March 2025 arXiv papers — page 118

Tom Rodemund, Shilong Li, Síle Nic Chormaic, Martina Hentschel

Non-Hermitian photonics has attracted significant interest and influences several key areas such as optical metamaterials, laser physics, and nonlinear optics. While non-Hermitian effects have been widely addressed in two-dimensional systems, we focus on realistic three-dimensional devices. To this end we generalize established phase space methods from mesos

Enrico D. Schiappacasse

Spin-$s$ light dark boson particles can exhibit wave-like behavior, capable of forming long-lived, coherent, spatially localized structures known as solitons. This work considers the possibility that a light spin-2 particle might be part of or all the dark matter content of the Universe, which could result in a significant fraction of solitons existing today

Yaling Ke

In this work, we investigate the influence of light-matter coupling on reaction dynamics and equilibrium properties of a single molecule inside an optical cavity. The reactive molecule is modeled using a triple-well potential, allowing two competing reaction pathways that yield distinct products. Dynamical and equilibrium simulations are performed using the

Weihan Li, Harshvardhan Samsukha, Bruis van Vlijmen, Lisen Yan

Degradation prediction for lithium-ion batteries using data-driven methods requires high-quality aging data. However, generating such data, whether in the laboratory or the field, is time- and resource-intensive. Here, we propose a method for the synthetic generation of capacity fade curves based on limited battery tests or operation data without the need fo

Abyad Enan, Mashrur Chowdhury

Computer vision plays a critical role in ensuring the safe navigation of autonomous vehicles (AVs). An AV perception module facilitates safe navigation. This module enables AVs to recognize traffic signs, traffic lights, and various road users. However, the perception module is vulnerable to adversarial attacks, which can compromise its accuracy and reliabil

Saeid Haghbin, Morteza Rezaei Larijani, MohammadReza Zolghadri, Shahin Hedayati Kia

The latest advancements and near-future trends in automotive battery packs, underlying regulatory compliance, and performance requirements are presented in this paper. In response to these specifications, high-level solutions that converge towards a standard architecture for passenger cars are provided. Transition to high-voltage enables ultra-fast charging

Manodip Routh, Anutosh Biswas, Manoranjan Kumar

The recent experimental realization of emergent quasi-particles, such as spinons, doublons, and quartons, in a spin-$1/2$ trimer chain has spurred new interest in low dimensional magnetic systems. In this study, we investigate the dynamical properties of the isotropic spin-$1$ trimer chain with intra and inter-trimer antiferromagnetic exchange couplings, ($J

Shosei Takeda

Several long-time limit theorems of one-dimensional L\'evy processes weighted and normalized by functions of its supremum are studied. The long-time limits are taken via the families of exponential times and that of constant times, called exponential clock and constant clock, respectively.

Yancheng Wang, Changyu Liu, Yingzhen Yang

Graph diffusion models have recently been proposed to synthesize entire graphs, such as molecule graphs. Although existing methods have shown great performance in generating entire graphs for graph-level learning tasks, no graph diffusion models have been developed to generate synthetic graph structures, that is, synthetic nodes and associated edges within a

Yuuho Tanaka

We classify weakly connected spanning closed (WCSC) subgraphs of $\overrightarrow{C_n^2}$, the square of a directed $n$-vertex cycle. Then we show that every spanning tree of $\overrightarrow{C_n^2}$ is contained in a unique nontrivial WCSC subgraph of $\overrightarrow{C_n^2}$. As a result, we obtain a purely combinatorial derivation of the formula for the n

Li Zheng, Hao Fei, Ting Dai, Zuquan Peng

With the continuous emergence of various social media platforms frequently used in daily life, the multimodal meme understanding (MMU) task has been garnering increasing attention. MMU aims to explore and comprehend the meanings of memes from various perspectives by performing tasks such as metaphor recognition, sentiment analysis, intention detection, and o

Xiao Wang, Qingyi Si, Jianlong Wu, Shiyu Zhu

Multimodal Large Language Models (MLLMs) have revolutionized video understanding, yet are still limited by context length when processing long videos. Recent methods compress videos by leveraging visual redundancy uniformly, yielding promising results. Nevertheless, our quantitative analysis shows that redundancy varies significantly across time and model la

ShuChang Yu, Jin Shang, YangRui Chen, Ran Li

This study used Speckle Visibility Spectroscopy to examine velocity fluctuations in a three-dimensional granular heap flow, where the mean velocity profile consists of a fast-flow surface layer and a creep layer beneath. The velocity spectra follow power-law scalings, $E(f) \propto f^{\alpha}$, with $\alpha \approx -0.85$ in the surface flow layer -- matchin

