December 2025 arXiv papers — page 73

Yicong Qiu, Qiye Zheng

Coarse-graining a chaotic bistable oscillator into a binary symbol sequence is a standard reduction, but it often obscures the geometry of the reduced state space and structural constraints of physically meaningful stochastic evolution. We develop a two-state framework that embeds coarse-grained left/right statistics of the driven Duffing oscillator into a $

Shiqian Guo, Tingxiang Ji, Jianqing Liu

Information retrieval from passive backscatter systems is widely used in digital applications with tight energy budgets, short communication distances, and low data rates. Due to the fundamental limits of classical wireless receivers, the achievable data rate cannot be increased without compromising either energy efficiency or communication range, thereby hi

Shao-Ting Chiu, Ioannis G. Kevrekidis, Ulisses Braga-Neto

We introduce BumpNet, a sparse multilayer perceptron (MLP) framework for PDE numerical solution and operator learning. BumpNet is based on basis function expansion, which makes them superficially similar to radial-basis function (RBF) networks. However, the basis functions in BumpNet are constructed from ordinary sigmoid activation functions in a sparse mult

Oleg B. Shiryaev

The susceptible-infected-recovered-vaccinated model (SIRV) of epidemic processes is treated for the combination of intercompartment transition rates yielding a high basic reproduction number, a condition corresponding to an intense epidemic. Multiple-timescale solutions to the SIRV model equations are presented, with the inverse of the basic reproduction num

Gerardo Palafox-Castillo, Ericka Fabiola Vázquez-Alcalá, Arturo Berrones-Santos

We study a symmetric two-disease SIR co-infection model on networks in which co-infected individuals recover at a rate distinct from that of single infections. The model explicitly represents all co-infection states and features absorbing recovered compartments for both diseases. Within a mean-field network approximation, we derive the basic reproduction num

Kai Liu, Leyang Chen, Wenbo Li, Zhikai Chen

Unifying multimodal understanding and generation has shown impressive capabilities in cutting-edge proprietary systems. However, evaluations of unified multimodal models (UMMs) remain decoupled, assessing their understanding and generation abilities separately with corresponding datasets. To address this, we propose UmniBench, a benchmark tailored for UMMs w

Bing He, Xiongze Zhang

Let \[ \sum_{n=0}^{\infty}A(n)q^{n} := \frac{(q^{2};q^{5})_{\infty}^{5}(q^{3};q^{5})_{\infty}^{5}}{(q;q^{5})_{\infty}^{5}(q^{4};q^{5})_{\infty}^{5}}, \] \[ \sum_{n=0}^{\infty} B(n)q^{n} := \frac{(q;q^{5})_{\infty}^{5} (q^{4};q^{5})_{\infty}^{5}} {(q^{2};q^{5})_{\infty}^{5}(q^{3}; q^{5})_{\infty}^{5}}, \] and \[ \sum_{n=0}^{\infty} D(n)q^{n} := \frac{(q^{5};q

Shengwei Zhao, Jingwen Yao, Sitong Wei, Linhai Xu

Multi-modal Retrieval-Augmented Generation (MMRAG) enables highly credible generation by integrating external multi-modal knowledge, thus demonstrating impressive performance in complex multi-modal scenarios. However, existing MMRAG methods fail to clarify the reasoning logic behind retrieval and response generation, which limits the explainability of the re

Sophie E. Deam, Hadrien A. R. Devillepoix, David Nesvorný, Patrick M. Shober

The population of Earth-impacting meteoroids and its size-dependent orbital elements are key to understanding the origin of meteorites and informing on planetary defence efforts. Outstanding questions include the role of collisions in depleting meteoroids on highly evolved orbits and the relative importance of delivery resonances. Those depend on size, with

Huixi Wang, Minzhi Zhao

This paper establishes a novel connection between null-recurrent CTMCs and electric networks, offering a systematic classification of null-recurrent behavior based on the first returning speed. By leveraging techniques from electric network theory, we present a general method for estimating the first returning speed of null recurrent birth-death processes an

Xiao Liang, Chenxi Liu, Zhi Ma, Di Wang

Medical Vision-Language Models (MedVLMs) show immense promise in clinical applicability. However, their reliability is hindered by hallucinations, where models often fail to derive answers from visual evidence, instead relying on learned textual priors. Existing mitigation strategies for MedVLMs have distinct limitations: training-based methods rely on costl

