July 2023 arXiv papers — page 3

Shibo Jie, Haoqing Wang, Zhi-Hong Deng

Current state-of-the-art results in computer vision depend in part on fine-tuning large pre-trained vision models. However, with the exponential growth of model sizes, the conventional full fine-tuning, which needs to store a individual network copy for each tasks, leads to increasingly huge storage and transmission overhead. Adapter-based Parameter-Efficien

Reza Gheissari, Eyal Lubetzky

We study Glauber dynamics for the low temperature $(2+1)$D Solid-On-Solid model on a box of side-length $n$ with a floor at height $0$ (inducing entropic repulsion) and a competing bulk external field $\lambda$ pointing down (the prewetting problem). In 1996, Cesi and Martinelli showed that if the inverse-temperature $\beta$ is large enough, then along a dec

Weikang Yu, Yonghao Xu, Pedram Ghamisi

Deep neural networks (DNNs) have risen to prominence as key solutions in numerous AI applications for earth observation (AI4EO). However, their susceptibility to adversarial examples poses a critical challenge, compromising the reliability of AI4EO algorithms. This paper presents a novel Universal Adversarial Defense approach in Remote Sensing Imagery (UAD-R

Jiehua Chen, Christian Hatschka, Sofia Simola

We investigate winner determination for two popular proportional representation systems: the Monroe and Chamberlin-Courant (abbrv. CC) systems. Our study focuses on (nearly) single-peaked resp. single-crossing preferences. We show that for single-crossing approval preferences, winner determination of the Monroe rule is polynomial, and for both rules, winner

Emily Kaczmarek, Olivier X. Miguel, Alexa C. Bowie, Robin Ducharme

The need for clear, trustworthy explanations of deep learning model predictions is essential for high-criticality fields, such as medicine and biometric identification. Class Activation Maps (CAMs) are an increasingly popular category of visual explanation methods for Convolutional Neural Networks (CNNs). However, the performance of individual CAMs depends l

Brent A. Wallace, Jennie Si

This work introduces new results in continuous-time reinforcement learning (CT-RL) control of affine nonlinear systems to address a major algorithmic challenge due to a lack of persistence of excitation (PE). This PE design limitation has previously stifled CT-RL numerical performance and prevented these algorithms from achieving control synthesis goals. Our

O. V. Kiyaeva, I. S. Izmailov, N. V. Narizhnaya, L. G. Romanenko

Star ADS 9173=WDS 14135+5147=Hip 69483 is a complex system. The B component has a spectroscopic companion, whose orbit with a period of 4.9 years has been known since 1986. The Gaia telescope has detected a distant faint pair over 100 arcsec away from the bright AB pair. In our article, we study the movement in a bright pair based on long-term observations w

Hoyoung Song

We define the mulati-parameter maximal function $\mathcal{M}$ as $$ \mathcal{M} f(x)=\sup _{0<h_1,h_2,\cdots,h_n<1} \frac{1}{h_1h_2\cdots h_n}\left|\int_0^{h_1}\cdots \int_0^{h_n} f(x-P(t_1,\cdots,t_n)) \mathrm{d}t_1\cdots \mathrm{d} t_n\right| $$ where $P(t_1,t_2,\cdots,t_n)$ is a real-valued multi-parameter polynomial of real variables $t_1,t_2,\cdots,t_n$

Miguel A. Martínez-García, Diego Martín-Cano

In this work we identify coherent electron-vibron interactions between near-resonant and non-resonant electronic levels that contribute beyond standard optomechanical models for off-resonant or resonance SERS. By developing an open-system quantum model using first molecular interaction principles, we show how the Raman interference of both resonant and non-r

Cole Jeznach

We provide several equivalent characterizations of locally flat, $d$-Ahlfors regular, uniformly rectifiable sets $E$ in $\mathbb{R}^n$ with density close to $1$ for any dimension $d \in \mathbb{N}$ with $1 \le d \le n-1$. In particular, we show that when $E$ is Reifenberg flat with small constant and has Ahlfors regularity constant close to $1$, then the Tol

Márton Naszódi, Zsombor Szilágyi, Mihály Weiner

Motivated by general probability theory, we say that the set $S$ in $\mathbb{R}^d$ is \emph{antipodal of rank $k$}, if for any $k+1$ elements $q_1,\ldots q_{k+1}\in S$, there is an affine map from $\mathrm{conv}(S)$ to the $k$-dimensional simplex $\Delta_k$ that maps $q_1,\ldots q_{k+1}$ bijectively onto the $k+1$ vertices of $\Delta_k$. For $k=1$, it coinci

