October 2025 arXiv papers — page 39

Moein Kazemi, Mehdi Keshavarz, Mark E. Turiansky, John L. Lyons

Efficient single-photon emitters are desirable for quantum technologies including quantum networks and photonic quantum computers. We investigate the T centre, a telecommunications-band emitter in silicon, and find a strong isotope dependence of its excited-state lifetime. In particular, the lifetime of the deuterium T centre is over five times longer than t

Rubén Montecinos, Carla Hernández, Irma Fuentes-Morales, Fernanda Alarcón

As scientific knowledge expands, science education may not always keep pace with the latest advancements in astrophysics. A solid scientific education is crucial for preparing students for 21st-century challenges. However, science education often focuses narrowly on specific content, neglecting frontier scientific research. To address this, a teaching sequen

Hyung Chan Cho, Go-Eum Cha, Yanfu Liu, Sooyeon Jeong

While the efficacy of social robots in supporting people in learning tasks has been extensively investigated, their potential impact in assisting students in self-studying contexts has not been investigated much. This study explores how a social robot can act as a peer study companion for college students during self-study tasks by delivering task-oriented g

Qiang Li, Mojtaba Safari, Shansong Wang, Huiqiao Xie

Low-dose computed tomography (LDCT) reduces patient radiation exposure but introduces substantial noise that degrades image quality and hinders diagnostic accuracy. Existing denoising approaches often require many diffusion steps, limiting real-time applicability. We propose a Regularization-Enhanced Efficient Diffusion Probabilistic Model (RE-EDPM), a rapid

Xiaobo Liu

A novel approach is presented for computing flexible body dynamics based on conventional structural dynamics models. This approach innovatively captures the rigid body motion component embedded within a flexible body's movement, generates and synthesizes single-step responses in a sequence of reconfigured inertial frames that follow the rigid body motion. By

Filipe R. Cogo, Gustavo A. Oliva, Ahmed E. Hassan

The rapid advancement of AI-assisted software engineering has brought transformative potential to the field of software engineering, but existing tools and paradigms remain limited by cognitive overload, inefficient tool integration, and the narrow capabilities of AI copilots. In response, we propose Compiler.next, a novel search-based compiler designed to e

Madelyn Broome, Ruth Murray-Clay, John McCann, James E Owen

Throughout their lives, short period exoplanets (<100 days) experience X-ray and extreme-UV (XUV) stellar irradiation that can heat and photoionize planets' upper atmospheres, driving transonic outflows. This photoevaporative mass loss plays a role in both evolution and observed demographics; however, mass loss rates are not currently directly observable and

Segev Shlomov, Alon Oved, Sami Marreed, Ido Levy

Agents are rapidly advancing in automating digital work, but enterprises face a harder challenge: moving beyond prototypes to deployed systems that deliver measurable business value. This path is complicated by fragmented frameworks, slow development, and the absence of standardized evaluation practices. Generalist agents have emerged as a promising directio

Carmine Autieri, Amar Fakhredine

Spin-momentum locking in altermagnets has been deeply explored in the non-relativistic limit. Including spin-orbit coupling, altermagnets exhibit antisymmetric exchange interactions, leading to spin cantings. Therefore, the spin-momentum locking differs among the three spin components Sx, Sy, and Sz, forming the relativistic spin-momentum locking. We conside

Jyotika Singh, Weiyi Sun, Amit Agarwal, Viji Krishnamurthy

In modern industry systems like multi-turn chat agents, Text-to-SQL technology bridges natural language (NL) questions and database (DB) querying. The conversion of tabular DB results into NL representations (NLRs) enables the chat-based interaction. Currently, NLR generation is typically handled by large language models (LLMs), but information loss or error

Yize Cheng, Arshia Soltani Moakhar, Chenrui Fan, Parsa Hosseini

Large language model (LLM) agents are increasingly used to interact with and execute tasks in dynamic environments. However, a critical yet overlooked limitation of these agents is that they, by default, assume a stationary context, failing to account for the real-world time elapsed between messages. We refer to this as "temporal blindness". This limitation

J. Díaz, J. Rodríguez-Fernández, J. Rubio-Zuazo

Magnetic anisotropy in disordered rare-earth-transition metals (RE-TM) compounds arises from RE atoms occupying asymmetric environments within the TM lattice. However, the underlying mechanism that promotes such environments remains not fully understood. In this study, we investigate amorphous NdCo$_{4.6}$ thin films deposited by magnetron sputtering, where

