November 2024 arXiv papers — page 159

Suyeong Jeong, Dae-Han Jung, Hee-Sung Han, Ganghwi Kim

Skyrmions, topologically stable magnetic solitons characterized by whirling magnetization in nanoscale magnetic elements, show promise information carriers in spintronics and spin-based quantum computing due to their unique properties: small size, stability, and controllability. In this study, we introduce a novel method of skyrmion generation through domain

Christopher Tralie, Ben Cantil

We present ``The Concatenator,'' a real time system for audio-guided concatenative synthesis. Similarly to Driedger et al.'s ``musaicing'' (or ``audio mosaicing'') technique, we concatenate a set number of windows within a corpus of audio to re-create the harmonic and percussive aspects of a target audio stream. Unlike Driedger's NMF-based technique, however

Lan-Huong Nguyen, Van-Linh Nguyen, Ren-Hung Hwang, Jian-Jhih Kuo

Many nations are promoting the green transition in the energy sector to attain neutral carbon emissions by 2050. Smart Grid 2.0 (SG2) is expected to explore data-driven analytics and enhance communication technologies to improve the efficiency and sustainability of distributed renewable energy systems. These features are beyond smart metering and electric su

Flavio Di Palo, Prateek Singhi, Bilal Fadlallah

Large Language Models (LLMs) face significant challenges at inference time due to their high computational demands. To address this, we present Performance-Guided Knowledge Distillation (PGKD), a cost-effective and high-throughput solution for production text classification applications. PGKD utilizes teacher-student Knowledge Distillation to distill the kno

Fraser Williams, Akila Pemasiri, Dhammika Jayalath, Terry Martin

The localization of directional RF emitters presents significant challenges for electronic warfare applications. Traditional localization methods, designed for omnidirectional emitters, experience degraded performance when applied to directional sources due to pronounced received signal strength (RSS) modulations introduced by directive beampatterns. This pa

Julia Zeiltinger, Sushmita Roy, Ferhat Ay, Anthony Mathelier

Predicting how genetic variation affects phenotypic outcomes at the organismal, cellular, and molecular levels requires deciphering the cis-regulatory code, the sequence rules by which non-coding regions regulate genes. In this perspective, we discuss recent computational progress and challenges towards solving this fundamental problem. We describe how cis-r

Alex Elchesen, Amit Patel

We develop a cohomological approach to M\"obius inversion using derived functors in the enriched categorical setting. For a poset $P$ and a closed symmetric monoidal abelian category $\mathcal{C}$, we define M\"obius cohomology as the derived functors of an enriched hom functor on the category of $P$-modules. We prove that the Euler characteristic of our coh

Guo-Li Liu, Xinbo Su, Fei Wang

Tiny Lorentz Invariance Violation (LIV) effects, potentially arising from quantum gravity-induced spacetime structures, may also manifest in the proton sector, offering a plausible pathway to test Planck-scale physics through high-energy cosmic phenomena. Our analysis reveals that even minuscule LIV effects in the proton sector can significantly elevate the

Takamasa Ando, Shinsei Ryu, Masataka Watanabe

In mixed quantum states, the notion of symmetry is divided into two types: strong and weak symmetry. While spontaneous symmetry breaking (SSB) for a weak symmetry is detected by two-point correlation functions, SSB for a strong symmetry is characterized by the Renyi-2 correlators. In this work, we present a way to construct various SSB phases for strong symm

Ruisheng Qi, Xiaojie Wang

In the present work, strong approximation errors are analyzed for both the spatial semi-discretization and the spatio-temporal fully discretization of stochastic wave equations (SWEs) with cubic polynomial nonlinearities and additive noises. The fully discretization is achieved by the standard Galerkin ffnite element method in space and a novel exponential t

Ayan Sengupta, Vaibhav Seth, Arinjay Pathak, Aastha Verma

Large Language Models (LLMs) are highly resource-intensive to fine-tune due to their enormous size. While low-rank adaptation is a prominent parameter-efficient fine-tuning approach, it suffers from sensitivity to hyperparameter choices, leading to instability in model performance on fine-tuning downstream tasks. This paper highlights the importance of effec

Han Yang, Sotiris Anagnostidis, Enis Simsar, Thomas Hofmann

We propose MegaPortrait. It's an innovative system for creating personalized portrait images in computer vision. It has three modules: Identity Net, Shading Net, and Harmonization Net. Identity Net generates learned identity using a customized model fine-tuned with source images. Shading Net re-renders portraits using extracted representations. Harmonization

