arXiv papers from November 2024

Fangyuan Ma, Junrong Feng, Feng Li, Ying Wu

We theoretically and numerically investigate Chern vector insulators and topological surface states in a three-dimensional lattice, based on phase-delayed temporal-periodic interactions within the tight-binding model. These Floquet interactions break time-reversal symmetry, effectively inducing a gauge field analogous to magnetic flux. This gauge field resul

Yi Teng, David D. Dai, Liang Fu

We introduce an attention-based fermionic neural network (FNN) to variationally solve the problem of two-dimensional Coulomb electron gas in magnetic fields, a canonical platform for fractional quantum Hall (FQH) liquids, Wigner crystals and other unconventional electron states. Working directly with the full Hilbert space of $N$ electrons confined to a disk

Yuhang Mei, Mohammad Al-Jarrah, Amirhossein Taghvaei, Yongxin Chen

This paper addresses the problem of steering an initial probability distribution to a target probability distribution through a deterministic or stochastic linear control system. Our proposed approach is inspired by the flow matching methodology, with the difference that we can only affect the flow through the given control channels. The motivation comes fro

Azul Fatalini, Ralf Schindler

It is a well-known result that, after adding one Cohen real, the transcendence degree of the reals over the ground-model reals is continuum. We extend this result for a set $X$ of finitely many Cohen reals, by showing that, in the forcing extension, the transcendence degree of the reals over a combination of the reals in the extension given by each proper su

Guanyi Yang, Srinivasan Murali

This paper examines the impact of racial discrimination in hiring on employment, wages, and wealth disparities between black and white workers. Using a labor search-and-matching model with racially prejudiced and non-prejudiced firms, we show that labor market frictions sustain discriminatory practices as an equilibrium outcome. These practices account for 5

Zilong Chang

According to perturbative quantum chromodynamic calculations, in $pp$ collisions at $\sqrt{s} = $~200 and 510 GeV studied at RHIC, jet production in mid-pseudorapidity, $|\eta| <$ 1, is dominated by quark-gluon and gluon-gluon scattering processes. Therefore jets at RHIC are direct probes of the gluon parton distribution functions (PDFs) for momentum fractio

Xunye Tian, Liuhua Peng, Zhijian Zhou, Mingming Gong

Learning effective data representations has been crucial in non-parametric two-sample testing. Common approaches will first split data into training and test sets and then learn data representations purely on the training set. However, recent theoretical studies have shown that, as long as the sample indexes are not used during the learning process, the whol

Trevor Camper

We obtain Szeg\H{o}-type Limit Theorems in the setting of Reproducing Kernel Hilbert Spaces on discs in $\mathbb{C}$. From this, we derive a formula for the density of the eigenvalues of compressions of Toeplitz operators. Examples for the Bergman and Segal-Bargmann-Fock space are also presented.

Yuzan Xiong, Andrew Christy, Muntasir Mahdi, Rui Sun

The needs for sensitively and reliably probing magnetization dynamics have been increasing in various contexts such as studying novel hybrid magnonic systems, in which the spin dynamics strongly and coherently couple to other excitations, including microwave photons, light photons, or phonons. Recent advances in quantum magnonics also highlight the need for

Yangxinyu Xie, Bhaswar B. Bhattacharya

Given a sequence of $r$-uniform hypergraphs $H_n$, denote by $T(H_n)$ the number of monochromatic hyperedges when the vertices of $H_n$ are colored uniformly at random with $c = c_n$ colors. In this paper, we study the joint distribution of monochromatic hyperedges for hypergraphs with multiple layers (multiplex hypergraphs). Specifically, we consider the jo

Andrew Root, Liam Jakubowski, Mounika Vanamala

It is well known that the usefulness of a machine learning model is due to its ability to generalize to unseen data. This study uses three popular cyberbullying datasets to explore the effects of data, how it's collected, and how it's labeled, on the resulting machine learning models. The bias introduced from differing definitions of cyberbullying and from d

Mohammad Sadeq Abolhasani, Rong Pan

Extracting relevant and structured knowledge from large, complex technical documents within the Reliability and Maintainability (RAM) domain is labor-intensive and prone to errors. Our work addresses this challenge by presenting OntoKGen, a genuine pipeline for ontology extraction and Knowledge Graph (KG) generation. OntoKGen leverages Large Language Models

