March 2025 arXiv papers — page 206

Wenchao Ma, Raphaël Pestourie, Steven G. Johnson

For linear electromagnetic systems possessing time-reversal symmetry, we present an approach to bound ratios of internal fields excited from different ports, using only the scattering matrix (S matrix), improving upon previous related bounds by Sounas and Al\`u (2017) [Phys. Rev. Lett. 118, 154302 (2017)]. By reciprocity, emitted-wave amplitudes from interna

Zhaohong Lu, Qingyu Liu, Haibo Zeng

In this paper, we investigate the delay performance of ad hoc broadcast networks in which random linear network coding is used to support M-to-N information dissemination. Multiple source nodes may inject coded packets for the same message, while multiple destination nodes decode the message after collecting a sufficient number of innovative degrees of freed

Hainiu Xu, Siya Qi, Jiazheng Li, Yuxiang Zhou

Theory-of-Mind (ToM), the ability to infer others' perceptions and mental states, is fundamental to human interaction but remains challenging for Large Language Models (LLMs). While existing ToM reasoning methods show promise with reasoning via perceptual perspective-taking, they often rely excessively on off-the-shelf LLMs, reducing their efficiency and lim

Abbas Edalat

We introduce the notion of a gauge and of a tagged partition (subordinate to a given gauge) by intersections of open and closed sets of a compact metric space extending the corresponding notions in Henstock-Kurzweil integration of real-valued functions with respect to the Lebesgue measure on the unit interval. We show that, for the integration of bounded fun

Irina Shchepochkina

Here, in every simple finite-dimensional vectorial Lie superalgebra considered with the standard grading where every indeterminate is of degree 1, the maximal graded solvable subalgebras are classified over $\mathbb{C}$.

Alexandre Benoit, Pedram Asef

We offer a new in-depth investigation of global path planning (GPP) for unmanned ground vehicles, an autonomous mining sampling robot named ROMIE. GPP is essential for ROMIE's optimal performance, which is translated into solving the traveling salesman problem, a complex graph theory challenge that is crucial for determining the most effective route to cover

Louis Gaillard

We identify a common scheme in several existing algorithms addressing computational problems on linear differential equations with polynomial coefficients. These algorithms reduce to computing a linear relation between vectors obtained as iterates of a simple differential operator known as pseudo-linear map. We focus on establishing precise degree bounds on

Anibal Sierra, Paola Pinilla, Laura Pérez, Myriam Benisty

Dust traps are the most promising mechanisms to explain the observed substructures in protoplanetary discs. In this work, we present high-angular resolution ($\sim$60 mas, 9.4 au) and high-sensitivity Atacama Large Millimetre/submillimetre Array (ALMA) observations at 3 mm of the transitional disc around LkCa15. The new data, combined with previous high-reso

Tiancheng Hu, Nigel Collier

Understanding how individuals perceive and react to information is fundamental for advancing social and behavioral sciences and developing human-centered AI systems. Current approaches often lack the granular data needed to model these personalized responses, relying instead on aggregated labels that obscure the rich variability driven by individual differen

Ellie Zontou, Antonia Kyprioti

This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (IoT) technology to enhance patient care, improve operational efficiency, and ensure data security in hospital settings. With the growing emphasis on utilizing advanced technologies in healthcare, this protocol ai

Melania Lembo, Ester Riccardi, Veronica Vinciotti, Ernst C. Wit

Dynamic networks models describe temporal interactions between social actors, and as such have been used to describe financial fraudulent transactions, dispersion of destructive invasive species across the globe, and the spread of fake news. An important question in all of these examples is what are the causal drivers underlying these processes. Current netw

G. Kishore, Nishant K. Singh

Previous studies have used magnetic energy and helicity spectra, the latter computed using the two-scale method, to search for signatures of mean-field dynamos. In this study, we compare cotemporal HMI and SOLIS magnetograms to illustrate the instrument-dependence of even qualitative features of the energy and helicity spectra. Around the minimum between sol

Ahmed E. Samy, Zekarias T. Kefato, Sarunas Girdzijauskas

Link prediction is a crucial task in many downstream applications of graph machine learning. To this end, Graph Neural Network (GNN) is a widely used technique for link prediction, mainly in transductive settings, where the goal is to predict missing links between existing nodes. However, many real-life applications require an inductive setting that accommod

