October 2024 arXiv papers — page 6

Angelo Caravano, Gabriele Franciolini, Sébastien Renaux-Petel

Violating the slow-roll regime during the final stages of inflation can significantly enhance curvature perturbations, a scenario often invoked in models producing primordial black holes and small-scale scalar induced gravitational waves. When perturbations are enhanced, one approaches the regime in which tree-level computations are insufficient, and nonline

Gregory Kendall

We classify the localising tensor ideal and colocalising hom-closed subcategories of the stable module category for $\mathrm{LH}\mathfrak{F}$ groups. Along the way we develop techniques to provide similar classifications for other categories of infinite groups.

Philipp Schleich, Marta Skreta, Lasse B. Kristensen, Rodrigo A. Vargas-Hernández

The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth increases expressivity, but also results in a detrimental accumulation of errors. Furthermore, the number of param

Pedro B. Melo, Sílvio M. Duarte Queirós, Diogo O. Soares-Pinto, Welles A. M. Morgado

Fisher Information (FI) is a quantity ubiquitously measured in such varied areas like metrology, machine learning, and biological complexity. Mathematically, it represents a lower bound in the variance of unknown parameters that are related to the distributions one has access, and a metric for probability manifolds. A stochastic analogous of the Fisher Infor

Mengyi Chen, Qianxiao Li

Macroscopic observables of a system are of keen interest in real applications such as the design of novel materials. Current methods rely on microscopic trajectory simulations, where the forces on all microscopic coordinates need to be computed or measured. However, this can be computationally prohibitive for realistic systems. In this paper, we propose a me

Chih-Hung Liu, Gleb Novikov

We develop a technique to design efficiently computable estimators for sparse linear regression in the simultaneous presence of two adversaries: oblivious and adaptive. We design several robust algorithms that outperform the state of the art even in the special case when oblivious adversary simply adds Gaussian noise. In particular, we provide a polynomial-t

Mohsen Rezaee-Hajidehi, Mahdi Neghabi, Stanislaw Stupkiewicz

While macroscopic experiments on polycrystalline shape memory alloys (SMAs) reveal significant thermomechanical coupling effects arising from the latent heat of transformation, the relevance of thermomechanical couplings in indentation tests remains ambiguous. This ambiguity is further emphasized by the rate effects observed in a number of micro/nano-indenta

Joyce A. Guzik, Helmut A. Abt, Jason Jackiewicz, Brian Kloppenborg

Deneb (alpha Cygni) is a bright (V magnitude 1.25) blue-white supergiant (spectral type A2 Ia) which shows variability in both radial velocity and photometric measurements. H. Abt reviewed radial velocity measurements by Paddock (1935) using the Lick observatory 36-inch telescope spectrograph during 1927-1935. Abt noticed resumptions of pulsations with a dom

Sergio Girón Pacheco, Kan Kitamura, Robert Neagu

We introduce the Cuntz-Thomsen picture of $\mathcal{C}$-equivariant Kasparov theory, denoted $\mathrm{KK}^\mathcal{C}$, for a unitary tensor category $\mathcal{C}$ with countably many isomorphism classes of simple objects. We use this description of $\mathrm{KK}^\mathcal{C}$ to prove the stable uniqueness theorem in this setting.

Anne-Marie George, Nic Wilson, Barry O'Sullivan

In this paper, we construct and compare algorithmic approaches to solve the Preference Consistency Problem for preference statements based on hierarchical models. Instances of this problem contain a set of preference statements that are direct comparisons (strict and non-strict) between some alternatives, and a set of evaluation functions by which all altern

Shanghaoran Quan, Tianyi Tang, Bowen Yu, An Yang

Recent advancements in Large Language Models (LLMs) have significantly enhanced their ability to process long contexts, yet a notable gap remains in generating long, aligned outputs. This limitation stems from a training gap where pre-training lacks effective instructions for long-text generation, and post-training data primarily consists of short query-resp

Bhupali Sharma

Variational Monte Carlo study has been done for the two hypernuclear systems $_\Lambda^4$H and $_\Lambda^4$H$^*$ for calculation of binding energies. For the two hypernuclear systems under study, different potential models have been used for the interactions involved in these hypernuclear systems. ArgonneV$_{18}$ NN, Urbana IX NNN and phenomenological $\Lamb

Ragnar Freij-Hollanti, Patricija Šapokaitė

We propose a novel definition of hypergraphical matroids, defined for arbitrary hypergraphs, simultaneously generalizing previous definitions for regular hypergraphs (Main, 1978), and for the hypergraphs of circuits of a matroid (Freij-Hollanti, Jurrius, Kuznetsova, 2023). As a consequence, we obtain a new notion of cycles in hypergraphs, and hypertrees. We