Shuangjie Li, Jiangqing Song, Baoming Zhang, Gaoli Ruan

Graph neural networks (GNNs) are prominent for their effectiveness in processing graph data for semi-supervised node classification tasks. Most works of GNNs assume that the observed structure accurately represents the underlying node relationships. However, the graph structure is inevitably noisy or incomplete in reality, which can degrade the quality of gr

Masaya Kaneda, Shun Tsuruoka, Hikari Shinya, Tetsuya Fukushima

Memristors, which are characterized by their unique input-voltage-history-dependent resistance, have garnered significant attention for the exploration of next-generation in-memory computing, reconfigurable logic circuits, and neural networks. Memristors are controlled by the applied input voltage; however, the latent potential of their magnetic field sensit

Nadezhda Semenova, Daniel Brunner

In recent years, the hardware implementation of neural networks, leveraging physical coupling and analog neurons has substantially increased in relevance. Such nonlinear and complex physical networks provide significant advantages in speed and energy efficiency, but are potentially susceptible to internal noise when compared to digital emulations of such net

Adam Bouland, Chenyi Zhang, Zixin Zhou

Characterizing the entanglement structure of ground states of local Hamiltonians is a fundamental problem in quantum information. In this work we study the computational complexity of this problem, given the Hamiltonian as input. Our main result is that to show it is cryptographically hard to determine if the ground state of a geometrically local, polynomial

Maximilian Ruth, Rogerio Jorge, David Bindel

When analyzing stellarator configurations, it is common to perform an asymptotic expansion about the magnetic axis. This so-called near-axis expansion is convenient for the same reason asymptotic expansions often are, namely, it reduces the dimension of the problem. This leads to convenient and quickly computed expressions of physical quantities, such as qua

Neal Lawton, Aram Galstyan, Greg Ver Steeg

A popular method for Neural Architecture Search (NAS) is based on growing networks via small local changes to the network's architecture called network morphisms. These methods start with a small seed network and progressively grow the network by adding new neurons in an automated way. However, it remains a challenge to efficiently determine which parts of t

Yeong-Seung Baek, Heung-Seon Oh

3D visual grounding (VG) aims to locate objects or regions within 3D scenes guided by natural language descriptions. While indoor 3D VG has advanced, outdoor 3D VG remains underexplored due to two challenges: (1) large-scale outdoor LiDAR scenes are dominated by background points and contain limited foreground information, making cross-modal alignment and co

Cihan Bayindir, Fatih Ozaydin, Azmi Ali Altintas, Tayyibe Eristi

In this study, we investigate the performance of the sparse identification of nonlinear dynamics (SINDy) algorithm and the neural ordinary differential equations (ODEs) in identification of the underlying mechanisms of open ocean Lagrangian drifter hydrodynamics with possible applications in coastal and port hydrodynamic processes. With this motivation we em

Patrick Bennett, Alan Frieze, Wesley Pegden

We study the intersection of a random geometric graph with an Erd\H{o}s-R\'enyi graph. Specifically, we generate the random geometric graph $G(n, r)$ by choosing $n$ points uniformly at random from $D=[0, 1]^2$ and joining any two points whose Euclidean distance is at most $r$. We let $G(n, p)$ be the classical Erd\H{o}s-R\'enyi graph, i.e. it has $n$ vertic

Xinhua Jiang, Tianpeng Liu, Li Liu, Zhen Liu

Occlusion is a longstanding difficulty that challenges the UAV-based object detection. Many works address this problem by adapting the detection model. However, few of them exploit that the UAV could fundamentally improve detection performance by changing its viewpoint. Active Object Detection (AOD) offers an effective way to achieve this purpose. Through De

Lucas Teyssier, Paul Thévenin

We prove sharp bounds on the virtual degrees introduced by Larsen and Shalev. This leads to improved bounds on characters of symmetric groups. We then sharpen bounds of Liebeck and Shalev concerning the Witten zeta function. Our main application is a characterization of the fixed-point free conjugacy classes whose associated random walk mixes in 2 steps.

