May 2025 arXiv papers — page 105

J. Mainik

The construction of regular polygons with a compass and straightedge is a well-known task and this problem has interested mathematicians for a long time. In particular, for a long time they could not answer the question of whether is it possible to construct a regular 17-gon with a compass and straightedge. C. F. Gauss solved this problem in 1796. He proved

Mohamed Wahib, Muhammed Abdullah Soyturk, Didem Unat

To reduce the computational and memory overhead of Large Language Models, various approaches have been proposed. These include a) Mixture of Experts (MoEs), where token routing affects compute balance; b) gradual pruning of model parameters; c) dynamically freezing layers; d) dynamic sparse attention mechanisms; e) early exit of tokens as they pass through m

N. V. Krylov

In recent years we witness growing interest in using Real Analysis methods and results in the theory of nondivergence form partial differential equations (PDEs) and the goal of this article is to give a brief and concise introduction into the applications of several results in Real Analysis to the theory of elliptic and parabolic equations in Sobolev and Mor

Nicolas Müller, Piotr Kawa, Wei-Herng Choong, Adriana Stan

We show how replay attacks undermine audio deepfake detection: By playing and re-recording deepfake audio through various speakers and microphones, we make spoofed samples appear authentic to the detection model. To study this phenomenon in more detail, we introduce ReplayDF, a dataset of recordings derived from M-AILABS and MLAAD, featuring 109 speaker-micr

Min Xiao, Paul Myers

This research study examined how the number of reviews, review scores product involvement, and product review valence affect consumers' shopping decisions. Specifically, two online experiments were conducted to examine how product review scores interact with the number of reviews, product involvement, and review valence in affecting bandwagon perceptions and

Akash Patel, Mario A. V. Saucedo, Nikolaos Stathoulopoulos, Viswa Narayanan Sankaranarayanan

Autonomous navigation in unknown environments is a fundamental challenge in robotics, particularly in coordinating ground and aerial robots to maximize exploration efficiency. This paper presents a novel approach that utilizes a hierarchical graph to represent the environment, encoding both geometric and semantic traversability. The framework enables the rob

Fernando Coutinho, Nicolas Lizarralde, Fernando Lizarralde

This work investigates the manufacturing of complex shapes parts with wire arc additive manufacturing (WAAM). In order to guarantee the integrity and quality of each deposited layer that composes the final piece, the deposition process is usually carried out in a flat position. However, for complex geometry parts with non-flat surfaces, this strategy causes

David A. Kosower, Sebastian Pögel

We study linear relations between color-ordered all-plus amplitudes at one loop in Yang--Mills theory. We show that on general grounds, there are $(n-1)!/2-2$ relations for $n\ge 5$, leaving only two independent color-ordered amplitudes. We present two complementary approaches to finding such relations: one using numerical linear algebra and the other using

Mahir Hadzic, Matthew Schrecker

We prove quantitative decay rates for the linearised Vlasov-Poisson system around compactly supported equilibria. More precisely, we prove decay of the gravitational potential induced by the radial dynamics of this system in the presence of a point mass source. Our result can be interpreted as the gravitational version of linear Landau damping in the radial

Raphaël Lafond-Mercier, Leonard Maler, Avner Wallach, André Longtin

Biological systems represent time from microseconds to years. An important gap in our knowledge concerns the mechanisms for encoding time intervals of hundreds of milliseconds to minutes that matter for tasks like navigation, communication, storage, recall, and prediction of stimulus patterns. A recently identified mechanism in fish thalamic neurons addresse

Shuwei Xing, Inaara Ahmed-Fazal, Utsav Pardasani, Uditha Jayarathne

Purpose: In conventional fluoroscopy-guided interventions, the 2D projective nature of X-ray imaging limits depth perception and leads to prolonged radiation exposure. Virtual fluoroscopy, combined with spatially tracked surgical instruments, is a promising strategy to mitigate these limitations. While magnetic tracking shows unique advantages, particularly

Maggie Hughes, Cassandra Overney, Ashima Kamra, Jasmin Tepale

Trust and transparency in civic decision-making processes, like neighborhood planning, are eroding as community members frequently report sending feedback "into a void" without understanding how, or whether, their input influences outcomes. To address this gap, we introduce Voice to Vision, a sociotechnical system that bridges community voices and planning o

Drishya Karki, Michiel Kamphuis, Angelecia Frey

EasyMath is a compact benchmark for practical math reasoning in small language models. It covers thirteen categories, from basic arithmetic and order of operations to word problems, algebraic expressions, edge cases, and omits specialist topics. We tested 23 models (14M to 4B parameters) using exact, numerical, and symbolic checks on free-form answers in a z