Zhenbao Yu, Banglei Guan, Shunkun Liang, Zibin Liu

Mobile devices equipped with a multi-camera system and an inertial measurement unit (IMU) are widely used nowadays, such as self-driving cars. The task of relative pose estimation using visual and inertial information has important applications in various fields. To improve the accuracy of relative pose estimation of multi-camera systems, we propose a global

Zhaoqilin Yang, Axin Xiang, Kedi Yang, Tianjun Liu

Spatial public goods games model collective dilemmas where individual payoffs depend on population-level strategy configurations. Most existing studies rely on evolutionary update rules or value-based reinforcement learning methods. These approaches struggle to represent payoff coupling and non-stationarity in large interacting populations. This work introdu

Yiqing Ma, Jung-Hua Liu

Large Language Models (LLMs) often exhibit behavioral artifacts such as laziness (premature truncation of responses or partial compliance with multi-part requests), decoding suboptimality (failure to select higher-quality sequences due to myopic decoding), and context degradation (forgetting or ignoring core instructions over long conversations). We conducte

Matias Quintana, Fangqi Liu, Jussi Torkko, Youlong Gu

Quantifying and assessing urban greenery is consequential for planning and development, reflecting the everlasting importance of green spaces for multiple climate and well-being dimensions of cities. Evaluation can be broadly grouped into objective (e.g., measuring the amount of greenery) and subjective (e.g., polling the perception of people) approaches, wh

Sandeep Neela

Financial crises emerge when structural vulnerabilities accumulate across sectors, markets, and investor behavior. Predicting these systemic transitions is challenging because they arise from evolving interactions between market participants, not isolated price movements alone. We present Systemic Risk Radar (SRR), a framework that models financial markets a

Y. Chen

HESS J1857+026 remains a mysterious gamma-ray emitter since its discovery in 2008. Despite the disclosure of a nearby pulsar and multiple studies in the high-energy (HE, E > 100 MeV) and very-high-energy (VHE, E > 100 GeV) regimes, there have been no confirmed counterparts (e.g., an SNR shell or other extended structure) in X-ray or other wavelengths. We pre

Gang Zhang

We present an innovative end-to-end framework for synthesizing semantically meaningful co-speech gestures and deploying them in real-time on a humanoid robot. This system addresses the challenge of creating natural, expressive non-verbal communication for robots by integrating advanced gesture generation techniques with robust physical control. Our core inno

Kelong Cheng, Jingwei Sun, Hong Zhang

High-order time-stepping schemes are crucial for simulating incompressible fluid flows due to their ability to capture complex turbulent behavior and unsteady motion. In this work, we propose a third-order accurate numerical scheme for the two-dimensional incompressible Navier-Stokes equation. Spatial and temporal discretization is achieved using Fourier pse

Anuj Sethia, Nasser Gohari Kamel, Daniel Oblak

The realization of scalable quantum networks for distribution of entanglement over long distances hinges on quantum repeaters. To outperform the exponential transmission loss in optical fibers, quantum repeaters must employ multiplexing schemes in the temporal, spectral, or spatial domain. The performance of such a multiplexed scheme is contingent on efficie

Maher Mesto, Francisco Cruz

Interactive reinforcement learning (IRL) has shown promise in enabling autonomous agents and robots to learn complex behaviours from human teachers, yet the dynamics of teacher selection remain poorly understood. This paper reveals an unexpected phenomenon in IRL: when given a choice between teachers with different reward structures, learning agents overwhel

Xiaotang Du, Rohit Saxena, Laura Perez-Beltrachini, Pasquale Minervini

We introduce a novel approach for long context summarisation, highlight-guided generation, that leverages sentence-level information as a content plan to improve the traceability and faithfulness of generated summaries. Our framework applies self-planning methods to identify important content and then generates a summary conditioned on the plan. We explore b

Qi Zhang, Yuxu Chen, Lei Deng, Lili Shen

Contrastive Language-Image Pretraining (CLIP) has achieved remarkable performance in various multimodal tasks. However, it still struggles with compositional image-text matching, particularly in accurately associating objects with their corresponding attributes, because its inherent global representation often overlooks fine-grained semantics for attribute b

Matthias Fresacher, Willow Stewart, Daniel Tubbenhauer

Classical diagram categories and monoids, including the Temperley--Lieb, Brauer, and partition cases, arise as special instances of the category of two dimensional cobordisms and admit additional twists that produce a large new family of diagram categories and monoids. In this paper we introduce this family and develop a unified approach to their representat

Huanhuan Wu, Yuhan Liu, Shengqiang Zhong, Yaozhi Yi

We propose a compact scheme based on ultrafast-rotating phase plates (URPPs) to achieve continuous spectral broadening of ultraviolet lasers. The rapid rotation elements behave as a random oscillator which induces Doppler frequency shift into the ultraviolet lasers. As an example, for a disk-shaped phase plate, with the beam acting on the edge at a radius of

Pablo Rocha

In this note we improve the parameter $q$ that appears in Theorem 1 obtained by the author in [Math. Ineq. \& appl., Vol 19 (3) (2016), 1013-1030].

