October 2025 arXiv papers — page 2

Pouya M. Ghari, Simone Sciabola, Ye Wang

Fine-tuning foundation models has emerged as a powerful approach for generating objects with specific desired properties. Reinforcement learning (RL) provides an effective framework for this purpose, enabling models to generate outputs that maximize a given reward function. However, in many applications such as text generation and drug discovery, it can be s

Pierre Bertrand, Wolfgang Stummer

For some smooth special case of generalized $\varphi-$divergences as well as of new divergences (called scaled shift divergences), we derive approximations of the omnipresent (weighted) $\ell_{1}-$distance and (weighted) $\ell_{1}-$norm.

Rajatsubhra Chakraborty, Ana Espinosa-Momox, Riley Haskin, Depeng Xu

Cell segmentation in single-shot quantitative phase microscopy (ssQPM) faces challenges from traditional thresholding methods that are sensitive to noise and cell density, while deep learning approaches using simple channel concatenation fail to exploit the complementary nature of polarized intensity images and phase maps. We introduce DM-QPMNet, a dual-enco

S. Quintero, R. Henao, J. P. Valencia

The open-source Python package, su4-branching, is introduced for the derivation of comprehensive spin S and isospin T branching rules for any SU(4) irreducible representation.The Wigner supermultiplet scheme in nuclear and hadronic physics is based on SU(4) symmetry. However, the community does not have easy access to practical calculations of branching rule

Filippo Radicchi, Filipi N. Silva, Alessandro Flammini, Santo Fortunato

We study the necessary condition to detect, by means of spectral modularity optimization, the ground-truth partition in networks generated according to the weighted planted-partition model with two equally sized communities. We analytically derive a general expression for the maximum level of mixing tolerated by the algorithm to retrieve community structure,

Vivek Devulapalli, Fedor F. Klimashin, Manuel Bärtschi, Stephan Waldner

Interfaces govern the unique mechanical response of amorphous multilayers. Here, we examine nanoindentation hardness and deformation behaviour of amorphous-amorphous Ta$_2$O$_5$/SiO$_2$ nanolaminates with bilayer thicknesses ranging from 2 nm to 334 nm. Whilst monolithic SiO$_2$ exhibits catastrophic failure through a single dominant shear band, multilayer a

Hoda E. Elgendy, Mashhoor A. Al-Wardat, Hassan B. Haboubi, Lin R. Benchi

This study utilizes "Al-Wardat's method for analysing binary and multiple stellar systems" to estimate a set of parameters for the triple system Hip70868. The method compares the system's observational magnitudes, color indices, and spectral energy distribution (SED) and synthetic SEDs generated through atmospheric modeling of each component. Feedback-adjust

Wenxuan Zhang, Peng Hu

The increasing adoption of satellite Internet with low-Earth-orbit (LEO) satellites in mega-constellations allows ubiquitous connectivity to rural and remote areas. However, weather events have a significant impact on the performance and reliability of satellite Internet. Adverse weather events such as snow and rain can disturb the performance and operations

Emilio Ancillotti, Loreto Pescosolido, Andrea Passarella

We consider a hybrid LiFi/WiFi network consisting of commercially available equipment, for mobile scenarios, where WiFi backs up communications, through vertical handovers, in case of insufficient LiFi QoS. When QoS requirements in terms of goodput are defined, tools are needed to anticipate the vertical handover relative to what is possible with standard ba

Yiquan Wang, Yahui Ma, Yuhan Chang, Jiayao Yan

Diffusion models have emerged as a leading framework in generative modeling, poised to transform the traditionally slow and costly process of drug discovery. This review provides a systematic comparison of their application in designing two principal therapeutic modalities: small molecules and therapeutic peptides. We dissect how the unified framework of ite

Enzo Ferreira Tomaz Silva, Pedro Henrique Silva Coutinho, Tiago Roux Oliveira, Miroslav Krstić

This paper addresses the multivariable gradient-based extremum seeking control (ESC) subject to saturation. Two distinct saturation scenarios are investigated here: saturation acting on the input of the function to be optimized, which is addressed using an anti-windup compensation strategy, and saturation affecting the gradient estimate. In both cases, the u

Di Hu, Xi Lu, Yunan Chen, Michelle Keller

De-identified health data are frequently used in research. As AI advances heighten the risk of re-identification, it is important to respond to concerns about transparency, data privacy, and patient preferences. However, few practical and user-friendly solutions exist. We developed iAGREE, a patient-centered electronic consent management portal that allows p

