July 2022 arXiv papers — page 51

Beren Millidge, Yuhang Song, Tommaso Salvatori, Thomas Lukasiewicz

Predictive coding (PC) is an influential theory in computational neuroscience, which argues that the cortex forms unsupervised world models by implementing a hierarchical process of prediction error minimization. PC networks (PCNs) are trained in two phases. First, neural activities are updated to optimize the network's response to external stimuli. Second,

Md Umar Hashmi, Arpan Koirala, Hakan Ergun, Dirk Van Hertem

{A curtailable and flexible resource activation framework for solving distribution network (DN) voltage and thermal congestions is used to quantify three important aspects with respect to modelling low voltage networks.} This framework utilizes the network states in the absence of such flexible or curtailable resources as the input for calculating flexibilit

Adam Villaflor, Zhe Huang, Swapnil Pande, John Dolan

Impressive results in natural language processing (NLP) based on the Transformer neural network architecture have inspired researchers to explore viewing offline reinforcement learning (RL) as a generic sequence modeling problem. Recent works based on this paradigm have achieved state-of-the-art results in several of the mostly deterministic offline Atari an

Mruganka Kashyap, Laurent Lessard

In this article, we consider a cooperative control problem involving a heterogeneous network of dynamically decoupled continuous-time linear plants. The (output-feedback) controllers for each plant may communicate with each other according to a fixed and known transitively closed directed graph. Each transmission incurs a fixed and known time delay. We provi

Dang-Khanh Nguyen, Sudarshan Pant, Ngoc-Huynh Ho, Guee-Sang Lee

Facial behavior analysis is a broad topic with various categories such as facial emotion recognition, age, and gender recognition. Many studies focus on individual tasks while the multi-task learning approach is still an open research issue and requires more research. In this paper, we present our solution and experiment result for the Multi-Task Learning ch

Zhengxin You, Qichao Ying, Sheng Li, Zhenxing Qian

Online social networks have stimulated communications over the Internet more than ever, making it possible for secret message transmission over such noisy channels. In this paper, we propose a Coverless Image Steganography Network, called CIS-Net, that synthesizes a high-quality image directly conditioned on the secret message to transfer. CIS-Net is compose

Nana Shumiya, Jia-Xin Yin, Guoqing Chang, Meng Yang

Topological insulators with intrinsic magnetic order are emerging as an exciting platform to realize fundamentally new excitations from topological quantum states of matter. To study these systems and their physics, people have proposed a variety of magnetic topological insulator systems, including HoSbTe, an antiferromagnetic weak topological insulator cand

Xiaoliang Liu, Furao Shen, Jian Zhao, Changhai Nie

Deep neural networks are powerful, but they also have shortcomings such as their sensitivity to adversarial examples, noise, blur, occlusion, etc. Moreover, ensuring the reliability and robustness of deep neural network models is crucial for their application in safety-critical areas. Much previous work has been proposed to improve specific robustness. Howev

Chenxi Wu, Min Zhu, Qinyang Tan, Yadhu Kartha

Physics-informed neural networks (PINNs) have shown to be an effective tool for solving forward and inverse problems of partial differential equations (PDEs). PINNs embed the PDEs into the loss of the neural network, and this PDE loss is evaluated at a set of scattered residual points. The distribution of these points are highly important to the performance

Shouzhe Dong, Jing Wang, Xiaoming Shi, Deshan Liang

Strain manipulation of the magnetic domains, such as the stripe domains and skyrmions, has attracted considerable attention because of its potential applications for magnetic logic and memory devices. Here, utilizing phase-field modeling, we demonstrate the deterministic modulation of the orientation and the configuration of the stripe domains and skyrmions

Wonwoo Cho, Jaegul Choo

In open-set recognition (OSR), classifiers should be able to reject unknown-class samples while maintaining high closed-set classification accuracy. To effectively solve the OSR problem, previous studies attempted to limit latent feature space and reject data located outside the limited space via offline analyses, e.g., distance-based feature analyses, or co

Yongqi Dong, Kejia Chen, Yinxuan Peng, Zhiyuan Ma

As the central nerve of the intelligent vehicle control system, the in-vehicle network bus is crucial to the security of vehicle driving. One of the best standards for the in-vehicle network is the Controller Area Network (CAN bus) protocol. However, the CAN bus is designed to be vulnerable to various attacks due to its lack of security mechanisms. To enhanc

