February 2025 arXiv papers — page 30

Aline Xavier Fidêncio, Felix Grün, Christian Klaes, Ioannis Iossifidis

Brain-computer interfaces (BCIs) provide alternative communication methods for individuals with motor disabilities by allowing control and interaction with external devices. Non-invasive BCIs, especially those using electroencephalography (EEG), are practical and safe for various applications. However, their performance is often hindered by EEG non-stationar

Zhining Wei

In this note, we derive a relative trace formula (RTF) using classical methods. We obtain a closed formula for the second moment of the central values of holomorphic cusp forms, a result originally established in Kuznetsov's preprint.

Matthias Schulz, Gwendal Jouan, Daniel Berger, Stefan Gavranovic

In recent years, augmentation of differentiable PDE solvers with neural networks has shown promising results, particularly in fluid simulations. However, most approaches rely on convolutional neural networks and custom solvers operating on Cartesian grids with efficient access to cell data. This particular choice poses challenges for industrial-grade solvers

Kenan Alkiek, Anna Wegmann, Jian Zhu, David Jurgens

Linguistic style is pivotal for understanding how texts convey meaning and fulfill communicative purposes, yet extracting detailed stylistic features at scale remains challenging. We present Neurobiber, a transformer-based system for fast, interpretable style profiling built on Biber's Multidimensional Analysis (MDA). Neurobiber predicts 96 Biber-style featu

Roumen Anguelov, Micaela Goddard, Yvette Hlophe, Kganya Letsoalo

This paper presents a mathematical model that explores the interactions between Cyclin-Dependent Kinase 1 (CDK1) and the Anaphase-Promoting Complex (APC) in cancer cells. Through the analysis of a dynamical system simulating the CDK1-APC network, we investigate the system's behavior and its implications for cancer progression and potential therapeutic interv

Amin Hakimi, S. Faezeh Mousavi, Amir Nader Askarpour

Mode division multiplexing (MDM) systems leveraging spatial modes carrying orbital angular momentum (OAM) present a promising approach to enhance communication capacity in free-space and fiber-optic networks. Efficient detection of OAM modes is critical for their practical implementation. Spiral phase plates (SPPs) are low-cost optical elements with simple s

M. Yuksel, M. P. Maksymowych, O. A. Hitchcock, F. M. Mayor

Nanoelectromechanical systems (NEMS) provide a platform for probing the quantum nature of mechanical motion in mesoscopic systems. This nature manifests most profoundly when the device vibrations are nonlinear and, currently, achieving vibrational nonlinearity at the single-phonon level is an active area of pursuit in quantum information science. Despite muc

M. E. Smith, N. Yilmaz, T. Watts, P. M. Scheikl

Existing tracheal tumor resection methods often lack the precision required for effective airway clearance, and robotic advancements offer new potential for autonomous resection. We present a vision-guided, autonomous approach for palliative resection of tracheal tumors. This system models the tracheal surface with a fifth-degree polynomial to plan tool traj

Sandipan Sengupta

We develop a Lagrangian formulation for gravity with matter where the gravitational couplings are universally treated as being field-dependent. The solutions for FLRW geometries and the associated time evolution of the Newton and cosmological couplings are found. The distance-redshift relations are shown to prefer a slowly growing Newton's coupling along wit

Vishal Tiwari, Chi-Ho Chan, Tamara Bogdanović, Yan-Fei Jiang

We present the first three-dimensional radiation magnetohydrodynamic (RMHD) simulation of a sub-Eddington circumbinary disk (CBD) around an equal-mass massive black hole binary (MBHB) with a total mass of $2\,\times\,10^7\,M_{\odot}$ on a circular orbit, separated by 100$\,GM_{\rm tot}/c^2$. The inclusion of radiation leads to a denser, thinner, and more fil

Anton Lavrouk, Tarek Naous, Alan Ritter, Wei Xu

The culture of the Post-Soviet states is complex, shaped by a turbulent history that continues to influence current events. In this study, we investigate the Post-Soviet cultural food knowledge of foundation models by constructing BORSch, a multimodal dataset encompassing 1147 and 823 dishes in the Russian and Ukrainian languages, centered around the Post-So

Brian Hu Zhang, Ioannis Anagnostides, Emanuel Tewolde, Ratip Emin Berker

$\Phi$-equilibria -- and the associated notion of $\Phi$-regret -- are a powerful and flexible framework at the heart of online learning and game theory, whereby enriching the set of deviations $\Phi$ begets stronger notions of rationality. Recently, Daskalakis, Farina, Fishelson, Pipis, and Schneider (STOC '24) -- abbreviated as DFFPS -- settled the existen

