February 2024 arXiv papers — page 120

Jodi Dianetti, Frank Riedel, Lorenzo Stanca

We consider the strategic interaction of traders in a continuous-time financial market with Epstein-Zin-type recursive intertemporal preferences and performance concerns. We derive explicitly an equilibrium for the finite player and the mean-field version of the game, based on a study of geometric backward stochastic differential equations of Bernoulli type

Meiyu, Pan, Christa Brelsford, Majbah Uddin

The increased use of personal vehicles presents environmental challenges, prompting the exploration of public transportation as an affordable, eco-friendly alternative. However, obstacles like fixed schedules, limited routes, and extended travel times impede widespread adoption. This study investigates the temporal evolution of spatial inequality in the trav

Jan Heuer, Christoph Wernhard

We show a projective Beth definability theorem for logic programs under the stable model semantics: For given programs $P$ and $Q$ and vocabulary $V$ (set of predicates) the existence of a program $R$ in $V$ such that $P \cup R$ and $P \cup Q$ are strongly equivalent can be expressed as a first-order entailment. Moreover, our result is effective: A program $

Claudio Landim, Jungkyoung Lee, Insuk Seo

Consider the elliptic operator given by \[ \mathscr{L}_\epsilon f=b\cdot\nabla f+\epsilon\Delta f \] for some smooth vector field $b:\mathbb{R}^d\to\mathbb{R}^d$ and $\epsilon>0$, and the initial-valued problem on $\mathbb{R}^d$ \[ \left\{\begin{aligned}&\partial_t u_\epsilon=\mathscr{L}_\epsilon u_\epsilon,\\ &u_\epsilon(0,\,\cdot)=u_0(\cdot), \end{aligned}

Adam Rouhiainen

The large-scale structure in cosmology is highly non-Gaussian at late times and small length scales, making it difficult to describe analytically. Parameter inference, data reconstruction, and data generation tasks in cosmology are greatly aided by various machine learning models. In order to retain as much information as possible while solving these problem

Juan Carlos Saez, Fernando Castro, Graziano Fanizzi, Manuel Prieto-Matias

Commodity multicore systems are increasingly adopting hardware support that enables the system software to partition the last-level cache (LLC). This support makes it possible for the operating system (OS) or the Virtual Machine Monitor (VMM) to mitigate shared-resource contention effects on multicores by assigning different co-running applications to variou

Yunsheng Tian, Ane Zuniga, Xinwei Zhang, Johannes P. Dürholt

Bayesian optimization has been successfully applied to optimize black-box functions where the number of evaluations is severely limited. However, in many real-world applications, it is hard or impossible to know in advance which designs are feasible due to some physical or system limitations. These issues lead to an even more challenging problem of optimizin

Georg K. J. Fischer, Max Bergau, D. Adriana Gómez-Rosal, Andreas Wachaja

Automated and autonomous industrial inspection is a longstanding research field, driven by the necessity to enhance safety and efficiency within industrial settings. In addressing this need, we introduce an autonomously navigating robotic system designed for comprehensive plant inspection. This innovative system comprises a robotic platform equipped with a d

Grigory A. Starkov

In this letter, we study how the spectrum of pseudo-Hermitian systems is influenced by the ambiguity in the choice of the pseudo-metric operator. In particular, we analyze the case when different parameter-independent choices of pseudo-metric are possible and how it can lead to the appearance of robust degenerate manifolds in the parameter space of the syste

Irina Alekseevskaia, Konstantin Arkhipenko

The use of third-party datasets and pre-trained machine learning models poses a threat to NLP systems due to possibility of hidden backdoor attacks. Existing attacks involve poisoning the data samples such as insertion of tokens or sentence paraphrasing, which either alter the semantics of the original texts or can be detected. Our main difference from the p

Norbert Tihanyi, Mohamed Amine Ferrag, Ridhi Jain, Tamas Bisztray

Large Language Models (LLMs) are increasingly used across various domains, from software development to cyber threat intelligence. Understanding all the different fields of cybersecurity, which includes topics such as cryptography, reverse engineering, and risk assessment, poses a challenge even for human experts. To accurately test the general knowledge of

Ethan Wilson, Azim Ibragimov, Michael J. Proulx, Sai Deep Tetali

Eye tracking is routinely being incorporated into virtual reality (VR) systems. Prior research has shown that eye tracking data, if exposed, can be used for re-identification attacks. The state of our knowledge about currently existing privacy mechanisms is limited to privacy-utility trade-off curves based on data-centric metrics of utility, such as predicti

