research community. In real-world attributed networks, a large portion of node classes

Li-Juan Liu, Zhi-Hui Li

Quantum key agreement requires all participants to recover the shared key together, so it is crucial to resist the participant attack. In this paper, we propose a verifiable multi-party quantum key agreement protocol based on the six-qubit cluster states. A verifiable distributor who preserves some subsequences of the six-qubit cluster states is introduced i

Gaurush Hiranandani, Harikrishna Narasimhan, Oluwasanmi Koyejo

What is a fair performance metric? We consider the choice of fairness metrics through the lens of metric elicitation -- a principled framework for selecting performance metrics that best reflect implicit preferences. The use of metric elicitation enables a practitioner to tune the performance and fairness metrics to the task, context, and population at hand.

Masaru Ikehata

This is a review article on the development of the probe and enclosure methods from past to present, focused on their central ideas together with various applications.

Xiangqiang Gao, Rongke liu, Aryan Kaushik

As the increase in satellite number and variety, satellite ground stations should be required to offer user services in a flexible and efficient manner. Network function virtualization (NFV) can provide a new paradigm to allocate network resources on-demand for user services over the underlying network. However, most of the existing work focuses on the virtu

Philippe Goulet Coulombe

I develop Macroeconomic Random Forest (MRF), an algorithm adapting the canonical Machine Learning (ML) tool to flexibly model evolving parameters in a linear macro equation. Its main output, Generalized Time-Varying Parameters (GTVPs), is a versatile device nesting many popular nonlinearities (threshold/switching, smooth transition, structural breaks/change)

Indranil Biswas, Jyoti Dasgupta, Krishna Hanumanthu, Bivas Khan

For a positive integer $n$, let $X_n \to X_{n-1} \to \ldots \to X_2 \to X_1 \to X_0$ be a Bott tower of height $n$, and let $L$ be a nef line bundle on $X_n$. We compute Seshadri constants $\varepsilon(X_n,L,x)$ of $L$ at any point $x \in X_n$ under some conditions.

Kristi Bushman, Konstantinos Pelechrinis, Alexandros Labrinidis

In the absence of pharmaceutical interventions to curb the spread of COVID-19, countries relied on a number of nonpharmaceutical interventions to fight the first wave of the pandemic. The most prevalent one has been stay-at-home orders, whose the goal is to limit the physical contact between people, which consequently will reduce the number of secondary infe

Ratan Othayoth, George Thoms, Chen Li

Effective locomotion in nature happens by transitioning across multiple modes (e.g., walk, run, climb). Despite this, far more mechanistic understanding of terrestrial locomotion has been on how to generate and stabilize around near-steady-state movement in a single mode. We still know little about how locomotor transitions emerge from physical interaction w

Anirvan Shukla, Amaresh Datta, Philip von Doetinchem, Diego-Mauricio Gomez-Coral

The possibility of antihelium production in interaction of cosmic rays with the interstellar gas is studied using large-scale Monte Carlo simulations. For this purpose, an energy-dependent coalescence mechanism developed previously is extended to estimate the production of light antinuclei (${}^3\overline{\text{He}}$ and ${}^4\overline{\text{He}}$). The unce

John Pardon

We construct the stable (representable) homotopy category of finite orbispectra, whose objects are formal desuspensions of finite orbi-CW-pairs by vector bundles and whose morphisms are stable homotopy classes of (representable) relative maps. The stable representable homotopy category of finite orbispectra admits a contravariant involution extending Spanier

Scott Wisdom, Efthymios Tzinis, Hakan Erdogan, Ron J. Weiss

In recent years, rapid progress has been made on the problem of single-channel sound separation using supervised training of deep neural networks. In such supervised approaches, a model is trained to predict the component sources from synthetic mixtures created by adding up isolated ground-truth sources. Reliance on this synthetic training data is problemati