Dhruv Motwani, Ankush Tyagi, Vipul Dabhi, Harshadkumar Prajapati

Manual attendance tracking at large-scale events, such as marriage functions or conferences, is often inefficient and prone to human error. To address this challenge, we propose an automated, cloud-based attendance tracking system that uses cameras mounted at the entrance and exit gates. The mounted cameras continuously capture video and send the video data

Yiqiong Yang, Yitian Yuan, Baoxing Ren, Ye Wu

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning

Zishun Liu, Saber Jafarpour, Yongxin Chen

We address the problem of safety verification for nonlinear stochastic systems, specifically the task of certifying that system trajectories remain within a safe set with high probability. To tackle this challenge, we adopt a set-erosion strategy, which decouples the effects of stochastic disturbances from deterministic dynamics. This approach converts the s

Saqib Qamar, Syed Furqan Qadri, Roobaea Alroobaea, Goram Mufarah M Alshmrani

Melanoma is a malignant tumor that originates from skin cell lesions. Accurate and efficient segmentation of skin lesions is essential for quantitative analysis but remains a challenge due to blurred lesion boundaries, gradual color changes, and irregular shapes. To address this, we propose ScaleFusionNet, a hybrid model that integrates a Cross-Attention Tra

David Algis, Bérenger Bramas, Emmanuelle Darles, Lilian Aveneau

The oceans cover the vast majority of the Earth. Therefore, their simulation has many scientific, industrial and military interests, including computer graphics domain. By fully exploiting the multi-threading power of GPU and CPU, current state-of-the-art tools can achieve real-time ocean simulation, even if it is sometimes needed to reduce the physical real

Guoyu Yang, Yuan Wang, Daming Shi, Yanzhong Wang

Recent real-time semantic segmentation models, whether single-branch or multi-branch, achieve good performance and speed. However, their speed is limited by multi-path blocks, and some depend on high-performance teacher models for training. To overcome these issues, we propose Golden Cudgel Network (GCNet). Specifically, GCNet uses vertical multi-convolution

Sukru C. Demirtas

This study as part of an ongoing research effort, empirically examines the relationship between foreign trade in the Istanbul Ataturk Airport Free Zone and exchange rate movements. Monthly data from 2003 to 2016 were analyzed through stationarity tests (Unit Root), followed by the Vector Autoregressive (VAR) model, Cointegration Analysis, and the Toda-Yamamo

Raian Lefgoum, Sezin Afsar, Pierre Carpentier, Jean-Philippe Chancelier

Hydrogen is an energy vector, and one possible way to reduce CO 2 emissions. This paper focuses on a hydrogen transport problem where mobile storage units are moved by trucks between sources to be refilled and destinations to meet demands, involving swap operations upon arrival. This contrasts with existing literature where inventories remain stationary. The

Seil Kang, Jinyeong Kim, Junhyeok Kim, Seong Jae Hwang

Large multimodal models (LMMs) "see" images by leveraging the attention mechanism between text and visual tokens in the transformer decoder. Ideally, these models should focus on key visual information relevant to the text token. However, recent findings indicate that LMMs have an extraordinary tendency to consistently allocate high attention weights to spec

Ilian Pihlajamaa, Lotte van Gessel, Corentin Laudicina, Luc van Burik

Previous studies have suggested a conundrum in the relaxation dynamics of polydisperse supercooled liquids. It has been shown that in two dimensions, the relative relaxation times of particles of different sizes become more similar as the material is cooled, whereas the opposite happens in three dimensions: they decouple. Here we resolve this conundrum. Firs

Anna de Crescenzo, Filippo de Feo, Huyên Pham

We study the linear-quadratic control problem for a class of non-exchangeable mean-field systems, which model large populations of heterogeneous interacting agents. We explicitly characterize the optimal control in terms of a new infinite-dimensional system of Riccati equations, for which we establish existence and uniqueness. To illustrate our results, we a

Wenting Wang, Xiaohu Yang, Yipeng Jing, Ashley J. Ross

We measure the luminosity functions (LFs) and stellar mass functions (SMFs) of photometric satellite galaxies around spectroscopically identified isolated central galaxies (ICGs). The photometric satellites are from the DESI Legacy Imaging Surveys (DR9), while the spectroscopic ICGs are selected from the DESI Year-1 BGS sample. We can measure satellite LFs d

Yacouba Boubacar Mainassara, Landy Rabehasaina, Armel Bra

In this paper, we present the asymptotic properties of the moment estimator for autoregressive (AR for short) models subject to Markovian changes in regime under the assumption that the errors are uncorrelated but not necessarily independent. We relax the standard independence assumption on the innovation process to extend considerably the range of applicati

