May 2023 arXiv papers — page 48

Linwei Hu, Vijayan N. Nair, Agus Sudjianto, Aijun Zhang

In the early days of machine learning (ML), the emphasis was on developing complex algorithms to achieve best predictive performance. To understand and explain the model results, one had to rely on post hoc explainability techniques, which are known to have limitations. Recently, with the recognition that interpretability is just as important, researchers ar

Jianxiong Li, Xiao Hu, Haoran Xu, Jingjing Liu

Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance. However, existing methods, effective as they are, suffer from suboptimal performance, limited adaptability, and unsatisfactory computational efficiency. We propose a novel framework

Jianqing Chen, Qian Zhang

In this paper, we consider the quasilinear Schr\"{o}dinger system in $\mathbb R^{N}$($N\geq3$): $$\left\{\begin{align} &-\Delta u+ A(x)u-\frac{1}{2}\triangle(u^{2})u=\frac{2\alpha }{\alpha+\beta}|u|^{\alpha-2}u|v|^{\beta},\\ &-\Delta v+ Bv-\frac{1}{2}\triangle(v^{2})v=\frac{2\beta }{\alpha+\beta}|u|^{\alpha}|v|^{\beta-2}v,\end{align}\right. $$ where $\alpha,

Min Zhang, Fuxun Yu, Yongbo Yu, Minjia Zhang

Federated Learning (FL) is a distributed learning paradigm that empowers edge devices to collaboratively learn a global model leveraging local data. Simulating FL on GPU is essential to expedite FL algorithm prototyping and evaluations. However, current FL frameworks overlook the disparity between algorithm simulation and real-world deployment, which arises

Ruixuan Liu, Yifan Sun, Changliu Liu

This paper studies automatic prototyping using LEGO. To satisfy individual needs and self-sustainability, this paper presents a framework that learns the assembly and disassembly sequences from human demonstrations. In addition, a digital twin is developed to verify the correctness of robot learning before deploying to the real world. Moreover, an end-effect

Peng-Lu Zhao, Xiao-Bin Qiang, Hai-Zhou Lu, X. C. Xie

In this Reply, we respond to the comments in Phys. Rev. Lett. 130, 219702 (2023) on our Phys. Rev. Lett. 127, 176601 (2021) ''Coulomb instabilities of a three-Dimensional higher-order topological insulator". We show the surface gap given in Phys. Rev. Lett. 130, 219701 (2023) is different from the expression derived by using the well-accepted approach and be

Oh-Hyun Kwon, Inho Hong, Woo-Sung Jung, Hang-Hyun Jo

The gravity model of human mobility has successfully described the deterrence of travels with distance in urban mobility patterns. While a broad spectrum of deterrence was found across different cities, yet it is not empirically clear if movement patterns in a single city could also have a spectrum of distance exponents denoting a varying deterrence dependin

W. Oliveira dos Santos, E. R. Bezerra de Mello

In this paper we investigate the vacuum polarization effects associated to a charged quantum massive scalar field on a $(D+1)$-dimensional anti-de Sitter background induced by a magnetic-flux-carrying cosmic string in the braneworld model context. We consider the brane parallel to the anti-de Sitter boundary and the cosmic string orthogonal to them. Moreover

Ke Hu, Bo Li, Tara N. Sainath, Yu Zhang

End-to-end models with large capacity have significantly improved multilingual automatic speech recognition, but their computation cost poses challenges for on-device applications. We propose a streaming truly multilingual Conformer incorporating mixture-of-expert (MoE) layers that learn to only activate a subset of parameters in training and inference. The

Changqing Wang, Oleksandr S Melnychuk, Crispin Contreras-Martinez, Yao Lu

High-quality cavities are crucial for various fundamental physical studies and applications. Here we find that by coupling two cavities directly or via a phase-tunable coupling channel, the photon lifetime of the local field can exceed that of the bare cavities. The cavity photon lifetime is modified by the phases of the initial states and the phase accumula

Natalie Maus, Yimeng Zeng, Daniel Allen Anderson, Phillip Maffettone

Inverse protein folding -- the task of predicting a protein sequence from its backbone atom coordinates -- has surfaced as an important problem in the "top down", de novo design of proteins. Contemporary approaches have cast this problem as a conditional generative modelling problem, where a large generative model over protein sequences is conditioned on the

Marzieh Alireza Mirhoseini, Matthew J. Zahr

This work introduces an empirical quadrature-based hyperreduction procedure and greedy training algorithm to effectively reduce the computational cost of solving convection-dominated problems with limited training. The proposed approach circumvents the slowly decaying $n$-width limitation of linear model reduction techniques applied to convection-dominated p