Daniel Alcalde Puente, Erik Lennart Weerda, Konrad Schröder, Matteo Rizzi

Projected entangled pair states (PEPS) on finite two-dimensional lattices are a natural ansatz for representing ground states of local many-body Hamiltonians, as they inherently satisfy the boundary law of entanglement entropy. In this paper, we propose the optimization of PEPS via an improved formulation of the time-dependent variational principle (TDVP), n

Sarvesh Baskar, Tanmay Tulsidas Verelakar, Srinivasan Parthasarathy, Manas Gaur

In multi-turn dialogues, large language models (LLM) face a critical challenge of ensuring coherence while adapting to user-specific information. This study introduces the persona knowledge gap, the discrepancy between a model's internal understanding and the knowledge required for coherent, personalized conversations. While prior research has recognized the

Mengdi Yin, Jing Zhang, Dimitri D Vvedensky

Several authors have suggested that the surfaces of vanishing potential generated by the electrostatic fields from a distribution of point charges resemble triply periodic minimal surfaces (TPMS) corresponding to the positions of the point charges. We provide a theoretical basis for this phenomenological comparison by starting with the Boltzmann equation to

Pamela C. Carvalho, Federico Zipoli, Alan C. Duriez, Marco Antonio Barroca

Metal-organic frameworks (MOFs) are porous materials composed of metal ions and organic linkers. Due to their chemical diversity, MOFs can support a broad range of applications in chemical separations. However, the vast amount of structural compositions encoded in crystallographic information files complicates application-oriented, computational screening an

Xianzu Wu, Zhenxin Ai, Harry Yang, Ser-Nam Lim

Recent advances in single-view 3D scene reconstruction have highlighted the challenges in capturing fine geometric details and ensuring structural consistency, particularly in high-fidelity outdoor scene modeling. This paper presents Niagara, a new single-view 3D scene reconstruction framework that can faithfully reconstruct challenging outdoor scenes from a

Tianyu Li, Yihang Qiu, Zhenhua Wu, Carl Lindström

Multi-traversal data, commonly collected through daily commutes or by self-driving fleets, provides multiple viewpoints for scene reconstruction within a road block. This data offers significant potential for high-quality novel view synthesis, which is crucial for applications such as autonomous vehicle simulators. However, inherent challenges in multi-trave

Abdullah Guvendi, Semra Gurtas Dogan, Omar Mustafa

This study investigates the dynamics of fermion-antifermion pairs within a traversable wormhole spacetime by solving the two-body covariant Dirac equation with a position-dependent mass.

Alexander Koebler, Ralf Gross, Florian Buettner, Ingo Thon

Flexible industrial production systems will play a central role in the future of manufacturing due to higher product individualization and customization. A key component in such systems is the robotic grasping of known or unknown objects in random positions. Real-world applications often come with challenges that might not be considered in grasping solutions

Amir Ayati, Hugh G. A. Burton, Patrick Bultinck, Stijn De Baerdemacker

We present a new application of the Generator Coordinate Method (GCM) as an electronic structure method for strong electron correlation in molecular systems. We identify spin fluctuations as an important generator coordinate responsible for strong static electron correlation that is associated with bond-breaking processes. Spin-constrained Unrestricted HF (c

Yuqi Sun, Qidong Liu, Haiping Zhu, Feng Tian

Sequential Recommender Systems (SRS) have become a cornerstone of online platforms, leveraging users' historical interaction data to forecast their next potential engagement. Despite their widespread adoption, SRS often grapple with the long-tail user dilemma, resulting in less effective recommendations for individuals with limited interaction records. The a

Shizhen Wu, Jinyang Dong, Xu Fang, Ning Sun

Motivated by the latest research on feasible space monitoring of multiple control barrier functions (CBFs) as well as polytopic collision avoidance, this paper studies the Polytope Volume Monitoring (PVM) problem, whose goal is to design a control law for inputs of nonlinear systems to prevent the volume of some state-dependent polytope from decreasing to ze

Zhaopan Xu, Pengfei Zhou, Weidong Tang, Jiaxin Ai

Multimodal large language models (MLLMs) have achieved remarkable success in vision-language tasks, but their reliance on vast, internet-sourced data raises significant privacy and security concerns. Machine unlearning (MU) has emerged as a critical technique to address these issues, enabling the selective removal of targeted information from pre-trained mod