Resmi Ramachandranpillai, Kishore Sampath, Ayaazuddin Mohammad, Malihe Alikhani

Biases in automated clinical decision-making using Electronic Healthcare Records (EHR) impose significant disparities in patient care and treatment outcomes. Conventional approaches have primarily focused on bias mitigation strategies stemming from single attributes, overlooking intersectional subgroups -- groups formed across various demographic intersectio

Stephen Elbourn

In the contemporary educational landscape, the advent of Generative Artificial Intelligence (AI) presents unprecedented opportunities for personalised learning, fundamentally challenging the traditional paradigms of education. This research explores the emerging trend where high school students, empowered by tailored educational experiences provided by Gener

Steffen Schotthöfer, Beckett Y. Zhou, Tim Albring, Nicolas R. Gauger

Unsteady Aerodynamic Shape Optimization presents new challenges in terms of sensitivity analysis of time-dependent objective functions. In this work, we consider periodic unsteady flows governed by the URANS equations. Hence, the resulting output functions acting as objective or constraint functions of the optimization are themselves periodic with unknown pe

Vit Kratky, Robert Penicka, Jiri Horyna, Petr Stibinger

In this paper, we introduce an algorithm designed to address the problem of time-optimal formation reshaping in three-dimensional environments while preventing collisions between agents. The utility of the proposed approach is particularly evident in mobile robotics, where agents benefit from being organized and navigated in formation for a variety of real-w

Peter C. Sercel, Matthew P. Hautzinger, Ruyi Song, Volker Blum

Recent activity in the area of chiroptical phenomena has been focused on the connection between structural asymmetry, electron spin configuration and light matter interactions in chiral semiconductors. In these systems, spin-splitting phenomena emerge due to inversion symmetry breaking and the presence of extended electronic states, yet the connection to chi

Benjamin Hall, Ian Njoroge, Colin Campbell, Bharath Thotakura

Column chromatography is an important process in downstream biopharmaceutical manufacturing that enables high-selectivity separation of proteins through various modalities, such as affinity, ion exchange, hydrophobic interactions, or a combination of the aforementioned modes. Current mechanistic models of column chromatography typically abstract particle-lev

Abdelrahman Abdallah, Jamshid Mozafari, Bhawna Piryani, Mohammed M. Abdelgwad

This paper presents DynRank, a novel framework for enhancing passage retrieval in open-domain question-answering systems through dynamic zero-shot question classification. Traditional approaches rely on static prompts and pre-defined templates, which may limit model adaptability across different questions and contexts. In contrast, DynRank introduces a dynam

Shweta Jain, Sandro Tacchella, Moein Mosleh

We present a systematic analysis of the spatially resolved star formation histories (SFHs) using Hubble Space Telescope imaging data of $\sim 997$, intermediate redshifts $0.5 \leq z \leq 2.0$ galaxies from the GOODS-S field, with stellar mass range $9.8 \leq \log \mathrm{M}_{\star}/\mathrm{M}_{\odot} \leq 11.5$. We estimate the SFHs in three spatial regions

Kazuyuki Kanaya, Ryo Ashikawa, Shinji Ejiri, Masakiyo Kitazawa

We study the finite-temperature critical point of QCD in the heavy-quark region by a scaling study of the Binder cumulant on large lattices. Extending our previous study at $N_t=4$, we perform simulations on $N_t=6$ and 8 lattices with spatial volumes up to the aspect ratio $LT=N_s/N_t=18$ and 15 ($N_s=108$ and 120), respectively, to determine the critical p

Lawrence Chen, Peter Schaldenbrand, Tanmay Shankar, Lia Coleman

A painting is more than just a picture on a wall; a painting is a process comprised of many intentional brush strokes, the shapes of which are an important component of a painting's overall style and message. Prior work in modeling brush stroke trajectories either does not work with real-world robotics or is not flexible enough to capture the complexity of h

Qiyao Xue, Xiangyu Yin, Boyuan Yang, Wei Gao

Text-to-video (T2V) generation has been recently enabled by transformer-based diffusion models, but current T2V models lack capabilities in adhering to the real-world common knowledge and physical rules, due to their limited understanding of physical realism and deficiency in temporal modeling. Existing solutions are either data-driven or require extra model

Uwe Franz, Amaury Freslon, Adam Skalski

We describe all Gaussian generating functionals on several easy quantum groups given by non-crossing partitions. This includes in particular the free unitary, orthogonal and symplectic quantum groups. We further characterize central Gaussian generating functionals and describe a centralization procedure yielding interesting (non-Gaussian) generating function