Daniel Diz-Castro, Manuel Febrero-Bande, Wenceslao González-Manteiga

It is well known that nonparametric regression estimation and inference procedures are subject to the curse of dimensionality. Moreover, model interpretability usually decreases with the data dimension. Therefore, model-free variable selection procedures and, in particular, covariate significance tests, are invaluable tools for regression modelling as they h

Shuheng Chen, Yong Si, Junyi Fan, Li Sun

Acute pancreatitis (AP) is a common and potentially life-threatening gastrointestinal disease that imposes a significant burden on healthcare systems. ICU readmissions among AP patients are common, especially in severe cases, with rates exceeding 40%. Identifying high-risk patients for readmission is crucial for improving outcomes. This study used the MIMIC-

J. C. Rodriguez E., L. Avilés-Félix, M. H. Aguirre, L. M. Rodriguez

Rare earth/transition metal (RE/TM) multilayers with perpendicular magnetic anisotropy are key ingredients for the development of spintronic applications. Their compensation temperature depends on the ratio of the thicknesses of rare earth and transition metal, allowing their magnetic properties to be tuned with temperature while maintaining their anisotropy

George Wang, Jiaqian Hu, Safinah Ali

We present MAATS, a Multi Agent Automated Translation System that leverages the Multidimensional Quality Metrics (MQM) framework as a fine-grained signal for error detection and refinement. MAATS employs multiple specialized AI agents, each focused on a distinct MQM category (e.g., Accuracy, Fluency, Style, Terminology), followed by a synthesis agent that in

Henry Fleischmann, Kiriaki Fragkia, Ratip Emin Berker

Infinitely repeated games support equilibrium concepts beyond those present in one-shot games (e.g., cooperation in the prisoner's dilemma). Nonetheless, repeated games fail to capture our real-world intuition for settings with many anonymous agents interacting in pairs. Repeated games with restarts, introduced by Berker and Conitzer [IJCAI '24], address thi

Daniya Najiha A. Kareem, Jean Lahoud, Mustansar Fiaz, Amandeep Kumar

Many practical medical imaging scenarios include categories that are under-represented but still crucial. The relevance of image recognition models to real-world applications lies in their ability to generalize to these rare classes as well as unseen classes. Real-world generalization requires taking into account the various complexities that can be encounte

Leif Johnson, Johan Engström, Aravinda Srinivasan, Ibrahim Özturk

Understanding collision avoidance behavior is of key importance in traffic safety research and for designing and evaluating advanced driver assistance systems and autonomous vehicles. While existing experimental work has primarily focused on response timing in traffic conflicts, the goal of the present study was to gain a better understanding of human evasiv

Shreya Gupta, Boyang Huang, Barna Saha, Yinzhan Xu

Despite the popularity of the Transformer architecture, the standard algorithm for computing Attention suffers from quadratic time complexity in context length $n$. Alman and Song [NeurIPS 2023] showed that when the head dimension $d = \Theta(\log n)$, subquadratic Attention is possible if and only if the inputs have small entries bounded by $B = o(\sqrt{\lo

P. L. Rodríguez-Kessler

In this study, we employ density functional theory (DFT) to investigate the structural and electronic properties B$_8$Cu$_3^-$ clusters -- boron-based frameworks doped with three copper atoms. The results indicate that the lowest-energy structure features a vertical Cu$_3$ triangle supported on a B$_8$ wheel geometry, whereas the horizontally supported confi

Hiba Arnaout, Noy Sternlicht, Tom Hope, Iryna Gurevych

Understanding the impact of scientific publications is crucial for identifying breakthroughs and guiding future research. Traditional metrics based on citation counts often miss the nuanced ways a paper contributes to its field. In this work, we propose a new task: generating nuanced, expressive, and time-aware impact summaries that capture both praise (conf

Jennifer Brown, David Jordan

We develop skein theory for 3-manifolds in the presence of codimension-one defects, focusing especially on defects arising from parabolic induction/restriction for quantum groups. We use these defects as a model for the quantum decorated character stacks of arXiv:2102.12283, thus extending them to 3-manifolds with surface defects. As a special case we obtain

Diego Ortiz Barbosa, Luis Burbano, Siwei Yang, Zijun Wang

We introduce an autonomous attack recovery architecture to add common sense reasoning to plan a recovery action after an attack is detected. We outline use-cases of our architecture using drones, and then discuss how to implement this architecture efficiently and securely in edge devices.