Chao Zhai, Yanlin Li

It is always a challenging task for multi-agent systems to achieve efficient and robust coverage in uncertain environments. The absence of global positioning information on the uncertain environment introduces significant complexity to the spatially distributed design of coverage control algorithms. To address this issue, this paper proposes a coverage contr

Bing Li, Sanju Velani, Bo Wang

Let $b\geq3$ be an integer and $C(b,D)$ be the set of real numbers in $[0,1]$ whose $b$-ary expansion consists of digits restricted to a given set $D\subseteq\{0,\ldots,b-1\}$. Given an integer $t\geq2$ and a real, positive function $\psi$, let $W_{t}(\psi)$ denote the set of $x$ in $[0,1]$ for which $|x-p/t^{n}|<\psi(n)$ for infinitely many $(p,n)\in\mathbb

Ripan Kumar Kundu, Istiak Ahmed, Khaza Anuarul Hoque

Artificial intelligence (AI)-driven augmented reality (AR) systems are becoming increasingly integrated into daily life, and with this growth comes a greater need for explainability in real-time user interactions. Traditional explainable AI (XAI) methods, which often rely on feature-based or example-based explanations, struggle to deliver dynamic, context-sp

Hengyu Zhang, Xuehan Wang, Xu Shi, Jintao Wang

High-mobility scenarios are becoming increasingly critical in next-generation communication systems. While multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) stands as a prominent technology, its performance in such scenarios is fundamentally limited by Doppler-induced inter-carrier interference (ICI). Rate splitting multip

Dmitry Pasechnyuk-Vilensky, Martin Takáč

We develop a homological duality framework based on a contravariant functor $D=\operatorname{Hom}_E(-,R)$ with dualizing object $R$. A morphism is called ethic when it satisfies the canonical double-dual compatibility $D^2(f)\eta=\eta f$. In the derived setting, the functor $\mathrm{RHom}_E(-,R)$ produces a graded family of Ext-groups that measure all failur

Nalin Arora, Aviral Chauhan, Siddhant Rana, Mahansh Aditya

Ultra-processed foods are increasingly linked to health issues like obesity, cardiovascular disease, type 2 diabetes, and mental health disorders due to poor nutritional quality. This first-of-its-kind study at such a scale uses machine learning to classify food processing levels (NOVA) based on the Open Food Facts dataset of over 900,000 products. Models in

Santu Nath, Smarajit Karmakar

Using extensive molecular dynamics simulations, we have performed finite-size scaling (FSS) in the aging regime of a model glass-forming liquid to investigate how the length scales associated with amorphous order (static length) and dynamic heterogeneity (dynamic length) evolve with waiting time. The $\alpha$-relaxation time in the aging regime reveals non-m

Zack Boone

We study removable sets for the Campanato, H\"{o}lder continuous, $L^p_{\text{loc}}$, and Lipschitz functions in Carnot groups. In the former three cases, we characterize removability through the use of capacities with respect to any left-invariant linear differential operator $\mathcal{L}$ for which $\mathcal{L}$ and $\mathcal{L}^t$ are hypoelliptic and sat

Harutaka Yamada, Sho Tsugawa, Mitsuo Yoshida

For effective social media marketing, identifying stable influencers-those who sustain their influence over an extended period-is more valuable than focusing on users who are influential only temporarily. This study addresses the challenge of distinguishing stable influencers from transient ones among users who are influential at a given point in time. We pa

Yu Qian, Alptekin Vardar, Konrad Seidel, David Lehninger

Computationally hard combinatorial optimization problems are pervasive in science and engineering, yet their NP-hard nature renders them increasingly inefficient to solve on conventional von Neumann architectures as problem size grows. Ising machines implemented using dynamical, digital and compute-in-memory (CiM) approaches offer a promising alternative, bu

Yu Yang, Feng Tian, Ping Chen

Query Expansion (QE) enriches queries and Document Expansion (DE) enriches documents, and these two techniques are often applied separately. However, such separate application may lead to semantic misalignment between the expanded queries (or documents) and their relevant documents (or queries). To address this serious issue, we propose TCDE, a dual expansio

Albert Herrero-Parareda, Nathaniel Furman, Tarek Mealy, Ricky Gibson

This erratum provides an updated fitting function for the lasing threshold of finite-length cavities operating at a stationary inflection point (SIP) or regular band edge (RBE) resonance, clarifying their asymptotic scaling with the number of unit cells of the periodic cavity.