Balázs Pál, Tze Goh, Gábor Rácz, István Szapudi

We present the results of a novel type of numerical simulation that realizes a rotating Universe with a shear-free, rigid body rotation inspired by a G\"{o}del-like metric. We run cosmological simulations of unperturbed glasses with various degrees of rotation in the Einstein-de Sitter and the $\Lambda$CDM cosmologies. To achieve this, we use the StePS N-bod

Shing-Hei Ho, Bao Thach, Minghan Zhu

We present LiDAR-EDIT, a novel paradigm for generating synthetic LiDAR data for autonomous driving. Our framework edits real-world LiDAR scans by introducing new object layouts while preserving the realism of the background environment. Compared to end-to-end frameworks that generate LiDAR point clouds from scratch, LiDAR-EDIT offers users full control over

Luca A. Lanzendörfer, Florian Grötschla, Uzeyir Valizada, Roger Wattenhofer

We introduce Audio Atlas, an interactive web application for visualizing audio data using text-audio embeddings. Audio Atlas is designed to facilitate the exploration and analysis of audio datasets using a contrastive embedding model and a vector database for efficient data management and semantic search. The system maps audio embeddings into a two-dimension

Eddy Li, Advaith Mopuri, Charles Zhang

A semidomain is a subsemiring of an integral domain. We call a semidomain $S$ additively reduced if $0$ is the only invertible element of the monoid $(S, +)$, while we say that $S$ is additively Furstenberg if every non-invertible element of $(S,+)$ can be expressed as the sum of an atom and an element of $S$. In this paper, we study a variant of the Goldbac

Jonah Botvinick-Greenhouse, Robert Martin, Yunan Yang

While invariant measures are widely employed to analyze physical systems when a direct study of pointwise trajectories is intractable, e.g., due to chaos or noise, they cannot uniquely identify the underlying dynamics. Our first result shows that, in contrast to invariant measures in state coordinates, e.g., $[x(t), y(t), z(t)]$, the invariant measure expres

Saifuddin Zafar, Mashaekh Tausif Ehsan, Sourav Das Suvro, Mahmudul Islam

Refractory high-entropy alloy (RHEA) is a promising class of materials with potential applications in extreme environments, where the dominant failure mode is thermal creep. The design of these alloys, therefore, requires an understanding of how their microstructure and local chemical distribution affect creep behavior. In this study, we performed high-fidel

Lukasz Stettner

In the paper we study dependence of long run functionals and limit characteristics assuming that Borel measurable Markov controls converge pointwise. We consider two kinds of functionals: average cost per unit time and long run risk sensitive. We impose uniform ergodicity assumption, which is later is relaxed and suitable convergence of controlled transition

Fred Heiding, Simon Lermen, Andrew Kao, Bruce Schneier

In this paper, we evaluate the capability of large language models to conduct personalized phishing attacks and compare their performance with human experts and AI models from last year. We include four email groups with a combined total of 101 participants: A control group of arbitrary phishing emails, which received a click-through rate (recipient pressed

Jiaming Liang

This paper studies the primal-dual convergence and iteration-complexity of proximal bundle methods for solving nonsmooth problems with convex structures. More specifically, we develop a family of primal-dual proximal bundle methods for solving convex nonsmooth composite optimization problems and establish the iteration-complexity in terms of a primal-dual ga

Alexey A. Kryukov

Spontaneous collapse models use non-linear stochastic modifications of the Schroedinger equation to suppress superpositions of eigenstates of the measured observable and drive the state to an eigenstate. It was recently demonstrated that the Born rule for transition probabilities can be modeled using the linear Schroedinger equation with a Hamiltonian repres

Frankie Chan, Ellen Weld

We provide a procedure for generating the irreducible representations of crystallography groups in any dimension. We also furnish a strategy to investigate the topology of the unitary dual of a crystallography group using sequences of matrices. All irreducible representations (up to unitary equivalence) of the dimension 3 crystallography group 90 and some ca

Gabriel I. Róis, José Tarciso S. S. Junior, Francisco S. N. Lobo, Manuel E. Rodrigues

In this work, we investigate static and spherically symmetric black hole solutions in $f(R,T)$ gravity, where $R$ is the curvature scalar and $T$ is the trace of the energy-momentum tensor, coupled to nonlinear electrodynamics (NLED). To construct our solutions, we adopt a linear functional form, $f(R,T) = R + \beta T$. In the limit $\beta = 0$, the theory r