Yuhan Deng, Akshay Srivatsan, Sebastian Ingino, Francis Chua

We describe a system for serverless computing where users, programs, and the underlying platform share a common representation of a computation: a deterministic procedure, run in an environment of well-specified data or the outputs of other computations. This representation externalizes I/O: data movement over the network is performed exclusively by the plat

Haoming Yan, Xinyu Chen, Yanran Wang, Zhengchao Luo

The discovery of effective molecular modulators is essential for advancing perovskite solar cells (PSCs), but the research process is hindered by the vastness of chemical space and the time-consuming and expensive trial-and-error experimental screening. Concurrently, machine learning (ML) offers significant potential for accelerating materials discovery. How

David Lüdke, Tom Wollschläger, Paul Ungermann, Stephan Günnemann

We introduce a novel framework that transforms the resource-intensive (adversarial) prompt optimization problem into an \emph{efficient, amortized inference task}. Our core insight is that pretrained, non-autoregressive generative LLMs, such as Diffusion LLMs, which model the joint distribution over prompt-response pairs, can serve as powerful surrogates for

Jacqueline Mitchell, Yasser Shaaban

``Vibe coding'' -- the practice of developing software through iteratively conversing with a large language model (LLM) -- has exploded in popularity within the last year. However, developers report key limitations including the accumulation of technical debt, security issues, and code churn to achieve satisfactory results. We argue that these pitfalls resul

Zhiyu Pei, Shuailing Ma, Maja Szymczak, Lukasz Marciniak

Series of the Mn2+-doped Mg2Y8(SiO4)6O2 phosphors were synthesized. Upon excitation at 408 nm, these phosphors exhibited intense orange emission originating from Mn2+, with concentration quenching observed beyond x = 0.07, and they also demonstrated excellent thermal stability. For optical thermometry, two independent parameters, emission band centroid ({\la

Lavinia Paiella, Manuel Arca Sedda, Benedetta Mestichelli, Cristiano Ugolini

The observational dearth of black holes (BHs) with masses between $\sim$100 and 100,000 $M_\odot$ raises questions about the nature of intermediate-mass black holes (IMBHs). Proposed formation channels for IMBHs include runaway stellar collisions and repeated binary BH (BBH) mergers driven by dynamical interactions in stellar clusters, but the formation effi

Jorick S. Vink, Gautham N. Sabhahit, Ethan R. J. Winch

At the end of their lives the most massive stars collapse into black holes (BHs). The detection of an 85 $M_{\odot}$ BH from GW 190521 appeared to challenge the upper-mass limit imposed by pair-instability (PI). Using systematic MESA calculations with new mass-loss implementations, we show that 100 $M_{\odot}$ stars at metallicities below 0.1 $Z_{\odot}$ can

Chun-Hao Yang, Bo-Han Feng, Tzu-Yuan Lai, Yan Yu Chen

Optimizing training performance in large language models (LLMs) remains an essential challenge, particularly in improving model performance while maintaining computational costs. This work challenges the conventional approach of training LLMs using next-token prediction (NTP), arguing that by predicting information-rich tokens during training, there is a mor

Oorja Majgaonkar, Zhiwei Fei, Xiang Li, Federica Sarro

The increasing deployment of Large Language Model (LLM) agents for complex software engineering tasks has created a need to understand their problem-solving behaviours beyond simple success metrics. While these agents demonstrate impressive capabilities in automated issue resolution, their decision-making processes remain largely opaque. This paper presents

Leonardo Alberro, Noura Limam, Raouf Boutaba

Next-generation services demand stringent Quality of Service (QoS) guarantees, such as per-flow bandwidth assurance, ultra-low latency, and traffic prioritization, posing significant challenges to 5G and beyond networks. As 5G network functions increasingly migrate to edge and central clouds, the transport layer becomes a critical enabler of end-to-end QoS c

Shengqian Wang, Israt Jahan Jui, Julie Thorpe

Crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) are important tools for researchers seeking to conduct studies with a broad, global participant base. Despite their popularity and demonstrated utility, we present evidence that suggests the integrity of data collected through Amazon MTurk is being threatened by the presence of puppeteers, appare

Jovial Cheukam Ngouonou, Ramiz Gindullin, Claude-Guy Quimper, Nicolas Beldiceanu

We present an improved incremental selection algorithm of the selection algorithm presented in [1] and prove all the selected conjectures.