Dongnan Gui, Kai Chen, Haisong Ding, Qiang Huo

There are more than 80,000 character categories in Chinese while most of them are rarely used. To build a high performance handwritten Chinese character recognition (HCCR) system supporting the full character set with a traditional approach, many training samples need be collected for each character category, which is both time-consuming and expensive. In th

Kwangjun Ahn, Ali Jadbabaie, Suvrit Sra

Modern machine learning applications have witnessed the remarkable success of optimization algorithms that are designed to find flat minima. Motivated by this design choice, we undertake a formal study that (i) formulates the notion of flat minima, and (ii) studies the complexity of finding them. Specifically, we adopt the trace of the Hessian of the cost fu

Masashi Wakamatsu

There recently appear some interesting attempts to explain the AB-effect through the interaction between the charged particle and the solenoid current mediated by the exchange of a virtual photon. A vital assumption of this approach is that AB-phase shift is proportional to the change of the interaction energy between the charged particle and solenoid along

Minh Q. Tram, Joseph M. Cloud, William J. Beksi

As robots become increasingly prominent in diverse industrial settings, the desire for an accessible and reliable system has correspondingly increased. Yet, the task of meaningfully assessing the feasibility of introducing a new robotic component, or adding more robots into an existing infrastructure, remains a challenge. This is due to both the logistics of

Lixin Mao

We introduce the concepts of generalized compatible and cocompatible bimodules in order to characterize Gorenstein projective, injective and flat modules over trivial ring extensions. Let $R\ltimes M$ be a trivial extension of a ring $R$ by an $R$-$R$-bimodule $M$ such that $M$ is a generalized compatible $R$-$R$-bimodule and $\textbf{Z}(R)$ is a generalized

Hsian-Hua Tseng

We consider K-theoretic Gromov-Witten theory of root constructions. We calculate some genus $0$ K-theoretic Gromov-Witten invariants of a root gerbe. We also obtain a K-theoretic relative/orbifold correspondence in genus $0$.

Ping Xiao, Yuanyuan Chen, Weining Bao

Generative AI tools such as ChatGPT have recently gained significant attention in higher education. This study aims to understand how universities establish policies regarding the use of AI tools and explore the factors that influence their decisions. Our study examines ChatGPT policies implemented at universities around the world, including their existence,

Yumiko Hironaka

We consider local densities for $p$-adic quaternion hermitian forms (hermitian forms over a division quaternion algebra over a ${\mathfrak p}$-adic field $k$). The author has studied such forms in connection with spherical functions on the space of quaternion hermitian forms in the previous paper. Obtaining good explicit formulas of local densities is an int

Hui Ouyang

We propose an alternating subgradient method with non-constant step sizes for solving convex-concave saddle-point problems associated with general convex-concave functions. We assume that the sequence of our step sizes is not summable but square summable. Then under the popular assumption of uniformly bounded subgradients, we prove that a sequence of convex

Han Gao, Huiyuan Luo, Fei Shen, Zhengtao Zhang

Although existing image anomaly detection methods yield impressive results, they are mostly an offline learning paradigm that requires excessive data pre-collection, limiting their adaptability in industrial scenarios with online streaming data. Online learning-based image anomaly detection methods are more compatible with industrial online streaming data bu

Jonathan Fisher, Lisa Jeffrey, Alessandro Malusà, Steven Rayan

Let $G$ be a compact Lie group. We study a class of Hamiltonian $(G \times S^{1})$-manifolds decorated with a function $s$ with certain equivariance properties, under conditions on the $G$-action which we call of (semi-)linear type. In this context, a close analogue of hyperk\"ahler reduction is defined, and our main result establishes surjectivity of an app

Xuzhi Tang

If there is a non-residually finite hyperbolic group, then there is a non-residually finite rigid hyperbolic group.

Zhian Jia, Dagomir Kaszlikowski

The measurement statistics for spatial and temporal quantum processes are produced through distinct mechanisms. Measurements that are space-like separated exhibit non-signaling behavior. However, time-like separated measurements can only result in one-way non-signaling, as the past is independent of the future, but the opposite is not true. This work present

Zhaoyou Wang, Mengzhen Zhang, Yat Wong, Changchun Zhong

Quantum transducers convert quantum signals through hybrid interfaces of physical platforms in quantum networks. Modeled as quantum communication channels, performance of unidirectional quantum transduction can be measured by the quantum channel capacity. However, characterizing performance of quantum transducers used for duplex quantum transduction where si