Paranjoy Chaki, Aparajita Bhattacharyya, Kornikar Sen, Ujjwal Sen

We discuss the idea of extracting energy from a quantum battery, applying a projective measurement on an auxiliary system. The battery is initially connected to the auxiliary system and allowed to interact with it. After some time, we execute a measurement on the auxiliary system which probabilistically projects the setup to a particular state, and the corre

David Viedma, Anselmo M. Marques, Ricardo G. Dias, Verònica Ahufinger

Square-root topology is one of the newest additions to the ever expanding field of topological insulators (TIs). It characterizes systems that relate to their parent TI through the squaring of their Hamiltonians. Extensions to $2^n$-root topology, where $n$ is the number of squaring operations involved in retrieving the parent TI, were quick to follow. Here,

Krzysztof Andrzejewski, Katarzyna Bolonek-Lasoń, Piotr Kosiński

For vanishing fidelity between initial and final states two important quantum speed limits, the Mandelstam-Tamm limit (involving energy dispersion) and Margolus-Levitin one (involving excitation energy expectation value) have been derived. While the generalization of the former limit to the case of arbitrary fidelity is straightforward, the relevant generali

Bastian Pradenas, Oleg Tchernyshyov

We present a nonlinear field theory of a three-sublattice hexagonal antiferromagnet. The order parameter is the spin frame, an orthogonal triplet of vectors related to sublattice magnetizations and spin chirality. The exchange energy, quadratic in spin-frame gradients, has three coupling constants, only two of which manifest themselves in the bulk. As a resu

Breno Tostes, Leonardo Ventura, Enrico Lovat, Matheus Martins

Indicators of Compromise (IOCs), such as IP addresses, file hashes, and domain names associated with known malware or attacks, are cornerstones of cybersecurity, serving to identify malicious activity on a network. In this work, we leverage real data to compare different parameterizations of IOC aging models. Our dataset comprises traffic at a real environme

Haoyang Liu, Maheep Chaudhary, Haohan Wang

The trustworthiness of machine learning has emerged as a critical topic in the field, encompassing various applications and research areas such as robustness, security, interpretability, and fairness. The last decade saw the development of numerous methods addressing these challenges. In this survey, we systematically review these advancements from a data-ce

Wanshu Yu, Haonan Shi, Hongyun Xu

As people's daily life becomes increasingly inseparable from various mobile electronic devices, relevant service application platforms and network operators can collect numerous individual information easily. When releasing these data for scientific research or commercial purposes, users' privacy will be in danger, especially in the publication of spatiotemp

Wenda Zhao, Abhishek Goudar, Mingliang Tang, Xinyuan Qiao

Ultra-wideband (UWB) time difference of arrival(TDOA)-based localization has emerged as a low-cost and scalable indoor positioning solution. However, in cluttered environments, the performance of UWB TDOA-based localization deteriorates due to the biased and non-Gaussian noise distributions induced by obstacles. In this work, we present a bi-level optimizati

Shohreh Deldari, Dimitris Spathis, Mohammad Malekzadeh, Fahim Kawsar

Limited availability of labeled data for machine learning on multimodal time-series extensively hampers progress in the field. Self-supervised learning (SSL) is a promising approach to learning data representations without relying on labels. However, existing SSL methods require expensive computations of negative pairs and are typically designed for single m

Alexander Alecio

Phase transitions and critical behaviour of a class of MV-SDEs, whose concomitant non-local Fokker-Planck equation includes the Granular Media equation with quadratic interaction potential as a special case, is studied. By careful analysis of an implicit auxiliary integral equation, it is shown for a wide class of potentials that below a certain `critical th

Amy L. Rankine, James Aird, Angel Ruiz, Antonis Georgakakis

In active galactic nuclei, the relationship between UV and X-ray luminosity is well studied (often characterised by $\alpha_\text{ox}$) but often with heterogeneous samples. We have parametrized the intrinsic distribution of X-ray luminosity, $L_\text{X}$, for the optically-selected sample of SDSS quasars in the Stripe 82 and XXL fields across redshifts 0.5-

Alba R. Piacenti, Casey Adam, Nicholas Hawkins, Ryan Wagner

Polymeric materials are widely used in industries ranging from automotive to biomedical. Their mechanical properties play a crucial role in their application and function and arise from the nanoscale structures and interactions of their constitutive polymer molecules. Polymeric materials behave viscoelastically, i.e. their mechanical responses depend on the

