May 2023 arXiv papers — page 104

Manuel Lagunas, Brayan Impata, Victor Martinez, Virginia Fernandez

Fine-grained classification is a challenging task that involves identifying subtle differences between objects within the same category. This task is particularly challenging in scenarios where data is scarce. Visual transformers (ViT) have recently emerged as a powerful tool for image classification, due to their ability to learn highly expressive represent

Magalie Bénéfice

The Lie groups $SU(2)$ and $SL(2,\mathbb{R})$ can be viewed as model spaces in subRiemannian geometry. Coupling two subelliptic Brownian motions on $SU(2)$ (resp. $SL(2,\mathbb{R})$) consists in coupling two Brownian motions on the sphere (resp. the hyperbolic plane) and simultaneously their swept areas. Using this approach we propose an explicit constructio

Gilles Dowek, Benjamin Werner

If the sequent (Gamma entails forall x exists y A) is provable in first order constructive natural deduction, then the theory (Gamma, forall x (f (x)/y)A), where f is a new function symbol, is a conservative extension of Gamma.

Shirong Xu, Will Wei Sun, Guang Cheng

Synthetic data algorithms are widely employed in industries to generate artificial data for downstream learning tasks. While existing research primarily focuses on empirically evaluating utility of synthetic data, its theoretical understanding is largely lacking. This paper bridges the practice-theory gap by establishing relevant utility theory in a statisti

Mrittika Chakraborty, Wreetbhas Pal, Sanghamitra Bandyopadhyay, Ujjwal Maulik

Deep learning models form one of the most powerful machine learning models for the extraction of important features. Most of the designs of deep neural models, i.e., the initialization of parameters, are still manually tuned. Hence, obtaining a model with high performance is exceedingly time-consuming and occasionally impossible. Optimizing the parameters of

Chengcheng Han, Liqing Cui, Renyu Zhu, Jianing Wang

Large pre-trained language models (PLMs) have garnered significant attention for their versatility and potential for solving a wide spectrum of natural language processing (NLP) tasks. However, the cost of running these PLMs may be prohibitive. Furthermore, PLMs may not be open-sourced due to commercial considerations and potential risks of misuse, such as G

Gilles Dowek

Given a first-order theory and a proof that it is consistent, can we design a proof-search method for this theory that fails in finite time when it attempts to prove the formula False?

Yi Zhou, Dingpeng Liao, Kun Zhang, Zijie Ma

The far-field resolution of optical imaging systems is restricted by the Abbe diffraction limit, a direct result of the wave nature of light. One successful technological approach to circumventing this limit is to reduce the effective size of a point-spread-function. In the past decades, great endeavors have been made to engineer an effective point-spread-fu

Hongrui Chen, Aditya Joglekar, Levent Burak Kara

We propose conditioning field initialization for neural network based topology optimization. In this work, we focus on (1) improving upon existing neural network based topology optimization, (2) demonstrating that by using a prior initial field on the unoptimized domain, the efficiency of neural network based topology optimization can be further improved. Ou

Siyue Wu, Hongzhan Chen, Xiaojun Quan, Qifan Wang

Knowledge distillation has attracted a great deal of interest recently to compress pre-trained language models. However, existing knowledge distillation methods suffer from two limitations. First, the student model simply imitates the teacher's behavior while ignoring the underlying reasoning. Second, these methods usually focus on the transfer of sophistica

Hadjer Benmeziane, Corey Lammie, Irem Boybat, Malte Rasch

The advancement of Deep Learning (DL) is driven by efficient Deep Neural Network (DNN) design and new hardware accelerators. Current DNN design is primarily tailored for general-purpose use and deployment on commercially viable platforms. Inference at the edge requires low latency, compact and power-efficient models, and must be cost-effective. Digital proce

Jinshun Shen, Deyun Wei

In this letter, based on the variational model, we propose a novel time-frequency post-processing technique to approximate the ideal time-frequency representation. Our method has the advantage of modularity, enabling "plug and play", independent of the performance of specific time-frequency analysis tool. Therefore, it can be easily generalized to the fracti

Shengxiang Lv

The nearly complete bipartite graph $G(m,n,k)$ is obtained by removing $k$ independent edges from the complete bipartite graph $K_{m,n}$. In this paper, we prove that for any nearly complete bipartite graph $G(m,n,k)$ with $m, n\geq 3$, and $(m,n,k)\notin\{(5,4,4)$, $(4,5,4)$, $(5,5,5)\}$, there exists a nonorientable genus embedding $\Pi$ satisfying $\tilde

