October 2025 arXiv papers — page 19

Omar Abdul-Aziz, Manuel Tuniz, Wibke Bronsch, Fulvio Parmigiani

We use time- and angle-resolved photoemission spectroscopy (TR-ARPES) combined with density functional theory to investigate ultrafast carrier dynamics in low-symmetry layered semiconducting PdSe$_2$. The indirect bandgap is determined to be 0.55~eV. Following photoexcitation above this gap, we resolve a valence band shift and broadening, both lasting less t

David R. Saykin, Qianni Jiang, Zhaoyu Liu, Chandra Shekhar

We report high resolution polar Kerr effect measurements on CsV3Sb5 and ScV6Sn6 single crystals in search for signatures of spontaneous polar Kerr effect (PKE) below the charge order transitions of these materials. Utilizing two separate zero-area loop Sagnac interferometers operating at 1550 nm and 830 nm wavelengths, we studied the temperature dependence o

Jerry Horgan, Alexander Nico-Katz, Shelbi L. Jenkins, Ashley N. Tittelbaugh

Zero Added Loss Multiplexing (ZALM) offers broadband, per channel heralded EPR pairs, with a rich parameter space that allows its performance to be tailored for specific applications. We present a modular ZALM simulator that demonstrates how design choices affect output rate and fidelity. Built in NetSquid with QSI controllers, it exposes 20+ tunable paramet

Ziji Chen, Steven W. D. Chien, Peng Qian, Noa Zilberman

Modern machine learning (ML) has grown into a tightly coupled, full-stack ecosystem that combines hardware, software, network, and applications. Many users rely on cloud providers for elastic, isolated, and cost-efficient resources. Unfortunately, these platforms as a service use virtualization, which means operators have little insight into the users' workl

Theresia Veronika Rampisela, Maria Maistro, Tuukka Ruotsalo, Christina Lioma

The development of Artificial Intelligence (AI), including AI in Science (AIS), should be done following the principles of responsible AI. Progress in responsible AI is often quantified through evaluation metrics, yet there has been less work on assessing the robustness and reliability of the metrics themselves. We reflect on prior work that examines the rob

Chung Hyun Lee, Bowoo Jang, Kyoungil Kwon, Kyung-Tae Kim

Accurate prediction of the radar cross section (RCS) of chaff clouds requires careful consideration of aerodynamic effects, as the orientation and spatial distribution of individual chaff elements evolve significantly after deployment. Building upon conventional six-degree-of-freedom (6-DoF) formulations for chaff aerodynamic analysis-which assumed straight

Rishika Bhagwatkar, Syrielle Montariol, Angelika Romanou, Beatriz Borges

Humans can naturally identify, reason about, and explain anomalies in their environment. In computer vision, this long-standing challenge remains limited to industrial defects or unrealistic, synthetically generated anomalies, failing to capture the richness and unpredictability of real-world anomalies. In this work, we introduce CAVE, the first benchmark of

Moyan Liu, Qin Huang, Upmanu Lall

Adaptive chaos control has been studied extensively for autonomous systems. For real world, non-autonomous systems, such as the planetary weather, observations of the system state in response to seasonally and diurnally varying forcing are available only at discrete times and locations, over which system trajectories are likely to have diverged given uncerta

Bai Li, Achilleas Kourtellis, Rong Cao, Joseph Post

Rapid and reliable incident detection is critical for reducing crash-related fatalities, injuries, and congestion. However, conventional methods, such as closed-circuit television, dashcam footage, and sensor-based detection, separate detection from verification, suffer from limited flexibility, and require dense infrastructure or high penetration rates, res

Eirini Poimenidou, K. A. Draziotis

In the present paper, we introduce a message-recovery attack based on the Modular Knapsack Problem, applicable to all variants of the NTRU-HPS cryptosystem. Assuming that a fraction $\epsilon$ of the coefficients of the message ${\bf{m}}\in\{-1,0,1\}^N$ and of the nonce vector ${\bf r}\in\{-1,0,1\}^N$ are known in advance at random positions, we reduce messa

Sergey G. Bobkov, Devraj Duggal

Pinsker-type inequalities are considered for the weighted total variation distance between probability measures in terms of the R\'enyi divergence powers. They are applied in derivation of transport-entropy inequalities under moment-type conditions.

