May 2025 arXiv papers — page 10

Lua F. T. Airoldi, Gustavo F. S. Alves, Yuber F. Perez-Gonzalez, Gabriel M. Salla

The discovery of ultra-high-energy neutrinos by IceCube marked the beginning of neutrino astronomy. Yet, the origin and production mechanisms of these neutrinos remain an open question. With the observation of several neutrino events with energies about the PeV, transient sources - astrophysical objects that emit particles in brief, localized bursts - have e

Ming-Hsun Yang

This work examines the multi-view compressive phase retrieval problem in a distributed sensor network, where each sensor device, limited by storage and sensing capabilities, can access only intensity measurements from an unknown part of the global sparse vector. The goal is to enable each sensor to recover its observable sparse signal when measurements are c

Huu-Thien Tran, Thanh-Dat Truong, Khoa Luu

Large vision-language models have become widely adopted to advance in various domains. However, developing a trustworthy system with minimal interpretable characteristics of large-scale models presents a significant challenge. One of the most prevalent terms associated with the fallacy functions caused by these systems is hallucination, where the language mo

Enrique Artal Bartolo, Willem Veys

Germs of rational functions~$h$ on points $p$ of smooth varieties~$S$ define germs of rational maps to the projective line. Assume that $p$ is in the indeterminacy locus of $h$. If $\pi:\hat{S}\to S$ is a birational map which is an isomorphism outside $p$, then $h$ lifts to a germ of a rational map on $(\hat{S}, \pi^{-1}(p))$. The exceptional components $E_i

Ruipeng Jia, Yunyi Yang, Yongbo Gai, Kai Luo

Reinforcement learning with verifiable rewards (RLVR) has enabled large language models (LLMs) to achieve remarkable breakthroughs in reasoning tasks with objective ground-truth answers, such as mathematics and code generation. However, a significant gap remains for non-verifiable tasks, like creative writing and open-ended dialogue, where quality assessment

Pouya Mehdipour, Somayeh Jangjooye Shaldehi

In this paper, we define the so-called square entropy and prove that n-to-1 full zip shift maps are intrinsically ergodic. Furthermore, we show that square entropy characterizes uniform n-to-1 transformations of $(m,l)$-Bernoulli type that are extended Bernoulli transformations.

Yiqun Sun, Qiang Huang, Anthony K. H. Tung, Jun Yu

Semantic Text Embedding is a fundamental NLP task that encodes textual content into vector representations, where proximity in the embedding space reflects semantic similarity. While existing embedding models excel at capturing general meaning, they often overlook ideological nuances, limiting their effectiveness in tasks that require an understanding of pol

Kequan Xia, Song Yang, Jianguo Lu, Min Yu

Conventional pressure sensors often integrate two distinct mechanisms to detect static and dynamic stimuli, hindering the development of high fidelity human-machine interfaces. Here, we present an intrinsic static/dynamic triboelectric sensor (iSD Sensor) capable of reliably perceiving both continuous static pressure and transient mechanical shocks through a

Ygor M. Jaques, Cristiano F. Woellner, Lucas M. Sassi, Marcelo L. Pereira

Transition metal dichalcogenides (TMDs), particularly monolayer MoS2, have received increased attention in materials science and have been exploited in diverse applications from photonics to catalysis. Defects in TMDs play a crucial role in modulating their properties, and understanding defect-induced dynamics is of great importance. This study investigates

Juan Wisznia, Cecilia Bolaños, Juan Tollo, Giovanni Marraffini

We introduce a novel framework for analyzing sorting algorithms in pairwise ranking prompting (PRP), re-centering the cost model around LLM inferences rather than traditional pairwise comparisons. While classical metrics based on comparison counts have traditionally been used to gauge efficiency, our analysis reveals that expensive LLM inferences overturn th

Masahiro Negishi, Thomas Gärtner, Pascal Welke

We investigate the distance function learned by message passing neural networks (MPNNs) in specific tasks, aiming to capture the functional distance between prediction targets that MPNNs implicitly learn. This contrasts with previous work, which links MPNN distances on arbitrary tasks to structural distances on graphs that ignore task-specific information. T

Chengzhi Wu, Qianliang Huang, Kun Jin, Julius Pfrommer

Contrastive learning is an essential method in self-supervised learning. It primarily employs a multi-branch strategy to compare latent representations obtained from different branches and train the encoder. In the case of multi-modal input, diverse modalities of the same object are fed into distinct branches. When using single-modal data, the same input und