Pedro Henrique Nogueira, Alice Zurlo, Sebastián Pérez, Camilo González-Ruilova

Episodic accretion is a low-mass pre-main sequence phenomenon characterized by sudden outbursts of enhanced accretion. These objects are classified into two: protostars with elevated levels of accretion that lasts for decades or more, called FUors, and protostars with shorter and repetitive bursts, called EXors. HBC 494 is a FUor object embedded in the Orion

Fedor Nazarov, Dmitry Ryabogin, Vladyslav Yaskin

We prove that the length of the projection of the vector joining the centers of mass of a convex body on the plane and of its boundary to an arbitrary direction does not exceed $\frac{1}{6}$ of the body width in this direction. It follows that the distance between these centers of mass does not exceed $\frac16$ of the diameter of the body and $\frac{1}{12}$

Fengran Mo, Kelong Mao, Yutao Zhu, Yihong Wu

In conversational search, the user's real search intent for the current turn is dependent on the previous conversation history. It is challenging to determine a good search query from the whole conversation context. To avoid the expensive re-training of the query encoder, most existing methods try to learn a rewriting model to de-contextualize the current qu

Zheyan Shen, Han Yu, Peng Cui, Jiashuo Liu

To ensure the out-of-distribution (OOD) generalization performance, traditional domain generalization (DG) methods resort to training on data from multiple sources with different underlying distributions. And the success of those DG methods largely depends on the fact that there are diverse training distributions. However, it usually needs great efforts to o

Site Bai, Brian Bullins

Federated learning (FL) approaches for saddle point problems (SPP) have recently gained in popularity due to the critical role they play in machine learning (ML). Existing works mostly target smooth unconstrained objectives in Euclidean space, whereas ML problems often involve constraints or non-smooth regularization, which results in a need for composite op

Shantanu Mandal

Increasing demands in software industry and scarcity of software engineers motivates researchers and practitioners to automate the process of software generation and configuration. Large scale automatic software generation and configuration is a very complex and challenging task. In this proposal, we set out to investigate this problem by breaking down autom

Huy Mai, Wen Huang, Wei Du, Xintao Wu

The shift between the training and testing distributions is commonly due to sample selection bias, a type of bias caused by non-random sampling of examples to be included in the training set. Although there are many approaches proposed to learn a classifier under sample selection bias, few address the case where a subset of labels in the training set are mis

Yuzhe Lu, Yilong Qin, Runtian Zhai, Andrew Shen

Out-of-distribution (OOD) data poses serious challenges in deployed machine learning models, so methods of predicting a model's performance on OOD data without labels are important for machine learning safety. While a number of methods have been proposed by prior work, they often underestimate the actual error, sometimes by a large margin, which greatly impa

Dai Shi, Zhiqi Shao, Yi Guo, Qibin Zhao

This paper presents a comprehensive theoretical analysis of the graph p-Laplacian regularized framelet network (pL-UFG) to establish a solid understanding of its properties. We conduct a convergence analysis on pL-UFG, addressing the gap in the understanding of its asymptotic behaviors. Further by investigating the generalized Dirichlet energy of pL-UFG, we

Benjamin Elder, Jeremy Sakstein

We compute bounds from atomic spectroscopy on chameleon fields that couple to the photon. Chameleons are a wide class of scalar field models that generically lead to screened fifth forces and a host of novel phenomenologies, particularly when the photon coupling is included. We account for perturbations to the atomic energy levels from both the scalar field

Jordan Kodner, Sarah Payne, Salam Khalifa, Zoey Liu

Morphological inflection is a popular task in sub-word NLP with both practical and cognitive applications. For years now, state-of-the-art systems have reported high, but also highly variable, performance across data sets and languages. We investigate the causes of this high performance and high variability; we find several aspects of data set creation and e

Xiaowei Li, Taixia Shi, Dong Ma, Yang Chen

In this article, analog microwave short-time Fourier transform (STFT) with improved measurement performance is implemented in the optical domain by employing stimulated Brillouin scattering (SBS) and channelization. By jointly using three optical frequency combs and filter- and SBS-based frequency-to-time mapping (FTTM), the time-frequency information of the

Xincheng Cao, Haochong Chen, Sukru Yaren Gelbal, Bilin Aksun-Guvenc

The current approach to connected and autonomous driving function development and evaluation uses model-in-the-loop simulation, hardware-in-the-loop simulation, and limited proving ground work followed by public road deployment of beta version of software and technology. The rest of the road users are involuntarily forced into taking part in the development

Jeff Shen, Joshua S. Speagle, J. Ted Mackereth, Yuan-Sen Ting

Stellar ages are key for determining the formation history of the Milky Way, but are difficult to measure precisely. Furthermore, methods that use chemical abundances to infer ages may entangle the intrinsic evolution of stars with the chemodynamical evolution of the Galaxy. In this paper, we present a framework for making probabilistic predictions of stella

Benjamin Dodson

In this paper we prove global well-posedness and scattering for the conformal, defocusing, nonlinear wave equation with radial initial data in the critical Sobolev space, for dimensions $d \geq 4$. This result extends a previous result proving sharp scattering in the three dimensional case.