Xiang Bai, Changhui Tan, Liutang Xue

We study the Cauchy problem of the compressible Euler system with strongly singular velocity alignment. We establish a global well-posedness theory for the system with small smooth initial data. Additionally, we derive asymptotic emergent behaviors for the system, providing time decay estimates with optimal decay rates. Notably, the optimal decay rate we obt

M. M. Castro, F. A. Grünbaum, I. Zurrián

The "time-and-band limiting" commutative property was found and exploited by D. Slepian, H. Landau and H. Pollak at Bell Labs in the 1960's, and independently by M. Mehta and later by C. Tracy and H. Widom in Random matrix theory. The property in question is the existence of local operators with simple spectrum that commute with naturally appearing global on

Jacob Tyo, Zachary C. Lipton

The acquisition of large-scale, precisely labeled datasets for person re-identification (ReID) poses a significant challenge. Weakly supervised ReID has begun to address this issue, although its performance lags behind fully supervised methods. In response, we introduce Contrastive Multiple Instance Learning (CMIL), a novel framework tailored for more effect

Saurabh Sihag, Gonzalo Mateos, Alejandro Ribeiro

Brain age is the estimate of biological age derived from neuroimaging datasets using machine learning algorithms. Increasing brain age with respect to chronological age can reflect increased vulnerability to neurodegeneration and cognitive decline. In this paper, we study NeuroVNN, based on coVariance neural networks, as a paradigm for foundation model for t

Michaela Borzechowski, John Fearnley, Spencer Gordon, Rahul Savani

We provide polynomial-time reductions between three search problems from three distinct areas: the P-matrix linear complementarity problem (P-LCP), finding the sink of a unique sink orientation (USO), and a variant of the $\alpha$-Ham Sandwich problem. For all three settings, we show that "two choices are enough", meaning that the general non-binary version

Marie Candito

The biaffine parser of Dozat and Manning (2017) was successfully extended to semantic dependency parsing (SDP) (Dozat and Manning, 2018). Its performance on graphs is surprisingly high given that, without the constraint of producing a tree, all arcs for a given sentence are predicted independently from each other (modulo a shared representation of tokens). T

Vicent Briva-Iglesias, Joao Lucas Cavalheiro Camargo, Gokhan Dogru

This study evaluates the machine translation (MT) quality of two state-of-the-art large language models (LLMs) against a tradition-al neural machine translation (NMT) system across four language pairs in the legal domain. It combines automatic evaluation met-rics (AEMs) and human evaluation (HE) by professional transla-tors to assess translation ranking, flu

Tanmoy Dam, Sanjay Bhargav Dharavath, Sameer Alam, Nimrod Lilith

Combining LiDAR and camera data has shown potential in enhancing short-distance object detection in autonomous driving systems. Yet, the fusion encounters difficulties with extended distance detection due to the contrast between LiDAR's sparse data and the dense resolution of cameras. Besides, discrepancies in the two data representations further complicate

Noureddine Snanou

In this paper, we study the isomorphism problem for central extensions. More precisely, in some new situations, we provide necessary and sufficient conditions for two central extensions to be isomorphic. We investigate the case when the quotient group is simple or purely non-abelian. Furthermore, we characterize isomorphisms leaving the quotient group invari

Gerald Kuba

Our main goal is to track down an algebraic basis of Hilbert space $\ell^2$ which is a connected and locally connected subset of the unit sphere.

Shiyu Li, Hannah Schieber, Niklas Corell, Bernhard Egger

Guidance for assemblable parts is a promising field for augmented reality. Augmented reality assembly guidance requires 6D object poses of target objects in real time. Especially in time-critical medical or industrial settings, continuous and markerless tracking of individual parts is essential to visualize instructions superimposed on or next to the target

Cecilia Tarpau, Ming Fang, Konstantinos C. Zygalakis, Marcelo Pereyra

This paper presents a statistical forward model for a Compton imaging system, called Compton imager. This system, under development at the University of Illinois Urbana Champaign, is a variant of Compton cameras with a single type of sensors which can simultaneously act as scatterers and absorbers. This imager is convenient for imaging situations requiring a

Kilian Pioch, Thomas Kriecherbauer, Michael Margaliot, Lars Grüne

The totally asymmetric simple exclusion process (TASEP) is a stochastic model for the unidirectional dynamics of interacting particles on a $1$D-lattice that is much used in systems biology and statistical physics. Its master equation describes the evolution of the probability distribution on the state space. The size of the master equation grows exponential