Jens-Joris Decorte, Jeroen Van Hautte, Chris Develder, Thomas Demeester

Labor market analysis relies on extracting insights from job advertisements, which provide valuable yet unstructured information on job titles and corresponding skill requirements. While state-of-the-art methods for skill extraction achieve strong performance, they depend on large language models (LLMs), which are computationally expensive and slow. In this

Varun Venkatesh, Amin Doostmohammadi

The interplay between active matter and its environment is central to understanding emergent behavior in biological and synthetic systems. Here, we show that coupling active nematic flows to small-amplitude deformations of a compliant substrate can fundamentally reorganize the system's dynamics. Using a model that combines active nematohydrodynamics with sub

Zahid Hassan Tushar, Adeleke Ademakinwa, Jianwu Wang, Zhibo Zhang

Cloud Optical Thickness (COT) is a critical cloud property influencing Earth's climate, weather, and radiation budget. Satellite radiance measurements enable global COT retrieval, but challenges like 3D cloud effects, viewing angles, and atmospheric interference must be addressed to ensure accurate estimation. Traditionally, the Independent Pixel Approximati

Marios Glytsos, Panagiotis P. Filntisis, George Retsinas, Petros Maragos

Accurate 6D object pose estimation is essential for robotic grasping and manipulation, particularly in agriculture, where fruits and vegetables exhibit high intra-class variability in shape, size, and texture. The vast majority of existing methods rely on instance-specific CAD models or require depth sensors to resolve geometric ambiguities, making them impr

Jiahao Ying, Wei Tang, Yiran Zhao, Yixin Cao

This paper introduces a Dual Evaluation Framework to comprehensively assess the multilingual capabilities of LLMs. By decomposing the evaluation along the dimensions of linguistic medium and cultural context, this framework enables a nuanced analysis of LLMs' ability to process questions within both native and cross-cultural contexts cross-lingually. Extensi

Xuzhi Wang, Wei Feng, Lingdong Kong, Liang Wan

LiDAR semantic segmentation plays a vital role in autonomous driving. Existing voxel-based methods for LiDAR semantic segmentation apply uniform partition to the 3D LiDAR point cloud to form a structured representation based on cartesian/cylindrical coordinates. Although these methods show impressive performance, the drawback of existing voxel-based methods

Andreas Blommaert, Adam Levine

Sine dilaton gravity is holographically related to DSSYK. We explain how to interpret sine dilaton as 2d quantum cosmology. This paves the way for using two copies of DSSYK as hologram for Big-Bang cosmologies. We study the most basic cosmological observable: the sphere amplitude. Via canonical quantization we find a finite answer that matches the on-shell a

E. Icking, F. Wörtche, A. W. Cummings, A. Wörtche

Proximity coupling of bilayer graphene (BLG) to transition metal dichalcogenides (TMDs) offers a promising route to engineer gate-tunable spin-orbit coupling (SOC) while preserving BLG's exceptional electronic properties. This tunability arises from the layer-asymmetric electronic structure of gapped BLG, where SOC acts predominantly on the layer in contact

Junyi Li, Hwee Tou Ng

Large language models (LLMs) have significantly advanced in reasoning tasks through reinforcement learning (RL) optimization, achieving impressive capabilities across various challenging benchmarks. However, our empirical analysis reveals a critical drawback: reasoning-oriented RL fine-tuning significantly increases the prevalence of hallucinations. We theor

Lotte Bransen, Tim Janssen, Jesse Davis

Penalties are fraught and game-changing moments in soccer games that teams explicitly prepare for. Consequently, there has been substantial interest in analyzing them in order to provide advice to practitioners. From a data science perspective, such analyses suffer from a significant limitation: they make the unrealistic simplifying assumption that goalkeepe

Hiroya Nishikawa, Dennis Kwaria, Atsuko Nihonyanagi, Fumito Araoka

Recently discovered helical polar fluid adopts a spontaneous chiral symmetry breaking (CSB) driven by polarization escape and conformational chirality. Ferroelectric nematic and smectic phases are intrinsically chiral in the ground state and can be stabilized in an extrinsic twisted configuration through surface anchoring. Herein, we introduce extrinsic CSB

Fu Luo, Yaoxin Wu, Zhi Zheng, Zhenkun Wang

Recent neural combinatorial optimization (NCO) methods have shown promising problem-solving ability without requiring domain-specific expertise. Most existing NCO methods use training and testing data with a fixed constraint value and lack research on the effect of constraint tightness on the performance of NCO methods. This paper takes the capacity-constrai

Ema Puljak, Maurizio Pierini, Artur Garcia-Saez

The pursuit of discovering new phenomena at the Large Hadron Collider (LHC) demands constant innovation in algorithms and technologies. Tensor networks are mathematical models on the intersection of classical and quantum machine learning, which present a promising and efficient alternative for tackling these challenges. In this work, we propose a tensor netw