Seonwoo Min, Nokyung Park, Siwon Kim, Seunghyun Park

Reducing the representational discrepancy between source and target domains is a key component to maximize the model generalization. In this work, we advocate for leveraging natural language supervision for the domain generalization task. We introduce two modules to ground visual representations with texts containing typical reasoning of humans: (1) Visual a

Zhanpeng Zeng, Sourav Pal, Jeffery Kline, Glenn M Fung

Transformers have emerged as a preferred model for many tasks in natural langugage processing and vision. Recent efforts on training and deploying Transformers more efficiently have identified many strategies to approximate the self-attention matrix, a key module in a Transformer architecture. Effective ideas include various prespecified sparsity patterns, l

Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada, Hiroshi Takahashi

This paper proposes a new loss function for adversarial training. Since adversarial training has difficulties, e.g., necessity of high model capacity, focusing on important data points by weighting cross-entropy loss has attracted much attention. However, they are vulnerable to sophisticated attacks, e.g., Auto-Attack. This paper experimentally reveals that

Liu Yang, Yinzhi Lu, Simon X. Yang, Yuanchang Zhong

Trustworthy and reliable data delivery is a challenging task in Wireless Sensor Networks (WSNs) due to unique characteristics and constraints. To acquire secured data delivery and address the conflict between security and energy, in this paper we present an evolutionary game based secure clustering protocol with fuzzy trust evaluation and outlier detection f

Hugo Esquivel, Arun Prakash, Guang Lin

The flow-driven spectral chaos (FSC) is a recently developed method for tracking and quantifying uncertainties in the long-time response of stochastic dynamical systems using the spectral approach. The method uses a novel concept called 'enriched stochastic flow maps' as a means to construct an evolving finite-dimensional random function space that is both a

Shi-Zhuo Looi

In this paper, we prove pointwise decay rates for cubic and higher order nonlinear wave equations, including quasilinear wave equations, on asymptotically flat and time-dependent spacetimes. We assume that the solution to the linear equation (rather than the nonlinear equation) satisfies a weaker form of the standard integrated local energy decay, or Morawet

Tian-Xing Xu, Yuan-Chen Guo, Yong-Liang Yang, Song-Hai Zhang

Point clouds captured by depth sensors are often contaminated by noises, obstructing further analysis and applications. In this paper, we emphasize the importance of point distribution uniformity to downstream tasks. We demonstrate that point clouds produced by existing gradient-based denoisers lack uniformity despite having achieved promising quantitative r

Yongqiang Mao, Kaiqiang Chen, Wenhui Diao, Xian Sun

The classification of airborne laser scanning (ALS) point clouds is a critical task of remote sensing and photogrammetry fields. Although recent deep learning-based methods have achieved satisfactory performance, they have ignored the unicity of the receptive field, which makes the ALS point cloud classification remain challenging for the distinguishment of

Yueling Yang, Kang Li, Zhenglin Li, Jinshu Huang

The current knowledge on the $D^{\ast}$ mesons are still inadequate. Encouraged by the positive development prospects of high-luminosity and high-precision experiments, the Cabibbo-favored nonleptonic $D^{\ast}$ ${\to}$ $\bar{K}{\pi}^{+}$, $\bar{K}^{\ast}{\pi}^{+}$, $\bar{K}{\rho}^{+}$ weak decays are studied with the naive factorization approach. It is foun

Ruixuan Xiao, Yiwen Dong, Haobo Wang, Lei Feng

Learning with Noisy Labels (LNL) has become an appealing topic, as imperfectly annotated data are relatively cheaper to obtain. Recent state-of-the-art approaches employ specific selection mechanisms to separate clean and noisy samples and then apply Semi-Supervised Learning (SSL) techniques for improved performance. However, the selection step mostly provid

Aquib Mustafa, Dimitra Panagou

This paper presents an adversary detection mechanism and a resilient control framework for multi-agent systems under spatiotemporal constraints. Safety in multi-agent systems is typically addressed under the assumption that all agents collaborate to ensure the forward invariance of a desired safe set. This work analyzes agent behaviors based on certain behav

Tengfei Jiao, Shutian Zhang, Min Sun, Decai Huang

This study presents numerical simulations of the resonance of a finite-length granular chain of dissipative grains driven by a harmonically vibrated tube. Multiple gradual resonant modes, namely, non-resonance mode, partial-resonance mode, and complete-resonance mode, are identified. With a fixed vibration frequency, increased vibration acceleration leads to

Chongyu Liu, Lianwen Jin, Yuliang Liu, Canjie Luo

Text removal has attracted increasingly attention due to its various applications on privacy protection, document restoration, and text editing. It has shown significant progress with deep neural network. However, most of the existing methods often generate inconsistent results for complex background. To address this issue, we propose a Contextual-guided Tex

