March 2025 arXiv papers — page 16

Sunoh Kim, Daeho Um

Weakly supervised video grounding aims to localize temporal boundaries relevant to a given query without explicit ground-truth temporal boundaries. While existing methods primarily use Gaussian-based proposals, they overlook the importance of (1) boundary prediction and (2) top-1 prediction selection during inference. In their boundary prediction, boundaries

Maroussia Slavtchova-Bojkova, Penka Mayster

The current paper focuses on studying the impact of immigration with an infinite mean, driven by a discrete-stable compound Poisson process, when it is entering the branching environment with infinite variance of reproduction. Our goal is to determine the explicit form of the probability generating function and subsequently to analyze the probability of exti

Ramachandran Balakrishnan, Priyambada Sahoo, Balamurugan Karuppannan, Ambesh Dixit

We conducted a detailed study of electronic transitions and defects induced Urbach tail bands in various functional perovskite oxides (V2O5, BaSnO3, PbZr0.52Ti0.48O3, BiMnO3, and BiFeO3) using diffuse reflectance spectroscopy (DRS). We analyzed their DRS spectra using the Kubelka-Munk (KM) function, the Tauc plot, and the first derivative of the reflectance

Pengyu Chen, Teng Fei, John A. Kupfer, Yunyan Du

Human-bear conflicts on the Tibetan Plateau threaten both local livelihoods and the conservation of Tibetan brown bears (Ursus arctos pruinosus). To address this challenge, we developed a low-power, network-independent deterrence system that combines computer vision with Internet of Things (IoT) hardware. The system integrates a YOLOv5-MobileNet detection mo

Bogdan C. Dumitru

We study the problem of finding positive integers $n$ such that all the decimal digits of $2^n$ are even, i.e., belong to $\{0, 2, 4, 6, 8\}$. Computational checks up to $n = 10^{15}$ reveal the known cases $n = 1, 2, 3, 6, 11$ and no additional instances. We present a self-contained argument, based on a dynamical Borel-Cantelli lemma, that establishes a met

Adiesha Liyanage, Braeden Sopp, Binhai Zhu

We formulate low-level malware detection using algorithms based on feature matching as Order-based Malware Detection with Critical Instructions (General-OMDCI): given a pattern in the form of a sequence \(M\) of colored blocks, where each block contains a critical character (representing a unique sequence of critical instructions potentially associated with

Emanuele Mezzi, Fabio Massacci, Katja Tuma

Several recent works have argued that Large Language Models (LLMs) can be used to tame the data deluge in the cybersecurity field, by improving the automation of Cyber Threat Intelligence (CTI) tasks. This work presents an evaluation methodology that other than allowing to test LLMs on CTI tasks when using zero-shot learning, few-shot learning and fine-tunin

Emanuele Mezzi, Asimina Mertzani, Michael P. Manis, Siyanna Lilova

Since the introduction of ChatGPT in 2022, Large language models (LLMs) and Large Multimodal Models (LMM) have transformed content creation, enabling the generation of human-quality content, spanning every medium, text, images, videos, and audio. The chances offered by generative AI models are endless and are drastically reducing the time required to generat

Mattia Opper, Roland Fernandez, Paul Smolensky, Jianfeng Gao

Transformers struggle with length generalisation, displaying poor performance even on basic tasks. We test whether these limitations can be explained through two key failures of the self-attention mechanism. The first is the inability to fully remove irrelevant information. The second is tied to position, even if the dot product between a key and query is hi

Maria Jose Pacifico, Fan Yang, Jiagang Yang, Gongran Yao

We study the existence and uniqueness of equilibrium states for continuous flows on a compact, locally maximal invariant set under weak, non-uniform versions of specification, expansivity, and the Bowen property, further improving the Climenhaga-Thompson Criterion.

Isaac M. Vitohekpon, Biswajit Deb, Ines G. Salako, V. A. Monwanou

This study provides a concise analysis of inflation under Rastall gravity by examining three types of potential such as the power law, natural, and hilltop potentials. Choosing a minimal interaction between matter and gravity, we derived the modified slow-roll parameters, the scalar spectral index $(n_s)$, the tensor spectral index $(n_T)$, and the tensor-to

Shayan Sepahvand, Niloufar Amiri, Farrokh Janabi-Sharifi

The problem of image-based visual servoing (IBVS) of an aerial robot using deep-learning-based keypoint detection is addressed in this article. A monocular RGB camera mounted on the platform is utilized to collect the visual data. A convolutional neural network (CNN) is then employed to extract the features serving as the visual data for the servoing task. T