Tushar Garg, Mike D. Sumption, Milan Majoros, Edward Collings

The magnetization of ReBCO tape stacks and tape stack cables in high magnetic fields (up to 30 T) are not commonly reported. Here we report magnetization measurements of tape stack cables in magnetic fields up to 30 T at 4.2 K. We observed that flux jumps, commonly relegated to low field regimes for single tapes, persisted up to 17 T in tape stacks, an effec

Chintan Shah, Pedro Amaro, Filipe Grilo, Ming Feng Gu

We report on comprehensive laboratory studies of the K-shell dielectronic recombination (DR) resonances of Fe XXV - XXI ions that prominently contribute to the hard X-ray spectrum of hot astrophysical plasmas. By scanning a monoenergetic electron beam to resonantly excite trapped Fe ions in an electron beam ion trap, and achieving a high electron-ion collisi

Harry Li, Gabriel Appleby, Kenneth Alperin, Steven R Gomez

Knowledge graphs (KGs) are powerful data structures, but exploring them effectively remains difficult for even expert users. Large language models (LLMs) are increasingly used to address this gap, yet little is known empirically about how their usage with KGs shapes user trust, exploration strategies, or downstream decision-making - raising key design challe

Wonje Jeung, Sangyeon Yoon, Albert No

Machine unlearning aims to selectively remove targeted knowledge from Large Language Models (LLMs), ensuring they forget specified content while retaining essential information. Existing unlearning metrics assess whether a model correctly answers retain queries and rejects forget queries, but they fail to capture real-world scenarios where forget queries rar

Altifani Rizky Hayyu, Stanisław Baran, Aleksandra Deptuch, Andrzej Szytuła

Polycrystalline samples of the RE$_{5}$Rh$_2$In$_4$ (RE = Gd--Tm) intermetallics have been investigated by means of X-ray diffraction (XRD) as well as by DC and AC magnetometric measurements. The XRD data confirm that the compounds crystallize with the orthorhombic Lu$_{5}$Ni$_2$In$_4$-type structure (space group Pbam, No. 55). With decreasing temperature, R

Size Peng, Yin Xu, Guanli Yi, Cixiao Zhang

Movable antenna (MA) has shown significant potential for improving the performance of integrated sensing and communication (ISAC) systems. In this paper, we model an MA-aided ISAC system operating in a communication full-duplex mono-static sensing framework. The self-interference channel is modeled as a function of the antenna position vectors under the near

Yang Lv, Brahmdutta Dixit, Jian-Ping Wang

The conventional computer architecture has been facing challenges answering the ever-increasing demands from emerging applications, such as AI, for energy-efficient computation and memory hardware systems. Computational Random Access Memory (CRAM) represents a true in-memory computing paradigm that integrates logic and memory functions within the same array.

Juan Nathaniel, Carla Roesch, Jatan Buch, Derek DeSantis

We use a deep Koopman operator-theoretic formalism to develop a novel causal discovery algorithm, Kausal. Causal discovery aims to identify cause-effect mechanisms for better scientific understanding, explainable decision-making, and more accurate modeling. Standard statistical frameworks, such as Granger causality, lack the ability to quantify causal relati

Yufan Zhuang, Liyuan Liu, Chandan Singh, Jingbo Shang

In standard autoregressive generation, an LLM predicts the next-token distribution, samples a discrete token, and then discards the distribution, passing only the sampled token as new input. To preserve this distribution's rich information, we propose Mixture of Inputs (MoI), a training-free method for autoregressive generation. After generating a token foll

Rohan Deb, Kiran Thekumparampil, Kousha Kalantari, Gaurush Hiranandani

Supervised fine-tuning (SFT) is a standard approach to adapting large language models (LLMs) to new domains. In this work, we improve the statistical efficiency of SFT by selecting an informative subset of training examples. Specifically, for a fixed budget of training examples, which determines the computational cost of fine-tuning, we determine the most in

Marios Andreou, Nan Chen, Erik Bollt

Causal inference is fundamental across scientific disciplines, yet existing methods struggle to capture instantaneous, time-evolving causal relationships in complex, high-dimensional systems. In this paper, assimilative causal inference (ACI) is developed, which is a methodological framework that leverages Bayesian data assimilation to trace causes backward

Yihong Liu, Mingyang Wang, Amir Hossein Kargaran, Felicia Körner

Large Language Models (LLMs) are capable of recalling multilingual factual knowledge present in their pretraining data. However, most studies evaluate only the final model, leaving the development of factual recall and crosslingual consistency throughout pretraining largely unexplored. In this work, we trace how factual recall and crosslingual consistency ev

Gabriel S. Rocha, Gabriel S. Denicol

We generalize a recent prescription for the relaxation time approximation for the relativistic Boltzmann equation for systems with multiple particle species at finite temperature. This is performed by adding counter-terms to the traditional Anderson-Witting ansatz for each particle species. Our approach allows for the use of momentum-dependent relaxation tim