Faranak Akbarifar, Nooshin Maghsoodi, Sean P Dukelow, Stephen Scott

Purpose: Visually Guided Reaching (VGR) on the Kinarm robot yields sensitive kinematic biomarkers but requires 40-64 reaches, imposing time and fatigue burdens. We evaluate whether time-series foundation models can replace unrecorded trials from an early subset of reaches while preserving the reliability of standard Kinarm parameters. Methods: We analyzed VG

Ali Satvaty, Suzan Verberne, Fatih Turkmen

Membership inference attacks (MIA) aim to infer whether a particular data point is part of the training dataset of a model. In this paper, we propose a new task in the context of LLM privacy: entity-level discovery of membership risk focused on sensitive information (PII, credit card numbers, etc). Existing methods for MIA can detect the presence of entire p

Ziliang Chen, Xin Huang, Quanlong Guan, Liang Lin

The vision community is undergoing the unprecedented progress with the emergence of Vision-Language Pretraining Models (VLMs). Prompt learning plays as the holy grail of accessing VLMs since it enables their fast adaptation to downstream tasks with limited resources. Whereas existing researches milling around single-prompt paradigms, rarely investigate the t

Andrea Macrì, Sebastian Jaimungal, Fabrizio Lillo

Reinforcement Learning (RL) applied to financial problems has been the subject of a lively area of research. The use of RL for optimal trading strategies that exploit latent information in the market is, to the best of our knowledge, not widely tackled. In this paper we study an optimal trading problem, where a trading signal follows an Ornstein-Uhlenbeck pr

Mahipal Gurram

This paper develops a generalized cotangent-type series, extending classical expansions to higher-order lattice sums. By introducing a new family of series indexed by integer powers, we derive closed form representations that combine trigonometric and hyperbolic structures, uncover recursive patterns, and establish integral connections to generalized Jacobi

Matteo De Berardinis, Silvio Ghilardi

We show that the category of finite $\textit{S5}$-algebras (dual to finite reflexive, symmetric and transitive Kripke frames) classifies the essentially algebraic theory whose models are Kan extensions of faithful actions of the finite symmetric groups.

Jan M. Armengol, Vicente Masip, Ada Barrantes, Gabriel M. Beltrami

With the rise of intelligent Internet of Things (IoT) systems in urban environments, new opportunities are emerging to enhance real-time environmental monitoring. While most studies focus either on IoT-based air quality sensing or physics-based modeling in isolation, this work bridges that gap by integrating low-cost sensors and AI-powered video-based traffi

David Farr, Lynnette Hui Xian Ng, Stephen Prochaska, Iain J. Cruickshank

Disinformation campaigns can distort public perception and destabilize institutions. Understanding how different populations respond to information is crucial for designing effective interventions, yet real-world experimentation is impractical and ethically challenging. To address this, we develop an agent-based simulation using Large Language Models (LLMs)

Frank Brückerhoff-Plückelmann, Jelle Dijkstra, Julian Büchel, Bottyan Batkai

Photonic processors use optical signals for computation, leveraging the high bandwidth and low loss of optical links. While many approaches have been proposed, including in memory photonic circuits, most efforts have focused on the physical design of photonic components rather than full architectural integration with electronic peripheral circuitry. In this

Roberto Morales

This article establishes a rigorous spectral framework for the mathematical analysis of SHAP values. We show that any predictive model defined on a discrete or multi-valued input space admits a generalized Fourier expansion with respect to an orthonormalisation tensor-product basis constructed under a product probability measure. Within this setting, each SH

Sami Davies, Venkatesan Guruswami, Xuandi Ren

We study bicriteria versions of Makespan Minimization on Unrelated Machines and Santa Claus by allowing a constrained number of rejections. Given an instance of Makespan Minimization on Unrelated Machines where the optimal makespan for scheduling $n$ jobs on $m$ unrelated machines is $T$, (Feige and Vondr\'ak, 2006) gave an algorithm that schedules a $(1-1/e

Gerard Czajkowski

We show how to calculate the linear and nonlinear optical functions of CdSe nanoplatelets, taking into account the effect of a dielectric confinement on excitonic states. We consider both stationary and non-stationary excitation regime. We obtain obtain analytical expressions for the absorption coefficient, the exciton resonance energy and binding energy of

Shaghayegh Fazliani, Madeleine Udell

Current LLM-driven approaches using test-time computing to generate PDE solvers execute a large number of solver samples to identify high-accuracy solvers. These paradigms are especially costly for complex PDEs requiring substantial computational resources for numerical evaluation. We introduce PDE-SHARP, a framework to reduce computational costs by replacin