Hariom Jani, Jheng-Cyuan Lin, Jiahao Chen, Jack Harrison

In the quest for post-CMOS technologies, ferromagnetic skyrmions and their anti-particles have shown great promise as topologically protected solitonic information carriers in memory-in-logic or neuromorphic devices. However, the presence of dipolar fields in ferromagnets, restricting the formation of ultra-small topological textures, and the deleterious sky

Saichethan Miriyala Reddy, Saisree Miriyala

Commercial companies that collect user data on a large scale have been the main beneficiaries of this trend since the success of deep learning techniques is directly proportional to the amount of data available for training. Massive data collection required for deep learning presents obvious privacy issues. Users personal, highly sensitive data such as photo

Hongyan Gu, Jingbin Huang, Lauren Hung, Xiang &#39;Anthony&#39; Chen

Despite the promises of data-driven artificial intelligence (AI), little is known about how we can bridge the gulf between traditional physician-driven diagnosis and a plausible future of medicine automated by AI. Specifically, how can we involve AI usefully in physicians&#39; diagnosis workflow given that most AI is still nascent and error-prone (e.g., in d

Orlando Romero, Subhro Das, Pin-Yu Chen, Sérgio Pequito

Out of the recent advances in systems and control (S\&C)-based analysis of optimization algorithms, not enough work has been specifically dedicated to machine learning (ML) algorithms and its applications. This paper addresses this gap by illustrating how (discrete-time) Lyapunov stability theory can serve as a powerful tool to aid, or even lead, in the anal

Grant Jensen

Given q non-attacking riders with r moves, the number of combinatorial types has not been found for r greater than 2 and q greater than 3. This paper aims to create upper and lower bound functions which can be applied to any q and r, regardless of size.

Zurab Janelidze, Ineke van der Berg

In this paper, we give an axiomatization of the ordinal number system, in the style of Dedekind&#39;s axiomatization of the natural number system. The latter is based on a structure $(N,0,s)$ consisting of a set $N$, a distinguished element $0\in N$ and a function $s\colon N\to N$. The structure in our axiomatization is a triple $(O,L,s)$, where $O$ is a cla

Arnab Sarker, Peter Fisher, Joseph E. Gaudio, Anuradha M. Annaswamy

Analysis and synthesis of safety-critical autonomous systems are carried out using models which are often dynamic. Two central features of these dynamic systems are parameters and unmodeled dynamics. This paper addresses the use of a spectral lines-based approach for estimating parameters of the dynamic model of an autonomous system. Existing literature has

Nelson Vadori, Sumitra Ganesh, Prashant Reddy, Manuela Veloso

We introduce a novel framework to account for sensitivity to rewards uncertainty in sequential decision-making problems. While risk-sensitive formulations for Markov decision processes studied so far focus on the distribution of the cumulative reward as a whole, we aim at learning policies sensitive to the uncertain/stochastic nature of the rewards, which ha

Andrew Obus, Tony Shaska

Let $\mathcal{C}$ be a smooth, projective, genus $g\geq 2$ curve, defined over $\mathbb{C}$. Then $\mathcal{C}$ has \emph{many automorphisms} if its corresponding moduli point $p \in \mathcal{M}_g$ has a neighborhood $U$ in the complex topology, such that all curves corresponding to points in $U \setminus \{p \}$ have strictly fewer automorphisms than $\math

Hongyan Gu, Yuan Liang, Yifan Xu, Christopher Kazu Williams

Recent developments in AI have provided assisting tools to support pathologists&#39; diagnoses. However, it remains challenging to incorporate such tools into pathologists&#39; practice; one main concern is AI&#39;s insufficient workflow integration with medical decisions. We observed pathologists&#39; examination and discovered that the main hindering facto

Viraj Mehta, Ian Char, Willie Neiswanger, Youngseog Chung

We introduce Neural Dynamical Systems (NDS), a method of learning dynamical models in various gray-box settings which incorporates prior knowledge in the form of systems of ordinary differential equations. NDS uses neural networks to estimate free parameters of the system, predicts residual terms, and numerically integrates over time to predict future states