William R. Green, Connor Lane, Benjamin Lyons, Shyam Ravishankar

We investigate dispersive estimates for the massless three dimensional Dirac equation with a potential. In particular, we show that the Dirac evolution satisfies a $\langle t\rangle^{-1}$ decay rate as an operator from $L^1$ to $L^\infty$ regardless of the existence of zero energy eigenfunctions. We also show this decay rate may be improved to $\langle t\ran

Raffaele Bolla, Roberto Bruschi, Chiara Lombardo, Sergio Mangialardi

In order to fulfill the stringent requirements and fast advancements of 5G and beyond applications, it is inevitable to develop research/industrial testbeds to examine the different proposed innovative features of 5G and beyond. In this paper, we propose a testbed including 5G and beyond technologies by combining open-source solutions and our developed Netwo

Daniel Kipping, Yixuan Zhang, Waldo Nogueira

Objective: In cochlear implant users with residual acoustic hearing, compound action potentials (CAPs) can be evoked by acoustic (aCAP) or electric (eCAP) stimulation and recorded through the electrodes of the implant. We propose a novel computational model to simulate aCAPs and eCAPs in humans, considering the interaction between combined electric-acoustic

Alessia Suprano, Danilo Zia, Emanuele Polino, Davide Poderini

Quantum Cellular Automaton (QCA) is a model for universal quantum computation and a natural candidate for digital quantum simulation of relativistic quantum fields. Here we introduce the first photonic platform for implementing QCA-simulation of a free relativistic Dirac quantum field in 1+1 dimension, through a Dirac Quantum Cellular Automaton (DQCA). Encod

Mazen Ali, Matthias Kabel

This work introduces a novel method for embedding continuous variables into quantum circuits via piecewise polynomial features, utilizing low-rank tensor networks. Our approach, termed Piecewise Polynomial Tensor Network Quantum Feature Encoding (PPTNQFE), aims to broaden the applicability of quantum algorithms by incorporating spatially localized representa

Eszter Gselmann, Christopher W. Doble, Yung-Fong Hsu

Iverson (2006) proposed the law of similarity \[ \xi_{s}(\lambda x)= \gamma(\lambda, s)\xi_{\eta(\lambda, s)}(x) \] for the sensitivity functions $\xi_{s}\, (s\in S)$. Compared to the former models, the generality of this one lies in that here $\gamma$ and $\eta$ can also depend on the variables $\lambda$ and $s$. In the literature, this model (or its specia

Jakob S. Stokke, Morten Jakobsen, Kundan Kumar, Florin A. Radu

In this work, we consider a fully dynamic Biot model that includes memory effects due to evolving permeability. Time integrals are used to account for the change in structure. We propose an iterative splitting scheme for this model, extending the fixed-stress split for the quasi-static Biot. We use finite elements in space and a backward Euler discretization

Maryam Mahdavifar, Farhad Khoeini, Francois M. Peeters

Vertical integration of dissimilar layered materials in a so-called van der Waals (vdW) heterostructure (HS) has emerged as a useful tool to engineer band alignments and interfaces. In this paper, we investigate thermoelectric currents in a phosphorene/graphene vdW nanoribbon consisting of an armchair graphene nanoribbon (AGNR) stacked on an armchair phospho

Patrick Cameron, Baptiste Courme, Daniele Faccio, Hugo Defienne

Quantum imaging enhances imaging systems performance, potentially surpassing fundamental limits such as noise and resolution. However, these schemes have limitations and are still a long way from replacing classical techniques. Therefore, there is a strong focus on improving the practicality of quantum imaging methods, with the goal of finding real-world app

Fabrizio Grandoni, Edin Husić, Mathieu Mari, Antoine Tinguely

In the maximum independent set of convex polygons problem, we are given a set of $n$ convex polygons in the plane with the objective of selecting a maximum cardinality subset of non-overlapping polygons. Here we study a special case of the problem where the edges of the polygons can take at most $d$ fixed directions. We present an $8d/3$-approximation algori

Victor Issa

It is well known that when the nonlinearity is convex, the Hamilton-Jacobi PDE admits a unique semi-convex weak solution, which is the viscosity solution. In this paper, motivated by problems arising from spin glasses, we show that if the Hamilton-Jacobi PDE with strictly convex nonlinearity and regular enough initial condition admits a semi-concave weak sol

Juan Carlos Saez, Fernando Castro, Manuel Prieto-Matias

Asymmetric multicore processors (AMPs) couple high-performance big cores and low-power small cores with the same instruction-set architecture but different features, such as clock frequency or microarchitecture. Previous work has shown that asymmetric designs may deliver higher energy efficiency than symmetric multicores for diverse workloads. Despite their

