December 2024 arXiv papers — page 202

Arnold O. Benz, Clemens R. Huber, Vincenzo Timmel, Christian Monstein

Solar type V radio bursts are associated with type III bursts. Several processes have been proposed to interpret the association, electron distribution, and emission. We present the observation of a unique type V event observed by e-CALLISTO on 7 May 2021. The type V radio emission follows a group of U bursts. Unlike the unpolarized U bursts, the type V burs

Wen-Dong Jiang, Chih-Yung Chang, Show-Jane Yen, Diptendu Sinha Roy

Deep learning has achieved remarkable success in processing and managing unstructured data. However, its "black box" nature imposes significant limitations, particularly in sensitive application domains. While existing interpretable machine learning methods address some of these issues, they often fail to adequately consider feature correlations and provide

Patrycja Stefańska

We present tabulated data for numerical calculations of relative para- and diamagentic contributions to the magnetic shielding constant ($\sigma$) of the Dirac one-electron atoms with a pointlike, spinless and motionless nuclei of charge $Ze$. Exploiting the analytical formulas for the diamagnetic ($\sigma_{d}$) and paramagnetic ($\sigma_{p}$) terms of $\sig

Paul-Albert Schneide, Michael Sorochan Armstrong, Neal Gallagher, Rasmus Bro

This paper introduces a novel deconvolution algorithm, shift-invariant multi-linearity (SIML), which significantly enhances the analysis of data from a comprehensive two-dimensional gas chromatograph coupled to a mass spectrometric detector (GC$\times$GC-TOFMS). Designed to address the challenges posed by retention time shifts and high noise levels, SIML inc

Silvia Lucia Sanna, Leonardo Regano, Davide Maiorca, Giorgio Giacinto

Nowadays, many tools are used to facilitate forensic tasks about data extraction and data analysis. In particular, some tools leverage Artificial Intelligence (AI) to automatically label examined data into specific categories (\ie, drugs, weapons, nudity). However, this raises a serious concern about the robustness of the employed AI algorithms against adver

David Buttsworth, Timothy Buttsworth

When the variations of surface temperature are measured both spatially and temporally, analytical expressions that correctly account for multi-dimensional transient conduction can be applied. To enhance the accessibility of these accurate multi-dimensional methods, expressions for converting between surface temperature and heat flux are presented as the sum

Gustavo Madeira, Leandro Esteves, Sebastien Charnoz, Elena Lega

Similarities in the non-mass dependent isotopic composition of refractory elements with the bulk silicate Earth suggest that both the Earth and the Moon formed from the same material reservoir. On the other hand, the Moon's volatile depletion and isotopic composition of moderately volatile elements points to a global devolatilization processes, most likely d

Jack Radford, Vytautas Gradauskas, Kevin J. Mitchell, Samuel Nerenberg

Optical brain imaging technologies are promising due to their relatively high temporal resolution, portability and cost-effectiveness. However, the highly scattering nature of near-infrared light in human tissue makes it challenging to collect photons emerging from more than 4 cm below the scalp, or with source-detector separation larger than several centime

Silvia Anna Cordieri, Chiara Bordin, Sambeet Mishra

Solar Organic Rankine Cycle (ORC)-based power generation plants leverage solar irradiation to produce thermal energy, offering a highly compatible renewable technology due to the alignment between solar irradiation temperatures and ORC operating requirements. Their superior performance compared to steam Rankine cycles in small-scale applications makes them p

M. Llerena, L. Pentericci, L. Napolitano, S. Mascia

Investigating the ionizing emission of star-forming galaxies is critical to understanding their contribution to reionization and their impact on the surrounding environment. The number of ionizing photons available to reionize the intergalactic medium (IGM) depends not only on the abundance of galaxies but also on their efficiency in producing ionizing photo

Carlos J. Costa

This paper investigates the evolving landscape of decentralized finance (DeFi) by examining its foundational concepts, research trends, and ecosystem. A bibliometric analysis was conducted to identify thematic clusters and track the evolution of DeFi research. Additionally, a thematic review was performed to analyze the roles and interactions of key particip

Huang Xie, Khazar Khorrami, Okko Räsänen, Tuomas Virtanen

This paper proposes to use similarities of audio captions for estimating audio-caption relevances to be used for training text-based audio retrieval systems. Current audio-caption datasets (e.g., Clotho) contain audio samples paired with annotated captions, but lack relevance information about audio samples and captions beyond the annotated ones. Besides, ma