Boxuan Ai, Shuo He, Xiang Zhao, Lin Yang

Existing quantum discrete adiabatic approaches are hindered by circuit depth that increases linearly with the number of evolution steps, a significant challenge for current quantum hardware with limited coherence times. To address this, we propose a co-designed framework that synergistically integrates dynamic circuit capabilities with real-time classical pr

Duo Zheng, Shijia Huang, Yanyang Li, Liwei Wang

Previous research has investigated the application of Multimodal Large Language Models (MLLMs) in understanding 3D scenes by interpreting them as videos. These approaches generally depend on comprehensive 3D data inputs, such as point clouds or reconstructed Bird's-Eye View (BEV) maps. In our research, we advance this field by enhancing the capability of MLL

Wenyuan Li, Guang Li, Keisuke Maeda, Takahiro Ogawa

To address the computational and storage challenges posed by large-scale datasets in deep learning, dataset distillation has been proposed to synthesize a compact dataset that replaces the original while maintaining comparable model performance. Unlike optimization-based approaches that require costly bi-level optimization, distribution matching (DM) methods

Giorgio Mentasti, Arad Nasiri

We study the covariant diffusion and drift of massless particles on the light cone within the context of quantum gravity phenomenology. Unlike modified dispersion relations that violate Lorentz invariance and grow with frequency, this model introduces a stochastic correction to the massless geodesic equation while preserving Lorentz invariance, and is domina

Vittorio Torri, Machteld J. Boonstra, Marielle C. van de Veerdonk, Deborah N. Kalkman

Objective: Heart failure (HF) patients present with diverse phenotypes affecting treatment and prognosis. This study evaluates models for phenotyping HF patients based on left ventricular ejection fraction (LVEF) classes, using structured and unstructured data, assessing performance and interpretability. Materials and Methods: The study analyzes all HF hospi

Victor Casamayor Pujol, Boris Sedlak, Tommaso Salvatori, Karl Friston

The Computing Continuum (CC) is an emerging Internet-based computing paradigm that spans from local Internet of Things sensors and constrained edge devices to large-scale cloud data centers. Its goal is to orchestrate a vast array of diverse and distributed computing resources to support the next generation of Internet-based applications. However, the distri

André H. Gomes, Winder A. Moura-Melo

We demonstrate that a conventional hollow conductor waveguide filled with a material exhibiting the coexistence of chiral magnetic and anomalous quantum Hall effects supports the propagation of transverse electromagnetic modes. This simple setup provides a direct and optically feasible method to probe the simultaneous presence of these phenomena, potentially

Nikita Martynov, Anastasia Mordasheva, Dmitriy Gorbetskiy, Danil Astafurov

We introduce POLLUX, a comprehensive open-source benchmark designed to evaluate the generative capabilities of large language models (LLMs) in Russian. Our main contribution is a novel evaluation methodology that enhances the interpretability of LLM assessment. For each task type, we define a set of detailed criteria and develop a scoring protocol where mode

Yan Liu, Zonglin Yang, Soujanya Poria, Thanh-Son Nguyen

In an era of exponential scientific growth, identifying novel research ideas is crucial and challenging in academia. Despite potential, the lack of an appropriate benchmark dataset hinders the research of novelty detection. More importantly, simply adopting existing NLP technologies, e.g., retrieving and then cross-checking, is not a one-size-fits-all soluti

Jiaxin Liu, Zeyue Pang, Hehanlin Wang, Zi-Yu Chen

Relativistic electron beams produced by intense lasers over short distances have important applications in high energy density physics and medical technologies. Vacuum laser acceleration with plasma mirrors injectors has garnered substantial research interest recently. However, a persistent challenge remains unresolved that electrons inevitably detach from t

Daniela Occhipinti, Marco Guerini, Malvina Nissim

Endowing dialogue agents with persona information has proven to significantly improve the consistency and diversity of their generations. While much focus has been placed on aligning dialogues with provided personas, the adaptation to the interlocutor's profile remains largely underexplored. In this work, we investigate three key aspects: (1) a model's abili

Sujoy Chatterjee, Everton Romanzini Colombo, Marcos Medeiros Raimundo

Explainability is crucial for improving the transparency of black-box machine learning models. With the advancement of explanation methods such as LIME and SHAP, various XAI performance metrics have been developed to evaluate the quality of explanations. However, different explainers can provide contrasting explanations for the same prediction, introducing t