Chun Huang

Tidal deformability of a 1.4 $M_\odot$ neutron star provides a pivotal window into the physics of dense nuclear matter, bridging gravitational-wave(GW), electromagnetic observations and nuclear physics. In this work, we present a novel, data-driven approach to constrain $\Lambda_{1.4}$ without invoking specific equation-of-state(EOS) models. By interpolating

Runze Zhao, Yue Yu, Adams Yiyue Zhu, Chen Yang

Continuous-time reinforcement learning (CTRL) provides a principled framework for sequential decision-making in environments where interactions evolve continuously over time. Despite its empirical success, the theoretical understanding of CTRL remains limited, especially in settings with general function approximation. In this work, we propose a model-based

Rushit N. Shah, Nikolaos Agadakos, Synthia Sasulski, Ali Farajzadeh

Imitation learning often assumes that demonstrations are close to optimal according to some fixed, but unknown, cost function. However, according to satisficing theory, humans often choose acceptable behavior based on their personal (and potentially dynamic) levels of aspiration, rather than achieving (near-) optimality. For example, a lunar lander demonstra

Lei Zhang, Soumya Mondal, Zhenshan Bing, Kaixin Bai

Dexterous robotic manipulation remains a longstanding challenge in robotics due to the high dimensionality of control spaces and the semantic complexity of object interaction. In this paper, we propose an object affordance-guided reinforcement learning framework that enables a multi-fingered robotic hand to learn human-like manipulation strategies more effic

Yihong Wu, Liheng Ma, Muzhi Li, Jiaming Zhou

Large Language Models (LLMs) equipped with modern Retrieval-Augmented Generation (RAG) systems often employ multi-turn interaction pipelines to interface with search engines for complex reasoning tasks. However, such multi-turn interactions inevitably produce long intermediate contexts, as context length grows exponentially with exploration depth. This leads

Leon Lin, Jun Zheng, Haidong Wang

Robustly evaluating the long-form storytelling capabilities of Large Language Models (LLMs) remains a significant challenge, as existing benchmarks often lack the necessary scale, diversity, or objective measures. To address this, we introduce WebNovelBench, a novel benchmark specifically designed for evaluating long-form novel generation. WebNovelBench leve

Kushagra Gupta, Surya Murthy, Mustafa O. Karabag, Ufuk Topcu

Cooperative bargaining games are widely used to model resource allocation and conflict resolution. Traditional solutions assume the mediator can access agents utility function values and gradients. However, there is an increasing number of settings, such as human AI interactions, where utility values may be inaccessible or incomparable due to unknown, nonaff

Zihan Lu, Tingying He, Jiayi Hong, Lijie Yao

We present a structured design methodology for creating semantically-resonant abstract patterns, making the pattern design process accessible to the general public. Semantically-resonant patterns are those that intuitively evoke the concept they represent within a specific set (e.g., in a vegetable concept set, small dots for olives and large dots for tomato

Muhammad Tayyab Khan, Lequn Chen, Wenhe Feng, Seung Ki Moon

Metal additive manufacturing (AM) involves complex interdependencies among processes, materials, feedstock, and post-processing steps. However, the underlying relationships and domain knowledge remain fragmented across literature and static databases that often require expert-level queries, limiting their applicability in design and planning. To address thes

Mingyang Wang, Lukas Lange, Heike Adel, Yunpu Ma

Reasoning language models (RLMs) excel at complex tasks by leveraging a chain-of-thought process to generate structured intermediate steps. However, language mixing, i.e., reasoning steps containing tokens from languages other than the prompt, has been observed in their outputs and shown to affect performance, though its impact remains debated. We present th

Harry Zhang, Kurt Partridge, Pai Zhu, Neng Chen

Spoken Keyword Spotting (KWS) is the task of distinguishing between the presence and absence of a keyword in audio. The accuracy of a KWS model hinges on its ability to correctly classify examples close to the keyword and non-keyword boundary. These boundary examples are often scarce in training data, limiting model performance. In this paper, we propose a m

Antoine Bigeard, Langston Nashold, Rayan Krishnan, Shirley Wu

Artificial Intelligence (AI) technology has emerged as a transformative force in financial analysis and the finance industry, though significant questions remain about the full capabilities of Large Language Model (LLM) agents in this domain. We present the Finance Agent Benchmark, featuring challenging and diverse real-world finance research problems that r

Antonia Kekkou

In this paper, we establish a lower bound on the level of a perfect complex with power torsion homology on positive degrees and a power torsion minimal generator for zero homology. Examples are provided to demonstrate that the bound is optimal. This result is applied to improve existing lower bounds on the level of a Koszul complex on various classes of sequ

A. Huster Zapke, P. C. W. Holdsworth

Using the Dirac string formalism for monopoles we expose an extensive analogy between magnetic monopole excitations in the dumbbell model of spin ice and those of the vacuum. In both cases the Dirac strings are defined in the space-time of monopole trajectories which are simulated in spin ice using transition graphs between initial and final configurations.