Christopher J. Fewster

The polarisation set of a vector-valued distribution generalises the wavefront set and captures fibre-directional information about its singularities in addition to their phase space description. Motivated by problems in quantum field theory on curved spacetimes, we consider normally hyperbolic operators on vector bundles over globally hyperbolic spacetimes,

Andrea Longhin

We present a new careful and comprehensive analysis the observations of the satellites of Jupiter from the Sidereus Nuncius that extends and complements previous similar studies. Each observation is compared to the predictions obtained using a modern sky simulator, verifying and trying to understand them individually. The work considers both the information

Peiran Wu, Yunze Liu, Miao Liu, Junxiao Shen

Humans excel at spatial-temporal reasoning, effortlessly interpreting dynamic visual events from an egocentric viewpoint. However, whether multimodal large language models (MLLMs) can similarly understand the 4D world remains uncertain. This paper explores multimodal spatial-temporal reasoning from an egocentric perspective, aiming to equip MLLMs with human-

Jiadong Zhou, Yadan Zeng, Huixu Dong, I-Ming Chen

Robotic kitting is a critical task in industrial automation that requires the precise arrangement of objects into kits to support downstream production processes. However, when handling complex kitting tasks that involve fine-grained orientation alignment, existing approaches often suffer from limited accuracy and computational efficiency. To address these c

Teng Wang, Zhangyi Jiang, Zhenqi He, Shenyang Tong

Recent studies show that Large Language Models (LLMs) achieve strong reasoning capabilities through supervised fine-tuning or reinforcement learning. However, a key approach, the Process Reward Model (PRM), suffers from reward hacking, making it unreliable in identifying the best intermediate step. In addition, the cost of annotating reasoning processes for

Robert de Mello Koch, Pedro Ornelas, Neelan Gounden, Bo-Qiang Lu

Topology has emerged as a fundamental property of many systems, manifesting in cosmology, condensed matter, high-energy physics and waves. Despite the rich textures, the topology has largely been limited to low dimensional systems that can be characterised by a single topological number, e.g., a Chern number in matter or a Skyrme number in waves. Here, using

Weiguang Zhao, Rui Zhang, Qiufeng Wang, Guangliang Cheng

3D semantic segmentation plays a fundamental and crucial role to understand 3D scenes. While contemporary state-of-the-art techniques predominantly concentrate on elevating the overall performance of 3D semantic segmentation based on general metrics (e.g. mIoU, mAcc, and oAcc), they unfortunately leave the exploration of challenging regions for segmentation

Wei Zhu, Abirath Raju, Abdulaziz Shamsah, Anqi Wu

This study presents an emotion-aware navigation framework -- EmoBipedNav -- using deep reinforcement learning (DRL) for bipedal robots walking in socially interactive environments. The inherent locomotion constraints of bipedal robots challenge their safe maneuvering capabilities in dynamic environments. When combined with the intricacies of social environme

Christopher J. Fewster

Hadamard states were originally introduced for quantised Klein-Gordon fields and occupy a central position in the theory of quantum fields on curved spacetimes. Subsequently they have been developed for other linear theories, such as the Dirac, Proca and Maxwell fields, but the particular features of each require slightly different treatments. The first aim

Lin-Chun Huang, Ching Chieh Tsao, Fang-Yi Su, Jung-Hsien Chiang

Image generative models, particularly diffusion-based models, have surged in popularity due to their remarkable ability to synthesize highly realistic images. However, since these models are data-driven, they inherit biases from the training datasets, frequently leading to disproportionate group representations that exacerbate societal inequities. Traditiona

Guibiao Liao, Qing Li, Zhenyu Bao, Guoping Qiu

3D Gaussian Splatting-based indoor open-world free-view synthesis approaches have shown significant performance with dense input images. However, they exhibit poor performance when confronted with sparse inputs, primarily due to the sparse distribution of Gaussian points and insufficient view supervision. To relieve these challenges, we propose SPC-GS, lever

Huajie Liang, Di Wang, Yuchao Lu, Mengke Song

With the advancement of Industry 4.0, intelligent manufacturing extensively employs sensors for real-time multidimensional data collection, playing a crucial role in equipment monitoring, process optimisation, and efficiency enhancement. Industrial data exhibit characteristics such as multi-source heterogeneity, nonlinearity, strong coupling, and temporal in

Haoqi Yuan, Yu Bai, Yuhui Fu, Bohan Zhou

Building autonomous robotic agents capable of achieving human-level performance in real-world embodied tasks is an ultimate goal in humanoid robot research. Recent advances have made significant progress in high-level cognition with Foundation Models (FMs) and low-level skill development for humanoid robots. However, directly combining these components often