Zhewei Kang, Xuandong Zhao, Dawn Song

Best-of-N selection is a key technique for improving the reasoning performance of Large Language Models (LLMs) through increased test-time computation. Current state-of-the-art methods often employ computationally intensive reward models for response evaluation and selection. Reward-free alternatives, like self-consistency and universal self-consistency, are

Adithya Sireesh, Abdulla Alhajri, M. S. Kim, Tobias Haug

Entangled quantum states are highly sensitive to noise, which makes it difficult to transfer them over noisy quantum channels or to store them in quantum memory. Here, we propose the disentangling quantum autoencoder (DQAE) to encode entangled states into single-qubit product states. The DQAE provides an exponential improvement in the number of copies needed

João Pedro C. Morais, Ruben Interian

This study introduces an algorithm that generates undirected graphs with three main characteristics of real-world networks: scale-freeness, short distances between nodes (small-world phenomenon), and large clustering coefficients. The main idea is to perform random walks across the network and, at each iteration, add special edges with a decreasing probabili

Amol Khanna, Fred Lu, Edward Raff

Linear $L_1$-regularized models have remained one of the simplest and most effective tools in data science. Over the past decade, screening rules have risen in popularity as a way to eliminate features when producing the sparse regression weights of $L_1$ models. However, despite the increasing need of privacy-preserving models for data analysis, to the best

Luo Feng, Zhao YongHeng, Liu Chao

Binary stars are fundamental to astrophysics, providing critical insights into stellar evolution, galactic dynamics, and fundamental physics. However, the high dimensionality of orbital parameters and observational constraints present significant challenges in statistically characterizing their properties. In this study, we propose and implement a novel algo

Jaemarie Solyst, Cindy Peng, Wesley Hanwen Deng, Praneetha Pratapa

Youth are active users and stakeholders of artificial intelligence (AI), yet they are often not included in responsible AI (RAI) practices. Emerging efforts in RAI largely focus on adult populations, missing an opportunity to get unique perspectives of youth. This study explores the potential of youth (teens under the age of 18) to engage meaningfully in RAI

Mark Hughes, Vishnu Jejjala, P. Ramadevi, Pratik Roy

Using the vertex model approach for braid representations, we compute polynomials for spin-1 placed on hyperbolic knots up to 15 crossings. These polynomials are referred to as 3-colored Jones polynomials or adjoint Jones polynomials. Training a subset of the data using a fully connected feedforward neural network, we predict the volume of the knot complemen

Szilárd Szalay, Péter Nyári

We show that all reduced states of nonproduct symmetric Dicke states of arbitrary number of qudits are genuinely multipartite entangled, and of nonpositive partial transpose with respect to any subsystem.

Radu Marinescu, Debarun Bhattacharjya, Junkyu Lee, Tigran Tchrakian

Large language models (LLMs) have achieved remarkable success in generative tasks, yet they often fall short in ensuring the factual accuracy of their outputs, thus limiting their reliability in real-world applications where correctness is critical. In this paper, we present FactReasoner, a novel neuro-symbolic based factuality assessment framework that empl

Nikita Elizarov, Vitali Wachtel

In this paper we consider two branching processes living in a joint random environment. Assuming that both processes are critical we address the following question: What is the probability that both populations survive up to a large time $n$? We show that this probability decays as $n^{-\theta}$ with $\theta>0$ which is determined by the random environment.

Nino Ephremidze, Chandrika Chandrashekar, Atınç Çağan Şengül, Cora Dvorkin

Mapping the small-scale structure of the universe through gravitational lensing is a promising tool for probing the particle nature of dark matter. Curved Arc Basis (CAB) has been proposed as a local lensing formalism in galaxy clusters, with the potential to detect low-mass dark matter substructure. In this work, we analyze the cluster lens Abell S1063 in s

Maxime Dupont, Tina Oberoi, Bhuvanesh Sundar

State-of-the-art classical optimization solvers set a high bar for quantum computers to deliver utility in this domain. Here, we introduce a quantum preconditioning approach based on the quantum approximate optimization algorithm. It transforms the input problem into a more suitable form for a solver with the level of preconditioning determined by the depth

M. Vivek, Dominika Wylezalek

Broad absorption line (BAL) quasars exhibit significant outflows, offering insights into active galactic nuclei (AGN) feedback. While typically associated with high Eddington ratios, BAL quasars also occur in low Eddington ratio regimes, which remain poorly understood. This study aims to compare BAL properties and variability across these regimes.We investig