Chuqi Cao

In this paper, we consider the cutoff Boltzmann equation near Maxwellian, we proved the global existence and uniqueness for the cutoff Boltzmann equation in polynomial weighted space for all $\gamma \in (-3, 1]$. We also proved initially polynomial decay for the large velocity in $L^2$ space will induce polynomial decay rate, while initially exponential deca

Yan Hong, Li Niu, Jianfu Zhang, Liqing Zhang

Learning to generate new images for a novel category based on only a few images, named as few-shot image generation, has attracted increasing research interest. Several state-of-the-art works have yielded impressive results, but the diversity is still limited. In this work, we propose a novel Delta Generative Adversarial Network (DeltaGAN), which consists of

Xuyu Zhang, Jingjing Gao, Yu Gan, Chunyuan Song

A channel should be built to transmit information from one place to another. Imaging is 2 or higher dimensional information communication. Conventionally, an imaging channel comprises a lens and free spaces of its both sides. The transfer function of each part is known; thus, the response of a conventional imaging channel is known as well. Replacing the lens

Bo Zhang, Li Niu, Xing Zhao, Liqing Zhang

Image cropping aims to find visually appealing crops in an image, which is an important yet challenging task. In this paper, we consider a specific and practical application: human-centric image cropping, which focuses on the depiction of a person. To this end, we propose a human-centric image cropping method with two novel feature designs for the candidate

P. Bryan Changala, Nadav Genossar, Joshua H. Baraban

Molecular electronic spectra can be represented in the time domain as auto-correlation functions of the initial vibrational wavepacket. We present a derivation of the harmonic vibrational auto-correlation function that is valid for both real and imaginary harmonic frequencies. The derivation rests on Lie algebra techniques that map otherwise complicated expo

Alexander P. Browning, Christopher Drovandi, Ian W. Turner, Adrianne L. Jenner

Heterogeneity is a dominant factor in the behaviour of many biological processes. Despite this, it is common for mathematical and statistical analyses to ignore biological heterogeneity as a source of variability in experimental data. Therefore, methods for exploring the identifiability of models that explicitly incorporate heterogeneity through variability

Weichuan Li, Mark A. Caprio, Stefan Frauendorf

The triaxiality of odd-mass nuclei is investigated by coupling a quasiparticle to an even-even core through the core-quasiparticle coupling model. Both soft and rigid triaxial cores are considered. The "soft core" is described by the collective model with rotation-vibrational motion, while the "rigid core" is described by the triaxial rotor model, which is a

Wenda Chu, Chulin Xie, Boxin Wang, Linyi Li

Federated learning (FL) allows agents to jointly train a global model without sharing their local data. However, due to the heterogeneous nature of local data, it is challenging to optimize or even define fairness of the trained global model for the agents. For instance, existing work usually considers accuracy equity as fairness for different agents in FL,

Yuquan Lin, Wensong Lin

Let $G$ be a graph and $k$ a positive integer. A strong $k$-edge-coloring of $G$ is a mapping $\phi: E(G)\to \{1,2,\dots,k\}$ such that for any two edges $e$ and $e'$ that are either adjacent to each other or adjacent to a common edge, $\phi(e)\neq \phi(e')$. The strong chromatic index of $G$, denoted as $\chi'_{s}(G)$, is the minimum integer $k$ such that $

Xin Qin, Jindong Wang, Yiqiang Chen, Wang Lu

Human activity recognition requires the efforts to build a generalizable model using the training datasets with the hope to achieve good performance in test datasets. However, in real applications, the training and testing datasets may have totally different distributions due to various reasons such as different body shapes, acting styles, and habits, damagi

Xuyu Zhan, Jingjing Gao, Yu Gan, Chunyuan Song

Scattering of light in complex media scrambles optical wavefronts and breaks the principles of conventional imaging methods. For decades, researchers have endeavored to conquer the problem by inventing approaches such as adaptive optics, iterative wavefront shaping, and transmission matrix measurement. That said, imaging through/into thick scattering media r

Hao Wu, Hans van Ditmarsch, Jinsheng Chen

Public announcement logic(PAL) is an extension of epistemic logic (EL) with some reduction axioms. In this paper, we propose a cut-free labelled sequent calculus for PAL, which is an extension of that for EL with sequent rules adapted from the reduction axioms. This calculus admits cut and allows terminating proof search.