Eric Myzelev

Szlam's Lemma began life as a way of getting upper bounds on the chromatic numbers of distance graphs in normed vector spaces. Now analogs are available in a variety of hypergraph settings, but the method always involves a shrewdly chosen 2-coloring of the vertex set of a hypergraph, together with a subset of the vertex set which satisfies certain requiremen

Toshiyuki Sugawa, Li-Mei Wang

P\'olya in 1926 showed that the hypergeometric function $F(z)=\null_2F_1(a,b;c;z)$ has a totally monotone sequence as its coefficients; that is, $F$ is the generating function of a Hausdorff moment sequence, when $0\le a\le 1$ and $0\le b\le c.$ In this paper, we give a complete characterization of such hypergeometric functions $F$ in terms of complex parame

Justin Kaidi, Yuji Tachikawa, Kazuya Yonekura

A uniform construction of non-supersymmetric 0-, 4-, 6- and 7-branes in heterotic string theory was announced and outlined in our letter \cite{Kaidi:2023tqo}. In this full paper, we provide details on their properties. Among other things, we discuss the charges carried by the branes, their topological and dynamical stability, the exact worldsheet description

Jairo S. Santos, Fabiano C. Simas, Adalto R. Gomes

In this work, we investigate the dynamics of a scalar field in the nonintegrable $\displaystyle \phi ^{4}$ model, restricted to the half-line. Here we consider singular solutions that interpolate the Dirichlet boundary condition $\phi(x=0,t)=H$ and their scattering with the regular kink solution. The simulations reveal a rich variety of phenomena in the fiel

Hailey Joren, Charles Marx, Berk Ustun

We propose a new approach to promote safety in classification tasks with established concepts. Our approach -- called a conceptual safeguard -- acts as a verification layer for models that predict a target outcome by first predicting the presence of intermediate concepts. Given this architecture, a safeguard ensures that a model meets a minimal level of accu

Elizabeth A Mullins, Adrian Portillo, Kristalys Ruiz-Rohena, Aritran Piplai

Large Language Models have become a valuable source of information for our daily inquiries. However, after training, its data source quickly becomes out-of-date, making RAG a useful tool for providing even more recent or pertinent data. In this work, we investigate how RAG pipelines, with the course materials serving as a data source, might help students in

Khandaker Tasnim Huq, Giovanni Luca Ciampaglia

Communities on the web rely on open conversation forums for a number of tasks, including governance, information sharing, and decision making. However these forms of collective deliberation can often result in biased outcomes. A prime example are Articles for Deletion (AfD) discussions on Wikipedia, which allow editors to gauge the notability of existing art

Alfredo Palacios, Simon Walker, Beth Woods, Catherine Hewitt

Hospital-to-home transitions are a critical component of effective healthcare delivery, especially for patients aged 75 and older. This study evaluates the cost-effectiveness of the 'Your Care Needs You' (YCNY) intervention, a patient-centred approach designed to empower older adults during discharge, compared to standard care. The analysis adopts the perspe

R. R. S. Oliveira

In this comment, we obtain the complete energy levels for Dvornikov's paper, that is, the energy levels dependent on two quantum numbers, namely, the radial quantum number (given by $N$) and the angular quantum number (given by $J_z$). In particular, what motivated us to do this was the fact that the quantized energy levels for particles (fermions or bosons)

Haoran Sun, Dominique Fourer, Hichem Maaref

Dynamic Range Compression (DRC) is a widely used audio effect that adjusts signal dynamics for applications in music production, broadcasting, and speech processing. Inverting DRC is of broad importance for restoring the original dynamics, enabling remixing, and enhancing the overall audio quality. Existing DRC inversion methods either overlook key parameter

Sajida Gufran, Adnan Aijaz

The fifth-generation (5G) mobile/cellular and time-sensitive networking (TSN) technologies are widely recognized as the key to shaping smart manufacturing for Industry 4.0 and beyond. Converged operation of the two offers end-to-end real-time and deterministic connectivity over hybrid wired and wireless segments. On the other hand, the augmented reality (AR)

He-Yen Hsieh, Ziyun Li, Sai Qian Zhang, Wei-Te Mark Ting

We present GazeGen, a user interaction system that generates visual content (images and videos) for locations indicated by the user's eye gaze. GazeGen allows intuitive manipulation of visual content by targeting regions of interest with gaze. Using advanced techniques in object detection and generative AI, GazeGen performs gaze-controlled image adding/delet

Mohamed Hussein Abo El-Ela, Ali Hamdi Fergany

This paper introduces a novel approach to urban pathfinding by transforming traditional heuristic-based algorithms into deep learning models that leverage real-time contextual data, such as traffic and weather conditions. We propose two methods: an enhanced A* algorithm that dynamically adjusts routes based on current environmental conditions, and a neural n

Rowena Mobbs, Fatima Nasrallah, Xuan Vinh To, John Magnussen

Objectives: This study examined whether currently existing clinical structural magnetic resonance imaging (MRI) and fluorodeoxyglucose positron emission tomography (18FDG-PET) capabilities and board-certified radiologists' reports and interpretations can assist with traumatic encephalopathy syndrome (TES) diagnosis. Design: retrospective case series. Setting