Minguk Kang, Jaesik Park

Conditional image generation is the task of generating diverse images using class label information. Although many conditional Generative Adversarial Networks (GAN) have shown realistic results, such methods consider pairwise relations between the embedding of an image and the embedding of the corresponding label (data-to-class relations) as the conditioning

Tianhong Wang, Vladimir Khudik, Gennady Shvets

Propagation distances of intense laser pulses and high-charge electron beams through the plasma are, respectively, limited by diffraction and self-deceleration. This imposes severe constraints on the performance of the two major advanced accelerator concepts: laser and plasma wakefield accelerators. Using numerical simulations, we demonstrate that when the t

Matthias J. Ehrhardt, Lindon Roberts

Variational regularization techniques are dominant in the field of mathematical imaging. A drawback of these techniques is that they are dependent on a number of parameters which have to be set by the user. A by now common strategy to resolve this issue is to learn these parameters from data. While mathematically appealing this strategy leads to a nested opt

Chang Wei Tan, Christoph Bergmeir, Francois Petitjean, Geoffrey I. Webb

This paper studies Time Series Extrinsic Regression (TSER): a regression task of which the aim is to learn the relationship between a time series and a continuous scalar variable; a task closely related to time series classification (TSC), which aims to learn the relationship between a time series and a categorical class label. This task generalizes time ser

Runzhou Ge, Zhuangzhuang Ding, Yihan Hu, Yu Wang

High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving. Most previous works try to solve it using anchor-based detection methods which come with two drawbacks: post-processing is relatively complex and computationally expensive; tuning anchor parameters is tricky.

Soumya Ghosh, William T. Stephenson, Tin D. Nguyen, Sameer K. Deshpande

Many modern data analyses benefit from explicitly modeling dependence structure in data -- such as measurements across time or space, ordered words in a sentence, or genes in a genome. A gold standard evaluation technique is structured cross-validation (CV), which leaves out some data subset (such as data within a time interval or data in a geographic region

Michael J. Kane, Simon Urbanek

Reproducible document standards, like R Markdown, facilitate the programmatic creation of documents whose content is itself programmatically generated. While these documents are generally not complete in the sense that they will not include prose content, generated by an author to provide context, a narrative, etc., programmatic generation can provide substa

Arijit Sehanobish, Neal G. Ravindra, David van Dijk

Graph Neural Networks (GNN) have been extensively used to extract meaningful representations from graph structured data and to perform predictive tasks such as node classification and link prediction. In recent years, there has been a lot of work incorporating edge features along with node features for prediction tasks. One of the main difficulties in using

Donggyu Kim, Seunghwa Ryu

The Cassie-Baxter state droplet has many local energy minima on the textured surface, while the amount of the energy barrier between them can be affected by the gravity. When the droplet cannot find any local energy minimum point on the surface, the droplet starts to slide. Based on the Laplace pressure equation, the shape of a two-dimensional Cassie-Baxter

Armin Shahmardi, Outi Tammisola, Mauro Chinappi, Luca Brandt

We study the role of surface topology, surface chemistry, and wall superheat temperature on the onset of boiling, bubble nucleation and growth, and the possible formation of an insulating vapour film by means of large-scale MD simulations. In the numerical experiments, we control the system pressure by imposing a constant force on a moving piston. The simula

Mehmet Aydar, Ozge Bozal, Furkan Ozbay

Neural relation extraction discovers semantic relations between entities from unstructured text using deep learning methods. In this study, we present a comprehensive review of methods on neural network based relation extraction. We discuss advantageous and incompetent sides of existing studies and investigate additional research directions and improvement i

Leopold Franz, Yash Raj Shrestha, Bibek Paudel

Augmentation of disease diagnosis and decision-making in healthcare with machine learning algorithms is gaining much impetus in recent years. In particular, in the current epidemiological situation caused by COVID-19 pandemic, swift and accurate prediction of disease diagnosis with machine learning algorithms could facilitate identification and care of vulne