Xinhua Wang, Caibo Feng, Xiangjun Fu, Chunxiao Liu

We propose an innovative enhancement to the Mamba framework by increasing the Hausdorff dimension of its scanning pattern through a novel Hilbert Selective Scan mechanism. This mechanism explores the feature space more effectively, capturing intricate fine-scale details and improving overall coverage. As a result, it mitigates information inconsistencies whi

Harin Lee, Min-hwan Oh

We study the problem of infrequent exploration in linear bandits, addressing a significant yet overlooked gap between fully adaptive exploratory methods (e.g., UCB and Thompson Sampling), which explore potentially at every time step, and purely greedy approaches, which require stringent diversity assumptions to succeed. Continuous exploration can be impracti

Hamid El Bahja

This paper establishes a probabilistic representation for the solution of the parabolic obstacle problem associated with the normalized $p$-Laplacian. We introduce a zero-sum stochastic tug-of-war game with noise in a space-time cylinder, where one player has the option to stop the game at any time to collect a payoff given by an obstacle function. We prove

Giulio Tononi, Melanie Boly

This overview of integrated information theory (IIT) emphasizes IIT's "consciousness-first" approach to what exists. Consciousness demonstrates to each of us that something exists--experience--and reveals its essential properties--the axioms of phenomenal existence. IIT formulates these properties operationally, yielding the postulates of physical existence.

Manu Redd, Tao Zhe, Dongjie Wang

Natural-language-to-SQL (NL-to-SQL) systems hold promise for democratizing access to structured data, allowing users to query databases without learning SQL. Yet existing systems struggle with realistic spatio-temporal queries, where success requires aligning vague user phrasing with schema-specific categories, handling temporal reasoning, and choosing appro

Fali Wang, Jihai Chen, Shuhua Yang, Runxue Bao

Test-Time Scaling (TTS) improves large language models (LLMs) by allocating additional computation during inference, typically through parallel, sequential, or hybrid scaling. However, prior studies often assume fixed collaboration architectures (e.g., topologies) and single-model usage, overlooking that optimal architectures and model combinations can vary

Riccardo Aiudi, Julien Despres, Roberto Menta, Ashkan Abedi

We study the impact of static disorder on a globally-controlled superconducting quantum computing architecture based on a quasi-two-dimensional ladder geometry [R. Menta et al., Phys. Rev. Research 7, L012065 (2025)]. Specifically, we examine how fabrication-induced inhomogeneities in qubit resonant frequencies and coupling strengths affect quantum state pro

Bradley Dirks

We show that the relation between multiplier ideals and $V$-filtration on the structure sheaf due to Budur-Musta\c{t}\u{a}-Saito generalizes to singular irreducible varieties, by replacing multiplier ideals with multiplier modules and the structure sheaf with the intersection complex Hodge module. This is applied to a Skoda theorem for such modules as well a

Yosef Shokeeb, Ludovic D. C. Jaubert, Han Yan

We generalize the Hyperbolic Fracton Model from the $\{5,4\}$ tessellation to generic tessellations, and investigate its core properties: subsystem symmetries, fracton mobility, and holographic correspondence. While the model on the original tessellation has features reminiscent of the flat-space lattice cases, the generalized tessellations exhibit a far ric

Darius Masoum Zadeh-Jousdani, Elvin Hajizada, Eyke Hüllermeier

Robotic systems operating at the edge require efficient online learning algorithms that can continuously adapt to changing environments while processing streaming sensory data. Traditional backpropagation, while effective, conflicts with biological plausibility principles and may be suboptimal for continuous adaptation scenarios. The Predictive Coding (PC) f

Yihe Deng, I-Hung Hsu, Jun Yan, Zifeng Wang

Large Language Models (LLMs) often struggle with problems that require multi-step reasoning. For small-scale open-source models, Reinforcement Learning with Verifiable Rewards (RLVR) fails when correct solutions are rarely sampled even after many attempts, while Supervised Fine-Tuning (SFT) tends to overfit long demonstrations through rigid token-by-token im

Simon Dirckx, Anna Yesypenko, Per-Gunnar Martinsson

An overlapping domain decomposition method is described for variable-coefficient elliptic boundary value problems on domains that can be decomposed into slabs or shells. The method represents the global solution through its traces on internal interfaces, coupled by local Dirichlet solution transfer operators posed on overlapping double slab domains. The key

Valentin Boussot, Cédric Hémon, Jean-Claude Nunes, Jean-Louis Dillenseger

In this work, we address the TrackRAD2025 challenge of real-time tumor tracking in cine-MRI sequences of the thoracic and abdominal regions under strong data scarcity constraints. Two complementary strategies were explored: (i) unsupervised registration with the IMPACT similarity metric and (ii) foundation model-based segmentation leveraging SAM 2.1 and its