Lishun Wang, Miao Cao, Xin Yuan

Video snapshot compressive imaging (SCI) uses a two-dimensional detector to capture consecutive video frames during a single exposure time. Following this, an efficient reconstruction algorithm needs to be designed to reconstruct the desired video frames. Although recent deep learning-based state-of-the-art (SOTA) reconstruction algorithms have achieved good

Alexander H. Liu, Heng-Jui Chang, Michael Auli, Wei-Ning Hsu

In this paper, we introduce self-distillation and online clustering for self-supervised speech representation learning (DinoSR) which combines masked language modeling, self-distillation, and online clustering. We show that these concepts complement each other and result in a strong representation learning model for speech. DinoSR first extracts contextualiz

Hafez M. Garmaroudi, S. Sandeep Pradhan, Jun Chen

We consider the rate-limited quantum-to-classical optimal transport in terms of output-constrained rate-distortion coding for both finite-dimensional and continuous-variable quantum-to-classical systems with limited classical common randomness. The main coding theorem provides a single-letter characterization of the achievable rate region of a lossy quantum

Thomas Vigouroux, Marius Bozga, Cristian Ene, Laurent Mounier

Given a boolean formula $\Phi$(X, Y, Z), the Max\#SAT problem asks for finding a partial model on the set of variables X, maximizing its number of projected models over the set of variables Y. We investigate a strict generalization of Max\#SAT allowing dependencies for variables in X, effectively turning it into a synthesis problem. We show that this new pro

Xiaote Xu, Zhong Lin Wang, Zhengbao Yang

Static metal-semiconductor contacts are classified into Ohmic contacts and Schottky contacts. As for dynamic metal-semiconductor contacts, the in-depth mechanism remains to be studied. We here define a "triboelectric junction" model for analyzing dynamic metal-semiconductor contacts, where a space charge region induced by the triboelectric effect dominates t

Geeta Rangwal, R. K. S. Yadav, D. Bisht, Alok Durgapal

We present the intra-cluster kinematics and dynamics of three open clusters: NGC 1193, NGC 2355, and King 12 by incorporating kinematical and photometric data from Gaia DR3, as well as a ground-based telescope. After selecting cluster members based on proper motion data, clusters' fundamental and structural parameters are investigated. We found the clusters

Albert Wong, Steven Whang, Emilio Sagre, Niha Sachin

Creating accurate predictions in the stock market has always been a significant challenge in finance. With the rise of machine learning as the next level in the forecasting area, this research paper compares four machine learning models and their accuracy in forecasting three well-known stocks traded in the NYSE in the short term from March 2020 to May 2022.

Yo Kobayashi

This study demonstrates that the soft biological tissues of humans can be used as a type of soft body in physical reservoir computing. Soft biological tissues possess characteristics such as stress-strain nonlinearity and viscoelasticity that satisfy the requirements for physical reservoir computing, including nonlinearity and memory. The aim of this study w

Haoming Ma, Xiaojun Yuan, Zhi Ding

To address the limitations of traditional over-the-air federated learning (OA-FL) such as limited server coverage and low resource utilization, we propose an OA-FL in MIMO cloud radio access network (MIMO Cloud-RAN) framework, where edge devices upload (or download) model parameters to the cloud server (CS) through access points (APs). Specifically, in every

Ricardo Gallego Torromé

It is shown that quantum illumination with three photons non-Gaussian states, where the signal is described by a two photons state and the idler is described by a one photon state, can outperform in sensitivity standard Gaussian quantum illumination in a high noisy background. In particular, there is a reduction in the probability due to an increase in the p

Leo Egghe

We define Hirsch-type equations and bundles being common generalizations of the defining equations of e.g. Hirsch-bundles, g-bundles and Kosmulski-bundles. In this way, common properties of alle these bundles can be proved. The main result proves basic inequalities for these bundles. They form the basis for convergence results as well as for criteria for the

Di Wang, Jinyuan Liu, Risheng Liu, Xin Fan

This research focuses on the discovery and localization of hidden objects in the wild and serves unmanned systems. Through empirical analysis, infrared and visible image fusion (IVIF) enables hard-to-find objects apparent, whereas multimodal salient object detection (SOD) accurately delineates the precise spatial location of objects within the picture. Their

Javier Cembrano, Felix Fischer, Max Klimm

We give new bounds for the single-nomination model of impartial selection, a problem proposed by Holzman and Moulin (Econometrica, 2013). A selection mechanism, which may be randomized, selects one individual from a group of $n$ based on nominations among members of the group; a mechanism is impartial if the selection of an individual is independent of nomin