Fanbin Lu, Zhisheng Zhong, Ziqin Wei, Shu Liu

Developing AI agents to autonomously manipulate graphical user interfaces is a long challenging task. Recent advances in data scaling law inspire us to train computer-use agents with a scaled instruction set, yet using behavior cloning to train agents still requires immense high-quality trajectories. To meet the scalability need, we designed STEVE, a step ve

Ning Li, Wenming Deng, Jiatan Chen

This research addresses the growing need to measure and understand AI literacy in the context of generative AI technologies. Through three sequential studies involving a total of 517 participants, we establish AI literacy as a coherent, measurable construct with significant implications for education, workforce development, and social equity. Study 1 (N=85)

Mehmet Kerem Turkcan, Mattia Ballo, Filippo Filicori, Zoran Kostic

We introduce specialized diffusion-based generative models that capture the spatiotemporal dynamics of fine-grained robotic surgical sub-stitch actions through supervised learning on annotated laparoscopic surgery footage. The proposed models form a foundation for data-driven world models capable of simulating the biomechanical interactions and procedural dy

Hunter Sawyer, Jesse Roberts, Kyle Moore

The field of psychology has long recognized a basic level of categorization that humans use when labeling visual stimuli, a term coined by Rosch in 1976. This level of categorization has been found to be used most frequently, to have higher information density, and to aid in visual language tasks with priming in humans. Here, we investigate basic-level categ

Ignacio Bono Parisi

In this paper, we explicitly provide expressions for a sequence of orthogonal polynomials associated with a weight matrix of size $N$ constructed from a collection of scalar weights $w_{1}, \ldots, w_{N}$: $$W(x) = T(x)\operatorname{diag}(w_{1}(x), \ldots, w_{N}(x))T(x)^{\ast},$$ where $T(x)$ is a specific polynomial matrix. We provide sufficient conditions

Kyle Moore, Jesse Roberts, Daryl Watson, Pamela Wisniewski

Recent work has sought to quantify large language model uncertainty to facilitate model control and modulate user trust. Previous works focus on measures of uncertainty that are theoretically grounded or reflect the average overt behavior of the model. In this work, we investigate a variety of uncertainty measures, in order to identify measures that correlat

Yang Yi, Kunqing Wang, Jinpu Zhang, Zhen Tan

Accurate and reliable estimation of biases of low-cost Inertial Measurement Units (IMU) is a key factor to maintain the resilience of Visual-Inertial Odometry (VIO), particularly when visual tracking fails in challenging areas. In such cases, bias estimates from the VIO can deviate significantly from the real values because of the insufficient or erroneous v

Youning Li

In this paper, we propose using the scattering surface area rather than the scattering cross section to characterize the scattering behavior of ellipsoidal rigid bodies. We examined the scattering behavior of ellipsoidal rigid bodies, focusing on the relationship between their surface area and total scattering cross-section. Building on the foundational work

Guandong Li, Zhaobin Chu

We propose EditID, a training-free approach based on the DiT architecture, which achieves highly editable customized IDs for text to image generation. Existing text-to-image models for customized IDs typically focus more on ID consistency while neglecting editability. It is challenging to alter facial orientation, character attributes, and other features thr

Patryk Marszałek, Kamil Książek, Oleksii Furman, Ulvi Movsum-zada

In recent years, there has been a growing interest in explainable AI methods. In addition to making accurate predictions, we also want to understand what the model's decision is based on. One of the fundamental levels of interpretability is to provide counterfactual examples explaining the rationale behind the decision and identifying which features, and to

Kyunghoon Bae, Eunbi Choi, Kibong Choi, Stanley Jungkyu Choi

We present EXAONE Deep series, which exhibits superior capabilities in various reasoning tasks, including math and coding benchmarks. We train our models mainly on the reasoning-specialized dataset that incorporates long streams of thought processes. Evaluation results show that our smaller models, EXAONE Deep 2.4B and 7.8B, outperform other models of compar

Chrysafis, Hartonas

We present an extension and generalization of Sahlqvist--Van Benthem correspondence to the case of distribution-free modal logic, with, or without negation and/or implication connectives. We follow a reductionist strategy, reducing the correspondence problem at hand to the same problem, but for a suitable system of sorted modal logic (the modal companion of

Zheyi Ding, F. Robert Wimmer-Schweingruber, Alexander Kollhoff, Patrick Kühl

Inverse velocity dispersion (IVD) events, characterized by higher-energy particles arriving later than lower-energy particles, challenge the classical understanding of SEP events and are increasingly observed by spacecraft, such as Parker Solar Probe (PSP) and Solar Orbiter (SolO). However, the mechanisms underlying IVD events remain poorly understood. This

