November 2025 arXiv papers — page 195

Haomin Li, Fangxin Liu, Chenyang Guan, Zongwu Wang

Barrett's algorithm is one of the most widely used methods for performing modular multiplication, a critical nonlinear operation in modern privacy computing techniques such as homomorphic encryption (HE) and zero-knowledge proofs (ZKP). Since modular multiplication dominates the processing time in these applications, computational complexity and memory limit

I. Bailo, F. Buonora, G. Ciarfaglia, L. T. Consoli

The energy transition is a key theme of the last decades to determine a future of eco-sustainability, and an area of such importance cannot disregard digitization, innovation and the new technological tools available. This is the context in which the Generative Artificial Intelligence models described in this paper are positioned, developed by Engineering In

Aloïs Duguet, Tobias Harks, Martin Schmidt, Julian Schwarz

Generalized Nash equilibrium problems with mixed-integer variables constitute an important class of games in which each player solves a mixed-integer optimization problem, where both the objective and the feasible set is parameterized by the rivals' strategies. However, such games are known for failing to admit exact equilibria and also the assumption of all

Hailin Sun, Xiaojun Chen

This paper introduces a class of two-stage stochastic minimax problems where the first-stage objective function is nonconvex-concave while the second-stage objective function is strongly convex-concave. We establish properties of the second-stage minimax value function and solution functions, and characterize the existence and relationships among saddle poin

Raphaël Mignon-Risse, Peggy Varniere, Fabien Casse

We present general-relativistic hydrodynamical simulations of inviscid circumbinary discs (CBDs) around near equal-mass binary black holes (BBH) in the binary-disc post-decoupling epoch. We use an approximate BBH spacetime with a post-Newtonian inspiral motion trajectory from ${\sim}80 (M/10^7 \mathrm{M_\odot}) \, \mbox{days}$ (separation of ${\sim}\,30$ gra

Riccardo Tripodi

This work introduces audio2chart, a framework for the automatic generation of Guitar Hero style charts directly from raw audio. The task is formalized as a sequence prediction problem, where models are trained to generate discrete chart tokens aligned with the audio on discrete time steps. An unconditional baseline demonstrates strong predictive performance,

A. Di Siena, C. Bourdelle, A. Bañón Navarro, G. Merlo

In this work, we present the first global gyrokinetic simulations of the ITER baseline scenario operating at 15 MA using GENE-Tango electrostatic and electromagnetic simulations. The modeled radial region spans close to the magnetic axis up to rho_tor = 0.6. Our results show a pronounced density peaking, moderated by electromagnetic fluctuations. The predict

Guillaume Aubian, Allen Ibiapina, Luis Kuffner, Reza Naserasr

The balanced chromatic number of a signed graph G is the minimum number of balanced sets that cover all vertices of G. Studying structural conditions which imply bounds on the balanced chromatic number of signed graphs is among the most fundamental problems in graph theory. In this work, we initiate the study of coloring hereditary classes of signed graphs.

Guozhen Zhang, Zixiang Zhou, Teng Hu, Ziqiao Peng

Due to the lack of effective cross-modal modeling, existing open-source audio-video generation methods often exhibit compromised lip synchronization and insufficient semantic consistency. To mitigate these drawbacks, we propose UniAVGen, a unified framework for joint audio and video generation. UniAVGen is anchored in a dual-branch joint synthesis architectu

Y. Alipour Fakhri

This paper develops a geometric and analytical extension of the Finsler--Ginzburg--Landau framework by introducing a distributed control field acting as a translation in the tangent bundle. Within this formulation, the classical Tonelli Lagrangian is deformed into a control--translated Finsler structure, whose Legendre dual induces a uniformly elliptic opera

Yi Yang, Yiming Xu, Timo Kaiser, Hao Cheng

In this report, we present our solution to the MOT25-Spatiotemporal Action Grounding (MOT25-StAG) Challenge. The aim of this challenge is to accurately localize and track multiple objects that match specific and free-form language queries, using video data of complex real-world scenes as input. We model the underlying task as a video retrieval problem and pr

István Gaál

In some previous works we gave algorithms for determining generators of power integral basis in sextic fields with a quadratic subfield, under certain restrictions. The purpose of the present paper is to extend those methods to the general case, when the relative integral basis of the sextic field over the quadratic subfield is of general form. This raises s

Yong-Ming Tian, Shuang Liang, Shao-Qun Zhang, Feng-Lei Fan

While deep learning has achieved remarkable success across a wide range of applications, its theoretical understanding of representation learning remains limited. Deep neural kernels provide a principled framework to interpret over-parameterized neural networks by mapping hierarchical feature transformations into kernel spaces, thereby combining the expressi