Jason Gibson, Anoushka Alavilli, Erica Tevere, Evangelos A. Theodorou

Rapid autonomous traversal of unstructured terrain is essential for scenarios such as disaster response, search and rescue, or planetary exploration. As a vehicle navigates at the limit of its capabilities over extreme terrain, its dynamics can change suddenly and dramatically. For example, high-speed and varying terrain can affect parameters such as tractio

Tingxu Han, Weisong Sun, Yanrong Hu, Chunrong Fang

Text-to-image diffusion models have shown an impressive ability to generate high-quality images from input textual descriptions. However, concerns have been raised about the potential for these models to create content that infringes on copyrights or depicts disturbing subject matter. Removing specific concepts from these models is a promising potential solu

Christina Runkel, Sinan Xiao, Nicolas Boullé, Yang Chen

Liquid composites moulding is an important manufacturing technology for fibre reinforced composites, due to its cost-effectiveness. Challenges lie in the optimisation of the process due to the lack of understanding of key characteristic of textile fabrics - permeability. The problem of computing the permeability coefficient can be modelled as the well-known

Alex Hanson, Allen Tu, Geng Lin, Vasu Singla

3D Gaussian Splatting (3D-GS) is a recent 3D scene reconstruction technique that enables real-time rendering of novel views by modeling scenes as parametric point clouds of differentiable 3D Gaussians. However, its rendering speed and model size still present bottlenecks, especially in resource-constrained settings. In this paper, we identify and address two

Mattson Ogg, Ritwik Bose, Jamie Scharf, Christopher Ratto

As we consider entrusting Large Language Models (LLMs) with key societal and decision-making roles, measuring their alignment with human cognition becomes critical. This requires methods that can assess how these systems represent information and facilitate comparisons with human understanding across diverse tasks. To meet this need, we adapted Representatio

Siyuan Lu, Yi-Lin Tsai

In [Amer. J. Math. 141 (2019), no. 5, 1281-1315], Ren and Wang proved the curvature estimates for the $n-1$ curvature equation. The purpose of this note is to give a simple proof of their theorem.

Juhyung Ha, Jong Sung Park, David Crandall, Eleftherios Garyfallidis

Medical image translation is the process of converting from one imaging modality to another, in order to reduce the need for multiple image acquisitions from the same patient. This can enhance the efficiency of treatment by reducing the time, equipment, and labor needed. In this paper, we introduce a multi-resolution guided Generative Adversarial Network (GA

Bruno Aguilar, Daibik Barik, Jetharam Bhambhu, Evan Frankel

The asymmetric coloring number of a graph is the minimum number of colors needed to color its vertices, so that no non-trivial automorphism preserves the color classes. We investigate the asymmetric coloring number of graphs that are disjoint unions of graphs. We will derive a general relationship between the asymmetric coloring number of disjoint copies of

Théo Fagnoni, Bellinda Mesbah, Mahsun Altin, Phillip Kingston

This paper introduces Opus, a novel framework for generating and optimizing Workflows tailored to complex Business Process Outsourcing (BPO) use cases, focusing on cost reduction and quality enhancement while adhering to established industry processes and operational constraints. Our approach generates executable Workflows from Intention, defined as the alig

Richard Evan Schwartz

Let $\epsilon<1/384$ and let $\Omega$ be a smooth embedded paper Moebius band of aspect ratio less than $\sqrt 3 + \epsilon$. We prove that $\Omega$ is within Hausdorff distance $18 \sqrt \epsilon$ of an equilateral triangle of perimeter $2 \sqrt 3$. This is an effective and fairly sharp version of our recent theorems in [{\bf S0\/}] about the optimal paper

Siddhant Dutta, Nouhaila Innan, Sadok Ben Yahia, Muhammad Shafique

The integration of fully homomorphic encryption (FHE) in federated learning (FL) has led to significant advances in data privacy. However, during the aggregation phase, it often results in performance degradation of the aggregated model, hindering the development of robust representational generalization. In this work, we propose a novel multimodal quantum f

Yupei Li, Manuel Milling, Lucia Specia, Björn W. Schuller

As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an art form and medium for entertainment, deeply rooted into human culture, is seeing an increased involvement of AI into its production. However, despite the effective application of A

CMS Collaboration

A direct search for new heavy neutral Higgs bosons A and H in the $\mathrm{t\bar{t}}$Z channel is presented, targeting the process pp $\to$ A $\to$ ZH with H $\to$ $\mathrm{t\bar{t}}$. For the first time, the channel with decays of the Z boson to muons or electrons in association with all-hadronic decays of the $\mathrm{t\bar{t}}$ system is targeted. The ana

