December 2025 arXiv papers — page 113

Carolina Montecinos, Doug Geisler, Cesar Muñoz, Sandro Villanova

Context. With CAPOS, we can mitigate the observational difficulties limiting access to bulge globular clusters in the optical and investigate them in more detail in the near-IR. Aims. To perform a rigorous abundance analysis of the metal-rich bulge globular cluster NGC 6304, in order to determine its detailed chemical composition and identify multiple popula

Atalay Denknalbant, Emre Sezdi, Zeki Furkan Kutlu

Financial exclusion constrains entrepreneurship, increases income volatility, and widens wealth gaps. Underbanked consumers in Istanbul often have no bureau file because their earnings and payments flow through informal channels. To study how such borrowers can be evaluated we create a synthetic dataset of one hundred thousand Istanbul residents that reprodu

F. Spada, A. C. Lanzafame

In recent years, ground- and space-based photometric surveys have characterized the rotational evolution of solar-like stars to an unprecedented level of detail. In this work we focus on the slow-rotator sequence, an emergent feature recognizable in the color-period diagram of Galactic open clusters. Understanding the evolution of this sequence is a promisin

Ruonan Xu, Xiye Yang

Using only retrospective data, we study the problem of predicting treatment effects for the same treatment/policy implemented in a different location or time period. We propose a distributionally robust estimator that minimizes the worst-case mean squared error for the prediction of treatment effect over a class of distributions defined by a Wasserstein neig

Ali Lakhal, Konstantin Stepanyantz

We consider an ${\cal N}=1$ nonrenormalizable supersymmetric gauge theory with the superpotential quartic in the chiral matter superfields. With the help of the Slavnov's higher covariant derivative regularization it is demonstrated that (in the lowest nontrivial order) the leading power divergent quantum correction to the gauge coupling constant is given by

Mohammad Abu-Shaira, Weishi Shi

Real-world datasets frequently exhibit evolving data distributions, reflecting temporal variations and underlying shifts. Overlooking this phenomenon, known as concept drift, can substantially degrade the predictive performance of the model. Furthermore, the presence of hyperparameters in online models exacerbates this issue, as these parameters are typicall

Zaid Tahir, Ahmed Sanaullah, Sahan Bandara, Ulrich Drepper

FPGA-based SmartNICs and IoT devices integrating soft-processors for network function execution have emerged to address the limited hardware reconfigurability of DPUs and MCUs. However, existing FPGA-based solutions lack a highly configurable many-core architecture specialized for network packet processing. This work presents VeBPF many-core architecture, a

Mosh Levy, Zohar Elyoseph, Shauli Ravfogel, Yoav Goldberg

Large Language Models (LLMs) can generate reasoning tokens before their final answer to boost performance on complex tasks. While these sequences seem like human thought processes, empirical evidence reveals that they are not a faithful explanation of the model's actual reasoning process. To address this gap between appearance and function, we introduce the

Haochong Chen, Xincheng Cao, Levent Guvenc, Bilin Aksun-Guvenc

Ensuring the safety of Vulnerable Road Users (VRUs) is a critical challenge in the development of advanced autonomous driving systems in smart cities. Among vulnerable road users, bicyclists present unique characteristics that make their safety both critical and also manageable. Vehicles often travel at significantly higher relative speeds when interacting w

Pedro Henrique Luz de Araujo, Michael A. Hedderich, Ali Modarressi, Hinrich Schuetze

Persona-assigned large language models (LLMs) are used in domains such as education, healthcare, and sociodemographic simulation. Yet, they are typically evaluated only in short, single-round settings that do not reflect real-world usage. We introduce an evaluation protocol that combines long persona dialogues (over 100 rounds) and evaluation datasets to cre

Hao Wang, Ashish Bastola, Chaoyi Zhou, Wenhui Zhu

As generative models become increasingly capable of producing high-fidelity visual content, the demand for efficient, interpretable, and editable image representations has grown substantially. Recent advances in 2D Gaussian Splatting (2DGS) have emerged as a promising solution, offering explicit control, high interpretability, and real-time rendering capabil

Reeteesha Roy

With the recent development and integration of autonomous vehicles (AVs) in transportation systems of the modern world, the emphasis on customizing user interfaces to optimize the overall user experience has been growing expediently. Therefore, understanding user needs and preferences is essential to the acceptance and trust of these technologies as they con

Gianfranco Cariolaro, Edi Ruffa, Amir Mohammad Yaghoobianzadeh, Jawad A. Salehi

The quantum Fourier transform for discrete variable (dvQFT) is an efficient algorithm for several applications. It is usually considered for the processing of quantum bits (qubits) and its efficient implementation is obtained with two elementary components: the Hadamard gate and the controlled--phase gate. In this paper, the quantum Fourier transform operati