Garima Gupta, Jaydev Dabas

This paper examines impulsive controls related to nonautonomous impulsive integro-differential equations in Hilbert space, highlighting their significance. We establish the existence of the mild solution by using fixed point approach and present conditions for approximate controllability using impulsive resolvent operators and the adjoint problem, supported

Aniket K. Singh, Debasis Chaudhuri, Manish P. Singh, Samiran Chattopadhyay

With the growing demand for interpretable deep learning models, this paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique aimed at providing a holistic view of feature importance across Convolutional Neural Networks (CNNs). Traditional gradient-based CAM methods, such as Grad-CAM and Grad-CAM++, primarily use final layer act

Chayan Tank, Shaina Mehta, Sarthak Pol, Vinayak Katoch

In recent times, more and more people are posting about their mental states across various social media platforms. Leveraging this data, AI-based systems can be developed that help in assessing the mental health of individuals, such as suicide risk. This paper is a study done on suicidal risk assessments using Reddit data leveraging Base language models to i

Martin Kapun

Chromosomal inversions are structural mutations resulting in the reversal of the gene order along the corresponding genomic region. Due to their influence on recombination patterns, they can have a major influence on genetic variation and the evolutionary process. Accordingly, inversions can act as supergenes that keep together co-adapted gene complexes that

Maria C. Bas, Vicente J. Bolos, Alvaro E. Prieto, Roberto Rodriguez-Echeverria

This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision support system for the evaluation of tra

A. Kumar, Z. Zhang, M. Bambach, M. Afrasiabi

The spark plasma sintering (SPS) process, a key technology for advanced material manufacturing, demands accurate and efficient simulation tools to capture the complex electro-thermal-mechanical interactions inherent in powder materials. This paper introduces a novel concurrent multiscale framework employing the Direct FE$^2$ method, designed for fully couple

Bobomurat Ahmedov, Maria Caruana, Jackson Levi Said, Konstantinos F. Dialektopoulos

We present in the form of a catalogue of the cosmological perturbations within the Bahamonde- Dialektopoulos-Levi Said (BDLS) theory, which serves as the teleparallel counterpart of Horndeski gravity. To understand structure formation in cosmological models, it is essential to study both the background and perturbative aspects of their cosmology. While exten

Rajesh Mangannavar, Alan Fern, Prasad Tadepalli

We present an online planning framework and a new benchmark dataset for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges. To address this limitation, we in

Jon Zubeltzu, Fernando Bresme, Matthew Dawber, Marivi Fernandez-Serra

Recent experiments show that the relative dielectric constant $\epsilon$ of water confined to a film of nanometric thickness reaches a strikingly low value of 2.1, barely above the bulk's 1.8 value for the purely electronic response. We argue that $\epsilon$ is not a well-defined measure for dielectric properties at sub-nanometer scales due to the ambiguous

Daniel Arnström, André M. H. Teixeira

Safety filters ensure that control actions that are executed are always safe, no matter the controller in question. Previous work has proposed a simple and stealthy false-data injection attack for deactivating such safety filters. This attack injects false sensor measurements to bias state estimates toward the interior of a safety region, making the safety f

Xiyu Han, Xian Zhong, Wenxin Huang, Xuemei Jia

Cloth-changing person re-identification (CC-ReID) aims to match individuals across surveillance cameras despite variations in clothing. Existing methods typically mitigate the impact of clothing changes or enhance identity (ID)-relevant features, but they often struggle to capture complex semantic information. In this paper, we propose a novel prompt learnin

Qianyi Chen, Ying Chen, Bo Li

This paper studies the performative policy learning problem, where agents adjust their features in response to a released policy to improve their potential outcomes, inducing an endogenous distribution shift. There has been growing interest in training machine learning models in strategic environments, including strategic classification and performative pred

Xiaomin Li, Xu Jia, Qinghe Wang, Haiwen Diao

Existing pretrained text-to-video (T2V) models have demonstrated impressive abilities in generating realistic videos with basic motion or camera movement. However, these models exhibit significant limitations when generating intricate, human-centric motions. Current efforts primarily focus on fine-tuning models on a small set of videos containing a specific