JiangDong Miao, Tatsuya Ikeda, Bisser Raytchev, Ryota Mizoguchi

Although 3D object editing has the potential to significantly influence various industries, recent research in 3D generation and editing has primarily focused on converting text and images into 3D models, often overlooking the need for fine-grained control over the editing of existing 3D objects. This paper introduces a framework that employs a pre-trained r

Linlin An, Peifeng Fan

The concept of periodic structures has driven the development of advanced materials like photonic and phononic crystals. These metamaterials typically rely on complex repeating units or meta-atoms, limiting their adaptability after fabrication. To overcome this limitation, we introduce the concept of metafields, which are repeating patterns of local magnetic

Benjamin J. Marshall, Yunda Yan, James Knowles, Chenguang Yang

A new disturbance observer based control scheme is developed for a quadrotor under the concurrent disturbances from a lightweight elastic tether cable and a lumped vertical disturbance. This elastic tether is unusual as it creates a disturbance proportional to the multicopter's translational movement. This paper takes an observer-based approach to estimate t

Rafael A. Garcia, Sylvain N. Breton, David Salabert, Sushant C. Tripathy

Solar magnetic activity follows regular cycles of about 11 years with an inversion of polarity in the poles every 22 years. This changing surface magnetism impacts the properties of the acoustic modes. The acoustic mode frequency shifts are a good proxy of the magnetic cycle. In this Letter we investigate solar magnetic activity cycles 23 and 24 through the

Ruslan Mirmominov, Johannes Wiesel

We consider multiperiod stochastic control problems with non-parametric uncertainty on the underlying probabilistic model. We derive a new metric on the space of probability measures, called the adapted $(p, \infty)$--Wasserstein distance $\mathcal{AW}_p^\infty$ with the following properties: (1) the adapted $(p, \infty)$--Wasserstein distance generates a to

Cristina Benso, Thomas Schwetz, Drona Vatsyayan

We consider an extended seesaw model which generates active neutrino masses via the usual type-I seesaw and leads to a large number of massless fermions as well as a sterile neutrino dark matter (DM) candidate in the $\mathcal{O}(10-100) {\rm~keV}$ mass range. The dark sector comes into thermal equilibrium with Standard Model neutrinos after neutrino decoupl

Isabeau Birindelli, Ariela Briani, Hitoshi Ishii

In this preprint we consider fully nonlinear equations in thin domains with oblique boundary condition, finding some new phenomena, in particular the limit equation contains "new terms" of the second, first and zeroth order which don't have an equivalent in the Neumann case treated in our previous work arXiv:2404.19577. The classical laplacian problem with N

Daniël Boer, Luca Maxia, Cristian Pisano

Azimuthal modulations in lepton and heavy-quark pair production in ultraperipheral collisions (UPCs) of highly charged ions are investigated. The modulations in the azimuthal angles of the sum and difference of the transverse momenta of the pair of particles in the final state, as well as of the transverse impact parameter, arise from the collisions of unpol

Juan Carlos Gonçalves-Dosantos, Ricardo Martínez, Joaquín Sánchez-Soriano

In this paper, we extend the museum pass problem to incorporate the market structure. To be more precise, we consider that museums are organized into several pass programs or consortia. Within this framework, we propose four allocation mechanisms based on the market structure and the principles of proportionality and egalitarianism. All these mechanisms sati

Atli Kosson, Bettina Messmer, Martin Jaggi

Learning Rate Warmup is a popular heuristic for training neural networks, especially at larger batch sizes, despite limited understanding of its benefits. Warmup decreases the update size $\Delta \mathbf{w}_t = \eta_t \mathbf{u}_t$ early in training by using lower values for the learning rate $\eta_t$. In this work we argue that warmup benefits training by k

Mario Liu

Thixotropic yields stress fluids are complex materials such as paint, drilling mud, and food products like ketchup or yogurt. They behave as a solid below a certain shear stress (called yield stress), and flows as a liquid above it. The viscosity decreases over time and recovers when being at rest again. The usual picture is that a web of interacting particl

Ali Behjatian, Ralf Blossey, Madhavi Krishnan

Electrostatics in the solution phase is governed by free electrical charges such as ions, as well as by bound charges that arise when a polarizable medium responds to an applied field. In a local medium, described by a constant dielectric permittivity, the sign of the far-field electrostatic potential distribution around an object is governed by its electric