M. H. Duong, C. M. Durbac, T. A. Han

In this paper, we study the problem of cost optimisation of individual-based institutional incentives (reward, punishment, and hybrid) for guaranteeing a certain minimal level of cooperative behaviour in a well-mixed, finite population. In this scheme, the individuals in the population interact via cooperation dilemmas (Donation Game or Public Goods Game) in

Qiao Pan, Zhaofang Mao

This paper addresses a realistic home health care and home care (HHC\&HC) problem which has become increasingly complex in the face of demographic aging and post-COVID-19 disruptions. The HHC\&HC sector, as the essential component of modern health care systems, faces unique challenges in efficiently scheduling and routing caregivers to meet the rising demand

Jorge Crispim Romão, Miguel Crispim Romão

We present a novel Artificial Intelligence approach for Beyond the Standard Model parameter space scans by augmenting an Evolutionary Strategy with Novelty Detection. Our approach leverages the power of Evolutionary Strategies, previously shown to quickly converge to the valid regions of the parameter space, with a \emph{novelty reward} to continue explorati

Lei Yu

This paper investigates three closely related topics -- R\'enyi resolvability, noise stability, and anti-contractivity. The R\'enyi resolvability problem refers to approximating a target output distribution of a given channel in the R\'enyi divergence when the input is set to a function of a given uniform random variable. This problem for the R\'enyi paramet

Yijie Zhang, Yuanchen Bei, Hao Chen, Qijie Shen

Representing information of multiple behaviors in the single graph collaborative filtering (CF) vector has been a long-standing challenge. This is because different behaviors naturally form separate behavior graphs and learn separate CF embeddings. Existing models merge the separate embeddings by appointing the CF embeddings for some behaviors as the primary

Dario Arnaldo Domanin, Matteo Pegoraro, Santi Trimarchi, Maurizio Domanin

Abdominal Aortic Aneurysm consists of a permanent dilation in the abodminal portion of the aorta and, along with its associated pathologies like calcifications and intraluminal thrombi, is one of the most important pathologies of the circulatory system. The shape of the aorta is among the primary drivers for these health issues, with particular reference to

Ayo Adedeji, Sarita Joshi, Brendan Doohan

In the rapidly evolving landscape of medical documentation, transcribing clinical dialogues accurately is increasingly paramount. This study explores the potential of Large Language Models (LLMs) to enhance the accuracy of Automatic Speech Recognition (ASR) systems in medical transcription. Utilizing the PriMock57 dataset, which encompasses a diverse range o

Riasat Ali, Xia Tiecheng, Rimsha Babar, Ali Övgün

In this article, we examine the gravitational deflection of particles in curved spacetime immersed in perfect fluid in the context of Rastall theory. We propose an infinite region approach to Gibbons-Werner to avoid singularity, given that the integral region is generally infinite. In the Rastall theory framework, the black hole solutions in the dust field a

Sofia Gil-Rodrigo, Massimiliano Zanin

The impact of air transport delays and their propagation has long been studied, mainly from environmental and mobility viewpoints, using a wide range of data analysis tools and simulations. Less attention has nevertheless been devoted to how delays create meso-scale structures around each airport. In this work we tackle this issue by reconstructing functiona

Ram Nandan Kumar, Sauvik Roy, Anand Dev Ranjan, Subhasish Dutta Gupta

Spin orbit interaction (SOI) due to tight focusing of light in optical tweezers has led to exciting and exotic avenues towards inducing rotation in microscopic particles. However, instances where the back action of the particles influences and modifies SOI effects so as to induce rotational motion are rarely known. Here, we tightly focus a vector beam having

Haoran Yin, Diederick Vermetten, Furong Ye, Thomas H. W. Bäck

When benchmarking optimization heuristics, we need to take care to avoid an algorithm exploiting biases in the construction of the used problems. One way in which this might be done is by providing different versions of each problem but with transformations applied to ensure the algorithms are equipped with mechanisms for successfully tackling a range of pro

Claire Chevallier, Joseph Vovrosh, Julius de Hond, Mario Dagrada

We design variational pulse sequences tailored for neutral atom quantum simulators and show that we can engineer layers of single-qubit and multi-qubit gates. As an application, we discuss how the proposed method can be used to perform refocusing algorithms, SWAP networks, and ultimately quantum chemistry simulations. While the theoretical protocol we develo