M. Balboni, S. Ettori, F. Gastaldello, R. Cassano

The thermal and non-thermal components in galaxy clusters have properties that, although shaped from different physical phenomena, can share some similarities, mainly driven by their halo mass and the accretion processes. Scaling relations have been proven to exist for both components and studied in X-ray (thermal) and radio (non-thermal) bands. At the radio

Chuanjie Zheng, Yang Huang, Jifeng Liu, Hongrui Gu

Spectroscopic observations are a crucial step in driving major discoveries in the era of time-domain surveys. However, the pace of current spectroscopic surveys is increasingly unable to meet the demands of rapidly advancing large-scale time-domain surveys. To address this issue, we propose the ``Frog-eyes" system, which employs a pair of narrow-band filters

Suchita Kulkarni, Joshua Lockyer, Matthew J. Strassler

We consider confining Hidden Valley/Dark Sector theories containing many dark quark flavors. These theories are in the ``conformal window'': they reach an infrared fixed point when their quarks are massless, and have unfamiliar confinement when the quark masses are non-zero but small. Their jets of hidden hadrons may be quite different from those familiar fr

Raghav Arora, Christoph Federrath, Mark Krumholz, Robi Banerjee

Context. Dense filaments/feathers are kpc-scale dusty features present in nearby main sequence galaxies. Distinct from the spiral arms, filaments constitute a major portion of dense gas concentration. They are expected to play an important role in star formation and are known to harbour star-forming regions and H II regions. Aims. We explore the origin of fi

Alexandros Ziampras, Richard P. Nelson, Sijme-Jan Paardekooper

While planet migration has been extensively studied for classical viscous disks, planet-disk interaction in nearly inviscid disks has mostly been explored with greatly simplified thermodynamics. In such environments, motivated by models of wind-driven accretion disks, even Earth-mass planets located interior to 1 au can significantly perturb the disk, carvin

Haydar Sahin, Mansoor B. A. Jalil, Ching Hua Lee

Metamaterials serve as versatile platforms for demonstrating condensed matter physics and non-equilibrium phenomena, with electrical circuits emerging as a particularly compelling medium. This review highlights recent advances in the experimental circuit realizations of topological, non-Hermitian, non-linear, Floquet and other notable phenomena. Initially pe

Aswini Bala, Sachin Jain, Dhruva K. S., Deep Mazumdar

The aim of this paper is to study three dimensional Lorentzian conformal field theories in twistor space. We formulate the conformal Ward identities and solve for two and three point Lorentzian Wightman functions. We found that the Helicity operators apart from the conformal generators play an important role in fixing their functional form. The equations tak

Yanzhe Zhang, H. J. Mo, Katherine E. Whitaker, Shuang Zhou

The spectrum of a galaxy is a complicated convolution of many properties of the galaxy, such as the star formation history (SFH), initial mass function, and metallicity. Inferring galaxy properties from the observed spectrum via spectral synthesis modeling is thus challenging. In particular, a simple yet flexible model for the SFH is required to obtain unbia

Hiroki Takeda, Takahiro Tanaka

The quantum nature of gravity remains an open question in fundamental physics, lacking experimental verification. Gravitational waves (GWs) provide a potential avenue for detecting gravitons, the hypothetical quantum carriers of gravity. However, by analogy with quantum optics, distinguishing gravitons from classical GWs requires the preservation of quantum

Zhoujian Zhang, Paul Mollière, Jonathan J. Fortney, Mark S. Marley

2MASS 1207 b, the first directly imaged planetary-mass companion, has been instrumental in advancing our understanding of exoplanets and brown dwarfs over the past 20 years. We have performed extensive atmospheric retrieval analyses of 2MASS 1207 b's JWST/NIRSpec spectrum using petitRADTRANS and a new atmospheric inhomogeneity framework, which characterizes

Rithwik Gupta, Daniel Muthukrishna, Nabeel Rehemtulla, Ved Shah

Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulations struggle with real data, models developed for one survey cannot be easily applied to another, and new surveys require prohibitively large amounts of labelled training data. Thes

Jianwei Lyu, Xuejuan Yang, Aigen Li, Fengwu Sun

Utilizing deep NIRCam/WFSS data from JWST's FRESCO program, we spectroscopically survey the 3.3 $\mu m$ aromatic and 3.4 $\mu m$ aliphatic C--H stretching emission bands of polycyclic aromatic hydrocarbon (PAH) molecules in galaxies at redshifts $z$$\sim$0.2--0.5. Unlike pre-JWST studies, largely limited to infrared (IR)-bright galaxies ($L_{\rm IR}\gtrsim10