Shenghua Wang, Zhen Yin

The rapid growth of open-access (OA) publications has intensified the challenge of identifying relevant scientific papers. Due to privacy constraints and limited access to user interaction data, recent efforts have shifted toward content-based recommendation, which relies solely on textual information. However, existing models typically treat papers as unstr

Natalia Shabala, Finja Tietjen, R. Matthias Geilhufe

Axial or circularly polarized phonons are collective lattice vibrations with angular momentum. Over the past decade they have emerged as a promising mechanism for the manipulation of magnetism, in parallel to well established optical protocols. In particular, coherent axial phonons were shown to induce magnetization in materials without spin-ordering, making

Jindong Hong, Tianjie Chen, Lingjie Luo, Chuanyang Zheng

A recent advancement in Multimodal Large Language Models (MLLMs) research is the emergence of "reasoning MLLMs" that offer explicit control over their internal thinking processes (normally referred as the "thinking mode") alongside the standard "non-thinking mode". This capability allows these models to engage in a step-by-step process of internal deliberati

Mario Bifulco, Luca Roversi

Quantum Annealing (QA) offers a promising framework for solving NP-hard optimization problems, but its effectiveness is constrained by the topology of the underlying quantum hardware. Solving an optimization problem $P$ via QA involves a hardware-aware circuit compilation which requires representing $P$ as a graph $G_P$ and embedding it into the hardware con

Irene Schicker, Marianne Bügelmayer-Blaschek, Annemarie Lexer, Katharina Baier

When Austrian hydropower production plummeted by 44% in early 2025 due to reduced snowpack, it exposed a critical vulnerability: standard meteorological and climatological datasets systematically fail in mountain regions that hold untapped renewable potential. This perspectives paper evaluates emerging solutions to the Alpine energy-climate data gap, analyzi

Sanjay Mishra

This expository article presents a self-contained introduction to simplicial homology for finite simplicial complexes, emphasizing concrete computation and geometric intuition. Beginning with orientations of simplices and the construction of free abelian chain groups, the boundary operators are defined via the alternating-sum formula and shown to satisfy the

A. Bulgarelli, N. Parmiggiani, L. Castaldini, R. Falco

We implemented a real-time data processor (rta-dp) framework that can be used to develop real-time analysis pipelines and data handling systems to manage high-throughput data streams with distributed applications in the context of ground and space astrophysical projects and high-energy instruments. The rta-dp is based on the ZeroMQ in-memory communication fr

Mauro Orazio Drago, Luca Carlini, Pelinsu Celebi Balyemez, Dennis Pierantozzi

Video Question Answering (VideoQA) in the surgical domain aims to enhance intraoperative understanding by enabling AI models to reason over temporally coherent events rather than isolated frames. Current approaches are limited to static image features, and available datasets often lack temporal annotations, ignoring the dynamics critical for accurate procedu

Tom R. Hepworth, Simon L. Cornish, Philip D. Gregory

Precise control over rotational angular momentum is at the heart of recent advances in quantum chemistry, quantum simulation, and quantum computation with ultracold bialkali molecules. Each rotational state comprises a rich manifold of hyperfine states arising from combinations of rotation and nuclear spins; this often yields hundreds of transitions availabl

Guy Bouchitté, Minh Phan

We study a problem of minimal surfaces with free boundary written in the form of a non convex minimization problem. Our aim is to characterize optimal solutions by finding a suitable calibration field. A natural upper bound of the infimum is given by a variant of the Cheeger problem that we solve explicitly proving the optimality thanks to the construction o

D. L. Boiko, P. Demolon, J. -F. Carlin, E. Eriksson

We report on unique features of ultra-thin GaN/AlN quantum wells and demonstrate a new field emission device for human-safe disinfection with an external quantum efficiency of 18% and superior reliability, as compared to far-UVC LEDs. We investigate the behavior of a Shockley-Read-Hall recombination via Al vacancies-oxygen complexes in AlN barriers as a func

Aakash Ravindra Shinde, Jukka K. Nurminen

Data dimensionality reduction techniques are often utilized in the implementation of Quantum Machine Learning models to address two significant issues: the constraints of NISQ quantum devices, which are characterized by noise and a limited number of qubits, and the challenge of simulating a large number of qubits on classical devices. It also raises concerns

Shouvik Sadhukhan, C. S. Narayanamurthy

We investigate the propagation of optical fields through polymethyl methacrylate (PMMA) rods under atmospheric turbulence conditions, employing a generalized Lorentz dipole oscillator model with nonlinear restoring forces and dipole-dipole coupling. The theoretical framework incorporates second- and third-order anharmonic terms ($\beta_i|r_i|r_i$ and $\alpha