Colin Cooper, Tomasz Radzik, Takeharu Shiraga

We consider a type of pull voting suitable for discrete numeric opinions which can be compared on a linear scale, for example, 1 ('disagree strongly'), 2 ('disagree'), $\ldots,$ 5 ('agree strongly'). On observing the opinion of a random neighbour, a vertex changes its opinion incrementally towards the value of the neighbour's opinion, if different. For opini

Mingyang Guo, Hongliang Lu, Xing Peng

Let $n,s,$ and $k$ be positive integers such that $k\geq 3$, $s\geq 3$ and $n\geq ks$. An $s$-matching $M_s$ in a $k$-uniform hypergraph is a set of $s$ pairwise disjoint edges. The anti-Ramsey number $\textrm{ar}(n,k,M_s)$ of an $s$-matching is the smallest integer $c$ such that each edge-coloring of the $n$-vertex $k$-uniform complete hypergraph with exact

Yong Jin Daniel Kim, David Vargas

We consider a downlink multicast and unicast superposition transmission in multi-layer Multiple-Input Multiple-Output (MIMO) Orthogonal Frequency Division Multiple Access (OFDMA) systems when only the statistical channel state information is available at the transmitter (CSIT). Multiple users can be scheduled by using the time/frequency resources in OFDMA, w

Liangyuan Na, Kimberly Villalobos Carballo, Jean Pauphilet, Ali Haddad-Sisakht

Problem definition: Access to accurate predictions of patients' outcomes can enhance medical staff's decision-making, which ultimately benefits all stakeholders in the hospitals. A large hospital network in the US has been collaborating with academics and consultants to predict short-term and long-term outcomes for all inpatients across their seven hospitals

Cheongho Han, Youn Kil Jung, Ian A. Bond, Andrew Gould

Recently, there have been reports of various types of degeneracies in the interpretation of planetary signals induced by planetary caustics. In this work, we check whether such degeneracies persist in the case of well-covered signals by analyzing the lensing event KMT-2021-BLG-1150, for which the light curve exhibits a densely and continuously covered short-

Shuhui Yu, Lijun Ji

A subspace of a finite field is called a Sidon space if the product of any two of its nonzero elements is unique up to a scalar multiplier from the base field. Sidon spaces, introduced by Roth et al. (IEEE Trans Inf Theory 64(6): 4412-4422, 2018), have a close connection with optimal full-length orbit codes. In this paper, we present two constructions of Sid

Vestislav Apostolov, Charles Cifarelli

For each partition of the positive integer $n= \ell +\sum_{j=1}^\ell d_j$, where $\ell\ge 1$ and $d_j \ge 0$ are integers, we construct a continuous $(\ell-1)$-parameter family of explicit complete gradient steady K\"ahler--Ricci solitons on $\mathbb{C}^n$ admitting a hamiltonian $2$-form of order $\ell$ and symmetry group ${\rm U}(d_1+ 1) \times \cdots \tim

Adrian Chapman, Samuel J. Elman, Ryan L. Mann

We show that a quantum spin system has an exact description by non-interacting fermions if its frustration graph is claw-free and contains a simplicial clique. The frustration graph of a spin model captures the pairwise anticommutation relations between Pauli terms of its Hamiltonian in a given basis. This result captures a vast family of known free-fermion

Y. Yamaguchi, W. Horiuchi, T. Ichikawa, N. Itagaki

Background: The four-neutron correlation has been attracting much attention for decades. In addition to the study on the tetra-neutron system, it is worthwhile to investigate the correlation in bound systems. Purpose: The $^8$He nucleus is a system where four neutrons are weakly bound around the $^4$He core. The dineutron ($2n$) correlation has been long dis

Blake Temple, Robin Young

We prove the existence of ``pure tone'' nonlinear sound waves of all frequencies. These are smooth, space and time periodic, oscillatory solutions of the $3\times3$ compressible Euler equations in one space dimension. Being perturbations of solutions of a linear wave equation, they provide a rigorous justification for the centuries old theory of Acoustics. I