Sumanta Roy, Manash Chakraborty

Suction stress is a fundamental component for applying the effective stress principle in unsaturated geotechnical engineering problems. The present paper aims to understand how the suction stress profiles get influenced if the $m$ and $n$ parameter of the van-Genuchten SWCC model is completely independent. Through the analysis, it is well noted that for cons

Zhenyue Qin, Yiqun Zhang, Yang Liu, Dylan Campbell

Generative text-to-image models, such as Stable Diffusion, have demonstrated a remarkable ability to generate diverse, high-quality images. However, they are surprisingly inept when it comes to rendering human hands, which are often anatomically incorrect or reside in the "uncanny valley". In this paper, we propose a method HandCraft for restoring such malfo

Zheyu Zhuang, Ville Kyrki, Danica Kragic

We present Robot-centric Pooling (RcP), a novel pooling method designed to enhance end-to-end visuomotor policies by enabling differentiation between the robots and similar entities or their surroundings. Given an image-proprioception pair, RcP guides the aggregation of image features by highlighting image regions correlating with the robot's proprioceptive

Tanishq Kumar, Zachary Ankner, Benjamin F. Spector, Blake Bordelon

Low precision training and inference affect both the quality and cost of language models, but current scaling laws do not account for this. In this work, we devise "precision-aware" scaling laws for both training and inference. We propose that training in lower precision reduces the model's "effective parameter count," allowing us to predict the additional l

Jierui Li, Hung Le, Yingbo Zhou, Caiming Xiong

Pre-trained on massive amounts of code and text data, large language models (LLMs) have demonstrated remarkable achievements in performing code generation tasks. With additional execution-based feedback, these models can act as agents with capabilities to self-refine and improve generated code autonomously. However, on challenging coding tasks with extremely

Manuel Nunez Martinez, Sonja Schmer-Galunder, Zoey Liu, Sangpil Youm

The unchecked spread of digital information, combined with increasing political polarization and the tendency of individuals to isolate themselves from opposing political viewpoints, has driven researchers to develop systems for automatically detecting political bias in media. This trend has been further fueled by discussions on social media. We explore meth

Chris Connell, Xianzhe Dai, Jesús Núñez-Zimbrón, Raquel Perales

We develop the barycenter technique of Besson--Courtois--Gallot so that it can be applied on RCD metric measure spaces. Given a continuous map $f$ from a non-collapsed RCD$(-(N-1),N)$ space $X$ without boundary to a locally symmetric $N$-manifold we show a version of BCG's entropy-volume inequality. The lower bound involves homological and homotopical indice

Thanadol Singkhornart, Olarik Surinta

The Multi-language Video Subtitle Dataset is a comprehensive collection designed to support research in text recognition across multiple languages. This dataset includes 4,224 subtitle images extracted from 24 videos sourced from online platforms. It features a wide variety of characters, including Thai consonants, vowels, tone marks, punctuation marks, nume

Jonathan Lee, Abhishek Rathod, Kshitij Goel, John Stecklein

Search and rescue environments exhibit challenging 3D geometry (e.g., confined spaces, rubble, and breakdown), which necessitates agile and maneuverable aerial robotic systems. Because these systems are size, weight, and power (SWaP) constrained, rapid navigation is essential for maximizing environment coverage. Onboard autonomy must be robust to prevent col

Emilio Méndez, Rocio Semino

New metal-organic frameworks (MOFs) are periodically synthesized all over the world due to the wide range of societally and environmentally relevant applications they possess. However, the mechanisms and thermodynamics associated to MOF self-assembly are poorly understood because of the difficulties in studying such a multi-scale process with molecular-level

Levin Gerdes, Carlos Pérez del Pulgar, Raúl Castilla Arquillo, Martin Azkarate

Proprioceptive sensors on planetary rovers serve for state estimation and for understanding terrain and locomotion performance. While inertial measurement units (IMUs) are widely used to this effect, force-torque sensors are less explored for planetary navigation despite their potential to directly measure interaction forces and provide insights into tractio

Yungeng Liu, Zan Chen, Yu Guang Wang, Yiqing Shen

Protein engineering is important for biomedical applications, but conventional approaches are often inefficient and resource-intensive. While deep learning (DL) models have shown promise, their training or implementation into protein engineering remains challenging for biologists without specialized computational expertise. To address this gap, we propose Au