Minghao Fu, Guo-Hua Wang, Tianyu Cui, Qing-Guo Chen

Text-to-image diffusion models deliver high-quality images, yet aligning them with human preferences remains challenging. We revisit diffusion-based Direct Preference Optimization (DPO) for these models and identify a critical pathology: enlarging the preference margin does not necessarily improve generation quality. In particular, the standard Diffusion-DPO

Sunwoo Park, Byungkwon Park, Eunsung Kim, Jiwon Yune

We present a Time-to-Digital Converter (TDC) implemented on a 16 nm Xilinx UltraScale Plus FPGA that achieves a resolution of 1.15 ps, RMS precision of 3.38 ps, a differential nonlinearity (DNL) of [-0.43, 0.24] LSB, and an integral nonlinearity (INL) of [-2.67, 0.15] LSB. This work introduces two novel hardware-independent post-processing techniques - Parti

Yen-Jhen Liu, Yi Yang

Axion-like particles (ALPs) can couple to photons in strong magnetic fields, producing characteristic fluctuations in X-ray spectra. Using data from NASA's Neutron Star Interior Composition Explorer (NICER), we analyzed three pulsars: PSR J2229+6114, PSR J1849-0001, and PSR B0531+21, to search for such features. Each spectrum was modeled with a sliding-windo

S. A. Franchino-Viñas, C. García-Pérez, F. D. Mazzitelli, S. Pla

Among the available perturbative approaches in quantum field theory, heat kernel techniques provide a powerful and geometrically transparent framework for computing effective actions in nontrivial backgrounds. In this work, resummation patterns within the heat kernel expansion are examined as a means of systematically extracting nonperturbative information.

Tetsuya Takaishi

The finite sample effect on the Hurst exponent (HE) of realized volatility time series is examined using Bitcoin data. This study finds that the HE decreases as the sampling period $\Delta$ increases and a simple finite sample ansatz closely fits the HE data. We obtain values of the HE as $\Delta \rightarrow 0$, which are smaller than 1/2, indicating rough v

Tahir Javaid, Li Yuan, Tongguang Cheng

In this manuscript, we present the latest differential measurements of Higgs boson cross sections with the CMS detector in bosonic and fermionic decay channels. Both fiducial differential cross section measurements and measurements in the simplified template cross section framework are presented. The fiducial measurements are then used to compute limits on H

Jiali Xu, Valeria Loscri, Romain Rouvoy

The advent of 5G networks, with network slicing as a cornerstone technology, promises customized, high-performance services, but also introduces novel attack surfaces beyond traditional threats. This article investigates a critical and underexplored integrity vulnerability: the manipulation of network slice allocation to compromise Quality of Service (QoS) a

Ziying Wang, Adolfo O. Fumega, Ana Vera Montoto, Mohammad Amini

Quantum spin liquids are quantum phases of matter featuring collectively entangled states and emergent fractional many-body excitations. While methods exist to probe three-dimensional quantum spin liquids experimentally, these techniques lack the sensitivity to probe two-dimensional quantum spin liquids. This seriously hampers the study of potential monolaye

Jing Peng, Yi Yang, Xu Li, Yu Xi

Recent advances in Speech Large Language Models (Speech LLMs) have paved the way for unified architectures across diverse speech understanding tasks. However, prevailing alignment paradigms rely heavily on large-scale audio-text paired data and computationally intensive training, yet often exhibit limited generalization to unseen domains or tasks. To address

Daniele Barbera, Miho Murata, Yoshihiro Shibata

In this paper, we consider the Q-tensor model of nematic liquid crystals, which couples the Navier-Stokes equations with a parabolic-type equation describing the evolution of the directions of the anisotropic molecules, in the half-space. The aim of this paper is to prove the global well-posedness for the Q-tensor model in the $L_p$-$L_q$ framework. Our proo

Ronny de la Bastida, Enzo Rongione, Karuppasamy Pandian Soundarapandian, Ioannis Vangelidis

Phase-sensitive terahertz (THz) detection enables applications ranging from astronomy to non-destructive testing. However, current THz detectors lack phase sensitivity, unless they are combined with external interferometers, or through photomixing. This implies a large footprint and sensitive dependence on alignment. Here, we demonstrate a graphene-enabled,

Susanne M. Schennach, Vincent Starck

We propose a new estimation methodology to address the presence of covariate measurement error by exploiting the availability of spatial data. The approach uses neighboring observations as repeated measurements, after suitably controlling for the random distance between the observations in a way that allows the use of operator diagonalization methods to esta