Alcides Buss, Siegfried Echterhoff

We revisit the procedure of deformation of $C^*$-algebras via coactions of locally compact groups and extend the methods to cover deformations for maximal, reduced, and exotic coactions for a given group $G$ and circle-valued Borel $2$-cocycles on $G$. In the special case of reduced (or normal) coactions our deformation method substantially differs from -- b

Ye-Cong Wan, Ming-Wen Shao, Yuan-Shuo Cheng, Yue-Xian Liu

Adverse conditions typically suffer from stochastic hybrid weather degradations (e.g., rainy and hazy night), while existing image restoration algorithms envisage that weather degradations occur independently, thus may fail to handle real-world complicated scenarios. Besides, supervised training is not feasible due to the lack of a comprehensive paired datas

Guy Bresler, Chenghao Guo, Yury Polyanskiy

We aim to understand the extent to which the noise distribution in a planted signal-plus-noise problem impacts its computational complexity. To that end, we consider the planted clique and planted dense subgraph problems, but in a different ambient graph. Instead of Erd\H{o}s-R\'enyi $G(n,p)$, which has independent edges, we take the ambient graph to be the

Jie Zhang, Qing-Tian Xu, Qiu-Shi Zhu, Zhen-Hua Ling

Time-domain single-channel speech enhancement (SE) still remains challenging to extract the target speaker without any prior information on multi-talker conditions. It has been shown via auditory attention decoding that the brain activity of the listener contains the auditory information of the attended speaker. In this paper, we thus propose a novel time-do

Weijia Xu, Andrzej Banburski-Fahey, Nebojsa Jojic

We introduce Reprompting, an iterative sampling algorithm that automatically learns the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, Reprompting infers the CoT recipes that work consistently well for a set of training samples by iteratively sampling new recipes using previously sampled recipes as parent

Hao Lan Zhang, Yun Xue, Yifan Lu, Sanghyuk Lee

Virtual Reality (VR), Augmented Reality (AR), Mixed Reality (MR), digital twin, Metaverse and other related digital technologies have attracted much attention in recent years. These new emerging technologies are changing the world significantly. This research introduces a fusion model, i.e. Fusion Universe (FU), where the virtual, physical, and cognitive wor

Ningbo Li, Pablo Borja, Arjan van der Schaft, Jacquelien M. A. Scherpen

This paper proposes a port-Hamiltonian framework for angle-based formation stabilization and maneuvers using bearing and velocity measurements with an underlying triangulated Laman graph. The corresponding port-Hamiltonian controller is designed using virtual couplings on the errors of angle constraints in angle space and then the angle constraints and agent

Xiaolin Chen, Xuemeng Song, Yinwei Wei, Liqiang Nie

Textual response generation is an essential task for multimodal task-oriented dialog systems.Although existing studies have achieved fruitful progress, they still suffer from two critical limitations: 1) focusing on the attribute knowledge but ignoring the relation knowledge that can reveal the correlations between different entities and hence promote the re

Song Jiang, Xiangxiang Su, Feng Xie

This paper concerns the sharp interface limit of solutions to the inhomogeneous incompressible Navier-Stokes/Allen-Cahn coupled system in a bounded domain $\Omega \subset \mathbb{R}^n,\ n =2,3$. Based on a relative energy method, we prove that the solutions to the Navier-Stokes/Allen-Cahn system converge to the corresponding solutions to a sharp interface mo

Heng Ma, Yan-Xia Ren

Consider a two-type reducible branching Brownian motion in which particles' diffusion coefficients and branching rates are influenced by their types. Here reducible means that type 1 particles can produce particles of type 1 and type 2, but type 2 particles can only produce particles of type 2. The maximum of this process is determined by two parameters: the

Jesper Jansson, Christos Levcopoulos, Andrzej Lingas, Valentin Polishchuk

We consider geometric problems on planar $n^2$-point sets in the congested clique model. Initially, each node in the $n$-clique network holds a batch of $n$ distinct points in the Euclidean plane given by $O(\log n)$-bit coordinates. In each round, each node can send a distinct $O(\log n)$-bit message to each other node in the clique and perform unlimited lo

Hyoung Suk Park, Young Jin Jeong, Kiwan Jeon

This paper presents a robust multi-domain network designed to restore low-quality amyloid PET images acquired in a short period of time. The proposed method is trained on pairs of PET images from short (2 minutes) and standard (20 minutes) scanning times, sourced from multiple domains. Learning relevant image features between these domains with a single netw