Kélian Sommer, Bertrand Plez, Johann Cohen-Tanugi, Marc Betoule

Ground-based surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time require photometric calibration that is both long-term stable and spatially uniform at the sub-per cent level, even during non-photometric conditions. Achieving this precision motivates new approaches to characterize atmospheric transmission, particularly to mitigate

Mengfei Liang, Yiting Qu, Yukun Jiang, Michael Backes

The rapid evolution of AI-generated images poses growing challenges to information integrity and media authenticity. Existing detection approaches face limitations in robustness, interpretability, and generalization across diverse generative models, particularly when relying on a single source of visual evidence. We introduce AIFo (Agent-based Image Forensic

Nicky Pochinkov, Yulia Volkova, Anna Vasileva, Sai V R Chereddy

Interpretability studies in language models often investigate forward-looking representations of activations. However, as language models become capable of doing ever longer time horizon tasks, methods for understanding activations often remain limited to testing specific concepts or tokens. We develop a framework of Residual Stream Decoders as a method of p

Xiang Li, Till Jahnke, Rebecca Boll, Jiaqi Han

Capturing the structural changes that molecules undergo during chemical reactions in real space and time is a long-standing dream and an essential prerequisite for understanding and ultimately controlling femtochemistry. A key approach to tackle this challenging task is Coulomb explosion imaging, which benefited decisively from recently emerging high-repetit

Rodrigo Avalos, Albachiara Cogo, Andoni Royo Abrego

It is well known in Riemannian geometry that the metric components have the best regularity in harmonic coordinates. These can be used to characterize the most regular element in the isometry class of a rough Riemannian metric. In this work, we study the conformal analogue problem on closed 3-manifolds: given a Riemannian metric $g$ of class $W^{2,q}$ with $

Hiba Ahsan, Byron C. Wallace

LLMs are increasingly being used in healthcare. This promises to free physicians from drudgery, enabling better care to be delivered at scale. But the use of LLMs in this space also brings risks; for example, such models may worsen existing biases. How can we spot when LLMs are (spuriously) relying on patient race to inform predictions? In this work we asses

Johannes Ruf, Ian Waudby-Smith

We derive concentration inequalities for sums of independent and identically distributed random variables that yield non-asymptotic generalizations of several strong laws of large numbers including some of those due to Kolmogorov [1930], Marcinkiewicz and Zygmund [1937], Chung [1951], Baum and Katz [1965], Ruf, Larsson, Koolen, and Ramdas [2023], and Waudby-

R. S. Marjoribanks, L. Zhao, F. W. Budnik, G. Kulcsar

Detailed spectra of harmonics produced from ultra-intense, sub-picosecond, high-contrast laser pulses incident on solid targets have shown the first observation of regular red- and blue-shifted satellites. Their frequency shift is slightly less than the frequency of a nominal, pure ion-plasma wave associated with electron critical density, where an ion-acous

Andrew Mackenzie

An agent observes a clue, and an analyst observes an inference: a ranking of events on the basis of how corroborated they are by the clue. We prove that if the inference satisfies the axioms of Villegas (1964) except for the classic qualitative probability axiom of monotonicity, then it has a unique normalized signed measure representation (Theorem 1). Moreo

Maryam Bibi

Rare $B$-meson decays, suppressed in the Standard Model (SM) by the GIM mechanism, are sensitive probes of new physics. In particular, loop-level $b \to s$ transitions can reveal effects of heavy virtual particles such as vector-like quarks (VLQs). VLQs, which do not require electroweak symmetry breaking for mass generation, can mix with SM quarks and induce

Rahul Ghosh, Baishali Chaudhury, Hari Prasanna Das, Meghana Ashok

Visual compliance verification is a critical yet underexplored problem in computer vision, especially in domains such as media, entertainment, and advertising where content must adhere to complex and evolving policy rules. Existing methods often rely on task-specific deep learning models trained on manually labeled datasets, which are costly to build and lim

Rye Howard-stone, Ion Mandoiu

Summary: We present AmpliconHunter2 (AHv2), a highly scalable in silico PCR engine written in C that can handle degenerate primers and uses a highly accurate melting temperature model. AHv2 implements a bit-mask IUPAC matcher with AVX2 SIMD acceleration, supports user-specified mismatches and 3' clamp constraints, calls amplicons in all four primer pair orie

Georgios C. Chasparis

Reinforcement-based learning has attracted considerable attention both in modeling human behavior as well as in engineering, for designing measurement- or payoff-based optimization schemes. Such learning schemes exhibit several advantages, especially in relation to filtering out noisy observations. However, they may exhibit several limitations when applied i