Marco Wiedmann, Maniraman Periyasamy, Daniel D. Scherer

VQC can be understood through the lens of Fourier analysis. It is already well-known that the function space represented by any circuit architecture can be described through a truncated Fourier sum. We show that the spectrum available to that truncated Fourier sum is not entirely determined by the encoding gates of the circuit, since the variational part of

Manshuo Lin, Saeed Rahmanian Koshkaki, Mohsen Yarmohammadi, Michael H. Kolodrubetz

There is a close theoretical connection between topological Floquet physics and cavity QED, yet this connection has not been realized experimentally due to complicated cavity QED models that often arise. We propose a simple, experimentally viable protocol to realize non-adiabatic topological photon pumping mediated by a single qubit, which we dub the anomalo

Chenxi Deng, Jan Rozendaal, Mark Veraar

We obtain polynomial decay rates for $C_{0}$-semigroups, assuming that the resolvent grows polynomially at infinity in the complex right half-plane. Our results do not require the semigroup to be uniformly bounded, and for unbounded semigroups we improve upon previous results by, for example, removing a logarithmic loss on non-Hilbertian Banach spaces.

Owen Visser, Somnath Datta

As single-cell gene expression data analysis continues to grow, the need for reliable clustering methods has become increasingly important. The prevalence of heuristic means for method choice could lead to inaccurate reports if comprehensive evaluation of the methods is omitted. Typical comparisons of methods fail to address the complexity presented by the d

Tomoki Matsumoto

In the era of Big Data, Markov chain Monte Carlo (MCMC) methods, which are currently essential for Bayesian estimation, face significant computational challenges owing to their sequential nature. To achieve a faster and more effective parallel computation, we emphasize the critical role of the overlapped area of the posterior distributions based on partition

D. R. Junior, L. E. Oxman

A scenario to understand the asymptotic properties of confinement between quark probes, based on a 4D mixed ensemble of percolating center-vortex worldsurfaces and chains, was initially proposed by one of us in a non-Abelian setting. More recently, the same physics was reobtained by means of a Schr\"odinger wavefunctional peaked at Abelian-projected configur

Carlos Huertas

Large Language Models (LLM) have brought numerous of new applications to Machine Learning (ML). In the context of tabular data (TD), recent studies show that TabLLM is a very powerful mechanism for few-shot-learning (FSL) applications, even if gradient boosting decisions trees (GBDT) have historically dominated the TD field. In this work we demonstrate that

Tri Minh Nguyen, Sherif Abdulkader Tawfik, Truyen Tran, Sunil Gupta

Discovering new solid-state materials requires rapidly exploring the vast space of crystal structures and locating stable regions. Generating stable materials with desired properties and compositions is extremely difficult as we search for very small isolated pockets in the exponentially many possibilities, considering elements from the periodic table and th

Morgane Austern, Lester Mackey

This work introduces a new, explicit bound on the Hellinger distance between a continuous random variable and a Gaussian with matching mean and variance. As example applications, we derive a quantitative Hellinger central limit theorem and efficient concentration inequalities for U-statistics.

Natalia Stepanova, Marie Turcicova

We observe an unknown regression function of $d$ variables $f(\boldsymbol{t})$, $\boldsymbol{t} \in[0,1]^d$, in the Gaussian white noise model of intensity $\varepsilon>0$. We assume that the function $f$ is regular and that it is a sum of $k$-variate functions, where $k$ varies from $1$ to $s$ ($1\leq s\leq d$). These functions are unknown to us and only fe

Claire Wonjeong Jo, Miki Wesołowska, Magdalena Wojcieszak

Short video platforms, such as YouTube, Instagram, or TikTok, are used by billions of users globally. These platforms expose users to harmful content, ranging from clickbait or physical harms to misinformation or online hate. Yet, detecting harmful videos remains challenging due to an inconsistent understanding of what constitutes harm and limited resources

Mohammadhossein Malekpour, Nour Shaheen, Foutse Khomh, Amine Mhedhbi

Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, whi

David Hyland-Wood, Sandra Johnson

This paper summarises the intersection of Web3 and AI technologies, synergies between these technologies, and gaps that we suggest exist in the conception of the possible integrations of these technologies. The summary is informed by a comprehensive literature review of current academic and industry papers, analyst reports, and Ethereum research community bl

Johannes O. Royset

Variational analysis provides the theoretical foundations and practical tools for constructing optimization algorithms without being restricted to smooth or convex problems. We survey the central concepts in the context of a concrete but broadly applicable problem class from composite optimization in finite dimensions. While prioritizing accessibility over m