Renato Purita Paes Leme, Cliff Stein, Yifeng Teng, Pratik Worah

We design efficient approximation algorithms for maximizing the expectation of the supremum of families of Gaussian random variables. In particular, let $\mathrm{OPT}:=\max_{\sigma_1,\cdots,\sigma_n}\mathbb{E}\left[\sum_{j=1}^{m}\max_{i\in S_j} X_i\right]$, where $X_i$ are Gaussian, $S_j\subset[n]$ and $\sum_i\sigma_i^2=1$, then our theoretical results inclu

Rory B. B. Lucyshyn-Wright

In the well-known settings of category theory enriched in a monoidal category V, the use of V-enriched functor categories and bifunctors demands that V be equipped with a symmetry, braiding, or duoidal structure. In this paper, we establish a theory of functor categories and bifunctors that is applicable relative to an arbitrary monoidal category V and appli

Charlie B. Tan, Avishek Joey Bose, Chen Lin, Leon Klein

Scalable sampling of molecular states in thermodynamic equilibrium is a long-standing challenge in statistical physics. Boltzmann generators tackle this problem by pairing normalizing flows with importance sampling to obtain uncorrelated samples under the target distribution. In this paper, we extend the Boltzmann generator framework with two key contributio

Ziheng Ouyang, Zhen Li, Qibin Hou

Recent studies have explored combining different LoRAs to jointly generate learned style and content. However, existing methods either fail to effectively preserve both the original subject and style simultaneously or require additional training. In this paper, we argue that the intrinsic properties of LoRA can effectively guide diffusion models in merging l

Xueguang Ma, Xi Victoria Lin, Barlas Oguz, Jimmy Lin

Large language models (LLMs) have demonstrated strong effectiveness and robustness while fine-tuned as dense retrievers. However, their large parameter size brings significant inference time computational challenges, including high encoding costs for large-scale corpora and increased query latency, limiting their practical deployment. While smaller retriever

E. Ceccotti, A. R. Offringa, L. V. E. Koopmans, F. G. Mertens

Studying the redshifted 21-cm signal from the the neutral hydrogen during the Epoch of Reionization and Cosmic Dawn is fundamental for understanding the physics of the early universe. One of the challenges that 21-cm experiments face is the contamination by bright foreground sources, such as Cygnus A, for which accurate spatial and spectral models are needed

Christian Schindler, Andreas Rausch

Detecting design pattern instances in unfamiliar codebases remains a challenging yet essential task for improving software quality and maintainability. Traditional static analysis tools often struggle with the complexity, variability, and lack of explicit annotations that characterize real-world pattern implementations. In this paper, we present a novel appr

Shibendu Gupta Choudhury, Purba Mukherjee, Anjan Ananda Sen

We investigate deviations from $\Lambda$CDM by independently parameterizing modifications in the background evolution and the growth of structures. The background is characterized by two parameters, $A$ and $B$, which reduce to $A=\Omega_{m0}$ and $B=2/3$ in the $\Lambda$CDM limit, while deviations in the growth of structures are captured through a fitting f

Patrick Mero, Aaron Pepsin, Chris Kreider

With a global shortage of cybersecurity students with the education and experience necessary to fill more than 3 million jobs, cybersecurity education is an international problem. Significant research within this field has explored this problem in depth, identifying a variety of shortcomings in the cybersecurity educational pipeline including lack of certifi

Liam Mazurowski, Jintian Zhu

We prove the existence of compact surfaces with prescribed constant mean curvature in asymptotically flat and asymptotically hyperbolic manifolds. More precisely, let $(M^3,g)$ be an asymptotically flat manifold with scalar curvature $R\ge 0$. Then, for each constant $c>0$, there exists a compact, almost-embedded, free boundary constant mean curvature surfac

Rohit Gheyi, Marcio Ribeiro, Jonhnanthan Oliveira

Popular IDEs frequently contain bugs in their refactoring implementations. Ensuring that a transformation preserves a program's behavior is a complex task. Traditional detection methods rely on predefined preconditions for each refactoring type, limiting their scalability and adaptability to new transformations. These methods often require extensive static a

Amir Al-Maamari

As artificial intelligence (AI) technologies increasingly enter important sectors like healthcare, transportation, and finance, the development of effective governance frameworks is crucial for dealing with ethical, security, and societal risks. This paper conducts a comparative analysis of AI risk management strategies across the European Union (EU), United

Dan Israel, Ilarion V. Melnikov, Yann Proto

We describe the general shift orbifold of a Narain CFT and use this to investigate decompactification limits in the heterotic Narain moduli space. We also comment on higher rank theories and describe some applications to the CFT based on the Leech lattice and its shift orbifolds.

