July 2022 arXiv papers — page 76

Sumiya Baasandorj, Sun-Sig Byun, Wontae Kim

We prove the self-improving property of very weak solutions to non-uniformly elliptic problems of double phase type in divergence form under sharp assumptions on the nonlinearity.

Yuqi Liu, Pengfei Xiong, Luhui Xu, Shengming Cao

Text-Video retrieval is a task of great practical value and has received increasing attention, among which learning spatial-temporal video representation is one of the research hotspots. The video encoders in the state-of-the-art video retrieval models usually directly adopt the pre-trained vision backbones with the network structure fixed, they therefore ca

Makoto Matsumoto, Kento Ogawa, Takayuki Okuda

We show that taking the set of primitive idempotents of commutative association schemes is a functor from the category of commutative association schemes with surjective morphisms to the category of finite sets with surjective partial functions. We then consider projective systems of commutative association schemes consisting of surjections (which we call pr

Viet Anh Trinh, Pegah Ghahremani, Brian King, Jasha Droppo

We present an approach to reduce the performance disparity between geographic regions without degrading performance on the overall user population for ASR. A popular approach is to fine-tune the model with data from regions where the ASR model has a higher word error rate (WER). However, when the ASR model is adapted to get better performance on these high-W

Weiqi Peng, Yong Chen

In this work, we consider the long-time asymptotics for the Cauchy problem of a fourth-order dispersive nonlinear Schr\"{o}dinger equation with nonzero boundary conditions at infinity. Firstly, in order to construct the basic Riemann-Hilbert problem associated with nonzero boundary conditions, we analysis direct scattering problem. Then we deform the corresp

Xiaoshan Chen, Xun Li, Fahuai Yi, Xiang Yu

This paper studies a finite horizon utility maximization problem on excessive consumption under a drawdown constraint. Our control problem is an extension of the one considered in Bahman et al. (2019) to the model with a finite horizon and an extension of the one considered in Jeon and Oh (2022) to the model with zero interest rate. Contrary to Bahman et al.

Xiaozhe Hu, Junyuan Lin

We consider effective preconditioners for solving Laplacians of general weighted graphs. Theoretically, spectral sparsifiers (SSs) provide preconditioners of optimal computational complexity. However, they are not easy to use for real-world applications due to the implementation complications. Multigrid (MG) methods, on the contrary, are computationally effi

Xuan Lin, Feng Xu, Alexander Schperberg, Dennis Hong

Convex model predictive controls (MPCs) with a single rigid body model have demonstrated strong performance on real legged robots. However, convex MPCs are limited by their assumptions such as small rotation angle and pre-defined gait, limiting the richness of potential solutions. We remove those assumptions and solve the complete mixed-integer non-convex pr

David R. Schipf, Matthew D. Guild, Caleb F. Sieck

Piezoelectric elastic metamaterials offer the ability to overcome the fixed, narrow bandwidth characteristics of passive elastic metamaterials. Interesting ultrasonic band gaps exist in piezoelectric plate metamaterials with periodic electrodes connected to shunted circuits. These band gaps result from an avoided crossing between electrical and mechanical ba

Klavs Hansen, Jos Engelen

The article in \cite{HolmlidAIPA2016} has recently come to our attention. It makes a number of extraordinary claims that each contradict well established facts in several fields, ranging from atomic and molecular physics to superconductivity and superfluidity, with practically no supporting evidence. We think it worthwhile to rectify the literature with this

Samuel Abreu, Ruth Britto, Claude Duhr, Einan Gardi

The diagrammatic coaction underpins the analytic structure of Feynman integrals, their cuts and the differential equations they admit. The coaction maps any diagram into a tensor product of its pinches and cuts. These correspond respectively to differential forms defining master integrals, and integration contours which place a subset of the propagators on s

Sota Kato, Kazuhiro Hotta

Dice loss is widely used for medical image segmentation, and many improvement loss functions based on such loss have been proposed. However, further Dice loss improvements are still possible. In this study, we reconsidered the use of Dice loss and discovered that Dice loss can be rewritten in the loss function using the cosine similarity through a simple equ

Yuri Yu. Tarasevich, Andrei V. Eserkepov, Irina V. Vodolazskaya

We have studied the resistance of two-dimensional random percolating networks of zero-width metallic nanowires (rings or sticks). We toke into account the nanowire resistance per unit length, the junction (nanowire/nanowire contact) resistance, and the busbar/nanowire contact resistance. Using a mean-field approximation (MFA), we derived the total resistance