Paiheng Xu, Yuhang Zhou, Bang An, Wei Ai

Given the growing concerns about fairness in machine learning and the impressive performance of Graph Neural Networks (GNNs) on graph data learning, algorithmic fairness in GNNs has attracted significant attention. While many existing studies improve fairness at the group level, only a few works promote individual fairness, which renders similar outcomes for

Xumei Xi, Christina Lee Yu, Yudong Chen

We consider offline Reinforcement Learning (RL), where the agent does not interact with the environment and must rely on offline data collected using a behavior policy. Previous works provide policy evaluation guarantees when the target policy to be evaluated is covered by the behavior policy, that is, state-action pairs visited by the target policy must als

Zinan Lin, Sivakanth Gopi, Janardhan Kulkarni, Harsha Nori

Generating differentially private (DP) synthetic data that closely resembles the original private data is a scalable way to mitigate privacy concerns in the current data-driven world. In contrast to current practices that train customized models for this task, we aim to generate DP Synthetic Data via APIs (DPSDA), where we treat foundation models as blackbox

Bikash Kanungo, Jeffrey Hatch, Paul M. Zimmerman, Vikram Gavini

The conventional approaches to the inverse density functional theory problem typically assume non-degeneracy of the Kohn-Sham (KS) eigenvalues, greatly hindering their use in open-shell systems. We present a generalization of the inverse density functional theory problem that can seamlessly admit degenerate KS eigenvalues. Additionally, we allow for fraction

Kazuto Akiba, Tatsuo C. Kobayashi

We investigated the electronic structure and superconducting properties of single-crystalline LaCuSb$_2$. The resistivity, magnetization, and specific heat measurements showed that LaCuSb$_2$ is a bulk superconductor. The observed Shubnikov-de Haas oscillation and magnetic field dependence of the Hall resistivity can be reasonably understood assuming a sligh

Zhong Yi Wan, Ricardo Baptista, Yi-fan Chen, John Anderson

We introduce a two-stage probabilistic framework for statistical downscaling using unpaired data. Statistical downscaling seeks a probabilistic map to transform low-resolution data from a biased coarse-grained numerical scheme to high-resolution data that is consistent with a high-fidelity scheme. Our framework tackles the problem by composing two transforma

Pranav Kulkarni, Sean Garin, Adway Kanhere, Eliot Siegel

As the adoption of Artificial Intelligence (AI) systems within the clinical environment grows, limitations in bandwidth and compute can create communication bottlenecks when streaming imaging data, leading to delays in patient care and increased cost. As such, healthcare providers and AI vendors will require greater computational infrastructure, therefore dr

Anthony Gruber, Kookjin Lee, Nathaniel Trask

Recent works have shown that physics-inspired architectures allow the training of deep graph neural networks (GNNs) without oversmoothing. The role of these physics is unclear, however, with successful examples of both reversible (e.g., Hamiltonian) and irreversible (e.g., diffusion) phenomena producing comparable results despite diametrically opposed mechan

Bogdan Alecu, Maria Chudnovsky, Sepehr Hajebi, Sophie Spirkl

The celebrated Erd\H{o}s-P\'{o}sa Theorem, in one formulation, asserts that for every $c\geq 1$, graphs with no subgraph (or equivalently, minor) isomorphic to the disjoint union of $c$ cycles have bounded treewidth. What can we say about the treewidth of graphs containing no induced subgraph isomorphic to the disjoint union of $c$ cycles? Let us call these

Ido Ben-Shaul, Ravid Shwartz-Ziv, Tomer Galanti, Shai Dekel

Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge. This paper presents an in-depth empirical analysis of SSL-trained representations, encompassing diverse models, architectures, and hyperparameters. Our study reveals an intriguing aspect of

R. N. Araújo dos Santos, D. Dreibelbis, M. F. Ribeiro, I. D. Santamaría Guarín

In this paper, we introduce a new regularity condition that characterizes the tameness of a composite singularity $H=G\circ F$ in a sharp way. Our approach provides a natural tool that links the topology of the Milnor tube fibrations through the Milnor fibers of the respective components of the map germs $F$, $G$ and $H = G\circ F$. We also study the invaria

Pavlo Melnyk, Michael Felsberg, Mårten Wadenbäck, Andreas Robinson

In this paper, we utilize hyperspheres and regular $n$-simplexes and propose an approach to learning deep features equivariant under the transformations of $n$D reflections and rotations, encompassed by the powerful group of O$(n)$. Namely, we propose O$(n)$-equivariant neurons with spherical decision surfaces that generalize to any dimension $n$, which we c