J. -L. Zhang, S. Mantry, J. K. Adkins, Y. Akiba

The recently approved Electron-Ion Collider (EIC) will provide a unique new opportunity for searches of charged lepton flavor violation (CLFV) and other new physics scenarios. In contrast to the $e \leftrightarrow \mu$ CLFV transition for which very stringent limits exist, there is still a relatively large discovery space for the $e \to \tau$ CLFV transition

C. B. Oliveira, A. C. Krabbe, J. A. Hernandez-Jimenez, O. L. Dors

The ionizing source of Low Ionization Nuclear Emission Regions (LINERs) is uncertain. Because of this, an empirical relation to determine the chemical abundances of these objects has not been proposed. In this work, for the first time, we derived two semi-empirical calibrations based on photoionization models to estimate the oxygen abundance of LINERS as a f

Xinye Peng

In this contribution, the final measurements of the centrality dependence of the nuclear modification factor ($R_{\rm AA}$) of non-prompt $\rm D^0$ in Pb--Pb collisions at $\sqrt{s_{\scriptscriptstyle \rm NN}}$ = 5.02 TeV will be presented. These measurements provide important constraints to the in-medium mass dependent of energy loss and hadronisation of th

Bo Miao, Mohammed Bennamoun, Yongsheng Gao, Ajmal Mian

Current semi-supervised video object segmentation (VOS) methods usually leverage the entire features of one frame to predict object masks and update memory. This introduces significant redundant computations. To reduce redundancy, we present a Region Aware Video Object Segmentation (RAVOS) approach that predicts regions of interest (ROIs) for efficient objec

Jeong-gi Kwak, Yuanming Li, Dongsik Yoon, Donghyeon Kim

Over the years, 2D GANs have achieved great successes in photorealistic portrait generation. However, they lack 3D understanding in the generation process, thus they suffer from multi-view inconsistency problem. To alleviate the issue, many 3D-aware GANs have been proposed and shown notable results, but 3D GANs struggle with editing semantic attributes. The

Dajian Zhong, Shujing Lyu, Palaiahnakote Shivakumara, Bing Yin

Scene text recognition is a challenging task due to the complex backgrounds and diverse variations of text instances. In this paper, we propose a novel Semantic GAN and Balanced Attention Network (SGBANet) to recognize the texts in scene images. The proposed method first generates the simple semantic feature using Semantic GAN and then recognizes the scene t

Ali Borji, Sikun Lin

We present SplitMixer, a simple and lightweight isotropic MLP-like architecture, for visual recognition. It contains two types of interleaving convolutional operations to mix information across spatial locations (spatial mixing) and channels (channel mixing). The first one includes sequentially applying two depthwise 1D kernels, instead of a 2D kernel, to mi

Moses Fayngold

Analysis of the recently proposed thought experiment with the path entangled photon pairs is extended here to spin entangled electron pairs. The detailed comparison of the two cases showed the range of distinctions and similarities in their monitoring. The general results contradict the Concurrency Rule stating that intimately linked characteristics of a sim

Samuel C. Gutekunst, Billy Jin, David P. Williamson

The symmetric circulant TSP is a special case of the traveling salesman problem in which edge costs are symmetric and obey circulant symmetry. Despite the substantial symmetry of the input, remarkably little is known about the symmetric circulant TSP, and the complexity of the problem has been an often-cited open question. Considerable effort has been made t

Nobuhito Maru, Haruki Takahashi, Yoshiki Yatagai

Grand gauge-Higgs unification of five dimensional SU(6) gauge theory on an orbifold $S^1/Z_2$ with localized gauge kinetic terms is discussed. The Standard model (SM) fermions on the boundaries and some massive bulk fermions coupling to the SM fermions on the boundary are introduced. Taking the power-law running contributions from the bulk fields into accoun

Yen-Jen Cheng, Sen-Peng Eu, Hsiang-Chun Hsu

A generalized Catalan matrix $(a_{n,k})_{n,k\ge 0}$ is generated by two seed sequences $\mathbf{s}=(s_0,s_1,\ldots)$ and $\mathbf{t}=(t_1,t_2,\ldots)$ together with a recurrence relation. By taking $s_\ell=2\ell+1$ and $t_\ell=\ell^2$ we can interpret $a_{n,k}$ as the number of partial permutations, which are $n\times n$ $0,1$-matrices of $k$ zero rows with

David J. W. Simpson

In diverse physical systems stable oscillatory solutions devolve into more complicated dynamical behaviour through border-collision bifurcations. Mathematically these occur when a stable fixed point of a piecewise-smooth map collides with a switching manifold as parameters are varied. The purpose of this paper is to highlight the extreme complexity possible