Gonzalo Martínez-Lema, Vitaly Chepel, Amos Breskin

We report on the first observation of electroluminescence and charge amplification with a Virtual Cathode Chamber (VCC) microstrips plate immersed in liquid xenon. Both were observed in an intense non-uniform electric field in the vicinity of 2-$\mu$m narrow anode strips deposited, with a 2~mm pitch, on a semiconductive glass substrate (S8900), with a cathod

Shoshana Reich, Edward Buskey, Clint Dawson, Eirik Valseth

We present an investigation comparing the potential impacts of offshore and onshore crude oil loading sites on surface trajectories of spilled oil particles in the regions near the Port of Corpus Christi, Texas. Oil transport is established in a two step procedure. First, the circulation and flow characteristics of seawater throughout the coastal ocean are e

Patawee Prakrankamanant, Shinji Watanabe, Ekapol Chuangsuwanich

This paper addresses the critical need for improved explainability in text-based depression detection. While offering predictive outcomes, current solutions often overlook the understanding of model predictions which can hinder trust in the system. We propose the use of Masked Hard Instance Mining (MHIM) to enhance the explainability in the depression detect

Panagiotis Rigas, Panagiotis Drivas, Charalambos Tzamos, Ioannis Chamodrakas

We present \textbf{GARLIC}, a representation learning approach for Euclidean approximate nearest neighbor (ANN) search in high dimensions. Existing partitions tend to rely on isotropic cells, fixed global resolution, or balanced constraints, which fragment dense regions and merge unrelated points in sparse ones, thereby increasing the candidate count when pr

C. Yao, J. Li, L. Hao, R. Yan

The saturation level of parametric instabilities critically determines their impact on fusion plasmas. We identify the resonance density range of two-plasmon decay as the critical parameter governing nonlinear saturation of ion density fluctuations and Langmuir waves, which drive hot-electron generation. Using this insight, we develop a predictive scaling mo

G. Cerretto, E. Cantoni, M. Sellone, C. E. Calosso

This study outlines the progress of a collaborative effort between INRIM and MUOGRAPHIX-The University of Tokyo, focusing on using muons from cosmic-ray-induced Extensive Air Showers (EAS) to synchronize atomic clocks and disseminate atomic time references. The approach, known as the Cosmic Time Synchronizer (CTS), proposed by the University of Tokyo, serves

Ivan Pereira-Sánchez, Julia Navarro, Ana Belén Petro, Joan Duran

This paper addresses the problem of reconstructing a high-resolution hyperspectral image from a low-resolution multispectral observation. While spatial super-resolution and spectral super-resolution have been extensively studied, joint spatio-spectral super-resolution remains relatively explored. We propose an end-to-end model-driven framework that explicitl

Beatriz Polo, Federico Centrone

We study precision charging in bosonic quantum batteries under a finite-energy constraint, using the signal-to-noise ratio (SNR) of delivered excitations as an operational metric directly tied to the energy measured at a load. At the state level, we derive a classical bound whose violation is equivalent to antibunching and certifies non-classicality, and a G

Omri Lev, Vishwak Srinivasan, Moshe Shenfeld, Katrina Ligett

Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning, with applications spanning computationally efficient optimization, coded computing, and federated learning. This operation also provides differential privacy guarantees due to its

Elena Demattè, Juan J. L. Velázquez

In this paper we consider a free boundary problem for the melting of ice where we assume that the heat is transported by conduction in both the liquid and the solid part of the material and also by radiation in the solid. Specifically, we study a one-dimensional two-phase Stefan-like problem which contains a non-local integral operator in the equation descri

Zekun Wang, Ethan L. Haarer, Nicki Barari, Christopher J. MacLellan

We introduce the concept of a \textbf{neuro-symbolic pair} -- neural and symbolic approaches that are linked through a common knowledge representation. Next, we present \textbf{taxonomic networks}, a type of discrimination network in which nodes represent hierarchically organized taxonomic concepts. Using this representation, we construct a novel neuro-symbo

Omer Nacar, Yasser Al-Habashi, Serry Sibaee, Adel Ammar

Arabic Optical Character Recognition (OCR) is essential for converting vast amounts of Arabic print media into digital formats. However, training modern OCR models, especially powerful vision-language models, is hampered by the lack of large, diverse, and well-structured datasets that mimic real-world book layouts. Existing Arabic OCR datasets often focus on

Peter Scholze

In this note, we consider the problem of constructing an enlargement of the category of Betti sheaves that supports an ``exponential local system'' on $\mathbb R$, and a Fourier equivalence defined on all sheaves. We show that there is a universal solution, recovering a construction of Tamarkin known also as ``enhanced sheaves''. The universality property im