Rei Inoue, Atsuo Kuniba, Xiaoyue Sun, Yuji Terashima

We construct a new solution to the tetrahedron equation by further pursuing the quantum cluster algebra approach in our previous works. The key ingredients include a symmetric butterfly quiver attached to the wiring diagrams for the longest element of type $A$ Weyl groups and the implementation of quantum $Y$-variables through the $q$-Weyl algebra. The solut

Joseph Adams

We prove well-posedness for higher-order equations in the so-called NLS hierarchy (also known as part of the AKNS hierarchy) in almost critical Fourier-Lebesgue spaces and in modulation spaces. We show the $j$th equation in the hierarchy is locally well-posed for initial data in $\hat H^s_r(\mathbb{R})$ for $s \ge \frac{j-1}{r'}$ and $1 < r \le 2$ and also i

Petr Malinský, Oleksander Romanenko, Vladimír Havránek, Mariapompea Cutroneo

In this study, novel flexible micro-scale humidity sensors were directly fabricated in graphene oxide (GO) and polyimide (PI) using ion beam writing without any further modifications, and then successfully tested in an atmospheric chamber. Two low fluences of carbon ions with an energy of 5 MeV were used, and structural changes in the irradiated materials we

Pinzari Gabriella, Scoppola Benedetto, Veglianti Matteo

We discuss a model describing the spin orbit resonance cascade. We assume that the primary has a two-layer (core-shell) structure: it is composed by a thin solid crust and an inner and heavier solid core that are interacting due to the presence of a fluid interface. We assume two sources of dissipation: a viscous one, depending on the relative angular veloci

S. Khakshournia, R. Mansouri

The effective Einstein equations on a 3-brane embedded in a 5-dimensional Riemann-Cartan bulk spacetime are revisited. Addressing the shortcomings in the hitherto published junction conditions on the brane in the presence of torsion, we have elaborated on our general form of the junction conditions recently published. Applying our general junction conditions

Chenchang Li, Zihao Ai, Tong Wu, Xiaosa Li

Manipulating deformable objects is a ubiquitous task in household environments, demanding adequate representation and accurate dynamics prediction due to the objects' infinite degrees of freedom. This work proposes DeformNet, which utilizes latent space modeling with a learned 3D representation model to tackle these challenges effectively. The proposed repre

Sophie Fischer, Carlos Gemmell, Niklas Tecklenburg, Iain Mackie

We tackle the challenge of building real-world multimodal assistants for complex real-world tasks. We describe the practicalities and challenges of developing and deploying GRILLBot, a leading (first and second prize winning in 2022 and 2023) system deployed in the Alexa Prize TaskBot Challenge. Building on our Open Assistant Toolkit (OAT) framework, we prop

Celina Kacperski, Tobias Vogel, Florian Kutzner

Connected and autonomous vehicles (CAVs) are often discussed as a solution to pressing issues of the current transport systems, including congestion, safety, social inclusion and ecological sustainability. Scientifically, there is agreement that CAVs may solve, but can also aggravate these issues, depending on the specific CAV solution. In the current paper,

Victor Turpaud, Thi-Hao-Nhi Nguyen, Natnicha Koompai, Jonathan Peltier

Dual-comb spectroscopy is a powerful technique to measure optical spectra in a wide spectral range with high-frequency resolution. The development of compact systems operating in the long-wave infrared wavelength range is of high interest for spectroscopic and sensing applications. Amongst the different techniques to obtain optical frequency-combs, electro-o

Isabelle Lorge, Dan W. Joyce, Niall Taylor, Alejo Nevado-Holgado

Difficult-to-treat depression (DTD) has been proposed as a broader and more clinically comprehensive perspective on a person's depressive disorder where despite treatment, they continue to experience significant burden. We sought to develop a Large Language Model (LLM)-based tool capable of interrogating routinely-collected, narrative (free-text) electronic

Parisa Ramezani, Özlem Tuğfe Demir, Emil Björnson

Source localization is the process of estimating the location of signal sources based on the signals received at different antennas of an antenna array. It has diverse applications, ranging from radar systems and underwater acoustics to wireless communication networks. Subspace-based approaches are among the most effective techniques for source localization

Samuel Winter, Yangyishi Zhang, Gan Zheng, Lajos Hanzo

Quantum annealing (QA) is proposed for vector perturbation precoding (VPP) in multiple input multiple output (MIMO) communications systems. The mathematical framework of VPP is presented, outlining the problem formulation and the benefits of lattice reduction algorithms. Lattice reduction aided quantum vector perturbation (LRAQVP) is designed by harnessing p