Amber Yijia Zheng, Jae Joong Lee, Bedrich Benes, Raymond A. Yeh

We present a vision-language model (VLM) that automatically edits website HTML to address violations of the Web Content Accessibility Guidelines 2 (WCAG2) while preserving the original design. We formulate this as a supervised image-conditioned program synthesis task, where the model learns to correct HTML given both the code and its visual rendering. We cre

Jianan Pan, Junbo Hao, Qixiang Gao, Xing Zhong

To address the non-optimal global design caused by the independent optimization of optical lenses, photodetectors, and computational processing subsystems in traditional remote sensing imaging system design, this paper proposes a holistic information theory for spatial remote sensing imaging. This theory integrates the optoelectronic imaging hardware front e

Xiaolei Yin, Guanwen Fang, Shiying Lu, Zesen Lin

The two-step galaxy morphology classification framework {\tt USmorph} successfully combines unsupervised machine learning (UML) with supervised machine learning (SML) methods. To enhance the UML step, we employed a dual-encoder architecture (ConvNeXt and ViT) to effectively encode images, contrastive learning to accurately extract features, and principal com

Liad Lea Didi, Kobi Cohen

We study distributed learning for spectrum access and sharing among multiple cognitive communication entities, such as cells, subnetworks, or cognitive radio users (collectively referred to as cells), in communication-constrained wireless networks modeled by interference graphs. Our goal is to achieve a globally stable and interference-aware channel allocati

Dianxing Shi, Dingjie Fu, Yuqiao Liu, Jun Wang

Vision-Language Models (VLMs) have shown strong performance in zero-shot image classification tasks. However, existing methods, including Contrastive Language-Image Pre-training (CLIP), all rely on annotated text-to-image pairs for aligning visual and textual modalities. This dependency introduces substantial cost and accuracy requirement in preparing high-q

Walter A. Strauss, Masahiro Suzuki

We consider a plasma that is created by a high voltage difference, which is known as a Townsend gas discharge. The plasma is confined to the region between two concentric spheres, one of which is a cathode and the other an anode. Ion-electron pairs are created by collisions inside the plasma. Additional electrons enter the plasma by collisions of ions with t

Changfu Xu, Jianxiong Guo, Jiandian Zeng, Houming Qiu

The Artificial Intelligence Generated Content (AIGC) technique has gained significant traction for producing diverse content. However, existing AIGC services typically operate within a centralized framework, resulting in high response times. To address this issue, we integrate collaborative Mobile Edge Computing (MEC) technology to reduce processing delays f

Shota Fujishima

This study investigates an adaptive pricing scheme aimed at achieving an efficient state in a traffic congestion game characterized by a diverse population of road users. While the planner possesses knowledge of players' preferences, their ability to observe only aggregate states limits the implementation of differentiated taxes. We propose a pricing approac

Zhaohua Tian, Yu Tian, Yadi Niu, Qi Liu

A optical waveform can be synthesized by complex-frequency waves as well as by real-frequency harmonic waves. While single complex-frequency wave with exponentially rising waveform can be perfectly absorbed in lossless structures. Here, we propose that diverse optical waveforms can be captured without any absorption through the synthesis of complex frequenci

Ravi Prasad

Monte Carlo algorithms are a foundational pillar of modern computational science, yet their effective application hinges on a deep understanding of their performance trade offs. This paper presents a critical analysis of the evolution of Monte Carlo algorithms, focusing on the persistent tension between statistical efficiency and computational cost. We descr

Yixuan Cheng, Maheen H. Mufti, Carrie He, Ying Cong Zuo

This paper introduces Bidirectional Technologies (BiTs), which is defined as technology that addresses the challenges within the aerospace and climate sectors simultaneously. BiTs presents a means to meet global development agendas, in particular, the United Nations 2030 Agenda for Sustainable Development and the Space2030 Agenda. These frameworks position a

Zhedong Zhang, Liang Li, Gaoxiang Cong, Chunshan Liu

Movie dubbing seeks to synthesize speech from a given script using a specific voice, while ensuring accurate lip synchronization and emotion-prosody alignment with the character's visual performance. However, existing alignment approaches based on visual features face two key limitations: (1)they rely on complex, handcrafted visual preprocessing pipelines, i