Rodrigo R. Lopes, Carlos Maquera, Régis Varão

We prove that an Anosov action of $\mathbb{R}^k$ over a compact manifold $M$ transitive on regular sub-cones satisfies the dichotomy: each stable and unstable leaf is dense or the Anosov action is topologically conjugated to a suspension of a $\mathbb{Z}^k$-Anosov action. This represents an important progress toward addressing Verjovsky's extended conjecture

Shuai Liu, Ning Cao, Yile Chen, Yue Jiang

Next location prediction plays a critical role in understanding human mobility patterns. However, existing approaches face two core limitations: (1) they fall short in capturing the complex, multi-functional semantics of real-world locations; and (2) they lack the capacity to model heterogeneous behavioral dynamics across diverse user groups. To tackle these

Beatriz Polo-Rodríguez, Federico Centrone, Gerardo Adesso, Mir Alimuddin

Continuous-variable quantum thermodynamics in the Gaussian regime provides a promising framework for investigating the energetic role of quantum correlations, particularly in optical systems. In this work, we introduce an entropy-free criterion for entanglement detection in bipartite Gaussian states, rooted in a distinct thermodynamic quantity: ergotropy--th

Andrei Chernov, Vitaliy Pozdnyakov, Ilya Makarov

Recent work in time series forecasting has explored reformulating regression as a classification task. By discretizing the continuous target space into bins and predicting over a fixed set of classes, these approaches benefit from more stable training, improved uncertainty modeling, and compatibility with modern deep learning architectures. However, most exi

Staci Hepler, Rob Erhardt

High dimensional space-time data pose known computational challenges when fitting spatio-temporal models. Such data show dependence across several dimensions of space as well as in time, and can easily involve hundreds of thousands of observations. Many spatio-temporal models result in a dependence structure across all observations and can be fit only at a s

Junzhuo Li, Bo Wang, Xiuze Zhou, Peijie Jiang

The interpretability of Mixture-of-Experts (MoE) models, especially those with heterogeneous designs, remains underexplored. Existing attribution methods for dense models fail to capture dynamic routing-expert interactions in sparse MoE architectures. To address this issue, we propose a cross-level attribution algorithm to analyze sparse MoE architectures (Q

Weebum Yoo, Sung Whan Yoon

Model robustness indicates a model's capability to generalize well on unforeseen distributional shifts, including data corruptions and adversarial attacks. Data augmentation is one of the most prevalent and effective ways to enhance robustness. Despite the great success of the diverse augmentations in different fields, a unified theoretical understanding of

Sherelyn Alejandro Merchan, Jacqueline K. Faherty, Genaro Suárez, Kelle L. Cruz

We present the a near complete spectral energy distribution (SED) for an extrasolar world: the T8 brown dwarf 2MASS~J04151954$-$0935066. Spanning from optical to mid-infrared (0.7--20.4 micron) wavelengths, the SED for this substellar atmosphere is constructed from new JWST NIRSpec G395H ($R\sim$2700) and Magellan FIRE echelle ($R\sim$8000) near-infrared spe

Chi-Hsi Kung, Frangil Ramirez, Juhyung Ha, Yi-Ting Chen

Understanding a procedural activity requires modeling both how action steps transform the scene, and how evolving scene transformations can influence the sequence of action steps, even those that are accidental or erroneous. Yet, existing work on procedure-aware video representations fails to explicitly learned the state changes (scene transformations). In t

Linsheng Fan, Gao Ye, Zhongliang Sun, Lingguo Cao

This paper addresses the challenges of monitoring optical-fiber channels subject to complex, multidimensional impairments-such as dynamic interference across polarization or modal dimensions-where conventional methods suffer from high equipment costs, poor impairment discrimination and limited scalability. We propose an in-service, frequency-domain joint mon

Thomas Pawlaschyk, Nikolay Shcherbina

We show that there exists a $q$-convex function in a neighborhood of a compact set $K$ in a complex manifold $\mathcal{M}$ if and only if the $q$-nucleus of this compact set is empty. The latter can be characterized as the maximal $q$-pseudoconcave subset of $K$, i.e., a subset of $K$ containing all other compact $q$-pseudoconcave subsets in $K$.

Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao

Primordial Black Holes~(PBHs) are hypothetical black holes with a wide range of masses that formed in the early universe. As a result, they may play an important cosmological role and provide a unique probe of the early universe. A PBH with an initial mass of approximately $10^{15}$~g is expected to explode today in a final burst of Hawking radiation. In thi

Alexander Temnykh, Ivan Temnykh

We developed, built, characterized on bench and beam-tested a permanent magnet (PM) Compact Wiggler (CW) prototype with a hydraulic assist gap-controlling mechanism. The prototype is of ~50cm long, 20cm wide and 40cm high and weights ~50kg. Magnetic structure has a 76.2mm period. At 6.5mm minimal gap the structure demonstrated ~2.3 Tesla peak field. At this

Sakhinana Sagar Srinivas, Shivam Gupta, Venkataramana Runkana

Recent advances in generative AI have accelerated the discovery of novel chemicals and materials. However, scaling these discoveries to industrial production remains a major bottleneck due to the synthesis gap -- the need to develop entirely new manufacturing processes. This challenge requires detailed engineering blueprints: PFDs for equipment layouts and m

Saeed Ibrahim, Yue Xiao, Dimitrios Tyrovolas, Sotiris A. Tegos

Cognitive radio rate-splitting multiple access (CR-RSMA) has emerged as a promising multiple access framework that can efficiently manage interference and adapt dynamically to heterogeneous quality-of-service (QoS) requirements. To effectively support such demanding access schemes, programmable wireless environments have attracted considerable attention, esp

Renlei Jiang, Yuzhen Fan, Di Fan, Chuanhou Gao

Molecular computation based on chemical reaction networks (CRNs) has emerged as a promising paradigm for designing programmable biochemical systems. However, the implementation of complex computations still requires excessively large and intricate network structures, largely due to the limited understanding of composability, that is, how multiple subsystems

Susan Terebey, Loraine Sandoval Ascencio, Lizxandra Flores-Rivera, Neal Turner

High-spatial-resolution observations of disks around young stars suggest planetary systems begin forming early, during the protostellar phase (< 1 Myr) when stars accrete most of their mass via infall from the surrounding cloud. During this era shocks are expected to be ubiquitous around the gaseous accretion disk due to supersonic infall that strikes the di

Omer Nacar, Anis Koubaa, Serry Sibaee, Yasser Al-Habashi

Semantic textual similarity (STS) is a critical task in natural language processing (NLP), enabling applications in retrieval, clustering, and understanding semantic relationships between texts. However, research in this area for the Arabic language remains limited due to the lack of high-quality datasets and pre-trained models. This scarcity of resources ha

Marat M. Rvachev, Benjamin Drukarch

In this commentary, we argue that El Hady and Machta's "surface wave" model for mechanical waves accompanying action potential (AP) propagation describes the same underlying process as the "axoplasmic pressure wave" model introduced earlier by Rvachev. Both models describe mechanical modes that store potential energy in the elastic components of the axon (ax

Chaoyu Liu, Yangming Li, Zhongying Deng, Chris Budd

Physical laws, such as the conversation of mass and momentum, are fundamental principles in many physical systems. Neural operators have achieved promising performance in learning the solutions to those systems, but often fail to ensure conservation. Existing methods typically enforce strict conservation via hand-crafted post-processing or architectural cons

Abhishek Chandra, Taniya Kapoor, Mitrofan Curti, Koen Tiels

Complex piezoelectric systems are foundational in industrial applications. Their performance, however, is challenged by the nonlinear voltage-displacement hysteretic relationships. Efficient characterization methods are, therefore, essential for reliable design, monitoring, and maintenance. Recently proposed neural operator methods serve as surrogates for sy

Francesco Barioli, Shaun M. Fallat, Himanshu Gupta, Zhongshan Li

Since the transformative workshop by the American Institute of Mathematics on the minimum rank of a graph, two longstanding open problems have captivated the community interested in the minimum rank of graphs: the graph complement conjecture and the $\delta$-conjecture. In this paper, we use a classical result of Mader (1972) to establish a weak version of t

Jie Zhang, Haoyin Yan, Xiaofei Li

It is promising to design a single model that can suppress various distortions and improve speech quality, i.e., universal speech enhancement (USE). Compared to supervised learning-based predictive methods, diffusion-based generative models have shown greater potential due to the generative capacities from degraded speech with severely damaged information. H

Hyuntak Kim, Byung-Hak Kim

Summarizing long-form narratives--such as books, movies, and TV scripts--requires capturing intricate plotlines, character interactions, and thematic coherence, a task that remains challenging for existing LLMs. We introduce NexusSum, a multi-agent LLM framework for narrative summarization that processes long-form text through a structured, sequential pipeli

Lilian Childress, Vincent Halde, Kayla Johnson, Andrew Lowther

Accurate measurement of vector magnetic fields is critical for applications including navigation, geoscience, and space exploration. Nitrogen-vacancy (NV) center spin ensembles offer a promising solution for high-sensitivity vector magnetometry, as their different orientations in the diamond lattice measure different components of the magnetic field. However