Daniel Saeedi, Denise Buckner, Jose C. Aponte, Amirali Aghazadeh

With upcoming sample return missions across the solar system and the increasing availability of mass spectrometry data, there is an urgent need for methods that analyze such data within the context of existing astrobiology literature and generate plausible hypotheses regarding the emergence of life on Earth. Hypothesis generation from mass spectrometry data

H. Yi, T. Z. Luan, W. Y. Hu, Cheng Shang

In this work, we construct three coupled optical modes systems to obtain effective Hamiltonian mediated by coherent dissipative coupling during adiabatic elimination of large dissipation mode. We investigate the cooperative effect of coherent and dissipative photon-photon couplings in an open cavity system, which leads to nonreciprocity with a considerably l

Ziming Chen, Xiaoqing Zhang

Recently, tensor fibered rank has demonstrated impressive performance by effectively leveraging the global low-rank property in all directions for low-rank tensor completion (LRTC). However, it still has some limitations. Firstly, the typical tensor fibered rank approximation based on tensor nuclear norm (TNN) processes fixed and data-independent transformat

Zewen Liu, Xiaoda Wang, Bohan Wang, Zijie Huang

Graph Neural Networks (GNNs) and differential equations (DEs) are two rapidly advancing areas of research that have shown remarkable synergy in recent years. GNNs have emerged as powerful tools for learning on graph-structured data, while differential equations provide a principled framework for modeling continuous dynamics across time and space. The interse

Zhao-Qian Yao, Yin-Zhen Xu, Daniele Binosi, Minghui Ding

A unified set of predictions for pion, kaon and nucleon gravitational form factors is obtained using a symmetry-preserving truncation of each relevant quantum field equation. A crucial aspect of the study is the self-consistent characterization of the dressed quark-graviton vertices, applied when probing each quark flavor inside mesons or nucleons. The calcu

Paul Haimerl, Stephan Smeekes, Ines Wilms

We consider panel data models where coefficients change smoothly over time and follow a latent group structure, being homogeneous within but heterogeneous across groups. To jointly estimate the group membership and group-specific coefficient trajectories, we propose FUSE-TIME, a pairwise adaptive group fused-Lasso estimator combined with polynomial spline si

Paul Balister, Emil Powierski, Alex Scott, Jane Tan

Consider a `dense' Erd\H{o}s--R\'enyi random graph model $G=G_{n,M}$ with $n$ vertices and $M$ edges, where we assume the edge density $M/\binom{n}{2}$ is bounded away from 0 and 1. Fix $k=k(n)$ with $k/n$ bounded away from 0 and~1, and let $S$ be a random subset of size $k$ of the vertices of $G$. We show that with probability $1-\exp(-n^{\Omega(1)})$, $G$

Yuxin Lu, Yu-Ying Chuang, R. Harald Baayen

A growing body of literature has demonstrated that semantics can co-determine fine phonetic detail. However, the complex interplay between phonetic realization and semantics remains understudied, particularly in pitch realization. The current study investigates the tonal realization of Mandarin disyllabic words with all 20 possible combinations of two tones,

Zhenyu Tang, Chaoran Feng, Xinhua Cheng, Wangbo Yu

3D Gaussian Splatting (3DGS) achieves impressive quality and rendering speed, but with millions of 3D Gaussians and significant storage and transmission costs. In this paper, we aim to develop a simple yet effective method called NeuralGS that compresses the original 3DGS into a compact representation. Our observation is that neural fields like NeRF can repr

Kris Kristoffer Dreher, Janek Groehl, Friso Grace, Leonardo A. Ayala

Significance: Optical imaging of blood oxygenation (sO$_2$) can be achieved based on the differential absorption spectra of oxy- and deoxy-haemoglobin. A key challenge in realising clinical validation of the sO$_2$ biomarkers is the absence of reliable sO$_2$ reference standards, including test objects. Aim: To enable quantitative testing of multispectral im

Moorad Alexanian

We introduce a modified Jaynes-Cummings model with single-photon cavity radiation field but with the atomic system instead of exchanging a single photon as in the Jaynes-Cummings model, it exchanges instead a squeezed photon. After a unitary transformation and requiring the decoupling of the spin up from the spin down, we diagonalize the resulting Hamiltonia

Peter J. Cameron

In 1935, Philip Hall published what is often referred to as ``Hall's marriage theorem'' in a short paper (P.~Hall, On Representatives of Subsets, \textit{J. Lond. Math. Soc.} (1) \textbf{10} (1935), no.1, 26--30.) This paper has been very influential. I state the theorem and outline Hall's proof, together with some equivalent (or stronger) earlier results, a

