December 2025 arXiv papers — page 20

Fabio Bellini, Muqiao Huang, Qiuqi Wang, Ruodu Wang

The Lambda Value-at-Risk (Lambda-VaR) is a generalization of the Value-at-Risk (VaR), which has been actively studied in quantitative finance. Over the past two decades, the Expected Shortfall (ES) has become one of the most important risk measures alongside VaR because of its various desirable properties in the practice of optimization, risk management, and

Yuan-Sen Ting

Attenuation bias -- the systematic underestimation of regression coefficients due to measurement errors in input variables -- affects astronomical data-driven models. For linear regression, this problem was solved by treating the true input values as latent variables to be estimated alongside model parameters. In this paper, we show that neural networks suff

Runzhi Zhou, Xi Luo

Integrating non-Euclidean brain imaging data with Euclidean tabular data, such as clinical and demographic information, poses a substantial challenge for medical imaging analysis, particularly in forecasting future outcomes. While machine learning and deep learning techniques have been applied successfully to cross-sectional classification and prediction tas

Junyeong Park, Jieun Han, Yeon Su Park, Youngbin Lee

For English as a Foreign Language (EFL) learners, code-switching (CSW), or alternating between their native language and the target language (English), can lower anxiety and ease communication barriers. Large language models (LLMs), with their multilingual abilities, offer new opportunities to support CSW in speaking practice. Yet, the pedagogical design of

Roshan Klein-Seetharaman, Daniel Rakita

Robots are typically described in software by specification files (e.g., URDF, SDF, MJCF, USD) that encode only basic kinematic, dynamic, and geometric information. As a result, downstream applications such as simulation, planning, and control must repeatedly re-derive richer data, leading to redundant computations, fragmented implementations, and limited st

Lang Yu, Nanjing Huang

This work proposes a novel and unified sparse recovery framework, termed the difference of convex Elastic Net (DCEN). This framework effectively balances strong sparsity promotion with solution stability, and is particularly suitable for high-dimensional variable selection involving highly correlated features. Built upon a difference-of-convex (DC) structure

Anqi Mao, Mehryar Mohri, Yutao Zhong

In applications with significant class imbalance or asymmetric costs, metrics such as the $F_\beta$-measure, AM measure, Jaccard similarity coefficient, and weighted accuracy offer more suitable evaluation criteria than standard binary classification loss. However, optimizing these metrics present significant computational and statistical challenges. Existin

Armstrong Foundjem, Lionel Nganyewou Tidjon, Leuson Da Silva, Foutse Khomh

Machine learning (ML) underpins foundation models in finance, healthcare, and critical infrastructure, making them targets for data poisoning, model extraction, prompt injection, automated jailbreaking, and preference-guided black-box attacks that exploit model comparisons. Larger models can be more vulnerable to introspection-driven jailbreaks and cross-mod

Yankang Li, Changsheng Li

Accurate identification of the penetration process relies heavily on prior feature values of penetration acceleration. However, these feature values are typically obtained through long simulation cycles and expensive computations. To overcome this limitation, this paper proposes a multi-layer Perceptron architecture, termed squeeze and excitation multi-layer

Jian Wang, Sixing Rong, Jiarui Xing, Yuling Xu

We present PathoSyn, a unified generative framework for Magnetic Resonance Imaging (MRI) image synthesis that reformulates imaging-pathology as a disentangled additive deviation on a stable anatomical manifold. Current generative models typically operate in the global pixel domain or rely on binary masks, these paradigms often suffer from feature entanglemen

Loris Di Cairano

We develop a geometric foundation of microcanonical thermodynamics in which entropy and its derivatives are determined from the geometry of phase space, rather than being introduced through an a priori ensemble postulate. Once the minimal structure needed to measure constant -- energy manifolds is made explicit, the microcanonical measure emerges as the natu

Yu Li, Tian Lan, Zhengling Qi

Direct Preference Optimization (DPO) and its variants have become standard for aligning Large Language Models due to their simplicity and offline stability. However, we identify two fundamental limitations. First, the optimal policy depends on arbitrary modeling choices (scalarization function, reference policy), yielding behavior reflecting parameterization

A. V. Afanasjev, E. Litvinova, B. Osei

Although there exists a clear and, in principle, exact theoretical formulation for the equation of motion for the response of a correlated fermionic system, its numerical implementations for atomic nuclei require feasible approximations. One of the widely accepted approximations is a truncated harmonic oscillator (HO) basis, whose wave functions are used to