Thales Sales Almeida, Rodrigo Nogueira, Hélio Pedrini

Continued pretraining extends a language model's capabilities by further exposing it to additional data, often tailored to a specific linguistic or domain context. This strategy has emerged as an efficient alternative to full retraining when adapting general-purpose models to new settings. In this work, we investigate this paradigm through Curi\'o 7B, a 7-bi

Sharim Khan, Paul Landes, Adam Cross, Jimeng Sun

Social Determinants of Health correlate with patient outcomes but are rarely captured in structured data. Recent attention has been given to automatically extracting these markers from clinical text to supplement diagnostic systems with knowledge of patients' social circumstances. Large language models demonstrate strong performance in identifying Social Det

Mohammad Jalili Torkamani, Israt Zarin

Voice-based interaction has emerged as a natural and intuitive modality for controlling IoT devices. However, speech-driven edge devices face a fundamental trade-off between cloud-based solutions, which offer stronger language understanding capabilities at the cost of latency, connectivity dependence, and privacy concerns, and edge-based solutions, which pro

Tianjiao Yu, Xinzhuo Li, Yifan Shen, Yuanzhe Liu

Recent advances in large multimodal models suggest that explicit reasoning mechanisms play a critical role in improving model reliability, interpretability, and cross-modal alignment. While such reasoning-centric approaches have been proven effective in language and vision tasks, their extension to 3D remains underdeveloped. CoRe3D introduces a unified 3D un

Thiparat Chotibut, Oleg Evnin, Weerawit Horinouchi

Training recurrent neuronal networks consisting of excitatory (E) and inhibitory (I) units with additive noise for working memory computation slows and diversifies inhibitory timescales, leading to improved task performance that is attributed to emergent marginally stable equilibria [PNAS 122 (2025) e2316745122]. Yet the link between trained network characte

Yihan Guo, Lek-Heng Lim

We show that some of the best-known matrix decompositions of some of the best-known random matrix ensembles give us the unique $G$-invariant uniform distributions on some of the best-known manifolds. The eigenvectors distributions of the Gaussian, Laguerre, and Jacobi ensembles are all given by the uniform distribution on the complete flag manifold. The sing

V. Prayag, L. Levin, M. Geyer, B. W. Stappers

We report the discovery of 12 new radio pulsars in the Large Magellanic Cloud (LMC) as part of the TRAPUM (TRAnsients and PUlsars with MeerKAT) Large Survey Project, using the MeerKAT L-band receivers (856--1712\,MHz). These pulsars, discovered in 18 new pointings with 2\,hour integration times, bring the total number of pulsars identified by this ongoing su

Gustavo A. L. Forão, Jonas Berx, Tan Van Vu, Carlos E. Fiore

Spontaneous symmetry breaking is a hallmark of equilibrium systems, typically characterized by a single critical point separating ordered and disordered phases. Recently, a novel class of non-equilibrium phase transitions was uncovered [Phys. Rev. Res. {\bf 7}, L032049 (2025)], showing that the combined effects of simultaneous contact with thermal baths at d

C. Vergu

We illustrate how methods from Landau analysis that have been developed for studying the properties of massive Feynman integrals in momentum space can be generalized to massless integrals. We consider integrals with both massive and massless propagators in arbitrary dimensions, paying attention to square root branch points. By focusing on a number of well-ch

Incheol Baek, Hyungbin Kim, Minseo Kim, Yon Dohn Chung

Federated Learning (FL) enables collaborative training across multiple clients while preserving data privacy, yet it struggles with data heterogeneity, where clients' data are not distributed independently and identically (non-IID). This causes local drift, hindering global model convergence. To address this, we introduce Federated Learning with Feedback Ali

Zhiquan Zhang, Omar Muhammetkulyyev, Tichakorn Wongpiromsarn, Melkior Ornik

We study the Stochastic Shortest Path (SSP) problem for autonomous systems with mixed max-sum cost aggregations under Linear Temporal Logic constraints. Classical SSP formulations rely on sum-aggregated costs, which are suitable for cumulative quantities such as time or energy but fail to capture bottleneck-style objectives such as avoiding high-risk transit

Sina Jani, Arman Heidari, Amirmohammad Anvari, Zahra Rahimi

The rapid acceleration of scientific publishing has created substantial challenges for researchers attempting to discover, contextualize, and interpret relevant literature. Traditional keyword-based search systems provide limited semantic understanding, while existing AI-driven tools typically focus on isolated tasks such as retrieval, clustering, or bibliom