Chris Cade, Marten Folkertsma, Sevag Gharibian, Ryu Hayakawa

Estimating the ground state energy of a local Hamiltonian is a central problem in quantum chemistry. In order to further investigate its complexity and the potential of quantum algorithms for quantum chemistry, Gharibian and Le Gall (STOC 2022) recently introduced the guided local Hamiltonian problem (GLH), which is a variant of the local Hamiltonian problem

Jose Ceniceros, Mohamed Elhamdadi, Josef Komissar, Hitakshi Lahrani

We study RNA foldings and investigate their topology using a combination of knot theory and embedded rigid vertex graphs. Knot theory has been helpful in modeling biomolecules, but classical knots place emphasis on a biomolecule's entanglement while ignoring their intrachain interactions. We remedy this by using stuck knots and links, which provide a way to

Md Umar Hashmi, Deepjyoti Deka, Ana Bušić, Dirk Van Hertem

To mitigate issues related to the growth of variable smart loads and distributed generation, distribution system operators (DSO) now make it binding for prosumers with inverters to operate under pre-set rules. In particular, the maximum active and reactive power set points for prosumers are based on local voltage measurements to ensure that inverter output d

Qidi Peng, Nan Rao

Based on an optimal rate wavelet series representation, we derive a local modulus of continuity result with a refined almost sure upper bound for fractional Brownian motion. \sloppy The obtained upper bound of the small fractional Brownian increments is of order $\mathcal O_{a.s.}\big(|h|^H\sqrt{\log\log |h|^{-1}}\big)$ as $|h|\to0$, and an upper bound of it

Aakash Varma Nadimpalli, Ajita Rattani

Facial forgery by deepfakes has raised severe societal concerns. Several solutions have been proposed by the vision community to effectively combat the misinformation on the internet via automated deepfake detection systems. Recent studies have demonstrated that facial analysis-based deep learning models can discriminate based on protected attributes. For th

Oskar van der Wal, Jaap Jumelet, Katrin Schulz, Willem Zuidema

Detecting and mitigating harmful biases in modern language models are widely recognized as crucial, open problems. In this paper, we take a step back and investigate how language models come to be biased in the first place. We use a relatively small language model, using the LSTM architecture trained on an English Wikipedia corpus. With full access to the da

A. Shekhter, K. A. Modic, L. E. Winter, Y. Lai

Anomalous transport behavior -- both longitudinal and Hall -- is the defining characteristic of the strange-metal state of High-Tc cuprates. The temperature, frequency, and magnetic field dependence of the resistivity is understood within strange metal phenomenology as resulting from energy-scale competition to set the inelastic relaxation rate. The anomalou

Longkang Dai, Xi Zhang, Jun Cui

The Venusian clouds originate from the binary condensation of H$_{2}$SO$_{4}$ and H$_{2}$O. The two components strongly interact with each other via chemistry and cloud formation. Previous works adopted sophisticated microphysical approaches to understand the clouds. Here we show that the observed vapor and cloud distributions on Venus can be well explained

Jinting Zhu, Julian Jang-Jaccard, Ian Welch, Harith Al-Sahaf

To enhance the efficiency of incident response triage operations, it is not cost-effective to defend all systems equally in a complex cyber environment. Instead, prioritizing the defense of critical functionality and the most vulnerable systems is desirable. Threat intelligence is crucial for guiding Security Operations Center (SOC) analysts' focus toward sp

Andrew Y. Chen, Jack McCoy

We characterize the structure and origins of missingness for 159 cross-sectional return predictors and study missing value handling for portfolios constructed using machine learning. Simply imputing with cross-sectional means performs well compared to rigorous expectation-maximization methods. This stems from three facts about predictor data: (1) missingness

Junho Choi, Namjung Kim, Youngjoon Hong

Machine learning methods have been lately used to solve partial differential equations (PDEs) and dynamical systems. These approaches have been developed into a novel research field known as scientific machine learning in which techniques such as deep neural networks and statistical learning are applied to classical problems of applied mathematics. In this p

George Z. Li, Dung Nguyen, Anil Vullikanti

It was observed in \citet{gupta2009differentially} that the Set Cover problem has strong impossibility results under differential privacy. In our work, we observe that these hardness results dissolve when we turn to the Partial Set Cover problem, where we only need to cover a $\rho$-fraction of the elements in the universe, for some $\rho\in(0,1)$. We show t