Melusi Malinga, Isaac Lupanda, Mike Wa Nkongolo, Phil van Deventer

South Africa and the Democratic Republic of Congo (DRC) present a complex linguistic landscape with languages such as Zulu, Sepedi, Afrikaans, French, English, and Tshiluba (Ciluba), which creates unique challenges for AI-driven translation and sentiment analysis systems due to a lack of accurately labeled data. This study seeks to address these challenges b

Aviad Susman

Autoencoders may lend themselves to the design of more accurate and computationally efficient recommender systems by distilling sparse high-dimensional data into dense lower-dimensional latent representations. However, designing these systems remains challenging due to the lack of theoretical guidance. This work addresses this by identifying three key mathem

Jasper R. Stroud, David F. Plusquellic

We describe an electro-optic dual-comb system that operates in the near-infrared (near-IR) region to generate optical frequency combs in the near-UV by sum frequency generation in two configurations. The near-IR frequency combs are generated using chirped pulses that down convert the optical information into the radio frequency (RF) domain by a difference in

Takuya Kiyokawa, Mahiro Muta, Weiwei Wan, Kensuke Harada

This study explores a pick-and-toss (PT) as an alternative to pick-and-place (PP), allowing a robot to extend its range and improve task efficiency. Although PT boosts efficiency in object arrangement, the placement environment critically affects the success of tossing. To achieve accurate and efficient object arrangement, we suggest choosing between PP and

Ying-Ying Lee, Chu-An Liu

We study causal inference in sample selection models where a continuous or multivalued treatment affects both outcome and their observability (eg., employment or survey response). We generalized the widely used Lee (2009)'s bounds for binary treatment effects. Our key innovation is a sufficient treatment value assumption that imposes weak restrictions on sel

Félix Helluin, Daniela Pinto-dias, Quentin Fontaine, Sylvain Ravets

Many systems, classical or quantum, closed or open, exhibit universal statistical properties. Exciton-polariton condensates, being intrinsically driven-dissipative, offer a promising platform for observing non-equilibrium universal features. By conducting extensive numerical simulations of an incoherently pumped and interacting condensate coupled to an excit

Brandon R. Feng, Eric Yanchenko, K. Lloyd Hill, Lindsey A. Rosman

Congestive heart failure (CHF) is a leading cause of morbidity, mortality and healthcare costs, impacting $>$23 million individuals worldwide. Large electronic health records data provide an opportunity to improve clinical management of diseases, but statistical inference on large amounts of relevant personal data is still challenging. Thus, accurately ident

Alhad Deshpande, Cory Hargus, Karthik Shekhar, Kranthi K. Mandadapu

We introduce a theory of "odd viscodiffusive fluids," which exhibit three-dimensional odd transport phenomena through the coupling of viscous and diffusive transport. In these fluids, diffusive fluxes may arise from orthogonal velocity gradients and, reciprocally, stresses may arise from concentration gradients. We examine microscopic fluctuations using the

Anand Syamkumar, Nora Tseng, Kaycie Barron, Shanglin Yang

Large language models (LLMs) offer promise in generating educational content, providing instructor feedback, and reducing teacher workload on assessments. While prior studies have focused on studying LLM-powered learning analytics, limited research has examined how effective LLMs are in a bilingual context. In this paper, we study the effectiveness of multil

Henri Lefebvre, Anirudh Subramanyam

We provide a correction to the sufficient conditions under which closed-form expressions for the optimal Lagrange multiplier are provided in arXiv:2112.13138 [math.OC]. We first present a simple counterexample where the original conditions are insufficient, highlight where the original proof fails, and then provide modified conditions along with a correct pr

Thiago Bergamaschi, Fernando Granha Jeronimo, Tushant Mittal, Shashank Srivastava

We give a construction of Quantum Low-Density Parity Check (QLDPC) codes with near-optimal rate-distance tradeoff and efficient list decoding up to the Johnson bound in polynomial time. Previous constructions of list decodable good distance quantum codes either required access to a classical side channel or were based on algebraic constructions that preclude

Ben Chen, Rohit Mokashi, Mamata Khadka, Robert Reyes

Amazon is the world number one online retailer and has nearly every product a person could need along with a treasure trove of product reviews to help consumers make educated purchases. Companies want to find a way to increase their sales in a very crowded market, and using this data is key. A very good indicator of how a product is selling is its sales rank

Hendrik Decker, Davide Martinenghi

We revisit integrity checking in relational and deductive databases with an approach that tolerates erroneous, inconsistent data. In particular, we relax the fundamental prerequisite that, in order to apply any method for simplified integrity checking, all data must initially have integrity. As opposed to a long-standing belief, integrity in the old state be