Tingchen Fu, Jiawei Gu, Yafu Li, Xiaoye Qu

Instruction-following is essential for aligning large language models (LLMs) with user intent. While recent reasoning-oriented models exhibit impressive performance on complex mathematical problems, their ability to adhere to natural language instructions remains underexplored. In this work, we introduce MathIF, a dedicated benchmark for evaluating instructi

María Gragera Garcés, Luis Gómez Orzechowski, Juan Francisco Rodríguez Hernández

Quantum computing is an emerging field with growing implications across science and industry, making early educational exposure increasingly important. This paper examines how quantum computing concepts can be introduced into high-school STEM curricula within existing structures to enhance foundational learning in mathematics, computer science, and physics.

Soo Min Kwon, Alec S. Xu, Can Yaras, Laura Balzano

The transformer's remarkable ability to perform in-context learning (ICL) has sparked a wide range of studies designed to understand its strengths and limitations. However, a theoretical understanding of when ICL can and cannot generalize beyond its pre-training data still remains unclear. This paper puts forth a minimal mathematical model that provably iden

Daniel Robbins, Subham Roy

We investigate (-1)-form symmetries using the framework of symmetry topological field theories. Previous studies of (-1)-form symmetries have primarily focused on SymTFTs with topological point operators. Here we examine SymTFTs devoid of point operators, constructed to realize zero-form symmetries of some physical theory. In this context we identify codimen

Minglu Zhao, Dehong Xu, Deqian Kong, Wen-Hao Zhang

The hippocampus supports spatial navigation by encoding cognitive maps through collective place cell activity. We model the place cell population as non-negative spatial embeddings derived from the spectral decomposition of multi-step random walk transition kernels. In this framework, inner product or equivalently Euclidean distance between embeddings encode

Ya-Chuan Hsu, Michael Defranco, Rutvik Patel, Stefanos Nikolaidis

In human-robot collaboration (HRC), it is crucial for robot agents to consider humans' knowledge of their surroundings. In reality, humans possess a narrow field of view (FOV), limiting their perception. However, research on HRC often overlooks this aspect and presumes an omniscient human collaborator. Our study addresses the challenge of adapting to the evo

Maxence Verhaverbeke, Julie A. Gramaccia, Richard Khoury

The 5W1H questions -- who, what, when, where, why and how -- are commonly used in journalism to ensure that an article describes events clearly and systematically. Answering them is a crucial prerequisites for tasks such as summarization, clustering, and news aggregation. In this paper, we design the first automated extraction pipeline to get 5W1H informatio

Yu Liu, Weiyao Tao, Tong Xia, Simon Knight

Survival analysis, which estimates the probability of event occurrence over time from censored data, is fundamental in numerous real-world applications, particularly in high-stakes domains such as healthcare and risk assessment. Despite advances in numerous survival models, quantifying the uncertainty of predictions from these models remains underexplored an

Iman Kazemian, Paritosh Ramanan, Murat Yildirim

Data engineering pipelines are essential - albeit costly - components of predictive analytics frameworks requiring significant engineering time and domain expertise for carrying out tasks such as data ingestion, preprocessing, feature extraction, and feature engineering. In this paper, we propose ADEPT, an automated data engineering pipeline via text embeddi

Emily J. King, Kylie Schnoor

In this paper, we explore a correspondence between frames with rational eigensteps and semistandard Young tableaux (SSYT), via the relation assigning a Gelfand-Tsetlin pattern to a frame via the frame's eigensteps. We will identify how certain key structures in SSYTs correlate with particular frame properties. For example, the weight of an SSYT yields the se

Winfried Hochstättler, Sophia Keip, Birol Yazici

The Varchenko matrix is known to have a well-structured determinant for complexes of oriented matroids (COMs). COMs can be characterized as partial cubes that do not have certain forbidden pc-minors. In this work, we generalize the Varchenko matrix and its determinant to partial cubes. We identify examples of partial cubes whose Varchenko determinants lack a

Bobo Hua, Linlin Sun, Jiaxuan Wang

We study semilinear elliptic equations on finite graphs with fully general exponential nonlinearities, thereby extending classical equations such as the Kazdan-Warner and Chern-Simons equations. A key contribution of this work is the development of new techniques for deriving a priori estimates in this generalized setting, which reduce the original finite gr