B. K. Sartayev

As it is known, the defining identities of a free Novikov algebra can be obtained from a commutative algebra with a derivation. In this paper, we consider a class of algebras obtained from the class of associative algebras with a derivation that generalizes Novikov algebras. Such objects are called noncommutative Novikov algebras. We construct a monomial bas

Abhranil Chatterjee, Partha Mukhopadhyay

Let $T$ be a matrix whose entries are linear forms over the noncommutative variables $x_1, x_2, \ldots, x_n$. The noncommutative Edmonds' problem (NSINGULAR) aims to determine whether $T$ is invertible in the free skew field generated by $x_1,x_2,\ldots,x_n$. Currently, there are three different deterministic polynomial-time algorithms to solve this problem:

Maximilian Buchmüller, Ivan Shutsko, Sven Oliver Schumacher, Patrick Görrn

Short-range surface plasmon polaritons (SR-SPPs) can arise due to the hybridization of surface plasmon polaritons propagating along the two interfaces of a thin metal slab. In optics, they have gained particular interest for imaging and sensing applications, because of their short wavelengths at optical frequencies along with strong field enhancement. Howeve

Nouf M. Almousa, Jacopo Assettini, Marco Gallo, Marco Squassina

In this paper we study quasiconcavity properties of solutions of Dirichlet problems related to modified nonlinear Schr\"odinger equations of the type $$-{\rm div}\big(a(u) \nabla u\big) + \frac{a'(u)}{2} |\nabla u|^2 = f(u) \quad \hbox{in $\Omega$},$$ where $\Omega$ is a convex bounded domain of $\mathbb{R}^N$. In particular, we search for a function $\varph

Bohan Lou

As a key low-power communication technique, backscatter communication has received significant attention since the rising of the Internet of Things (IoT). We revisit the state-of-the-art backscatter system, RBLE [1]. It solves several key reliability issues of backscatter system including unreliable two-step modulation, productive-data dependency, and lack o

Fatemeh Azimi, Fahim Mannan, Felix Heide

In this work, we study self-supervised multiple object tracking without using any video-level association labels. We propose to cast the problem of multiple object tracking as learning the frame-wise associations between detections in consecutive frames. To this end, we propose differentiable soft object assignment for object association, making it possible

Jorge Marco-Blanco, Rubén Cuevas

Time series data, spanning applications ranging from climatology to finance to healthcare, presents significant challenges in data mining due to its size and complexity. One open issue lies in time series clustering, which is crucial for processing large volumes of unlabeled time series data and unlocking valuable insights. Traditional and modern analysis me

S. Toxvaerd

Computer simulation of the time evolution in a classical system is a standard numerical method, used in numerous scientific articles in Natural Science. Almost all the simulations are performed by discrete Molecular Dynamics (MD). The algorithm used in MD was originally formulated by I. Newton at the beginning of his book $Principia$. Newton's discrete dynam

Haokun Wen, Xuemeng Song, Jianhua Yin, Jianlong Wu

The composed image retrieval (CIR) task aims to retrieve the desired target image for a given multimodal query, i.e., a reference image with its corresponding modification text. The key limitations encountered by existing efforts are two aspects: 1) ignoring the multi-faceted query-target matching factors; 2) ignoring the potential unlabeled reference-target

Shigeng Sun, Yuchen Xie

Many machine learning applications and tasks rely on the stochastic gradient descent (SGD) algorithm and its variants. Effective step length selection is crucial for the success of these algorithms, which has motivated the development of algorithms such as ADAM or AdaGrad. In this paper, we propose a novel algorithm for adaptive step length selection in the

Amit Puri, John Jose, Tamarapalli Venkatesh, Vijaykrishnan Narayanan

Memory disaggregation has emerged as an alternative to traditional server architecture in data centers. This paper introduces DRackSim, a simulation infrastructure to model rack-scale hardware disaggregated memory. DRackSim models multiple compute nodes, memory pools, and a rack-scale interconnect similar to GenZ. An application-level simulation approach sim

Yaroslav D. Krivenko-Emetov, Andriy I. Smetana

This article explores the van der Waals gas model proposed to describe the hadronic stages of nuclear fireball evolution during the cooling stage. Two different models were proposed for the early and late stages of hadronization. At the initial stage, a two-component meson model consisting of $\pi^0$ and $\pi^+$ mesons was suggested, and at the later stage,