Wolf-Jürgen Beyn

In a series of papers in the 1960's, S. G\"ahler defined and investigated so-called m-metric spaces and their topological properties. An m-metric assigns to any tuple of m+1 elements a real value (more generally an element in a partially odered set) which satisfies the generalized metric axioms of semidefiniteness, symmetry, and simplex inequality. In this c

Rémi Abgrall, Yongle Liu, Walter Boscheri

In this paper, we explore the use of the Virtual Element Method concepts to solve scalar and system hyperbolic problems on general polygonal grids. The new schemes stem from the active flux approach \cite{AF1}, which combines the usage of point values at the element boundaries with an additional degree of freedom representing the average of the solution with

Sheikh Shafayat, Dongkeun Yoon, Woori Jang, Jiwoo Choi

In this work, we propose and evaluate the feasibility of a two-stage pipeline to evaluate literary machine translation, in a fine-grained manner, from English to Korean. The results show that our framework provides fine-grained, interpretable metrics suited for literary translation and obtains a higher correlation with human judgment than traditional machine

Jaskirat Singh, Lindsey Li, Weijia Shi, Ranjay Krishna

Text-based adversarial guidance using a negative prompt has emerged as a widely adopted approach to steer diffusion models away from producing undesired concepts. While useful, performing adversarial guidance using text alone can be insufficient to capture complex visual concepts or avoid specific visual elements like copyrighted characters. In this paper, f

Konrad Deka, Emil Zak

In fault-tolerant quantum computing, the cost of calculating Hamiltonian eigenvalues using the quantum phase estimation algorithm is proportional to the constant scaling the Hamiltonian matrix block-encoded in a unitary circuit. We present a method to reduce this scaling constant for the electronic Hamiltonians represented as a linear combination of unitarie

Radek Holeňák, Kevin Vomschee, Eleni Ntemou, Svenja Lohmann

Fast dynamic processes between electrons in solids and a foreign atom represent a fundamental challenge for describing interactions in many-body systems and are a prerequisite for modelling materials modification. We experimentally determined the charge state distributions of slow He and Ne projectiles after transmission through thin single-crystalline silic

M. V. Tkachuk

The aim of this work is to weaken the conditions of monogenity for functions that take values in subspaces of one concrete three-dimensional commutative algebras over the field of complex numbers. The monogenity of the function understood as a combination of its continuity with the existence of a Gato derivative.

Victor Feuillard

Heavy-flavor hadrons are one of the most prominent probes to study the quark-gluon plasma and to test models based on Quantum Chromodynamics (QCD). This contribution presents the latest results regarding heavy-flavor production in ALICE, ATLAS, CMS and LHCb.

Junwei Deng, Weijing Tang, Jiaqi W. Ma

Influence function, a technique rooted in robust statistics, has been adapted in modern machine learning for a novel application: data attribution -- quantifying how individual training data points affect a model's predictions. However, the common derivation of influence functions in the data attribution literature is limited to loss functions that can be de

Yanzu Huang, Mengfan Liang, Lin Chen

Complex Hadamard matrices (CHMs) are intimately related to the number of distinct matrix elements. We investigate CHMs containing exactly three distinct elements, which is also the least number of distinct elements. In this paper, we show that such CHMs can only be complex equivalent to two kind of matrices, one is $H_2$-reducible and the other is the Tao ma

Yang Wu, Yao Wan, Zhaoyang Chu, Wenting Zhao

Code summarization facilitates program comprehension and software maintenance by converting code snippets into natural-language descriptions. Over the years, numerous methods have been developed for this task, but a key challenge remains: effectively evaluating the quality of generated summaries. While human evaluation is effective for assessing code summary

J. Lerendegui-Marco, C. Guerrero, E. Mendoza, J. M. Quesada

Accurate neutron capture cross sections are essential for the design and operation of fast reactors using MOX fuels. For $^{242}$Pu, the Nuclear Energy Agency (NEA) recommends 8--12% accuracy in the fast energy region (2--500 keV), compared to the current uncertainty of 35%. Moreover, integral experiments and previous measurements suggest the evaluated $^{24

Elizabeth Remfry, Rafael Henkin, Michael R Barnes, Aakanksha Naik

Electronic healthcare records (EHR) contain a huge wealth of data that can support the prediction of clinical outcomes. EHR data is often stored and analysed using clinical codes (ICD10, SNOMED), however these can differ across registries and healthcare providers. Integrating data across systems involves mapping between different clinical ontologies requirin