Tiancheng Su, Meicong Zhang, Guoxiu He

Speculative decoding (SD) accelerates large language model (LLM) inference by using a lightweight draft model to propose tokens and a stronger target model to verify them. However, standard SD is mainly designed for acceleration, and its output quality is typically constrained by the target model. In this work, we propose Entropy-Aware Speculative Decoding (

Kang-Chung Yang, Shinsheng Yuan

In epidemiological research, modeling the cumulative effects of time-dependent exposures on survival outcomes presents a challenge due to their intricate temporal dynamics. Conventional spline-based statistical methods, though effective, require repeated data transformation for each spline parameter tuning, with survival analysis computations relying on the

Dongmin Gang, Byoungyoon Park, Huijoon Sohn

In 2003, Hikami and Kirillov uncovered an intriguing connection between torus knots $\mathcal{K}_{(P,Q)}$ and Virasoro minimal models $\mathcal{M}(P,Q)$ by relating the Kashaev invariants of the knots to the characters of the corresponding minimal models. In this work, we recover and extend this connection by combining the 3D--3D correspondence with a bulk--

Kacper Kluk, Jesper Nederlof

We give unconditional parameterized complexity lower bounds on pure dynamic programming algorithms - as modeled by tropical circuits - for connectivity problems such as the Traveling Salesperson Problem. Our lower bounds are higher than the currently fastest algorithms that rely on algebra and give evidence that these algebraic aspects are unavoidable for co

Christoph Schlager, Lutz Bornmann, Gerald Schweiger

Multiple organisational affiliations are an increasingly common feature of research systems, yet their implications for organisational performance had received limited systematic attention. We developed a scalable, network-based analytical framework that represents simultaneous researcher affiliations as relational links between organisations and applied it

Dennis Delali Kwesi Wayo, Rodrigo Alves Dias, Leonardo Goliatt, Sven Groppe

We introduce DifGa, a fully differentiable error-mitigation framework for continuous-variable (CV) quantum photonic circuits operating under Gaussian loss and weak non-Gaussian noise. The approach is demonstrated using analytic simulations with the default.gaussian backend of PennyLane, where quantum states are represented by first and second moments and opt

Urna Basu, Satya N. Majumdar, Alberto Rosso

We investigate the dynamics of a run-and-tumble particle in a double-well potential and demonstrate that, in stark contrast to Brownian particles, active dynamics can lead to strong ergodicity breaking. When the barrier height exceeds a critical threshold, the long-time position distribution depends crucially on the initial condition: if the particle starts

Lázaro O. Rodríguez Díaz

The Jacobian conjecture is thought to have been proposed by O. H. Keller in 1939. However, we have found that the statement of the conjecture is precisely the main result of a paper published by L. Kraus in 1884. Although the final step of Kraus's proof is flawed, the ideas he introduced anticipated approaches to the problem that would only emerge more t

Bao-Jun Cai, Bao-An Li, Yu-Gang Ma

Nucleon short-range correlations (SRCs) and the associated high-momentum tail (HMT) in its momentum distribution $n(k)$ represent a universal feature of strongly interacting Fermi systems. In nuclear matter, SRCs arise primarily from the spin-isospin dependence of the tensor and short-range components of the nucleon-nucleon interaction, leading to a substant

Yilin Bi, Junhao Bian, Shuyan Wan, Shuaijia Wang

Link prediction, a foundational task in complex network analysis, has extensive applications in critical scenarios such as social recommendation, drug target discovery, and knowledge graph completion. However, existing evaluations of algorithmic often rely on experiments conducted on a limited number of networks, assuming consistent performance rankings acro

Yang Yu, Lei Zhang, Emanuel Gull, Xiaodong Cao

We present the combination of a complex-time tensor-network impurity solver with an analytic continuation scheme based on exponential fitting as an efficient framework for single and multi-orbital dynamical mean-field calculations. By performing time-evolution along a complex-time contour, the approach balances computational cost with the difficulty of spect

David Regan, Paola Borri, Wolfgang Langbein

Biological membranes are known to form various structural motifs, from lipid bilayers to tubular filaments and networks facilitating e.g. adhesion and cell-cell communication. To understand the biophysical processes underpinning lipid-lipid interactions in these systems, synthetic membrane models are crucial. Here, we demonstrate the formation of tubular net