Mohammad Khalil, Ronas Shakya, Qinyi Liu

The increasing adoption of data-driven applications in education such as in learning analytics and AI in education has raised significant privacy and data protection concerns. While these challenges have been widely discussed in previous works, there are still limited practical solutions. Federated learning has recently been discoursed as a promising privacy

Brian Davenport, Thomas Kennedy, E. M. May, Emily Rauscher

We present analyses of Spitzer InfraRed Array Camera (IRAC) 3.6 $\mu$m and 4.5 $\mu$m phase curve observations of hot Jupiters WASP-77Ab and WASP-121b. For WASP-121b, we find amplitudes of 1771 $\pm$ 95 ppm (3.6 $\mu$m) and 2048 $\pm$ 109 ppm (4.5 $\mu$m), and near-zero offsets of -0.78 $\pm$ 1.87$^{\circ}$ (3.6 $\mu$m) and 0.42 $\pm$ 1.74$^{\circ}$ (4.5 $\m

Cenk Tüysüz, Abhijith Jayakumar, Carleton Coffrin, Marc Vuffray

Analog quantum computation is an attractive paradigm for the simulation of time-dependent quantum systems. Programmable analog quantum computers have been realized in hardware using a variety of physical principles, including neutral-atom and superconducting technologies. The input parameters of the physical Hamiltonians that are used to program the quantum

Taein Kwon, Zador Pataki, Mahdi Rad, Marc Pollefeys

Self-supervised temporal sequence alignment can provide rich and effective representations for a wide range of applications. However, existing methods for achieving optimal performance are mostly limited to aligning sequences of the same activity only and require separate models to be trained for each activity. We propose a novel framework that overcomes the

Tomer Adar, Eldar Fischer, Amit Levi

The conditional sampling model, introduced by Cannone, Ron and Servedio (SODA 2014, SIAM J. Comput. 2015) and independently by Chakraborty, Fischer, Goldhirsh and Matsliah (ITCS 2013, SIAM J. Comput. 2016), is a common framework for a number of studies concerning strengthened models of distribution testing. A core task in these investigations is that of esti

Parisa Ramezani, Alva Kosasih, Emil Björnson

While fully-digital precoding achieves superior performance in massive MIMO systems, it comes with significant drawbacks in terms of computational complexity and power consumption, particularly when operating at millimeter-wave (mmWave) frequencies and beyond. Hybrid analog-digital architectures address this by reducing radio frequency (RF) chains while main

Sergei Balashev, Daria Kosenko, Pasquier Noterdaeme

Molecular hydrogen (H$_2$) is by far the most abundant molecule in the Universe. However, due to the low emissivity of H$_2$, carbon monoxide (CO) is widely used instead to trace molecular gas in galaxies. The relative abundances of these molecules is expected to depend on both physical (e.g., density) and chemical (e.g., metal enrichment) properties of the

Pan Du, Delin An, Chaoli Wang, Jian-Xun Wang

Image-based modeling is essential for understanding cardiovascular hemodynamics and advancing the diagnosis and treatment of cardiovascular diseases. Constructing patient-specific vascular models remains labor-intensive, error-prone, and time-consuming, limiting their clinical applications. This study introduces a deep-learning framework that automates the c

Andrew Dane, Karthik Balakrishnan, Brent Wacaser, Li-Wen Hung

Superconducting qubits have been used in the most advanced demonstrations of quantum information processing, and they can be manufactured at-scale using proven semiconductor techniques. This makes them one of the leading technologies in the race to demonstrate useful quantum computers. Since their initial demonstration, advances in design, fabrication, and m

Yikai Deng, Zongnan Li, Zhiyuan Li, Lijie Liu

We present a study of giant molecular cloud (GMC) properties in the Andromeda galaxy (M31) using CO(3-2) data from the James Clerk Maxwell Telescope (JCMT) in selected regions across the disc and in the nuclear ring, and comparing them with CO(1-0) observations from the IRAM 30m telescope in the same regions. We find that GMCs in the centre of M31 generally

Xun Jiang, Haoran Lu, Yuxuan Zhao, Jiarui Wang

As the scaling of semiconductor devices nears its limits, utilizing the back-side space of silicon has emerged as a new trend for future integrated circuits. With intense interest, several works have hacked existing backend tools to explore the potential of synthesizing double-side clock trees via nano Through-Silicon-Vias (nTSVs). However, these works lack