Cheng Li, Saifei Sun, Yichen Zhu

Spatial Gaussian process regression models typically contain finite dimensional covariance parameters that need to be estimated from the data. We study the Bayesian estimation of covariance parameters including the nugget parameter in a general class of stationary covariance functions under fixed-domain asymptotics, which is theoretically challenging due to

Alexei M. Frolov

The governing equations of Maxwell electrodynamics in multi-dimensional spaces are derived from the variational principle of least action which is applied to the action function of the electromagnetic field. The Hamiltonian approach for the electromagnetic field in multi-dimensional pseudo-Euclidean (flat) spaces has also been developed and investigated. Bas

Longkang Dai, Xi Zhang, Wencheng D. Shao, Carver J. Bierson

The current Venus climate is largely regulated by globally-covered concentrated sulfuric acid clouds from binary condensation of sulfuric acid (H2SO4) and water (H2O). To understand this complicated H2SO4-H2O gas-cloud system, previous theoretical studies either adopted complicated microphysical calculations or assumed that both H2SO4 and H2O vapor follow th

Chien-Yu Lin, Anish Prabhu, Thomas Merth, Sachin Mehta

Recent isotropic networks, such as ConvMixer and vision transformers, have found significant success across visual recognition tasks, matching or outperforming non-isotropic convolutional neural networks (CNNs). Isotropic architectures are particularly well-suited to cross-layer weight sharing, an effective neural network compression technique. In this paper

Ali Cheraghi

In this paper, we prove the relation between special cycles on a Rapoport-Smithling-Zhang Shimura variety and special values of the derivative of a Hilbert Eisenstein series.

Arsalan Adil, Andreas Albrecht, Lloyd Knox

Several cosmological tensions have emerged in light of recent data, most notably in the inferences of the parameters $H_0$ and $\sigma_8$. We explore the possibility of alleviating both these tensions {\it simultaneously} by means of the Albrecht-Skordis ``quintessence'' potential. The field can reduce the size of the sound horizon $r_s^*$ while concurrently

Md Umar Hashmi, Arpan Koirala, Hakan Ergun, Dirk Van Hertem

For market-based procurement of low voltage (LV) flexibility, DSOs identify the amount of flexibility needed for resolving probable distribution network (DN) voltage and thermal congestion. A framework is required to avoid over or under procurement of flexibility in the presence of uncertainty. To this end, we propose a scenario-based robust chance-constrain

Jingyuan Chen, Zhenwei Yao

Elucidating the interplay of stress and geometry is a fundamental scientific question arising in multiple fields. In this work, we investigate the geometric frustration of crystalline caps confined on the sphere in both elastic and plastic regimes. Based on the revealed quasi-conformal ordering, we discover the partial, but uniform screening of the substrate

Ruijie Du, Ran Qi, Peng Zhang

By generalizing Bo Gao's approach [Phys. Rev. A 58, 1728 (1998)] for solving the Schrödinger equation for an isotropic van der Waals (vdW) potential to the systems with a multi-scale anisotropic long-range interaction, we derive the solutions for the Schrödinger equation for an anisotropic dipole-dipole interaction plus an isotropic attractive vdW potent

Nawaf Nazir, Shushman Choudhury, Stephen Zoepf, Ke Ma

In this paper we formulate a dynamic mixed integer program for optimally zoning curbside parking spaces subject to transportation policy-inspired constraints and regularization terms. First, we illustrate how given some objective of curb zoning valuation as a function of zone type (e.g., paid parking or bus stop), dynamically rezoning involves unrolling this

Sven Wang, Youssef Marzouk

We study the convergence properties, in Hellinger and related distances, of nonparametric density estimators based on measure transport. These estimators represent the measure of interest as the pushforward of a chosen reference distribution under a transport map, where the map is chosen via a maximum likelihood objective (equivalently, minimizing an empiric

Hafez M. Garmaroudi, Zikai Dou, Shengtian Yang, Jun Chen

This paper studies optimal linear power control for battery-limited energy harvesting communications. It provides a systematic analysis of linear power control policies, covering the greedy and fixed-fraction policies as special cases. Three optimality notions are introduced: the maximin optimal linear policy for a given battery capacity and mean-to-capacity

Wei Sun, Mei Wang, Lili Qiu

During the Covid, online meetings have become an indispensable part of our lives. This trend is likely to continue due to their convenience and broad reach. However, background noise from other family members, roommates, office-mates not only degrades the voice quality but also raises serious privacy issues. In this paper, we develop a novel system, called S