Mollie Shichman, Claire Bonial, Austin Blodgett, Taylor Hudson

During Human Robot Interactions in disaster relief scenarios, Large Language Models (LLMs) have the potential for substantial physical reasoning to assist in mission objectives. However, these reasoning capabilities are often found only in larger models, which are not currently reasonable to deploy on robotic systems due to size constraints. To meet our prob

Christopher C. Stark, Sarah Steiger, Armen Tokadjian, Dmitry Savransky

Estimating the exoplanet scientific productivity of the Habitable Worlds Observatory requires estimating science exposure times. From exoplanet yields to spectral retrievals, exposure times are at the heart of our understanding of the capabilities of this future mission. As such, ensuring accuracy and consistency between different exposure time calculators (

Lukas W. Lindwasser

The Lie algebra of symmetries generated by the left-moving current $j=\partial_-\phi$ in the $2d$ single scalar conformal field theory is infinite dimensional, exhibiting mutually commuting subalgebras. The infinite dimensional mutually commuting subalgebras define integrable deformations of the $2d$ single scalar conformal field theory which preserve the Po

Erick da Silva Farias, Eduardo Palhares Junior

The automatic detection of human conflicts through videos is a crucial area in computer vision, with significant applications in monitoring and public safety policies. However, the scarcity of public datasets and the complexity of human interactions make this task challenging. This study investigates the integration of advanced deep learning techniques, incl

Jonathan C. Marcks, Benjamin Pingault, Jiefei Zhang, Cyrus Zeledon

Semiconductors are the backbone of modern technology, garnering decades of investment in high quality materials and devices. Electron spin systems in semiconductors, including atomic defects and quantum dots, have been demonstrated in the last two decades to host quantum coherent spin qubits, often with coherent spin-photon interfaces and proximal nuclear sp

Jiajun Huang, Sheng Di, Xiaodong Yu, Yujia Zhai

With the ever-increasing computing power of supercomputers and the growing scale of scientific applications, the efficiency of MPI collective communication turns out to be a critical bottleneck in large-scale distributed and parallel processing. The large message size in MPI collectives is particularly concerning because it can significantly degrade overall

Yuxiang Wei, Olivier Duchenne, Jade Copet, Quentin Carbonneaux

The recent DeepSeek-R1 release has demonstrated the immense potential of reinforcement learning (RL) in enhancing the general reasoning capabilities of large language models (LLMs). While DeepSeek-R1 and other follow-up work primarily focus on applying RL to competitive coding and math problems, this paper introduces SWE-RL, the first approach to scale RL-ba

Irina Saparina, Mirella Lapata

Handling ambiguity and underspecification is an important challenge in natural language interfaces, particularly for tasks like text-to-SQL semantic parsing. We propose a modular approach that resolves ambiguity using natural language interpretations before mapping these to logical forms (e.g., SQL queries). Although LLMs excel at parsing unambiguous utteran

Albert Rico

Recently, a toolkit of highly symmetric techniques employing matrix inequalities has been developed to detect entanglement in various ways. Here we unifiedly explain in detail these methods, and expand them to a new family of positive maps with further detection capabilities. In the simplest case, we generalize the reduction map to detect more generic states

F. Capotondi, A. Maznev, F. Bencivenga, S. Bonetti

We report the observation of dynamic fringe patterns in the diffuse scattering of extreme ultraviolet light from surfaces, following femtosecond optical excitation. At each point on the detector, the diffuse scattering intensity exhibits oscillations at well-defined frequencies that correspond to surface phonons with wave vectors determined by the scattering

Michael Ruderman, Gianluca Giostra, Matteo Sette

Dynamic systems with a large and non-smooth hysteresis in the feedforward channel challenge the design of feedback control since the instantaneous input gain is varying during the operation, in the worst case between zero and infinity. Magnetic shape memory (MSM) actuators with multi-stable transitions represent such untypical system plant with only the outp

Jake Poznanski, Aman Rangapur, Jon Borchardt, Jason Dunkelberger

PDF documents have the potential to provide trillions of novel, high-quality tokens for training language models. However, these documents come in a diversity of types with differing formats and visual layouts that pose a challenge when attempting to extract and faithfully represent the underlying content for language model use. Traditional open source tools

Yanning Yin, Stefan Willitsch

Coulomb crystals -- ordered structures of cold ions confined in ion traps -- find applications in a variety of research fields. The number and temperature of the ions forming the Coulomb crystals are two key attributes of interest in many trapped-ion experiments. Here, we present a fast and accurate approach to determining these attributes from fluorescence