Tianyang Zhou, Ziyi Zhang, Haowen Lin, Somesh Jha

Translating software written in C to Rust has significant benefits in improving memory safety. However, manual translation is cumbersome, error-prone, and often produces unidiomatic code. Large language models (LLMs) have demonstrated promise in producing idiomatic translations, but offer no correctness guarantees. We propose SACTOR, an LLM-driven C-to-Rust

Antoine Van Proeyen

Following the initial construction of pure supergravity in 1976, various methods were developed to couple supergravity with supersymmetric matter. This contribution to 'Half a century of supergravity' provides a personal perspective on the key steps, techniques and results developed in the first decade. These developments form the foundation for numerous app

Wei Zhang, Zhaohong Deng, Guanjin Wang, Kup-Sze Choi

Representation learning has emerged as a crucial focus in machine and deep learning, involving the extraction of meaningful and useful features and patterns from the input data, thereby enhancing the performance of various downstream tasks such as classification, clustering, and prediction. Current mainstream representation learning methods primarily rely on

Kyle L. Walker, Hsing-Yu Chen, Alix J. Partridge, Lucas Cruz da Silva

The use of soft and compliant manipulators in marine environments represents a promising paradigm shift for subsea inspection, with devices better suited to tasks owing to their ability to safely conform to items during contact. However, limitations driven by material characteristics often restrict the reach of such devices, with the complexity of obtaining

Guangqian Guo, Yong Guo, Xuehui Yu, Wenbo Li

Despite their success, Segment Anything Models (SAMs) experience significant performance drops on severely degraded, low-quality images, limiting their effectiveness in real-world scenarios. To address this, we propose GleSAM, which utilizes Generative Latent space Enhancement to boost robustness on low-quality images, thus enabling generalization across var

Zhongju Yuan, Geraint Wiggins, Dick Botteldooren

Auditory working memory is essential for various daily activities, such as language acquisition, conversation. It involves the temporary storage and manipulation of information that is no longer present in the environment. While extensively studied in neuroscience and cognitive science, research on its modeling within neural networks remains limited. To addr

Wei-Chang Yeh

Network reliability assessment is pivotal for ensuring the robustness of modern infrastructure systems, from power grids to communication networks. While exact reliability computation for binary-state networks is NP-hard, existing approximation methods face critical tradeoffs between accuracy, scalability, and data efficiency. This study evaluates 20 machine

Zhaopan Xu, Pengfei Zhou, Jiaxin Ai, Wangbo Zhao

Reasoning is an essential capacity for large language models (LLMs) to address complex tasks, where the identification of process errors is vital for improving this ability. Recently, process-level reward models (PRMs) were proposed to provide step-wise rewards that facilitate reinforcement learning and data production during training and guide LLMs toward c

Maarten Derickx

The main result of this article is that all but finitely many points of small enough degree on a curve can be written as a pullback of a smaller degree point. The main theorem has several corollaries that yield improvements on results of Kadets and Vogt, Khawaja and Siksek, and Vojta under a slightly stronger assumption on the degree of the points.

Jonathan Holland, George Sparling

This article gives an invariant representation of the curvature of a plane wave spacetime in terms of the Schwarzian of a curve in the Lagrangian Grassmannian. It develops a general theory of cross ratios and Schwarzians of curves in what it terms the middle Grassmannian. Most of the theory is developed in infinite dimensions, where the middle Grassmannian i

Jingyang Zhao, Mingyu Xiao, Shunwang Wang

The Capacitated Location Routing Problem is an important planning and routing problem in logistics, which generalizes the capacitated vehicle routing problem and the uncapacitated facility location problem. In this problem, we are given a set of depots and a set of customers where each depot has an opening cost and each customer has a demand. The goal is to

A. L. Siu-Tapia, L. R. Bellot Rubio, D. Orozco Suárez, R. Gafeira

The Mg I b$_2$ line at 5173 \r{A} is primarily magnetically sensitive to heights between the mid photosphere and the low chromosphere, a region that has not been sufficiently explored in the solar atmosphere but is crucial for understanding the magnetic coupling between the two layers. New generation solar observatories are now performing polarimetric observ

Xu Guo, Xiangwang Hou, Minrui Xu, Jianrui Chen

Collaborative underwater target hunting, facilitated by multiple autonomous underwater vehicles (AUVs), plays a significant role in various domains, especially military missions. Existing research predominantly focuses on designing efficient and high-success-rate hunting policy, particularly addressing the target's evasion capabilities. However, in real-worl