Torstein Ulsnaes

We compute the K-theory of a collection of C*-algebras, which we refer to as boundary C*-algebras, arising as the crossed product C*-algebras of lattice actions on the maximal Furstenberg boundaries of symmetric spaces of noncompact type. As a result, we add new examples to the collection of known isomorphic boundary C*-algebras which are not spatially isomo

Hongwei Jia, Jing Hu, Ruo-Yang Zhang, Yixin Xiao

Topologically protected edge states have been extensively studied in systems characterized by the topological invariants in band gaps (also called line gaps). In this study, we unveil a whole new form of edge states that transcends the established paradigms of band-gap topology. In contrast to the traditional stable edge states in topological insulators with

Florian Lecourt, Madalina Croitoru, Konstantin Todorov

This work investigates the capabilities of large language models (LLMs) in detecting and understanding human emotions through text. Drawing upon emotion models from psychology, we adopt an interdisciplinary perspective that integrates computational and affective sciences insights. The main goal is to assess how accurately they can identify emotions expressed

Peiding Wang, Li Zhang, Fang Liu, Lin Shi

Large Language Models (LLMs) have demonstrated exceptional performance in code generation tasks and have become indispensable programming assistants for developers. However, existing code generation benchmarks primarily assess the functional correctness of code generated by LLMs in single-turn interactions. They offer limited insight into LLMs' abilities to

Xi Zhu, Haochen Xue, Ziwei Zhao, Wujiang Xu

Text-Attributed Graphs (TAGs), where each node is associated with text descriptions, are ubiquitous in real-world scenarios. They typically exhibit distinctive structure and domain-specific knowledge, motivating the development of a Graph Foundation Model (GFM) that generalizes across diverse graphs and tasks. Despite large efforts to integrate Large Languag

Itzhak Rasooly, Roberto Rozzi

In this paper, we conduct a large-scale field experiment to investigate the manipulability of prediction markets. The main experiment involves randomly shocking prices across 817 separate markets; we then collect hourly price data to examine whether the effects of these shocks persist over time. We find that prediction markets can be manipulated: the effects

He-bin Zhang, Yuanjiang Tang, Yong-Chun Liu

Electromagnetically induced transparency (EIT) is an important quantum optical phenomenon which provides a crucial tool for light manipulation. However, typically the transparency window is broad, limited by the coherence time of the metastable state. Here we show that extremely narrow transparency window can be realized using nuclear spin induced transparen

C. Brandon Ogbunugafor, Sudam Surasinghe

Asymptomatic infection has gained notoriety as an important feature of infectious disease dynamics. Despite increasing attention, there have been few rigorous examinations of how asymptomatic transmission influences pathogen evolution. In this study, we apply evolutionary invasion analysis to compute optimal strategies for viruses evolving in a system with a

Aleksandra Deptuch, Marcin Kozieł, Marcin Piwowarczyk, Magdalena Urbańska

The liquid crystalline compound, forming the glass of the smectic C$_A$* phase, is investigated by the X-ray diffraction in the 18-298 K range. The characteristic distances within the smectic C$_A$* phase are determined. The electron density profile along the smectic layer normal is inferred and compared with the results of the density functional theory calc

Jie He, Tao Wang, Deyi Xiong, Qun Liu

Does neural machine translation yield translations that are congenial with common sense? In this paper, we present a test suite to evaluate the commonsense reasoning capability of neural machine translation. The test suite consists of three test sets, covering lexical and contextless/contextual syntactic ambiguity that requires commonsense knowledge to resol

Ji Zhao, Banglei Guan, Zibin Liu, Laurent Kneip

For event cameras, current sparse geometric solvers for egomotion estimation assume that the rotational displacements are known, such as those provided by an IMU. Thus, they can only recover the translational motion parameters. Recovering full-DoF motion parameters using a sparse geometric solver is a more challenging task, and has not yet been investigated.