Romain Boutelet, Chih-Li Sung

Simulating complex physical processes across a domain of input parameters can be very computationally expensive. Multi-fidelity surrogate modeling can resolve this issue by integrating cheaper simulations with the expensive ones in order to obtain better predictions at a reasonable cost. We are specifically interested in computer experiments where real-value

Mohammadreza Pourreza, Shayan Talaei, Ruoxi Sun, Xingchen Wan

Text-to-SQL is a challenging task involving multiple reasoning-intensive subtasks, including natural language understanding, database schema comprehension, and precise SQL query formulation. Existing approaches often rely on handcrafted reasoning paths with inductive biases that can limit their overall effectiveness. Motivated by the recent success of reason

Lídia M. André, Jennifer L. Wadsworth, Raphaël Huser

Likelihood-free approaches are appealing for performing inference on complex dependence models, either because it is not possible to formulate a likelihood function, or its evaluation is very computationally costly. This is the case for several models available in the multivariate extremes literature, particularly for the most flexible tail models, including

S. Mignemi

A long time ago C.N. Yang proposed a generalization of the Snyder model to the case of a curved background spacetime, based on an algebra isomorphic to $so(1,5)$, which includes as subalgebras both the Snyder and the de Sitter algebras. His proposal can therefore be interpreted as a model of noncommutative curved spacetime, and could be useful for relating p

Idriss Boutaayamou, Fouad Et-tahri, Lahcen Maniar, Francisco Periago

This paper addresses the exact controllability of trajectories in the one-dimensional Fisher-Stefan problem--a reaction-diffusion equation that models the spatial propagation of biological, chemical, or physical populations within a free-end domain, governed by Stefan's law. We establish the local exact controllability to the trajectories by reformulating th

Jiaxin An, Siqi Yi, Yao Lyu, Houjiang Liu

There has been vast literature that studies Conversational Agents (CAs) in facilitating older adults' health. The vast and diverse studies warrants a comprehensive review that concludes the main findings and proposes research directions for future studies, while few literature review did it from human-computer interaction (HCI) perspective. In this study, we

Harald Garcke, Robert Nürnberg, Quan Zhao

We propose and analyze stable finite element approximations for Willmore flow of planar curves. The presented schemes are based on a novel weak formulation which combines an evolution equation for curvature with the curvature formulation originally proposed by Barrett, Garcke and N\"urnberg (BGN) in \cite{BGN07}. Under discretization in space with piecewise

Marina Ritthaler, Andy Regensky, André Kaup

Efficient compression of 360-degree video content requires the application of advanced motion models for interframe prediction. The Motion Plane Adaptive (MPA) motion model projects the frames on multiple perspective planes in the 3D space. It improves the motion compensation by estimating the motion on those planes with a translational diamond search. In th

Francescopaolo Lopez, Nicola Bartolo

In order to shed light on the quantum-to-classical transition of the primordial perturbations in single field inflation, we investigate the decoherence and associated quantum corrections to the correlation functions of superhorizon scalar curvature perturbations. The latter are considered as an open quantum system which undergoes quantum decoherence induced

Chenglong Ma, Yuanfeng Ji, Jin Ye, Lu Zhang

Counterfactual medical image generation enables clinicians to explore clinical hypotheses, such as predicting disease progression, facilitating their decision-making. While existing methods can generate visually plausible images from disease progression prompts, they produce silent predictions that lack interpretation to verify how the generation reflects th

Hunter C. Meyer, Conner B. Dooley, Victoria L. Fontenot, Kyungmin Ham

X-ray interferometry is an emerging imaging modality with a wide variety of potential clinical applications, including lung and breast imaging, as well as in non-destructive testing, such as additive manufacturing and porosimetry. A grating interferometer uses a diffraction grating to produce a periodic interference pattern and measures how a patient or samp

Chathika Gunaratne, Mason Stott, Debraj De, Gautam Malviya Thakur

Digital twin technologies help practitioners simulate, monitor, and predict undesirable outcomes in-silico, while avoiding the cost and risks of conducting live simulation exercises. Virtual reality (VR) based digital twin technologies are especially useful when monitoring human Patterns of Life (POL) in secure nuclear facilities, where live simulation exerc

S. Meljanac, S. Mignemi

The Yang model describes a noncommutative geometry in a curved spacetime by means of an orthogonal algebra $o(1,5)$, whose 15 generators are identified with phase space variables and Lorentz generators together with an additional scalar generator. In this paper we show that it is possible to define a nonlinear algebra with the same structure, but with only 1