Yaqian Liang, Shanshan Zhao, Baosheng Yu, Jing Zhang

Recently, self-supervised pre-training has advanced Vision Transformers on various tasks w.r.t. different data modalities, e.g., image and 3D point cloud data. In this paper, we explore this learning paradigm for 3D mesh data analysis based on Transformers. Since applying Transformer architectures to new modalities is usually non-trivial, we first adapt Visi

Ngoc M Tran

Extreme value statistics is the max analogue of classical statistics, while tropical geometry is the max analogue of classical geometry. In this paper, we review recent work where insights from tropical geometry were used to develop new, efficient learning algorithms with leading performance on benchmark datasets in extreme value statistics. We give intuitio

Chulin Xie, Pin-Yu Chen, Qinbin Li, Arash Nourian

Federated learning (FL) enables distributed resource-constrained devices to jointly train shared models while keeping the training data local for privacy purposes. Vertical FL (VFL), which allows each client to collect partial features, has attracted intensive research efforts recently. We identified the main challenges that existing VFL frameworks are facin

Elijah Cole, Kimberly Wilber, Grant Van Horn, Xuan Yang

Weakly supervised object localization (WSOL) aims to learn representations that encode object location using only image-level category labels. However, many objects can be labeled at different levels of granularity. Is it an animal, a bird, or a great horned owl? Which image-level labels should we use? In this paper we study the role of label granularity in

Robert S. Maier

Many combinatorial and other number triangles are solutions of recurrences of the Graham-Knuth-Patashnik (GKP) type. Such triangles and their defining recurrences are investigated analytically. They are acted on by a transformation group generated by two involutions: a left-right reflection and an upper binomial transformation, acting row-wise. The group als

Zhenpeng Chen, Jie M. Zhang, Max Hort, Mark Harman

Unfair behaviors of Machine Learning (ML) software have garnered increasing attention and concern among software engineers. To tackle this issue, extensive research has been dedicated to conducting fairness testing of ML software, and this paper offers a comprehensive survey of existing studies in this field. We collect 100 papers and organize them based on

Amir Weiss, Toros Arikan, Gregory W. Wornell

Direct localization (DLOC) methods, which use the observed data to localize a source at an unknown position in a one-step procedure, generally outperform their indirect two-step counterparts (e.g., using time-difference of arrivals). However, underwater acoustic DLOC methods require prior knowledge of the environment, and are computationally costly, hence sl

Won Joon Yun, Jae Pyoung Kim, Soyi Jung, Jihong Park

Quantum federated learning (QFL) has recently received increasing attention, where quantum neural networks (QNNs) are integrated into federated learning (FL). In contrast to the existing static QFL methods, we propose slimmable QFL (SlimQFL) in this article, which is a dynamic QFL framework that can cope with time-varying communication channels and computing

Alejandro Corichi, Juan D Reyes, Tatjana Vukašinac

The notion of Isolated Horizons has played an important role in gravitational physics, being useful from the characterization of the endpoint of black hole mergers to (quantum) black hole entropy. In particular, the definition of {\it weakly} isolated horizons (WIHs) as quasilocal generalizations of event horizons is purely geometrical, and is independent of

Sai Xu, Yanan Du, Jiliang Zhang, Jiangzhou Wang

This paper proposes to leverage intelligent reflecting surface (IRS) backscatter to realize radio-frequency-chain-free uplink-transmissions (RFCF-UT). In this communication paradigm, IRS works as an information carrier, whose elements are capable of adjusting their amplitudes and phases to collaboratively portray an electromagnetic image like a dynamic quick

Eric Pulick, Shubham Bharti, Yiding Chen, Vladimir Menkov

As machine learning (ML) is more tightly woven into society, it is imperative that we better characterize ML's strengths and limitations if we are to employ it responsibly. Existing benchmark environments for ML, such as board and video games, offer well-defined benchmarks for progress, but constituent tasks are often complex, and it is frequently unclear ho

Nigel Bernard, Hoa Nguyen, Aman Chandan, Savyasachi Jagdeeshan

With the growing complexity of big data workloads that require abundant data and computation, data centers consume a tremendous amount of power daily. In an effort to minimize data center power consumption, several studies developed power models that can be used for job scheduling either reducing the number of active servers or balancing workloads across ser

Johannes Köhler, Melanie N. Zeilinger

To address feasibility issues in model predictive control (MPC), most implementations relax state constraints by using slack variables and adding a penalty to the cost. We propose an alternative strategy: relaxing the initial state constraint with a penalty. Compared to state-of-the-art soft constrained MPC formulations, the proposed formulation has two key