Biman Bagchi

Upon rapid quenching of temperature of a glass forming liquid, the system falls out of equilibrium due its finite relaxation time. Additionally, the relaxation becomes progressively slower with time. The created nonequilibrium state of the glassy system is conveniently described by introducing a fictive temperature which provides the instantaneous state of t

Jiping Luo, Erfan Delfani, Mehrdad Salimnejad, Nikolaos Pappas

Future wireless networks must support real-time, data-driven cyber-physical systems in which communication is tightly coupled with sensing, inference, control, and decision-making. Traditional communication paradigms centered on accuracy, throughput, and latency are increasingly inadequate for these systems, where the value of information depends on its sema

Yue Jiang, Dingkang Yang, Minghao Han, Jinghang Han

Despite rapid progress in multimodal large language models (MLLMs) and emerging omni-modal architectures, current benchmarks remain limited in scope and integration, suffering from incomplete modality coverage, restricted interaction to text-centric outputs, and weak interdependence and complementarity among modalities. To bridge these gaps, we introduce Fys

Tingwei Cao, Yan Xu

High penetration of renewable energy sources (RES) introduces significant uncertainty and intermittency into microgrid operations, posing challenges to economic and reliable scheduling. To address this, this paper proposes an end-to-end decision-focused framework that jointly optimizes probabilistic forecasting and robust operation for microgrids. A multilay

Elena Bellomi, John A. ZuHone, Nhut Truong, Irina Zhuravleva

High-resolution X-ray spectroscopy with XRISM has revealed complex, non-monotonic velocity dispersion profiles in the Perseus cluster, pointing to a complex interplay between at least two physical drivers of motions caused by dynamical processes within the intracluster medium (ICM). To further explore this conclusion, we perform a suite of idealized, control

Hanbeot Park, Yunjeong Cho, Hunhee Kim

Restoring speech communication from neural signals is a central goal of brain-computer interface research, yet EEG-based speech reconstruction remains challenging due to limited spatial resolution, susceptibility to noise, and the absence of temporally aligned acoustic targets in imagined speech. In this study, we propose an EEG-to-Voice paradigm that direct

Elisabetta Carlini, Ahmad Zorkot

We address the numerical solution of second-order Mean Field Game problems through Newton iterations in infinite dimensions, introduced in [14], where quadratic convergence of the method was rigorously established. Building upon this theoretical framework, we develop new numerical discretization techniques, including both a finite difference and a semi-Lagra

Balasaravanan Thoravi Kumaravel

Creating new documents by synthesizing information from existing sources is an important part of knowledge work in many domains. This process often involves gathering content from multiple documents, organizing it, and then transforming it into new forms such as reports, slides, or spreadsheets. While recent advances in Generative AI have shown potential in

Zhenya Yang, Zhe Liu, Yuxiang Lu, Liping Hou

Physics-aware driving world model is essential for drive planning, out-of-distribution data synthesis, and closed-loop evaluation. However, existing methods often rely on a single diffusion model to directly map driving actions to videos, which makes learning difficult and leads to physically inconsistent outputs. To overcome these challenges, we propose Gen

Sahil Bhola, Karthik Duraisamy

Learning surrogate models for physical systems with latent uncertainty remains challenging in data-scarce regimes: deterministic neural operators fail to characterize uncertainty, while generative approaches require large ensembles of high-fidelity solution operator simulations and often sacrifice resolution generalizability. In this work, we propose a resid

Yajie Yang, Yuqing Zhao, Xiaochao Xi, Yinan Zhu

Artificial Intelligence Generated Content (AIGC) assisting image production triggers controversy in journalism while attracting attention from media agencies. Key issues involve misinformation, authenticity, semantic fidelity, and interpretability. Most AIGC tools are opaque "black boxes," hindering the dual demands of content accuracy and semantic alignment

Shiyun Wen

To find all two-dimensional equivariant symplectic submanifolds in symplectic toric manifolds, we combine the convex geometry of Delzant polytopes with local equivariant symplectic models and obtain a criterion for determining when a two-dimensional submanifold is an equivariant symplectic submanifold in a toric manifold.