Chenshuo Wang, Shaoguang Mao, Tao Ge, Wenshan Wu

Enhancing word usage is a desired feature for writing assistance. To further advance research in this area, this paper introduces "Smart Word Suggestions" (SWS) task and benchmark. Unlike other works, SWS emphasizes end-to-end evaluation and presents a more realistic writing assistance scenario. This task involves identifying words or phrases that require im

Kai Wang, Siqiang Luo, Dan Lin

We study Graph Neural Networks (GNNs)-based embedding techniques for knowledge graph (KG) reasoning. For the first time, we link the path redundancy issue in the state-of-the-art KG reasoning models based on path encoding and message passing to the transformation error in model training, which brings us new theoretical insights into KG reasoning, as well as

Abhranil Chatterjee, Sumanta Ghosh, Rohit Gurjar, Roshan Raj

VBP is the class of polynomial families that can be computed by the determinant of a symbolic matrix of the form $A_0 + \sum_{i=1}^n A_ix_i$ where the size of each $A_i$ is polynomial in the number of variables (equivalently, computable by polynomial-sized algebraic branching programs (ABP)). A major open problem in geometric complexity theory (GCT) is to de

Kwangho Lee, Patrick Kwon, Myung Ki Lee, Namhyuk Ahn

While current talking head models are capable of generating photorealistic talking head videos, they provide limited pose controllability. Most methods require specific video sequences that should exactly contain the head pose desired, being far from user-friendly pose control. Three-dimensional morphable models (3DMM) offer semantic pose control, but they f

Dillon Reis, Jordan Kupec, Jacqueline Hong, Ahmad Daoudi

This paper presents a generalized model for real-time detection of flying objects that can be used for transfer learning and further research, as well as a refined model that achieves state-of-the-art results for flying object detection. We achieve this by training our first (generalized) model on a data set containing 40 different classes of flying objects,

Sela Fried, Toufik Mansour

Recently, we initiated the study of random walk labelings of graphs. These are graph labelings that are obtainable by performing a random walk on the graph, such that each vertex is labeled upon its first visit. In this work, we calculate the number of random walk labelings of several natural graph families: The wheel, fan, barbell, lollipop, tadpole, friend

T. Mageshwaran, Kimitake Hayasaki

We construct a numerical model of steady-state, general relativistic (GR) super-Eddington accretion flows in an optically thick, advection-dominated regime, motivated by tidal disruption events wherein super-Eddington accretion assumes a pivotal role. Our model takes into account the loss of angular momentum due to radiation and the scale-height derivative i

Sharanya Sur, Kandaswamy Subramanian

Using magnetohydrodynamic simulations of fluctuation dynamos in turbulent flows with rms Mach numbers $\mathcal{M}_{\rm rms} = 0.2, 1.1$ and $3$, we show that magnetic pressure forces play a crucial role in dynamo saturation in supersonic flows. First, as expected when pressure forces oppose compression, an increase in anticorrelation between density and mag

Yonglong Ding

In order to gain a deeper understanding of complex systems and infer key information using minimal data, I classify all configurations based on classical probability, starting from the dimensions of energy and different categories of configurations. By utilizing the principle of maximum entropy, it is concluded that all possible configurations with the same

Johnathan Chiu, Andi Gu, Matt Zhou

In this work, we introduce a novel deep learning architecture, Variable Length Embeddings (VLEs), an autoregressive model that can produce a latent representation composed of an arbitrary number of tokens. As a proof of concept, we demonstrate the capabilities of VLEs on tasks that involve reconstruction and image decomposition. We evaluate our experiments o

Yong Li, Sven Schewe, Moshe Y. Vardi

We introduce a method for translating an alternating weak B\"uchi automaton (AWA), which corresponds to a Linear Dynamic Logic (LDL) formula, to an unambiguous B\"uchi automaton (UBA). Our translations generalise constructions for Linear Temporal Logic (LTL), a less expressive specification language than LDL. In classical constructions, LTL formulas are firs

Eric Yanchenko, Tsuyoshi Murata, Petter Holme

Influence maximization (IM) is the task of finding the most important nodes in order to maximize the spread of influence or information on a network. This task is typically studied on static or temporal networks where the complete topology of the graph is known. In practice, however, the seed nodes must be selected before observing the future evolution of th

Ningbo Li, Zhiyong Sun, Arjan van der Schaft, Jacquelien M. A. Scherpen

This paper proposes a passivity-based port-Hamiltonian (pH) framework for multi-agent displacement-based and rigid formation control and velocity tracking. The control law consists of two parts, where the internal feedback is to track the velocity and the external feedback is to achieve formation stabilization by steering variables of neighboring agents that