Nikola Ljubešić, Ivan Porupski, Peter Rupnik

Automating primary stress identification has been an active research field due to the role of stress in encoding meaning and aiding speech comprehension. Previous studies relied mainly on traditional acoustic features and English datasets. In this paper, we investigate the approach of fine-tuning a pre-trained transformer model with an audio frame classifica

Michael D. Higgins, J. Golak, R. Skibinski, K. Topolnicki

A number of recent references have pointed out that an N-particle system having short-range interactions at S-wave and/or P-wave unitarity can exhibit modified threshold behavior for various reactive processes. But the question of how close to unitarity one must get in order to observe such modifications has not been addressed. The present study quantities t

Ajinkya Kulkarni, Francisco Teixeira, Enno Hermann, Thomas Rolland

Children are one of the most under-represented groups in speech technologies, as well as one of the most vulnerable in terms of privacy. Despite this, anonymization techniques targeting this population have received little attention. In this study, we seek to bridge this gap, and establish a baseline for the use of voice anonymization techniques designed for

Yucheng Pan, Wenchang Sun

We study the pointwise convergence of Landau type Schr\"odinger operators within the fractional Sobolev space $W^{s,p}(\mathbb R)$. Our results extend those established by Bailey (Rev. Mat. Iberoam., 29 (2): 531-546, 2013) and Yuan, Zhao and Zheng (Nonlinear Anal., 208: Paper No. 112312, 28, 2021). Furthermore, we also analyze the convergence rate of Landau

Qinghe Ma, Jian Zhang, Lei Qi, Qian Yu

Both limited annotation and domain shift are prevalent challenges in medical image segmentation. Traditional semi-supervised segmentation and unsupervised domain adaptation methods address one of these issues separately. However, the coexistence of limited annotation and domain shift is quite common, which motivates us to introduce a novel and challenging sc

Mehrdad Khodapanahandeh, Parviz Zolfaghari, Hakan Urey

This work presents the design, simulation, fabrication, and characterization of a novel architectural compact two-dimensional (2D) resonant MEMS scanning mirror actuated by thin-film lead zirconate titanate (PZT). The device employs an innovative mechanically coupled dual-axis architecture fabricated using a three-mask process on an SOI-PZT deposited wafer,

Brian Kintu

In this follow-up paper, we again inspect a surprising relationship between the set of fixed points of a polynomial map $\varphi_{d, c}$ defined by $\varphi_{d, c}(z) = z^d + c$ for all $c, z \in \mathcal{O}_{K}$ or $\in \mathbb{Z}_{p}$ or $\in \mathbb{F}_{p}[t]$ and the coefficient $c$, where $K$ is any number field of degree $n > 1$, $p>2$ is any prime, $\

Yu-Cheng Chang, Federico Chianese, Naveen Shetty, Johanna Huhtasaari

Exploiting quantum interference of charge carriers, epitaxial graphene grown on silicon carbide emerges as a game-changing platform for ultra-sensitive bolometric sensing, featuring an intrinsic resistive thermometer response unmatched by any other graphene variant. By achieving low and uniform carrier densities, we have accessed a new regime of strong charg

Diana Shakirova, Adrià Canós Valero, Daniil Riabov, Hatice Altug

Identifying the handedness of chiral molecules is of fundamental importance in chemistry, biology, pharmacy, and medicine. Nanophotonic structures allow us to control light at the nanoscale and offer powerful tools for chiral sensing, enabling the detection of small analyte volumes and low molecular concentrations by harnessing optical resonances. Most exist

Noah Stevenson, Ian Tice

We give the first mathematical construction of two-dimensional traveling bore wave solutions to the free boundary incompressible Navier-Stokes equations for a single finite depth layer of constant density fluid. Our construction is based on a rigorous justification of the formal shallow water limit, which postulates that in a certain scaling regime the full

Ioannis Tsiamas, David Dale, Marta R. Costa-jussà

Current translation systems, despite being highly multilingual, cover only 5% of the world's languages. Expanding language coverage to the long-tail of low-resource languages requires data-efficient methods that rely on cross-lingual and cross-modal knowledge transfer. To this end, we propose a character-based approach to improve adaptability to new language

Z. H. Zhang, F. Navarete, M. C. Galvez-Ortiz, H. R. A. Jones

Benchmark brown dwarfs in wide binary systems are crucial for characterizing substellar objects and calibrating atmospheric and evolutionary models. However, brown dwarf benchmarks with subsolar metallicity, very cool temperatures, or suitability for dynamical mass measurements are rare, limiting our understanding across the full range of mass, age, and meta