Satyam Sinha, Rekha Kumari, Junaid Majeed Bhat, Abhishek Dhar

The observed robustly quantized Hall conductance in quantum Hall systems and Chern insulators (CI) is normally understood in terms of the bulk topology of isolated systems, not coupled to leads. It is assumed that the leads act as inert reservoirs. Within a model of a CI coupled to leads with a cylindrical geometry, we show that this is not always true. We i

C. Afonso, J. -N. Albert, R. Ansari, E. Aubourg

The EROS project (Exp\'erience de Recherche d'Objets Sombres) carried out photometric surveys of dense stellar fields towards the Magellanic Clouds (LMC and SMC), the Galactic Bulge and Galactic spiral arms, over the period 1990-2003. The main goal of the experiment was to search for the Galactic Dark Matter in the form of massive compact objects (machos), t

Cheng-Cheng Li, Xiong-Hui Cao, Feng-Kun Guo

We generalize chiral perturbation theory with spinless matter fields in the fundamental representation of ${\rm SU}(N)$ to curved spacetime in the presence of an external gravitational field. This work is motivated by recent interest in investigating energy-momentum tensor matrix elements of matter fields. The complete chiral Lagrangian is constructed, inclu

Pingping Yin, Xiyun Jiao

We propose a unified framework that employs variational inference (VI) with (conditional) normalizing flows (NFs) to train both between-model and within-model proposals for reversible jump Markov chain Monte Carlo, enabling efficient trans-dimensional Bayesian inference. In contrast to the transport reversible jump (TRJ) of Davies et al. (2023), which optimi

Chuangkai Li, Feng Ye, Hong Jin, Xuanyi Liu

We demonstrate a compact all-fiber nonlinear-polarization-evolution (NPE) fiber laser capable of outputting wavelength-manipulated multiple laser states in the C + L band. Leveraging the intracavity birefringence-induced filtering effect, without external spectral filters, the laser achieves wavelength-tunable conventional solitons (CSs) and soliton molecule

Dezhi Yi, Wei Guo, Wenyang Cui, Wenxuan He

Sequential recommendation aims to model users' evolving preferences based on their historical interactions. Recent advances leverage Transformer-based architectures to capture global dependencies, but existing methods often suffer from high computational overhead, primarily due to discontinuous memory access in temporal encoding and dense attention over long

Wen-Chia Lo, Chao-Yuan Wang, Yu-Tung Tsai, Sheng-Yao Huang

Entanglement, one of the most representative phenomena in quantum mechanics, has been widely used for fundamental studies and modern quantum technologies. In this paper, we report the observation of nonlocal cancellation and addition of optical rotations with polarization-entangled photons in fructose solutions. The entanglement also enables probing optical

V. A. Vassiliev

We list all connected components of sets of non-discriminant functions near all {\em parabolic} function singularities (which are the second most important family of singularity classes of smooth functions after {\em simple} singularities). Thus, we prove (and improve in one particular case) all the corresponding conjectures from the previous work \cite{para

Syeda Zunaira Ahmed, Hejab Tahira Beg, Maryam Khalid

Quality of Experience (QoE) prediction is a critical component of modern multimedia systems, particularly for adaptive video streaming in 5G networks. Accurate QoE estimation enables intelligent resource management and supports user centric service delivery. Existing QoE prediction approaches primarily rely on limited datasets and assume uniform user percept

Zhimin Chen, Bryan Kelly, Semyon Malamud

Machine learning (ML) methods are highly flexible, but their ability to approximate the true data-generating process is fundamentally constrained by finite samples. We characterize a universal lower bound, the Limits-to-Learning Gap (LLG), quantifying the unavoidable discrepancy between a model's empirical fit and the population benchmark. Recovering the tru

Zhan Wang, Yuxin Wang, Kun Jiang, Jiangping Hu

The discovery of high-T$_c$ superconductor in Ruddlesden-Popper nickelate materials represented by La$_3$Ni$_2$O$_7$ has opened new directions in the quest for unconventional superconductivity. A central unresolved issue concerns the pairing symmetry of the superconducting order. In this paper, we model the superconducting order of La$_3$Ni$_2$O$_7$ using th

Chengyuan Wu, Heran Xiong, Shi Jia, Zhengyang Zhang

Common envelope evolution is a critical but still poorly understood phase in binary evolution. It plays a key role in forming close binaries such as hot subdwarfs, double white dwarfs, X-ray binaries, and double neutron stars. However, its outcomes remain highly uncertain. Depending on the efficiency of envelope ejection, a system may either survive as a clo

Georgy Ishmaev, Emmanuelle Anceaume, Davide Frey, François Taïani

Layer 2 rollups improve throughput and fees, but can reintroduce risk through operator discretion and information asymmetry. We ask which operator and governance designs produce ethically problematic user risk. We adapt Ethical Risk Analysis to rollup architectures, build a role-based taxonomy of decision authority and exposure, and pair the framework with t

Yuriy N. Bakhvalov

This paper studies a machine learning regression problem as a multivariate approximation problem using the framework of the theory of random functions. An ab initio derivation of a regression method is proposed, starting from postulates of indifference. It is shown that if a probability measure on an infinite-dimensional function space possesses natural symm