Lucía Verdegay, Bingcheng Zeng, Daniel Y. Knapp, Jack C. Roth

The helium dimer in its metastable triplet state is a promising candidate to be the first laser-cooled homonuclear molecule. An ultracold gas of He$_2^*$ would enable a new generation of precision measurements to test quantum electrodynamics for three- and four-electron molecules through Rydberg spectroscopy. Nearly diagonal Franck-Condon factors are obtaine

Abdullah Al Omar, Xin Yang, Euijin Choo, Omid Ardakanian

Federated Learning (FL) is susceptible to privacy attacks, such as data reconstruction attacks, in which a semi-honest server or a malicious client infers information about other clients' datasets from their model updates or gradients. To enhance the privacy of FL, recent studies combined Multi-Key Homomorphic Encryption (MKHE) and FL, making it possible to

Melanie Cornelius, Greg Cross, Shilpika Shilpika, Matthew T. Dearing

As supercomputers grow in size and complexity, power efficiency has become a critical challenge, particularly in understanding GPU power consumption within modern HPC workloads. This work addresses this challenge by presenting a data co-analysis approach using system data collected from the Polaris supercomputer at Argonne National Laboratory. We focus on GP

S. E. Chorfi, L. Maniar, R. Morales

This review examines classical and recent results on controllability and inverse problems for hyperbolic and dispersive equations with dynamic boundary conditions. We aim to illustrate the applicability of Carleman estimates to establish exact controllability of such equations and derive Lipschitz stability estimates for inverse problems of source terms and

Kedong Wang, Cody L. Covington, Kalman Varga

A real-space, real-time time-dependent density functional theory (RT-TDDFT) with Ehrenfest dynamics is used to simulate intermolecular Coulombic decay (ICD) processes following the ionization of an inner-valence electron. The approach has the advantage of treating both nuclear and electronic motion simultaneously, allowing for the study of electronic excitat

Naga Dileep Varikuti, Soumik Bandyopadhyay, Philipp Hauke

Non-stabilizerness, alongside entanglement, is a crucial ingredient for fault-tolerant quantum computation and achieving a genuine quantum advantage. Despite recent progress, a complete understanding of the generation and thermalization of non-stabilizerness in circuits that mix Clifford and non-Clifford operations remains elusive. While Clifford operations

Attila Szabó

Based on the recently proposed character theory of projective representations of finite groups proposed, we generalise several algorithms for computing character tables and matrices of irreducible linear representations to projective representations. In particular, we present an algorithm based on that of Burnside to compute the characters of all irreducible

Paolo Alessandro Xavier Tognini, Leonardo Banchi, Giacomo De Palma

We propose a new quantum neural network for image classification, which is able to classify the parity of the MNIST dataset with full resolution with a test accuracy of up to 97.5% without any classical pre-processing or post-processing. Our architecture is based on a mixture of experts whose model function is the sum of the model functions of each expert. W

Valeria Coenda, Gustavo Baume, Tali Palma, Carlos Feinstein

We investigate the structural and dynamical properties of Open Clusters (OCs) classified as single, in pairs, or in groups. By analysing their mass, size, age, fractality, and mass segregation, we aim to identify systematic differences among these categories and evaluate the role of the Galactic environment in their evolution. Our sample comprises 420 single

Sergey E. Koposov, Ting S. Li, C. Allende Prieto, G. E. Medina

In this paper we present the stellar Value-Added Catalogue (VAC) based on the DESI Data Release 1. This VAC contains stellar parameter, abundance and radial velocity measurements for more than 4 million stars. It also contains, for the first time, measurements from individual epochs for more than a million stars with at least two observations. The main contr

Natsuko Yamaguchi, Kareem El-Badry, Sahar Shahaf

Astrometry from the Gaia mission has revealed a large population of white dwarf (WD) + main sequence (MS) binaries with periods of $100 - 1000\,$d. These systems have separations intermediate to predictions from standard binary evolution scenarios, challenging models of binary interaction and mass transfer. Because the selection function of Gaia astrometric

Guillermo Lara, Maxence Corman, Peter James Nee, Harald P. Pfeiffer

We conduct numerical simulations of inspiraling, oppositely-charged black holes in the class of scalar-Gauss-Bonnet theories that exhibit spontaneous black hole scalarization. For quasi-circular, equal-mass binaries near the existence threshold for scalarized solutions, we find a new phenomenon whereby one of the component black holes can suddenly flip the s