Akhila Vangara, Alex Egg

Uniform random exploration in decision-making systems supports off-policy learning via supervision but incurs high regret, making it impractical for many applications. Conversely, non-uniform exploration offers better immediate performance but lacks support for off-policy learning. Recent research suggests that regression oracles can bridge this gap by combi

Ruben Ohana, Michael McCabe, Lucas Meyer, Rudy Morel

Machine learning based surrogate models offer researchers powerful tools for accelerating simulation-based workflows. However, as standard datasets in this space often cover small classes of physical behavior, it can be difficult to evaluate the efficacy of new approaches. To address this gap, we introduce the Well: a large-scale collection of datasets conta

Caleb Rotello

Quantum amplitude amplification and estimation have shown quadratic speedups to unstructured search and estimation tasks. We show that a coherent combination of these quantum algorithms also provides a quadratic speedup to calculating the expectation value of a random-exist quantified oracle. In this problem, Nature makes a decision randomly, i.e. chooses a

Roberto Bada-Nerin, Oleg Bulashenko, Osvaldo Gramaxo Freitas, José A. Font

Gravitational lensing of gravitational waves (GWs) provides a unique opportunity to study cosmology and astrophysics at multiple scales. Detecting microlensing signatures, in particular, requires efficient parameter estimation methods due to the high computational cost of traditional Bayesian inference. In this paper we explore the use of deep learning, name

Keith A. Kearnes, Emil W. Kiss

We answer the question in the title. In the process, we correct an error in our AMS Memoir The Shape of Congruence Lattices.

Mitsuo Oka, Kazuo Makishima, Toshio Terasawa

Particles are accelerated to very high, non-thermal energies in space, solar, and astrophysical plasma environments. In cosmic ray physics, the "Hillas limit" is often used as a rough estimate (or the necessary condition) of the maximum energy of particles. This limit is based on the concepts of one-shot direct acceleration by a system-wide motional electric

Gennaro Auricchio, Jie Zhang

In this paper, we study the Facility Location Problem with Scarce Resources (FLPSR) under the assumption that agents' type follow a probability distribution. In the FLPSR, the objective is to identify the optimal locations for one or more capacitated facilities to maximize Social Welfare (SW), defined as the sum of the utilities of all agents. The total capa

Peihao Dong, Jibin Jia, Shen Gao, Fuhui Zhou

Wideband spectrum sensing (WSS) is critical for orchestrating multitudinous wireless transmissions via spectrum sharing, but may incur excessive costs of hardware, power and computation due to the high sampling rate. In this article, a deep learning based WSS framework embedding the multicoset preprocessing is proposed to enable the low-cost sub-Nyquist samp

Dusa McDuff, Kyler Siegel

We solve the stabilized symplectic embedding problem for four-dimensional ellipsoids into the four-dimensional round ball. The answer is neatly encoded by a piecewise smooth function which exhibits a phase transition from an infinite Fibonacci staircase to an explicit rational function related to symplectic folding. Our approach is based on a bridge between

Long Qian, Bingke Zhu, Yingying Chen, Ming Tang

Overfitting has traditionally been viewed as detrimental to anomaly detection, where excessive generalization often limits models' sensitivity to subtle anomalies. Our work challenges this conventional view by introducing Controllable Overfitting-based Anomaly Detection (COAD), a novel framework that strategically leverages overfitting to enhance anomaly dis

Łukasz Grzybowski, Jakub Pokrywka, Michał Ciesiółka, Jeremi I. Kaczmarek

Large Language Models (LLMs) have demonstrated significant potential in handling specialized tasks, including medical problem-solving. However, most studies predominantly focus on English-language contexts. This study introduces a novel benchmark dataset based on Polish medical licensing and specialization exams (LEK, LDEK, PES) taken by medical doctor candi

Yunjoo Kim, Bongsuk Kwon, Jeongsik Yoon

We study the formation of singularities in the Camassa-Holm (CH) equation, providing a detailed description of the blow-up dynamics and identifying the precise H\"older regularity of the gradient blow-up solutions. To this end, we first construct self-similar blow-up profiles and examine their properties, including the asymptotic behavior at infinity, which

Michail Dontas, Yutong He, Naoki Murata, Yuki Mitsufuji

This paper considers blind inverse image restoration, the task of predicting a target image from a degraded source when the degradation (i.e. the forward operator) is unknown. Existing solutions typically rely on restrictive assumptions such as operator linearity, curated training data or narrow image distributions limiting their practicality. We introduce L