Luke Marrinan, Farzad Yousefian, Uday V. Shanbhag

We consider the computation of an equilibrium of a stochastic Nash equilibrium problem, where the player objectives are assumed to be $L_0$-Lipschitz continuous and convex given rival decisions with convex and closed player-specific feasibility sets. To address this problem, we consider minimizing a suitably defined value function associated with the Nikaido

Evgeny Ukhanov

The proliferation of decentralized financial (DeFi) systems and smart contracts has underscored the critical need for software correctness. Bugs in such systems can lead to catastrophic financial losses. Formal verification offers a path to achieving mathematical certainty about software behavior. This paper presents the formal verification of the core logic

Enrico Lampetti

In this paper we give construct good moduli spaces for constructible sheaves and Stokes functors. Derived enhancement of such are also considered.

Wanting Huang, Weiran Wang

Contextual biasing improves automatic speech recognition (ASR) by integrating external knowledge, such as user-specific phrases or entities, during decoding. In this work, we use an attention-based biasing decoder to produce scores for candidate phrases based on acoustic information extracted by an ASR encoder, which can be used to filter out unlikely phrase

You-Jin Kim, Myungin Lee, Marko Peljhan, JoAnn Kuchera-Morin

Spatial Orchestra demonstrates how easy it is to play musical instruments using basic input like natural locomotion, which is accessible to most. Unlike many musical instruments, our work allows individuals of all skill levels to effortlessly create music by walking into virtual bubbles. Our Augmented Reality experience involves interacting with ever-shiftin

Joel Poncha Lemayian, Ghyslain Gagnon, Kaiwen Zhang, Pascal Giard

Cryptocurrency blockchain networks safeguard digital assets using cryptographic keys, with wallets playing a critical role in generating, storing, and managing these keys. Wallets, typically categorized as hot and cold, offer varying degrees of security and convenience. However, they are generally software-based applications running on microcontrollers. Cons

Julius Kullig, Qi Zhong, Jan Wiersig, Ramy El-Ganainy

One of the most fundamental questions in laser physics is the following: Which mode of an optical cavity will reach the lasing threshold first when gain is applied? Intuitively, the answer appears straightforward: When a particular mode is both temporally well confined (i.e., exhibits the highest quality factor) and experiences initially the largest increase

Grace Byun, Rebecca Lipschutz, Sean T. Minton, Abigail Lott

Detecting mental health crisis situations such as suicide ideation, rape, domestic violence, child abuse, and sexual harassment is a critical yet underexplored challenge for language models. When such situations arise during user--model interactions, models must reliably flag them, as failure to do so can have serious consequences. In this work, we introduce

Cameron M. Pike, Brad Oney, Gabriel Hepner, Animesh Yadav

Nonlinearities in power amplifiers adversely affect multi-carrier modulation techniques. Accurate prediction of nonlinear distortion is essential for making design trade-offs between output power and network throughput. We use the series form of the characteristic function (ch.f.) method to predict distortion spectra for sparse multi-carrier transmissions. T

Aissa Boukarou

In this paper, we investigate the Cauchy problem for the coupled generalized Korteweg-de Vries system driven by white noise. We prove local well-posedness for data in $ H^{s} \times H^{s},$ with $ s>1/2$. The key ingredients that we used in this paper are multilinear estimates in Bourgain spaces, the It\^o formula and a fixed point argument. Our result impro

Carmine Monetta, Víctor Sotomayor

In this paper we study arithmetical and structural features of a finite group that possesses exactly two conjugacy class sizes that are composite numbers.

Cameron Berg, Diogo de Lucena, Judd Rosenblatt

Large language models sometimes produce structured, first-person descriptions that explicitly reference awareness or subjective experience. To better understand this behavior, we investigate one theoretically motivated condition under which such reports arise: self-referential processing, a computational motif emphasized across major theories of consciousnes

You-Jin Kim, Andrew D. Wilson, Jennifer Jacobs, Tobias Höllerer

Reality Distortion Room (RDR) is a proof-of-concept augmented reality system using projection mapping and unencumbered interaction with the Microsoft RoomAlive system to study a user's locomotive response to visual effects that seemingly transform the physical room the user is in. This study presents five effects that augment the appearance of a physical roo

John C. Howell, Pieter H. Neethling, Tjaart P. J. Kruger

We consider the use of digital signal processing to reconstruct continuous reflectance spectra using a small finite set of randomly illuminated light emitting diodes (LEDs). We simulate the use of LEDs having identical spectral distance and Gaussian bandwidth whose illumination overlaps its nearest neighbors. An object, whose reflectance spectrum is to be de