Haoqin Zhao, Zan Li, Jiangbo Si, Rui Huang

Owing to the openness of wireless channels, wireless communication systems are highly susceptible to malicious jamming. Most existing anti-jamming methods rely on the assumption of accurate sensing and optimize parameters on a single timescale. However, such methods overlook two practical issues: mismatched execution latencies across heterogeneous actions an

Theodore Modis

Use is made of rigorous definitions for the terms normal, natural, and harmonic to reveal a number of unfamiliar aspects about them. The Gaussian distribution is not sufficient to determine who is normal, and fluctuations above or below a natural-growth curve may or may not be natural. A recipe for harmonically sustained natural growth requires that the over

Ruth H. Tichauer, Maksim Lednev, Gerrit Groenhof, Johannes Feist

In the context of light-matter interactions between organic chromophores and confined photons of (plasmonic) nano-resonators, we introduce a general framework that couples ab initio QM/MM molecular dynamics with few-mode field quantization to simulate light-matter interactions of molecular emitters at the nanoscale. Arbitrary, lossy, and spatially inhomogene

Xiaoyun Wang, Yutong Zhang, Sen Wang, Sun Qi

Future mobile networks in the sixth generation (6G) are poised for a paradigm shift from conventional communication services toward comprehensive information services, driving the evolution of radio access network (RAN) architectures toward enhanced cooperation, intelligence, and service orientation. Building upon the concept of centralized, collaborative, c

Yueyi Sun, Dexing Liu, Jiefei Zhu, Siming Liu

This work presents a platform that enables surface acoustic wave (SAW) modulation of both intralayer and interlayer excitons in MoS2/WSe2 heterostructures. Harnessing the coupled piezoelectric and strain fields of SAWs, this integrated approach allows for dynamic, precise, and fully contactless control of excitonic properties, a capability essential for the

Anil Kumar, V. K. Chandrasekar, D. V. Senthilkumar

Synchrony patterns characterize network states in which nodes organize into clusters based on their synchronized dynamics. The synchronized clusters may further exhibit either active or inactive states. The simultaneous invariance of active and inactive clusters of synchronized nodes poses a dynamical constraint because fluctuations from active clusters must

A. R. Shelke, C. -W. Chuang, S. Hamamoto, M. Oura

The layered $3d$ transition metal dichalcogenide (TMD) CoTe$_2$ is a topological Dirac Type-II metal. However, the Co $3d$-bands in CoTe$_2$ do not exhibit the expected correlation-induced band narrowing seen in CoO. We address this conundrum by studying the electronic structure of CoTe$_2$ using hard x-ray photoemission spectroscopy (HAXPES), x-ray absorpti

Oleg Senkevich, Siyang Xu, Tianyi Jiang, Alexander Radionov

Approximate Nearest Neighbor Search (ANNS) is a cornerstone algorithm for information retrieval, recommendation systems, and machine learning applications. While x86-based architectures have historically dominated this domain, the increasing adoption of ARM-based servers in industry presents a critical need for ANNS solutions optimized on ARM architectures.

Leonardo Pedroso, Andrea Agazzi, W. P. M. H. Heemels, Mauro Salazar

We study a dynamic game with a large population of players who choose actions from a finite set in continuous time. Each player has a state in a finite state space that evolves stochastically with their actions. A player's reward depends not only on their own state and action but also on the distribution of states and actions across the population, capturing

Bennet Gebken

It is well-known by now that the BFGS method is an effective method for minimizing nonsmooth functions. However, despite its popularity, theoretical convergence results are almost non-existent. One of the difficulties when analyzing the nonsmooth case is the fact that the secant equation forces certain eigenvalues of the quasi-Newton matrix to vanish, which

Giulia Battilotti, Rosapia Lauro Grotto

A crucial issue both in cognitive and psychoanalytical theories deals with the origin of mental representations. In order to explore this issue, the paper analyzes a pre-logical setting, by considering a formalized approach to the foundations of psychoanalysis in logic, interpreting and integrating the views by Freud, Matte Blanco, Klein and Bion. The formal

Mauro Cettolo, Marco Gaido, Matteo Negri, Sara Papi

Automatic evaluation of ST systems is typically performed by comparing translation hypotheses with one or more reference translations. While effective to some extent, this approach inherits the limitation of reference-based evaluation that ignores valuable information from the source input. In MT, recent progress has shown that neural metrics incorporating t