Xue Yao, Simeon C. Calvert, Serge P. Hoogendoorn

Current approaches to identifying driving heterogeneity face challenges in capturing the diversity of driving characteristics and understanding the fundamental patterns from a driving behaviour mechanism standpoint. This study introduces a comprehensive framework for identifying driving heterogeneity from an Action-chain perspective. First, a rule-based segm

Diego A. Tejada-Arango

This paper summarises the main multi-year investment modelling approaches in energy planning models. Therefore, here we will go from a simple (basic) formulation to a more complex (general) one to understand different levels of detail, including examples to make more accessible the understanding of the concepts.

Prashant Kocherlakota, Luciano Rezzolla, Rittick Roy, Maciek Wielgus

Astrophysical black holes (BHs) are universally expected to be described by the Kerr metric, a stationary, vacuum solution of general relativity (GR). Indeed, by imaging M87$^\star$ and Sgr A$^\star$ and measuring the size of their shadows, we have substantiated this hypothesis through successful null tests. Here we discuss the potential of upcoming improved

Luca Geatti, Alessandro Gianola, Nicola Gigante, Sarah Winkler

We study Linear Temporal Logic Modulo Theories over Finite Traces (LTLfMT), a recently introduced extension of LTL over finite traces (LTLf) where propositions are replaced by first-order formulas and where first-order variables referring to different time points can be compared. In general, LTLfMT was shown to be semi-decidable for any decidable first-order

Lucy Downes

Understanding the interactions between atoms and light is at the heart of atomic physics. Being able to `experiment' with various system parameters, produce plots of the results and interpret these is very useful, especially for those new to the field. This tutorial aims to provide an introduction to the equations governing near-resonant atom-light interacti

Nicholas J. Harmon, Emma Z. Kurth, Dana Coleman, Lana Flanigan

The spin-orbit interaction is frequently the mechanism by which spin and charge are coupled for spintronic applications. The discovery of spin, a century ago, relied on spin-charge coupling by a magnetic field gradient; this mechanism has received scant attention as a means for generating spin and charge currents in semiconductors. Through the derivation of

Johnny Sierra, Daphne Der-Fen Liu, Jessica Toy

An antimagic labeling of a graph $G(V,E)$ is a bijection $f: E \to \{1,2, \dots, |E|\}$ so that $\sum_{e \in E(u)} f(e) \neq \sum_{e \in E(v)} f(e)$ holds for all $u, v \in V(G)$ with $u \neq v$, where $E(v)$ is the set of edges incident to $v$. We call $G$ antimagic if it admits an antimagic labeling. A forest is a graph without cycles; equivalently, every

Arthur Vesperini, Ghofrane Bel-Hadj-Aissa, Lorenzo Capra, Roberto Franzosi

We show that the manifold of quantum states is endowed with a rich and nontrivial geometric structure. We derive the Fubini-Study metric of the projective Hilbert space of a multi-qubit quantum system, endowing it with a Riemannian metric structure, and investigate its deep link with the entanglement of the states of this space. As a measure, we adopt the En

Mayank Singh, Emily Ray, Marc Ferradou, Andrea Barraza-Urbina

Accuracy measures such as Recall, Precision, and Hit Rate have been a standard way of evaluating Recommendation Systems. The assumption is to use a fixed Top-N to represent them. We propose that median impressions viewed from historical sessions per diner be used as a personalized value for N. We present preliminary exploratory results and list future steps

T. Engel, F. Hagelstein, M. Rocco, V. Sharkovska

We present the complete next-to-next-to-leading order (NNLO) pure pointlike QED corrections to lepton-proton scattering, including three-photon-exchange contributions, and investigate their impact in the case of the MUSE experiment. These corrections are computed with no approximation regarding the energy of the emitted photons and taking into account lepton

Sungho Shin, François Pacaud, Mihai Anitescu

This paper introduces a framework for solving alternating current optimal power flow (ACOPF) problems using graphics processing units (GPUs). While GPUs have demonstrated remarkable performance in various computing domains, their application in ACOPF has been limited due to challenges associated with porting sparse automatic differentiation (AD) and sparse l

Nguyen Chau Giang, Thiem Hoang

Thermal dust polarization is a powerful tool to probe magnetic fields ($\textbf{B}$) and grain properties. However, a systematic study of the dependence of dust polarization on grain properties in protostellar environments is not yet available. In this paper, we post-process a non-ideal MHD simulation of a collapsing protostellar core with our updated POLARI