Mohammad Ali Gorji

We introduce a higher-derivative action for dark matter whose energy-momentum tensor describes an imperfect fluid with nonzero pressure, energy flux, and anisotropic stress. In the limit where the higher-derivative couplings are switched off, the energy-momentum tensor reduces to pressureless dust. A systematic derivation follows from extending the singular

Ali Amhaz, Shreya Khisa, Mohamed Elhattab, Chadi Assi

We study a coordinated multi-point (CoMP) transmission where two base stations (BSs), each supported by a pinching antenna system (PASS), are deployed to jointly serve communication users under spatial division multiple access (SDMA) technology. Pinching Antenna technology was introduced as a promising solution to overcome the large-scale fading that has bee

Mehran Erfanifam, Mahnoush Madani, Reza Shaebani, Maniya Maleki

Understanding how granular materials respond to shear stress remains a central challenge in soft matter physics. We report direct observations of persistent granular convection in the bulk shear zones of spherical particle packings -- a phenomenon previously associated primarily with particle shape anisotropy or boundary effects. By employing various bead-co

Enrico Lampetti

We construct good moduli spaces from moduli of objects in the sense of To\"en-Vaqui\'e. As an application, we construct good moduli spaces for perverse sheaves.

Boyan Sirakov, Aelson Sobral

We show that the classical Keller-Osserman theorem on the solvability of the equation $\mathcal{L}[u] = f(u)$ is valid when $\mathcal{L}$ is a general operator in divergence form with unbounded coefficients in the natural regime of local integrability. This has been open up to now, earlier results concerned operators with locally bounded ingredients. We also

Zach Hensel

Since early 2020, several theories have suggested that a distribution of restriction endonuclease recognition sites in the SARS-CoV-2 genome indicates a synthetic origin. The most influential of these, a 2022 preprint by Bruttel et al. claimed: "The BsaI/BsmBI restriction map of SARS-CoV-2 is unlike any wild-type coronavirus, and it is unlikely to evolve fro

Evan Allen, Karim Said, Robert Calderbank, Lingjia Liu

As 6G technologies advance, international bodies and regulatory agencies are intensifying efforts to extend seamless connectivity especially for high-mobility scenarios such as Mobile Ad-Hoc Networks (\textit{MANETs}) types such as Vehicular Ad-Hoc Networks (\textit{VANETs}) and Flying Ad-Hoc Networks (\textit{FANETs}). For these environments to be considere

Jiasong Duan, Hongmei Zhang, Xianzheng Huang

We propose a Bayesian variable selection method in the framework of modal regression for heavy-tailed responses. An efficient expectation-maximization algorithm is employed to expedite parameter estimation. A test statistic is constructed to exploit the shape of the model error distribution to effectively separate informative covariates from unimportant ones

Syon Bhattacharjee, Subhra Sankar Dhar

This article studies statistical estimation of $\pi$ based on the fact that the ratio of the volumes of a $d$-dimensional hypersphere and a $d$-dimensional hypercube is a certain function of $\pi$, and the function depends on the dimension $d$. The estimation of $\pi$ is carried out for various choices of $d$ (strictly speaking, $d\in\{1, 2, \ldots, 20\}$) u

Jingwang Diao, Xingjiang Zhu

A gravitational-wave background (GWB) arising from the superposition of numerous unresolved gravitational-wave signals has yet to be detected. Potential contributing sources to such a background include compact binary coalescences (CBCs) and core-collapse supernovae (CCSNe). In this work, we place upper limits on the gravitational-wave energy emitted by CCSN

Mena Attia, Aashiq Muhamed, Mai Alkhamissi, Thamar Solorio

We present a comprehensive evaluation of the ability of large language models (LLMs) to process culturally grounded language, specifically to understand and pragmatically use figurative expressions that encode local knowledge and cultural nuance. Using figurative language as a proxy for cultural nuance and local knowledge, we design evaluation tasks for cont

Xicheng Xu, Ahmed Adel Mahmoud, Noah Gorgichuk, Ronny Thomale

Theoretical studies and experiments in the last six years have revealed the potential for novel behaviours and functionalities in device physics through the synthetic engineering of negatively-curved spaces. For instance, recent developments in hyperbolic band theory have unveiled the emergence of higher-dimensional eigenstates -- features fundamentally abse

Moises Bravo-Gaete, Fabiano F. Santos, Xiangdong Zhang

This paper aims to propose a Cardy-like formula characterized by the mass, charge, and angular components of the black hole, along with their corresponding solitonic configuration, obtained through a double Wick rotation. The expression also incorporates the dynamical exponent and effective spatial dimensionality as key elements. To validate the proposal, we