Hannah Kerner, Gabriel Tseng, Inbal Becker-Reshef, Catherine Nakalembe

Spatial information on cropland distribution, often called cropland or crop maps, are critical inputs for a wide range of agriculture and food security analyses and decisions. However, high-resolution cropland maps are not readily available for most countries, especially in regions dominated by smallholder farming (e.g., sub-Saharan Africa). These maps are e

Nan Wang, Luigi Lombardo, Marj Tonini, Weiming Cheng

The persistence over space and time of flash flood disasters -- flash floods that have caused either economical or life losses, or both -- is a diagnostic measure of areas subjected to hydrological risk. The concept of persistence can be assessed via clustering analyses, performed here to analyse the national inventory of flash floods disasters in China occu

Erhan Bayraktar, Asaf Cohen, April Nellis

The current COVID-19 pandemic and subsequent lockdowns have highlighted the close and delicate relationship between a country&#39;s public health and economic health. Macroeconomic models that use preexisting epidemic models to calculate the impacts of a disease outbreak are therefore extremely useful for policymakers seeking to evaluate the best course of a

Kateryna Maksymyk

We study algebraic properties on a group G such that if the discrete group G has these properties then every locally compact shift continuous topology on G with adjoined zero is either compact, or discrete. We introduce electorally flexible and electorally stable groups and establish their properties. In particular, we prove that every group with an infinite

Keqin Liu

The purpose of this paper is to introduce the concept of the automatic integration and present a new way of approximating definite integrals using the automatic integration based on an associative algebra with zero divisors.

S. A. M. Mohsenialhosseini

This paper introduce a new class of operators and contraction mapping for a cyclical map T on G-metric spaces and the approximately fixed point properties. Also,we prove two general lemmas regarding approximate fixed Point of cyclical contraction mapping on G-metric spaces. Using these results we prove several approximate fixed point theorems for a new class

Julius M. Kernbach, Victor E. Staartjes

In the emerging era of big data, larger available clinical datasets and computational advances have sparked a massive interest in machine learning-based approaches. The number of manuscripts related to machine learning or artificial intelligence has exponentially increased over the past years. As analytical machine learning tools become readily available for

Sumra Bari, Nicole L. Vike, Khrystyna Stetsiv, Linda Papa

Collision sports athletes experience many head acceleration events (HAEs) per season. The effects of these subconcussive events are largely understudied since HAEs may produce no overt symptoms, and are likely to diffusely manifest across multiple scales of study (e.g., molecular, cellular network, and behavior). This study integrated resting-state fMRI with

Andrés Franco, Hernán Giraldo, Pedro Rizzo

In this paper we develop combinatorial techniques for the case of string algebras with the aim to give a characterization of string complexes with infinite minimal projective resolution. These complexes will be called \textit{periodic string complexes}. As a consequence of this characterization, we give two important applications. The first one, is a suffici

A. Arbieto, A. M. López, Y. Sánchez

In hyperbolic dynamics, a well-known result is that every hyperbolic attracting set, have a finite pseudo-orbit tracing property (FPOTP). It&#39;s natural to wonder if this result is maintained in the sectional-hyperbolic dynamics; Komuro in [Lorenz attractors do not have the pseudo-orbit tracing property], provides a negative answer for this question, by pr

Kun Su, Xiulong Liu, Eli Shlizerman

We present a novel system that gets as an input video frames of a musician playing the piano and generates the music for that video. Generation of music from visual cues is a challenging problem and it is not clear whether it is an attainable goal at all. Our main aim in this work is to explore the plausibility of such a transformation and to identify cues a

Sheir Yarkoni, Florian Neukart, Eliane Moreno Gomez Tagle, Nicole Magiera

The Web Summit conference in Lisbon, Portugal, is one of the biggest technology conferences in Europe, attended by tens of thousands of people every year. The high influx of people into Lisbon causes significant stress on the city&#39;s transit services for the duration of the conference. For the Web Summit 2019, Volkswagen AG partnered with the city of Lisb