Eyal Z. Goren, Jonathan R. Love

Let $\mathcal{O}$ be a maximal order in the quaternion algebra over $\mathbb{Q}$ ramified at $p$ and $\infty$. We prove two theorems that allow us to recover the structure of $\mathcal{O}$ from limited information. The first says that for any infinite set $S$ of integers coprime to $p$, $\mathcal{O}$ is spanned as a $\mathbb{Z}$-module by elements with norm

ATLAS Collaboration

The identification of jets originating from quarks and gluons, often referred to as quark/gluon tagging, plays an important role in various analyses performed at the Large Hadron Collider, as Standard Model measurements and searches for new particles decaying to quarks often rely on suppressing a large gluon-induced background. This paper describes the measu

Susmita Ghosh, Abhiroop Chatterjee

The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods. Computed Tomography (CT) scan images have emerged as a valuable tool for detecting the disease. In this article, we present a novel deep learning approach for automated COVID-19 CT scan classification where a modified Xception model is proposed which incorporates a newly

Kazuki Sone, Motohiko Ezawa, Yuto Ashida, Nobuyuki Yoshioka

As first demonstrated by the characterization of the quantum Hall effect by the Chern number, topology provides a guiding principle to realize robust properties of condensed matter systems immune to the existence of disorder. The bulk-boundary correspondence guarantees the emergence of gapless boundary modes in a topological system whose bulk exhibits nonzer

Saizhuo Wang, Hang Yuan, Leon Zhou, Lionel M. Ni

One of the most important tasks in quantitative investment research is mining new alphas (effective trading signals or factors). Traditional alpha mining methods, either hand-crafted factor synthesizing or algorithmic factor mining (e.g., search with genetic programming), have inherent limitations, especially in implementing the ideas of quants. In this work

Amador Martin-Pizarro, Martin Ziegler

A first-order theory is Noetherian with respect to the collection of formulae $\mathcal{F}$ if every definable set is a Boolean combination of instances of formulae in $\mathcal{F}$ and the topology whose subbasis of closed sets is the collection of instances of arbitrary formulae in $\mathcal{F}$ is Noetherian. Noetherianity is a strengthening of equational

Yizhong Pan, Xiao Liu, Xiangyu Liao, Yuanzhouhan Cao

With sufficient paired training samples, the supervised deep learning methods have attracted much attention in image denoising because of their superior performance. However, it is still very challenging to widely utilize the supervised methods in real cases due to the lack of paired noisy-clean images. Meanwhile, most self-supervised denoising methods are i

Zakir Hussain Shaik, Erik G. Larsson

Cell-free massive multiple-input-multiple-output (CF-mMIMO) is a next-generation wireless access technology that offers superior coverage and spectral efficiency compared to conventional MIMO. With many future applications in unlicensed spectrum bands, networks will likely experience and may even be limited by out-of-system (OoS) interference. The OoS interf

Simone Cerreia-Vioglio, Paolo Leonetti, Fabio Maccheroni, Massimo Marinacci

We show that a normalized capacity $\nu: \mathcal{P}(\mathbf{N})\to \mathbf{R}$ is invariant with respect to an ideal $\mathcal{I}$ on $\mathbf{N}$ if and only if it can be represented as a Choquet average of $\{0,1\}$-valued finitely additive probability measures corresponding to the ultrafilters containing the dual filter of $\mathcal{I}$. This is obtained

J. G. De la Varga, S. Pineda, J. M. Morales, Á. Porras

The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable in real time, indicating the existence of multiple states that result in identical values for the available measurements. Certain exi

Farzaneh Tajidini, Mohammad-Javad Kheiri

Computer-aided diagnosis (CAD), a vibrant medical imaging research field, is expanding quickly. Because errors in medical diagnostic systems might lead to seriously misleading medical treatments, major efforts have been made in recent years to improve computer-aided diagnostics applications. The use of machine learning in computer-aided diagnosis is crucial.