Anjiang Wei, Tarun Suresh, Jiannan Cao, Naveen Kannan

Inductive program synthesis, or programming by example, requires synthesizing functions from input-output examples that generalize to unseen inputs. While large language model agents have shown promise in programming tasks guided by natural language, their ability to perform inductive program synthesis is underexplored. Existing evaluation protocols rely on

Sudipta Chakraborty, Amrita Manna, T. Daniel Crawford, Achintya Kumar Dutta

We present an efficient implementation of four-component linear response coupled cluster singles and doubles (4c-LRCCSD) theory that enables accurate and computationally efficient calculation of polarizabilities for systems containing heavy elements. We have observed that the frozen natural spinor (FNS)-based truncation scheme is not suitable for linear resp

Marco Bresciani

We study free-discontinuity functionals in nonlinear elasticity, where discontinuities correspond to the phenomenon of cavitation. The energy comprises two terms: a volume term accounting for the elastic energy; and a surface term concentrated on the boundaries of the cavities in the deformed configuration that depends on their unit normal. First, we prove t

Shuyang Bai, Jiemiao Chen

We introduce the notion of multiple extremal integrals as an extension of single extremal integrals, which have played important roles in extreme value theory. The multiple extremal integrals are formulated in terms of a product-form random sup measure derived from the $\alpha$-Fr\'{e}chet random sup measure. We establish a LePage-type representation similar

Qingmeng Wen, Yu-Kun Lai, Ze Ji, Seyed Amir Tafrishi

Skeletonization is a powerful tool for shape analysis, rooted in the inherent instinct to understand an object's morphology. It has found applications across various domains, including robotics. Although skeletonization algorithms have been studied in recent years, their performance is rarely quantified with detailed numerical evaluations. This work focuses

Raffaele Berzi, Daniela Bubboloni, Michele Gori

We study the manipulability of social choice correspondences in situations where individuals have incomplete information about others' preferences. We propose a general concept of manipulability that depends on the extension rule used to derive preferences over sets of alternatives from preferences over alternatives, as well as on individuals' level of infor

Maximilian Nguyen

The classic heuristic formula for calculating the mean arterial blood pressure (MAP) in the human cardiac cycle using 1/3 of the systolic pressure (P_S) plus 2/3 of the diastolic pressure (P_D) importantly neglects the nonlinear effect of heart rate (HR) on blood pressure. Regression analysis on blood pressure data from patients experiencing a variety of hea

Yanwei Sun, Zhe Liu, Chiwei Yan

Motivated by applications such as urban traffic control and make-to-order systems, we study a fluid model of a single-server, on-off system that can accommodate multiple queues. The server visits each queue in order: when a queue is served, it is "on", and when the server is serving another queue or transitioning between queues, it is "off". Customers arrive

Donghe Li, Zuchen Li, Ye Yang, Li Sun

Communication encryption is crucial in computer technology, but existing algorithms struggle with balancing cost and security. We propose EncGPT, a multi-agent framework using large language models (LLM). It includes rule, encryption, and decryption agents that generate encryption rules and apply them dynamically. This approach addresses gaps in LLM-based mu

Tuo Liang, Zhe Hu, Jing Li, Hao Zhang

Understanding humor-particularly when it involves complex, contradictory narratives that require comparative reasoning-remains a significant challenge for large vision-language models (VLMs). This limitation hinders AI's ability to engage in human-like reasoning and cultural expression. In this paper, we investigate this challenge through an in-depth analysi

Arturo Tozzi, Michel Planat

A theoretical framework bridging General Relativity (GR) and Quantum Dynamics (QD) is introduced through the application of Kripke semantics and linear logic. While conventional unification efforts often rely on structural or geometrical formulations, we instead treat causality, energy and information as finite, non-replicable resources constraining physical

Ao Wang, Hui Chen, Zijia Lin, Jungong Han

Vision network designs, including Convolutional Neural Networks and Vision Transformers, have significantly advanced the field of computer vision. Yet, their complex computations pose challenges for practical deployments, particularly in real-time applications. To tackle this issue, researchers have explored various lightweight and efficient network designs.