Min Dai, Shu Wang, Biwei Jiang, Ying Li

The binarity of red supergiants (RSGs) influences their evolution and the fate of supernovae. We investigate the binary fraction of RSGs in the Andromeda Galaxy (M31) and Triangulum Galaxy (M33) using photometry from the Hubble Space Telescope (HST), which offers high spatial resolution to resolve more RSGs. A preliminary step involves identifying a reliable

Simone Cammarasana, Giuseppe Patanè

We introduce a novel weighted convolution operator that enhances traditional convolutional neural networks (CNNs) by integrating a spatial density function into the convolution operator. This extension enables the network to differentially weight neighbouring pixels based on their relative position to the reference pixel, improving spatial characterisation a

Jiahao Hu, Yanlan Xiao, Honglei Yang, Siyi Xue

Optical frequency division (OFD) implements the conversion of ultra-stable optical frequencies into microwave frequencies through an optical frequency comb flywheel, generating microwave oscillators with record-low phase noise and time jitter. However, conventional OFD systems face significant trade-off between division complexity and noise suppression due t

Na Young Ahn, Dong Hoon Lee

Data remanence in NAND flash complicates complete deletion on IoT SSDs. We design an adaptive architecture offering four privacy levels (PL0-PL3) that select among address, data, and parity deletion techniques. Quantitative analysis balances efficacy, latency, endurance, and cost. Machine-learning adjusts levels contextually, boosting privacy with negligible

Iván Angiono, Leandro Vendramin

We present algorithms to compute generalized root systems of Nichols algebras of diagonal type and of contragredient Lie superalgebras. As a consequence, we obtain an algorithm to compute the Lyndon words in the Kharchenko PBW basis associated to each positive root, along with their corresponding hyperwords. This data is essential for obtaining a minimal pre

Viorica Rozina Chifu, Tudor Cioara, Cristina Bianca Pop, Ionut Anghel

This paper proposes a decentralized model of energy cooperation between microgrids, in which decisions are made locally, at the level of the microgrid community. Each microgrid is modeled as an autonomous agent that adopts a Hawk or Dove strategy, depending on the level of energy stored in the battery and its role in the energy trading process. The interacti

Anna Sofia Lippolis, Minh Davide Ragagni, Paolo Ciancarini, Andrea Giovanni Nuzzolese

The availability of Large Language Models (LLMs) presents a unique opportunity to reinvigorate research on Knowledge Engineering (KE) automation. This trend is already evident in recent efforts developing LLM-based methods and tools for the automatic generation of Competency Questions (CQs), natural language questions used by ontology engineers to define the

Ye Eun Chun, Taeyoon Hwang, Seung-won Hwang, Byung-Hak Kim

Understanding complex character relations is crucial for narrative analysis and efficient script evaluation, yet existing extraction methods often fail to handle long-form narratives with nuanced interactions. To address this challenge, we present CREFT, a novel sequential framework leveraging specialized Large Language Model (LLM) agents. First, CREFT build

Urooj Tariq, Rishu Raj, Dan Kilper

The open radio access network (O-RAN) Alliance developed an architecture and specifications for open and disaggregated cellular networks including many elements that are being widely adopted and implemented in both commercial and research networks. In this paper, we develop transaction-based power consumption models of a centralized O-RAN architecture based

Leonid Kondrashov, Lazar Cvetković, Hancheng Wang, Boxi Zhou

Serverless platforms face a trade-off: conventional cluster managers like Kubernetes offer compatibility for co-locating Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) components of serverless applications, at the cost of high cold-start latency, whereas specialized FaaS-only systems like Dirigent achieve low latency by sacrificing compatibilit

Xiaoang Xu, Shuo Wang, Xu Han, Zhenghao Liu

Large Reasoning Models (LRMs) achieve superior performance by extending the thought length. However, a lengthy thinking trajectory leads to reduced efficiency. Most of the existing methods are stuck in the assumption of overthinking and attempt to reason efficiently by compressing the Chain-of-Thought, but this often leads to performance degradation. To addr

Linda Greggio, Rémi Robin, Mazyar Mirrahimi, Alexandru Petrescu

Under strong drives, which are becoming necessary for fast high-fidelity operations, transmons can be structurally unstable. Due to chaotic effects, the computational manifold is no longer well separated from the remainder of the spectrum, which correlates with enhanced offset-charge sensitivity and destructive effects in readout. We show here that these det

Jendrik Gördes, Ivar Kumberg, Chowdhury S. Awsaf, Marcel Walter

We investigate the ultrafast magnetization dynamics of an interlayer-exchange-coupled Co/Mn/Co trilayer system after excitation with an ultrafast optical pump. We probe element- and time-resolved ferromagnetic order by X-ray magnetic circular dichroism in resonant reflectivity. We observe an accelerated Co demagnetization time in the case of weak total paral