Liyou Luo, Pengfei Zhao, Jensen Li

Illusion effects-where one object appears as another-arise from the non-uniqueness of physical systems, in which different material configurations yield identical external responses. Conventional approaches, such as coordinate transformation, map equivalent configurations but provide only specific solutions, while analytical or numerical optimization methods

Yixiao Zhang, Wanying Kang, John Marshall

A poleward-thinning ice shell can drive circulation in the subsurface oceans of icy moons by imposing a meridional temperature gradient--colder at the equator than the pole--through the freezing point suppression due to pressure. This temperature gradient sets a buoyancy gradient, whose sign depends on the thermal expansion coefficient determined by ocean sa

Kevin Phan, William Mitchell, David Chaparro, Enrique De Alba

Traditional lost-in-space algorithms, such as those implemented in astrometry.net, solve for spacecraft orientation by matching observed star fields to celestial catalogs using geometric asterisms alone. In this work, we propose a novel extension to astrometry.net that incorporates stellar spectral type, which is derived from hyperspectral imagery, into the

Andrew W. Rosemberg, Joaquim Dias Garcia, François Pacaud, Robert B. Parker

Differentiating through constrained optimization problems is increasingly central to learning, control, and large-scale decision-making systems, yet practical integration remains challenging due to solver specialization and interface mismatches. This paper presents a general and streamlined framework-an updated DiffOpt.jl-that unifies modeling and differenti

Francisco M. F. R. Gonçalves, Ryan M. Bena, Néstor O. Pérez-Arancibia

Over the past few decades, continuous quaternion-based attitude control has been proven highly effective for driving rotational systems that can be modeled as rigid bodies, such as satellites and drones. However, methods rooted in this approach do not enforce the existence of a unique closed-loop (CL) equilibrium attitude-error quaternion (AEQ); and, for rot

Stephen Landsittel, Sudipta Das

In this paper, we initiate a systematic study of the generalized Hilbert-Kunz multiplicity for families of ideals in a Noetherian local ring (R,m) of positive characteristic, and introduce a new asymptotic invariant called the Amao-type multiplicity. We establish that, for a p-family of ideals, the generalized Hilbert-Kunz multiplicity arises as the limit of

J. Jon Ryu, Pavan Yeddanapudi, Xiangxiang Xu, Gregory W. Wornell

The InfoNCE objective, originally introduced for contrastive representation learning, has become a popular choice for mutual information (MI) estimation, despite its indirect connection to MI. In this paper, we demonstrate why InfoNCE should not be regarded as a valid MI estimator, and we introduce a simple modification, which we refer to as InfoNCE-anchor,

Chaithanya Naik Mude, Linipun Phuttitarn, Satvik Maurya, Kunal Sinha

Neutral atom quantum computers hold promise for scaling up to hundreds of thousands or more qubits, but their progress is constrained by slow qubit readout. Parallel measurement of qubit arrays currently takes milliseconds, much longer than the underlying quantum gate operations-making readout the primary bottleneck in deploying quantum error correction. Bec

J. Oliveira-Cony, C. Farina

We study the influence of perturbations in the three dimensional isotropic harmonic oscillator problem considering different perturbing force laws and apply our results in the context of celestial mechanics, particularly in the movement of stars in stellar clusters. We use a method based on the Runge-Lenz tensor, so that our results are valid for any eccentr

Brandon Massett, Walter Shmayda

In certain applications, pressure transducers may be exposed to high pressures either deliberately or accidentally, raising concerns about their functionality afterwards. We compared the performance of two MKS Granville-Phillips 390 Micro-Ion Gauges against each other, one that had been exposed to 10,000 Torr and the other had never been exposed to pressures

Dinghong Song, Yuan Feng, Yiwei Wang, Shangye Chen

Large Language Models (LLMs) are widely used in generative applications such as chatting, code generation, and reasoning. However, many realworld workloads such as classification, question answering, recommendation, and text embedding rely solely on the prefill stage of inference, where the model encodes input sequences without performing autoregressive deco

You-Jin Kim, Radha Kumaran, Jingjing Luo, Tom Bullock

Augmented reality is projected to be a primary mode of information consumption on the go, seamlessly integrating virtual content into the physical world. However, the potential perceptual demands of viewing virtual annotations while navigating a physical environment could impact user efficacy and safety, and the implications of these demands are not well und

Dinghong Song, Jierui Xu, Weichu Yang, Pengfei Su

Emerging AI accelerators have started to gain attention and offer new opportunities for efficient inference of large language models (LLMs). Trainium, an AI accelerator recently developed by Amazon Web Services (AWS), provides an attractive option for LLM inference through its heterogeneous architecture. However, leveraging Trainium architecture for high per