Kaile Du, Linyan Li, Fan Lyu, Fuyuan Hu

Existing class-incremental lifelong learning studies only the data is with single-label, which limits its adaptation to multi-label data. This paper studies Lifelong Multi-Label (LML) classification, which builds an online class-incremental classifier in a sequential multi-label classification data stream. Training on the data with Partial Labels in LML clas

Siu-Wing Cheng, Man Ting Wong

We present experimental and theoretical results on a method that applies a numerical solver iteratively to solve several non-negative quadratic programming problems in geometric optimization. The method gains efficiency by exploiting the potential sparsity of the intermediate solutions. We implemented the method to call quadprog of MATLAB iteratively. In com

Mohammad Alawieh, George Yammine, Ernst Eberlein, Birendra Ghimire

Physical effects such as reflection, refraction, and diffraction cause a radio signal to arrive from a transmitter to a receiver in multiple replicas that have different amplitude and rotation. Bandwidth-limited signals, such as positioning reference signals, have a limited time resolution. In reality, the signal is often reflected in the close vicinity of a

Mohammad Alawieh, Ernst Eberlein, Stephan Jäckel, Norbert Franke

Positioning benefits from channel models that capture geometric effects and, in particular, from the signal properties of the first arriving path and the spatial consistency of the propagation condition of multiple links. The models that capture the physical effects observed in a realistic deployment scenario are essential for assessing the potential benefit

Mayank Singhal, Suman Banerjee

In recent times, Group Trip Planning Query (henceforth referred to as GTP Query) is one of the well\mbox{-}studied problems in Spatial Databases. The inputs to the problem are a road network where the vertices represent the Point-of-Interests (mentioned as POIs henceforth) and they are grouped into different categories, edges represent the road segments, and

Fangzhou Yu, Ryan Batke, Jeremy Dao, Jonathan Hurst

For legged robots to match the athletic capabilities of humans and animals, they must not only produce robust periodic walking and running, but also seamlessly switch between nominal locomotion gaits and more specialized transient maneuvers. Despite recent advancements in controls of bipedal robots, there has been little focus on producing highly dynamic beh

Biswanath Layek, Deepthi Godaba Venkata, Pradeepkumar Yadav

We revisit the mechanism of vortex unpinning caused by the neutron-vortex scattering \cite{prad1} in the inner crust of a pulsar. The strain energy released by the crustquake is assumed to be absorbed in some part of the inner crust and causes pair-breaking quasi-neutron excitations from the existing free neutron superfluid in the bulk of the inner crust. Th

Zonghan Zhang, Zhiqian Chen

Given its vast application on online social networks, Influence Maximization (IM) has garnered considerable attention over the last couple of decades. Due to the intricacy of IM, most current research concentrates on estimating the first-order contribution of the nodes to select a seed set, disregarding the higher-order interplay between different seeds. Con

William Chang, Hassan Hamad, Keith M. Chugg

Standard artificial neural networks (ANNs) use sum-product or multiply-accumulate node operations with a memoryless nonlinear activation. These neural networks are known to have universal function approximation capabilities. Previously proposed morphological perceptrons use max-sum, in place of sum-product, node processing and have promising properties for c

Mayank Singhal, Suman Banerjee

In this paper, we study the problem of \textsc{Utility Driven Job Selection} on Road Networks for which the inputs are: a road network with the vertices as the set of Point-Of-Interests (Henceforth mentioned as POI) and the edges are road segments joining the POIs, a set of jobs with their originating POI, starting time, duration, and the utility. A worker c

Poonam Sharma, Suman Banerjee

Now-a-days, \emph{Online Social Networks} have been predominantly used by commercial houses for viral marketing where the goal is to maximize profit. In this paper, we study the problem of Profit Maximization in the two\mbox{-}phase setting. The input to the problem is a \emph{social network} where the users are associated with a cost and benefit value, and

Subramanya Nageshrao, Yousaf Rahman, Vladimir Ivanovic, Mrdjan Jankovic

There has been significant progress in sensing, perception, and localization for automated driving, However, due to the wide spectrum of traffic/road structure scenarios and the long tail distribution of human driver behavior, it has remained an open challenge for an intelligent vehicle to always know how to make and execute the best decision on road given a

Cong Wang, Jinshan Pan, Xiao-Ming Wu

We propose an effective Structural Prior guided Generative Adversarial Transformer (SPGAT) to solve low-light image enhancement. Our SPGAT mainly contains a generator with two discriminators and a structural prior estimator (SPE). The generator is based on a U-shaped Transformer which is used to explore non-local information for better clear image restoratio