Shiyu Zhao, Zhenting Wang, Felix Juefei-Xu, Xide Xia

Prevailing Multimodal Large Language Models (MLLMs) encode the input image(s) as vision tokens and feed them into the language backbone, similar to how Large Language Models (LLMs) process the text tokens. However, the number of vision tokens increases quadratically as the image resolutions, leading to huge computational costs. In this paper, we consider imp

Lihui Liu, Zihao Wang, Hanghang Tong

Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences. These knowledge graphs function as comprehensive repositories of human knowledge, facilitating the inference of new information. Traditional symbolic reasoning, despite its strengths, struggles with the challenges posed by incom

Muqing Cao, Xinhang Xu, Yizhuo Yang, Jianping Li

Robot navigation in dense human crowds poses a significant challenge due to the complexity of human behavior in dynamic and obstacle-rich environments. In this work, we propose a dynamic weight adjustment scheme using a neural network to predict the optimal weights of objectives in an optimization-based motion planner. We adopt a spatial-temporal trajectory

Yue Zhou, Barbara Di Eugenio, Lu Cheng

This paper studies the performance of large language models (LLMs), particularly regarding demographic fairness, in solving real-world healthcare tasks. We evaluate state-of-the-art LLMs with three prevalent learning frameworks across six diverse healthcare tasks and find significant challenges in applying LLMs to real-world healthcare tasks and persistent f

Roberto Cavassi, Antonio Cicone, Enza Pellegrino, Haomin Zhou

The decomposition of a signal is a fundamental tool in many fields of research, including signal processing, geophysics, astrophysics, engineering, medicine, and many more. By breaking down complex signals into simpler oscillatory components we can enhance the understanding and processing of the data, unveiling hidden information contained in them. Tradition

F. F. Nascimento, V. B. Bezerra, J. M. Toledo, G. A. Marques

We obtain a class of solutions corresponding to a generalization of the Hayward black hole by solving the Einstein equations coupled to a particular nonlinear electromagnetic field. The generalization is realized by considering, additionally, the presence of the cosmological constant and a source corresponding to an anisotropic fluid, namely, a fluid of stri

Marek Putresza

This article present a new, direct and simple formula for constructing Mignotte sequences.

Saloua Zammali, Siddhant Dutta, Sadok Ben Yahia

In the field of Recommender Systems (RS), neural collaborative filtering represents a significant milestone by combining matrix factorization and deep neural networks to achieve promising results. Traditional methods like matrix factorization often rely on linear models, limiting their capability to capture complex interactions between users, items, and cont

Sebastian Seemann, Francesca Zaffalon

We study the set of linear subspaces of a fixed dimension intersecting a given polytope. To describe this set as a semialgebraic subset of a Grassmannian, we introduce a Schubert arrangement of the polytope, defined by the Chow forms of the polytope's faces of complementary dimension. We show that the set of subspaces intersecting a specified family of faces

Leonardo Balart, Grigoris Panotopoulos, Angel Rincon

We introduce two new static, spherically symmetric regular black hole solutions that can be obtained from non-linear electrodynamics models. For each solution, we investigate the dynamic stability with respect to arbitrary linear fluctuations of the metric and electromagnetic field, and also examine the energy conditions that those black holes satisfy. Moreo

Jebish Purbey, Siddhant Gupta, Nikhil Manali, Siddartha Pullakhandam

This paper presents the system description of our entry for the COLING 2025 FMD challenge, focusing on misinformation detection in financial domains. We experimented with a combination of large language models, including Qwen, Mistral, and Gemma-2, and leveraged pre-processing and sequential learning for not only identifying fraudulent financial content but

Yuta Takahashi, Shin-ichiro Sakai

This paper presents a learning-based current calculation model to achieve power-optimal magnetic-field interaction for multi-agent formation and attitude control. In aerospace engineering, electromagnetic coils are referred to as magnetorquer (MTQ) coils and used as satellite attitude actuators in Earth's orbit and for long-term formation and attitude contro

Tianshuo Xu, Zhifei Chen, Leyi Wu, Hao Lu

Recent advances in video generation have shown promise for generating future scenarios, critical for planning and control in autonomous driving and embodied intelligence. However, real-world applications demand more than visually plausible predictions; they require reasoning about object motions based on explicitly defined boundary conditions, such as initia