Roman Beliy, Amit Zalcher, Jonathan Kogman, Navve Wasserman

Reconstructing images seen by people from their fMRI brain recordings provides a non-invasive window into the human brain. Despite recent progress enabled by diffusion models, current methods often lack faithfulness to the actual seen images. We present "Brain-IT", a brain-inspired approach that addresses this challenge through a Brain Interaction Transforme

Sina Bagheri Nezhad, Yao Li, Ameeta Agrawal

Large Language Models (LLMs) often struggle with complex mathematical reasoning, where prose-based generation leads to unverified and arithmetically unsound solutions. Current prompting strategies like Chain of Thought still operate within this unreliable medium, lacking a mechanism for deterministic verification. To address these limitations, we introduce S

Mengtian Guo, David Gotz, Yue Wang

Predictive modeling has the potential to enhance human decision-making. However, many predictive models fail in practice due to problematic problem formulation in cases where the prediction target is an abstract concept or construct and practitioners need to define an appropriate target variable as a proxy to operationalize the construct of interest. The cho

A. A. Araújo Filho, N. Heidari, Francisco S. N. Lobo

In this work, we study how the spin of particle modes influences particle creation, greybody factors, absorption, and evaporation of a black hole within the framework of modified electrodynamics in $f(R,T)$ gravity, recently proposed in Ref. [1]. All spin sectors -- scalar, vector, tensor, and spinorial -- are analyzed to obtain the corresponding features. F

Huan Tran, Hieu-Chi Dam, Christopher Kuenneth, Tuoc N. Vu

The last two decades have witnessed a tremendous number of computational predictions of hydride-based (phonon-mediated) superconductors, mostly at extremely high pressures, i.e., hundreds of GPa. These discoveries were heavily driven by Migdal-\'{E}liashberg theory (and its first-principles computational implementations) for electron-phonon interactions, the

Sung-Hoon Yoon, Minghan Li, Gaspard Beaudouin, Congcong Wen

Rectified flow models have become a de facto standard in image generation due to their stable sampling trajectories and high-fidelity outputs. Despite their strong generative capabilities, they face critical limitations in image editing tasks: inaccurate inversion processes for mapping real images back into the latent space, and gradient entanglement issues

B. C. Lütfüoğlu

We analyze quasinormal modes, grey-body factors, and absorption cross-sections of a massive scalar field in the background of a Schwarzschild black hole surrounded by a Hernquist dark-matter halo. The quasinormal spectrum is obtained through the higher-order WKB method and verified by time-domain evolution, showing consistent results. The field mass increase

Andrii Arman, Jaskaran Singh Kaire, Andriy Prymak

Hadwiger's covering conjecture states that every $n$-dimensional convex body can be covered by at most $2^n$ of its smaller positive homothetic translates, with $2^n$ copies required only for affine images of the $n$-cube. In this note, we confirm Hadwiger's conjecture for the class of cap bodies in all dimensions, bridging recently established cases

Mohsinul Kabir, Tasnim Ahmed, Md Mezbaur Rahman, Polydoros Giannouris

Machine Translation (MT) is widely employed to address resource scarcity in low-resource languages by generating synthetic data from high-resource counterparts. While sentiment preservation in translation has long been studied, a critical but underexplored factor is the role of cultural alignment between source and target languages. In this paper, we hypothe

Karolina Hutchison, David Yevick

Delayed generalization, termed grokking, in a machine learning calculation occurs when the increase in test accuracy is delayed relative to the training accuracy. This paper examines grokking in the context of a dense neural network trained to classify 2D Ising model configurations into 4 equally spaced energy regions in the presence of weight decay. Partial

Luoyan Zhong, Heather Jin Hee Kim, Dylan P. Losey, Cara M. Nunez

Flexible tactile sensors are increasingly used in real-world applications such as robotic grippers, prosthetic hands, wearable gloves, and assistive devices, where they need to conform to curved and irregular surfaces. However, most existing tactile sensors are calibrated only on flat substrates, and their accuracy and consistency degrade once mounted on cur

Ritesh Kanchi, Miya Natsuhara, Matt X. Wang

It is critically important to make computing courses accessible for disabled students. This is particularly challenging in large computing courses, which face unique challenges due to the sheer scale of course content and staff. In this experience report, we share our attempts to scale accessibility efforts for a large university-level introductory programmi

Izzy Grosof, Daniela Hurtado-Lange

A well-designed scheduling policy can unlock significant performance improvements with no additional resources. Multiserver SRPT (SRPT-$k$) is known to achieve asymptotically optimal mean response time in the heavy traffic limit, as load approaches capacity. No better policy is known for the M/G/$k$ queue in any regime. We introduce a new policy, SRPT-Except