Devin M. McAfee, Elizabeth A. Barnes

Faithful uncertainty quantification (UQ) is paramount in high stakes climate prediction. Deep ensembles, or ensembles of probabilistic neural networks, are state of the art for UQ in machine learning (ML) and are growing increasingly popular for weather and climate prediction. However, detailed analyses of the mechanisms, strengths, and limitations of ensemb

Nan Zhou, Huandong Wang, Jiahao Li, Yang Li

Fine-grained fire prediction plays a crucial role in emergency response. Infrared images and fire masks provide complementary thermal and boundary information, yet current methods are predominantly limited to binary mask modeling with inherent signal sparsity, failing to capture the complex dynamics of fire. While world models show promise in video generatio

Yoshinori Hashimoto, Bruno Mera, Tomoki Ozawa

We prove the rigidity of isotropic harmonic maps from a 2-torus to a complex projective space, when they are constructed from holomorphic embeddings associated to complete linear systems. We also prove that this rigidity holds for any holomorphic embeddings without special hyperosculation points, with an extra assumption on the pullbacks of Fubini--Study sym

Yanzhen Wang, Yiyang Jiang, Diana Golovanova, Kamal Das

We introduce QMBench, a comprehensive benchmark designed to evaluate the capability of large language model agents in quantum materials research. This specialized benchmark assesses the model's ability to apply condensed matter physics knowledge and computational techniques such as density functional theory to solve research problems in quantum materials sci

Rui Liu, Runsheng Zhang, Shixiao Wang

This study investigates the task of dwell time prediction and proposes a Transformer framework based on interaction behavior modeling. The method first represents user interaction sequences on the interface by integrating dwell duration, click frequency, scrolling behavior, and contextual features, which are mapped into a unified latent space through embeddi

Joseph C. Chapman, Muneer Alshowkan, Jack Postlewaite, Saikat Guha

High-quality quantum communications that enable important capabilities, such as distributed quantum computing and sensing, will require quantum repeaters for providing high-quality entanglement. To realize high-rate heralded entanglement for quantum repeaters, Chen et al. [Phys. Rev. Appl. 19, 054209 (2023)] proposed a scheme for heralded-multiplexed generat

Kaz Gary, Ji Wang, Anusha Pai Asnodkar, Ian Wong

Due to their high equilibrium temperatures ($T_{eq}$ $>$ 2000 K), ultra-hot Jupiters (UHJs) are the best characterized exoplanets to date. However, many questions about their formation, evolution, and atmospheres remain unanswered. Phase curve observations can reveal answers to these questions by constraining multiple atmospheric properties including circula

Huixin Zhan

Genomic Foundation Models (GFMs), such as Evolutionary Scale Modeling (ESM), have demonstrated remarkable success in variant effect prediction. However, their security and robustness under adversarial manipulation remain largely unexplored. To address this gap, we introduce the Secure Agentic Genomic Evaluator (SAGE), an agentic framework for auditing the ad

Josh Barber, Rourke Young, Cameron Coombe, Will Browne

Reasoning under uncertainty is a key challenge in AI, especially for real-world tasks, where problems with sparse data demands systematic generalisation. Existing approaches struggle to balance accuracy and simplicity when evaluating multiple candidate solutions. We propose a Solomonoff-inspired method that weights LLM-generated hypotheses by simplicity and

Taylan Demir

In this paper, we propose a fractional time extension of the Quan tum Master Equation. We introduce a Caputo-type fractional derivative in time as an extension of the exponential decay of the Lindblad framework through the incorporation of fractional derivatives into the Lindblad framework. We show that the analytical and numerical results of our analytical

Pōhai Kūkea-Shultz, Frank Brockmann

This paper presents the design and evaluation of a community-based artificial intelligence (AI) workflow developed for the Kaiapuni Assessment of Educational Outcomes (K\=A'EO) program, the only native language assessment used for federal accountability in the United States. The project explored whether document-grounded language models could ethically and e

Preston Tranbarger

Building upon the work of Stucker, Vennos, and Young we derive generalized Dedekind sums arising from period integrals applied to holomorphic Eisenstein series attached to pairs of primitive non-trivial Dirichlet characters. Furthermore, we explore a variety of properties of these generalized Dedekind sums: we develop a finite sum formula, demonstrate their

Dawnena Key

This paper presents a real-time American Sign Language (ASL) recognition system utilizing a hybrid deep learning architecture combining 3D Convolutional Neural Networks (3D CNN) with Long Short-Term Memory (LSTM) networks. The system processes webcam video streams to recognize word-level ASL signs, addressing communication barriers for over 70 million deaf a