Hao Xia, Qing Xue, Yanping Liu, Binggui Zhou

Recently, reconfigurable intelligent surface (RIS) has been widely used to enhance the performance of millimeter wave (mmWave) communication systems, making beam alignment more challenging. To ensure efficient communication, this paper proposes a novel intelligent angle map-based beam alignment scheme for both general user equipments (UEs) and RIS-aided UEs

Xinghao Wang, Pengyu Wang, Bo Wang, Dong Zhang

Large language models (LLMs) have revolutionized numerous applications, yet their deployment remains challenged by memory constraints on local devices. While scaling laws have enhanced LLM capabilities, the primary bottleneck has shifted from \textit{capability} to \textit{availability}, emphasizing the need for efficient memory management. Traditional compr

Laura Abatangelo, Veronica Felli

We study double eigenvalues of Aharonov-Bohm operators with Dirichlet boundary conditions in planar domains containing the origin. We focus on the behavior of double eigenvalues when the potential's circulation is a fixed half-integer number and the operator's pole is moving on straight lines in a neighborhood of the origin. We prove that bifurcation occurs

Davide Celestini, Daniele Gammelli, Tommaso Guffanti, Simone D'Amico

Model predictive control (MPC) has established itself as the primary methodology for constrained control, enabling general-purpose robot autonomy in diverse real-world scenarios. However, for most problems of interest, MPC relies on the recursive solution of highly non-convex trajectory optimization problems, leading to high computational complexity and stro

Dmitriy Kunisky, Timm Oertel, Nicola Wengiel, Peiyuan Zhang

We study the matrix discrepancy problem in the average-case setting. Given a sequence of $m \times m$ symmetric matrices $A_1,\ldots,A_n$, its discrepancy is defined as the minimal spectral norm over all signed sums $\sum_{i=1}^n x_iA_i$ with $x_1,\ldots,x_n \in \{\pm1\}$. Our contributions are twofold. First, we study the asymptotic discrepancy of random ma

Guy David, Stefano Decio, Max Engelstein, Svitlana Mayboroda

The present paper establishes that the Robin harmonic measure is quantitatively mutually absolutely continuous with respect to the surface measure on any Ahlfors regular set in any (quantifiably) connected domain for any elliptic operator. This stands in contrast with analogous results for the Dirichlet boundary value problem and also contradicts the expecta

Nic Wilson, Anne-Marie George

We analyse preference inference, through consistency, for general preference languages based on lexicographic models. We identify a property, which we call strong compositionality, that applies for many natural kinds of preference statement, and that allows a greedy algorithm for determining consistency of a set of preference statements. We also consider dif

Simon A. Pope, Diane J. Roth, Aakash Bansal, Mostafa Mousa

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the

Fu-Chieh Chang, Yu-Ting Lee, Hui-Ying Shih, Yi Hsuan Tseng

The reasoning abilities of large language models (LLMs) have improved with chain-of-thought (CoT) prompting, allowing models to solve complex tasks stepwise. However, training CoT capabilities requires detailed reasoning data, which is often scarce. The self-taught reasoner (STaR) framework addresses this by using reinforcement learning to automatically gene

Tomohiro Okuma, Kei-ichi Watanabe, Ken-ichi Yoshida

Let $A$ be an excellent two-dimensional normal local ring containing an algebraically closed field. Then $A$ is called an elliptic singularity if $p_f(A)=1$, where $p_f$ denotes the fundamental genus. On the other hand, the concept of almost Gorenstein rings was introduced by Barucci and Fr\"oberg for one-dimensional local rings and generalized by Goto, Taka

Ruisi He, Nicola D. Cicco, Bo Ai, Mi Yang

Accurate channel models are the prerequisite for communication-theoretic investigations as well as system design. Channel modeling generally relies on statistical and deterministic approaches. However, there are still significant limits for the traditional modeling methods in terms of accuracy, generalization ability, and computational complexity. The fundam

Nikita Durasov, Rafid Mahmood, Jiwoong Choi, Marc T. Law

3D object detection is an essential task for computer vision applications in autonomous vehicles and robotics. However, models often struggle to quantify detection reliability, leading to poor performance on unfamiliar scenes. We introduce a framework for quantifying uncertainty in 3D object detection by leveraging an evidential learning loss on Bird's Eye V

Amir Khan, Imran Akhtar, Muhammad Saif Ullah Khalid

Inspired by the natural motion of insects, fish, and other animals, flapping airfoils have gained significant importance due to their applications in fields such as ship propulsion, micro aerial vehicles, and autonomous underwater vehicles. Over the past two decades, extensive research has focused on understanding the dynamics of these airfoils, their thrust