Yani Ziani, Nikolai Kosmatov, Frédéric Loulergue, Daniel Gracia Pérez

The Trusted Platform Module (TPM) is a cryptoprocessor designed to protect integrity and security of modern computers. Communications with the TPM go through the TPM Software Stack (TSS), a popular implementation of which is the open-source library tpm2-tss. Vulnerabilities in its code could allow attackers to recover sensitive information and take control o

Fabian Barras, Nicolas Brantut

Earthquakes are produced by the propagation of rapid slip along tectonic faults. The propagation dynamics is governed by a balance between elastic stored energy in the surrounding rock, and dissipated energy at the propagating tip of the slipping patch. Energy dissipation is dictated by the mechanical behaviour of the fault, which is itself the result of fee

N. Belousov, S. Derkachov, S. Kharchev, S. Khoroshkin

In the previous paper we showed that the wave functions of the quantum Ruijsenaars hyperbolic system diagonalize Baxter Q-operators. Using this property and duality relation we prove orthogonality and completeness relations for the wave functions or, equivalently, unitarity of the corresponding integral transform.

Xuanang Chen, Ben He, Le Sun, Yingfei Sun

Neural ranking models (NRMs) have undergone significant development and have become integral components of information retrieval (IR) systems. Unfortunately, recent research has unveiled the vulnerability of NRMs to adversarial document manipulations, potentially exploited by malicious search engine optimization practitioners. While progress in adversarial a

Serafim Teknowijoyo, Armen Gulian

We report on magnetron deposition of thin superconducting rhenium films on sapphire substrates. During the deposition, substrates were held at ambient temperature. Critical temperature of the films is Tc~3.6 K. Films have polycrystalline structure, and grazing incidence X-ray diffractometry indicates that crystalline lattice parameters are somewhat larger co

Richard D. James, Kunlun Qi, Li Wang

In this paper, we develop a multiscale hierarchy framework for objective molecular dynamics (OMD), a reduced order molecular dynamics with a certain symmetry, that connects it to the statistical kinetic equation, and the macroscopic hydrodynamic model. In the mesoscopic regime, we exploit two interaction scalings that lead, respectively, to either a mean-fie

Kun Yuan, Zishang Kong, Chuanchuan Zheng, Ming Sun

Video Quality Assessment (VQA), which aims to predict the perceptual quality of a video, has attracted raising attention with the rapid development of streaming media technology, such as Facebook, TikTok, Kwai, and so on. Compared with other sequence-based visual tasks (\textit{e.g.,} action recognition), VQA faces two under-estimated challenges unresolved i

Doug Lenat, Gary Marcus

Generative AI, the most popular current approach to AI, consists of large language models (LLMs) that are trained to produce outputs that are plausible, but not necessarily correct. Although their abilities are often uncanny, they are lacking in aspects of reasoning, leading LLMs to be less than completely trustworthy. Furthermore, their results tend to be b

John Joseph M. Carrasco, Nicolas H. Pavao

We identify novel structure in newly computed multi-loop amplitudes and quantum actions for even-point effective field theories, including both the nonlinear sigma model (NLSM) and double-copy gauge theories such as Born-Infeld and its supersymmetric generalizations. We exploit special properties of all even-point theories towards efficient unitarity based a

Angus R. Williams, Hannah Rose Kirk, Liam Burke, Yi-Ling Chung

Public figures receive a disproportionate amount of abuse on social media, impacting their active participation in public life. Automated systems can identify abuse at scale but labelling training data is expensive, complex and potentially harmful. So, it is desirable that systems are efficient and generalisable, handling both shared and specific aspects of

Farzane Tajidini

To provide the reader with a historical perspective on cancer classification approaches, we first discuss the fundamentals of the area of cancer diagnosis in this article, including the processes of cancer diagnosis and the standard classification methods employed by clinicians. Current methods for cancer diagnosis are deemed ineffective, calling for new and

Souheib Allout, Abderrahmane Belkacem, Abdelghani Zeghib

We construct compact Lorentz manifolds without closed geodesics.

Valeria Bruschi, Michela Cantarini, Luca Serafini, Stefano Nobili

Snoring is a common disorder that affects people's social and marital lives. The annoyance caused by snoring can be partially solved with active noise control systems. In this context, the present work aims at introducing an enhanced system based on the use of a convolutional recurrent neural network for snoring activity detection and a delayless subband app

Samuel A. Lopes

This is an abridged version of our Habilitation thesis. In these notes, we aim to summarize our research interests and achievements as well as motivate what drives our work: symmetry, structure and invariants. The paradigmatic example which permeates and often inspires our research is the Weyl algebra $\mathbb{A}_{1}$.