Mingjie Li, Rui Liu, Guangsi Shi, Mingfei Han

Long-term time-series forecasting (LTSF) is fundamental to various real-world applications, where Transformer-based models have become the dominant framework due to their ability to capture long-range dependencies. However, these models often experience overfitting due to data redundancy in rolling forecasting settings, limiting their generalization ability

Wentao Chen, Zhang Zhang, Wei Wang, Liang Wang

Few-shot learning (FSL) aims to recognize novel queries with only a few support samples through leveraging prior knowledge from a base dataset. In this paper, we consider the domain shift problem in FSL and aim to address the domain gap between the support set and the query set. Different from previous cross-domain FSL work (CD-FSL) that considers the domain

Richard Kohar, François Rivest, Alain Gosselin

In reinforcement learning, agents have successfully used environments modeled with Markov decision processes (MDPs). However, in many problem domains, an agent may suffer from noisy observations or random times until its subsequent decision. While partially observable Markov decision processes (POMDPs) have dealt with noisy observations, they have yet to dea

Zhenyuan Yuan, Minghui Zhu

This paper considers the problem of online multi-robot motion planning with general nonlinear dynamics subject to unknown external disturbances. We propose dSLAP, a distributed safe learning and planning framework that allows the robots to safely navigate through the environments by coupling online learning and motion planning. Gaussian process regression is

Yao Tian, Xi Zhao, Xiaofang Zhou

Among many solutions to the high-dimensional approximate nearest neighbor (ANN) search problem, locality sensitive hashing (LSH) is known for its sub-linear query time and robust theoretical guarantee on query accuracy. Traditional LSH methods can generate a small number of candidates quickly from hash tables but suffer from large index sizes and hash bounda

Sepideh Mahabadi, David P. Woodruff, Samson Zhou

We introduce data structures for solving robust regression through stochastic gradient descent (SGD) by sampling gradients with probability proportional to their norm, i.e., importance sampling. Although SGD is widely used for large scale machine learning, it is well-known for possibly experiencing slow convergence rates due to the high variance from uniform

Chen-Huan Wu

By considering energy flow, we construct the one-dimensional (1d) model consisting of the quasiparticles caused by asymmetric hopping (in carrier position space) or the complex bosonic potential whose varying gradience with a chiral ordering plays the role of ingredience of quasiparticles. A bosonic potential can be generated and the chaotic dynamics of chir

Kook Tae Kim, Yeong Jae Shinb, Sung-Jin Kang, Ryung Kim

Functional devices with ultrathin ferroelectric layers have been attracted as a promising candidate for next-generation memory and logic device applications. Using the ultrathin ferroelectric layers, particularly approaching the two-dimensional limit, however, it is still challenging to control ferroelectric switching and to observe ferroelectricity by spect

Lei Zhu, Qian Chen, Lujia Jin, Yunfei You

Classification activation map (CAM), utilizing the classification structure to generate pixel-wise localization maps, is a crucial mechanism for weakly supervised object localization (WSOL). However, CAM directly uses the classifier trained on image-level features to locate objects, making it prefers to discern global discriminative factors rather than regio

Menglu Yu, Bo Ji, Hridesh Rajan, Jia Liu

Powered by advances in deep learning (DL) techniques, machine learning and artificial intelligence have achieved astonishing successes. However, the rapidly growing needs for DL also led to communication- and resource-intensive distributed training jobs for large-scale DL training, which are typically deployed over GPU clusters. To sustain the ever-increasin

Michael Shoemate, Kevin Jett, Ethan Cowan, Sean Colbath

Speech data is expensive to collect, and incredibly sensitive to its sources. It is often the case that organizations independently collect small datasets for their own use, but often these are not performant for the demands of machine learning. Organizations could pool these datasets together and jointly build a strong ASR system; sharing data in the clear,

Junxuan Li, Hongdong Li

This paper tackles the task of uncalibrated photometric stereo for 3D object reconstruction, where both the object shape, object reflectance, and lighting directions are unknown. This is an extremely difficult task, and the challenge is further compounded with the existence of the well-known generalized bas-relief (GBR) ambiguity in photometric stereo. Previ