Markus J. Aschwanden

This review is focused on recent {\sl Self-Organized Criticality (SOC)} literature of astrophysical phenomena, covering the last decade of (2015-2025), while previous SOC literature (1987-2014) is reviewed elsewhere. The selection of literature is mostly based on searches with the NASA-supported {\sl Astrophysics Data System (ADS)}. The discussed astrophysic

Murad Ismayilov, Edwin Meriaux, Shuo Wen, Gregory Dudek

Coordinating multiple autonomous agents in shared environments under decentralized conditions is a long-standing challenge in robotics and artificial intelligence. This work addresses the problem of decentralized goal assignment for multi-agent path planning, where agents independently generate ranked preferences over goals based on structured representation

H0DN Collaboration, Stefano Casertano, Gagandeep Anand, Richard I. Anderson

The direct, empirical determination of the local value of the Hubble constant (H0) has markedly advanced thanks to improved instrumentation, measurement techniques, and distance estimators. However, combining determinations from different estimators is non-trivial, due to correlated calibrations and different analysis methodologies. Using covariance weightin

Zhenyu Zhang, Tianyi Chen, Weiran Xu, Alex Pentland

Long-horizon tasks requiring multi-step reasoning and dynamic re-planning remain challenging for large language models (LLMs). Sequential prompting methods are prone to context drift, loss of goal information, and recurrent failure cycles, while hierarchical prompting methods often weaken cross-level continuity or incur substantial runtime overhead. We intro

Łukasz Delong, Mario Wüthrich

Calibration of mean estimates for predictions is a crucial property in many applications, particularly in the fields of financial and actuarial decision-making. In this paper, we first review classical approaches for validating mean-calibration, and we discuss the Likelihood Ratio Test (LRT) within the Exponential Dispersion Family (EDF). Then, we investigat

Pedro Cortes dos Santos, Matheus Becali Rocha, Renato A Krohling

Industrial processes generate complex data that challenge fault detection systems, often yielding opaque or underwhelming results despite advanced machine learning techniques. This study tackles such difficulties using the Tennessee Eastman Process, a well-established benchmark known for its intricate dynamics, to develop an innovative fault detection framew

Iagoba Apellaniz, Manuel Gessner, Géza Tóth

We study magnetometry of gradients and homogeneous background fields along the three orthogonal directions using two spatially separated spin ensembles. We derive trade-off relations for the achievable estimation precision of these parameters. Dicke states, optimal for homogeneous field estimation, can be locally rotated into states sensitive to magnetic gra

Tong Bao, Gabriele Ponti, Frank Haberl, Samaresh Mondal

Across the Galactic disk lies a diverse population of X-ray sources, with the fainter end remaining poorly understood due to past survey sensitivity limits. We aim to classify and characterize faint X-ray sources detected in the eROSITA All-Sky Survey (eRASS1) towards the inner Galactic disk ($350^\circ < l < 360^\circ$, $-1^\circ < b < 1^\circ$) using deepe

Jonathan Clivio

We describe the Goresky-Hingston coproduct on the free loop space with real coefficients via the quasi-isomorphism $C_*(\Lambda M)\simeq C_*(M,C_*(\Omega M))$. This lets us describe the coproduct on the Leray-Serre spectral sequence as the diagonal on the manifold and the coproduct on the based loop space. With this description, we compute the coproduct with

Jonathan Clivio

We construct a lift of the Goresky-Hingston coproduct on the based loop space. Using this lift, we produce formulas for the interaction of the coproduct with the $\pi_1$-action and with maps of manifolds. These results lets us compute the coproduct on the based loop space for manifolds of the form $S^n/G$.

Mehdi Nategh, Zhenbo Qin, Shuguang Wang

We study decomposable N^d-indexed persistence modules via higher dimensional partitions. Their barcodes are defined in terms of the extended interior of the corresponding Young diagrams. For two decomposable N^d-indexed persistence modules, we present a necessary and sufficient condition, in terms of the partitions, for their rank invariants to be the same.