Natalya Weber, Werner Koch, Ozan Erdem, Tom Froese

Hopfield networks are an attractive choice for solving many types of computational problems because they provide a biologically plausible mechanism. The Self-Optimization (SO) model adds to the Hopfield network by using a biologically founded Hebbian learning rule, in combination with repeated network resets to arbitrary initial states, for optimizing its ow

Esra Sablevice, Peter Millington

This paper explores quantum field theories with pseudo-Hermitian Hamiltonians, where PT-symmetric Hamiltonians serve as a special case. In specific regimes, these pseudo-Hermitian Hamiltonians have real eigenspectra, orthogonal eigenstates, and unitary time evolution. So far, most pseudo-Hermitian quantum field theories have been constructed using analytic c

Jana Kolassa, Manisha Ganeshan, Erica McGrath-Spangler, Oreste Reale

Soil moisture conditions can influence the evolution of a tropical cyclone (TC) that is partially or completely over land. Hence, better constraining soil moisture initial conditions in a numerical weather prediction model can potentially improve predictions of TC evolution near or over land. This study examines the impact of assimilating observations from t

Yue Zhang, Hehe Fan, Yi Yang, Mohan Kankanhalli

In this technical report, we present our findings from the research conducted on the Human-Object Interaction 4D (HOI4D) dataset for egocentric action segmentation task. As a relatively novel research area, point cloud video methods might not be good at temporal modeling, especially for long point cloud videos (\eg, 150 frames). In contrast, traditional vide

Lucy Martinez, Doron Zeilberger

We use three kinds of computations: simulation, numeric, and symbolic, to guide risk-averse gamblers in general, and offer particular advice on how to resolve the famous St. Petersburg paradox.

Li Liu, Ziang Meng, Xiaoning Wang, Hongyu Chen

The very recent claim on the discovery of ambient-pressure room-temperature superconductivity in modified lead-apatite has immediately excited sensational attention in the entire society, which is fabricated by sintering lanarkite (Pb2SO5) and copper(I) phosphide (Cu$_3$P). To verify this exciting claim, we have successfully synthesized Pb$_2$SO$_5$, Cu$_3$P

Yangrui Hu, Sabrina Pasterski

This paper presents a systematic cataloging of the generators of celestial symmetries on phase space. Starting from the celestial OPEs, we first show how to extract a representation of the general-spin analog of the wedge subalgebra of $w_{1+\infty}$ on the phase space of massless matter fields of arbitrary helicity. These generators can be expressed as ligh

Yunfei Bai, Libo Zhang, Xiaobing Luo, A. A. Shanenko

Nucleation of the pair condensate near surfaces above the upper critical magnetic field and the pair-condensate enhancement/suppression induced by changes in the electron-phonon interaction at interfaces are the most known examples of the surface superconductivity. Recently, another example has been reported, when the surface enhancement of the critical supe

Tirthak Patel, Daniel Silver, Aditya Ranjan, Harshitta Gandhi

Quantum computing is an emerging paradigm that has shown great promise in accelerating large-scale scientific, optimization, and machine-learning workloads. With most quantum computing solutions being offered over the cloud, it has become imperative to protect confidential and proprietary quantum code from being accessed by untrusted and/or adversarial agent

Felix Biessmann

Online social media have become an important forum for exchanging political opinions. In response to COVID measures citizens expressed their policy preferences directly on these platforms. Quantifying political preferences in online social media remains challenging: The vast amount of content requires scalable automated extraction of political preferences --

Yachong Yang, Arun Kumar Kuchibhotla, Eric Tchetgen Tchetgen

Series or orthogonal basis regression is one of the most popular non-parametric regression techniques in practice, obtained by regressing the response on features generated by evaluating the basis functions at observed covariate values. The most routinely used series estimator is based on ordinary least squares fitting, which is known to be minimax rate opti

Andrea Lama, Mario di Bernardo

We study the shepherding control problem where a group of "herders" need to orchestrate their collective behaviour in order to steer the dynamics of a group of "target" agents towards a desired goal. We relax the strong assumptions of targets showing cohesive collective behavior in the absence of the herders, and herders owning global sensing capabilities. W

Diana Waldmannstetter, Ivan Ezhov, Benedikt Wiestler, Francesco Campi

Accurate image registration is pivotal in biomedical image analysis, where selecting suitable registration algorithms demands careful consideration. While numerous algorithms are available, the evaluation metrics to assess their performance have remained relatively static. This study addresses this challenge by introducing a novel evaluation metric termed La

Yuri Lavinas, Marcelo Ladeira, Gabriela Ochoa, Claus Aranha

The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from their components. These automatically desi