Simon Marionnet

In this article we answer a question asked by Chien et al. in arXiv:2304.06050 in which they study the numerical range of weighted cyclic matrices under permutation of their entries. Namely, we are interested in how $w(A_σ)$ fluctuates for various permutations $σ\in S_n$ and fixed $0\leq a_1<\cdots<a_n$ with $A_σ=\begin{pmatrix} 0&a_{σ(1)}&{}&{}&{}\cr {}&0&a

Ranran Guo, Jin Wu, Mingmei Xu, Xiaosong Chen

We develop the eigen-microstate framework as a new approach to identify criticality in relativistic heavy-ion collisions. We construct the original microstate, defined as the final-state particle fluctuations of a single event. By examining ensembles of such original microstates with and without critical signals, we demonstrate that the corresponding eigen-m

Jonathan Katzy, Razvan Mihai Popescu, Arie van Deursen, Maliheh Izadi

The recent rise in the popularity of large language models has spurred the development of extensive code datasets needed to train them. This has left limited code available for collection and use in the downstream investigation of specific behaviors, or evaluation of large language models without suffering from data contamination. To address this problem, we

Mary Kathleen Caucci, Billy E. Yang, Gerald R. Bejger, Jacob T. Sivak

Cerium-based lanthanide high-entropy oxides (LN-HEOs) are promising candidates for solid-state electrolyte (mass transport) applications due to their ability to accommodate high concentrations of oxygen vacancies while retaining a fluorite-derived structure. However, synthesis often yields undesired ordered oxygen-deficient phases, such as bixbyite, dependin

Achintya Paradkar, Paul Nicaise, Karim Dakroury, Fabian Resare

We demonstrate the efficient modulation of flux-tunable superconducting resonators (FTRs) using flip-chip or on-chip-based input coils. The FTRs we use are aluminum-based quarter-wave coplanar waveguide resonators terminated with 100um or 200um-wide square loop dc superconducting quantum interference devices (SQUIDs) employing 1um-sized Josephson junctions.

Mengyao Guo, Kexin Nie, Jinda Han, Guanyou Li

Space exploration has advanced rapidly, but the emotional needs of astronauts on long-duration missions remain underexplored. We present ReHome Earth, a dual-component design approach addressing space homesickness: 1) a future-oriented installation concept integrating transparent OLED displays with spaceship windows for real-time Earth connectivity, and 2) a

Daniel S. Levine, Nicholas Liesen, Lauren Chua, James Diffenderfer

Polymers-macromolecular systems composed of repeating chemical units-constitute the molecular foundation of living organisms, while their synthetic counterparts drive transformative advances across medicine, consumer products, and energy technologies. While machine learning (ML) models have been trained on millions of quantum chemical atomistic simulations f

Muhammad Idrees Khan, Sauro Succi, Giacomo Falcucci

The simulation of turbulent flows remains a central challenge, as even our most powerful computers cannot resolve the finest scales of motion in many flows of practical interest. As a result, the effects of unresolved scales on large eddies must be modelled via closures and coarse-graining procedures. Large-eddy simulation (LES) traditionally coarse-grains N

Eilon Solan, Avraham Tabbach, Chang Zhao

We analyze a two-period principal-agent model in which the principal faces a budget constraint, and the agent's private costs of performing tasks across the two periods may be correlated. We examine the optimal design of the reward scheme and the cost correlation structure. Our findings reveal that when the budget is low, the optimal reward scheme employs \t

M. H. Wagner, T. Dufour, A. Geraci, E. Oddo

Turnip (Brassica rapa) is a native species of the Mediterranean area, spread from northwest France to south Algeria. In this study, dormancy and germination traits were assessed for 61 wild Brassica rapa populations collected across the Mediterranean region. Seed dormancy is a key factor influencing germination and seedling establishment. Three dormancy-brea

Nicholas R. Rasmussen, Rodrigue Rizk, Longwei Wang, Arun Singh

The early detection of Parkinsons disease remains a critical challenge in clinical neuroscience, with electroencephalography offering a noninvasive and scalable pathway toward population level screening. While machine learning has shown promise in this domain, many reported results suffer from methodological flaws, most notably patient level data leakage, in

Anas El Balali, Alessio Marrani

We introduce the Lense-Thirring Acoustic Black Hole (LTABH), motivated by the relevance of analogue models for black holes embedded in various physical systems, such as the cosmological microwave background or quantum superfluids. We investigate the LTABH spacetime geometry, showing that the roots of the metric function determine a partition of the spacetime