Alejandra L. Collopy, Julian Schmidt, Dietrich Leibfried, David R. Leibrandt

We use quantum-logic spectroscopy (QLS) and interrogate rotational transitions of a single CaH+ ion with a highly coherent frequency comb, achieving a fractional statistical uncertainty for a transition line center of 4 x 10^-13. We also improve the resolution in measurement of the Stark effect due to the radio-frequency (rf) electric field experienced by a

Boris Khesin, Klas Modin

We describe the geometry of the incompressible porous medium (IPM) equation: we prove that it is a gradient dynamical system on the group of area-preserving diffeomorphisms and has a special double-bracket form. Furthermore, we show its similarities and differences with the dispersionless Toda system. The Toda flow describes an integrable interaction of seve

James Hong, Haotian Zhang, Michaël Gharbi, Matthew Fisher

We introduce the task of spotting temporally precise, fine-grained events in video (detecting the precise moment in time events occur). Precise spotting requires models to reason globally about the full-time scale of actions and locally to identify subtle frame-to-frame appearance and motion differences that identify events during these actions. Surprisingly

Jorge Medina, Roberto Rojas-Cessa, Ziqian Dong, Vatcharapan Umpaichitra

While vaccinations continue to be rolled out to curb the ongoing COVID-19 pandemic, their verification is becoming a requirement for the re-incorporation of individuals into many social activities or travel. Blockchain technology has been widely proposed to manage vaccination records and their verification in many politically-bound regions. However, the high

Robert F. Allen, Colin M. Jackson

In this paper, we study the differentiation operator acting on discrete function spaces; that is spaces of functions defined on an infinite rooted tree. We discuss, through its connection with composition operators, the boundedness and compactness of this operator. In addition, we discuss the operator norm and spectrum, and consider when such an operator can

Joshua Combes, Austin P. Lund

Homodyne measurements are a widely used quantum measurement. Using a coherent state of large amplitude as the local oscillator, it can be shown that the quantum homodyne measurement limits to a field quadrature measurement. In this work, we give an example of a general idea: injecting non-classical states as a local oscillator can led to non-classical measur

Pierre Cardaliaguet, Benjamin Seeger, Panagiotis Souganidis

We study the forward-backward system of stochastic partial differential equations describing a mean field game for a large population of small players subject to both idiosyncratic and common noise. The unique feature of the problem is that the idiosyncratic noise coefficient may be degenerate, so that the system does not admit smooth solutions in general. W

C. M. Newman, N. Read, D. L. Stein

We consider short-range Ising spin glasses in equilibrium at infinite system size, and prove that, for fixed bond realization and a given Gibbs state drawn from a suitable metastate, each translation- and locally-invariant function (for example, self-overlaps) of a single pure state in the decomposition of the Gibbs state takes the same value for all the pur

J. Talukdar, D. Blume

The collective dynamics of two non-interacting two-level emitters, which are coupled to a structured wave guide that supports two-photon bound states, is investigated. Tuning the energy of the two emitters such that they are in resonance with the two-photon bound state energy band, we identify parameter regimes where the system displays fractional population

Loren Coquille, Christof Kuelske, Arnaud Le Ny

We consider $\mathbb Z$-valued $p$-SOS-models with nearest neighbor interactions of the form $|\omega_v-\omega_w|^p$, and finite-spin ferromagnetic models on regular trees. This includes the classical SOS-model, the discrete Gaussian model and the Potts model. We exhibit a family of extremal inhomogeneous (i.e. tree automorphism non-invariant) Gibbs measures

Fatima Albreiki, Sultan Abughazal, Jean Lahoud, Rao Anwer

In recent years, significant progress has been achieved for 3D object detection on point clouds thanks to the advances in 3D data collection and deep learning techniques. Nevertheless, 3D scenes exhibit a lot of variations and are prone to sensor inaccuracies as well as information loss during pre-processing. Thus, it is crucial to design techniques that are

Shamin Achari, Ling Cheng

With advancements in telecommunications, data transmission over increasingly harsher channels that produce synchronisation errors is inevitable. Coding schemes for such channels are available through techniques such as the Davey-MacKay watermark coding; however, this is limited to memoryless channel estimates. Memory must be accounted for to ensure a realist

J. Talukdar, D. Blume

We consider two non-interacting two-level emitters that are coupled weakly to a one-dimensional non-linear wave guide. Due to the Kerr-like non-linearity, the wave guide considered supports -- in addition to the scattering continuum -- a two-body bound state. As such, the wave guide models a bath with non-trivial mode structure. Solving the time-dependent Sc