Maria Lyssenko, Christoph Gladisch, Christian Heinzemann, Matthias Woehrle

Safety is of utmost importance for perception in automated driving (AD). However, a prime safety concern in state-of-the art object detection is that standard evaluation schemes utilize safety-agnostic metrics to argue sufficient detection performance. Hence, it is imperative to leverage supplementary domain knowledge to accentuate safety-critical misdetecti

W. Barker, C. Marzo

In the context of weak-field metric-affine (i.e. Palatini) gravity near Minkowski spacetime, we compute the particle spectra in the simultaneous presence of all independent contractions quadratic in Ricci-type tensors. Apart from the full metric-affine geometry, we study kinematic limits with vanishing torsion (i.e. a symmetric connection) and vanishing non-

Puneet Kumar, Sarthak Malik, Balasubramanian Raman, Xiaobai Li

The ability to generate sentiment-controlled feedback in response to multimodal inputs comprising text and images addresses a critical gap in human-computer interaction. This capability allows systems to provide empathetic, accurate, and engaging responses, with useful applications in education, healthcare, marketing, and customer service. To this end, we ha

Nir Weingarten, Zohar Yakhini, Moshe Butman, Ran Gilad-Bachrach

Deep Neural Nets (DNNs) learn latent representations induced by their downstream task, objective function, and other parameters. The quality of the learned representations impacts the DNN's generalization ability and the coherence of the emerging latent space. The Information Bottleneck (IB) provides a hypothetically optimal framework for data modeling, yet

Sauvik Roy, Nirmalya Ghosh, Ayan Banerjee, Subhasish Dutta Gupta

We observe clear precessional motion of highly birefringent liquid crystal (LC) particles trapped in a spherically aberrated optical trap which is built around a tilted refractive index stratified medium. For input circularly polarized light, the breaking of azimuthal symmetry induced by the tilt leads to an asymmetric intensity distribution in the radial di

Sabyasachi Ghosh, Ajit Rajwade

Recovery of signals with elements defined on the nodes of a graph, from compressive measurements is an important problem, which can arise in various domains such as sensor networks, image reconstruction and group testing. In some scenarios, the graph may not be accurately known, and there may exist a few edge additions or deletions relative to a ground truth

Hans-Otto Walther

Differential equations with state-dependent delays define a semiflow of continuously differentiable solution operators in general only on the associated {\it solution manifold} in the Banach space $C^1_n=C^1([-h,0],\mathbb{R}^n)$. For a prototypic example we develop a new proof that its solution manifold is diffeomorphic to an open subset of the subspace giv

Rui Song, Chenwei Liang, Hu Cao, Zhiran Yan

Collaborative perception in automated vehicles leverages the exchange of information between agents, aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However, these approaches fall short in offering a comprehensive 3D e

Zecheng Li, Zening Zeng, Yuqi Liang, Jin-Gang Yu

Weakly supervised instance segmentation (WSIS) using only image-level labels is a challenging task due to the difficulty of aligning coarse annotations with the finer task. However, with the advancement of deep neural networks (DNNs), WSIS has garnered significant attention. Following a proposal-based paradigm, we encounter a redundant segmentation problem r

Jingshu Li, Yitian Yang, Renwen Zhang, Q. Vera Liao

Providing well-calibrated AI confidence can help promote users' appropriate trust in and reliance on AI, which are essential for AI-assisted decision-making. However, calibrating AI confidence -- providing confidence score that accurately reflects the true likelihood of AI being correct -- is known to be challenging. To understand the effects of AI confidenc

Xue Gong, Desmond J. Higham, Konstantinos Zygalakis, Ginestra Bianconi

Higher-order networks encode the many-body interactions existing in complex systems, such as the brain, protein complexes, and social interactions. Simplicial complexes are higher-order networks that allow a comprehensive investigation of the interplay between topology and dynamics. However, simplicial complexes have the limitation that they only capture und

Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh V. Chawla

Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user's questions, our method provides textual replies and highlights the relevant parts of the graph. While existing works integrate large language models (LLMs) and graph neural netwo

Antoine Bendimerad-Hohl, Denis Matignon, Ghislain Haine, Laurent Lefèvre

First, two examples of 1D distributed port-Hamiltonian systems with dissipation, given in explicit (descriptor) form, are considered: the Dzekster model for the seepage of underground water and a nanorod model with non-local viscous damping. Implicit representations in Stokes-Lagrange subspaces are formulated. These formulations lead to modified Hamiltonian