Jiarui Ji, Runlin Lei, Xuchen Pan, Zhewei Wei

The emergence of Large Language Models (LLMs) demonstrates their potential to encapsulate the logic and patterns inherent in human behavior simulation by leveraging extensive web data pre-training. However, the boundaries of LLM capabilities in social simulation remain unclear. To further explore the social attributes of LLMs, we introduce the CiteAgent fram

Valentin Blomer, Junxian Li

While several instances of shifted convolution problems for GL(3) x GL(2) have been solved, the case where one factor is the classical divisor function and one factor is a GL(3) Fourier coefficient has remained open. We solve this case in the present paper. The proof involves two intertwined applications of different types of delta symbol methods. As an appl

Qiuyuan Yang, Cunhua Pan, Ruidong Li, Zhenkun Zhang

The integration of sensing and communication (ISAC) has significant potential for future wireless systems, enabling efficient spectrum utilization and novel application scenarios. In this paper, we propose a cooperative ISAC framework for synthetic aperture radar (SAR) imaging by leveraging orthogonal frequency division multiplexing (OFDM) communication sign

Zhiyuan Zhai, Shuyan Hu, Wei Ni, Xiaojun Yuan

Decentralized machine learning (DML) supports collaborative training in large-scale networks with no central server. It is sensitive to the quality and reliability of inter-device communications that result in time-varying and stochastic topologies. This paper studies the impact of unreliable communication on the convergence of DML and establishes a direct c

Jinhao Yi, Weijun Gao, Chong Han

The ever-increasing demand for ultra-high data rates in space-air-ground integrated networks (SAGINs) has rendered terahertz THz communications a promising technology owing to its exceptionally broad and continuous spectrum resources. Nevertheless, in air-ground (AG) scenarios, the high mobility of aircraft induces intense and rapidly fluctuating turbulence,

Tian Bai, Zhiyi Huang, Chui Shan Lee, Dongchen Li

There are two major models of value uncertainty in the optimal stopping literature: the secretary model, which assumes no prior knowledge, and the prophet inequality model, which assumes full information about value distributions. In practice, decision makers often rely on machine-learned priors that may be erroneous. Motivated by this gap, we formulate the

Peter Raffai, Dominika E. R. Kis, Dávid A. Ködmön, Adrienn Pataki

We present a local-to-global cosmological framework in which cosmic acceleration emerges from structure formation in an inhomogeneous Einstein-de Sitter (iEdS) universe, without dark energy. The model exhibits a quasilinear coasting evolution toward an effective Milne state driven by growing inhomogeneities. We test the iEdS model with ${H_0=72.5\ \mathrm{km

Yuhang Du, Yuxuan Wu, Hannah L. Cebull, Bangquan Liao

Accurate prediction of aortic expansion in uncomplicated type B aortic dissection (TBAD) can help identify patients who may benefit from timely thoracic endovascular aortic repair. This study investigates associations between biomechanical predictors derived from reduced-order fluid-structure interaction (FSI) analysis and aortic growth outcomes. Baseline an

Rakeshkumar H Sodha

Problem. "Thinking" LLMs (TLLMs) expose explicit or hidden reasoning traces and are widely believed to generalize better on complex tasks than direct LLMs. Whether this promise carries to noisy, heavy-tailed and regime-switching financial data remains unclear. Approach. Using Indian equities (NIFTY constituents), we run a rolling 48m/1m walk-forward evaluati

Ehud Shapiro

Global digital platforms are distributed systems designed to serve entire populations, with some already serving billions of people. Here we propose atomic transactions-based multiagent transition systems and protocols as a formal framework to study them; introduce essential agents---minimal sets of agents the removal of which makes communication impossible;

Qingyuan Zhang, Ning Lyu, Le Liu, Yuxi Wang

This study addresses the problem of anomaly detection and root cause tracing in microservice architectures and proposes a unified framework that combines graph neural networks with temporal modeling. The microservice call chain is abstracted as a directed graph, where multidimensional features of nodes and edges are used to construct a service topology repre

Zhiyuan Zhai, Xiaojun Yuan, Xin Wang, Geoffrey Ye Li

Decentralized federated learning (DFL) is an emerging paradigm to enable edge devices collaboratively training a learning model using a device-to-device (D2D) communication manner without the coordination of a parameter server (PS). Aggregation weights, also known as mixing weights, are crucial in DFL process, and impact the learning efficiency and accuracy.