Fekadu L. Bayisa, Markus Ådahl, Patrik Rydén, Ottmar Cronie

This study investigates the spatial distribution of emergency alarm call events to identify spatial covariates associated with the events and discern hotspot regions for the events. The study is motivated by the problem of developing optimal dispatching strategies for prehospital resources such as ambulances. To achieve our goals, we model the spatially vary

Vainavi Viswanath, Kaushik Shivakumar, Justin Kerr, Brijen Thananjeyan

Cables are ubiquitous in many settings and it is often useful to untangle them. However, cables are prone to self-occlusions and knots, making them difficult to perceive and manipulate. The challenge increases with cable length: long cables require more complex slack management to facilitate observability and reachability. In this paper, we focus on autonomo

Weiqing Ren, Yuben Qu, Chao Dong, Yuqian Jing

With the vigorous development of artificial intelligence (AI), the intelligent applications based on deep neural network (DNN) change people's lifestyles and the production efficiency. However, the huge amount of computation and data generated from the network edge becomes the major bottleneck, and traditional cloud-based computing mode has been unable to me

Xiao-Feng He, Liang Li, Stephane Lanteri, Kun Li

A non-intrusive model order reduction (MOR) method for solving parameterized electromagnetic scattering problems is proposed in this paper. A database collecting snapshots of high-fidelity solutions is built by solving the parameterized time-domain Maxwell equations for some values of the material parameters using a fullwave solver based on a high order disc

Yubing Qian, Weizhong Fu, Weiluo Ren, Ji Chen

Accurate ab initio calculations are of fundamental importance in physics, chemistry, biology, and materials science, which have witnessed rapid development in the last couple of years with the help of machine learning computational techniques such as neural networks. Most of the recent efforts applying neural networks to ab initio calculation have been focus

Siu-Wing Cheng, Haoqiang Huang

We present new approximation results on curve simplification and clustering under Fr\'echet distance. Let $T = \{\tau_i : i \in [n] \}$ be polygonal curves in $R^d$ of $m$ vertices each. Let $l$ be any integer from $[m]$. We study a generalized curve simplification problem: given error bounds $\delta_i > 0$ for $i \in [n]$, find a curve $\sigma$ of at most $

Desheng Li, Ruijing Wang, Luyan Zhou

In this note we present some generalized versions of the Krein-Rutman theorem for sectorial operators. They are formulated in a fashion that can be easily applied to elliptic operators. Another feature of these generalized versions is that they contain some information on the generalized eigenspaces associated with non-principal eigenvalues, which are helpfu

Kaito Kimura

Let R be a commutative noetherian ring, I an ideal of R, and M a finitely generated R-module. The asymptotic behavior of the quotient modules M/I^n M of M is an actively studied subject in commutative algebra. The main result of this paper asserts that the depth of the localization of M/I^n M at any prime ideal of R is stable for large integers n that do not

Yifeng Tian, Chaofeng Wang, Ashish Asutosh, Junghoon Woo

Infrastructure is critical for enabling society to function and the economy to thrive, but there is an increasing mismatch between the need for infrastructure investments and available capital, which is in consequence of constraints on public resources and limited capacity to leverage the private sector co-financing under the current system. With the emergen

Eric Rosen, Doruk Senkal

In this paper, we report a hierarchical deep learning model for classification of complex human activities using motion sensors. In contrast to traditional Human Activity Recognition (HAR) models used for event-based activity recognition, such as step counting, fall detection, and gesture identification, this new deep learning model, which we refer to as CHA

Yifeng Tian, Chaofeng Wang, Junghoon Woo, Zheng Lu

Spurred by the emerging blockchain technology and increased interest in tokenization, this forecasting research built on extensive literature and aggregated expertise to explore the potential implementation of blockchain-enabled tokenization in infrastructure investment and development. The Delphi-based scenario analysis approach was applied to gather long-t

M. Buuck, A. Mishra, E. Charles, N. Di Lalla

The GammaTPC is an MeV-scale single-phase liquid argon time-projection-chamber gamma-ray telescope concept with a novel dual-scale pixel-based charge-readout system. It promises to enable a significant improvement in sensitivity to MeV-scale gamma-rays over previous telescopes. The novel pixel-based charge readout allows for imaging of the tracks of electron

Christopher Stokes

In this paper we establish a connection between the Gauss factorials and Iwasawa's cyclotomic $\lambda$-invariant for an imaginary quadratic field $K$. As a result, we will explain a corespondance between the 1-exceptional primes of Cosgrave and Dilcher for $m = 3$ and $m = 4$, and the primes for which the $\lambda$-invariants for $K = \mathbb{Q}(\sqrt{-3})$