Joaquín Figueroa, Ivan Gonzalez, Daniel Salinas-Arizmendi

We present an analytical framework for studying quantum tunneling through multiple Dirac delta potential barriers in one dimension. Using the transfer matrix method, we derive a closed-form expression for the total transfer matrix of a system composed of $N$ equally spaced delta barriers. In a systematic manner, a compact expression is obtained for the first

Wei-Shu Hou, Mohamed Krab

We investigate the LHC discovery prospects for a second Higgs doublet through $A \to ZH$ weak decay. The latter is identified as the $\textit{smoking gun signature}$ of two Higgs doublet models (2HDMs) with first-order electroweak (EW) phase transition, a necessary condition for EW baryogenesis. In the general 2HDM (G2HDM) that has flavor-changing neutral Hi

Bisheng Wei, Ruichen Zhang, Ruihong Jiang, Mugen Peng

With the rapid growth of the low-altitude economy, there is increasing demand for real-time data collection using UAV-assisted wireless sensor networks. This paper investigates the problem of minimizing the age of information (AoI) in UAV-assisted wireless sensor networks by optimizing the UAV flight routing. We formulate the AoI minimization task and propos

Alexander Vogel, Omar Moured, Yufan Chen, Jiaming Zhang

Recently, Vision Language Models (VLMs) have increasingly emphasized document visual grounding to achieve better human-computer interaction, accessibility, and detailed understanding. However, its application to visualizations such as charts remains under-explored due to the inherent complexity of interleaved visual-numerical relationships in chart images. E

Boyi Ma, Yanguang Zhao, Jie Wang, Guankun Wang

The DeepSeek models have shown exceptional performance in general scene understanding, question-answering (QA), and text generation tasks, owing to their efficient training paradigm and strong reasoning capabilities. In this study, we investigate the dialogue capabilities of the DeepSeek model in robotic surgery scenarios, focusing on tasks such as Single Ph

Michaël Darche, Raphaël Assier, Sébastien Guenneau, Bruno Lombard

Time modulation of the physical parameters offers interesting new possibilities for wave control. Examples include amplification of waves, harmonic generation and non-reciprocity, without resorting to non-linear mechanisms. Most of the recent studies focus on the time-modulation of the bulk physical properties. However, as the temporal modulation of these pr

Tristan Tsoi, Jiajun Deng, Yaolong Ju, Benno Weck

Music similarity retrieval is fundamental for managing and exploring relevant content from large collections in streaming platforms. This paper presents a novel cross-modal contrastive learning framework that leverages the open-ended nature of text descriptions to guide music similarity modeling, addressing the limitations of traditional uni-modal approaches

Jean-Eric Campagne

Generative models have recently revolutionized image generation tasks across diverse domains, including galaxy image synthesis. This study investigates the statistical learning and consistency of three generative models: light-weight-gan (a GAN-based model), Glow (a Normalizing Flow-based model), and a diffusion model based on a U-Net denoiser, all trained o

PASTA Collaboration, L. Rosignoli, A. Della Croce, E. Leitinger

In the big data era of Astrophysics, the improvement of visualization techniques can greatly enhance the ability to identify and interpret key features in complex datasets. This aspect of data analysis will become even more relevant in the near future, with the expected growth of data volumes. With our studies, we aim to drive progress in this field and insp

Shuhao Fu, Andrew Jun Lee, Anna Wang, Ida Momennejad

The visual world is fundamentally compositional. Visual scenes are defined by the composition of objects and their relations. Hence, it is essential for computer vision systems to reflect and exploit this compositionality to achieve robust and generalizable scene understanding. While major strides have been made toward the development of general-purpose, mul

Camilla Beneduce, Diogo E. P. Pinto, Lorenzo Rovigatti, Flavio Romano

Classical nucleation theory (CNT) is built upon the capillarity approximation, i.e., the assumption that the nucleation properties can be inferred from the bulk properties of the melt and the crystal. Although CNT's simplicity and usefulness cannot be overstated, experiments and simulations regularly uncover significant deviations from its predictions, which

A. Pastorello, A. Reguitti, L. Tartaglia, G. Valerin

We discuss the results of the spectroscopic and photometric monitoring of the type IIn supernova (SN) 2023ldh. Survey archive data show that the SN progenitor experienced some erratic outbursts in the years before exploding. From May 2023, the source shows a general slow luminosity rise lasting over four months with some superposed luminosity fluctuations. I

Damian de la Fuente

In a recent project, Castillo, Libedinsky, Plaza, and the author established a deep connection between the size of lower Bruhat intervals in affine Weyl groups and the volume of the permutohedron, showing that the former can be expressed as a linear combination of the latter. In this paper, we provide a formula for the volume of this polytope in terms of Dyc

Guohong Huang, Ling-An Zeng, Zexin Zheng, Shengbo Gu

We propose a novel approach for generating text-guided human-object interactions (HOIs) that achieves explicit joint-level interaction modeling in a computationally efficient manner. Previous methods represent the entire human body as a single token, making it difficult to capture fine-grained joint-level interactions and resulting in unrealistic HOIs. Howev