Jingzhe Ding, Shengda Long, Changxin Pu, Huan Zhou

Recent advances in coding agents suggest rapid progress toward autonomous software development, yet existing benchmarks fail to rigorously evaluate the long-horizon capabilities required to build complete software systems. Most prior evaluations focus on localized code generation, scaffolded completion, or short-term repair tasks, leaving open the question o

Yimin Huang, Honghui Liu, Cosimo Bambi, Adam Ingram

We present a new non-relativistic reflection model, DAO, designed to calculate reflection spectra in the rest frame of accretion disks in X-ray binaries and active galactic nuclei. The model couples the XSTAR code, which treats atomic processes, with the Feautrier method for solving the radiative transfer equation. A key feature of DAO is the incorporation o

Sudhanshu Sekhar Tripathy, Bichitrananda Behera

Network Intrusion Detection Systems (NIDS) are essential for securing networks by identifying and mitigating unauthorized activities indicative of cyberattacks. As cyber threats grow increasingly sophisticated, NIDS must evolve to detect both emerging threats and deviations from normal behavior. This study explores the application of machine learning (ML) me

Dinggao Liu, Robert Ślepaczuk, Zhenpeng Tang

Accurately forecasting daily exchange rate returns represents a longstanding challenge in international finance, as the exchange rate returns are driven by a multitude of correlated market factors and exhibit high-frequency fluctuations. This paper proposes EXFormer, a novel Transformer-based architecture specifically designed for forecasting the daily excha

Vasyl Kovalchuk, Ewa Eliza Rożko, Barbara Gołubowska

A generalization of the Euler's elastic problem, i.e., finding a stationary configuration (planar elastica) of the Bernoulli's thin ideal elastic rod with boundary conditions defined through fixed endpoints and/or tangents at the endpoints, for the chosen nonlocal differential constitutive stress-strain relation (i.e., nonlocal theory of elasticity) is consi

Sayan Jana, Lea Sirota

We present a theoretical investigation of wave dynamics in two-dimensional non-Hermitian $\mathcal{PT}$-symmetric lattices, where onsite, as well as inter-site control couplings are employed. Our analysis shows that these couplings can be tuned to achieve a direction-sensitive group velocity enhancement beyond what is possible in the uncontrolled (Hermitian)

Jiachen Tian, Yu Han, Xiao Li, Shi Jin

Mid-band extra-large-scale multiple-input multiple-output (XL-MIMO), emerging as a critical enabler for future communication systems, is expected to deliver significantly higher throughput by leveraging the extended bandwidth and enlarged antenna aperture. However, power consumption remains a significant concern due to the enlarged system dimension, undersco

Yigen Peng, Jiayin Che, Ruihua Xu, Shang Wang

In strong laser-atom interactions, the Coulomb potential can affect the trajectories of rescattering electron in high-order harmonic generation (HHG). Here, by constructing a semi-analytical Coulomb-included model and comparing it with numerical experiments that allow for direct observation of electron trajectories, we identify the role of Coulomb potential

Ankit Anand, Shoucheng Wang

We analytically investigate the Widom line and universal supercritical crossover for charged AdS black holes threaded by a global monopole. We compute thermodynamic variables in both the extended and canonical ensembles. We derive the scaled variance $\Omega$ using the Gibbs free energy and locate the Widom line as the extrema of this. Using mean-field expan

Tarik Viehmann, Daniel Swoboda, Samridhi Kalra, Himanshu Grover

CLIPS is a rule-based programming language for building knowledge-driven applications, well suited for the complex task of coordinating autonomous robots. Inspired by the CLIPS-Executive originally developed for the lesser known Fawkes robotics framework, we present an Integration of CLIPS into the ROS ecosystem. Additionally, we show the flexibility of CLIP

Michael Geiwitz, Owen Rivers Page, Marina E. Nichols, Tio Marello

Graphene Field-Effect Transistors (GFETs) are increasingly employed as biochemical sensors due to their exceptional electronic properties, surface sensitivity, and potential for miniaturization. A critical challenge in deploying GFETs is determining the optimal electrical readout strategy. GFETs are typically operated with either of two modalities: one measu

Mingxuan Liu, Mateusz Ruszkowski, Ellen Zweibel, Xiaochen Sun

Shear flows, ubiquitous in space and astrophysical plasmas, can accelerate particles through turbulence excited by the Kelvin-Helmholtz instability. We present the first numerical study of particle acceleration in sustained, subsonic, non-relativistic, and magnetized turbulence driven purely by velocity shear, including full particle backreaction. Using two-