Jiahao Qi, Zhiqiang Gong, Xingyue Liu, Kangcheng Bin

Deep learning methodology contributes a lot to the development of hyperspectral image (HSI) analysis community. However, it also makes HSI analysis systems vulnerable to adversarial attacks. To this end, we propose a masked spatial-spectral autoencoder (MSSA) in this paper under self-supervised learning theory, for enhancing the robustness of HSI analysis sy

Zhiyin Shao, Xinyu Zhang, Meng Fang, Zhifeng Lin

Text-to-image person re-identification (ReID) aims to search for pedestrian images of an interested identity via textual descriptions. It is challenging due to both rich intra-modal variations and significant inter-modal gaps. Existing works usually ignore the difference in feature granularity between the two modalities, i.e., the visual features are usually

Irtaza Khalid, Carrie A. Weidner, Edmond A. Jonckheere, Sophie G. Shermer

Robustness of quantum operations or controls is important to build reliable quantum devices. The robustness-infidelity measure (RIM$_p$) is introduced to statistically quantify the robustness and fidelity of a controller as the p-order Wasserstein distance between the fidelity distribution of the controller under any uncertainty and an ideal fidelity distrib

Kevin O'Bryant

A Sidon set is a set of integers containing no nontrivial solutions to the equation $a+b=c+d$. We improve on the lower bound on the diameter of a Sidon set with $k$ elements: if $k$ is sufficiently large and ${\cal A}$ is a Sidon set with $k$ elements, then $diam({\cal A})\ge k^2-1.99405 k^{3/2}$. Alternatively, if $n$ is sufficiently large, then the largest

Seongjin Ahn, Sankar Das Sarma

Considering theoretically the transition between hydrodynamic and ballistic regimes in 2D semiconductors, we show that electrons in high-mobility 2D GaAs are by far the best system for the direct observation of collective hydrodynamic effects even in bulk transport properties independent of complicated transport features in narrow constrictions and small sys

Daqian Shi, Xiaolei Diao, Lida Shi, Hao Tang

Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results. Existing methods have dedicated efforts to restoring degraded character images. However, the denoising results obtained by these methods do not appear to improve character recognition performance. This is mainly because current

Lei Hsiung, Yun-Yun Tsai, Pin-Yu Chen, Tsung-Yi Ho

Prior literature on adversarial attack methods has mainly focused on attacking with and defending against a single threat model, e.g., perturbations bounded in Lp ball. However, multiple threat models can be combined into composite perturbations. One such approach, composite adversarial attack (CAA), not only expands the perturbable space of the image, but a

Roulan Jiang, Xiang Zhan, Tianying Wang

In microbiome studies, it is of interest to use a sample from a population of microbes, such as the gut microbiota community, to estimate the population proportion of these taxa. However, due to biases introduced in sampling and preprocessing steps, these observed taxa abundances may not reflect true taxa abundance patterns in the ecosystem. Repeated measure

Daqian Shi, Xiaolei Diao, Hao Tang, Xiaomin Li

Constructing high-quality character image datasets is challenging because real-world images are often affected by image degradation. There are limitations when applying current image restoration methods to such real-world character images, since (i) the categories of noise in character images are different from those in general images; (ii) real-world charac

L. S. Moriggi, M. V. T. Machado

In this paper, dedicated to the memory of the late Prof. Jean Cleymans, the nuclear modification factors, $R_{xA}$, are investigated for pion production in small system collisions, measured by PHENIX experiment at RHIC (Relativistic Heavy Ion Collider). The theoretical framework is the transverse momentum $k_T$-factorization formalism for hard processes at s

Xiaoyu Liang, Yaguan Qian, Jianchang Huang, Xiang Ling

Adversarial training, as one of the most effective defense methods against adversarial attacks, tends to learn an inclusive decision boundary to increase the robustness of deep learning models. However, due to the large and unnecessary increase in the margin along adversarial directions, adversarial training causes heavy cross-over between natural examples a

Herbert J. Bernstein, Lawrence C. Andrews, Mario Xerri

Characterization of crystallographic lattices is an important tool in structure solution, crystallographic database searches and clustering of diffraction images in serial crystallography. Characterization of lattices by Niggli-reduced cells (based on the three shortest non-coplanar lattice edge vectors) or by Delaunay-reduced cells (based on four edge vecto