Gabriele Torri, Rosella Giacometti, Gianluca Farina

We introduce a model for the loss distribution of a credit portfolio considering a contagion mechanism for the default of names which is the result of two independent components: an infection attempt generated by defaulting entities and a failed defence from healthy ones. We then propose an efficient recursive algorithm for the loss distribution. Then we ext

Tine Colman, Patrick Hennebelle, Noé Brucy, Pierre Dumond

In this work, we explore the link between star formation, turbulence and the thermal state of the multi-phase ISM. We analyse a suite of stratified box simulations modelling a realistic ISM that aims to probe environments similar to those found in the Milky Way. Turbulence is injected through stellar feedback and an external large-scale driving force. We fin

Mhd Modar Halimeh, Matteo Torcoli, Philipp Grundhuber, Emanuël A. P. Habets

Neural audio signal codecs have attracted significant attention in recent years. In essence, the impressive low bitrate achieved by such encoders is enabled by learning an abstract representation that captures the properties of encoded signals, e.g., speech. In this work, we investigate the relation between the latent representation of the input signal learn

Yi-Fan Lu, Xian-Ling Mao, Tian Lan, Tong Zhang

Automatic evaluation for Open Domain Event Detection (ODED) is a highly challenging task, because ODED is characterized by a vast diversity of un-constrained output labels from various domains. Nearly all existing evaluation methods for ODED usually first construct evaluation benchmarks with limited labels and domain coverage, and then evaluate ODED methods

Akash Yadav, Eulalia Nualart

Accurate time series prediction is challenging due to the inherent nonlinearity and sensitivity to initial conditions. We propose a novel approach that enhances neural network predictions through differential learning, which involves training models on both the original time series and its differential series. Specifically, we develop a differential long sho

Xiong Liu, Wenhua Wang, Tiantian Zhao, Hongliang Zuo

Let $(\mathbb X,d,μ)$ be a space of homogeneous type in the sense of Coifman--Weiss, let $X$ be a ball quasi-Banach function space on $\mathbb X$ under suitable maximal-function and associate-space assumptions, and let $L$ be a non-negative self-adjoint operator on $L^2(\mathbb X)$. Assume that, for every $t>0$, the semigroup $e^{-tL}$ admits an integral ker

Aïda Elamrani

With the significant progress of artificial intelligence (AI) and consciousness science, artificial consciousness (AC) has recently gained popularity. This work provides a broad overview of the main topics and current trends in AC. The first part traces the history of this interdisciplinary field to establish context and clarify key terminology, including th

Hannes Rosenbusch, Erdem Ozan Meral

Finding enjoyable fiction books can be challenging, partly because stories are multi-faceted and one's own literary taste might be difficult to ascertain. Here, we introduce the ISAAC method (Introspection-Support, AI-Annotation, and Curation), a pipeline which supports fiction readers in gaining awareness of their literary preferences and finding enjoyable

Julia Hindel, Rohit Mohan, Jelena Bratulic, Daniele Cattaneo

LiDAR semantic segmentation models are typically trained from random initialization as universal pre-training is hindered by the lack of large, diverse datasets. Moreover, most point cloud segmentation architectures incorporate custom network layers, limiting the transferability of advances from vision-based architectures. Inspired by recent advances in univ

Hong Lin, Mengmeng Liu, Xiaomin Guo, Yue Luo

True random numbers are extracted through measurements of vacuum fluctuations in quantum state components. We propose an improved scheme utilizing an optimization-based simulation methodology to enhance the temporal resolution of quantum state detection and processing efficiency of vacuum fluctuation signals in continuous-variable quantum random number gener

Vahid Reza Shajiee

We propose a black hole microstate counting method based on the canonical quantization of the asymptotic symmetries of a two-dimensional dilaton-gravity system at future null infinity. This dilaton-gravity is obtained from the s-wave reduction of an $N$-dimensional Einstein gravity over a generic asymptotically flat black hole solution. We show that a Cardy-

B. N. Khabibullin

We solve the following three problems. 1. How much can the radial growth of an entire function $f$ be reduced by multiplying it by some nonzero entire function? We give the answer in terms of the growth of the integral means of $\ln|f|$ over the circles centered at the origin. 2. We estimate the smallest possible radial growth of non zero entire functions th

Yasushi Fukazawa

The Compton camera is a sensitive imaging detector for soft gamma-rays. Compton Reconstruction can not only give imaging capability but also remove background events to achieve good sensitivity. However, the angular resolution is in principle limited to several degrees. In this paper, we propose a novel concept of Compton camera incorporating shadow effects.