Alvaro Velasquez, Susmit Jha, Ismail R. Alkhouri

This paper surveys studies on the use of neural networks for optimization in the training-data-free setting. Specifically, we examine the dataless application of neural network architectures in optimization by re-parameterizing problems using fully connected (or MLP), convolutional, graph, and quadratic neural networks. Although MLPs have been used to solve

Amanda Glazer

We present a tractable framework for detecting changes in performance metrics and apply these methods to Major League Baseball (MLB) batting and pitching data from the 2023 and 2024 seasons. We propose a changepoint detection algorithm that combines a likelihood-based approach with split-sample inference to better control false positives, using either nonpar

Zeynep Yasemin Erdogan, Shishir Nagaraja, Chuadhry Mujeeb Ahmed, Ryan Shah

In this paper, we present a framework that uses acoustic side-channel analysis (ASCA) to monitor and verify whether a robot correctly executes its intended commands. We develop and evaluate a machine-learning-based workflow verification system that uses acoustic emissions generated by robotic movements. The system can determine whether real-time behavior is

Gabriel Meyniel, Fei Zhou

In this article, we put forward a practical but generic approach towards constructing a large family of $(3+1)$ dimension lattice models which can naturally lead to a single Weyl cone in the infrared (IR) limit. Our proposal relies on spontaneous charge $U(1)$ symmetry breaking to evade the usual no-go theorem of a single Weyl cone in a 3d lattice. We have e

Lukas Pfromm, Alish Kanani, Harsh Sharma, Janardhan Rao Doppa

Due to reduced manufacturing yields, traditional monolithic chips cannot keep up with the compute, memory, and communication demands of data-intensive applications, such as rapidly growing deep neural network (DNN) models. Chiplet-based architectures offer a cost-effective and scalable solution by integrating smaller chiplets via a network-on-interposer (NoI

Radha Kumaran, You-Jin Kim, Anne E Milner, Tom Bullock

Head-worn augmented reality (AR) is a hotly pursued and increasingly feasible contender paradigm for replacing or complementing smartphones and watches for continual information consumption. Here, we compare three different AR navigation aids (on-screen compass, on-screen radar and in-world vertical arrows) in a wide-area outdoor user study (n=24) where part

Conor K. Trygstad, Nestor O. Perez-Arancibia

We present force characterizations of two newly developed insect-scale propulsors--one single-tailed and one double-tailed--for microrobotic swimmers that leverage fluid-structure interaction (FSI) to generate thrust. The designs of these two devices were inspired by anguilliform swimming and are driven by soft tails excited by high-work-density (HWD) actuat

Lynn Metz, Rachel Haggard, Michael Moszczynski, Samer Asbah

The reliability of routine health data in low and middle-income countries (LMICs) is often constrained by reporting delays and incomplete coverage, necessitating the exploration of novel data sources and analytics. Geospatial Foundation Models (GeoFMs) offer a promising avenue by synthesizing diverse spatial, temporal, and behavioral data into mathematical e

Prathamesh Tamhane

Modern astronomical surveys such as the Sloan Digital Sky Survey (SDSS) provide extensive astronomical databases enabling researchers to access vast amount of diverse data. However, retrieving data from archives requires knowledge of query languages and familiarity with their schema, which presents a barrier for non-experts. This work investigates the use of

Tcharlies Schmitz

This paper introduces Modular Linear Tokenization (MLT), a reversible and deterministic technique for encoding high-cardinality categorical identifiers into compact numerical vectors. Unlike traditional hashing or one-hot encodings, MLT preserves bijective mappings by leveraging modular arithmetic over finite fields and invertible linear transformations. The

Sounak Banerjee, Daphne Cornelisse, Deepak Gopinath, Emily Sumner

People's goal-directed behaviors are influenced by their cognitive biases, and autonomous systems that interact with people should be aware of this. For example, people's attention to objects in their environment will be biased in a way that systematically affects how they perform everyday tasks such as driving to work. Here, building on recent work in compu

D. Manzoni, A. Ferrara

Order-unity star formation efficiencies (SFE) in early galaxies may explain the overabundance of bright galaxies observed by JWST at high redshift. Here we show that Lyman-$\alpha$ (Ly$\alpha$) radiation pressure limits the gas mass converted into stars, particularly in primordial environments. We develop a shell model including Ly$\alpha$ feedback, and vali

Şahin Koçak

The attractors of iterated function systems are usually obtained as the Hausdorff limit of any non-empty compact subset under iteration. In this note we show that an iterated function system on a boundedly compact metric space has compact, invariant subspaces so that the attractor of the IFS can also be expressed as the intersection of a sequence of decreasi