Ujjwal, Shikha C. Patel, Bansari K. Shah, Nicholas Ogbonna

Drought has been perceived as a persistent threat globally and the complex mechanism of various factors contributing to its emergence makes it more troublesome to understand. Droughts and their severity trends have been a point of concern in the USA as well, since the economic impact of droughts has been substantial, especially in parts that contribute major

Sam Armon, Joshua P. Swanson

Thrall's problem asks for the Schur decomposition of the higher Lie modules $\mathcal{L}_\lambda$, which are defined using the free Lie algebra and decompose the tensor algebra as a general linear group module. Although special cases have been solved, Thrall's problem remains open in general. We generalize Thrall's problem to the free Lie superalgebra, and p

David Gamarnik, Bobak T. Kiani, Alexander Zlokapa

Preparing thermal (Gibbs) states is a common task in physics and computer science. Recent algorithms mimic cooling via system-bath coupling, where the cost is determined by mixing time, akin to classical Metropolis-like algorithms. However, few methods exist to demonstrate slow mixing in quantum systems, unlike the well-established classical tools for system

Hyunjae Suh, Mahan Tafreshipour, Jiawei Li, Adithya Bhattiprolu

Artificial Intelligence (AI) techniques, especially Large Language Models (LLMs), have started gaining popularity among researchers and software developers for generating source code. However, LLMs have been shown to generate code with quality issues and also incurred copyright/licensing infringements. Therefore, detecting whether a piece of source code is w

Hellina Hailu Nigatu, Zeerak Talat

Mainstream Natural Language Processing (NLP) research has ignored the majority of the world's languages. In moving from excluding the majority of the world's languages to blindly adopting what we make for English, we first risk importing the same harms we have at best mitigated and at least measured for English. However, in evaluating and mitigating harms ar

Selain K. Kasereka

Modeling and simulation approaches for infectious disease dynamics have proven to be essential tools for effective control of the spread of epidemics in the population. Among these approaches, it is obvious that compartmental mathematical models, such as SIS, SIR, SEIR, etc. are the most widely used by researchers. However, they are difficult to apply in a m

Gabriele Coci, Jiaxing Zhao, Susanne Glässel, Viktar Kireyeu

We investigate light cluster and anti-cluster production in heavy-ion collisions from SIS to RHIC energies within the Parton-Hadron-Quantum-Molecular Dynamics (PHQMD) microscopic transport approach which propagates (anti-)baryons using n-body QMD dynamics. In PHQMD the clusters are formed dynamically by potential interactions between baryons - and recognized

Stephen Pasteris, Chris Hicks, Vasilios Mavroudis

Motivated by the need to remove discrimination in certain applications, we develop a meta-algorithm that can convert any efficient implementation of an instance of Hedge (or equivalently, an algorithm for discrete bayesian inference) into an efficient algorithm for the equivalent contextual bandit problem which guarantees exact statistical parity on every tr

Eduardo Barredo-Alamilla, Luis F. Urrutia, Maxim A. Gorlach

Chiral matter exhibits unique electromagnetic responses due to the macroscopic manifestation of the chiral anomaly as anomalous transport currents. Here, we study the modification of electromagnetic radiation in isotropic chiral matter characterized by an axion coupling that varies linearly over time $\theta(t) = b_0 t$. Using Carroll-Field-Jackiw electrodyn

Antonio A. Chaves, Mauricio G. C. Resende, Martin J. A. Schuetz, J. Kyle Brubaker

This paper introduces the Random-Key Optimizer (RKO), a versatile and efficient stochastic local search method tailored for combinatorial optimization problems. Using the random-key concept, RKO encodes solutions as vectors of random keys that are subsequently decoded into feasible solutions via problem-specific decoders. The RKO framework is able to combine

Varsha Gupta

This paper proposes a theoretical framework for modeling and optimizing the bounded functions based on the Fourier series approximation and Ricci flow. Specifically, the initial manifold, $\mathcal{M}_0$ is approximated using Fourier series approximation in conjunction with the center and boundary sampling procedure introduced in the paper. The manifold is i

Saketh Bachu, Erfan Shayegani, Rohit Lal, Trishna Chakraborty

Vision-language models (VLMs) have improved significantly in their capabilities, but their complex architecture makes their safety alignment challenging. In this paper, we reveal an uneven distribution of harmful information across the intermediate layers of the image encoder and show that skipping a certain set of layers and exiting early can increase the c