Md. Abbas Ali Khan, Mphammad Hanif Ali, AKM Fazlul Haque, Md. Tarek Habib

Driver identification is a momentous field of modern decorated vehicles in the controller area network (CAN-BUS) perspective. Many conventional systems are used to identify the driver. One step ahead, most of the researchers use sensor data of CAN-BUS but there are some difficulties because of the variation of the protocol of different models of vehicle. Our

Lin Sok

In this paper, we study Euclidean and Hermitian hulls of generalized Reed-Solomon codes and twisted generalized Reed-Solomon codes, as well as the Hermitian hulls of Roth-Lempel typed codes. We present explicit constructions of MDS and AMDS linear codes for which their hull dimensions are well determined. As an application, we provide several classes of enta

Mengyuan Li, Arman Kazemi, Ann Franchesca Laguna, X. Sharon Hu

Experience replay is an essential component in deep reinforcement learning (DRL), which stores the experiences and generates experiences for the agent to learn in real time. Recently, prioritized experience replay (PER) has been proven to be powerful and widely deployed in DRL agents. However, implementing PER on traditional CPU or GPU architectures incurs s

Fanglin Chen, Xiao Liu, Bo Tang, Feiyu Xiong

We utilize an offline reinforcement learning (RL) model for sequential targeted promotion in the presence of budget constraints in a real-world business environment. In our application, the mobile app aims to boost customer retention by sending cash bonuses to customers and control the costs of such cash bonuses during each time period. To achieve the multi-

Yanran Wang, James O'Keeffe, Qiuchen Qian, David Boyle

Accuracy and stability are common requirements for Quadrotor trajectory tracking systems. Designing an accurate and stable tracking controller remains challenging, particularly in unknown and dynamic environments with complex aerodynamic disturbances. We propose a Quantile-approximation-based Distributional-reinforced Uncertainty Estimator (QuaDUE) to accura

Reza Hosseini, Albert Chen, Kaixu Yang, Sayan Patra

Forecasts help businesses allocate resources and achieve objectives. At LinkedIn, product owners use forecasts to set business targets, track outlook, and monitor health. Engineers use forecasts to efficiently provision hardware. Developing a forecasting solution to meet these needs requires accurate and interpretable forecasts on diverse time series with su

Lorenzo Giambagli, Lucille Calmon, Riccardo Muolo, Timoteo Carletti

The study of reaction-diffusion systems on networks is of paramount relevance for the understanding of nonlinear processes in systems where the topology is intrinsically discrete, such as the brain. Until now reaction-diffusion systems have been studied only when species are defined on the nodes of a network. However, in a number of real systems including, e

Nicki Mullins, Gabriel S. Denicol, Jorge Noronha

We investigate the far-from-equilibrium behavior of the Boltzmann equation for a gas of massless scalar field particles with quartic (tree level) self-interactions ($\lambda \phi^4$) in Friedmann-Lemaitre-Robertson-Walker spacetime. Using a new covariant generating function for the moments of the Boltzmann distribution function, we analytically determine a s

Frederik Werner Isaksen, Ulrik Lund Andersen

A mechanical system can be optimally controlled through continuous measurements of its position followed by feedback. We revisit the complete formalism for predicting the performance of such as system without invoking the standard rotating wave approximations and the adiabatic approximation. Using this formalism we deduce both the conditional and uncondition

Evan J. Arena

Exotic objects such as the Ellis wormhole are expected to act as gravitational lenses. Much like their nonexotic counterparts, information about these lenses can be found by considering the strong and weak lensing fields they induce. In this work, we consider how weak gravitational lensing flexion can provide information beyond that of shear. We find that di

Kyle Min, Sourya Roy, Subarna Tripathi, Tanaya Guha

Active speaker detection (ASD) in videos with multiple speakers is a challenging task as it requires learning effective audiovisual features and spatial-temporal correlations over long temporal windows. In this paper, we present SPELL, a novel spatial-temporal graph learning framework that can solve complex tasks such as ASD. To this end, each person in a vi

Jiawei Xu, Onur Dizdar, Bruno Clerckx

In this letter, we investigate Rate-Splitting Multiple Access (RSMA) for an uplink communication system with finite blocklength. Considering a two-user Single-Input Single-Output (SISO) Multiple Access Channel (MAC), we study the impact of Signal-to-Noise Ratio (SNR), blocklength, power allocation and target rate on the error probability performance of RSMA