Quentin Labriet, Lucia Morey, Luc Vinet

An algebraic interpretation of matrix-valued orthogonal polynomials (MVOPs) is provided. The construction is based on representations of a ($q$-deformed) Lie algebra $\mathfrak{g}$ into the algebra $\operatorname{End}_{M_n(\mathbb{C})}(M)$ of $M_n(\mathbb{C})$-linear maps over a $M_n(\mathbb{C})$-module $M$. Cases corresponding to the Lie algebras $\mathfrak

Peilin Tan, Chuanqi Shi, Dian Tu, Liang Xie

Stock trend prediction is crucial for profitable trading strategies and portfolio management yet remains challenging due to market volatility, complex temporal dynamics and multifaceted inter-stock relationships. Existing methods struggle to effectively capture temporal dependencies and dynamic inter-stock interactions, often neglecting cross-sectional marke

Negar Foroutan, Paul Teiletche, Ayush Kumar Tarun, Antoine Bosselut

The impact of different multilingual data mixtures in pretraining large language models (LLMs) has been a topic of ongoing debate, often raising concerns about potential trade-offs between language coverage and model performance (i.e., the curse of multilinguality). In this work, we investigate these assumptions by training 1.1B and 3B parameter LLMs on dive

Brandon Massett, Walter Shmayda

Tritium plays a critical role in nuclear fusion power plant designs and dryer beds are an essential tool for managing tritiated water vapor. A series of tests were performed to investigate the ability of a saturated dryer to preferentially adsorb heavy water vapor. The design of passive tritiated control systems is feasible by utilizing a dryer's ability to

Atsuhiro Yaginuma, Aster G. Taylor, Darryl Z. Seligman

We investigate the thermal evolution of 3I/ATLAS, the third macroscopic interstellar object discovered on 2025 July 1. By comparing modeled thermal profiles with observations of volatile activity, it is possible to constrain bulk physical properties of a cometary nucleus. 3I/ATLAS is actively producing a variety of cometary volatiles. In this paper, we calcu

Jolanta Śliwa

In recent years, the pen and paper RPG market has experienced significant growth. As a result, companies are increasingly exploring the integration of AI technologies to enhance player experience and gain a competitive edge. One of the key challenges faced by publishers is designing new opponents and estimating their challenge level. Currently, there are no

Anton Varlukhin, Mackenzie Smith, Fahad Alam, Amandeep Tagger

Purpose: To develop a virtual reality simulator for high dose rate prostate brachytherapy and to test whether participation is associated with immediate gains in self-reported confidence across predefined procedural domains in two cohorts. Methods: Two modules were developed and implemented using Unreal Engine: patient preparation and template guided needle

Ann Huang, Mitchell Ostrow, Satpreet H. Singh, Leo Kozachkov

In control problems and basic scientific modeling, it is important to compare observations with dynamical simulations. For example, comparing two neural systems can shed light on the nature of emergent computations in the brain and deep neural networks. Recently, Ostrow et al. (2023) introduced Dynamical Similarity Analysis (DSA), a method to measure the sim

Bernd Ulmann

The Achilles heel of classic analog computers was the complex, error prone, and time consuming process of programming. This typically involved manually patching hundreds or even thousands of connections between individual computing elements as well as setting many precision 10-turn potentiometers manually, often taking hours, or even days. Albeit being simpl

André V. Duarte, Xuying li, Bin Zeng, Arlindo L. Oliveira

If we cannot inspect the training data of a large language model (LLM), how can we ever know what it has seen? We believe the most compelling evidence arises when the model itself freely reproduces the target content. As such, we propose RECAP, an agentic pipeline designed to elicit and verify memorized training data from LLM outputs. At the heart of RECAP i

Sarah K. Wyse, Eric Foxall, Rebecca C. Tyson

It is well-established that human activity is driving extreme weather patterns, and that these extreme events influence human behaviour. However, few models allow for human behaviours and the climate to dynamically interact. The models presented in this paper expand on previous work and serve as an initial framework to extend current models by using a dynami

Juha Tiihonen, David Trejo-Garcia, Tapio T. Rantala, Marco Ornigotti

We present a path-integral Monte Carlo estimator for calculating the dipole polarizability of interacting Coulomb plasma in the long-wavelength limit, i.e., the optical region. We present comprehensive details and method validation studies for our approach based on both collective and one-particle dipole autocorrelation functions in the imaginary time. The s

Markus Reineke

Punctual noncommutative Hilbert schemes are projective varieties parametrizing finite codimensional left ideals in noncommutative formal power series rings. We determine their motives and intersection cohomology, by constructing affine pavings and small resolutions of singularities.