Vinicius P. Campos, Diego Szczupak, Tales Santini, Afonso C. Silva

Purpose: Noise in diffusion-weighted MRI (dMRI) is often spatially correlated due to different acquisition and reconstruction strategies, which is not fully accounted for in current denoising strategies. Thus, we propose a novel model-based denoising method for dMRI that effectively accounts for the different noise characteristics of data. Methods: We propos

Puyang Wang, Pengfei Guo, Keyi Chai, Jinyuan Zhou

Clinical MRI encompasses diverse imaging protocols--spanning anatomical targets (cardiac, brain, knee), contrasts (T1, T2, mapping), sampling patterns (Cartesian, radial, spiral, kt-space), and acceleration factors--yet current deep learning reconstructions are typically protocol-specific, hindering generalization and deployment. We introduce Scalable Deep U

Chunyang Meng, Eduardo B. Sandoval, Ricardo Sosa, Francisco Cruz

As the global population ages, many seniors face the problem of loneliness. Companion robots offer a potential solution. However, current companion robots often lack advanced functionality, while task-oriented robots are not designed for social interaction, limiting their suitability and acceptance by seniors. Our work introduces a senior-oriented system for

Hanwu Li

In this paper, we introduce a new type of backward stochastic differential equations (BSDEs), called conditional expectation BSDEs, whose drivers depend not only on the value of the solutions but also on their conditional expectations with respect to a certain sub-{\sigma}-algebra. The collection of these sub-{\sigma}-algebra forms a subfiltration, which sta

Xuenan Li, Chun Liu, Di Qi

We present a new asymptotic strategy for general micro-macro models which analyze complex viscoelastic fluids governed by coupled multiscale dynamics. In such models, the elastic stress appearing in the macroscopic continuum equation is derived from the microscopic kinetic theory, which makes direct numerical simulations computationally expensive. To address

Talha Akyildiz, Hessam Mahdavifar

We consider vehicular networking scenarios where existing vehicle-to-vehicle (V2V) links can be leveraged for an effective uploading of large-size data to the network. In particular, we consider a group of vehicles where one vehicle can be designated as the \textit{leader} and other \textit{follower} vehicles can offload their data to the leader vehicle or d

Alexander G. Mountogiannakis, Stefano Pirandola

We investigate the optimization of epsilon-security parameters in quantum key distribution (QKD), aiming to improve the achievable secure key rate under a fixed overall composable security level. For this purpose, we employ a continuous genetic algorithm (CGA) to optimize the epsilon-security components of two representative protocols: the homodyne protocol

Jacob A. Barandes, Mahmudul Hasan, David Kagan

We reformulate the CHSH game in terms of indivisible stochastic processes. Using Barandes&#39;s stochastic-quantum correspondence and its associated definition of causal locality, we present a novel proof of the Tsirelson bound. In particular, we show that unlike the no-signaling principle alone, the postulates defining causally local, indivisible stochastic

Md. Shahriar Hossain Shuvo, Xing He, Mithun Ghosh, Ding-Shyue Yang

Interfaces play a crucial role in energy transport at the nanoscale. However, direct experimental observations of interfacial thermal conductance across molecular junctions have remained challenging due to the high spatiotemporal resolution required for probing. Here, we report dynamic energy transport processes across multi-component molecular junctions obs

Sunil Arora, John Hastings

Securing Agentic Artificial Intelligence (AI) systems requires addressing the complex cyber risks introduced by autonomous, decision-making, and adaptive behaviors. Agentic AI systems are increasingly deployed across industries, organizations, and critical sectors such as cybersecurity, finance, and healthcare. However, their autonomy introduces unique secur

Roger A. Finger, Eduardo G. Cortes, Sandro J. Rigo, Gabriel de O. Ramos

Processing overlapping narrative documents, such as legal testimonies or historical accounts, often aims not for compression but for a unified, coherent, and chronologically sound text. Standard Multi-Document Summarization (MDS), with its focus on conciseness, fails to preserve narrative flow. This paper formally defines this challenge as a new NLP task: Na

Brahim Mahmoudi, Zacharie Chenail-Larcher, Naouel Moha, Quentin Stiévenart

Large Language Models (LLMs) have gained massive popularity in recent years and are increasingly integrated into software systems for diverse purposes. However, poorly integrating them in source code may undermine software system quality. Yet, to our knowledge, there is no formal catalog of code smells specific to coding practices for LLM inference. In this