Yuhan Wang, Yu Li, Yaodong Yang, Yuanpei Chen

Objects with large base areas become ungraspable when they exceed the end-effector's maximum aperture. Existing approaches address this limitation through extrinsic dexterity, which exploits environmental features for non-prehensile manipulation. While grippers have shown some success in this domain, dexterous hands offer superior flexibility and manipulatio

Sundar Aditya, Morteza Varasteh, Bruno Clerckx

For integrated sensing and communications, an intriguing question is whether information-bearing channel-coded signals can be reused for sensing - specifically ranging. This question forces the hitherto non-overlapping fields of channel coding (communications) and sequence design (sensing) to intersect by motivating the design of error-correcting codes that

Zhi Liu, Wenchang Zhu, Sarah Rankin, Nikhil Garg

We tackle the challenge brought to urban library systems by the {holds system} -- which allows users to request books available at other branches to be transferred for local pickup. The holds system increases usage of the entire collection, at the expense of an in-person browser's experience at the source branch. We study the optimization of usage and browse

Zachary Nason

Let $R$ be a commutative noetherian local differential graded (DG) ring. In this paper we propose a definition of a maximal Cohen-Macaulay DG-complex over $R$ that naturally generalizes a maximal Cohen-Macaulay complex over a noetherian local ring, as studied by Iyengar, Ma, Schwede, and Walker. Our proposed definition extends the work of Shaul on Cohen-Maca

Abdesalem Houari, Peter Bloechl

Recently, a first successful synthesis of the unknown bulk manganese monocarbide (MnC) has been reported. Suggested as a potential superhard material, the compound is found to crystallize in the zincblende (B3) structure. In the present work, we report an exhaustive density functional investigation on structural, electronic and magnetic properties of MnC, us

Himanshu Chaudhary, Saddam Hussain

Perfect cosmological hyperfluids generalize the concept of a perfect fluid within the framework of metric affine gravity. These hyperfluids encode the microstructure of matter including shear, dilation, and spin via the hypermomentum tensor. In this paper, we focus on the observational constraints of the recently introduced Yano-Schr\"odinger hyperfluid, whi

Sophie Rosu

Cookies are enjoyed best when they are both crispy and soft. I investigate in which proportion the cookies are crispy and soft, and disentangle whether it makes them biscuits, cakes, or none of the above. I baked cookies for colleagues at KTH, Stockholm, and University of Geneva, Switzerland, adopting my mum's mum's mum's etc. recipe. I created a dedicated s

David van Duren, Monika Mościbrodzka

Accurate synchrotron transfer coefficients are essential for modeling radiation processes in astrophysics. However, their current calculation methods face significant challenges. Analytical approximations of the synchrotron emissivity, absorptivity, and rotativity are limited to a few simple electron distribution functions that inadequately capture the compl

Jianyuan Xiao, Jian liu

We present a symplectic electromagnetic fully-kinetic particle-in-cell simulation of microinstabilities in plasma, using parameters from the Cyclone Base Case [Dimits, et al., Physics of Plasmas 7, 969 (2000)]. The results show that the growth rates of unstable modes, including ion temperature gradient (ITG), trapped electron mode (TEM), and kinetic ballooni

Robi Bhattacharjee, Karolin Frohnapfel, Ulrike von Luxburg

SHAP is one of the most popular local feature-attribution methods. Given a function f and an input x, it quantifies each feature's contribution to f(x). Recently, SHAP has been increasingly used for global insights: practitioners average the absolute SHAP values over many data points to compute global feature importance scores, which are then used to discard

Katarzyna Rybarczyk, Grzegorz Serafin

The random intersection graph model $\mathcal G(n,m,p)$ is considered. Due to substantial edge dependencies, studying even fundamental statistics such as the subgraph count is significantly more challenging than in the classical binomial model $\mathcal G(n,p)$. First, we establish normal approximation bound in both the Wasserstein and the Kolmogorov distanc

Xiaolu Liu, Ruizi Yang, Song Wang, Wentong Li

Reliable high-definition (HD) map construction is crucial for the driving safety of autonomous vehicles. Although recent studies demonstrate improved performance, their generalization capability across unfamiliar driving scenes remains unexplored. To tackle this issue, we propose UIGenMap, an uncertainty-instructed structure injection approach for generaliza