Robert A. Bridges, Thomas R. Mitchell, Mauricio Muñoz, Ted Henriksson

The advent of Large Language Models (LLMs) promised to resolve the long-standing paradox in honeypot design: achieving high-fidelity deception with low operational risk. Since late 2022, a flurry of research has demonstrated steady progress from ideation to prototype implementation. While promising, evaluations show only incremental progress in real-world de

Shakib Khan, A. Ben Hamza, Amr Youssef

Adversarial attacks present a significant risk to the integrity and performance of graph neural networks, particularly in tasks where graph structure and node features are vulnerable to manipulation. In this paper, we present a novel model, called fixed-point iterative graph convolutional network (Fix-GCN), which achieves robustness against adversarial pertu

Oliver Carmona, Peter Ludwig Rodríguez-Kessler, Sebastián Salazar-Colores, Alvaro Muñoz-Castro

We present an adaptive and parallel implementation of the Basin Hopping (BH) algorithm for the global optimization of atomic clusters interacting via the Lennard-Jones (LJ) potential. The method integrates local energy minimization with adaptive step-size Monte Carlo moves and simultaneous evaluation of multiple trial structures, enabling efficient explorati

Antía Dorado, Iván Folgueira, Sofía Martín, Gonzalo Martín

CodeSight is an end-to-end system designed to anticipate deadline compliance in software development workflows. It captures development and deployment data directly from GitHub, transforming it into process mining logs for detailed analysis. From these logs, the system generates metrics and dashboards that provide actionable insights into PR activity pattern

Jipeng Li, Yannning Shen

Graph Neural Networks (GNNs) are valuable intellectual property, yet many watermarks rely on backdoor triggers that break under common model edits and create ownership ambiguity. We present InvGNN-WM, which ties ownership to a model's implicit perception of a graph invariant, enabling trigger-free, black-box verification with negligible task impact. A lightw

Nissan Yaron, Dan Bystritsky, Ben-Etzion Yaron

We introduce Humans-Junior, a 3.8B model that matches GPT-4o on the FACTS Grounding public subset within a $\pm 5$ pp equivalence margin. Results. On Q1--Q500 under identical judges, GPT-4o scores 73.5% (95% CI 69.5--77.2) and Humans-Junior 72.7% (95% CI 68.7--76.5); the paired difference is 0.8 pp (bootstrap 95% CI $-3.1$ to $+4.7$; permutation $p = 0.72$;

Soufiane Essahli, Oussama Sarsar, Ahmed Bentajer, Anas Motii

Social platforms distribute information at unprecedented speed, which in turn accelerates the spread of misinformation and threatens public discourse. We present FakeZero, a fully client-side, cross-platform browser extension that flags unreliable posts on Facebook and X (formerly Twitter) while the user scrolls. All computation, DOM scraping, tokenization,

Brendan Creutz

Let $X$ be a closed subvariety of an abelian variety $A$ over a global function field $k$ such that the base change of $A$ to an algebraic closure does not have any positive dimensional isotrivial quotient. We prove that every adelic point on $X$ which is the limit of a sequence of $k$-rational points on $A$ is a limit of $k$-rational points on $X$. Assuming

Ziyi Wang, Carmine Ventre, Maria Polukarov

Algorithmic collusion has emerged as a central question in AI: Will the interaction between different AI agents deployed in markets lead to collusion? More generally, understanding how emergent behavior, be it a cartel or market dominance from more advanced bots, affects the market overall is an important research question. We propose a hierarchical multi-ag

Miguel Cruz, Joaquin Housset, Samuel Lepe, Joel Saavedra

Focusing on the description of cosmic evolution at late times, this study examines a generalized holographic dark energy (HDE) framework constructed via a polynomial expansion in the Hubble parameter, which includes contributions proportional to $H^{2}$, $H^{4}$, and $H^{6}$, introduced through a variable parameter within the standard holographic formula. Th

Arvin Shahbazi-Moghaddam

What happens at spacetime singularities is poorly understood. The Penrose-Wall singularity theorem constrains possible scenarios, but until recently its key assumption--the generalized second law (GSL)--had only been proven perturbatively, severely limiting this application. We highlight that recent progress enables a proof of the GSL in holographic brane-wo

Chen Wang, Xunzhuo Liu, Yue Zhu, Alaa Youssef

LLM serving systems process heterogeneous query workloads where different categories exhibit different characteristics. Code queries cluster densely in embedding space while conversational queries distribute sparsely. Content staleness varies from minutes (stock data) to months (code patterns). Query repetition patterns range from power-law (code) to uniform