Yannian Gu, Wenhui Lei, Hanyu Chen, Xiaofan Zhang

Automated CT report generation plays a crucial role in improving diagnostic accuracy and clinical workflow efficiency. However, existing methods lack interpretability and impede patient-clinician understanding, while their static nature restricts radiologists from dynamically adjusting assessments during image review. Inspired by interactive segmentation tec

Alexandre Göttel, Vivien Raymond

We introduce a novel logarithmic spectral estimation method for dark matter searches using gravitational-wave detectors, integrating established dark matter search techniques with insights from computer music analysis. By leveraging symmetries between the time and frequency domains, this method matches the computational efficiency of FFT based algorithms wit

Mi-Jin Jin, Guang Yang, Doo-Seung Um, Jacob Linder

Functional oxides and hybrid structures with interfacial spin orbit coupling and the Rashba-Edelsterin effect (REE) are promising materials systems for thermal tolerance spintronic device applications. Here, we demonstrate efficient spin-to-charge conversion through enhanced interfacial spin orbit coupling at the all-oxide interface of La1-xCaxMnO3 with quas

Ahsen Topbas, Cagri Ari, Onur Kaya, Elif Uysal

We propose Goal-Oriented Random Access (GORA), where transmitters jointly optimize what to send and when to access the shared channel to a common access point, considering the ultimate goal of the information transfer at its final destination. This goal is captured by an objective function, which is expressed as a general (not necessarily monotonic) function

Mahmoud Abo Khamis, Vasileios Nakos, Dan Olteanu, Dan Suciu

Estimating the cardinality of the output of a query is a fundamental problem in database query processing. In this article, we overview a recently published contribution that casts the cardinality estimation problem as linear optimization and computes guaranteed upper bounds on the cardinality of the output for any full conjunctive query. The objective of th

Ulrich Galander, Maximilian Prinz, Lukas W. Perner, Oliver H. Heckl

We present the methods and results for broadband group delay dispersion measurements for all-monocrystalline and amorphous-crystalline hybrid supermirrors, as well as an all-amorphous mirror in the wavelength range from \SIrange{2.5}{4.8}{\micro \metre}. Measurements are performed using a custom-built white light interferometer that allows for balanced and u

Pankaj Chavan, Tapomoy Guha Sarkar, Chandrachud B. V. Dash, Anjan A Sen

The signature of Baryon Acoustic Oscillation in the clustering of dark-matter tracers allows us to measure $(D_A(z), H(z))$ independently. Treating these as conjugate variables, we are motivated to study cosmological evolution in the phase space of dimensionless variables $x = H_0 D_A/c$ and $p = dx/dz$. The dynamical variables $(x(z),p(z))$ can be integrate

Youngjoon Jang, Jeongsoo Choi, Junseok Ahn, Joon Son Chung

The objective of this work is to align asynchronous subtitles in sign language videos with limited labelled data. To achieve this goal, we propose a novel framework with the following contributions: (1) we leverage fundamental grammatical rules of British Sign Language (BSL) to pre-process the input subtitles, (2) we design a selective alignment loss to opti

Yi He, Lei Yang, Shilin Wang

This paper introduces a novel approach to Visual Forced Alignment (VFA), aiming to accurately synchronize utterances with corresponding lip movements, without relying on audio cues. We propose a novel VFA approach that integrates a local context-aware feature extractor and employs multi-task learning to refine both global and local context features, enhancin

Khoi Anh Nguyen, Linh Yen Vu, Thang Dinh Duong, Thuan Nguyen Duong

Visual Question Answering (VQA) is a multimodal task requiring reasoning across textual and visual inputs, which becomes particularly challenging in low-resource languages like Vietnamese due to linguistic variability and the lack of high-quality datasets. Traditional methods often rely heavily on extensive annotated datasets, computationally expensive pipel

Lei Zhao, Chuanjiang He

Guided image filtering (GIF) is a popular smoothing technique, in which an additional image is used as a structure guidance for noise removal with edge preservation. The original GIF and some of its subsequent improvements are derived from a two-parameter local affine model (LAM), where the filtering output is a local affine transformation of the guidance im

Pavel Kharyuk, Sergey Matveev, Ivan Oseledets

Drawing parallels with the way biological networks are studied, we adapt the treatment--control paradigm to explainable artificial intelligence research and enrich it through multi-parametric input alterations. In this study, we propose a framework for investigating the internal inference impacted by input data augmentations. The internal changes in network

Yijie Chu, Ziniu Wu, Yong Yue, Eng Gee Lim

Unmanned Surface Vehicles (USVs) are increasingly applied to water operations such as environmental monitoring and river-map modeling. It faces a significant challenge in achieving precise autonomous docking at ports or stations, still relying on remote human control or external positioning systems for accuracy and safety which limits the full potential of h

Yalei Huang, Na Zuo, Zheyi Zhang, Chunqiang Xu

The van der Waals, pseudo-binary chalcogenides (ACh)m(Pn2Ch3)n (A = Ge, Mn, Pb, etc.; Pn = Sb or Bi; Ch = Te, Se) have recently been reported to host a vast landscape of topological phases of matter, including the quantum anomalous Hall state and topological axion state with quantized magnetoelectric effect. A subgroup in this series, like MnSb4Te7 and GeSb4