Mohsen Dehghankar, Abolfazl Asudeh

Large language models (LLMs) have quickly emerged as practical and versatile tools that provide new solutions for a wide range of domains. In this paper, we consider the application of LLMs on symmetric tasks where a query is asked on an (unordered) bag of elements. Examples of such tasks include answering aggregate queries on a database table. In general, w

Hadi Mohasel Afshar, Gilad Francis, Sally Cripps

Particle-based methods include a variety of techniques, such as Markov Chain Monte Carlo (MCMC) and Sequential Monte Carlo (SMC), for approximating a probabilistic target distribution with a set of weighted particles. In this paper, we prove that for any set of particles, there is a unique weighting mechanism that minimizes the Kullback-Leibler (KL) divergen

Daniel Kyselica, Marek Šuppa, Jiří Šilha, Roman Ďurikovič

Space debris presents a critical challenge for the sustainability of future space missions, emphasizing the need for robust and standardized identification methods. However, a comprehensive benchmark for rocket body classification remains absent. This paper addresses this gap by introducing the RoBo6 dataset for rocket body classification based on light curv

Noah Lee, Jiwoo Hong, James Thorne

Large language models (LLMs) have shown potential as general evaluators along with the evident benefits of speed and cost. While their correlation against human annotators has been widely studied, consistency as evaluators is still understudied, raising concerns about the reliability of LLM evaluators. In this paper, we conduct extensive studies on the two a

Jiangmeng Li, Zehua Zang, Qirui Ji, Chuxiong Sun

Representations learned by self-supervised approaches are generally considered to possess sufficient generalizability and discriminability. However, we disclose a nontrivial mutual-exclusion relationship between these critical representation properties through an exploratory demonstration on self-supervised learning. State-of-the-art self-supervised methods

Negin Amirshirzad, Mehmet Arda Eren, Erhan Oztop

In this paper, we propose a novel lightweight learning from demonstration (LfD) model based on reservoir computing that can learn and generate multiple movement trajectories with prediction intervals, which we call as Context-based Echo State Network with prediction confidence (CESN+). CESN+ can generate movement trajectories that may go beyond the initial L

Meinolf Geck

These are unpublished notes from about 1992-1993 which, retrospectively, may be regarded as a complement to Lusztig's recent paper on the trace of Coxeter elements. Our notes include explicit tables for those traces. The proofs rely on a connection with Lusztig's work on Coxeter orbits and eigenspaces of Frobenius, which may be of independent interest.

Bastián González-Bustamante

The TextClass Benchmark project is an ongoing, continuous benchmarking process that aims to provide a comprehensive, fair, and dynamic evaluation of LLMs and transformers for text classification tasks. This evaluation spans various domains and languages in social sciences disciplines engaged in NLP and text-as-data approach. The leaderboards present performa

Ayush Mohanty, Jason Dekarske, Stephen K. Robinson, Sanjay Joshi

Robotic manipulators are critical in many applications but are known to degrade over time. This degradation is influenced by the nature of the tasks performed by the robot. Tasks with higher severity, such as handling heavy payloads, can accelerate the degradation process. One way this degradation is reflected is in the position accuracy of the robot's end-e

Mahalakshmi Sabanayagam, Lukas Gosch, Stephan Günnemann, Debarghya Ghoshdastidar

Machine learning models are highly vulnerable to label flipping, i.e., the adversarial modification (poisoning) of training labels to compromise performance. Thus, deriving robustness certificates is important to guarantee that test predictions remain unaffected and to understand worst-case robustness behavior. However, for Graph Neural Networks (GNNs), the

G. Juarez Rangel, B. M. Rodríguez-Lara

We explore static noise in a discrete quantum random walk over a homogeneous cyclic graph, focusing on spectral and dynamical properties. Using a three-parameter unitary coin, we control the spectral structure of the noiseless step operator on the unit circle. One parameter induces two spectral bands separated by a gap proportional to its value, while the ha

Bytedance-Seed-Foundation-Code-Team, :, Yao Cheng, Jianfeng Chen

As the capabilities of code large language models (LLMs) continue to expand, their applications across diverse code intelligence domains are rapidly increasing. However, most existing datasets only evaluate limited application domains. To address this gap, we have developed a comprehensive code evaluation dataset FullStack Bench focusing on full-stack progra

Yuchen Shi, Huaxin Pei, Liang Feng, Yi Zhang

Agent faults pose a significant threat to the performance of multi-agent reinforcement learning (MARL) algorithms, introducing two key challenges. First, agents often struggle to extract critical information from the chaotic state space created by unexpected faults. Second, transitions recorded before and after faults in the replay buffer affect training une