Sumanta Roy, Bahador Bahmani, Ioannis G. Kevrekidis, Michael D. Shields

The predictive accuracy of operator learning frameworks depends on the quality and quantity of available training data (input-output function pairs), often requiring substantial amounts of high-fidelity data, which can be challenging to obtain in some real-world engineering applications. These datasets may be unevenly discretized from one realization to anot

A. Aleksejevs, S. Barkanova, A. I. Davydychev

Ab initio predictions of two-loop electroweak contributions to observables are increasingly essential for precision collider experiments, yet their evaluation remains very challenging. We connect recurrence techniques and dispersive method in order to evaluate complex multi-loop Feynman diagrams. By expressing multi-point Passarino-Veltman functions in a two

Gabriel Assant, Astrid Eichhorn, Benjamin Knorr

Ultralight scalar dark matter may couple to the Standard Model through dimension-five operators that contain the field-strength tensors of the gauge interactions. Recent progress in nuclear clocks is projected to increase the sensitivity to such couplings by several orders of magnitude. Future experimental constraints may even have Planck-scale sensitivity,

Hamid R. Tizhoosh

Despite their successes in vision and language, foundation models have stumbled in pathology, revealing low accuracy, instability, and heavy computational demands. These shortcomings stem not from tuning problems but from deeper conceptual mismatches: dense embeddings cannot represent the combinatorial richness of tissue, and current architectures inherit fl

Samuel Chevalier, Duncan Starkenburg, Robert Parker, Noah Rhodes

Solving for globally optimal line switching decisions in AC transmission grids can be intractability slow. Machine learning (ML) models, meanwhile, can be trained to predict near-optimal decisions at a fraction of the speed. Verifying the performance and impact of these ML models on network operation, however, is a critically important step prior to their ac

Xueying Chen, Jianfeng Ke, Lauren Flynn, Giovanni Parmigiani

Purpose: Hereditary cancer risk is key to guiding screening and prevention strategies. Cancer risks can vary by individual due to the presence or absence of high- and moderate-risk pathogenic variants (PV) in cancer-associated genes, in addition to sex, age, and other risk factors. We previously developed Fam3PRO, a flexible multi-gene, multi-cancer Mendelia

Adela DePavia, Vasileios Charisopoulos, Rebecca Willett

Adaptive gradient methods such as Adam and Adagrad are widely used in machine learning, yet their effect on the generalization of learned models -- relative to methods like gradient descent -- remains poorly understood. Prior work on binary classification suggests that Adam exhibits a ``richness bias,'' which can help it learn nonlinear decision boundaries c

Christopher W. Churchill

In this two-paper series, we present a straightforward mathematical model for synthesizing quasar absorption line profiles from sight lines through idealized, spatial-kinematic models of the circumgalactic medium (CGM) and their host galaxies. In Paper I, we developed the spatial components of the galaxy/CGM structures (disk, halo, wind, accretion) and their

Nathan Paek, Yongyi Zang, Qihui Yang, Randal Leistikow

While sparse autoencoders (SAEs) successfully extract interpretable features from language models, applying them to audio generation faces unique challenges: audio's dense nature requires compression that obscures semantic meaning, and automatic feature characterization remains limited. We propose a framework for interpreting audio generative models by mappi

M. D. Medina, H. I. Giron, K. Paucar, A. Talledo

Chromium thin films deposited on silicon substrates by DC magnetron sputtering were systematically investigated as a function of film thickness, using a DC power of 50 W and a post-deposition annealing temperature of 200 C. Two types of grounded substrate holders, copper and stainless steel, were employed to assess substrate-dependent effects. The intrinsic

P. Sherpa, R. Hingorani, A. Menon, I. Vinograd

We report a pronounced Hebel-Slichter coherence peak in the zero field nuclear quadrupolar resonance (NQR) spin-lattice relaxation rate of the topological crystalline superconductor LaNiGa$_2$ in the superconducting state. Previously, a two-band internally antisymmetric non-unitary triplet pairing (INT) state was proposed for this system, with equal spin-pai

Yujia Sun, Yang Han, Xingya Wang, Szu-Yu Tang

Transitioning from Phase 2 to Phase 3 in drug development, at a rate of $\approx$40%, is the most stringent among phase transitions (Hay et al. (2014)). Yet, success rate at Phase 3 leading to approval is only $\approx$50% (Arrowsmith (2011b)). To improve Confirmability, we propose a methodological shift: replacing multiple hypothesis testing with inference

Evangelia Takou, Kenneth R. Brown

Decoders of quantum error correction (QEC) experiments make decisions based on detected errors and the expected rates of error events, which together comprise a detector error model. Here we show that the syndrome history of QEC experiments is sufficient to detect and estimate coherent errors, removing the need for prior device benchmarking experiments. Impo

A. Zecchetto, J. -R. Coudevylle, M. Morassi, A. Lemaître

Harnessing topological effects offers a promising route to protect quantum states of light from imperfections, potentially enabling more robust platforms for quantum information processing. This capability is particularly relevant for active photonic circuits that generate quantum light directly on-chip. Here, we explore topological effects on photon-pair ge