Meysam Motaharfar, Parampreet Singh

We investigate the linear static response of three covariant loop quantum black holes, namely, the two models proposed by Zhang, Lewandowski, Ma, and Yang (ZLMY) and the Alonso-Bardaji, Brizuela, and Vera (ABV) model, to an external tidal field. Using effective spacetime description, we uniquely extract the tidal Love numbers (TLNs) using perturbative soluti

Michal Bílek, Hongsheng Zhao

The Fornax dwarf spheroidal galaxy has five massive globular clusters (GCs). They are often used for testing different dark matter and modified gravity theories, because it is difficult to reconcile their old stellar ages with the short time they need to settle in the center of the galaxy due to dynamical friction. Using high resolution $N$-body simulations

Benjamin D. Oppenheimer, G. Mark Voit, Yannick M. Bahé, Nicolas Battaglia

We develop and present the Descriptive Parametric Model (DPM), a tool for generating profiles of gaseous halos (pressure, electron density, and metallicity) as functions of radius, halo mass, and redshift. The model assumes single-phase, spherically symmetric, volume-filling warm/hot gas. The DPM framework enables mock observations of the circumgalactic medi

Luiz A. Silva-Lima, Dimitri A. Gadotti, Lucimara P. Martins, Tutku Kolcu

Galaxy evolution is driven by spatially distributed processes with varying timescales. Integral field spectroscopy provides spatially-resolved information about these processes. Nevertheless, disentangling these processes, which are related to both the underlying stellar populations and the interstellar medium can be challenging. We present a case study on N

Thibault Lechien, Selma E. de Mink, Ruggero Valli, Amanda C. Rubio

Binary stars and their interactions shape the formation of compact binaries, supernovae, and gravitational wave sources. The efficiency of mass transfer - the fraction of mass retained by the accretor during binary interaction - is a critical parameter that significantly impacts the final fate of these systems. However, this parameter is observationally poor

V. M. Sabarish, Marcus Brüggen, Kai Schmidt-Hoberg, Moritz S. Fischer

Dark matter (DM) spikes around supermassive black holes (SMBHs) may lead to interesting physical effects such as enhanced DM annihilation signals or dynamical friction within binary systems, shortening the merger time and possibly addressing the `final parsec problem'. They can also be promising places to study the collisionality of DM because their velocity

Allison Lin, Maria Charisi, Zoltan Haiman

Supermassive black hole binary (SMBHB) systems are expected to form as a consequence of galaxy mergers. At sub-parsec separations, SMBHBs can be identified as quasars with periodic variability with previous periodicity searches uncovering significant candidates. However, these searches focused primarily on sinusoidal signals, while theoretical models and hyd

Mingquan Feng, Yixin Huang, Yifan Fu, Shaobo Wang

The design of optimization algorithms for neural networks remains a critical challenge, with most existing methods relying on heuristic adaptations of gradient-based approaches. This paper introduces KO (Kinetics-inspired Optimizer), a novel neural optimizer inspired by kinetic theory and partial differential equation (PDE) simulations. We reimagine the trai

B. Dalla Barba, M. Berton, L. Foschini, E. Sani

We have analyzed publicly available optical spectra of PMN J0948+0022 obtained with the Sloan Digital Sky Survey, X-Shooter, and the Multi Unit Spectroscopic Explorer (MUSE). Initially, PMN J0948+0022 was classified as a jetted narrow-line Seyfert 1 galaxy, but X-Shooter and MUSE observations, which have better spectral resolution, revealed a different profi

Frederik K. Marqversen, Janus H. Wesenberg, Nikolaj T. Zinner, Ulrik L. Andersen

Quantum error correction is essential for achieving fault-tolerant quantum computing. Gottesman-Kitaev-Preskill (GKP) codes are particularly effective at correcting continuous noise, such as Gaussian noise and loss, and can significantly reduce overhead when concatenated with qubit error-correcting codes like surface codes. GKP error correction can be implem

Deepen Garg, Ruth Durrer, Jennifer Schober

The nature of magnetic fields in the voids of the large-scale structure of the Universe has been a multifaceted open puzzle for decades. On one hand, their origin is not clear with most of the magnetogenesis models using physics beyond the standard model in the early Universe, and on the other hand, their existence and potential role in explaining the spectr

Alessandro Gavardi, Rebecca von Kuk, Matthew A. Lim

We study the use of higher-order resummation for transverse observables to achieve NNLO+PS matching within the GENEVA framework. In particular, we embed $q_T$ resummation for colour-singlet production at N$^3$LL obtained via soft-collinear effective theory and implemented in the library SCETlib within GENEVA. We also study for the first time the use of the g