Huagui Lai, Selina Olthof, Shengqiang Ren, Radha K. Kothandaraman

Tin perovskites are emerging as promising alternatives to their lead-based counterparts for high-performance and flexible perovskite solar cells (PSCs). However, their rapid crystallization often leads to inadequate film quality and poor device performance. In this study, the role of GeI2 as an additive is investigated for controlling the nucleation and crys

Rodrigo Veiga, Anastasia Remizova, Nicolas Macris

We investigate the test risk of continuous-time stochastic gradient flow dynamics in learning theory. Using a path integral formulation we provide, in the regime of a small learning rate, a general formula for computing the difference between test risk curves of pure gradient and stochastic gradient flows. We apply the general theory to a simple model of wea

Yifan Zhang, Yifan Luo, Yang Yuan, Andrew C Yao

We present Autonomous Data Selection (AutoDS), a method that leverages base language models themselves as zero-shot "generative classifiers" to automatically curate high-quality mathematical texts. Unlike prior approaches that require human annotations or training a dedicated data filter, AutoDS relies solely on a model's logits to determine whether a given

Mark de Rooij, Dion Woestenburg, Frank Busing

We propose a new mapping tool for supervised and unsupervised analysis of multivariate binary data with multiple items, questions, or response variables. The mapping assumes an underlying proximity response function, where participants can have multiple reasons to disagree or say ``no'' to a question. The probability to endorse, or to agree with an item depe

Atsuhisa Ota

We study the fluctuation-dissipation relation for sound waves in the cosmic microwave background (CMB), employing effective field theory (EFT) for fluctuating hydrodynamics. Treating sound waves as the linear response to thermal radiation, we establish the fluctuation-dissipation relation within a cosmological framework. While dissipation is elucidated in es

Gennaro Ciampa, Gianluca Crippa, Stefano Spirito

The aim of this note is to study the Cauchy problem for the 2D Euler equations under very low regularity assumptions on the initial datum. We prove propagation of regularity of logarithmic order in the class of weak solutions with $L^p$ initial vorticity, provided that $p\geq 4$. We also study the inviscid limit from the 2D Navier-Stokes equations for vortic

Petros Ellinas, Rahul Nellikath, Ignasi Ventura, Jochen Stiasny

Verification of Neural Networks (NNs) that approximate the solution of Partial Differential Equations (PDEs) is a major milestone towards enhancing their trustworthiness and accelerating their deployment, especially for safety-critical systems. If successful, such NNs can become integral parts of simulation software tools which can accelerate the simulation

Sudan Hansraj, Chevarra Hansraj, Njabulo Mkhize, Abdelghani Errehymy

The trace-free Einstein equations contain one equation less than the complete field equations. In a static and spherically symmetric spacetime, the number of field equations is thus reduced to two. The equation of pressure isotropy of general relativity, however, is preserved thus showing that any known perfect fluid spacetime is a suitable candidate for the

Yuyang Yan, Wafaa Aljbawi, Sami O. Simons, Visara Urovi

COVID-19 has affected more than 223 countries worldwide and in the Post-COVID Era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identific

B. Ohayon, J. E. Padilla-Castillo, S. C. Wright, G. Meijer

Nuclear charge radius differences in the silver isotopic chain have been reported through different combinations of experiment and theory, exhibiting a tension of two combined standard errors. This study investigates this issue by combining high-accuracy calculations for six low-lying states of atomic silver with an improved measurement of the $5s ^2S_{1/2}

Yuyang Xue, Chen Qin, Sotirios A. Tsaftaris

Reconstruction of magnetic resonance imaging (MRI) data has been positively affected by deep learning. A key challenge remains: to improve generalisation to distribution shifts between the training and testing data. Most approaches aim to address this via inductive design or data augmentation. However, they can be affected by misleading data, e.g. random noi

José D. Guimarães, Antonio Ruiz-Molero, James Lim, Mikhail I. Vasilevskiy

Noise in quantum devices is generally considered detrimental to computational accuracy. However, the recent proposal of noise-assisted simulation has demonstrated that noise can be an asset in digital quantum simulations of open systems on Noisy Intermediate-Scale Quantum (NISQ) devices. In this context, we introduce an optimized decoherence rate control sch

Jianhui Pang, Fanghua Ye, Derek Fai Wong, Xin He

Large language models (LLMs) predominantly employ decoder-only transformer architectures, necessitating the retention of keys/values information for historical tokens to provide contextual information and avoid redundant computation. However, the substantial size and parameter volume of these LLMs require massive GPU memory. This memory demand increases with