Josimar Chire, Khalid Mahmood, Zhao Liang

Data classification techniques partition the data or feature space into smaller sub-spaces, each corresponding to a specific class. To classify into subspaces, physical features e.g., distance and distributions are utilized. This approach is challenging for the characterization of complex patterns that are embedded in the dataset. However, complex networks r

Laécio Carvalho de Barros, Estevão Esmi, Francielle Santo Pedro Simões, Mina Shahidi

This article presents a theory of differential and integral calculus for mapping between Banach spaces formed by subsets of fuzzy numbers called A-linearly correlated fuzzy numbers, where both the domain and codomain are spaces composed of fuzzy numbers. This is one of the main contributions of this study from a theoretical point of view, as well-known appro

Robert Wilms

In this paper we express any intersection number $(L_1\cdot\ldots\cdot L_d)$ of ample line bundles on an irreducible projective variety as the mixed volume $V(\Delta_{Y_\bullet}(L_1),\dots,\Delta_{Y_\bullet}(L_d))$ of their Newton-Okounkov bodies. The admissible flag $Y_\bullet$ of subvarieties is constructed from sections of the line bundles using Bertini's

Matthew Gaughan, Kaylea Champion, Sohyeon Hwang, Aaron Shaw

README and CONTRIBUTING files can serve as the first point of contact for potential contributors to free/libre and open source software (FLOSS) projects. Prominent open source software organizations such as Mozilla, GitHub, and the Linux Foundation advocate that projects provide community-focused and process-oriented documentation early to foster recruitment

Chanwoo Park, Seungju Han, Xingzhi Guo, Asuman Ozdaglar

Leveraging multiple large language models (LLMs) to build collaborative multi-agentic workflows has demonstrated significant potential. However, most previous studies focus on prompting the out-of-the-box LLMs, relying on their innate capability for collaboration, which may not improve LLMs' performance as shown recently. In this paper, we introduce a new po

Pedro Sequeira, Vidyasagar Sadhu, Melinda Gervasio

In this paper we present ToMCAT (Theory-of-Mind for Cooperative Agents in Teams), a new framework for generating ToM-conditioned trajectories. It combines a meta-learning mechanism, that performs ToM reasoning over teammates' underlying goals and future behavior, with a multiagent denoising-diffusion model, that generates plans for an agent and its teammates

Yafei Ou, Mahdi Tavakoli

A number of recent studies have focused on developing surgical simulation platforms to train machine learning (ML) agents or models with synthetic data for surgical assistance. While existing platforms excel at tasks such as rigid body manipulation and soft body deformation, they struggle to simulate more complex soft body behaviors like cutting and suturing

Adam Gonstal, Marek Lewicki, Bogumila Swiezewska

We study gravitational waves from supercooled cosmological first-order phase transitions. If such a transition is followed by inefficient reheating, the evolution history of the universe is modified by a period of early matter domination. This leaves an imprint on the predicted gravitational-wave spectra. Using Fisher analysis we show the parameter space in

Yizhe Zhang, Richard Bai, Zijin Gu, Ruixiang Zhang

Language models usually use left-to-right (L2R) autoregressive factorization. However, L2R factorization may not always be the best inductive bias. Therefore, we investigate whether alternative factorizations of the text distribution could be beneficial in some tasks. We investigate right-to-left (R2L) training as a compelling alternative, focusing on multip

Hend ElGhazaly, Bahman Mirheidari, Nafise Sadat Moosavi, Heidi Christensen

This study investigates factors influencing Automatic Speech Recognition (ASR) systems' fairness and performance across genders, beyond the conventional examination of demographics. Using the LibriSpeech dataset and the Whisper small model, we analyze how performance varies across different gender representations in training data. Our findings suggest a comp

Phuong-Nam Nguyen

We introduce a novel mathematical framework that unifies neural population dynamics, hippocampal sharp wave-ripple (SpWR) generation, and cognitive consistency constraints inspired by Heider's theory. Our model leverages low-dimensional manifold representations to capture structured neural drift and incorporates a balance energy function to enforce coherent

Laura Burri

Renyi reflected entropies of order $n \geq 2$ are correlation measures that have been introduced in the field of holography. In this work, we put the spotlight on the min-reflected entropy, i.e., the Renyi reflected entropy in the limit $n \rightarrow \infty$. We show that, for general bipartite quantum states, this measure is identical to another measure or