Jungtaek Kim

Bayesian optimization has attracted huge attention from diverse research areas in science and engineering, since it is capable of efficiently finding a global optimum of an expensive-to-evaluate black-box function. In general, a probabilistic regression model is widely used as a surrogate function to model an explicit distribution over function evaluations g

Gaotang Li, Danai Koutra, Yujun Yan

We address the key challenge of size-induced distribution shifts in graph neural networks (GNNs) and their impact on the generalization of GNNs to larger graphs. Existing literature operates under diverse assumptions about distribution shifts, resulting in varying conclusions about the generalizability of GNNs. In contrast to prior work, we adopt a data-driv

Daniel Domínguez-Vázquez, Gustaaf B. Jacobs

A predictive, point-cloud tracer is presented that determines with a quantified uncertainty the Lagrangian motion of a group of point-particles within a finite region. The tracer assumes a random forcing within confidence intervals to account for the empiricism of data-driven force models and stochasticity related to the chaotic nature of the subcloud scale

G. Jordan Maclay

Understanding the hydrogen atom has been at the heart of modern physics. Exploring the symmetry of the most fundamental two body system has led to advances in atomic physics, quantum mechanics, quantum electrodynamics, and elementary particle physics. In this pedagogic review we present an integrated treatment of the symmetries of the Schrodinger hydrogen at

YoonHaeng Hur, Tengyuan Liang

Detecting weak, systematic distribution shifts and quantitatively modeling individual, heterogeneous responses to policies or incentives have found increasing empirical applications in social and economic sciences. Given two probability distributions $P$ (null) and $Q$ (alternative), we study the problem of detecting weak distribution shift deviating from th

Halil Ibrahim Aysel, Xiaohao Cai, Adam Prügel-Bennett

Semantic segmentation is a critical task in computer vision aiming to identify and classify individual pixels in an image, with numerous applications in for example autonomous driving and medical image analysis. However, semantic segmentation can be highly challenging particularly due to the need for large amounts of annotated data. Annotating images is a ti

Unnati Kashyap, Manoneeta Chakraborty, Sudip Bhattacharyya, Biki Ram

We report a broadband investigation of the Z-type neutron star (NS) low mass X-ray binary (LMXB) GX 349+2 using AstroSat and NICER. AstroSat observed the source exhibiting large scale variability in its normal branch (NB) /flaring branch (FB) vertex and flaring branch (FB) and a moderate evolution during NICER observations. The power spectra exhibit very low

Fabrizio Arrigoni Battaia, Aura Obreja, Chian-Chou Chen, Marta Nowotka

We conducted a systematic survey of the environment of high-z quasars at submillimeter wavelengths to unveil and characterize the surrounding distribution of dusty submillimeter galaxies (SMGs). We took sensitive JCMT/SCUBA-2 observations for 3 enormous Lyman-alpha nebulae (ELANe) and 17 quasar fields in the redshift range 2<z<4.2 selected from recent Ly$\al

Guy Tennenholtz, Martin Mladenov, Nadav Merlis, Robert L. Axtell

While popularity bias is recognized to play a crucial role in recommmender (and other ranking-based) systems, detailed analysis of its impact on collective user welfare has largely been lacking. We propose and theoretically analyze a general mechanism, rooted in many of the models proposed in the literature, by which item popularity, item quality, and positi

Eran Hirsch, Valentina Pyatkin, Ruben Wolhandler, Avi Caciularu

Tasks involving text generation based on multiple input texts, such as multi-document summarization, long-form question answering and contemporary dialogue applications, challenge models for their ability to properly consolidate partly-overlapping multi-text information. However, these tasks entangle the consolidation phase with the often subjective and ill-

Amer Alrakik, Miguel Escobar Azor, Véronique Brumas, Gian Luigi Bendazzoli

In its original version, the Thomson problem consists of the search for the minimum-energy configuration of a set of point-like electrons that are confined to the surface of a two-dimensional sphere (${\cal S}^2$) that repel each other according to Coulomb's law, in which the distance is the Euclidean distance in the embedding space of the sphere, {\em i.e.}

Artur P. Toshev, Gianluca Galletti, Johannes Brandstetter, Stefan Adami

We contribute to the vastly growing field of machine learning for engineering systems by demonstrating that equivariant graph neural networks have the potential to learn more accurate dynamic-interaction models than their non-equivariant counterparts. We benchmark two well-studied fluid-flow systems, namely 3D decaying Taylor-Green vortex and 3D reverse Pois