Hiep Truong Cong, Ajay Kumar Sigatapu, Arindam Das, Yashwanth Sharma

Accurate motion understanding of the dynamic objects within the scene in bird's-eye-view (BEV) is critical to ensure a reliable obstacle avoidance system and smooth path planning for autonomous vehicles. However, this task has received relatively limited exploration when compared to object detection and segmentation with only a few recent vision-based approa

Yinghua Li, Wenlin Ye

In this paper, we investigate a system coupled by nonhomogeneous incompressible Navier-Stokes equations and Allen-Cahn equations describing a diffuse interface for two-phase flow of viscous fluids with different densities in a bounded domain $\Omega\subset\mathbb R^d (d=2, 3)$. The mobility is allowed to depend on phase variable but non-degenerate. We first

Jun Li, Che Liu, Wenjia Bai, Rossella Arcucci

Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domain, particularly for abnormality detection and localization within medical images, remains underexplored. A major challenge is the complex and abstract nature of medical terminology, which makes it difficult to di

Luana Testa

Particle Therapy (PT) has emerged as a powerful tool in cancer treatment, leveraging the unique dose distribution of charged particles to deliver high radiation levels to the tumor while minimizing damage to surrounding healthy tissue. Despite its advantages, further improvements in Treatment Planning Systems (TPS) are needed to address uncertainties related

Jiayi Zhang, Yiming Zhang, Yuan Zheng, Yuchen Wang

Urban traffic optimization is critical for improving transportation efficiency and alleviating congestion, particularly in large-scale dynamic networks. Traditional methods, such as Dijkstra's and Floyd's algorithms, provide effective solutions in static settings, but they struggle with the spatial-temporal complexity of real-world traffic flows. In this wor

Jingying Zeng, Hui Liu, Zhenwei Dai, Xianfeng Tang

With the advancement of conversational large language models (LLMs), several LLM-based Conversational Shopping Agents (CSA) have been developed to help customers smooth their online shopping. The primary objective in building an engaging and trustworthy CSA is to ensure the agent's responses about product factoids are accurate and factually grounded. However

Komal Malik

We consider a model of two-sided matching market where buyers and sellers trade indivisible goods with the feature that each buyer has unit demand and seller has unit supply. The result of the existence of Walrasian equilibrium and lattice structure of equilibrium price vectors is known. We provide an alternate proof for existence and lattice structure using

Alfreds Lapkovskis, Boris Sedlak, Sindri Magnússon, Schahram Dustdar

Ensuring Service Level Objectives (SLOs) in large-scale architectures, such as Distributed Computing Continuum Systems (DCCS), is challenging due to their heterogeneous nature and varying service requirements across different devices and applications. Additionally, unpredictable workloads and resource limitations lead to fluctuating performance and violated

T. Pietrangeli, R. Foffi, R. Stocker, C. Ybert

Dispersal is essential to the plethora of motile microorganisms living in porous environments, yet how it relates to movement patterns and pore space structure remains largely unknown. Here we investigate numerically the long-time dispersal of a run-and-tumble microorganism that remains trapped at solid surfaces and escapes from them by tumbling. We find tha

Li Lun, Kunyu Feng, Qinglong Ni, Ling Liang

Spiking neural networks (SNNs) have shown their competence in handling spatial-temporal event-based data with low energy consumption. Similar to conventional artificial neural networks (ANNs), SNNs are also vulnerable to gradient-based adversarial attacks, wherein gradients are calculated by spatial-temporal back-propagation (STBP) and surrogate gradients (S

Michael K. -H. Kiessling, A. Shadi Tahvildar-Zadeh

We review the formulation of a Lorentz-covariant bispinorial wave function and wave equation for a single photon on a flat background. We show the existence of a 10-dimensional set of conservation laws for this equation, and prove that 8 of these can be used to obtain global, gauge-invariant, ADM-like quantities that together define a covariantly constant se

Beilin Chu, Xuan Xu, Yufei Zhang, Weike You

As one of the prominent AI-generated content, Deepfake has raised significant safety concerns. Although it has been demonstrated that temporal consistency cues offer better generalization capability, existing methods based on CNNs inevitably introduce spatial bias, which hinders the extraction of intrinsic temporal features. To address this issue, we propose