Youqing Ji, Yuanhang Zhang

For a quasinilpotent operator $T$ on a separable Hilbert space $\mathcal{H}$, Douglas and Yang define $k_x=\limsup\limits_{\lambda\rightarrow 0}\frac{\ln\|(\lambda-T)^{-1}x\|}{\ln\|(\lambda-T)^{-1}\|}$ for each nonzero vector $x$, and call $\Lambda(T)=\{k_x:x\ne 0\}$ the power set of $T$. In this paper, we prove that $\Lambda(T)$ is right closed, that is, $\

Lih-King Lim, Cunzhong Lou, Chushun Tian

Understanding fluctuation phenomena plays a dominant role in the development of many-body physics. The time evolution of entanglement is essential to a broad range of subjects in many-body physics, ranging from exotic quantum matter to quantum thermalization. Stemming from various dynamical processes of information, fluctuations in entanglement evolution dif

Mingyu Hao, Keyang Qian, Sid Chi-Kin Chau

Despite its popularity, the nature of solar energy is highly uncertain and weather dependent, affecting the business viability and investment of solar energy generation, especially for household users. To stabilize the income from solar energy generation, there have been limited traditional options, such as using energy storage to pool excessive solar energy

Mobasshir Mahbub, Raed M. Shubair

Interactive reflecting surfaces (IRSs) are a remarkable technology that will be integrated into 6G wireless networks to enhance the electromagnetic propagation environment in a programmable or adaptable way in order to improve communication between both transmission and reception devices. The work intends to broaden coverage by including IRS into micro radio

Md Azim Ullah

mContain was developed (and sparsely deployed) by MD2K center at University of Memphis in the early stages of COVID-19 pandemic to help reduce community transmission in Shelby County and Memphis metropolitan area. The application counts and displays the number of daily proximity encounters with other app users. To reduce the chances of entering crowded place

Xiaoxiao Hu, Zhiqiang Li, Yu Guo, Yajiang Chen

We investigate, both analytically and numerically, the scattering of one-dimensional quantum droplets by a P\"{o}schl-Teller reflectionless potential well, confirming that there is a sharp transition between full reflection and full transmission at a certain critical incident speed for both small droplets and large flat-top droplets. We observe sharp differe

Youichi Ohyama, Shusuke Onishi, Takao Nakagawa, Kosei Matsumoto

We investigated the inner buried nucleus of a nearby luminous infrared galaxy NGC 4418 using high-resolution spectroscopy of fundamental carbon monoxide (CO) ro-vibrational absorptions around $4.67 \mu$m for the first time. This method allowed us to examine the physical and kinematical properties in the hot inner region of this nucleus. We detected a series

Haoyu Liu, Ningyi Liao, Siqiang Luo

Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. neighboring nodes are dissimilar, due to their local and uniform aggregation. Existing attempts of heterophilous GNNs incorporate long-range or global aggregations to distinguish nodes in the graph. However, these aggregations

Diego García-Martín, Martin Larocca, M. Cerezo

It is well known that artificial neural networks initialized from independent and identically distributed priors converge to Gaussian processes in the limit of a large number of neurons per hidden layer. In this work we prove an analogous result for Quantum Neural Networks (QNNs). Namely, we show that the outputs of certain models based on Haar random unitar

Natalie Frank, Jonathan Niles-Weed

We study the consistency of surrogate risks for robust binary classification. It is common to learn robust classifiers by adversarial training, which seeks to minimize the expected $0$-$1$ loss when each example can be maliciously corrupted within a small ball. We give a simple and complete characterization of the set of surrogate loss functions that are \em

Shangbin Feng, Weijia Shi, Yuyang Bai, Vidhisha Balachandran

By design, large language models (LLMs) are static general-purpose models, expensive to retrain or update frequently. As they are increasingly adopted for knowledge-intensive tasks, it becomes evident that these design choices lead to failures to generate factual, relevant, and up-to-date knowledge. To this end, we propose Knowledge Card, a modular framework

Zhaoji Zhang, Yuhao Chi, Qinghua Guo, Ying Li

Massive Machine-Type Communications (mMTC) features a massive number of low-cost user equipments (UEs) with sparse activity. Tailor-made for these features, grant-free random access (GF-RA) serves as an efficient access solution for mMTC. However, most existing GF-RA schemes rely on strict synchronization, which incurs excessive coordination burden for the l