Stefano Almi, Elisa Davoli, Anna Kubin, Emanuele Tasso

We prove that E. De Giorgi's conjecture for the nonlocal approximation of free-discontinuity problems extends to the case of functionals defined in terms of the symmetric gradient of the admissible field. After introducing a suitable class of continuous finite-difference approximants, we show the compactness of deformations with equibounded energies, as well

Vasileios Tzouras, Lazaros Nalpantidis, Ronja Güldenring

In precision agriculture, vision models often struggle with new, unseen fields where crops and weeds have been influenced by external factors, resulting in compositions and appearances that differ from the learned distribution. This paper aims to adapt to specific fields at low cost using Unsupervised Domain Adaptation (UDA). We explore a novel domain shift

Hao Zhang, Lei Cao, Jiayi Ma

Existing multi-modal image fusion methods fail to address the compound degradations presented in source images, resulting in fusion images plagued by noise, color bias, improper exposure, \textit{etc}. Additionally, these methods often overlook the specificity of foreground objects, weakening the salience of the objects of interest within the fused images. T

Qinqian Lei, Bo Wang, Robby T. Tan

Detecting Human-Object Interactions (HOI) in zero-shot settings, where models must handle unseen classes, poses significant challenges. Existing methods that rely on aligning visual encoders with large Vision-Language Models (VLMs) to tap into the extensive knowledge of VLMs, require large, computationally expensive models and encounter training difficulties

Augustin Lemesle, Julien Lehmann, Tristan Le Gall

As AI systems are becoming more and more popular and used in various critical domains (health, transport, energy, ...), the need to provide guarantees and trust of their safety is undeniable. To this end, we present PyRAT, a tool based on abstract interpretation to verify the safety and the robustness of neural networks. In this paper, we describe the differ

Matyas Juhasz, Kalyan Dutia, Henry Franks, Conor Delahunty

Climate decision making is constrained by the complexity and inaccessibility of key information within lengthy, technical, and multi-lingual documents. Generative AI technologies offer a promising route for improving the accessibility of information contained within these documents, but suffer from limitations. These include (1) a tendency to hallucinate or

Evgeni Dimitrov

We investigate a class of line ensembles whose local structure is described by independent geometric random walk bridges, which have been conditioned to interlace with each other. The latter arise naturally in the context Schur processes, including their versions in a half-space and a finite interval with free or periodic boundary conditions. We show that un

Tung Sum Thomas Kwok, Chi-hua Wang, Guang Cheng

Data collaboration via Data Clean Room offers value but raises privacy concerns, which can be addressed through synthetic data and multi-table synthesizers. Common multi-table synthesizers fail to perform when subjects occur repeatedly in both tables. This is an urgent yet unresolved problem, since having both tables with repeating subjects is common. To imp

Phil Wee, Riyadh Baghdadi

Recently, there has been an explosion of large language models created through fine-tuning with data from larger models. These small models able to produce outputs that appear qualitatively similar to significantly larger models. However, one of the key limitations that have been observed with these models is their propensity to hallucinate significantly mor

Biswajit Pandey, Anindita Nandi

We analyze the evolution of red and blue galaxies in different cosmic web environments from redshift $z=3$ to $z=0$ using the IllustrisTNG simulation. We use Otsu's method to classify the red or blue galaxies at each redshift and determine their geometric environments from the eigenvalues of the deformation tensor. Our analysis shows that initially, blue gal

Sumner B. Harris, Ruth Fajardo, Alexander A. Puretzky, Kai Xiao

The rapid validation of newly predicted materials through autonomous synthesis requires real-time adaptive control methods that exploit physics knowledge, a capability that is lacking in most systems. Here, we demonstrate an approach to enable the real-time control of thin film synthesis by combining in situ optical diagnostics with a Bayesian state estimati

Pooria Madani

Code metamorphism refers to a computer programming exercise wherein the program modifies its own code (partial or entire) consistently and automatically while retaining its core functionality. This technique is often used for online performance optimization and automated crash recovery in certain mission-critical applications. However, the technique has been

Hamidreza Eivazi, André Hebenbrock, Raphael Ginster, Steffen Blömeke

Battery degradation remains a critical challenge in the pursuit of green technologies and sustainable energy solutions. Despite significant research efforts, predicting battery capacity loss accurately remains a formidable task due to its complex nature, influenced by both aging and cycling behaviors. To address this challenge, we introduce a novel general-p