Song Bo, Bernard T. Agyeman, Xunyuan Yin, Jinfeng Liu

Reinforcement learning (RL) is an area of significant research interest, and safe RL in particular is attracting attention due to its ability to handle safety-driven constraints that are crucial for real-world applications. This work proposes a novel approach to RL training, called control invariant set (CIS) enhanced RL, which leverages the advantages of ut

Michael Gogins

This essay recounts my personal journey towards a deeper understanding of the mathematical foundations of algorithmic music composition. I do not spend much time on specific mathematical algorithms used by composers; rather, I focus on general issues such as fundamental limits and possibilities, by analogy with metalogic, metamathematics, and computability t

Gaku Liu, Alexander Mason

We study the behavior of $h$-vectors associated to matroid complexes under weak maps, or inclusions of matroid polytopes. Specifically, we show that the $h$-vector of the order complex of the lattice of flats of a matroid is component-wise non-increasing under a weak map. This result extends to the flag $h$-vector. We note that the analogous result also hold

Qiaohao Liang, Martin Z. Bazant

As the design of single-component battery electrodes has matured, the battery industry has turned to hybrid electrodes with blends of two or more active materials to enhance battery performance. Leveraging the best properties of each material while mitigating their drawbacks, multi-component hybrid electrodes open a vast new design space that could be most e

Hiroki Yagisita

The standard Laplacian $-\triangle_{\mathbb R^n}$ in $L^2(\mathbb R^n)$ is self-adjoint and translation invariant on the finite-dimensional linear space $\mathbb R^n$. In this paper, we define a translation invariant operator $-\triangle_{\mathbb R^\infty}$ on $\mathbb R^\infty$ as a non-negative self-adjoint operator in some non-separable Hilbert space $L^2

Suzanna Parkinson, Greg Ongie, Rebecca Willett

Neural networks often operate in the overparameterized regime, in which there are far more parameters than training samples, allowing the training data to be fit perfectly. That is, training the network effectively learns an interpolating function, and properties of the interpolant affect predictions the network will make on new samples. This manuscript expl

Pengcheng Jiang, Shivam Agarwal, Bowen Jin, Xuan Wang

The mission of open knowledge graph (KG) completion is to draw new findings from known facts. Existing works that augment KG completion require either (1) factual triples to enlarge the graph reasoning space or (2) manually designed prompts to extract knowledge from a pre-trained language model (PLM), exhibiting limited performance and requiring expensive ef

Jamison W. Weber, Dhanush R. Giriyan, Devendra R. Parkar, Dimitri P. Bertsekas

In this work we consider a generalization of the well-known multivehicle routing problem: given a network, a set of agents occupying a subset of its nodes, and a set of tasks, we seek a minimum cost sequence of movements subject to the constraint that each task is visited by some agent at least once. The classical version of this problem assumes a central co

Alexander Davydov, Veronica Centorrino, Anand Gokhale, Giovanni Russo

In this article, we provide a novel and broadly-applicable contraction-theoretic approach to continuous-time time-varying convex optimization. For any parameter-dependent contracting dynamics, we show that the tracking error is asymptotically proportional to the rate of change of the parameter and that the proportionality constant is upper bounded by Lipschi

Haonan Duan, Adam Dziedzic, Nicolas Papernot, Franziska Boenisch

Large language models (LLMs) are excellent in-context learners. However, the sensitivity of data contained in prompts raises privacy concerns. Our work first shows that these concerns are valid: we instantiate a simple but highly effective membership inference attack against the data used to prompt LLMs. To address this vulnerability, one could forego prompt

Xuan Li

The high-luminosity high-energy Electron-Ion Collider (EIC) to be built at Brookhaven National Laboratory (BNL) will provide a clean environment to study several fundamental questions in the high energy and nuclear physics fields. A high granularity and low material budget vertex and tracking detector is required to provide precise measurements of primary an

Leonardo V. Santoro, Victor M. Panaretos

We establish a strong law of large numbers and a central limit theorem in the Bures-Wasserstein space of covariance operators -- or equivalently centred Gaussian measures -- over a general separable Hilbert space. Specifically, we show that empirical barycentre sequences indexed by sample size are almost certainly relatively compact, with accumulation points

Yunhao Ge, Yuecheng Li, Di Wu, Ao Xu

In Lifelong Learning (LL), agents continually learn as they encounter new conditions and tasks. Most current LL is limited to a single agent that learns tasks sequentially. Dedicated LL machinery is then deployed to mitigate the forgetting of old tasks as new tasks are learned. This is inherently slow. We propose a new Shared Knowledge Lifelong Learning (SKI