Yaroslav V. Kartashov

I consider higher-order topological insulator (HOTI) created in chi(2) nonlinear medium and based on two-dimensional generalization of the Su-Schrieffer-Heeger waveguide array, where transition between trivial and topological phases is achieved by shift of the four waveguides in the unit cell towards its center or towards its periphery. Such HOTI can support

Frederikus Hudi, Genta Indra Winata, Ruochen Zhang, Alham Fikri Aji

Reasoning is a fundamental capability of large language models (LLMs), enabling them to comprehend, analyze, and solve complex problems. In this paper, we introduce TextGames, an innovative benchmark specifically crafted to assess LLMs through demanding text-based games that require advanced skills in pattern recognition, spatial awareness, arithmetic, and l

Hayder Tirmazi

Pulsars exhibit signals with precise inter-arrival times that are on the order of milliseconds to seconds, depending on the individual pulsar. There are subtle variations in the timing of pulsar signals. We show that these variations can serve as a natural entropy source for the creation of Random Number Generators (RNGs). We also explore the effects of usin

István Tomon

The $\gamma_2$-norm of Boolean matrices plays an important role in communication complexity and discrepancy theory. In this paper, we study combinatorial properties of this norm, and provide new applications, involving Zarankiewicz type problems. We show that if $M$ is an $m\times n$ Boolean matrix such that $\gamma_2(M)<\gamma$ and $M$ contains no $t\times

Alice Guionnet, Vanessa Piccolo

We study the asymptotic spectral distribution of the conjugate kernel random matrix $YY^\top$, where $Y= f(WX)$ arises from a two-layer neural network model. We consider the setting where $W$ and $X$ are random rectangular matrices with i.i.d.\ entries, where the entries of $W$ follow a heavy-tailed distribution, while those of $X$ have light tails. Our assu

Mike Thelwall, Xiaorui Jiang

This article compares (1) citation analysis with OpenAlex and Scopus, testing their citation counts, document type/coverage and subject classifications and (2) three citation-based indicators: raw counts, (field and year) Normalised Citation Scores (NCS) and Normalised Log-transformed Citation Scores (NLCS). Methods (1&2): The indicators calculated from 28.6

Lea K. Northcote, Matthew S. Teynor, Gemma C. Solomon

Quantum algorithms have the potential to revolutionize our understanding of open quantum systems in chemistry. In this work, we demonstrate that a repeated interaction model, which could serve as the foundation for a digital quantum algorithm, can effectively reproduce non-Markovian electron transfer dynamics under four different donor-acceptor parameter reg

Nils Wandel, David Stotko, Alexander Schier, Reinhard Klein

Grading student assignments in STEM courses is a laborious and repetitive task for tutors, often requiring a week to assess an entire class. For students, this delay of feedback prevents iterating on incorrect solutions, hampers learning, and increases stress when exercise scores determine admission to the final exam. Recent advances in AI-assisted education

Miles Williams, George Chrysostomou, Vitor Jeronymo, Nikolaos Aletras

Language models (LMs) excel at tasks across diverse domains, yet require substantial computational resources during inference. Compression techniques such as pruning and quantization offer a practical path towards efficient LM deployment, exemplified by their ability to preserve performance on general-purpose benchmarks. However, general-purpose LM compressi

Zohar Ringel, Noa Rubin, Edo Mor, Moritz Helias

Deep learning algorithms have made incredible strides in the past decade, yet due to their complexity, the science of deep learning remains in its early stages. Being an experimentally driven field, it is natural to seek a theory of deep learning within the physics paradigm. As deep learning is largely about learning functions and distributions over function

Jens Hoppe

Apart from relating interesting quantum mechanical systems to equations describing a parabolic discrete minimal surface, the quantization of a cubic minimal surface in $\mathbb{R}^4$ is considered.

Omar Cabrera, Silvia Cingolani, Tobias Weth

We consider the planar logarithmic Choquard equation $$- \Delta u + a(x)u + (\log|\cdot| \ast u^2)u = 0,\qquad \text{in } \mathbb{R}^2$$ in the strongly indefinite and possibly degenerate setting where no sign condition is imposed on the linear potential $a \in L^\infty(\mathbb{R}^2)$. In particular, we shall prove the existence of a sequence of high energy

Yiyuan Chen, Jonas Helsen, Maris Ozols

The Sachdev--Ye--Kitaev (SYK) model is a prominent model of strongly interacting fermions that serves as a toy model of quantum gravity and black hole physics. In this work, we study the Trotter error and gate complexity of the quantum simulation of the SYK model using Lie--Trotter--Suzuki formulas. Building on recent results by Chen and Brand\~{a}o (arXiv:2