Zhiyuan Zhai, Wei Ni, Xin Wang, Dusit Niyato

UAV swarms can form virtual antenna arrays to exploit additional spatial degrees of freedom and enhance integrated sensing and communication (ISAC). The optimization of UAV positions is challenging due to the distributed nature of swarms and the lack of a global view at individual UAVs. This paper presents a new decentralized optimization framework that allo

Xinyu Ning, Yan Zhuo, Xian Wang, Chan-In Devin Sio

With the continuous advancement of technology, the application of generative artificial intelligence (AI) in various fields is gradually demonstrating great potential, particularly when combined with Extended Reality (XR), creating unprecedented possibilities. This survey article systematically reviews the applications of generative AI in XR, covering as muc

Zhenlin Tan, Lizhi Xie, Jiaxin Han, Yisheng Qiu

We introduce a mock galaxy catalog built for the CSST extragalactic surveys using the primary runs of the Jiutian $N$-body simulation suites. The catalogs are built by coupling the GAlaxy Evolution and Assembly (GAEA) semi-analytical model of galaxy formation with merger trees extracted from the simulations using the Hierarchical Bound-Tracing (HBT+) algorit

Rob Romijnders, Gabriele Cesa, Christos Louizos, Kumar Pratik

From molecular imaging to wireless communications, the ability to align and reconstruct signals from multiple misaligned observations is crucial for system performance. We study the problem of multi-reference alignment (MRA), which arises in many real-world problems, such as cryo-EM, computer vision, and, in particular, wireless communication systems. Using

Ning Lyu, Yuxi Wang, Ziyu Cheng, Qingyuan Zhang

As cloud computing and microservice architectures become increasingly prevalent, API rate limiting has emerged as a critical mechanism for ensuring system stability and service quality. Traditional rate limiting algorithms, such as token bucket and sliding window, while widely adopted, struggle to adapt to dynamic traffic patterns and varying system loads. T

Wenda Yang, Xin Zhang

Seismic surface wave tomography uses surface wave information to obtain velocity structures in the subsurface. Due to data noise and nonlinearity of the problem, surface wave tomography often has non-unique solutions. It is therefore required to quantify uncertainty of the results in order to better interpret the resulting images. Bayesian inference is the m

Yi Ouyang, Chenhao Zhang

In this paper, we establish a real closed analogue of Bertini's theorem. Let $R$ be a real closed field and $X$ a formally real integral algebraic variety over $R$. We show that if the zero locus of a nonzero global section $s$ of an invertible sheaf on $X$ has a formally real generic point, then $s$ does not change sign on $X$, and vice versa under certain

Jinjie Ni, Qian Liu, Longxu Dou, Chao Du

Under strictly controlled pre-training settings, we observe a Crossover: when unique data is limited, diffusion language models (DLMs) consistently surpass autoregressive (AR) models by training for more epochs. The crossover shifts later with more or higher-quality data, earlier with larger models, and persists across dense and sparse architectures. We attr

Taishi Katsuragawa, Shin'ichi Nojiri, Sergei D. Odintsov

We study wormhole geometries embedded in an expanding universe within a four-scalar non-linear $\sigma$ model, where the target-space metric is identified with the spacetime Ricci tensor. In this framework, wormholes can remain stable even when conventional energy conditions are violated. However, once cosmological expansion is included, the effective energy

A. V. Butkevich, S. V. Luchuk

The reduced cross section of the semiexclusive $(l,l'p)$ lepton scat tering process irrespective of the type of interaction is determined mainly by bound nucleon momentum distribution in target and nucleon final state interaction with residual nucleus. These cross sections can be identified with distorted nuclear spectral functions and therefore are similar

Dimiter Prodanov

Diffusion within porous media, such as biological tissues, exhibits departures from conventional Fick's laws, which could result in space-fractional diffusion. The paper considers a reaction-diffusion system with two spatial compartments -- a proximal one of finite radius having a source, and an outer one extending to infinity where the source is not present

Xiu Fang Lu, Xue-Jin Zhang, Naizhou Wang, Jin Cao

In materials with broken inversion symmetry, nonreciprocal magneto-transport (NRMT) manifests as a bilinear dependence of charge conductivity on applied electric (E) and magnetic (B) fields. This phenomenon is deeply rooted in symmetry and electronic quantum geometry, holding promise for novel rectification and detector technologies. Existing experimental st

Shuangquan Lyu, Jian Mao, Yue Ma

Diffusion-based text-to-video models are increasingly capable, but mask-based editing over hundreds of frames remains challenging: naïve long-video generation suffers from memory blow-up, window seams, and temporal drift, while existing editors often require specialized modules or heavy fine-tuning. We present Overlapping High-Order Co-Denoising, a lightweig