Hyeongju Kim, Jinhyeok Yang, Yechan Yu, Seunghun Ji

We introduce SupertonicTTS, a novel text-to-speech (TTS) system designed for efficient and streamlined speech synthesis. SupertonicTTS comprises three components: a speech autoencoder for continuous latent representation, a text-to-latent module leveraging flow-matching for text-to-latent mapping, and an utterance-level duration predictor. To enable a lightw

Xiaochun Rong

Let $X$ be a compact Gromov-Hausdorff limit space of a collapsing sequence of compact $n$-manifolds, $M_i$, of Ricci curvature $\text{Ric}_{M_i}\ge -(n-1)$ and all points in $M_i$ are $(\delta,\rho)$-local rewinding Reifenberg points, or sectional curvature $\text{sec}_{M_i}\ge -1$, respectively. We conjecture that if $M_i$ is an aspherical manifold of funda

Chao Tao, Dandan Zhong, Weiliang Mu, Zhuofei Du

The traditional deep learning paradigm that solely relies on labeled data has limitations in representing the spatial relationships between farmland elements and the surrounding environment.It struggles to effectively model the dynamic temporal evolution and spatial heterogeneity of farmland. Language,as a structured knowledge carrier,can explicitly express

Yifan Xu, Vineet Kamat, Carol Menassa

The global rise in the number of people with physical disabilities, in part due to improvements in post-trauma survivorship and longevity, has amplified the demand for advanced assistive technologies to improve mobility and independence. Autonomous assistive robots, such as smart wheelchairs, require robust capabilities in spatial segmentation and semantic r

Paul Caillon, Erwan Fagnou, Alexandre Allauzen

Recurrent neural networks (RNNs) have recently demonstrated strong performance and faster inference than Transformers at comparable parameter budgets. However, the recursive gradient computation with the backpropagation through time (or BPTT) algorithm remains the major computational bottleneck. In this work, we propose a novel method that replaces BPTT with

Shunpu Tang, Yuhao Chen, Qianqian Yang, Ruichen Zhang

Semantic communication has emerged as a promising paradigm for enhancing communication efficiency in sixth-generation (6G) networks. However, the broadcast nature of wireless channels makes SemCom systems vulnerable to eavesdropping, which poses a serious threat to data privacy. Therefore, we investigate secure SemCom systems that preserve data privacy in th

Beibei Li

The accurate forecasting of geomagnetic activity is important. In this work, we present a novel multimodal Transformer based framework for predicting the 3 days and 5 days planetary Kp index by integrating heterogeneous data sources, including satellite measurements, solar images, and KP time series. A key innovation is the incorporation of the Wasserstein d

Enrico Marchesini, Benjamin Donnot, Constance Crozier, Ian Dytham

Reinforcement learning (RL) can provide adaptive and scalable controllers essential for power grid decarbonization. However, RL methods struggle with power grids' complex dynamics, long-horizon goals, and hard physical constraints. For these reasons, we present RL2Grid, a benchmark designed in collaboration with power system operators to accelerate progress

Zehua Liu, Han Wu, Ruifeng She, Xiaojin Fu

Mixture of Experts (MoE) has become a key architectural paradigm for efficiently scaling Large Language Models (LLMs) by selectively activating a subset of parameters for each input token. However, standard MoE architectures face significant challenges, including high memory consumption and communication overhead during distributed training. In this paper, w

Eric Cabezas, Manuel Saavedra

We introduce the super-shadowing property in linear dynamics, where pseudotrajectories are approximated by sequences of the form $(\lambda_nT^nx)$, with $(\lambda_n)_n$ being complex scalars. For compact operators on Banach spaces, we characterize the operators that possess the positive super-shadowing property and the positive limit super-shadowing property

Ray Wai Man Kong, Ding Ning, Theodore Ho Tin Kong

This research article explores the optimization of aluminium extrusion processes through advanced line balancing techniques, focusing on maximizing marginal profit by increasing melting and casting outputs. By employing mixed integer linear programming (MILP), we identify strategies to minimize idle costs and enhance production efficiency. The study demonstr

Amrita Ghosh, Mugdha Sarkar, Ying-Jer Kao, Pochung Chen

We propose the use of the ``spin-opstring", derived from Stochastic Series Expansion Quantum Monte Carlo (QMC) simulations as machine learning (ML) input data. It offers a compact, memory-efficient representation of QMC simulation cells, combining the initial state with an operator string that encodes the state's evolution through imaginary time. Using super

Nina Dörnemann, Miles E. Lopes

Under certain conditions, the largest eigenvalue of a sample covariance matrix undergoes a well-known phase transition when the sample size $n$ and data dimension $p$ diverge proportionally. In the subcritical regime, this eigenvalue has fluctuations of order $n^{-2/3}$ that can be approximated by a Tracy-Widom distribution, while in the supercritical regime