Stephen Doty, Anthony Giaquinto, Stuart Martin

For $q$ generic, Jimbo showed that $q$-tensor space $V_q^{\otimes r}$ (where $V_q$ is the $n$-dimensional vector representation) satisfies Schur--Weyl duality with respect to the commuting actions of the quantized enveloping algebra $\mathbf{U}_q(\mathfrak{gl}_n)$ and the Iwahori--Hecke algebra $\mathbf{H}_q(\mathfrak{S}_r)$, with the latter action derived f

Jinling Zhou, Xin Liu, Jiawang Nie, Xindong Tang

This paper studies how to compute global minimizers of the cubic-quartic regularization (CQR) problem \[ \min_{s \in \mathbb{R}^n} \quad f_0+g^Ts+\frac{1}{2}s^THs+\fracβ{6}\| s \|^3+ \fracσ{4} \| s\|^4, \] where $f_0$ is a constant, $g$ is an $n$-dimensional vector, $H$ is an $n$-by-$n$ symmetric matrix, and $\| s \|$ denotes the Euclidean norm of $s$. The p

Paul Honore Takam, Nathalie Fruiba

In this paper, we consider a domestic standalone microgrid equipped with local renewable energy generation such as photovoltaic panels, consumption units, and battery storage to balance supply and demand and investigate the stochastic optimal control problem for its cost-optimal management. As a special feature, the manager does not have access to the power

Shiman Zhang, Jinghan Zhou, Zhoufan Yu, Ningai Leng

To improve decision-making and planning efficiency in back-end centralized redundant supply chains, this paper proposes a decision model integrating deep learning with intelligent particle swarm optimization. A distributed node deployment model and optimal planning path are constructed for the supply chain network. Deep learning such as convolutional neural

Nikolai Husung

Finding a complete and yet minimal on-shell basis of operators of a given mass-dimension that are compatible with a specific set of transformation properties is the first step in any Effective Field Theory description. This step is the main bottleneck for systematic studies of leading logarithmic corrections to integer-power lattice artifacts in Symanzik Eff

Theophile Jegou du Laz, Michael W. Coughlin, Peter Bachant, Jacob E. Simones

With the arrival of ever higher throughput wide-field surveys and a multitude of multi-messenger and multi-wavelength instruments to complement them, software capable of harnessing these associated data streams is urgently required. To meet these needs, a number of community supported alert brokers have been built, currently focused on processing of Zwicky T

Stefan Goessner

In this follow-up article to Symplectification of Circular Arcs and Arc Splines, biarc geometry is examined from a purely geometric point of view. Two given points together with their associated tangent vectors in the plane are sufficient to define two directed, consecutive circular arcs. However, there remains one degree of freedom to determine the join poi

Michael D. Moffitt

The Abstraction and Reasoning Corpus remains one of the most compelling and challenging benchmarks for tracking progress toward achieving Artificial General Intelligence. In contrast to other evaluation datasets designed to assess an agent's task-specific skills or accumulated knowledge, the ARC-AGI suite is specifically targeted at measuring skill acquisiti

Yubo Su, Melaine Saillenfest

Context: The Solar System giant planets harbour a wide variety of moons. Moons around exoplanets are plausibly similarly abundant, even though most of them are likely too small to be easily detectable with modern instruments. Moons are known to affect the long-term dynamics of the spin of their host planets; however, their influence on warm exoplanets (i.e.\

Katherine A. Rosenfeld, Cliff C. Kerr, Jessica Lundin

Modern software programs are built on stacks that are often undergoing changes that introduce updates and improvements, but may also break any project that depends upon them. In this paper we explore the use of Large Language Models (LLMs) for code migration, specifically the problem of maintaining compatibility with a dependency as it undergoes major and mi

Kemal Oksuz, Alexandru Buburuzan, Anthony Knittel, Yuhan Yao

The emergence of multi-modal foundation models has markedly transformed the technology for autonomous driving, shifting away from conventional and mostly hand-crafted design choices towards unified, foundation-model-based approaches, capable of directly inferring motion trajectories from raw sensory inputs. This new class of methods can also incorporate natu

Miguel Escudero, Clara Garcia-Perez, Maksym Ovchynnikov

We provide a fresh look at the cosmological constraints on axion-like particles (ALPs) that couple predominantly to photons, focusing on lifetimes $\tau_{a} \lesssim 10^{4}\, {\rm s}$ and masses $m_a\lesssim 10\,{\rm GeV}$. We consider Big Bang Nucleosynthesis (BBN) and Cosmic Microwave Background (CMB) bounds and explore how these limits depend upon the unk