Bryce Iversen

The Gregorian calendar -- first established for daily use on Friday, October 15th, 1582 by Pope Gregory XIII in Catholic countries -- is presently the most pervasive calendar in the world. As such, algorithms for performing various calendrical computations in accurate, performant, and easily implementable ways are extremely useful in fields like software eng

Kianoosh Ashouritaklimi, Tom Rainforth

Active learning has the potential to be especially useful for messy, uncurated pools where datapoints vary in relevance to the target task. However, state-of-the-art approaches to this problem currently rely on using fixed, unsupervised representations of the pool, focusing on modifying the acquisition function instead. We show that this model setup can unde

Ehsan Ghaderi, Mohamad Ali Bijarchi, Siamak Kazemzadeh Hannani, Ali Nouri Boroujerdi

In this study, the capabilities of the Physics-Informed Neural Network (PINN) method are investigated for three major tasks: modeling, simulation, and optimization in the context of the heat conduction problem. In the modeling phase, the governing equation of heat transfer by conduction is reconstructed through equation discovery using fractional-order deriv

Manuel Iglesias-Alonso, Felix Schur, Julius von Kügelgen, Jonas Peters

We consider the problem of estimating a causal effect in a multi-domain setting. The causal effect of interest is confounded by an unobserved confounder and can change between the different domains. We assume that we have access to a proxy of the hidden confounder and that all variables are discrete or categorical. We propose methodology to estimate the caus

Dmitrii E. Vavilov, Daniel Hestroffer

Robust impact monitoring of near-Earth objects is an essential task of planetary defense. Current systems such as NASA's Sentry-II, the University of Pisa's CLOMON2, and ESA's Aegis have been highly successful, but independent approaches are essential to ensure reliability and to cross-validate predictions of possible impacts. We present NEOForCE (Near-Earth

Masfiqur Rahaman, Maoyejatun Hasana, Shahad Shahriar Rahman, MD Sajid Mostafiz Noor

Cycle rickshaw pullers are highly vulnerable to extreme heat, yet little is known about how their physiological biomarkers respond under such conditions. This study collected real-time weather and physiological data using wearable sensors from 100 rickshaw pullers in Dhaka, Bangladesh. In addition, interviews with 12 pullers explored their knowledge, percept

Nikola L. Kolev, Tommaso Rodani, Neil J. Curson, Taylor J. Z. Stock

Scanning tunnelling microscopy (STM) enables atomic-resolution imaging and atom manipulation, but its utility is often limited by tip degradation and slow serial data acquisition. Fabrication adds another layer of complexity since the tip is often subjected to large voltages, which may alter the shape of its apex, requiring it to be conditioned. Here, we pro

Flavio Giuliani, Francesco Guidarelli Mattioli, Yuhan Chen, Daniele Dragoni

Phase-change materials (PCMs) based on group IV, V, and VI elements, such as Ge, Sb, and Te, exhibit distinctive liquid-state features, including thermodynamic anomalies and unusual dynamical properties, which are believed to play a key role in their fast and reversible crystallization behavior. Antimony (Sb), a monoatomic PCM with ultrafast switching capabi

Shivanshu Dwivedi, Kalum Palandage

Arrays of semiconductor quantum dots provide a powerful platform to design correlated quantum matter from the bottom up. We establish a predictive framework for engineering local electron pairing in these artificial molecules by systematically deploying three control levers: lattice geometry, orbital hybridization, and external electric fields. Using Hartree

Brandon P. Ashley, Michael T. Schultz

We study geometric aspects of horizontal 2-plane distributions on the complement of the zero section in the 5-dimensional total space of a rank-3 vector bundle equipped with connection over a surface. We show that any surface in 3-dimensional projective space can be associated to such a geometric structure in 5-dimensions, and establish a dictionary between

Mehdi Karbalayghareh, David J. Love, Christopher G. Brinton

The performance of federated learning (FL) over wireless networks critically depends on accurate and timely channel state information (CSI) across distributed devices. This requirement is tightly linked to how rapidly the channel gains vary, i.e., the coherence intervals. In practice, edge devices often exhibit unequal coherence times due to differences in m

Henrik Kaiser

We study the recovery of the distribution function $F_X$ of a random variable $X$ that is subject to an independent additive random error $\varepsilon$. To be precise, it is assumed that the target variable $X$ is available only in the form of a blurred surrogate $Y = X + \varepsilon$. The distribution function $F_Y$ then corresponds to the convolution of $F