Yize Liu, Yunyun Hou, Aina Sui

Large Language Models (LLMs) have been widely deployed across various applications, yet their potential security and ethical risks have raised increasing concerns. Existing research employs red teaming evaluations, utilizing multi-turn jailbreaks to identify potential vulnerabilities in LLMs. However, these approaches often lack exploration of successful dia

Jin-woo Lee, Junhwa Choi, Bongkyu Hwang, Jinho Choo

We revisit continual pre-training for large language models and argue that progress now depends more on scaling the right structure than on scaling parameters alone. We introduce SCALE, a width upscaling architecture that inserts lightweight expansion into linear modules while freezing all pre-trained parameters. This preserves the residual and attention top

Ville Nordstrom

We prove a conjecture by Belmans, Fu and Krug concerning the Hochschild homology of the symmetric powers of a small dg category $\mathscr{C}$. More precisely, we show that these groups decompose into pieces that only depend on the Hochschild homology of the dg category $\mathscr{C}$.

Tonghui Wang, Guoqing Zhang, Haocheng Wen

The total pressure gain (PG) characteristics of the quasi-2D rotating detonation engine (RDE) are numerically investigated in this study, based on an abstract check valve model and the quasi-1D assumption. The influence of back-propagating pressure suppression on PG and its underlying mechanism are examined. An abstract check valve model is established to si

Bingyang Guo, Hongjie Li, Ruiyun Yu, Hanzhe Liang

3D anomaly detection (3D-AD) plays a critical role in industrial manufacturing, particularly in ensuring the reliability and safety of core equipment components. Although existing 3D datasets like Real3D-AD and MVTec 3D-AD offer broad application support, they fall short in capturing the complexities and subtle defects found in real industrial environments.

Harsh Sharma, Sampriti Saha, A. S. Majumdar, Manik Banik

Quantifying multipartite entanglement in quantum many-body systems and hybrid quantum computing architectures is a fundamental yet challenging task. In recent years, thermodynamic quantities such as the maximum extractable work from an isolated system (the ergotropy) have allowed for entanglement measures that are operationally more accessible. However, thes

Yuan Hua, Jilin Zhang, Yingtao Zhang, Wenqi Gu

Inspired by the brain's spike-based computation, spiking neural networks (SNNs) inherently possess temporal activation sparsity. However, when it comes to the sparse training of SNNs in the structural connection domain, existing methods fail to achieve ultra-sparse network structures without significant performance loss, thereby hindering progress in energy-

Vaishali Aggarwal, Nicolas Gillis, Punit Sharma

For a given set $\Omega \subseteq \mathbb{C}$, a matrix pair $(E,A)$ is called $\Omega$-admissible if it is regular, impulse-free and its eigenvalues lie inside the region $\Omega$. In this paper, we provide a dissipative Hamiltonian characterization for the matrix pairs that are $\Omega$-admissible where $\Omega$ is an LMI region. We then use these results

Manuel Ratz, Alessandro Parente, Miguel Alfonso Mendez

Data-driven modal decompositions are useful tools for compressing data or identifying dominant structures. Popular ones like the dynamic mode decomposition (DMD) and the proper orthogonal decomposition (POD) are defined with continuous inner products. These are usually approximated with samples of data uniform in space and time. However, not every dataset fu

Fatemeh Shahhosseini, Arash Marioriyad, Ali Momen, Mahdieh Soleymani Baghshah

Scientific idea generation is central to discovery, requiring the joint satisfaction of novelty and scientific soundness. Unlike standard reasoning or general creative generation, scientific ideation is inherently open-ended and multi-objective, making its automation particularly challenging. Recent advances in large language models (LLMs) have enabled the g

Ruizhe Zheng, Lingyan Mao, Dingding Han, Tian Luo

Precise, generalizable subject-agnostic seizure prediction (SASP) remains a fundamental challenge due to the intrinsic complexity and significant spectral variability of electrophysiological signals across individuals and recording modalities. We propose FAPEX, a novel architecture that introduces a learnable fractional neural frame operator (FrNFO) for adap

Ranul Dayarathne, Uvini Ranaweera, Upeksha Ganegoda

Retrieval Augmented Generation (RAG) is emerging as a powerful technique to enhance the capabilities of Generative AI models by reducing hallucination. Thus, the increasing prominence of RAG alongside Large Language Models (LLMs) has sparked interest in comparing the performance of different LLMs in question-answering (QA) in diverse domains. This study comp

Delong Kong, Yu Tian, Hongbao Zhang

We give a comprehensive analysis of the dynamic and thermodynamic stability of neutron stars composed of superconducting-superfluid mixtures within the Iyer-Wald formalism. We derive the first law of thermodynamics and the necessary and sufficient condition under which dynamic equilibrium implies thermodynamic equilibrium. By constructing the phase space and