Mohammad Rasoolinejad

Viruses display striking diversity in structure, transmission mode, immune interaction, and evolutionary behavior. Despite this diversity, viral strategies are not unconstrained. Here we present a unifying framework that treats viral evolution as a problem of constrained optimization governed by physical decay, immune pressure, mutation robustness, and trans

Chuen Hei Chan, Charu Jain, Ezra Kissel, Wenji Wu

Developing and deploying advanced Quantum Repeater (QR) technologies will be necessary to scale quantum networks to longer distances. Depending on the error mitigation mechanisms adopted to suppress loss and errors, QRs are typically classified into memory-based or all-photonic QRs; and each type of QR may be best suited for a specific type of underlying qua

M. Waseem Akram, Binita Shahi, M. Javed Akram

This research analyses the axiomatic link among health expenditures, inflation rate, and gross national income (GNI) per capita concerning the child mortality (CMU5) rate in D-8 nations, employing panel data analysis from 1995 to 2014. Utilising conventional panel unit root tests and linear regression models, we establish that education expenditures, in conj

Paul M. Thompson

Modern generative and vision-language models (VLMs) are increasingly used in scientific and medical decision support, where predicted probabilities must be both accurate and well calibrated. Despite strong empirical results with moderate data, it remains unclear when such predictions generalize uniformly across inputs, classes, or subpopulations, rather than

Henry Adams, Alexandre Karassev, Ziga Virk

We show that if $X$ is a finite-dimensional Polish metric space, then the natural bijection $\mathrm{VR}(X;r)\to \mathrm{VR^m}(X;r)$ from the (open) Vietoris-Rips complex to the Vietoris-Rips metric thickening is a homotopy equivalence. This occurs, for example, if $X$ is a Riemannian manifold. The same is true for the map $\mathrm{\check{C}}(X;r)$ to $\math

Hossein Vaziri, Mohammad Reza Shojaei, Pere Masjuan

We present a detailed study of the nucleon electromagnetic form factors in the spacelike region by combining three complementary approaches: two GPD-based contributions and a vector-meson exchange component. By fitting experimental data, we extract the optimal weights and shape parameters describing the proton and neutron form factors. Global Pad\'e-based fi

Sebastián Muñoz-Thon, Sean Richardson

Guillarmou's normal operator over a closed Anosov manifold is analogous to the classical normal operator of the geodesic X-ray transform over manifolds with boundary. In this paper, we generalize this normal operator, under some dynamical assumptions, to thermostat flows as well as to the case of the magnetic flows. In particular, we show that these generali

Jonathon Sendall

What happens to a unified conscious field when its physical implementation straddles a black hole event horizon? This paper addresses that question for integration-based theories, including Integrated Information Theory, Global Workspace Theory, and Predictive Processing. These views share a structural commitment: unity requires a single strongly connected c

Ramesh Radhakrishnan, Gerald Cleaver, Delaram Mirfendereski, Eric Davis

We investigate scalar field perturbations of the rotating Teo wormhole. We also compute the quasinormal mode (QNM) spectrum using first order WKB approximation. After separation of variables, we obtain a Schroedinger type radial equation and a smooth barrier potential which is shaped by the localized frame-dragging effects of the wormhole throat. This barrie

Peter Messina, Daniel Rakita

High-quality visualizations are an essential part of robotics research, enabling clear communication of results through figures, animations, and demonstration videos. While Blender is a powerful and freely available 3D graphics platform, its steep learning curve and lack of robotics-focused integrations make it difficult and time-consuming for researchers to

Krzysztof Golec-Biernat

We investigate the concept of partonic entropy of the proton within the Dokshitzer--Gribov--Lipatov--Altarelli--Parisi (DGLAP) evolution scheme of collinear parton distributions. We show that such entropy increases monotonically with the evolution scale. The mechanism underlying this growth is illustrated using a simplified model of DGLAP evolution, highligh

Amarda Shehu, Adonyas Ababu, Asma Akbary, Griffin Allen

Claims about whether large language model (LLM) chatbots "reason" are typically debated using curated benchmarks and laboratory-style evaluation protocols. This paper offers a complementary perspective: a student-led field experiment embedded as a midterm project in UNIV 182 (AI4All) at George Mason University, a Mason Core course designed for undergraduates

Ricardo Mendes, Alessandro Minuzzo, Marco Radeschi

We prove that a compact simply connected manifold $M$ with a variationally complete $G$-action satisfying certain mild conditions (e.g. trivial principal isotropy, or simply connected principal orbits) is rationally elliptic if and only if $M/G$ is flat. This answers several conjectures and problems regarding the rational homotopy of manifolds with symmetrie