Franziska Menti, José A. Caballero, Mark C. Wyatt, Antonio García Muñoz

We present the database of potential targets for the Large Interferometer For Exoplanets (LIFE), a space-based mid-infrared nulling interferometer mission proposed for the Voyage 2050 science program of the European Space Agency (ESA). The database features stars, their planets and disks, main astrophysical parameters, and ancillary observations. It allows u

Hangyu Zhou, Chia-Hsiang Kao, Cheng Perng Phoo, Utkarsh Mall

Clouds in satellite imagery pose a significant challenge for downstream applications. A major challenge in current cloud removal research is the absence of a comprehensive benchmark and a sufficiently large and diverse training dataset. To address this problem, we introduce the largest public dataset -- $\textit{AllClear}$ for cloud removal, featuring 23,742

Séamus Lankford, Andy Way

In an evolving landscape of crisis communication, the need for robust and adaptable Machine Translation (MT) systems is more pressing than ever, particularly for low-resource languages. This study presents a comprehensive exploration of leveraging Large Language Models (LLMs) and Multilingual LLMs (MLLMs) to enhance MT capabilities in such scenarios. By focu

Armand Kassaï Koupaï, Jorge Mifsut Benet, Yuan Yin, Jean-Noël Vittaut

Solving parametric partial differential equations (PDEs) presents significant challenges for data-driven methods due to the sensitivity of spatio-temporal dynamics to variations in PDE parameters. Machine learning approaches often struggle to capture this variability. To address this, data-driven approaches learn parametric PDEs by sampling a very large vari

Marek Vlk, Marek Jaros, Zdenek Hanzalek

This paper addresses the problem of scheduling non-preemptive tasks with release jitter and execution time variation on a uniprocessor. We show that the schedulability analysis based on schedule graph generation, proposed by Nasri and Brandenburg [RTSS 2017], produces negative results when it could be easily avoided by slightly reformalizing the notion of no

Fabian Hummer, Lorenz Emberger, Frank Simon

The SiPM-on-Tile technology for highly granular calorimeters, where small plastic scintillator tiles are directly read out with SiPMs, has been developed for the CALICE Analog Hadron Calorimeter, and has been adopted for parts of the hadronic section of the CMS High Granularity Calorimeter. For future electron-positron colliders, a single cell time stamping

Koji Nagano, Karera Mori, Kiwamu Izumi

Future gravitational wave observation in space will demand improvement in the sensitivity of the local sensor for the drag-free control. This paper presents the proposal, design, and demonstration of a new laser interferometric sensor named Quadrature Interferometric Metrology of Translation and Tilt (QUIMETT) for the drag-free local sensor. QUIMETT enables

Pan Li

In this paper, we investigate the Hilbert space factorisation problem of two-sided black holes in high dimensions. We demonstrate that the Hilbert space of two-sided black holes can be factorized into the tensor product of two one-sided bulk Hilbert spaces when the effect of non-perturbative replica wormholes is taken into account. We further interpret the o

Marvin Rübenacke, Andreas Zunker, Felix Krieg, Stephan ten Brink

In this paper, we propose a data-driven algorithm to design rate- and length-flexible polar codes. While the algorithm is very general, a particularly appealing use case is the design of codes for automorphism ensemble decoding (AED), a promising decoding algorithm for ultra-reliable low-latency communications (URLLC) and massive machine-type communications

Khurram Yamin, Shantanu Gupta, Gaurav R. Ghosal, Zachary C. Lipton

The ability to robustly identify causal relationships is essential for autonomous decision-making and adaptation to novel scenarios. However, accurately inferring causal structure requires integrating both world knowledge and abstract logical reasoning. In this work, we investigate the interaction between these two capabilities through the representative tas

Rena Gao, Xuetong Wu, Siwen Luo, Caren Han

Out-of-distribution (OOD) detection in multimodal contexts is essential for identifying deviations in combined inputs from different modalities, particularly in applications like open-domain dialogue systems or real-life dialogue interactions. This paper aims to improve the user experience that involves multi-round long dialogues by efficiently detecting OOD

Zihang Song, Matteo Zecchin, Bipin Rajendran, Osvaldo Simeone

Sequence models have demonstrated the ability to perform tasks like channel equalization and symbol detection by automatically adapting to current channel conditions. This is done without requiring any explicit optimization and by leveraging not only short pilot sequences but also contextual information such as long-term channel statistics. The operating pri

Daniel May, Alessandro Tundo, Shashikant Ilager, Ivona Brandic

The deployment of ML models on edge devices is challenged by limited computational resources and energy availability. While split computing enables the decomposition of large neural networks (NNs) and allows partial computation on both edge and cloud devices, identifying the most suitable split layer and hardware configurations is a non-trivial task. This pr