Anrafel Fernandes Pereira, Maria Teresa Baldassarre, Daniel Mendez, Marcos Kalinowski

Poorly formulated research problems can compromise the practical relevance of Software Engineering studies by not reflecting the complexities of industrial practice. This vision paper explores the use of artificial intelligence agents to support SE researchers during the early stage of a research project, the formulation of the research problem. Based on the

Sehyun Kim, Hye Jun Lee, Jiwoo Lee, Changgyun Kim

The spinal angle is an important indicator of body balance. It is important to restore the 3D shape of the human body and estimate the spine center line. Existing mul-ti-image-based body restoration methods require expensive equipment and complex pro-cedures, and single image-based body restoration methods have limitations in that it is difficult to accurate

Mattia Catellani, Marta Gabbi, Lorenzo Sabattini

We address the problem of coordinating a team of robots to cover an unknown environment while ensuring safe operation and avoiding collisions with non-cooperative agents. Traditional coverage strategies often rely on simplified assumptions, such as known or convex environments and static density functions, and struggle to adapt to real-world scenarios, espec

Xuanzhang Liu, Jianglun Feng, Zhuoran Zhuang, Junzhe Zhao

Large Language Model (LLM) agents trained with reinforcement learning (RL) show great promise for solving complex, multi-step tasks. However, their performance is often crippled by "Context Explosion", where the accumulation of long text outputs overwhelms the model's context window and leads to reasoning failures. To address this, we introduce CoDA, a Conte

Peng Jing, Yichao Dang, Lejing Zhang, Yang He

In the established paradigm of jet quenching in relativistic heavy-ion collisions, jets from initial hard parton scatterings are suppressed due to their interaction with the quark-gluon plasma (QGP), serving as crucial tomographic probes of QGP properties. Within the linear Boltzmann transport model, we find that the QGP is also capable of absorbing and repr

Noam Nissan

We compute the delta power operation for morava E-theory of height 2 at the prime 3. The delta power operation was defined using the notion of higher semi additivity by Shachar Carmeli, Tomer M. Schlank and Lior Yanovski. We briefly survey the basic definitions in higher semi-additivity. Using explicit formulas for moduli spaces of elliptic curves and comput

Harol Moreno Fernandez, Mohammad Amirabbasi, Crizaldo Jr. Mempin, Andrea Trapletti

Controlling lattice oxygen reactivity in earth abundant OER catalysts requires precise tuning of defect chemistry in the oxide lattice. Here, we combine DFT+U calculations with plasma assisted synthesis to show how O2 and H2O in the discharge govern vacancy formation, electronic structure, and catalytic predisposition in NiO thin films. Oxygen rich plasmas g

Yuanyuan Xu, Qiang Zeng

We establish large deviation principles for the extremal eigenvalues of the Ginibre ensembles with good rate functions. In contrast to the typical estimates for the extremal eigenvalues, the large deviations for the real Ginibre ensemble come from the eigenvalues lying on the real line. Moreover, we also derive deviation estimates for the second leading term

Stefan Balauca, Ada-Astrid Balauca, Adrian Iftene

Hybrid quantum-classical models represent a crucial step toward leveraging near-term quantum devices for sequential data processing. We present Quantum Recurrent Neural Networks (QRNNs) and Quantum Convolutional Neural Networks (QCNNs) as hybrid quantum language models, reporting the first empirical demonstration of generative language modeling trained and e

Chiara Barà, Yuri Antonacci, Laura Sparacino, Helder Pinto

Assessing the synergistic high-order behaviors (HOBs) that emerge from underlying structural mechanisms is crucial to characterize complex systems. This work leverages the combined use of predictability and information measures to detect and quantify HOBs in synthetic and physiological network systems. After providing formal definitions of mechanisms and beh

Anthime Valin

We study optimal trade execution with a hard-zero terminal inventory constraint, modeled via Hamilton-Jacobi-Bellman (HJB) equations. Vanilla PINNs often under-enforce this constraint and produce unstable controls. We propose a Multi-Trajectory PINN (MT-PINN) that adds a rollout-based trajectory loss and propagates a terminal penalty on terminal inventory vi

Hugo Roger Paz

Risk-based AI regulation has become the dominant paradigm in AI governance, promising proportional controls aligned with anticipated harms. This paper argues that such frameworks often fail for structural reasons: they implicitly assume linear causality, stable system boundaries, and largely predictable responses to regulation. In practice, AI operates withi

Enhong Mu, Minami Yoda, Yan Zhang, Mingyue Zhang

The widespread adoption of the "Games as a Service" model necessitates frequent content updates, placing immense pressure on quality assurance. In response, automated game testing has been viewed as a promising solution to cope with this demanding release cadence. However, existing automated testing approaches typically create a dichotomy: code-centric metho