Marc Boubnovski Martell, Kristofer Linton-Reid, Sumeet Hindocha, Mitchell Chen

The rich chemical information from tissue metabolomics provides a powerful means to elaborate tissue physiology or tumor characteristics at cellular and tumor microenvironment levels. However, the process of obtaining such information requires invasive biopsies, is costly, and can delay clinical patient management. Conversely, computed tomography (CT) is a c

Murat Gozu, Mumin Tolga Emirler, Ismail Meric Can Uygan, Tevfik Ali Boke

This paper is on the automated driving architecture and operation of a light commercial vehicle. Simple longitudinal and lateral dynamic models of the vehicle and a more detailed CarSim model are developed and used in simulations and controller design and evaluation. Experimental validation is used to make sure that the models used represent the actual respo

Andres A. Contreras Hip, Ewain Gwynne

Let $h$ be a log-correlated Gaussian field on $\R^d$, let $\gamma \in (0,\sqrt{2d}),$ let $\mu_h$ be the $\gamma$-Gaussian multiplicative chaos measure, and let $D_h$ be an exponential metric associated with $h$ satisfying certain natural axioms. In the special case when $d=2$, this corresponds to the Liouville quantum gravity (LQG) measure and metric. We sh

Salijona Dyrmishi, Salah Ghamizi, Maxime Cordy

Natural Language Processing (NLP) models based on Machine Learning (ML) are susceptible to adversarial attacks -- malicious algorithms that imperceptibly modify input text to force models into making incorrect predictions. However, evaluations of these attacks ignore the property of imperceptibility or study it under limited settings. This entails that adver

Ahmed A. Elhag, Yuyang Wang, Joshua M. Susskind, Miguel Angel Bautista

We present Manifold Diffusion Fields (MDF), an approach that unlocks learning of diffusion models of data in general non-Euclidean geometries. Leveraging insights from spectral geometry analysis, we define an intrinsic coordinate system on the manifold via the eigen-functions of the Laplace-Beltrami Operator. MDF represents functions using an explicit parame

António Girão, Kevin Hendrey, Freddie Illingworth, Florian Lehner

Scott and Seymour conjectured the existence of a function $f \colon \mathbb{N} \to \mathbb{N}$ such that, for every graph $G$ and tournament $T$ on the same vertex set, $\chi(G) \geqslant f(k)$ implies that $\chi(G[N_T^+(v)]) \geqslant k$ for some vertex $v$. In this note we disprove this conjecture even if $v$ is replaced by a vertex set of size $\mathcal{O

Julio Arroyo, Pietro Perona, Elijah Cole

Annotating data for multi-label classification is prohibitively expensive because every category of interest must be confirmed to be present or absent. Recent work on single positive multi-label (SPML) learning shows that it is possible to train effective multi-label classifiers using only one positive label per image. However, the standard benchmarks for SP

Mingxiao Li, Tingyu Qu, Ruicong Yao, Wei Sun

Diffusion Probabilistic Models (DPM) have shown remarkable efficacy in the synthesis of high-quality images. However, their inference process characteristically requires numerous, potentially hundreds, of iterative steps, which could exaggerate the problem of exposure bias due to the training and inference discrepancy. Previous work has attempted to mitigate

Debarati Das, David Ma, Dongyeop Kang

Text style transfer is an exciting task within the field of natural language generation that is often plagued by the need for high-quality paired datasets. Furthermore, training a model for multi-attribute text style transfer requires datasets with sufficient support across all combinations of the considered stylistic attributes, adding to the challenges of

Fan Dong, Ali Abbasi, Henry Leung, Xin Wang

Federated learning offers a promising approach under the constraints of networking and data privacy constraints in aerial and space networks (ASNs), utilizing large-scale private edge data from drones, balloons, and satellites. Existing research has extensively studied the optimization of the learning process, computing efficiency, and communication overhead

Eric Hedlin, Gopal Sharma, Shweta Mahajan, Hossam Isack

Text-to-image diffusion models are now capable of generating images that are often indistinguishable from real images. To generate such images, these models must understand the semantics of the objects they are asked to generate. In this work we show that, without any training, one can leverage this semantic knowledge within diffusion models to find semantic

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The associated production of prompt $J/\psi$ and $\mathit{\mathit{\Upsilon}}$ mesons in $pp$ collisions at a centre-of-mass energy of $\sqrt{s}=13\,\mathrm{TeV}$ is studied using LHCb data, corresponding to an integrated luminosity of $4\,\mathrm{fb}^{-1}$. The measurement is performed for $J/\psi$ ($\mathit{\Upsilon}$) mesons with a transverse momentum $p_{