Mohit Thakre, Praveen Kumar Dhankar, Behnam Pourhassan, Safiqul Islam

This study combines theoretical advancements with observational limitations to investigate the cosmological implications of a bulk viscous modified Chaplygin gas (MCG) in a Friedmann--Robertson--Walker (FRW) in (3+1) dimensional spacetime framework. We provide analytical solutions for both viscous and non-viscous cases, pointing out variations in the energy

Kenta Sawamura, Kensuke Araki, Naoki Maruyama, Renichiro Haba

Efficient production planning is essential in modern manufacturing to improve performance indicators such as lead time and to reduce reliance on human intuition. While mathematical optimization approaches, formulated as job shop scheduling problems, have been applied to automate this process, solving large-scale production planning problems remains computati

Jing Ma, Hanlin Li, Xiang Xiang

Entropy Minimization (EM) is beneficial to reducing class overlap, bridging domain gap, and restricting uncertainty for various tasks in machine learning, yet its potential is limited. To study the internal mechanism of EM, we reformulate and decouple the classical EM into two parts with opposite effects: cluster aggregation driving factor (CADF) rewards dom

Nikolina Tomic, Roshni Bhatnagar, Sarthak Jain, Connor Lau

Deep learning (DL) has the potential to revolutionize image acquisition and interpretation across medicine, however, attention to data imbalance and missingness is required. Ultrasound data presents a particular challenge because in addition to different views and structures, it includes several sub-modalities-such as greyscale and color flow doppler (CFD)-t

Jianqiao Wang, Chushu Fang, Zhibin Qiu, Yang Zhao

Low-temperature refrigeration technology constitutes a crucial component in space exploration. The small-scale, low-vibration Stirling-type pulse tube refrigerators hold significant application potential for space cooling. However, the efficient operation of current Stirling-type pulse tube cryocoolers in space cooling applications remains challenging due to

Taiyo Fukai, Keisuke Kawata, Mizuki Komura, Takahiro Toriyabe

This study analyzes the gender gap in desired wages using large administrative data of public job referrals, which allows us to look at the desired salaries of individuals from a wider wage distribution. We conduct a decomposition analysis using available information on age, desired work region, and desired occupation. We find that of the three factors, desi

Zhibin Wang, Zhixing Zhang, Shuqi Wang, Xuanting Xie

Graph Neural Networks (GNNs) have demonstrated impressive performance on task-specific benchmarks, yet their ability to generalize across diverse domains and tasks remains limited. Existing approaches often struggle with negative transfer, scalability issues, and high adaptation costs. To address these challenges, we propose GMoPE (Graph Mixture of Prompt-Ex

Ryuta Miyata, Koki Fujikawa, Rina Harata, Yoshitaka Itow

Radon-induced backgrounds, particularly from $^{222}$Rn and its beta-emitting progeny, present a critical challenge for next-generation liquid xenon (LXe) detectors aimed at probing dark matter down to the neutrino fog. To address this, we developed a compact hermetic gaseous xenon (GXe) detector. This device physically isolates the active volume from extern

Gutierrez-Florensa, F. Sanniti, D. Tedeschi, L. Sigrist

A precise estimation of the Rate of Change of Frequency (RoCoF) is crucial for secure power system operation. In fact, RoCoF is strictly related to the amount of the available physical and/or virtual inertia of the system and the severity of the active power unbalance following a disturbance. For this reason, it is widely exploited in different protection sy

Junhao Li, Jiahao Chen, Zhou Feng, Chunyi Zhou

Recent advances in multi-modal Large Language Models (M-LLMs) have demonstrated a powerful ability to synthesize implicit information from disparate sources, including images and text. These resourceful data from social media also introduce a significant and underexplored privacy risk: the inference of sensitive personal attributes from seemingly daily media

Amy Chang, Nicholas Conley, Harish Santhanalakshmi Ganesan, Adam Swanda

Open-weight models provide researchers and developers with accessible foundations for diverse downstream applications. We tested the safety and security postures of eight open-weight large language models (LLMs) to identify vulnerabilities that may impact subsequent fine-tuning and deployment. Using automated adversarial testing, we measured each model's res

Kangrou Guo, Shigeru Ida, Masahiro Ogihara

Microlensing observations suggest that the mass distribution of free-floating planets (FFPs) follows a declining power-law with increasing mass. The origin of such distribution is unclear. Using a population synthesis framework, we investigate the formation channel and properties of FFPs, and compare the predicted mass function with observations. Assuming FF