Chen Peng, Xinfu Zheng, Duanfu Chen, Hanxiao Zhang

We propose an efficient scheme for achieving mode-tunable unidirectional reflection lasing (URL) by establishing a coherent gain atomic system to amplify the probe field and ingeniously designing the one-dimensional (1D) defective atomic lattice. This lattice not only replaces the resonant cavity to provide a distributed feedback mechanism but also breaks th

Sougata Bhattacharyya, Sovik Roy

This work investigates the topological structure of multipartite entanglement in symmetric Dicke states $|D_n^{(k)}\rangle$. By viewing qubits as topological loops, we establish a direct correspondence between the recursive measurement dynamics of Dicke states and the stability of $n$-Hopf links. We utilize the Schmidt rank to quantify bipartite entanglement

Boyuan Li, Sipeng Zheng, Bin Cao, Ruihua Song

Extracting human motion from large-scale web videos offers a scalable solution to the data scarcity issue in character animation. However, some human parts in many video frames cannot be seen due to off-screen captures or occlusions. It brings a dilemma: discarding the data missing any part limits scale and diversity, while retaining it compromises data qual

Edmondo Valvo, Michele Jakob, Patrick Del Vecchio, Maximilian Rimbach-Russ

Hole spin qubits in planar germanium heterostructures are frontrunners for scalable semiconductor quantum computing. However, their current performance is mostly limited by large dot-to-dot variability that leads to uncontrolled qubit energies and random tilts in the spin quantization axis. Here, we propose a systematic and local method to engineer the spin

Xue Li, Xiaonan Song, Henry Hu

Real-world deployment of Vision-Language Models (VLMs) is hindered by high computational demands, as existing architectures inefficiently process all tokens uniformly. We introduce Adaptive Token Pruning (ATP), a dynamic inference mechanism that retains only the most informative tokens based on contextual relevance. ATP operates at the vision-language interf

Xiaolei Yang

In this paper, we propose a new method for calculating integrals for a special class of integrands. As an application, we show how this method can be used to derive optimal pointwise temporal estimates for a class of nonlocal evolution equations. Compared with other methods, our approach can obtain both upper bound and lower bound simultaneously.

Anrafel Fernandes Pereira, Maria Teresa Baldassarre, Daniel Mendez, Jürgen Börstler

[Background] A well-formulated research problem is essential for achieving practical relevance in Software Engineering (SE), yet there is a lack of structured guidance in this early phase. [Aims] Our goal is to introduce and evaluate seven attributes identified in the SE literature as relevant for formulating research problems (practical problem, context, im

Julian Chaidez, Yijie Pan

We introduce the notion of a pseudo-Anosov contact structure, which admits a type of singular contact form with pseudo-Anosov Reeb flow. We prove that contact homology detects the free homotopy classes of closed orbits of any pseudo-Anosov Reeb flow and that any pseudo-Anosov contact structure is universally tight and torsion free. Many applications are give

Jing-Ya Zhao, Tong-Yu He, Jia-Jun Yin, Zhan-Wen Han

We propose a parameterized equation of state for dark energy and perform observational tests with the Hubble parameter measurements, the Pantheon supernova sample, and DESI DR2 data. We obtain the best-fit values for the parameters as: $H_0 = 73.28\pm 0.15~\mathrm{km\,s^{-1}\, Mpc^{-1}}$, $Ω_{\rm m} = 0.316\pm 0.011$, and $α= 0.00032\pm 0.00046$, demonstrati

Raquel Izquierdo García

The holographic dictionary is well developed for gravity in asymptotically anti de Sitter $A(AdS_{d+1})$ spacetimes. However, this approach is limited, since many physically relevant configurations, such as bubbling geometries dual to heavy operators, do not arise as an uplift of a lower dimensional $A(AdS_{d+1})$ solution. Instead, they are intrinsic soluti

Anthony Mudet, Souhail Bakkali

Large-scale digitization initiatives have unlocked massive collections of historical newspapers, yet effective computational access remains hindered by OCR corruption, multilingual orthographic variation, and temporal language drift. We develop and evaluate a multilingual Retrieval-Augmented Generation pipeline specifically designed for question answering on

Sumantrak Mukherjee, Serafima Lebedeva, Valentin Margraf, Jonas Hanselle

We propose a novel Bayesian framework for efficient exploration in contextual multi-task multi-armed bandit settings, where the context is only observed partially and dependencies between reward distributions are induced by latent context variables. In order to exploit these structural dependencies, our approach integrates observations across all tasks and l