Evandro A. Rodrigues, Edwin E. Mozo Luis, Thiago A. de Assis, Fernando A. Oliveira

The Family-Vicsek relation is a seminal universal relation obtained for the global roughness at the interface of two media in the growth process. In this work, we revisit the scaling analysis and, through both analytical and computational means, show that the Family-Vicsek relation can be generalized to a new scaling independent of the size, substrate dimens

Wen Gu, Zhaoxing Li, Jan Buermann, Jim Dilkes

Consensus building is inherently challenging due to the diverse opinions held by stakeholders. Effective facilitation is crucial to support the consensus building process and enable efficient group decision making. However, the effectiveness of facilitation is often constrained by human factors such as limited experience and scalability. In this research, we

A. L. Siu-Tapia, L. R. Bellot Rubio, D. Orozco Suárez

Aims. We examined the capabilities of methods based on the weak-field approximation and line bisectors to extract fast and reliable information about the height stratification of the magnetic field and line-of-sight velocities, respectively, from high spatial resolution observations of the Mg I b$_2$ line at 5173 \r{A}. Methods. The Mg I b$_2$ line was analy

Jian-Ping Mei, Weibin Zhang, Jie Chen, Xuyun Zhang

Malicious users attempt to replicate commercial models functionally at low cost by training a clone model with query responses. Timely prevention of such model-stealing attacks is challenging, as it requires achieving robust protection, maintaining utility, and ensuring low deployment overhead at the same time. In this paper, we propose a novel non-parametri

Tianyuan Qu, Longxiang Tang, Bohao Peng, Senqiao Yang

The rise of Large Vision-Language Models (LVLMs) has significantly advanced video understanding. However, efficiently processing long videos remains a challenge due to the ``Sampling Dilemma'': low-density sampling risks missing critical information, while high-density sampling introduces redundancy. To address this issue, we introduce LSDBench, the first be

Xuan Ma, Zewen Lv, Chengcai Ma, Tao Zhang

Extremely degraded grassland on the Qinghai-Tibetan Plateau (QTP) presents a significant environmental challenge due to overgrazing, climate change, and rodent activity, which degrade vegetation cover and soil quality. These extremely degraded grassland on QTP, commonly referred to as black-soil area, require accurate assessment to guide effective restoratio

Dapeng Zhao

Recently, deep learning-based 3D face reconstruction methods have demonstrated promising advancements in terms of quality and efficiency. Nevertheless, these techniques face challenges in effectively handling occluded scenes and fail to capture intricate geometric facial details. Inspired by the principles of GANs and bump mapping, we have successfully addre

Pingyuan Wei, Lei Zhang

This work is devoted to studying normally hyperbolic invariant manifolds (NHIMs) for a class of quasi-periodically forced systems subject to additional stochastic noise. These systems can be understood as skew-product systems. The existence of NHIMs is established by developing a parameterization method in random settings and applying the Implicit Function T

Dapeng Zhao

Recently, deep learning-based 3D face reconstruction methods have demonstrated promising advancements in terms of quality and efficiency. Nevertheless, these techniques face challenges in effectively handling occluded scenes and fail to capture intricate geometric facial details. Inspired by the principles of GANs and bump mapping, we have successfully addre

Hongli Liang, Yuanting Zhang, Qingye Meng, Shuangshuang He

This study introduces a cutting-edge regional weather forecasting model based on the SwinTransformer 3D architecture. This model is specifically designed to deliver precise hourly weather predictions ranging from 1 hour to 5 days, significantly improving the reliability and practicality of short-term weather forecasts. Our model has demonstrated generally su

Ziran Qin, Yuchen Cao, Mingbao Lin, Wen Hu

Large language models (LLMs) excel at processing long sequences, boosting demand for key-value (KV) caching. While recent efforts to evict KV cache have alleviated the inference burden, they often fail to allocate resources rationally across layers with different attention patterns. In this paper, we introduce Cascading and Adaptive KV cache Eviction (CAKE),

Zilun Zhang, Haozhan Shen, Tiancheng Zhao, Bin Chen

The application of Vision-Language Models (VLMs) in remote sensing (RS) has demonstrated significant potential in traditional tasks such as scene classification, object detection, and image captioning. However, current models, which excel in Referring Expression Comprehension (REC), struggle with tasks involving complex instructions (e.g., exists multiple co

Amir Shakouri, Henk J. van Waarde, M. Kanat Camlibel

In this letter, we provide new insight into Willems et al.'s fundamental lemma by studying the concept of universal inputs. An input is called universal if, when applied to any controllable system, it leads to input-output data that parametrizes all finite trajectories of the system. By the fundamental lemma, inputs that are persistently exciting of sufficie

A. E. S. Hartmann

English translation of "Bemerkungen zur allgemein-relativistischen Fassung der Quantentheorie", originally published in {\em Sitzber. kgl.-preu{\ss}. Akad. Wiss. Berlin, Sitzung der phys.-math. Klasse} {\bf XXIV} (1932) 346--354.