Jiachen Yu, Haotan Han, Kristina G. Wolinski, Ruihua Fan

Many topological phases host gapless boundary modes that can be dramatically modified by electronic interactions. Even for the long-studied edge modes of quantum Hall phases, forming at the boundaries of two-dimensional (2D) electron systems, the nature of such interaction-induced changes has been elusive. Despite advances made using local probes, key experi

Sascha Desmettre, Devika Khurana, Amira Meddah

The first-passage time is a key concept in stochastic modeling, representing the time at which a process first reaches a specified threshold. In this work, we consider a jump-diffusion (JD) model with a time-dependent threshold, providing a more flexible framework for describing stochastic dynamics. We are interested in the Exact simulation method developed

Justin Yu, Yide Shentu, Di Wu, Pieter Abbeel

Imitation learning from human demonstrations offers a promising approach for robot skill acquisition, but egocentric human data introduces fundamental challenges due to the embodiment gap. During manipulation, humans actively coordinate head and hand movements, continuously reposition their viewpoint and use pre-action visual fixation search strategies to lo

Sadra Jazayeri, Xi Tong, Yuhang Zhu

Motivated by cosmological observations, we push the cosmological bootstrap program beyond the de Sitter invariance lamppost by considering correlators that explicitly break scale invariance, thereby exhibiting primordial features. For exchange processes involving heavy fields with time-dependent masses and sound speeds, we demonstrate that locality in the bu

Snehalata Sahu, Pier-Emmanuel Tremblay, Detlev Koester, Mairi W. O'Brien

Gaia observations have reignited interest in the optical and ultraviolet (UV) opacity problems of cool white dwarfs ($T_{\rm eff} \leq 6000$ K), which were thought to be resolved nearly two decades ago through the inclusion of Lyman $\alpha$ red wing opacity arising from H-H$_2$ collisions in atmospheric models. Recent studies have revealed that their masses

Christopher L. Baldwin

Adiabatic reverse annealing (ARA) has been proposed as an improvement to conventional quantum annealing for solving optimization problems, in which one takes advantage of an initial guess at the solution to suppress problematic phase transitions. Here we interpret the performance of ARA through its effects on the free energy landscape, and use the intuition

Hannah Bossi, Yi Chen, Yu-Chen Chen, Max Jaarsma

Quantum Chromodynamics (QCD) is a remarkably rich theory exhibiting numerous emergent degrees of freedom, from flux tubes to hadrons. Their description in terms of the underlying quarks and gluons of the QCD Lagrangian remains a central challenge of modern physics. Colliders offer a unique opportunity to probe these phenomena experimentally: high energy part

Yash Gondhalekar, Richard M. Feder, Matthew J. Graham, Ajit K. Kembhavi

Ground-based astronomical observations will continue to produce resolution-limited images due to atmospheric seeing. Deconvolution reverses such effects and thus can benefit extracted science in multifaceted ways. We apply the Scaled Gradient Projection (SGP) algorithm for the single-band deconvolution of several observed images from the Zwicky Transient Fac

L. Eyer, P. Huijse, N. Chornay, J. De Ridder

The Gaia mission has observed over 2 billion stars repeatedly across the entire sky over 10 years, revealing the many astronomical objects that vary on human timescales from seconds to years. Its repeated astrometric, photometric, spectrophotometric and spectroscopic measurements create an unprecedented dataset to probe the variable celestial sources down to

Michael J Landry, Mingda Li

Symmetry is central to how we classify phases of matter: solids break spatial translations, superfluids break particle-number conservation, and superconductors "break" gauge symmetry. Mixed anomalies involving higher-form symmetries, however, present a generalization of spontaneous symmetry breaking that admits a wider and more versatile set of possibilities

Abhishek Banerjee, Reza Ebadi, Surjeet Rajendran

We introduce a Rydberg-based single photon detector (SPD) for probing dark matter in the 0.1-10 meV mass range (20 GHz-2 THz). The Rydberg SPD absorbs photons produced and focused by the BREAD dish antenna and trades them for free, detectable electrons. At the lower end of the mass range, photons drive Rydberg-Rydberg transitions, which are read out via stat

Alex S. Arvanitakis

I define topological twists of supersymmetric field theories in the case when the supercharges involved obey an ``open'' algebra. Using the Batalin-Vilkovisky field-antifield formalism, I construct twisted theories algorithmically from the supersymmetry data, and explain supersymmetric localisation in terms of anticanonical transformations. I also treat equi