Ali Arab, Dev Arora, Jialin Lu, Martin Ester

Subgroup discovery is a descriptive and exploratory data mining technique to identify subgroups in a population that exhibit interesting behavior with respect to a variable of interest. Subgroup discovery has numerous applications in knowledge discovery and hypothesis generation, yet it remains inapplicable for unstructured, high-dimensional data such as ima

Sandeep Chowdhary, Iacopo Iacopini, Federico Battiston

From sports to science, the recent availability of large-scale data has allowed to gain insights on the drivers of human innovation and success in a variety of domains. Here we quantify human performance in the popular game of chess by leveraging a very large dataset comprising of over 120 million games between almost 1 million players. We find that individu

Runhua Xu, Nathalie Baracaldo, Yi Zhou, Ali Anwar

Federated learning has emerged as a privacy-preserving machine learning approach where multiple parties can train a single model without sharing their raw training data. Federated learning typically requires the utilization of multi-party computation techniques to provide strong privacy guarantees by ensuring that an untrusted or curious aggregator cannot ob

Sajjad Feizollah, Christine L. Tardif

Diffusion MRI (dMRI) is a valuable imaging technique to study the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various acquisition strategies have been developed to achieve high resolutions, but they require long scan times. Imaging at ultra-high fields (UHF) could further increase the SN

Ramón Pozuelo, Qiang Li, Philipp Schlatter, Ricardo Vinuesa

With the availability of new high-Reynolds-number ($Re$) databases of turbulent boundary layers (TBLs) it has been possible to identify in detail certain regions of the boundary layer with more complex behavior. In this study we consider a unique database at moderately-high $Re$, with a near-constant adverse pressure gradient (APG) (Pozuelo {\it et al.}, {\i

Minho Jin, Chelsea J. -T. Ju, Zeya Chen, Yi-Chieh Liu

We address performance fairness for speaker verification using the adversarial reweighting (ARW) method. ARW is reformulated for speaker verification with metric learning, and shown to improve results across different subgroups of gender and nationality, without requiring annotation of subgroups in the training data. An adversarial network learns a weight fo

Jacob Fox, Yuval Wigderson

Extending an earlier conjecture of Erd\H{o}s, Burr and Rosta conjectured that among all two-colorings of the edges of a complete graph, the uniformly random coloring asymptotically minimizes the number of monochromatic copies of any fixed graph $H$. This conjecture was disproved independently by Sidorenko and Thomason. The first author later found quantitati

Lucas Aimaretto, Diego Dujovne

One of the essential requirements of wireless industrial Internet of Things (IoT) systems is to have an extremely high packet delivery rate, generally over 99.9% and comply wih realtime deadline constraints. In industrial IoT networks, packets arriving after the deadline become part of packet loss and lose meaning when they arrive late. However, currently av

Haohe Liu, Xubo Liu, Xinhao Mei, Qiuqiang Kong

Few-shot bioacoustic event detection is a task that detects the occurrence time of a novel sound given a few examples. Previous methods employ metric learning to build a latent space with the labeled part of different sound classes, also known as positive events. In this study, we propose a segment-level few-shot learning framework that utilizes both the pos

Chun-Hua Guo, Wen-Wei Lin, Ching-Sung Liu

We propose a new modification of Newton iteration for finding some nonnegative Z-eigenpairs of a nonnegative tensor. The method has local quadratic convergence to a nonnegative eigenpair of a nonnegative tensor, under the usual assumption guaranteeing the local quadratic convergence of the original Newton iteration.

Lei Zhang, Tianying Chen, Olivia Seow, Tim Chong

Digital communication is often brisk and automated. From auto-completed messages to "likes," research has shown that such lightweight interactions can affect perceptions of authenticity and closeness. On the other hand, effort in relationships can forge emotional bonds by conveying a sense of caring and is essential in building and maintaining relationships.

Nikita Markarian, Alexander Polishchuk

We prove that several Feigin-Odesskii Poisson brackets associated with normal elliptic curves in ${\mathbb P}^n$ are compatible if and only if they are contained in a scroll or in a Veronese surface in ${\mathbb P}^5$ (with an exception of one case when $n=3$). In the case $n=3$ we determine the quartic corresponding to the Schouten bracket of two (non-compa

Vinod Subramanian, Siddharth Gururani, Emmanouil Benetos, Mark Sandler

Loss-gradients are used to interpret the decision making process of deep learning models. In this work, we evaluate loss-gradient based attribution methods by occluding parts of the input and comparing the performance of the occluded input to the original input. We observe that the occluded input has better performance than the original across the test datas