Zeping Sui, Hongming Zhang, Yu Xin, Tong Bao

A spatial modulation-aided orthogonal time frequency space (SM-OTFS) scheme is proposed for high-Doppler scenarios, which relies on a low-complexity distance-based detection algorithm. We first derive the delay-Doppler (DD) domain input-output relationship of our SM-OTFS system by exploiting an SM mapper, followed by characterizing the doubly-selective chann

K. Shima, T. S. Cheng, C. J. Mellor, P. H. Beton

Cathodoluminescence (CL) spectroscopy is a powerful technique for studying emission properties of optoelectronic materials because CL is free from excitable bandgap limits and from ambiguous signals due to simple light scattering and resonant Raman scattering potentially involved in the photoluminescence (PL) spectra. However, direct CL measurements of atomi

Huanyin Chen, Marjan Sheibani

We present the generalized Drazin inverse for certain anti-triangular operator matrices. Let $E,F,EF^{\pi}\in \mathcal{B}(X)^d$. If $EFEF^{\pi}=0$ and $F^2EF^{\pi}=0$, we prove that $M=\left( \begin{array}{cc} E&I F&0 \end{array} \right)$ has g-Drazin inverse and its explicit representation is established. Moreover, necessary and sufficient conditions are gi

Md Mahadi Rajib, Namita Bindal, Ravish Kumar Raj, Brajesh Kumar Kaushik

Multistate memory systems have the ability to store and process more data in the same physical space as binary memory systems, making them a potential alternative to existing binary memory systems. In the past, it has been demonstrated that voltage-controlled magnetic anisotropy (VCMA) based writing is highly energy-efficient compared to other writing method

Eun Jung Chung, Chang Won Lee, Woojin Kwon, Mario Tafalla

We present 850 $\mu$m polarization and $\rm C^{18}O (3-2)$ molecular line observations toward the X-shaped nebula in the California molecular cloud using the JCMT SCUBA-2/POL-2 and HARP instruments. The 850 $\mu$m emission shows that the observed region includes two elongated filamentary structures (Fil1 and Fil2) having chains of regularly spaced cores. We

Kentaro Takemoto, Moyuru Yamada, Tomotake Sasaki, Hisanao Akima

Human-Object Interaction (HOI) detection is a task to localize humans and objects in an image and predict the interactions in human-object pairs. In real-world scenarios, HOI detection models need systematic generalization, i.e., generalization to novel combinations of objects and interactions, because the train data are expected to cover a limited portion o

Mingyuan Meng, Bingxin Gu, Michael Fulham, Shaoli Song

Survival prediction is a major concern for cancer management. Deep survival models based on deep learning have been widely adopted to perform end-to-end survival prediction from medical images. Recent deep survival models achieved promising performance by jointly performing tumor segmentation with survival prediction, where the models were guided to extract

Jordan T. Bishop, Marcus Gallagher, Will N. Browne

Interest in reinforcement learning (RL) has recently surged due to the application of deep learning techniques, but these connectionist approaches are opaque compared with symbolic systems. Learning Classifier Systems (LCSs) are evolutionary machine learning systems that can be categorised as eXplainable AI (XAI) due to their rule-based nature. Michigan LCSs

Josh E. Baker

As Nature's version of machine learning, evolution has solved many extraordinarily complex problems, none perhaps more remarkable than learning to harness an increase in chemical entropy (disorder) to generate directed chemical forces (order). Using muscle as a model system, here I unpack the basic mechanism by which life creates order from disorder. In shor

Jigang Kim, Daesol Cho, H. Jin Kim

While reinforcement learning (RL) has achieved great success in acquiring complex skills solely from environmental interactions, it assumes that resets to the initial state are readily available at the end of each episode. Such an assumption hinders the autonomous learning of embodied agents due to the time-consuming and cumbersome workarounds for resetting

L. Arceo, J. Félix

The Multi-Pixel Photon Counter (MPPC) or Silicon Photomultiplier (SiPM) can detect from a single photon to several thousand ones; it has high gain -$10^{5}$ to $10^{6}$-, low operation voltage -65 to 75 Vdc-, and small size -typically 3.85 mm $\times$ 4.35 mm $\times$ 1.45 mm-; it is suitable for very low light level detection applications, like in cosmic ra

Anaelia Ovalle, Palash Goyal, Jwala Dhamala, Zachary Jaggers

Transgender and non-binary (TGNB) individuals disproportionately experience discrimination and exclusion from daily life. Given the recent popularity and adoption of language generation technologies, the potential to further marginalize this population only grows. Although a multitude of NLP fairness literature focuses on illuminating and addressing gender b