Gabriel Mantegna, Ziting Huang, Guillaume Van Caelenberg, Bethany Frew

The reliability of the electric grid has in recent years become a larger concern for regulators, planners, and consumers due to several high-impact outage events, as well as the potential for even more impactful events in the future. These concerns are largely the result of decades-old resource adequacy (RA) planning frameworks being insufficiently adapted t

Yiyuan Pan, Yunzhe Xu, Zhe Liu, Hesheng Wang

Humans navigate unfamiliar environments using episodic simulation and episodic memory, which facilitate a deeper understanding of the complex relationships between environments and objects. Developing an imaginative memory system inspired by human mechanisms can enhance the navigation performance of embodied agents in unseen environments. However, existing V

Ali Shiraee Kasmaee, Mohammad Khodadad, Mohammad Arshi Saloot, Nicholas Sherck

Recent advancements in language models have started a new era of superior information retrieval and content generation, with embedding models playing an important role in optimizing data representation efficiency and performance. While benchmarks like the Massive Text Embedding Benchmark (MTEB) have standardized the evaluation of general domain embedding mod

Hans Matthew Abello, Maxine Beatriz Badiola, Mark John Custer, Lorane Bernadeth Fausto

Push notifications are brief messages that users frequently encounter in their daily lives. However, the volume of notifications can lead to information overload, making it challenging for users to engage effectively. This study investigates how notification behavior and color influence user interaction and perception. To explore this, we developed an app pr

Edith Haim, Natalie Fischer, Salvatore Citraro, Giulio Rossetti

Creativity is a fundamental skill of human cognition. We use textual forma mentis networks (TFMN) to extract network (semantic/syntactic associations) and emotional features from approximately one thousand human- and GPT3.5-generated stories. Using Explainable Artificial Intelligence (XAI), we test whether features relative to Mednick's associative theory of

Thomas Saupe, Sebastian Götschel, Thibaut Lunet, Daniel Ruprecht

As supercomputers grow in hardware complexity, their susceptibility to faults increases and measures need to be taken to ensure the correctness of results. Some numerical algorithms have certain characteristics that allow them to recover from some types of faults. It has been demonstrated that adaptive Runge-Kutta methods provide resilience against transient

André E. Kézdy, Jenő Lehel

The length polyhedron of an interval order $P$ is the convex hull of integer vectors representing the interval lengths in possible interval representations of $P$ in which all intervals have integer endpoints. This polyhedron is an integral translation of a polyhedral cone, with its apex corresponding to the canonical interval representation of $P$ (also kno

Siyu Cen, Bangti Jin, Xiyao Li, Zhi Zhou

In this work we develop a novel algorithm, termed as mixed least-squares deep neural network (MLS-DNN), to recover an anisotropic conductivity tensor from the internal measurements of the solutions. It is based on applying the least-squares formulation to the mixed form of the elliptic problem, and approximating the internal flux and conductivity tensor simu

Matyas Bohacek, Hany Farid

AI-generated video generation continues its journey through the uncanny valley to produce content that is increasingly perceptually indistinguishable from reality. To better protect individuals, organizations, and societies from its malicious applications, we describe an effective and robust technique for distinguishing real from AI-generated human motion us

Quang Duc Nguyen, Tung Nguyen, Duc Anh Nguyen, Linh Ngo Van

Uncovering hidden topics from short texts is challenging for traditional and neural models due to data sparsity, which limits word co-occurrence patterns, and label sparsity, stemming from incomplete reconstruction targets. Although data aggregation offers a potential solution, existing neural topic models often overlook it due to time complexity, poor aggre

Carlo Silvestri, Xiaoqiong Qi, Thomas Taimre, Aleksandar D. Rakić

We present a method for engineering harmonic frequency combs (HFCs) in the terahertz spectral region. This approach involves interfacing a quantum cascade laser (QCL) with an external reflector featuring frequency-dependent reflectivity. A notable advantage of this method over existing ones is its dual functionality in shaping HFCs, allowing for control over

Sumit Banik, Guglielmo Coloretti, Andreas Crivellin, Howard E. Haber

We examine the correlations between new scalar boson decays to photons and electric dipole moments (EDMs) in the CP-violating flavor-aligned two-Higgs-doublet model (2HDM). It is convenient to work in the Higgs basis $\{{H}_1, {H}_2\}$ where only the first Higgs doublet field ${H}_1$ acquires a vacuum expectation value. In light of the LHC Higgs data, which