Abraham Holtermann, Jacquelyn Noronha-Hostler, Anne M. Sickles, Xiaoning Wang

Correlations of two or more particles have been an essential tool for understanding the hydrodynamic behavior of the quark-gluon plasma created in ultra-relativistic nuclear collisions. In this paper, we extend that framework to introduce a mathematical construction of multi-particle correlators that utilize correlations between arbitrary numbers of particle

Kyle Duffy, Satwik Bhattamishra, Phil Blunsom

Large-scale pre-training has made progress in many fields of natural language processing, though little is understood about the design of pre-training datasets. We propose a methodology for obtaining a quantitative understanding of structural overlap between machine translation tasks. We apply our methodology to the natural language to Bash semantic parsing

Pavel Galashin, Thomas Lam

We define monotone links on a torus, obtained as projections of curves in the plane whose coordinates are monotone increasing. Using the work of Morton-Samuelson, to each monotone link we associate elements in the double affine Hecke algebra and the elliptic Hall algebra. In the case of torus knots (when the curve is a straight line), we recover symmetric fu

Joshua D. Martin, Duff Neill, A. Roggero, Huaiyu Duan

Neutrino gases are expected to form in high density astrophysical environments, and accurately modeling their flavor evolution is critical to understanding such environments. In this work we study a simplified model of such a dense neutrino gas in the regime for which neutrino-neutrino coherent forward scattering is the dominant mechanism contributing to the

Zihan Zhang, Lei Shi, Ding-Xuan Zhou

Deep neural networks (DNNs) trained with the logistic loss (i.e., the cross entropy loss) have made impressive advancements in various binary classification tasks. However, generalization analysis for binary classification with DNNs and logistic loss remains scarce. The unboundedness of the target function for the logistic loss is the main obstacle to derivi

Thomas Gobet

In this note, we give a new proof of a result of Matthew Dyer stating that in an arbitrary Coxeter group $W$, every pair $t,t'$ of distinct reflections lie in a unique maximal dihedral reflection subgroup of $W$. Our proof only relies on the combinatorics of words, in particular we do not use root systems at all. As an application, we deduce a new proof of a

Meng Xu, Vasilii Vadimov, Malte Krug, J. T. Stockburger

The dynamics of open quantum systems is formulated in a minimally extended state space comprising the degrees of freedom of a system of interest and a finite set of non-unitary, pure-state reservoir modes. This formal structure, derived from the Feynman-Vernon path integral for the reduced density, is shown to lead to an exact time-local evolution equation i

Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu

Despite the advancements of open-source large language models (LLMs), e.g., LLaMA, they remain significantly limited in tool-use capabilities, i.e., using external tools (APIs) to fulfill human instructions. The reason is that current instruction tuning largely focuses on basic language tasks but ignores the tool-use domain. This is in contrast to the excell

Robert Brown, Julie A. Adams

The Defense Advanced Research Projects Agency (DARPA) OFFensive Swarm-Enabled Tactics program's goal of launching 250 unmanned aerial and ground vehicles from a limited sized launch zone was a daunting challenge. The swarm's aerial vehicles were primarily multirotor platforms, which can efficiently be launched en masse. Each field exercise expected the deplo

Blair Attard-Frost, David Gray Widder

Researchers, practitioners, and policymakers with an interest in AI ethics need more integrative approaches for studying and intervening in AI systems across many contexts and scales of activity. This paper presents AI value chains as an integrative concept that satisfies that need. To more clearly theorize AI value chains and conceptually distinguish them f

Olivier Lamarre, Shantanu Malhotra, Jonathan Kelly

The success of a multi-kilometre drive by a solar-powered rover at the lunar south pole depends upon careful planning in space and time due to highly dynamic solar illumination conditions. An additional challenge is that the rover may be subject to random faults that can temporarily delay long-range traverses. The majority of existing global spatiotemporal p

Bokai Liu, Yizheng Wang, Timon Rabczuk, Thomas Olofsson

Polyurethane (PU) possesses excellent thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane (PU) has proven to be an effective strategy for enhancing building envelopes. This innovative design substantially enhances indoor thermal stability and minimizes fluctuations in

Noga Alon

A bipartite covering of a (multi)graph $G$ is a collection of bipartite graphs, so that each edge of $G$ belongs to at least one of them. The capacity of the covering is the sum of the numbers of vertices of these bipartite graphs. In this note we establish a (modest) strengthening of old results of Hansel and of Katona and Szemer\'edi, by showing that the c