Joon-Hwi Kim

We establish an exact relation between the S-symplectomorphism and the S-matrix by means of the phase space formulation of quantum mechanics. The adjoint action of the S-matrix defines a fuzzy diffeomorphism on phase space whose classical limit is the S-symplectomorphism. The relation between classical and quantum eikonals is immediate via $\hbar$-deformatio

Igor Chaban, Nikita Nekrasov

These lectures study two correspondences between gauge theories and integrable many-body systems. The first arises from infinite-dimensional Hamiltonian reduction and relates gauge-theoretic dynamics directly to Calogero--Moser-type systems and their quantum counterparts. The second emerges in supersymmetric gauge theory through instanton counting and non-pe

Tian-Ning Yang, Ren-Fang Geng, Jing Zhang, Chong Yang

Context. Among options for definition of the lunar reference time, the option taking Lunar Coordinate Time (O1) has its simplicity but cannot be realized by any clock without steering, while another option adopting the lunar geoid (selenoid) proper time (O2) has its convenience for users on the lunar surface but would bring a new scaling of spatial coordinat

John E. Darges, Babak Maboudi Afkham, Matthias Chung

We introduce Neural Optimal Design of Experiments, a learning-based framework for optimal experimental design in inverse problems that avoids classical bilevel optimization and indirect sparsity regularization. NODE jointly trains a neural reconstruction model and a fixed-budget set of continuous design variables representing sensor locations, sampling times

Yingru Li, Ziniu Li, Jiacai Liu

We present a unified framework for Large Language Model (LLM) fine-tuning that integrates Imitation Learning and Reinforcement Learning. By analyzing the gradient of a composite objective combining trajectory-level KL divergence with task rewards, we derive a natural decomposition into two components: (1) an analytically computable Dense Gradient for token-l

Mario Colosi, Reza Farahani, Maria Fazio, Radu Prodan

Data within a specific context gains deeper significance beyond its isolated interpretation. In distributed systems, interdependent data sources reveal hidden relationships and latent structures, representing valuable information for many applications. This paper introduces Osmotic Learning (OSM-L), a self-supervised distributed learning paradigm designed to

Pranav Kumar Sasikumar

Zero-dimensional cardiovascular models provide a computationally efficient framework for studying global hemodynamic behavior, yet the influence of model complexity on parameter sensitivity remains insufficiently understood. This work investigates two lumped-parameter cardiovascular models, a simplified single-ventricle configuration and a detailed four-cham

Javier A. Almonacid, Nilima Nigam, James M. Wakeling

There has been debate for over 70-years about whether active skeletal muscle is dynamically stable at lengths greater than its optimal length. The stability of computational muscle models is a critical issue, as it directly affects our ability to simulate muscle deformation across different operating lengths, especially at lengths where muscles are known to

Hossien Hossieni

We propose a new analytical potential function to model proton transfer in the adenine-thymine base pair and develop a non-adiabatic quantum mechanical framework to calculate genetic mutation probabilities. This potential has been used to calculate the probability of mutation in a non-adiabatic process. The results of the new model have been shown to be cons

Ananya Drishti, Mahfuza Farooque

Alzheimer's disease (AD) and its prodromal stage, Mild Cognitive Impairment (MCI), are associated with subtle declines in memory, attention, and language that often go undetected until late in progression. Traditional diagnostic tools such as MRI and neuropsychological testing are invasive, costly, and poorly suited for population-scale monitoring. Social pl

P. Boyvalenkov, D. Cherkashin, P. Dragnev, D. Yorgov

In this article, we show that the minimal vectors of the extremal even unimodular lattices in $\mathbb{R}^{32}$ are $T$-avoiding universally optimal for suitable sets $T$. Moreover, they are minimal $T$-avoiding spherical designs and maximal $T$-avoiding codes for appropriate choices of $T$.