Hiwot Belay Tadesse, Alihan Hüyük, Yaniv Yacoby, Weiwei Pan

When explaining black-box machine learning models, it's often important for explanations to have certain desirable properties. Most existing methods `encourage' desirable properties in their construction of explanations. In this work, we demonstrate that these forms of encouragement do not consistently create explanations with the properties that are suppose

Fabian Haak, Björn Engelmann, Christin Katharina Kreutz, Philipp Schaer

Search query suggestions affect users' interactions with search engines, which then influences the information they encounter. Thus, bias in search query suggestions can lead to exposure to biased search results and can impact opinion formation. This is especially critical in the political domain. Detecting and quantifying bias in web search engines is diffi

Gaétan Berthe, Marin Bougeret, Daniel Gonçalves, Jean-Florent Raymond

In this paper, we investigate the existence of parameterized algorithms running in subexponential time for two fundamental cycle-hitting problems: Feedback Vertex Set (FVS) and Triangle Hitting (TH). We focus on the class of pseudo-disk graphs, which forms a common generalization of several graph classes where such results exist, like disk graphs and square

Shuocang Zhang, Qiang Shi

The time-evolving matrix product operator (TEMPO) method is a powerful tool for simulating open system quantum dynamics. Typically, it is used in problems with diagonal system-bath coupling, where analytical expressions for discretized influence functional are available. In this work, we aim to address issues related to off-diagonal coupling by extending the

Liyi Chen, Panrong Tong, Zhongming Jin, Ying Sun

Large Language Models (LLMs) have shown remarkable reasoning capabilities on complex tasks, but they still suffer from out-of-date knowledge, hallucinations, and opaque decision-making. In contrast, Knowledge Graphs (KGs) can provide explicit and editable knowledge for LLMs to alleviate these issues. Existing paradigm of KG-augmented LLM manually predefines

Tianyun Tang, Kim-Chuan Toh

In this paper, we study linearly constrained optimization problems (LCP). After applying Hadamard parametrization, the feasible set of the parametrized problem (LCPH) becomes an algebraic variety, with conducive geometric properties which we explore in depth. We derive explicit formulas for the tangent cones and second-order tangent sets associated with the

Alexander Lercher, Christian Macho, Clemens Bauer, Martin Pinzger

Developers require accurate descriptions of REpresentational State Transfer (REST) Application Programming Interfaces (APIs) for a successful interaction between web services. The OpenAPI Specification (OAS) has become the de facto standard for documenting REST APIs. Manually creating an OpenAPI description is time-consuming and error-prone, and therefore se

Zhiyuan Cheng, Yaojia Wang, Heng Wu, Mazhar N. Ali

Kagome materials are known to be an ideal platform that hosts a plethora of interesting phases such as topological states, electronic correlation, and magnetism, owing to their unique band structure and geometry. We report magnetotransport measurement in Kagome metal Yb$_{0.5}$Co_3Ge$_3$ as a function of pressure. Below $\sim25^\circ$ K the temperature depen

Eunji Kim, Sriya Mantena, Weiwei Yang, Chandan Singh

While large transformer models excel in predictive performance, their lack of interpretability restricts their usefulness in high-stakes domains. To remedy this, we propose the Generalized Induction-Head Model (GIM), an interpretable model for next-token prediction inspired by the observation of "induction heads" in LLMs. GIM is a retrieval-based module that

Tomáš Roubal, Jan Valdman

This paper investigates a path-following method inspired by the semismooth$^*$ approach for solving algebraic inclusions, with a primary emphasis on the role of uniform subregularity. Uniform subregularity is crucial for ensuring the robustness and stability of path-following methods, as it provides a framework to uniformly control the distance between the i

Steve Bakos, Pooria Madani, Heidar Davoudi

This study investigates a counterintuitive phenomenon in adversarial machine learning: the potential for noise-based defenses to inadvertently aid evasion attacks in certain scenarios. While randomness is often employed as a defensive strategy against adversarial examples, our research reveals that this approach can sometimes backfire, particularly when faci

Javier Cembrano, José Correa, Ulrike Schmidt-Kraepelin, Alexandros Tsigonias-Dimitriadis

The apportionment problem constitutes a fundamental problem in democratic societies: How to distribute a fixed number of seats among a set of states in proportion to the states' populations? This--seemingly simple--task has led to a rich literature and has become well known in the context of the US House of Representatives. In this paper, we connect the desi