Saurabh Kumar, Jacob Buckman, Carles Gelada, Sean Zhang

Transformers with linear attention offer significant computational advantages over softmax-based transformers but often suffer from degraded performance. The symmetric power (sympow) transformer, a particular type of linear transformer, addresses some of this performance gap by leveraging symmetric tensor embeddings, achieving comparable performance to softm

Noam Tur, Ismail Nassar, Ido Schwartz, Joseph Avron

We study, experimentally and theoretically, temporal correlations between the polarization of photon pairs emitted during the biexciton-exciton radiative cascade from a single semiconductor quantum dot, optically excited by a continuous-wave light source. The system is modeled by a Lindbladian coupled to two Markovian baths: One bath represents the continuou

Gazi Tanbhir, Md. Farhan Shahriyar

Dementia, a neurological disorder impacting millions globally, presents significant challenges in diagnosis and patient care. With the rise of privacy concerns and security threats in healthcare, federated learning (FL) has emerged as a promising approach to enable collaborative model training across decentralized datasets without exposing sensitive patient

T. Y. S. S Santosh, Mahmoud Aly, Oana Ichim, Matthias Grabmair

Analyzing large volumes of case law to uncover evolving legal principles, across multiple cases, on a given topic is a demanding task for legal professionals. Structured topical reports provide an effective solution by summarizing key issues, principles, and judgments, enabling comprehensive legal analysis on a particular topic. While prior works have advanc

Ping Chen, Xingpeng Zhang, Zhaoxiang Liu, Huan Hu

In this research, we propose a novel denoising diffusion model based on shortest-path modeling that optimizes residual propagation to enhance both denoising efficiency and quality. Drawing on Denoising Diffusion Implicit Models (DDIM) and insights from graph theory, our model, termed the Shortest Path Diffusion Model (ShortDF), treats the denoising process a

Thokchom Premkumar Meitei, Lenin S. Shagolsem

The dynamics of a stiff filament (made by connecting beads) embedded in size-polydisperse hard sphere fluid is investigated by means of molecular dynamics simulations with focus on how the degree of size-polydispersity, characterized by polydispersity index ($\delta$), affects the dynamics in this model heterogeneous system. Polydispersity of the fluid as we

Christos Pappas, Antonios Prapas, Theodoros Moschos, Manos Kirtas

The ever-increasing volume of data has necessitated a new computing paradigm, embodied through Artificial Intelligence (AI) and Large Language Models (LLMs). Digital electronic AI computing systems, however, are gradually reaching their physical plateaus, stimulating extensive research towards next-generation AI accelerators. Photonic Neural Networks (PNNs),

Nadya Abdel Madjid, Abdulrahman Ahmad, Murad Mebrahtu, Yousef Babaa

As the potential for autonomous vehicles to be integrated on a large scale into modern traffic systems continues to grow, ensuring safe navigation in dynamic environments is crucial for smooth integration. To guarantee safety and prevent collisions, autonomous vehicles must be capable of accurately predicting the trajectories of surrounding traffic agents. O

Yichong Zhao, Susumu Goto

Multiple previous studies have reported suboptimal performance of LLMs in biomedical text mining. By analyzing failure patterns in these evaluations, we identified three primary challenges for LLMs in biomedical corpora: (1) LLMs fail to learn implicit dataset-specific nuances from supervised data, (2) The common formatting requirements of discriminative tas

Farokhnaz Hosseinifar, Shahin Mamedov, Filip Studnička, Hassan Hassanabadi

In this work, we explore the critical parameters that delineate the existence of black holes, identifying the permissible ranges that facilitate their formation. A comprehensive thermodynamic analysis of black holes is conducted, leading to the calculation of black hole remnants. We investigate the trajectory of light, establishing an upper limit for the par

Gangwei Xu, Jiaxin Liu, Xianqi Wang, Junda Cheng

State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. Directly applying 2D convolutions for cost aggregation often results in edge blurring, detail loss, and mismatches in textureless regions. Some complex operations, like deformable c

Runlin Lei, Jiarui Ji, Haipeng Ding, Lu Yi

With the rise of large language models (LLMs), there has been growing interest in Graph Foundation Models (GFMs) for graph-based tasks. By leveraging LLMs as predictors, GFMs have demonstrated impressive generalizability across various tasks and datasets. However, existing research on LLMs as predictors has predominantly focused on static graphs, leaving the