Dominik Soukup, Richard Plný, Daniel Vašata, Tomáš Čejka

Machine learning (ML) represents an efficient and popular approach for network traffic classification. However, network traffic classification is a challenging domain, and trained models may degrade soon after deployment due to the obsolete datasets and quick evolution of computer networks as new or updated protocols appear. Moreover, significant change in t

Esha Saha, Hao Wang

Advances in data acquisition and computational methods have accelerated the use of differential equation based modelling for complex systems. Such systems are often described by coupled (or more) variables, yet governing equation is typically available for one variable, while the remaining variable can be accessed only through data. This mismatch between kno

Yury Belousov

The problem of enumerating meanders -- pairs of simple plane curves with transverse intersections -- was formulated about forty years ago and is still far from solved. Recently, it was discovered that meanders admit a factorization into prime components. This factorization naturally leads to a broader class of objects, which we call singular meanders, in whi

Armin Berger, Manuela Bergau, Helen Schneider, Saad Ahmad

Recent Reinforcement Learning (RL) advances for Large Language Models (LLMs) have improved reasoning tasks, yet their resource-constrained application to medical imaging remains underexplored. We introduce ChexReason, a vision-language model trained via R1-style methodology (SFT followed by GRPO) using only 2,000 SFT samples, 1,000 RL samples, and a single A

Brayden Miao, Zain Rehman, Xin Miao, Siming Liu

Chest X-ray (CXR) imaging is widely used for screening and diagnosing pulmonary abnormalities, yet automated interpretation remains challenging due to weak disease signals, dataset bias, and limited spatial supervision. Foundation models for medical image segmentation (MedSAM) provide an opportunity to introduce anatomically grounded priors that may improve

Hans van Ditmarsch, Djanira Gomes, David Lehnherr, Valentin Müller

Simplicial models have become a crucial tool for studying distributed computing. These models, however, are only able to account for the knowledge, but not for the beliefs of agents. We present a new semantics for logics of belief. Our semantics is based on directed hypergraphs, a generalization of ordinary directed graphs in which edges are able to connect

Yingru Li, Jiawei Xu, Jiacai Liu, Yuxuan Tong

Reinforcement Learning (RL) for Large Language Models (LLMs) faces a fundamental tension: the numerical divergence between high-throughput inference engines and numerically precise training engines. Although these systems share the same parameters, they produce slightly different probability distributions, creating a training-inference mismatch. We prove tha

Teng Liu, Xuezhi Niu, Mingli Zhang, Gaoke Hu

We introduce the eigen microstate entropy ($S_{\text{EM}}$), a novel metric of complexity derived from the probabilities of statistically independent eigen microstates. After establishing its scaling behavior in equilibrium systems and demonstrating its utility in critical phenomena (mean spherical, Ising, and Potts models), we apply $S_{\text{EM}}$ to non-e

Ran Hao, Yuttana Itsarachaiyot, Yen-Chun Chen, M. Cenk Çavuşoğlu

This paper presents a forward kinematics and analytical Jacobian computation approach for real-time control of a novel magnetic resonance imaging (MRI)-actuated robotic catheter. The MRI-actuated robotic catheter is modeled as a series of rigid and flexible segments and actuated by magnetic torques generated on a set of current-carrying microcoils embedded o

Amina Halima Arrouche, Benharrat Belaïdi

In this paper, we study the growth of solutions to higher-order complex linear differential equations in the unit disc, where the analytic coefficients are of finite ({\alpha},\b{eta},{\gamma})-order. By employing the concepts of ({\alpha},\b{eta},{\gamma})-order and ({\alpha},\b{eta},{\gamma})-type, we establish new results concerning the growth of such sol

Wangqian Miao, Chu Li

We systematically investigate the emergence of electron crystal phases in rhombohedral multilayer graphene using comprehensive self-consistent Hartree Fock calculations combined with \textit{ab initio} tight binding model. As the carrier density increases, we uncover an isospin cascade sequence of phase transitions that gives rise to a rich variety of ordere

Taha Saeed Khan

Synchronization in power systems is traditionally achieved through physical network coupling, whereby inverter-based resources (IBRs) and synchronous machines converge to a common frequency via oscillatory swing dynamics. In conventional operation, secondary control acts on a slow time scale and is typically engaged only after the primary dynamics have large

Tim C. Pearce, Ahmed Ibrahim

The discovery of novel odorant molecules is key for the fragrance and flavor industries, yet efficiently navigating the vast chemical space to identify structures with desirable olfactory properties remains a significant challenge. Generative artificial intelligence offers a promising approach for \textit{de novo} molecular design but typically requires larg

Yotam Smilansky

The {\alpha}-Kakutani substitution rule splits the unit interval into two subintervals of lengths alpha and 1 - {\alpha}, for a fixed {\alpha} in (0,1). A simple inflation-substitution procedure produces tilings of the real line and their associated Delone sets. We show that there are precisely five distinct values of min({\alpha}, 1 - {\alpha}) for which th