Yaorui Tan, Maolin Bo

Despite the extensive applications of perovskite compounds, the precise nature of non-Hermitian bonding in these materials remains poorly understood. In this study, density functional theory calculations were performed to determine the electronic structures of perovskite compounds. In particular, the bandgaps of Ba$_2$ScNbO$_6$ and Ba$_2$LuNbO$_6$ were found

Florentin Goyens, Clément W. Royer

The difficulty of minimizing a nonconvex function is in part explained by the presence of saddle points. This slows down optimization algorithms and impacts worst-case complexity guarantees. However, many nonconvex problems of interest possess a favorable structure for optimization, in the sense that saddle points can be escaped efficiently by appropriate al

Peter Orbanz

Consider a convex function that is invariant under an group of transformations. If it has a minimizer, does it also have an invariant minimizer? Variants of this problem appear in nonparametric statistics and in a number of adjacent fields. The answer depends on the choice of function, and on what one may loosely call the geometry of the problem -- the inter

Rasmus Ulfsnes, Nils Brede Moe, Viktoria Stray, Marianne Skarpen

Generative AI (GenAI) has fundamentally changed how knowledge workers, such as software developers, solve tasks and collaborate to build software products. Introducing innovative tools like ChatGPT and Copilot has created new opportunities to assist and augment software developers across various problems. We conducted an empirical study involving interviews

Nicolas Kainz, Dirk Lebiedz

In this paper, we explore the local geometry of dynamical systems $\dot{x}=F(x)$ with real time parameterization, where $F$ is holomorphic on connected open subsets of $\mathbb{C}\stackrel{\sim}{=}\mathbb{R}^2$. We describe the geometry of first-order equilibria. For equilibria of higher orders, we establish an equivalent condition for "definite directions",

Fabian Reede

Let $X$ be an Enriques surface. Using Beauville's result about the triviality of the Brauer map of $X$, we define a new involution on the category of coherent sheaves on the canonically covering K3 surface $\overline{X}$. We relate the fixed locus of this involution to certain Picard schemes of the noncommutative pair $(X,\mathcal{A})$, where $\mathcal{A}$ i

Haoyu Wang, Guozheng Ma, Ziqiao Meng, Zeyu Qin

Self-alignment is an effective way to reduce the cost of human annotation while ensuring promising model capability. However, most current methods complete the data collection and training steps in a single round, which may overlook the continuously improving ability of self-aligned models. This gives rise to a key query: What if we do multi-time bootstrappi

S. K. Mahatha, J. Phillips, J. Corral-Sertal, D. Subires

The emergence of correlated phenomena arising from the combination of 1$\mathrm{T}$ and 1$\mathrm{H}$ van der Waals layers is the focus of intense research. Here, we synthesize a novel self-stacked 6$\mathrm{R}$ phase in NbSeTe, showing a perfect alternating 1T and 1H layers that grow coherently along the c-direction, as revealed by scanning transmission ele

Gurkirat Singh, Surajit Bera, Vijay B. Shenoy

We explore phases of free fermions on arenas that do not tessellate a manifold. Specializing to arboreal arenas described by tree graphs which possess a notion of translation symmetry, we study possible fermionic phases in the BDI symmetry class on the $p$-coordinated Bethe lattice. We find that there are $p$ distinct obstructed atomic insulating phases that

S. Sundar

Is every product system of Hilbert spaces over a semigroup $P$ concrete, i.e. isomorphic to the product system of an $E_0$-semigroup over $P$? The answer, in general, is no. We record a non-example when $P$ is cancellative and is not embeddable in a group. However, we show that the answer is yes for a reasonable class of semigroups which includes solid, Bore

Vladimir Grujić, Tanja Stojadinović

In a series of recent talks Richard Stanley introduced a symmetric function associated to digraphs called the Redei-Berge symmetric function. This symmetric function enumerates descent sets of permutations corresponding to digraphs. We show that such constructed symmetric function arises from a suitable structure of combinatorial Hopf algebra on digraphs. Th

Shi-Xin Zhang, Jiaqi Miao, Chang-Yu Hsieh

Variational quantum algorithms (VQAs), as one of the most promising routes in the noisy intermediate-scale quantum (NISQ) era, offer various potential applications while also confront severe challenges due to near-term quantum hardware restrictions. In this work, we propose a framework to enhance the expressiveness of variational quantum ansatz by incorporat

Amir Džambić, Kristian Holm, Ralf Köhl

Let $n \geqslant 2$. We prove that, up to conjugation, $\mathrm{Sp}_{2n} (\mathbf{Z})$ is the unique lattice in $\mathrm{Sp}_{2n} (\mathbf{R})$ of the smallest covolume.