Katherine Abramski, Riccardo Improta, Giulio Rossetti, Massimo Stella

Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a similar approach for investigating the knowledge encoded in LLMs has emerged, specifically as a method for investigating LLM biases. However, the absence of large-scale LLM-generated fr

Chitran Ghosal, Siheon Ryee, Zamin Mamiyev, Niklas Witt

Graphene, renowned for its exceptional electronic and optical properties as a robust 2D material, traditionally lacks electronic correlation effects. Proximity coupling offers a promising method to endow quantum materials with novel properties. In this study, we achieve such a proximity coupling by intercalating Sn between the buffer layer of graphene on SiC

Philippe Lalanda, German Vega, Denis Morand

Pervasive computing promotes the integration of smart electronic devices in our living and working spaces to provide advanced services. Recently, two major evolutions are changing the way pervasive applications are developed. The first deals with moving computation and storage to the edge. The second is the massive use of machine learning techniques to build

Hu Sun, Qingfei Fu, Chiyu Xie, Bingqiang Ji

Rheology of bubble suspensions is critical for the prediction and control of bubbly flows in a wide range of industrial processes. It is well-known that the bubble suspension exhibits a shear-thinning behavior due to the bubble shape deformation under pure shear, but how the shear rheology response to dilatation remains unexplored. Here, we report a compress

Kangyu Lin, Toshiyuki Ohtsuka

This study focuses on using direct methods (first-discretize-then-optimize) to solve optimal control problems for a class of nonsmooth dynamical systems governed by differential variational inequalities (DVI), called optimal control problems with equilibrium constraints (OCPEC). In the discretization step, we propose a class of novel approaches to smooth the

André Sandmann, Florian Azendorf, Michael Eiselt

Distributed fiber sensing based on correlation-aided phase-sensitive optical time domain reflectometry is presented. The focus is on correlation as an enabler for high spatial resolution. Results from different applications are presented.

Satoru Takano, Tomofumi Shimoda, Yuka Oshima, Ching Pin Ooi

The Torsion-Bar Antenna (TOBA) is a torsion pendulum-based gravitational detector developed to observe gravitational waves in frequencies between 1 mHz and 10 Hz. The low resonant frequency of the torsion pendulum enables observation in this frequency band on the ground. The final target of TOBA is to observe gravitational waves with a 10 m detector and expa

Spyros Rigas, Michalis Papachristou, Ioannis Sotiropoulos, Georgios Alexandridis

Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cause of machinery failures, often resulting in costly downtime, reduced productivity, and, in extreme cases, catastrophic damage. This study p

Pei Xu, Yunpeng Wu, Srikanth Pilla, Gang Li

Designing fiber-reinforced polymer composites (FRPCs) with a tailored nonlinear stress-strain response can enable innovative applications across various industries. Currently, no efforts have achieved the inverse design of FRPCs that target the entire stress-strain curve. Here, we develop PC3D_Diffusion, a 3D spatial diffusion model designed for the inverse

Florian Azendorf, André Sandmann, Michael Eiselt

A deployed fiber with in-house and underground sections is interrogated with a coherent correlation OTDR. The origin and propagation speed of a hammer-generated pressure wave in the underground section is detected and acoustic signals are monitored.

Yuma Furuta

We propose a novel approach to exploring duality defects in the $c=2$ compact boson conformal field theory (CFT). This study is motivated by the desire to classify categorical symmetries, particularly duality defects, in CFTs. While the $c=1$ case has been extensively studied, and the types of realizable duality defects are largely understood, the situation

Wenhui Chen, Ryo Ikehata

In this paper, we study large time asymptotic behavior of the elastic displacement $u$ and the temperature difference $\theta$ for the thermoelastic systems of type II and type III in the whole space $\mathbb{R}^n$ without using the thermal displacement transformation. For the type III model with hyperbolic thermal effect, we derive optimal growth/decay esti

Zhiyuan Li, Jingzheng Wu, Xiang Ling, Tianyue Luo

The widespread application of large language models (LLMs) underscores the importance of deep learning (DL) technologies that rely on foundational DL libraries such as PyTorch and TensorFlow. Despite their robust features, these libraries face challenges with scalability and adaptation to rapid advancements in the LLM community. In response, tech giants like