Jianping Mei, Michael Moses, Jan Waelty, Yucheng Yang

We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models. Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods against modern deep architectures, including multi-modal models that fuse tabular and image

Saraswati Soedarmadji, Yunyue Wei, Chen Zhang, Yisong Yue

Discovering effective reward functions remains a fundamental challenge in motor control of high-dimensional musculoskeletal systems. While humans can describe movement goals explicitly such as "walking forward with an upright posture," the underlying control strategies that realize these goals are largely implicit, making it difficult to directly design rewa

Bouzid Mosbahi, Imed Basdouri, Jean Lerbet

In this paper, we introduce and study Reynolds--Nijenhuis operators on associative algebras a novel hybrid structure that simultaneously satisfies the defining identities of both Reynolds and Nijenhuis operators. We investigate their connections with Rota-Baxter and modified Rota-Baxter operators. We develop a representation theory for Reynolds--Nijenhuis as

Mingyuan Zhang, Yue Bai, Yifan Wang, Yiyang Huang

Explorations in fine-tuning Vision-Language Models (VLMs), such as Low-Rank Adaptation (LoRA) from Parameter Efficient Fine-Tuning (PEFT), have made impressive progress. However, most approaches rely on explicit weight updates, overlooking the extensive representational structures already encoded in pre-trained models that remain underutilized. Recent works

Bernard Parent, Felipe Martin Rodriguez Fuentes

Accurate prediction of electron temperature ($T_{\rm e}$) is critical for non-equilibrium plasma applications ranging from hypersonic flight to plasma-assisted combustion. We recently proposed a thermodynamically consistent model for vibrational-electron heating [Phys. Fluids 37, 096141 (2025)] that enforces the convergence of $T_{\rm e}$ to the vibrational

Krishna Harsha Kovelakuntla Huthasana, Alireza Olama, Andreas Lundell

Federated Learning (FL) is a distributed machine learning setting that requires multiple clients to collaborate on training a model while maintaining data privacy. The unaddressed inherent sparsity in data and models often results in overly dense models and poor generalizability under data and client participation heterogeneity. We propose FL with an L0 cons

Shuhuan Wang, Yuzhen Xie, Jiayi Li, Yinliang Diao

Selective State Space Models (SSMs) achieve linear-time inference, yet their gradient-based sensitivity analysis remains bottlenecked by O(L) memory scaling during backpropagation. This memory constraint precludes genomic-scale modeling (L > 10^5) on consumer-grade hardware. We introduce Phase Gradient Flow (PGF), a framework that computes exact analytical d

Fady Rezk, Yuangang Pan, Chuan-Sheng Foo, Xun Xu

Personalized alignment from preference data has focused primarily on improving personal reward model (RM) accuracy, with the implicit assumption that better preference ranking translates to better personalized behavior. However, in deployment, computational constraints necessitate inference-time adaptation such as reward-guided decoding (RGD) rather than per

Houcine Abdelkader Cherief, Brahim Mahmoudi, Zacharie Chenail-Larcher, Naouel Moha

Grey literature is essential to software engineering research as it captures practices and decisions that rarely appear in academic venues. However, collecting and assessing it at scale remains difficult because of their heterogeneous sources, formats, and APIs that impede reproducible, large-scale synthesis. To address this issue, we present GLiSE, a prompt

Melikşah Türker, A. Ebrar Kızıloğlu, Onur Güngör, Susan Üsküdarlı

Since the inception of BERT, encoder-only Transformers have evolved significantly in computational efficiency, training stability, and long-context modeling. ModernBERT consolidates these advances by integrating Rotary Positional Embeddings (RoPE), FlashAttention, and refined normalization. Despite these developments, Turkish NLP lacks a monolingual encoder

Matthieu Destrade, Oumayma Bounou, Quentin Le Lidec, Jean Ponce

Building deep learning models that can reason about their environment requires capturing its underlying dynamics. Joint-Embedded Predictive Architectures (JEPA) provide a promising framework to model such dynamics by learning representations and predictors through a self-supervised prediction objective. However, their ability to support effective action plan

Victor Amberger

This article improves the estimate of $|S_1(t_2)-S_1(t_1)|$, which is the definite integral of the argument of the Riemann zeta-function between $t_1$ and $t_2$. Estimates of this quantity are needed to apply Turing's method to compute the exact number of zeta zeros up to a given height.