Aleksandra V. Sharonova, Kirill A. Grishin, Igor V. Chilingarian, Gary A. Mamon

Compact elliptical (cE) galaxies are compact stellar systems with stellar masses of $10^8 \leq M_*/M_\odot \leq 10^{10}$ and radii typically < 0.6 kpc. Here we investigate the properties of 13 cE galaxies in the Coma cluster, six newly identified. Our goal in this paper is to explore whether these cEs form directly in the cluster environment or are pre-proce

Andreas Blommaert, Jonah Kudler-Flam, Erez Y. Urbach

We investigate the no-boundary proposal for closed universes with an observer. We argue that the observer's no-boundary state is the identity operator on the physical Hilbert space, i.e., the maximum entropy state and show this explicitly in Jackiw-Teitelboim gravity. Geometrically, the no-boundary state is a bra-ket wormhole. Expectation values in the no-bo

Farida Farsian, Tiziana Trombetti, Carlo Burigana, Francesco Schilliró

The Cosmic Microwave Background (CMB) data analysis and the map-making process rely heavily on the use of spherical harmonics. For suitable pixelizations of the sphere, the (forward and inverse) Fourier transform plays a crucial role in computing all-sky map from spherical harmonic expansion coefficients -- or from angular power spectrum -- and vice versa. W

Shayarneel Kundu, Alessandro Russo, Philip Schuster, Natalia Toro

We introduce a formalism for coupling a bosonic Continuous-Spin field to familiar spin-1/2 matter. To do this, we describe the matter using the supersymmetric worldline formalism. We construct currents that are local functions of worldline kinematics, and respect both the worldline supersymmetry and the conservation condition required for consistent coupling

N. G. C. Brunken, A. C. A. Boogert, E. F. van Dishoeck, N. J. Evans

The evolution of interstellar ices can be studied with thermal tracers such as the vibrational modes of CO$_2$ ice that show great diversity depending on their local chemical and thermal environment. In this work we present JWST observations of the 15.2 $\mu$m bending mode, the 4.39 $\mu$m stretching mode and the 2.70 $\mu$m combination mode of $^{12}$CO$_2$

Orsolya E. Kovacs, Norbert Werner, Akos Bogdan, Jelle de Plaa

We present the X-ray analysis of seven local compact elliptical galaxies (cEGs), selected for their morphological resemblance to high-redshift red nuggets. As likely descendants of the red nugget population, cEGs offer a unique window into the early Universe, enabling the study of early galaxy evolution and the interplay between black holes, stellar bulges,

Meng Cheng, Seth Musser, Amir Raz, Nathan Seiberg

We discuss the possible topological order/topological quantum field theory of different quantum Hall systems. Given the value of the Hall conductivity, we constrain the global symmetry of the low-energy theory and its anomaly. Specifically, the one-form global symmetry and its anomaly are presented as the organizing principle of these systems. This informati

Sucheng Ren, Qihang Yu, Ju He, Alan Yuille

Diffusion-based Transformers have demonstrated impressive generative capabilities, but their high computational costs hinder practical deployment, for example, generating an $8192\times 8192$ image can take over an hour on an A100 GPU. In this work, we propose GRAT (\textbf{GR}ouping first, \textbf{AT}tending smartly), a training-free attention acceleration

Katerina Slavicinska, Łukasz Tychoniec, María Gabriela Navarro, Ewine F. van Dishoeck

Water is detected in environments representing every stage of star and solar system formation, but its chemical evolution throughout these stages remains poorly constrained. Deuterium ratios offer a means of probing chemical links between water in different cosmic regions because of their sensitivity to physicochemical conditions. Here, we present the first

Nikhil Prakash, Natalie Shapira, Arnab Sen Sharma, Christoph Riedl

How do language models (LMs) represent characters' beliefs, especially when those beliefs may differ from reality? This question lies at the heart of understanding the Theory of Mind (ToM) capabilities of LMs. We analyze LMs' ability to reason about characters' beliefs using causal mediation and abstraction. We construct a dataset, CausalToM, consisting of s

Haolei Xu, Yuchen Yan, Yongliang Shen, Wenqi Zhang

Large language models (LLMs) have achieved remarkable progress on mathematical tasks through Chain-of-Thought (CoT) reasoning. However, existing mathematical CoT datasets often suffer from Thought Leaps due to experts omitting intermediate steps, which negatively impacts model learning and generalization. We propose the CoT Thought Leap Bridge Task, which ai

Chaorui Deng, Deyao Zhu, Kunchang Li, Chenhui Gou

Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. In this work, we introduce BAGEL, an open-source foundational model that natively supports multimodal understanding and generation. BAGEL is a unified, decoder-only model pretrained on trillions of tokens curated from large-scale interleave