Akash Kharita, Marine Denolle, Alexander R Hutko, J. Renate Hartog

Accurately separating tectonic, anthropogenic, and geomorphologic seismic sources is essential for Pacific Northwest (PNW) monitoring but remains difficult as networks densify and signals overlap. Prior work largely treats binary discrimination and seldom compares classic ML (feature-engineered) and deep learning (end-to-end) approaches under a common, multi

Jun Liu, Tao Zhou, Jiarui Li, Xiaohui Zhong

Tropical cyclones (TCs) are highly destructive and inherently uncertain weather systems. Ensemble forecasting helps quantify these uncertainties, yet traditional systems are constrained by high computational costs and limited capability to fully represent atmospheric nonlinearity. FuXi-ENS introduces a learnable perturbation scheme for ensemble generation, r

Michael Hemsworth, TJ Hammond, Arthur K. Mills, David J. Jones

When pulses propagate in gas-filled anti-resonant hollow-core fibers (AR-HCFs) modulational instability (MI) can lead to pulse break-up and loss of coherence. In pulse broadening and compression schemes, MI is a parasitic effect that induces significant shot-to-shot fluctuations of the peak power of compressed pulses and increases rapidly over a narrow range

Stephen R. Kane

Exoplanetary systems exhibit a broad range of architectures which, in turn, enable a variety of dynamical environments. Many of the known planetary systems do not transit the host star, and so we measure the minimum masses of their planets, making it difficult to fully assess the dynamical environment within the system. Astrometry can resolve the mass ambigu

Matthew Corbelli

We study superselection sectors in two-dimensional quantum spin systems with an on-site action of a compact abelian group $G$. Naaijkens and Ogata (2022) arXiv:2102.07707 showed that for states quasi-equivalent to a product state, the superselection structure is trivial, reflecting the absence of long-range entanglement. We consider a symmetry-compatible ref

Jason Brown, Kevin Carlson, Sophie Libkind, David Jaz Myers

(Pseudo) double categories have two sorts of morphisms: tight ones which compose strictly, and loose ones which compose up to coherent isomorphism. In this paper, we consider bimodules between double categories in the loose direction. We provide two formulation of this concept -- first as pseudo-bimodules between pseudo-categories in the 2-category of catego

Sourav Pal, Nitin Tomar

A commuting pair of Hilbert space operators having the closed symmetrized bidisc \[ \Gamma=\{(z_1+z_2, z_1z_2) \in \mathbb C^2 \ : \ |z_1| \leq 1, |z_2| \leq 1\} \] as a spectral set is called a \textit{$\Gamma$-contraction}. A $\Gamma$-contraction $(S,P)$ is called \textit{$\Gamma$-distinguished} if $(S,P)$ is annihilated by a polynomial $q \in \mathbb C[z_

Calvin W. Johnson

RHODIUM is a postprocessing code for nuclear structure physics. It can be used to compute density matrices, spectroscopic amplitudes, and other information, from wave function and basis files created by the configuration-interaction shell-model code BIGSTICK. The source code is available at github.com/cwjsdsu/Rhodium. This manual gives detailed instructions

Joohwan Ko, Aristofanis Rontogiannis, Yih-En Andrew Ban, Axel Elaldi

Protein design using structure prediction models such as AlphaFold2 has shown remarkable success, but existing approaches like relaxed sequence optimization (RSO) rely on single-path gradient descent and ignore sequence-space constraints, limiting diversity and designability. We introduce Relaxed Sequence Sampling (RSS), a Markov chain Monte Carlo (MCMC) fra

D. J. P. de Sousa, N. Roldan-Levchenko, C. O. Ascencio, J. D. S. Forte

Recent theoretical advances have highlighted the role of Bloch state intrinsic properties in enabling unconventional electro-optic (EO) phenomena in bulk metals, offering novel strategies for dynamic optical control in quantum materials. Here, we identify an alternative EO mechanism in bulk metallic systems that arises from the interplay between Berry curvat

Jonas Bucher, Ueli Grossniklaus, Deepak Kumar Tanwar

DNA cytosine methylation is a critical epigenetic mark regulating gene expression and thus playing an important role in development and differentiation across eukaryotes. Existing tools for high-throughput methylation analysis often lack cross-species flexibility or require command-line expertise. We present Methylator, a novel, end-to-end DNA methylation an