Sylwia Antoniuk, Christian Reiher

For a bounded measurable set $A\subseteq \mathbb{R}$ we denote the Lebesgue measure of $\{(x, y)\in A^2\colon x\le y\le x+1\}$ by $\Phi(A)$. We prove that if $I=A_1\cup\dots\cup A_{k+1}$ partitions an interval $I$ of length $L$ into $k+1$ measurable pieces, then $\sum_{i=1}^{k+1} \Phi(A_i)\ge (\sqrt{k^2+1}-k)L-1$, where the multiplicative constant $\sqrt{k^2

Benjamin Howson, Sarah Filippi, Ciara Pike-Burke

We study the cooperative stochastic $k$-armed bandit problem, where a network of $m$ agents collaborate to find the optimal action. In contrast to most prior work on this problem, which focuses on extending a specific algorithm to the multi-agent setting, we provide a black-box reduction that allows us to extend any single-agent bandit algorithm to the multi

Yanjie Jiang, Hui Liu, Lu Zhang

Source code identifiers often contain abbreviations. Such abbreviations may reduce the readability of the source code, which in turn hinders the maintenance of the software applications. To this end, accurate and automated approaches to expanding abbreviations in source code are desirable and abbreviation expansion has been intensively investigated. However,

L. Chen, X. Huang, E. Park, R. Wang

This paper introduces a novel staggered discontinuous Galerkin (SDG) method tailored for solving elliptic equations on polytopal meshes. Our approach utilizes a primal-dual grid framework to ensure local conservation of fluxes, significantly improving stability and accuracy. The method is hybridizable and reduces the degrees of freedom compared to existing a

Anat Levin, Marina Alterman

Transmission matrices, mapping the propagation of light from one end of the tissue to the other, form an important mathematical tool in the analysis of tissue scattering and the design of wavefront shaping systems. To understand the relationship between their content and the volumetric structure of the tissue, we wish to fit them with multi-slice models, com

Soumen Das, Anit Sane, Satyanu Bhadra, Shankar Ghosh

We study a monolayer of metal balls under periodic chiral driving in the horizontal plane. Energy dissipation occurs in this system via (i) inelastic collisions and (ii) frictional interaction with the substrate. We show that below a density-dependent critical drive, the system phase separates into a fluid phase coexisting with a solid phase. Unlike ordinary

Klea Ziu, Slavomír Hanzely, Loka Li, Kun Zhang

Learning the structure of Directed Acyclic Graphs (DAGs) presents a significant challenge due to the vast combinatorial search space of possible graphs, which scales exponentially with the number of nodes. Recent advancements have redefined this problem as a continuous optimization task by incorporating differentiable acyclicity constraints. These methods co

Hao Yang, Lizhen Qu, Ehsan Shareghi, Gholamreza Haffari

Large Multimodal Models (LMMs) have demonstrated the ability to interact with humans under real-world conditions by combining Large Language Models (LLMs) and modality encoders to align multimodal information (visual and auditory) with text. However, such models raise new safety challenges of whether models that are safety-aligned on text also exhibit consis

Janis Lenz, Theo Gruner, Daniel Palenicek, Tim Schneider

Robotic insertion tasks remain challenging due to uncertainties in perception and the need for precise control, particularly in unstructured environments. While humans seamlessly combine vision and touch for such tasks, effectively integrating these modalities in robotic systems is still an open problem. Our work presents an extensive analysis of the interpl

Yutaka Takeuchi

The Poisson Boolean percolation on a metric measure space is one of the percolation models. Intuitively, this model is obtained by collecting random balls whose centers form a Poisson point process. In 2008, Gou\'{e}r\'{e} proved that for $n \geq 2$, the Poisson Boolean percolation on $\mathbb{R}^n$ has the subcritical regime if and only if the radius distri

Kentaro Hino, Yuki Kurashige

A neural network-based machine learning potential energy surface (PES) expressed in a matrix product operator (NN-MPO) is proposed. The MPO form enables efficient evaluation of high-dimensional integrals that arise in solving the time-dependent and time-independent Schr\"odinger equation and effectively overcomes the so-called curse of dimensionality. This s

Kecheng Liu, Yidong Zhou, Haochen Luo, Lingjun Xiong

In this paper, we propose an efficient compilation method for distributed quantum computing (DQC) using the Linear Nearest Neighbor (LNN) architecture. By exploiting the LNN topology's symmetry, we optimize quantum circuit compilation for High Local Connectivity, Sparse Full Connectivity (HLC-SFC) algorithms like Quantum Approximate Optimization Algorithm (Q