Prateek Kumar Vishwakarma

Lusztig showed that invertible totally nonnegative (TNN) matrices form a semigroup generated by positive diagonal matrices and Chevalley generators. From its Grassmann analogue, we introduce Chevalley operations on index sets, which we show have a rich variety of applications. We first completely classify all inequalities that are quadratic in Plucker coordi

Orion Weller, Kathryn Ricci, Eugene Yang, Andrew Yates

We introduce Rank1, the first reranking model trained to take advantage of test-time compute. Rank1 demonstrates the applicability within retrieval of using a reasoning language model (i.e. OpenAI's o1, Deepseek's R1, etc.) for distillation in order to rapidly improve the performance of a smaller model. We gather and open-source a dataset of more than 600,00

Alexander Groshev, Anastasiia Iashchenko, Pavel Paramonov, Denis Dimitrov

While the task of face swapping has recently gained attention in the research community, a related problem of head swapping remains largely unexplored. In addition to skin color transfer, head swap poses extra challenges, such as the need to preserve structural information of the whole head during synthesis and inpaint gaps between swapped head and backgroun

Zhuoqin Yang, Jiansong Zhang, Xiaoling Luo, Zheng Lu

Recent advancements in deep learning for image classification predominantly rely on convolutional neural networks (CNNs) or Transformer-based architectures. However, these models face notable challenges in medical imaging, particularly in capturing intricate texture details and contextual features. Kolmogorov-Arnold Networks (KANs) represent a novel class of

Ali Forootani, Raffaele Iervolino, Massimo Tipaldi, Mohammad Khosravi

Dynamic Programming suffers from the curse of dimensionality due to large state and action spaces, a challenge further compounded by uncertainties in the environment. To mitigate these issue, we explore an off-policy based Temporal Difference Approximate Dynamic Programming approach that preserves contraction mapping when projecting the problem into a subspa

Henry Peng Zou, Siffi Singh, Yi Nian, Jianfeng He

Generalized Category Discovery (GCD) is a practical and challenging open-world task that aims to recognize both known and novel categories in unlabeled data using limited labeled data from known categories. Due to the lack of supervision, previous GCD methods face significant challenges, such as difficulty in rectifying errors for confusing instances, and in

Jane Pan, Ryan Shar, Jacob Pfau, Ameet Talwalkar

Programming is a fundamentally interactive process, yet coding assistants are often evaluated using static benchmarks that fail to measure how well models collaborate with users. We introduce an interactive evaluation pipeline to examine how LLMs incorporate different types of feedback in a collaborative setting. Specifically, we perturb static coding benchm

Paula Santos

This study presents a comparative evaluation of a Variational Autoencoder (VAE) enhanced with Minimum Description Length (MDL) regularization against a Standard Autoencoder for reconstructing high-dimensional gynecological data. The MDL-VAE exhibits significantly lower reconstruction errors (MSE, MAE, RMSE) and more structured latent representations, driven

Xiangyu Zhao, Shengyuan Ding, Zicheng Zhang, Haian Huang

Recent advancements in open-source multi-modal large language models (MLLMs) have primarily focused on enhancing foundational capabilities, leaving a significant gap in human preference alignment. This paper introduces OmniAlign-V, a comprehensive dataset of 200K high-quality training samples featuring diverse images, complex questions, and varied response f

Young-Chae Hong, Bei Xiao, Yangho Chen

Time series forecasting has long been a focus of research across diverse fields, including economics, energy, healthcare, and traffic management. Recent works have introduced innovative architectures for time series models, such as the Time-Series Mixer (TSMixer), which leverages multi-layer perceptrons (MLPs) to enhance prediction accuracy by effectively ca

Keqiang Yan, Montgomery Bohde, Andrii Kryvenko, Ziyu Xiang

Machine learning interatomic potentials (MLIPs) can predict energy, force, and stress of materials and enable a wide range of downstream discovery tasks. A key design choice in MLIPs involves the trade-off between invariant and equivariant architectures. Invariant models offer computational efficiency but may not perform as well, especially when predicting h

F. La Barbera, A. Vazdekis, A. Pasquali

We present abundance ratio estimates of individual elements, namely C, N, Na, and the so-called alpha elements, Mg, O, Si, Ca, and Ti, for the bulge of M31. The analysis is based on long-slit, high-quality spectroscopy of the bulge, taken with the OSIRIS spectrograph at the Gran Telescopio CANARIAS (GTC). Abundance ratios, [X/Fe]s, are inferred by comparing