Gustavo Ardila-Tafurth, Andrés Flórez, Cristian Rodríguez, Maud Sarazin

We perform a feasibility study to probe dark matter (DM) production at the LHC within a global $U(1)_L$ scotogenic model. The study is conducted using the Markov Chain Monte Carlo numerical method, considering the viable parameter space of the model allowed by experimental constraints such as neutrino oscillation data, the Higgs to invisible branching fracti

Xinyue Yu, Hayden Schaeffer

Operator learning is a data-driven approximation of mappings between infinite-dimensional function spaces, such as the solution operators of partial differential equations. Kernel-based operator learning can offer accurate, theoretically justified approximations that require less training than standard methods. However, they can become computationally prohib

Mohamed Belfkir

This paper presents the first dedicated study of the boosted $HH \to b\bar{b}γγ$ topology as a key probe of physics beyond the Standard Model (SM) in the high-energy double-Higgs boson regime. The analysis presented in this paper, focuses on two classes of new-physics scenarios: non-resonant deviations of the quartic gauge--Higgs interaction, parameterized b

Ami Pandat, Punna Rajasekhar, G. Aravamuthan, Gopika Vinod

Accurate camera models are essential for photogrammetry applications such as 3D mapping and object localization, particularly for long distances. Various stereo-camera based 3D localization methods are available but are limited to few hundreds of meters&#39; range. This is majorly due to the limitation of the distortion models assumed for the non-linearities

Lei Zhu, Dale Gottlieb, Vincent Maioli, Antoine Hubert

Multiphoton microscopy is widely used for live imaging. However, its acquisition speed remains limited by fluorophore emission rates and photodamage. To increase the pixel rate of a two-photon microscope beyond a few megahertz (MHz), multi-point parallelized schemes have been proposed. Two-photon (2P) light-sheet microscopy emerges as an effective approach f

Anastasiia Kultaeva, Volodymyr Vasylkovskyi, Andreas Sperlich, Eugenio Otal

Donor-$π$-acceptor (D-$π$-A) functionalization of MOF linkers can enhance visible-light photocatalytic activity, yet the mechanisms responsible for these effects remain unclear. Here we combine EPR spectroscopy, transient photoluminescence, and first-principles calculations to examine how diazo-coupled anisole, diphenylamine (DPA), and N,N-dimethylaniline (N

Michael Meixner, Matthias Reitner, Thomas Schäfer, Alessandro Toschi

By means of cellular dynamical mean-field theory (CDMFT) we study how short-range correlations drive the breakdown of the self-consistent perturbation theory in two-dimensional systems and the most relevant physical consequences associated to it. To this aim, we first derive in a structured and consistent way the Bethe-Salpeter equation (BSE) formalism at th

Yuri Calleo

The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders th

Zhou Lu, Mohammed El-Hajjar, Lie-liang Yang

Research results on Affine Frequency Division Multiplexing (AFDM) reveal that it experiences the same Peak-to-Average Power Ratio (PAPR) problem as conventional Orthogonal Frequency-Division Multiplexing (OFDM). On the other side, some references and also our studies demonstrate that AFDM involves an unneeded matrix, which is based on a parameter typically r

Yulong Qiao, Richard. Matthias Geilhufe

The advent of high-intensity ultrafast laser pulses has opened new opportunities for controlling and designing quantum materials. In particular, terahertz (THz) pulses can resonantly drive optical phonon modes, enabling dynamic manipulation of lattice degrees of freedom. In this work, we investigate the ultrafast quantum thermodynamics of optical phonon mode

Xiaopeng Yuan, Peng Wu, Xinran Wang, Yulin Hu

In this paper, we investigate an integrated sensing-and-communication (ISAC) network enabled by an unmanned aerial vehicle (UAV). The UAV is supposed to fly along a periodical circular trajectory at a fixed height for ISAC service supply from the sky. We consider on-demand sensing services, where on-demand detection and on-demand localization requests may be

Lukas Schamriß, Louis Garbe, Peter Rabl

We discuss a conceptually simple scheme for cooling a one dimensional gas of microwave photons in a superconducting transmission line. By shunting one end of the transmission line by a nonlinear Josephson element, we show how a cooling mechanism can be engineered that transfers photons from high- into low-frequency modes, while preserving their total number.