Zhanke Zhou, Rong Tao, Jianing Zhu, Yiwen Luo

This paper investigates an under-explored challenge in large language models (LLMs): chain-of-thought prompting with noisy rationales, which include irrelevant or inaccurate reasoning thoughts within examples used for in-context learning. We construct NoRa dataset that is tailored to evaluate the robustness of reasoning in the presence of noisy rationales. O

Xinke Jiang, Rihong Qiu, Yongxin Xu, Wentao Zhang

Graph Neural Networks (GNNs) have become essential in interpreting relational data across various domains, yet, they often struggle to generalize to unseen graph data that differs markedly from training instances. In this paper, we introduce a novel framework called General Retrieval-Augmented Graph Learning (RAGraph), which brings external graph data into t

Chenyu Li, Minghui Zhang, Chuyan Zhang, Yun Gu

Accurate airway anatomical labeling is crucial for clinicians to identify and navigate complex bronchial structures during bronchoscopy. Automatic airway anatomical labeling is challenging due to significant individual variability and anatomical variations. Previous methods are prone to generate inconsistent predictions, which is harmful for preoperative pla

Juan M. Torres-Rincon, Angels Ramos, Joel Rufí

The proton-deuteron correlation function measured by the ALICE collaboration in high multiplicity p+p collisions shows a momentum dependence which is in contradiction with the predictions of the Lednick\'y-Lyuboshitz formalism of the two-body interaction. This result motivated a more sophisticated three-body description in terms of a composite deuteron. Enco

Ming Li, Zhentao Shi, Yapeng Zheng

We develop a unified estimation and inference framework for dyadic network formation with individual fixed effects, covering both transferable-utility (TU) and nontransferable-utility (NTU) links under general link functions. Under NTU, bilateral consent makes the fixed effects non-additive and the log-likelihood non-concave in the high-dimensional fixed eff

Georgios Peikos, Pranav Kasela, Gabriella Pasi

This paper introduces a system that integrates large language models (LLMs) into the clinical trial retrieval process, enhancing the effectiveness of matching patients with eligible trials while maintaining information privacy and allowing expert oversight. We evaluate six LLMs for query generation, focusing on open-source and relatively small models that re

Michael Schlichtkrull, Yulong Chen, Chenxi Whitehouse, Zhenyun Deng

The Automated Verification of Textual Claims (AVeriTeC) shared task asks participants to retrieve evidence and predict veracity for real-world claims checked by fact-checkers. Evidence can be found either via a search engine, or via a knowledge store provided by the organisers. Submissions are evaluated using AVeriTeC score, which considers a claim to be acc

Tianyun Tang, Kim-Chuan Toh

Semidefinite programming (SDP) problems are challenging to solve because of their high dimensionality. However, solving sparse SDP problems with small tree-width are known to be relatively easier because: (1) they can be decomposed into smaller multi-block SDP problems through chordal conversion; (2) they have low-rank optimal solutions. In this paper, we st

Maciej Gancewski, Hubert Jóźwiak, Hubert Cybulski, Piotr Wcisło

The intensities of all rovibrational electric quadrupole absorption lines in $^{16}$O$_2$($X^{3}\Sigma^{-}_{g}$), for which the vibrational quantum number is $v \leq 35$ and the total angular momentum quantum number is $J \leq 40$, are calculated in the intermediate coupling using a new $\textit{ab initio}$ quadrupole moment curve of the ground electronic st

Dmitri Finkelshtein, Yuri Kondratiev, Eugene Lytvynov, Maria Joao Oliveira

The paper describes known and new results about finite difference calculus on configuration spaces. We describe finite difference geometry on configuration spaces, connect finite difference operators with cannonical commutation relations, find explicit form for certain finite difference Markov generators on configuration spaces, and describe spaces of Newton

Edyta Brzychczy, Tomasz Pełech-Pilichowski, Ziemowit Dworakowski

Process mining gains increasing popularity in business process analysis, also in heavy industry. It requires a specific data format called an event log, with the basic structure including a case identifier (case ID), activity (event) name, and timestamp. In the case of industrial processes, data is very often provided by a monitoring system as time series of

Julius T. Gohsrich, Ayan Banerjee, Flore K. Kunst

The non-Hermitian (NH) skin effect is a truly NH feature, which manifests itself as an accumulation of states, known as skin states, on the boundaries of a system. In this perspective, we discuss several aspects of the NH skin effect focusing on the most interesting facets of this phenomenon. Beyond reviewing necessary requirements to see the NH skin effect,