Chawaphon Prayoonyong, Crispin Szydzik, Guanghui Ren, Bill Corcoran

We propose an alternative way to determine GaAs carrier lifetime using pump-probe measurement based on fibre optics and integrated waveguides. We find that our GaAs samples have the lifetime ranging from 30-80 ps, supporting the bandwidth $\geq$ 12.5 GHz. The platform utilised in this work could offer a cost-effective way to investigate photocarrier lifetime

María J. Carro, Carlos Pérez, Rodolfo H. Torres

A version of the Fr\'echet-Kolmogorov theorem for the compactness of operators in weighted mixed Lebesgue spaces is proved and a corresponding compact extrapolation theory a la Rubio de Francia is developed. Several applications are presented too.

Kepeng Wu, Zecheng Li, Hezhen Hu, Wengang Zhou

Pre-training has been proven to be effective in boosting the performance of Isolated Sign Language Recognition (ISLR). Existing pre-training methods solely focus on the compact pose data, which eliminates background perturbation but inevitably suffers from insufficient semantic cues compared to raw RGB videos. Nevertheless, learning representation directly f

Jiakang Chen, Selim F. Yilmaz, Di You, Pier Luigi Dragotti

Joint source-channel coding systems based on deep neural networks (DeepJSCC) have recently demonstrated remarkable performance in wireless image transmission. Existing methods primarily focus on minimizing distortion between the transmitted image and the reconstructed version at the receiver, often overlooking perceptual quality. This can lead to severe perc

Wenkai Wan, Aiying Yang, Peng Guo, Zhe Zhao

Chromatic dispersion compensation (CDC), implemented in either the time-domain or frequency-domain, is crucial for enhancing power efficiency in the digital signal processing of modern optical fiber communication systems. Developing low-complexity CDC schemes is essential for hardware implemention, particularly for high-speed and long-haul optical fiber comm

T. Clayton, R. Duddu, T. Hageman, E. Martínez-Pañeda

Iceberg calving at glacier termini results in mass loss from ice sheets, but the associated fracture mechanics is often poorly represented using simplistic (empirical or elementary mechanics-based) failure criteria. Here, we propose an advanced Mohr-Coulomb failure criterion that drives cracking based on the visco-elastic stress state in ice. This criterion

A. Lopez-Cazalilla, C. Serafim, J. Kimari, M. Ghaemi

Copper and hydrogen are among the most common elements that are widely used in industrial and fundamental research applications. Copper surfaces are often exposed to hydrogen in the form of charged ions. The hydrogen ions can accelerate towards the surface, resulting in an accumulation of hydrogen below the surface. Harmless in low concentrations, prolonged

Raj Gupta, Harshita Goyal, Dhruv Kumar, Apurv Mehra

The advent of Large Language Models (LLMs) is reshaping education, particularly in programming, by enhancing problem-solving, enabling personalized feedback, and supporting adaptive learning. Existing AI tools for programming education struggle with key challenges, including the lack of Socratic guidance, direct code generation, limited context retention, mi

Zhiyu Liang, Dongrui Cai, Chenyuan Zhang, Zheng Liang

Model selection has been raised as an essential problem in the area of time series anomaly detection (TSAD), because there is no single best TSAD model for the highly heterogeneous time series in real-world applications. However, despite the success of existing model selection solutions that train a classification model (especially neural network, NN) using

Rafael Montezuma, Idalina Ribeiro

What one obtains when the min-max methods for the distance function are applied on the space of pairs of points of a Riemannian two-sphere? This question is studied in details in the present article. We show that the associated min-max width do not always coincide with half of the length of a simple closed geodesic which is the union of two minimizing geodes

Daniel Majaess, Tina A. Harriott, Halis Seuret, Cercis Morera-Boado

A debate persists regarding the correlation between the DIBs 9577 and 9632 \r{A}, and whether they share a common molecular carrier (i.e., C$_{60}^{+}$). A robust high correlation determination emerges after bridging the baseline across an order of magnitude ($\simeq 50 - 700$ m\r{A}, $r=0.93\pm0.02$), and nearly doubling the important higher equivalent widt