Lia Gruber, Sonja Wogrin

This paper develops a multi-objective optimization framework to analyze the trade-offs between annual costs and resilience in energy communities. Under this framework, three energy community operation strategies are analyzed: a reference case where all assets are member-owned, implementing a communal battery electric storage system, and subsidizing energy-po

Yuelyu Ji, Rui Meng, Zhuochun Li, Daqing He

Multi-hop question answering (QA) requires models to retrieve and reason over multiple pieces of evidence. While Retrieval-Augmented Generation (RAG) has made progress in this area, existing methods often suffer from two key limitations: (1) fixed or overly frequent retrieval steps, and (2) ineffective use of previously retrieved knowledge. We propose MIND (

Andrea Boscolo Camiletto, Jian Wang, Eduardo Alvarado, Rishabh Dabral

Egocentric motion capture with a head-mounted body-facing stereo camera is crucial for VR and AR applications but presents significant challenges such as heavy occlusions and limited annotated real-world data. Existing methods rely on synthetic pretraining and struggle to generate smooth and accurate predictions in real-world settings, particularly for lower

LHCb collaboration

A second major LHCb detector upgrade will be installed during long shutdown 4 (LS4) of the CERN Large Hadron Collider. The new detector will provide excellent performance for studies of Quantum Chromodynamics at high temperature and density, as achieved in collisions of heavy nuclei. The high granularity of the tracking system will allow lead-lead collisions

Yige Chen, Zelong Li, Cindy Zhang, Changbing Yang

Chinese word segmentation is a foundational task in natural language processing (NLP), with far-reaching effects on syntactic analysis. Unlike alphabetic languages like English, Chinese lacks explicit word boundaries, making segmentation both necessary and inherently ambiguous. This study highlights the intricate relationship between word segmentation and sy

Sungmin Sohn, Namwoo Kim, Mark Hansen, Yoonjin Yoon

Urban air mobility (UAM) introduces new challenges for infrastructure planning, requiring data driven approaches for sustainable site selection. This study proposes USE-LFA (Urban Site Evaluation using Latent Factor Analysis), a framework designed to support equitable and environmentally conscious siting of urban ports. Applying latent factor analysis to 25

Chunhao Cai, Yiwu Shang

This paper introduces a new kind of seasonal fractional autoregressive process (SFAR) driven by fractional Gaussian noise (fGn). The new model includes a standard seasonal AR model and fGn. {The estimation of the parameters of this new model has to solve two problems: nonstationarity from the seasonal structure and long memory from fGn. We innovatively solve

Himanshu Beniwal, Reddybathuni Venkat, Rohit Kumar, Birudugadda Srivibhav

This work introduces UnityAI-Guard, a framework for binary toxicity classification targeting low-resource Indian languages. While existing systems predominantly cater to high-resource languages, UnityAI-Guard addresses this critical gap by developing state-of-the-art models for identifying toxic content across diverse Brahmic/Indic scripts. Our approach achi

LHCb collaboration

A second major upgrade of the LHCb detector is necessary to allow full exploitation of the HL-LHC for flavour physics. The new detector will be installed during long shutdown 4 (LS4), and will operate at instantaneous luminosity up to $1.5 \times 10^{34}\,{\rm cm}^{-2}{\rm s}^{-1}$. By upgrading all subsystems and adding new detection capability it will be p

Mingzhu Xue, Xin Li, Shilei Ding, Qixin Li

Selective doping with different R-site ions in rare-earth perovskite RFeO3 compounds offers an effective way to achieve atomic-scale tuning of the complex exchange interactions. In this study, the spin reorientation temperature of Tb-doped SmFeO3 (Sm0.7Tb0.3FeO3) single crystal is lowered to approximately 350 K, making it more suitable for room-temperature a

Maya M. Lassiter, Jungho Lee, Kyle Skelil, Li Xu

While miniaturization has been a goal in robotics for nearly 40 years, roboticists have struggled to access sub-millimeter dimensions without making sacrifices to on-board information processing due to the unique physics of the microscale. Consequently, microrobots often lack the key features that distinguish their macroscopic cousins from other machines, na

Yihuai Hong, Dian Zhou, Meng Cao, Lei Yu

Large language models (LLMs) excel on a variety of reasoning benchmarks, but previous studies suggest they sometimes struggle to generalize to unseen questions, potentially due to over-reliance on memorized training examples. However, the precise conditions under which LLMs switch between reasoning and memorization during text generation remain unclear. In t