Hao-Tse Huang, Allison W. S. Man, Federico Lelli, Carlos De Breuck

We present new ALMA observations of a starburst galaxy at cosmic noon hosting a radio-loud active galactic nucleus: PKS 0529-549 at $z=2.57$. To investigate the conditions of its cold interstellar medium, we use ALMA observations which spatially resolve the [CI] fine-structure lines, [CI] (2-1) and [CI] (1-0), CO rotational lines, CO (7-6) and CO (4-3), and

Amelia Chambliss, Elizabeth Paul, Stuart Hudson

Even if the magnetic field in a stellarator is integrable, phase-space integrability for energetic particle guiding center trajectories is not guaranteed. Both trapped and passing particle trajectories can experience convective losses, caused by wide phase-space island formation, and diffusive losses, caused by phase-space island overlap. By locating traject

Sudarsan Balakrishnan, Laura E. Linhardt, Jeffery C. Blackmon, Catherine M. Deibel

Background: The rate of energy production in the hot-CNO cycle and breakout to the rapid-proton capture process in Type I X-ray bursts is strongly related to the $^{14}$O($\alpha,p$)$^{17}$F reaction rate. The properties of states in $^{18}$Ne near $E_x=6.1-6.3$ MeV are important for understanding this reaction rate. Experiment: The RESOLUT radioactive-ion b

F. A. Gawish, Z. S. Mansour

We establish two theorems that illustrate the uniqueness of inverse q-Sturm-Liouville problems based on a specified set of spectral data. The first uniqueness theorem employs the method of transformation operators to provide a q-analog of the Levinson-Marchenko uniqueness theorem. The second uniqueness theorem is a q-analog of the Ashrafyan uniqueness theore

Wonseong Kim, Choong Lyol Lee

This study explores the influence of FOMC sentiment on market expectations, focusing on cognitive differences between experts and non-experts. Using sentiment analysis of FOMC minutes, we integrate these insights into a bounded rationality model to examine the impact on inflation expectations. Results show that experts form more conservative expectations, an

Thomas Frost, Kezhi Li, Steve Harris

The task of predicting long-term patient outcomes using supervised machine learning is a challenging one, in part because of the high variance of each patient's trajectory, which can result in the model over-fitting to the training data. Temporal difference (TD) learning, a common reinforcement learning technique, may reduce variance by generalising learning

Chen Ling, Mina Ghashami, Vianne Gao, Ali Torkamani

Security controls are mechanisms or policies designed for cloud based services to reduce risk, protect information, and ensure compliance with security regulations. The development of security controls is traditionally a labor-intensive and time-consuming process. This paper explores the use of Generative AI to accelerate the generation of security controls.

Ainesh Bakshi, John Bostanci, William Kretschmer, Zeph Landau

We study the problem of finding a (pure) product state with optimal fidelity to an unknown $n$-qubit quantum state $\rho$, given copies of $\rho$. This is a basic instance of a fundamental question in quantum learning: is it possible to efficiently learn a simple approximation to an arbitrary state? We give an algorithm which finds a product state with fidel

Sohail Bahmani

We derive a fundamental trade-off between standard and adversarial risk in a rather general situation that formalizes the following simple intuition: "If no (nearly) optimal predictor is smooth, adversarial robustness comes at the cost of accuracy." As a concrete example, we evaluate the derived trade-off in regression with polynomial ridge functions under m

Haolin Chen, Yihao Feng, Zuxin Liu, Weiran Yao

Large language models (LLMs) have shown impressive capabilities, but still struggle with complex reasoning tasks requiring multiple steps. While prompt-based methods like Chain-of-Thought (CoT) can improve LLM reasoning at inference time, optimizing reasoning capabilities during training remains challenging. We introduce LaTent Reasoning Optimization (LaTRO)

Marco Fiandri, Alberto Maria Metelli, Francesco Trov`o

This paper is in the field of stochastic Multi-Armed Bandits (MABs), i.e. those sequential selection techniques able to learn online using only the feedback given by the chosen option (a.k.a. $arm$). We study a particular case of the rested bandits in which the arms' expected reward is monotonically non-decreasing and concave. We study the inherent sample co

Malcolm Wolff, Kin G. Olivares, Boris Oreshkin, Sunny Ruan

Demand forecasting faces challenges induced by Peak Events (PEs) corresponding to special periods such as promotions and holidays. Peak events create significant spikes in demand followed by demand ramp down periods. Neural networks like MQCNN and MQT overreact to demand peaks by carrying over the elevated PE demand into subsequent Post-Peak-Event (PPE) peri