Youhe Jiang, Fangcheng Fu, Xupeng Miao, Xiaonan Nie

Large-scale deep learning models contribute to significant performance improvements on varieties of downstream tasks. Current data and model parallelism approaches utilize model replication and partition techniques to support the distributed training of ultra-large models. However, directly deploying these systems often leads to sub-optimal training efficien

Cyrille Praz

Decays of $B$ mesons involving the transition of a $b$ quark into an $s$ quark are good probes of physics beyond the standard model. Such decays are studied at the Belle II experiment, a detector located along the SuperKEKB electron-positron collider, and with data corresponding to an integrated luminosity of $189\,\mathrm{fb}^{-1}$ collected at the energy o

Haohui Wang, Baoyu Jing, Kaize Ding, Yada Zhu

In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitigate class imbalances and enhance the overall performance. Despite the notable success, there is very limited literature that provides a theoretical tool for characterizing the beha

Shuang Guo, Han-Sheng Wang, Kai Zhou, Guo-Liang Ma

Collective flow has been found to be similar between small colliding systems ($p$ $+$ $p$ and $p$ $+$ A collisions) and large colliding systems (peripheral A $+$ A collisions) at the CERN Large Hadron Collider. In order to study the differences of collective flow between small and large colliding systems, we employ a point cloud network to identify $p$ $+$ P

Seiji Tomita

In this paper, this author proved that $A^4 + hB^4 = C^4 + hD^4$ always has the integral solutions for $h < 20000.$ Then we conjecture the equation $A^4 + hB^4 = C^4 + hD^4$ always has the integral solutions.

Maria A. Terres, Aiyou Chen, Ruixuan Rachel Zhou, Claire M. McLeod

Autonomous vehicles are continually increasing their presence on public roads. However, before any new autonomous driving software can be approved, it must first undergo a rigorous assessment of driving quality. These quality evaluations typically focus on estimating the frequency of (undesirable) behavioral events. While rate estimation would be straight-fo

Sarira Sahu, B. Medina-Carrillo, G. Sánchez-Colón, Subhash Rajpoot

Observation of several very high energy (VHE) flaring events of the BL Lac object VER J0521+211 were reported by the VERITAS and MAGIC collaborations between 2009 and 2014. The redshift of this source is uncertain and several analysis have derived different limits for it. In the framework of the photohadronic model, and using three different extragalactic ba

Yingzhe Xu, Cheng Lu, Zhibin Deng, Ya-Feng Liu

In this paper, we propose some new semidefinite relaxations for a class of nonconvex complex quadratic programming problems, which widely appear in the areas of signal processing and power system. By deriving new valid constraints to the matrix variables in the lifted space, we derive some enhanced semidefinite relaxations of the complex quadratic programmin

Steve Macenski, Alberto Soragna, Michael Carroll, Zhenpeng Ge

The Robot Operating System 2 (ROS 2) is the second generation of ROS representing a step forward in the robotic framework. Several new types of nodes and executor models are integral to control where, how, and when information is processed in the computational graph. This paper explores and benchmarks one of these new node types -- the Component node -- whic

Simon Donaldson, Fabian Lehmann

We study an intrinsic volume form defined on a pseudoconvex hypersurface in a complex Calabi-Yau manifold. We compute first and second variation formulae and discuss possible analogues of the affine isoperimetric inequality. In the last section of the paper we explore infinite dimensional aspects, including moment maps for diffeomorphism group actions.

Ganghua Wang, Ali Payani, Myungjin Lee, Ramana Kompella

The issue of group fairness in machine learning models, where certain sub-populations or groups are favored over others, has been recognized for some time. While many mitigation strategies have been proposed in centralized learning, many of these methods are not directly applicable in federated learning, where data is privately stored on multiple clients. To

Harrison Delecki, Anthony Corso, Mykel J. Kochenderfer

Estimating the distribution over failures is a key step in validating autonomous systems. Existing approaches focus on finding failures for a small range of initial conditions or make restrictive assumptions about the properties of the system under test. We frame estimating the distribution over failure trajectories for sequential systems as Bayesian inferen

Kangbo Li, Hsin-Yu Ko, Robert A. DiStasio, Anil Damle

We provide a new variational definition for the spread of an orbital under periodic boundary conditions (PBCs) that is continuous with respect to the gauge, consistent in the thermodynamic limit, well-suited to diffuse orbitals, and systematically adaptable to schemes computing localized Wannier functions. Existing definitions do not satisfy all these deside