J. Gonzalez, T. Collett, K. Rojas, K. Bechtol

We present a systematic comparison of three independent machine learning (ML)-based searches for strong gravitational lenses applied to the Dark Energy Survey (Jacobs et al. 2019a,b; Rojas et al. 2022; Gonzalez et al. 2025). Each search employs a distinct ML architecture and training strategy, allowing us to evaluate their relative performance, completeness,

Peilin He, Tananun Songdechakraiwut

The human brain is highly adaptive: its functional connectivity reconfigures on multiple timescales during cognition and learning, enabling flexible information processing. By contrast, artificial neural networks typically rely on manually-tuned learning-rate schedules or generic adaptive optimizers whose hyperparameters remain largely agnostic to a model's

Omran Abbas, Abdullah Zayat, Loıc Markley, Anas Chaaban

Stacked intelligent surfaces (SIS) are a promising technology for next-generation wireless systems, offering an opportunity to enhance communication performance with low power consumption. Typically, an SIS is modelled as a surface that imparts phase shifts on impinging electromagnetic signals to achieve desired communication objectives. However, this mode o

Jon Kristian Dahl, Ann-Cecilie Larsen, Noritaka Shimizu, Yutaka Utsuno

We address the microscopic origin of the low-energy enhancement (LEE) in \(^{50}\)V with large-scale shell-model calculations to obtain $E1$ and $M1$ transitions within the same theoretical framework. The valence space spans the three major shells $sd$, $pf$ and $sdg$ and is treated with the SDPFSDG-MU interaction using the KSHELL code. With a \(1 \hbar \ome

Halyna Hodovanets, Hyunsoo Kim, Tristin Metz, Yasuyuki Nakajima

We present a detailed study of magnetization, resistivity, heat capacity, and X-ray and neutron powder diffraction measurements performed on single crystals of non-stoichiometric CeAuBi$_2$, Au deficiency 18$\%$, a strongly correlated antiferromagnet with N\'eel temperature T$_N$ = 13.2 K. Field-dependent magnetization measurements reveal a large magnetic an

Viktor Chust, Flávio U. Coelho

We give a necessary condition for the existence of a path of n irreducible morphisms between indecomposable modules whose composition lies in the (n + 1)-power of the radical. In order to do that, we consider the general criterion given by C. Chaio, P. Le Meur and S. Trepode, which relates these compositions with zero paths in the mesh category, and then stu

P. Ranaivomanana, C. Johnston, G. Iorio, P. J. Groot

The unprecedented volume and quality of data from space- and ground-based telescopes present an opportunity for machine learning to identify new classes of variable stars and peculiar systems that may have been overlooked by traditional methods. Extending prior methodological work, this study investigates the potential of an unsupervised learning approach to

Aryan Mathur, Asaduddin Ahmed, Pushti Amit Vasoya, Simeon Kandan Sonar

The increasing realism of AI-generated imagery poses challenges for verifying visual authenticity. We present an explainable image authenticity detection system that combines a lightweight convolutional classifier ("Faster-Than-Lies") with a Vision-Language Model (Qwen2-VL-7B) to classify, localize, and explain artifacts in 32x32 images. Our model achieves 9

Francesco Crescimbeni

We present a theory-agnostic framework to search for extra-fundamental modes in the ringdown phase of black hole mergers. These modes, distinct from standard General Relativity (GR) deviations, originate from modifications of the theory of gravity. Applying our method to the events GW150914, GW190521, and GW200129, we find no significant evidence for extra m

Kun Cheng, Tao Han, Sokratis Trifinopoulos

We investigate quantum-information-theoretic observables in electron-proton scattering at the Electron-Ion Collider (EIC). Our analysis focuses on entanglement and magic, two complementary indicators of non-classicality in quantum states. We show that while unpolarized and longitudinally polarized beams yield unentangled separable outcomes, transverse beam p

Vivek Veeriah, Federico Barbero, Marcus Chiam, Xidong Feng

The rapid advancement of Generative AI has raised significant questions regarding its ability to produce creative and novel outputs. Our recent work investigates this question within the domain of chess puzzles and presents an AI system designed to generate puzzles characterized by aesthetic appeal, novelty, counter-intuitive and unique solutions. We briefly

Mattia Di Mauro, Yanhan Wang

We present a comprehensive study of secluded dark matter (DM) $\chi$, where the relic abundance is set by annihilations into lighter dark mediators $\phi$ that couple only feebly to the Standard Model (SM). In contrast to canonical WIMPs, which are now strongly constrained by direct and indirect searches, secluded models still achieve the observed relic abun