Hongxuan Zhang, Dian Gong, Yiling Zhou, Zhangrong Qin

With increasing water scarcity, membrane distillation technology has gained widespread attention as an innovative method for seawater desalination.However,existing studies often overlook the influence of membrane characteristics on mass transfer efficiency. This study, based on the lattice Boltzmann method,proposes a model for a novel Poly(tetraethynylpyrene

Gangwei Xu, Haotong Lin, Zhaoxing Zhang, Hongcheng Luo

Event cameras deliver visual information characterized by a high dynamic range and high temporal resolution, offering significant advantages in estimating optical flow for complex lighting conditions and fast-moving objects. Current advanced optical flow methods for event cameras largely adopt established image-based frameworks. However, the spatial sparsity

Qingyuan Jiang, Zhouyang Chi, Xiao Ma, Qirong Mao

To address the modality learning degeneration caused by modality imbalance, existing multimodal learning~(MML) approaches primarily attempt to balance the optimization process of each modality from the perspective of model learning. However, almost all existing methods ignore the modality imbalance caused by unimodal data sampling, i.e., equal unimodal data

Martin Kuhn, Joscha Grüger, Tobias Geyer, Ralph Bergmann

The rapid progress in modern medicine presents physicians with complex challenges when planning patient treatment. Techniques from the field of Predictive Business Process Monitoring, like Next-activity-prediction (NAP) can be used as a promising technique to support physicians in treatment planning, by proposing a possible next treatment step. Existing pati

Yuxi Fu, Yangluo Zheng, Qizhe Yang

Reachability of vector addition systems with states (VASS) is Ackermann complete~\cite{leroux2021reachability,czerwinski2021reachability}. For $d$-dimensional VASS reachability it is known that the problem is NP-complete~\cite{HaaseKreutzerOuaknineWorrell2009} when $d=1$, PSPACE-complete~\cite{BlondinFinkelGoellerHaaseMcKenzie2015} when $d=2$, and in $\mathb

Jiebin Yan, Ziwen Tan, Jiale Rao, Lei Wu

Blind panoramic image quality assessment (BPIQA) has recently brought new challenge to the visual quality community, due to the complex interaction between immersive content and human behavior. Although many efforts have been made to advance BPIQA from both conducting psychophysical experiments and designing performance-driven objective algorithms, \textit{l

Haodong Jiang, Xiang Zheng, Yanglin Zhang, Qingcheng Zeng

We present SCORE, a visual relocalization system that achieves unprecedented map compactness by adopting semantically labeled 3D line maps. SCORE requires only 0.01\%-0.1\% of the storage needed by structure-based or learning-based baselines, while maintaining practical accuracy and comparable runtime. The key innovation is a novel robust estimation mechanis

Jinhao Zhang, Zhexuan Zhou, Wenlong Xia, Youmin Gong

Efficient and safe trajectory planning plays a critical role in the application of quadrotor unmanned aerial vehicles. Currently, the inherent trade-off between constraint compliance and computational efficiency enhancement in UAV trajectory optimization problems has not been sufficiently addressed. To enhance the performance of UAV trajectory optimization,

Guoyang Rong, Ying Chen, Thorsten Koch, Keisuke Honda

As research in the Scientometric deepens, the impact of data quality on research outcomes has garnered increasing attention. This study, based on Web of Science (WoS) and Crossref datasets, systematically evaluates the differences between data sources and the effects of data merging through matching, comparison, and integration. Two core metrics were employe

Erica Cooper, Sébastien Le Maguer, Esther Klabbers, Junichi Yamagishi

This document is provided as a guideline for reviewers of papers about speech synthesis. We outline some best practices and common pitfalls for papers about speech synthesis, with a particular focus on evaluation. We also recommend that reviewers check the guidelines for authors written in the paper kit and consider those as reviewing criteria as well. This

Benjamin D. Boizelle, Xueyi Li, Nicholas LeVar, Sam Norcross

We present an M87 molecular line search from archival Atacama Large Millimeter/sub-millimeter Array (ALMA) imaging, covering the circumnuclear disk (CND) as well as ionized gas filaments and dusty cloud regions. We find no evidence for CO emission in the central $\sim$kpc and place an upper limit of $M_\mathrm{H_2} < 2.3\times 10^5$ $M_\odot$ in the atomic g

Alexander Pushnitski, František Štampach

We introduce and study a new theoretical concept of \textit{spectral pair} for a Schr\"{o}dinger operator $H$ in $L^2(\mathbb{R}_{+})$ with a bounded \textit{complex-valued} potential. The spectral pair consists of a scalar measure and a complex-valued function. We show that in many ways, the spectral pair generalises the classical spectral measure to the no