Mahir Labib Dihan, Tanzima Hashem, Mohammed Eunus Ali, Md Rizwan Parvez

LLM-based agents often operate in a greedy, step-by-step manner, selecting actions solely based on the current observation without considering long-term consequences or alternative paths. This lack of foresight is particularly problematic in web environments, which are only partially observable-limited to browser-visible content (e.g., DOM and UI elements)-w

Suprabha Mukhopadhyay, Yuto Bekki, Xiaojue Zhu, Laurent Gizon

Solar inertial modes are believed to play important diagnostic and dynamical roles in the Sun's differentially rotating convection zone. However, the coupling of these modes to the radiative interior has not yet been discussed. We aim to understand the dependence of the modes on the uniformly rotating sub-adiabatic region below the convection zone and determ

Yongcan Yu, Lingxiao He, Shuo Lu, Lijun Sheng

Recent advances in vision-language models (VLMs) reasoning have been largely attributed to the rise of reinforcement Learning (RL), which has shifted the community's focus away from the supervised fine-tuning (SFT) paradigm. Many studies suggest that introducing the SFT stage not only fails to improve reasoning ability but may also negatively impact model tr

Mansour El Alami, Adam Innan, Nouhaila Innan, Muhammad Shafique

Credit card fraud detection is a critical task in financial security, as fraudulent transactions are rare, highly imbalanced, and often resemble legitimate ones. A wide range of classical machine learning methods, as well as more recent quantum machine learning approaches, have been investigated to address this challenge, each providing valuable progress but

Dongseok Kim, Hyoungsun Choi, Mohamed Jismy Aashik Rasool, Gisung Oh

Prompt engineering is widely used to shape large language model behavior, yet it is often treated as a practical heuristic rather than as a form of natural-language control. This paper develops a cognitive-semantic account in which prompts function as semantic conditions on how a fixed model interprets inputs, foregrounds information, and structures tasks. W

Marwa Ennaceur

We introduce almost periodic Banach--Malcev algebras as a non-associative extension of Bohr's classical theory. Our framework is based on the relative compactness of adjoint orbits $\{e^{t\,\mathrm{ad}(x)}(y)\}$, which yields the spectral characterization $\sigma(\mathrm{ad}(x)) \subseteq i\mathbb{R}$, uniform boundedness of orbit closures in the strong oper

Samarth Sarin, Lovepreet Singh, Bhaskarjit Sarmah, Dhagash Mehta

Agentic memory is emerging as a key enabler for large language models (LLM) to maintain continuity, personalization, and long-term context in extended user interactions, critical capabilities for deploying LLMs as truly interactive and adaptive agents. Agentic memory refers to the memory that provides an LLM with agent-like persistence: the ability to retain

Maryam Arif, Soban Saeed

Our study investigates the interplay between young women's empowerment and Pakistan's economic growth, focusing on how social media use enhances their businesses and drives economic advancement. We utilize a mixed-methods research design, integrating both online and offline random sampling, for our survey of 51 respondents. We also utilized existing datasets

M. Griebel, H. Harbrecht

Kernel interpolation in tensor product reproducing kernel Hilbert spaces allows for the use of sparse grids to mitigate the curse of the dimension. Typically, besides the generic constant, only a dimension dependent power of a logarithm term enters here into complexity estimates. We show that optimized sparse grids can avoid this logarithmic factor when the

Yeray Cordero, Paula García-Molina, Fernando Vilariño

Implicit neural representations (INRs) have become a powerful paradigm for continuous signal modeling and 3D scene reconstruction, yet classical networks suffer from a well-known spectral bias that limits their ability to capture high-frequency details. Quantum Implicit Representation Networks (QIREN) mitigate this limitation by employing parameterized quant

Zisong Cao

In this paper we generalize Vasiliev's higher-spin gravity theory in 3d into $\mathcal{N} = (0, 2)$ case, by which we mean that the asymptotic symmetry of such a gravity theory have the structure of 2d $\mathcal{N} = (0, 2)$ superconformal algebra. While the construction is limited to linearized level, asymptotic symmetry and possible matter content of such

Hung Viet Chu, Steven J. Miller, Garrett Tresch

For two relatively prime positive integers $a, b\in \mathbb{N}$, it is known that exactly one of the two Diophantine equations $$ax + by \ =\ \frac{(a-1)(b-1)}{2}\ \mbox{ and }\ 1 + ax + by \ =\ \frac{(a-1)(b-1)}{2}$$ has a nonnegative integral solution $(x, y)$. Furthermore, the solution is unique. In this note, we summarize recent results and some new ones