Soumitra Kundu, Gargi Panda, Saumik Bhattacharya, Aurobinda Routray

Non-contact electrocardiogram (ECG) reconstruction from radar signals offers a promising approach for unobtrusive cardiac monitoring. We present LifWavNet, a lifting wavelet network based on a multi-resolution analysis and synthesis (MRAS) model for radar-to-ECG reconstruction. Unlike prior models that use fixed wavelet approaches, LifWavNet employs learnabl

Jorinde van de Vis, Philipp Schicho, Lauri Niemi, Benoit Laurent

We examine theoretical uncertainties in state-of-the-art calculations of the bubble wall velocity during first-order cosmological phase transitions. By utilising the software WallGo for two extensions of the Standard Model, we find several $O(1)$ uncertainties arising from the number of particles taken out of equilibrium, the logarithmically and power enhanc

Wen-Yuan Ai, Sebastian A. R. Ellis, Josef Pradler

Gravitational waves are thought to propagate unattenuated through matter due to a cancellation between graviton absorption and stimulated emission inferred from leading-order soft-graviton arguments. We revisit this reasoning and show that it fails for the converse problem: the effect of a gravitational-wave background on matter. For unstable particles, real

Anton Molnar, Cosmin Pohoata, Michael Zheng, Daniel G. Zhu

We show that the Kneser graph of triangulations of a convex $n$-gon has chromatic number $n-2$.

Chenze Shao, Darren Li, Fandong Meng, Jie Zhou

The efficiency of large language models (LLMs) is fundamentally limited by their sequential, token-by-token generation process. We argue that overcoming this bottleneck requires a new design axis for LLM scaling: increasing the semantic bandwidth of each generative step. To this end, we introduce Continuous Autoregressive Language Models (CALM), a paradigm s

Karol Horodecki, Chirag Srivastava, Leonard Sikorski, Siddhartha Das

Quantum resource theories use distillation protocols to convert less resourceful states into fully resourceful ones. However, these protocols often also generate an additional, unused output-referred to as a residual. We propose a framework for the quantum residual management, in which states discarded after a resource distillation protocol are repurposed as

Seungjae Son

We quantitatively study the mixing rate of randomly shifted alternating shears on the torus. This flow was introduced by Pierrehumbert '94, and was recently shown to be exponentially mixing. In this work, we quantify the dependence of the exponential mixing rate on the flow amplitude. Our approach is based on constructing an explicit Lyapunov function and a

Hrushikesh Sable, Subrata Das, Vito W. Scarola

We demonstrate characteristics of a bosonic fractional quantum Hall (FQH) state in a one-dimensional extended Bose-Hubbard model (eBHM) with a static tilt. In the large tilt limit, quenched kinetic energy leads to emergent dipole moment conservation, enabling mapping to a model generating FQH states. Using exact diagonalization, density matrix renormalizatio

Xiangyu Fan, Zesong Qiu, Zhuguanyu Wu, Fanzhou Wang

Distribution Matching Distillation (DMD) distills score-based generative models into efficient one-step generators, without requiring a one-to-one correspondence with the sampling trajectories of their teachers. Yet, the limited capacity of one-step distilled models compromises generative diversity and degrades performance in complex generative tasks, e.g.,

Jinsu Kim, Yunhun Nam, Minseon Kim, Sangpil Kim

Recent advances in text-to-image models have increased the exposure of powerful image editing techniques as a tool, raising concerns about their potential for malicious use. An emerging line of research to address such threats focuses on implanting "protective" adversarial noise into images before their public release, so future attempts to edit them using t

Zexu Li, M. Amin Rahimian, Lei Fang

Collective foragers, from animals to robotic swarms, must balance exploration and exploitation to locate sparse resources efficiently. While social learning is known to facilitate this balance, how the range of information sharing shapes group-level outcomes remains unclear. Here, we develop a minimal collective foraging model in which individuals combine in

Paolo Antonelli, Yuri Cacchiò

In this article, we study the small dispersion limit of the Euler-Korteweg system in a domain with a smooth boundary and no-flux boundary conditions. We exploit a relative energy approach to study the convergence of finite energy weak solutions towards strong solutions to the compressible Euler system. Given the boundary conditions under consideration, our a

Sean Kelley, David De Cremer, Christoph Riedl

As AI becomes more deeply embedded in knowledge work, building assistants that support human creativity and expertise becomes more important. Yet achieving synergy in human-AI collaboration is not easy. Providing AI with detailed information about a user's demographics, psychological attributes, divergent thinking, and domain expertise may improve performanc