Luis F. V. Thomazini, Alexandre F. Fonseca

Titanium MXenes are two-dimensional inorganic structures composed of titanium and carbon or nitrogen elements, with distinctive electronic, thermal and mechanical properties. Despite the extensive experimental investigation, there is a paucity of computational studies at the level of classical molecular dynamics (MD). As demonstrated in a preceding study, kn

Soham Pramanik, Vimal William, Arnab Raha, Debayan Das

The rapid advancement in AI architectures and the proliferation of AI-enabled systems have intensified the need for domain-specific architectures that enhance both the acceleration and energy efficiency of AI inference, particularly at the edge. This need arises from the significant resource constraints-such as computational cost and energy consumption-assoc

Itay Hen

Exponential divided differences arise in numerical linear algebra, matrix-function evaluation, and quantum Monte Carlo simulations, where they serve as kernel weights for time evolution and observable estimation. Efficient and numerically stable evaluation of high-order exponential divided differences for dynamically evolving node sets remains a significant

Xu Zhang, Yanli Ai, Yanxia Zhang, Yuming Fu

Quasar samples remain severely incomplete at low Galactic latitudes because of strong extinction and source confusion. We conduct a systematic search for quasars behind the Galactic plane using X-ray sources from the Chandra Source Catalog (CSC 2.1), combined with optical data from Gaia DR3 and mid-infrared data from CatWISE2020. Using spectroscopically conf

Antoni Mestre, Xavier Naya, Manoli Albert, Vicente Pelechano

The diffusion of artificial intelligence, particularly generative models, is expected to transform labor markets in uneven ways across sectors, territories, and social groups. This paper proposes a methodological framework to estimate the potential exposure of employment to AI using sector based data, addressing the limitations of occupation centered approac

David B. Massey

By using our previous results on L\^e modules and an upper-bound on the betti numbers which we proved with L\^e, we investigate the cohomology of Milnor fibers and the internal local systems given by the vanishing cycles of hypersurfaces with one-dimensional singular sets and small L\^e numbers.

Corwin Cheung, Marcos Johnson-Noya, Michael Xiang, Dominic Chang

We construct the first physics-informed neural-network (PINN) surrogates for relativistic magnetohydrodynamics (RMHD) using a hybrid PDE and data-driven workflow. Instead of training for the conservative form of the equations, we work with Jacobians or PDE characteristics directly in terms of primitive variables. We further add to the trainable system the di

Min Zhu, Jingmin Sun, Zecheng Zhang, Hayden Schaeffer

Partial differential equations (PDEs) govern a wide range of physical systems, and recent multimodal foundation models have shown promise for learning PDE solution operators across diverse equation families. However, existing multi-operator learning approaches are data-hungry and neglect physics during training. Here, we propose a physics-informed multimodal

Jamie J. Alnasir

Traditional flight computers -- including mechanical "whiz-wheels" (e.g. E6B, CRP series) and electronic flight calculators (e.g. ASA CX-3, Sportys E6-B) -- have long played a central role in flight planning and training within general aviation (GA). While these tools remain pedagogically valuable, their fixed form factors, constrained interaction models, an

Shuntian Zheng, Jiaqi Li, Guangming Wang, Minzhe Ni

Millimeter-wave (mmWave) radar enables privacy-preserving, illumination-invariant Human Pose Estimation (HPE). However, current mmWave-based HPE systems face a signal-noise dilemma: Heatmaps retain human reflections but embed environmental clutter, while Point Clouds (PC) suppress noise through aggressive thresholding but discard informative human reflection

Ken Huang, Jerry Huang

Reinforcement learning is increasingly used to transform large language models into agentic systems that act over long horizons, invoke tools, and manage memory under partial observability. While recent work has demonstrated performance gains through tool learning, verifiable rewards, and continual training, deployed self-improving agents raise unresolved se

Emanuela Furfaro, Simone Mosciatti

As generative AI becomes increasingly embedded in everyday life, the thoughtful and intentional integration of AI-based tools into statistics education has become essential. We address this need with a focus on homework assignments. We propose the use of LLMs as an opportunity for instructors to integrate more pedagogical approaches into homework design, by

Romain Branchereau

We define a regularized lift from harmonic weak Maass forms of weight $2-N$ to differential forms of degree $N-1$ on the symmetric space $\SL_N(\R)/\SO(N)$, that are smooth outside of certain modular symbols. We show that this lift is adjoint to the derivative of a theta lift. We compute periods of the regularized lift over tori and relate them to Fourier co