Igor V. Bondarev

We study theoretically the properties of a two-level quantum dipole emitter near an ultrathin transdimensional plasmonic film. Our model system mimics a solid-state single-photon source device. Using realistic experimental parameters, we compute the spontaneous and stimulated emission intensity profiles as functions of the excitation frequency and film thick

Eric Luxenberg, Stephen Boyd, Mykel Kochenderfer, Misha van Beek

We address the problem of strategic asset allocation (SAA) with portfolios that include illiquid alternative asset classes. The main challenge in portfolio construction with illiquid asset classes is that we do not have direct control over our positions, as we do in liquid asset classes. Instead we can only make commitments; the position builds up over time

Pingzhi Li, Johannes W. van der Jagt, Maarten Beens, Julian Hintermayr

All-optical switching (AOS) of magnetization by a single femtosecond laser pulse in Co/Gd based synthetic ferrimagnets is the fastest magnetization switching process. On the other hand, He ion irradiation has become a promising tool for interface engineering of spintronic material platforms, giving rise to significant modification of magnetic properties. In

Hilson Shrestha, Kathleen Cachel, Mallak Alkhathlan, Elke Rundensteiner

Fair consensus building combines the preferences of multiple rankers into a single consensus ranking, while ensuring any group defined by a protected attribute (such as race or gender) is not disadvantaged compared to other groups. Manually generating a fair consensus ranking is time-consuming and impractical -- even for a fairly small number of candidates.

Atreyee Kundu

This paper deals with input/output-to-state stability (IOSS) of continuous-time switched nonlinear systems. Given a family of systems, possibly containing unstable dynamics, and a set of restrictions on admissible switches between the subsystems and admissible dwell times on the subsystems, we identify a class of switching signals that obeys these restrictio

Diego Fallas Padilla, Han Pu, Guo-Jing Cheng, Yu-Yu Zhang

We map a quantum Rabi ring, consisting of $N$ cavities arranged in a ring geometry, into an effective magnetic model containing the XY exchange and the Dzyaloshinskii Moriya (DM) interactions. The analogue of the latter is induced by an artificial magnetic field, which modulates photon hopping between nearest-neighbor cavities with a phase. The mean-field be

Angelot Behajaina, Roghayeh Maleki, Andriaherimanana Sarobidy Razafimahatratra

A subset $\mathcal{F}$ of a finite transitive group $G\leq \operatorname{Sym}(\Omega)$ is \emph{intersecting} if any two elements of $\mathcal{F}$ agree on an element of $\Omega$. The \emph{intersection density} of $G$ is the number $$\rho(G) = \max\left\{ \mathcal{|F|}/|G_\omega| \mid \mathcal{F}\subset G \mbox{ is intersecting} \right\},$$ where $\omega \i

Hunter Ray, Ryan Singer, Nisar Ahmed

The domain of public safety in the form of search \& rescue, wildland firefighting, structure firefighting, and law enforcement operations have drawn great interest in the field of aerospace engineering, human-robot teaming, autonomous systems, and robotics. However, a divergence exists in the assumptions made in research and how state-of-the-art technologie

Marius Lemm, Oliver Siebert

A physical system is said to satisfy a thermal area law if the mutual information between two adjacent regions in the Gibbs state is controlled by the area of their boundary. Thermal area laws have been derived for systems with bounded local interactions such as quantum spin systems. However, for lattice bosons these arguments break down because the interact

Qi Chang, Danish Ahmad, Jennifer Toth, Rebecca Bascom

Lung cancer tends to be detected at an advanced stage, resulting in a high patient mortality rate. Thus, much recent research has focused on early disease detection Bronchoscopy is the procedure of choice for an effective noninvasive way of detecting early manifestations (bronchial lesions) of lung cancer. In particular, autofluorescence bronchoscopy (AFB) d

Ralvi Isufaj, Marsel Omeri, Miquel Angel Piera, Jaume Saez Valls

Present air traffic complexity metrics are defined considering the interests of different management layers of ATM. These layers have different objectives which in practice compete to maximize their own goals, which leads to fragmented decision making. This fragmentation together with competing KPAs requires transparent and neutral air traffic information to

Yizhe Xu, Nikolaos Ignatiadis, Erik Sverdrup, Scott Fleming

Estimation of conditional average treatment effects (CATEs) plays an essential role in modern medicine by informing treatment decision-making at a patient level. Several metalearners have been proposed recently to estimate CATEs in an effective and flexible way by re-purposing predictive machine learning models for causal estimation. In this chapter, we summ