Danyal Maqbool, Changhee Lee, Zachary Huemann, Samuel D. Church

Generating automated reports for 3D positron emission tomography (PET) is an important and challenging task in medical imaging. PET plays a vital role in oncology, but automating report generation is difficult due to the complexity of whole-body 3D volumes, the wide range of potential clinical findings, and the limited availability of annotated datasets. To

Joyoni Dey, Hunter C. Meyer, Murtuza S. Taqi

Low-dose computed tomography (LDCT) is the current standard for lung cancer screening, yet its adoption and accessibility remain limited. Many regions lack LDCT infrastructure, and even among those screened, early-stage cancer detection often yield false positives, as shown in the National Lung Screening Trial (NLST) with a sensitivity of 93.8 percent and a

Roy Goldner, Jonathan Stern, Drummond Fielding, Claude-André Faucher-Giguère

Simulations suggest that turbulence is ubiquitous in the circumgalactic medium (CGM), though the source and properties of CGM turbulence is uncertain. Using analytic considerations and hydrodynamic simulations we study how CGM turbulence is driven by gas accretion, thus providing a baseline for additional turbulence driving processes such as galaxy feedback.

Bo Li, Duyuan Zheng, Xinyang Liu, Qingwen Li

Person re-identification (ReID) in surveillance is challenged by occlusion, viewpoint distortion, and poor image quality. Most existing methods rely on complex modules or perform well only on clear frontal images. We propose Sh-ViT (Shuffling Vision Transformer), a lightweight and robust model for occluded person ReID. Built on ViT-Base, Sh-ViT introduces th

Md Abdul Hannan, Ronghao Ni, Chi Zhang, Limin Jia

Large language models (LLMs) have demonstrated impressive capabilities across a wide range of coding tasks, including summarization, translation, completion, and code generation. Despite these advances, detecting code vulnerabilities remains a challenging problem for LLMs. In-context learning (ICL) has emerged as an effective mechanism for improving model pe

Vanessa Cruz Molina, Daniel Sanchez Guzman, Teodoro Rivera Montalvo, Ricardo Garcia-Salcedo

Physics teaching in engineering programmes poses discipline-specific demands that intertwine conceptual modelling, experimental inquiry, and computational analysis. This study examines nine teaching competences for physics instruction derived from international and regional frameworks and interpreted within engineering contexts. Nineteen university instructo

Mariam R. Alshamsi, Mashhoor A. Al-Wardat

Understanding the diurnal behavior of lee wave clouds on Mars provides critical insight into the planet's mesoscale atmospheric dynamics and their interaction with surface topography. Lee wave clouds exhibit distinct spatial and temporal patterns that vary over the Martian day. In this study, we investigate the diurnal distribution and frequency of lee wave

Caleb Ziems, William Held, Jane Yu, Amir Goldberg

To serve global users safely and productively, LLMs need culture-specific knowledge that might not be learned during pre-training. How do we find such knowledge that is (1) salient to in-group users, but (2) unknown to LLMs? The most common solutions are single-initiative: either researchers define challenging questions that users passively answer (tradition

Frank Cascarano, David Marasco

Founded in 2007, the Foothill College Physics Show has served nearly a quarter of a million attendees in the two decades that have followed. This demo show features both performances for the public and field trips for students from local Title 1 schools. The college's students play an important role, acting as both on-stage talent, leading tours of the colle

Saeed Hashemi Sababe

Motivated by practical applications, I present a novel and comprehensive framework for operator-valued positive definite kernels. This framework is applied to both operator theory and stochastic processes. The first application focuses on various dilation constructions within operator theory, while the second pertains to broad classes of stochastic processes

Wei Zhang, Zekun Guo, Yingce Xia, Peiran Jin

Structure-based drug design (SBDD), which maps target proteins to candidate molecular ligands, is a fundamental task in drug discovery. Effectively aligning protein structural representations with molecular representations, and ensuring alignment between generated drugs and their pharmacological properties, remains a critical challenge. To address these chal

Xiuzhen Ye, Wentao Tang

This work presents a nonparametric framework for dissipativity learning in reproducing kernel Hilbert spaces, which enables data-driven certification of stability and performance properties for unknown nonlinear systems without requiring an explicit dynamic model. Dissipativity is a fundamental system property that generalizes Lyapunov stability, passivity,

Zong-Gang Mou, Bipasha Chakraborty

In the Hamiltonian formulation, Quantum Field Theory calculations scale exponentially with spatial volume, making real-time simulations intractable on classical computers and motivating quantum computation approaches. In Hamiltonian quantisation, bosonic fields introduce the additional challenge of an infinite-dimensional Hilbert space. We present a scalable