Sebastian von Hausegger, Nathan Secrest, Harry Desmond, Mohamed Rameez

The cosmic dipole anomaly -- the observation of a significant mismatch between the dipole observed in the matter distribution and that expected given the kinematic interpretation of the cosmic microwave background dipole -- poses a serious challenge to the Cosmological Principle upon which the standard model of cosmology rests. Measurements of the dipole ($\

Philipp Denzel, Yann Billeter, Frank-Peter Schilling, Elena Gavagnin

We present the first systematic study of multi-domain map-to-map translation in galaxy formation simulations, leveraging deep generative models to predict diverse galactic properties. Using high-resolution magneto-hydrodynamical simulation data, we compare conditional generative adversarial networks and diffusion models under unified preprocessing and evalua

Timothy De Deyn, Sam K. Miller

We prove that, given the Balmer spectrum of any essentially small monoidal-triangulated category, one has a classification of semiprime thick tensor-ideals arising in terms of a "pseudo-Hochster-dual" of the noncommutative Balmer spectrum. This extends Balmer's classification of radical thick tensor-ideals to noncommutative tensor-triangular geometry. To ach

Ramshankar Bhuvaneswaran, Handan Liu

The pursuit of efficient Large Language Models (LLMs) has led to increasingly complex techniques like extreme quantization and dynamic routing. While individual benefits of these methods are well-documented, their compositional effects remain poorly understood. This paper introduces BitSkip, a hybrid architectural framework for systematically exploring these

Noam Goldberg, Michael Poss, Yariv Marmor

We study a robust extensible bin packing problem with budgeted uncertainty, under a budgeted uncertainty model where item sizes are defined to lie in the intersection of a box with a one-norm ball. We propose a scenario generation algorithm for this problem, which alternates between solving a master robust bin-packing problem with a finite uncertainty set an

Riya Gupta, Alexander Chowdhury, Sahil Nalawade

Federated Learning (FL) has emerged as a transformative paradigm in medical AI, enabling collaborative model training across institutions without direct data sharing. This study benchmarks three prominent FL frameworks NVIDIA FLARE, Flower, and Owkin Substra to evaluate their suitability for medical imaging applications in real-world settings. Using the Path

Abigail Loe, Susan Murray, Zhenke Wu

Alternating recurrent events, where subjects experience two potentially correlated event types over time, are common in healthcare, social, and behavioral studies. Often there is a primary event of interest that, when triggered, initiates a period of treatment and recovery measured via a secondary time-to-event. For example, cancer patients can experience re

Siyin Wang, Jinlan Fu, Feihong Liu, Xinzhe He

Recent advances in Multimodal Large Language Models (MLLMs) have driven rapid progress in Vision-Language-Action (VLA) models for robotic manipulation. Although effective in many scenarios, current approaches largely rely on explicit instructions, whereas in real-world interactions, humans rarely issue instructions directly. Effective collaboration requires

Raimondo Pala

This paper addresses the challenges of giving a causal interpretation to vector autoregressions (VARs). I show that under independence assumptions VARs can identify average treatment effects, average causal responses, or a mix of the two, depending on the distribution of the policy. But what about situations in which the economist cannot rely on independence

Kevin Han, Siddharth Maddikayala, Tim Knappe, Om Patel

We introduce TDFlow, a novel test-driven agentic workflow that frames repository-scale software engineering as a test-resolution task, specifically designed to solve human-written tests. Given a set of tests, TDFlow repeatedly proposes, revises, and debugs repository-scale patches using precisely engineered sub-agents and tightly constrained tools. The workf

Maxim Dvornikov

We study neutrino oscillations in external fields using the approach based on the quantum field theory (QFT). Neutrinos are virtual particles in this formalism. Neutrino mass eigenstates are supposed to be Dirac fermions. We consider two cases of external fields: the neutrino electroweak interaction with background matter and the interaction with an external

Marlon Estanislau

Let $G$ be a cyclic $p$-group for some prime number $p>0$ and let $R$ be a complete discrete valuation ring in mixed characteristic. In this paper, we present a generalization of two results that characterize $RG$-permutation modules, extending previous work by B. Torrecillas and Th. Weigel. Their original results were established under the assumption that $

Viktor Chust, Flávio U. Coelho

The idea of using Riedtmann's well-behaved functors to study compositions of irreducible morphisms has been explored in a number of articles. Here we introduce the concept of mesh-comparable components of the Auslander-Reiten quiver, which are components for which a Riedtmann functor exists without the necessity of taking a covering, such as the universal or