Ali Alsayegh, Tariq Masood

Voice-based human-machine interaction has become a primary means of accessing intelligent systems, yet individuals with dysarthria are systematically excluded by persistent gaps in recognition accuracy. Although automatic speech recognition (ASR) achieves word error rates (WER) below 5% on typical speech, performance degrades sharply for dysarthric speakers,

Carlos Saji, Roberto E. Troncoso

We study how topological crystalline defects--dislocations--reshape the real-space quantum geometric tensor and act as tunable sources of quantum geometry. We show that dislocations strongly enhance the quantum metric, establishing a direct link between lattice topology and the Hilbert-space geometry of states. We characterize the quantum geometry of topolog

Lishan Fang, Hua-Lin Huang

This paper addresses two fundamental problems posed by Qi regarding the sufficiency of eigenvalues for the classification of symmetric tensors in the two-dimensional setting. For $2\times2\times2$ and $2\times2\times2\times2$ complex symmetric tensors, we establish their complete set of equivalence classes via a one-to-one correspondence with the canonical f

Alexander A. Tsirlin, Oleg Janson, Ioannis Rousochatzakis

We report a comprehensive microscopic study of the frustrated quantum magnet PHCC, (C$_4$H$_{12}$N$_2$)Cu$_2$Cl$_6$, using density-functional band-structure calculations combined with numerical quantum many-body simulations of the underlying spin Hamiltonian. We show that the magnetism of PHCC is captured by a one-dimensional model of the frustrated spin cha

Satoshi Tanioka, Terri Pearce, Yuta Michimura, Kazuhiro Agatsuma

High-quality test mass substrates play essential roles in laser interferometric gravitational wave detectors. Inhomogeneous birefringence distribution in test mass substrates, however, can degrade the sensitivity of the detector by introducing the optical loss and disturbing the interferometer controls. In this paper, we present a heterodyne polarimetry meth

Byeongha Moon, Yujin Yang, Kyoung-Soo Lee, Eric Gawiser

Ly$α$ blobs (LABs) are large, spatially extended Ly$α$-emitting objects whose nature remains unclear. Their statistical properties such as number densities and luminosity functions are still uncertain because of small sample sizes and large cosmic variance. The One-hundred-deg$^2$ DECam Imaging in Narrowbands (ODIN) survey, with its large volume, offers an o

Zhi Ma, Cheng Wen, Zhexin Su, Xiao Liang

Automating the translation of natural language (NL) software requirements into formal specifications remains a critical challenge in scaling formal verification practices to industrial settings, particularly in safety-critical domains. Existing approaches, both rule-based and learning-based, face significant limitations. While large language models (LLMs) li

Fang-Xiang Wang, Sheng-Teng Zheng, Long Huang, Guo-We Zhang

Fully connected quantum networks enable simultaneously connecting every user to every other user and are the most versatile and robust networking architecture. However, the scalability of such networks remains great challenge for practical applications. Here we construct a large-scale fully connected quantum network founded on two-photon Hong-Ou-Mandel (HOM)

Hao Dai, Yue Zhang

Minimum uncertainty states of the conventional Heisenberg uncertainty relation have been extensively studied and are often regarded as the most classical quantum states from the perspective of uncertainty, providing valuable insight into the nature of quantumness and its potential applications. In this work, we investigate the minimum uncertainty states asso

Roman Firt, Neel Misciasci, Jonathan E. Mueller, Triet Friedhoff

We evaluate the Sample-based Krylov Quantum Diagonalization (SKQD) algorithm on one- and two-dimensional Heisenberg models, including strongly correlated regimes in which the ground state is dense. Using problem-informed initial states and magnetization-sector sweeps, SKQD accurately reproduces ground-state energies and field-dependent magnetization across a

Chiara Di Vece, Zhehua Mao, Netanell Avisdris, Brian Dromey

Accurate fetal growth assessment from ultrasound (US) relies on precise biometry measured by manually identifying anatomical landmarks in standard planes. Manual landmarking is time-consuming, operator-dependent, and sensitive to variability across scanners and sites, limiting the reproducibility of automated approaches. There is a need for multi-source anno

Xinqun Mei, Guofang Wang, Liangjun Weng

In this article, we introduce a $k$-th capillary area measure for capillary convex bodies in the Euclidean half-space, which serves as a boundary counterpart to the classical concept of area measure (see, e.g., \cite[Chapter 8]{Sch}). We then propose a Christoffel-Minkowski problem for capillary convex bodies, to find a capillary convex body in the Euclidean

A. Jesse Jiryu Davis, Murat Demirbas, Lingzhi Deng

Raft is a leading consensus algorithm for replicating writes in distributed databases. However, distributed databases also require consistent reads. To guarantee read consistency, a Raft-based system must either accept the high communication overhead of a safety check for each read, or implement leader leases. Prior lease protocols are vaguely specified and