Wanyu Wu, Wei Wang, Zheng Wang, Kui Jiang

Most existing Low-Light Image Enhancement (LLIE) methods are primarily designed to improve brightness in dark regions, which suffer from severe degradation in nighttime images. However, these methods have limited exploration in another major visibility damage, the glow effects in real night scenes. Glow effects are inevitable in the presence of artificial li

Muhittin Evren Aydin, Aykut Has, Beyhan Yilmaz

In this paper, we study the rectifying curves in multiplicative Euclidean space of dimension 3, i.e., those curves for which the position vector always lies in its rectifying plane. Since the definition of rectifying curve is affine and not metric, we are directly able to perform multiplicative differential-geometric concepts to investigate such curves. Havi

Kun Zhang, Kwangmin Yu, Kun Hao, Vladimir Korepin

Quantum computers provide a promising method to study the dynamics of many-body systems beyond classical simulation. On the other hand, the analytical methods developed and results obtained from the integrable systems provide deep insights on the many-body system. Quantum simulation of the integrable system not only provides a valid benchmark for quantum com

Jesse Heyninck, Badran Raddaoui, Christian Straßer

In formal argumentation, a distinction can be made between extension-based semantics, where sets of arguments are either (jointly) accepted or not, and ranking-based semantics, where grades of acceptability are assigned to arguments. Another important distinction is that between abstract approaches, that abstract away from the content of arguments, and struc

Hrishikesh Kulkarni, Sean MacAvaney, Nazli Goharian, Ophir Frieder

Retrieval approaches that score documents based on learned dense vectors (i.e., dense retrieval) rather than lexical signals (i.e., conventional retrieval) are increasingly popular. Their ability to identify related documents that do not necessarily contain the same terms as those appearing in the user's query (thereby improving recall) is one of their key a

Jiho Jin, Jiseon Kim, Nayeon Lee, Haneul Yoo

The Bias Benchmark for Question Answering (BBQ) is designed to evaluate social biases of language models (LMs), but it is not simple to adapt this benchmark to cultural contexts other than the US because social biases depend heavily on the cultural context. In this paper, we present KoBBQ, a Korean bias benchmark dataset, and we propose a general framework t

Alex J. Meyer, Harrison F. Agrusa, Derek C. Richardson, R. Terik Daly

With the successful impact of the NASA DART spacecraft in the Didymos-Dimorphos binary asteroid system, we provide an initial analysis of the post-impact perturbed binary asteroid dynamics. To compare our simulation results with observations, we introduce a set of "observable elements" calculated using only the physical separation of the binary asteroid, rat

Giampiero Palatucci, Mirco Piccinini, Letizia Temperini

We investigate some effects of the lack of compactness in the critical Sobolev embedding in the Heisenberg group.

Marie-Hélène Tomé

Deep work by Shintani in the 1970's describes Hecke $L$-functions associated to narrow ray class group characters of totally real fields $F$ in terms of what are now known as Shintani zeta functions. However, for $[F:\mathbb{Q}] = n \geq 3$, Shintani's method was ineffective due to its crucial dependence on abstract fundamental domains for the action of tota

Yisheng Tu, Zhi-Yun Li, Ka Ho Lam, Kengo Tomida

Stars form from the gravitational collapse of turbulent, magnetized molecular cloud cores. Our non-ideal MHD simulations reveal that the intrinsically anisotropic magnetic resistance to gravity during the core collapse naturally generates dense gravo-magneto-sheetlets within inner protostellar envelopes -- disrupted versions of classical sheet-like pseudodis

Tianxing Wu, Xudong Cao, Yipeng Zhu, Feiyue Wu

To easily obtain the knowledge about autism spectrum disorder and help its early screening and diagnosis, we create AsdKB, a Chinese knowledge base on autism spectrum disorder. The knowledge base is built on top of various sources, including 1) the disease knowledge from SNOMED CT and ICD-10 clinical descriptions on mental and behavioural disorders, 2) the d

Nima Alibabaei

Feng--Huang (2016) introduced weighted topological entropy and pressure for factor maps between dynamical systems and established its variational principle. Tsukamoto (2022) redefined those invariants quite differently for the simplest case and showed via the variational principle that the two definitions coincide. We generalize Tsukamoto's approach, redefin

Monika Henzinger, Barna Saha, Martin P. Seybold, Christopher Ye

{\em Algorithms with predictions} incorporate machine learning predictions into algorithm design. A plethora of recent works incorporated predictions to improve on worst-case optimal bounds for online problems. In this paper, we initiate the study of complexity of dynamic data structures with predictions, including dynamic graph algorithms. Unlike in online