Xin Yan, Yuxuan Cai, Qiuyue Wang, Yuan Zhou

We introduce Presto, a novel video diffusion model designed to generate 15-second videos with long-range coherence and rich content. Extending video generation methods to maintain scenario diversity over long durations presents significant challenges. To address this, we propose a Segmented Cross-Attention (SCA) strategy, which splits hidden states into segm

Balázs Bursics, Zoltán Vidnyánszky

We investigate the behavior of countable Borel equivalence relations (CBERs) on topological Ramsey spaces. First, we give a simple proof of the fact that every CBER on $[\mathbb{N}]^{\mathbb{N}}$ is hyperfinite on some set of the form $[A]^{\mathbb{N}}$. Using the idea behind the proof, we show the analogous result for every topological Ramsey space.

Hervé Oyono-Oyono, Guoliang Yu

We define for families of finite metric spaces quantitative assembly map estimates that take into account propagation phenomena for pseudo-differential calculus. We relate these estimates to the Novikov conjecture and we show that they fit nicely under coarse decompositions. As an application, we provide a geometric proof of Novikov conjecture for groups wit

Ofer Aharony, Netanel Barel, Tal Sheaffer

Effective string theory describes the physics of long confining strings in theories, like Yang-Mills theory, where the mass gap $M_{gap}^2$ is of the same order as the string tension $T$. In $2+1$ dimensions, there is a class of confining theories, including massive QED$_3$ as first analyzed by Polyakov, for which $M_{gap}^2\ll T$. These theories are weakly

Kevin A. Pimbblet, Lesley J. Morrell

The emergence of conversational natural language processing models presents a significant challenge for Higher Education. In this work, we use the entirety of a UK physics undergraduate (BSc with Honours) degree including all examinations and coursework to test if ChatGPT (GPT-4) can pass a degree. We adopt a "maximal cheating" approach wherein we permit our

Timon Scheiber, Paul Haubenwallner, Matthias Heller

Quantum error mitigation is regarded as a possible path to near-term quantum utility. The methods under the quantum error mitigation umbrella term, such as probabilistic error cancellation (PEC), zero-noise extrapolation (ZNE) or Clifford data regression (CDR) are able to significantly reduce the error for the estimation of expectation values, although at an

Anna Fehérkuti, Péter Major, Gabriella Pásztor

For most high-precision experiments in particle physics, it is essential to know the luminosity at highest accuracy. The luminosity is determined by the convolution of particle densities of the colliding beams. In special van der Meer transverse beam separation scans, the convolution function is sampled along the horizontal and vertical axes with the purpose

Francis Hindle, Lotta Kuuliala, Meriem Mouelhi, Arnaud Cuisset

High resolution rotational Terahertz (THz) spectroscopy has been widely applied to the studies of numerous polar gas phase molecules, in particular volatile organic compounds (VOCs). During the storage of foodstuffs packed under a protective atmosphere, microbial activity will lead to the generation of a complex mixture of trace gases that could be used as f

Francis Hindle, Alexandra Khabbaz, Anthony Roucou, Jean-Francois Lampin

We demonstrate the advantages of THz frequency combs for high-resolution spectroscopy. This benefits from wide spectral coverage and the exact knowledge of the frequency position of each comb component. Heterodyne detection combined with a fast Fourier spectrometer enables rapid and simultaneous measurement of more than 80 frequency comb modes covering a 7.5

Yu-Ming Yang, Zhao-Chen Zhang, Xiao-Jun Bi, Peng-Fei Yin

Recent observations have uncovered a remarkably flat and extremely diffuse stellar distribution within the almost dark dwarf galaxy Nube, posing a challenge to the standard cold dark matter scenario. In this study, we employ numerical simulations to explore the possibility that this anomalous stellar distribution can be attributed to the dynamical heating ef

Christian Gapp, Elias Tappeiner, Martin Welk, Rainer Schubert

Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is of utmost interest due to its huge potential for medical procedure such as diagnosis and patient treatment planning. In

Abdurrahman Javid Shaikh, Fauzi Packeer, Mirza Muhammad Ali Baig, Othman Sidek

Performance optimization associated with optical modulators requires reasonably accurate predictive models for key figures of merit. Interleaved PN-junction topology offers the maximum mode/junction overlap and is the most efficient modulator in depletion-mode of operation. Due to its structure, the accurate modelling process must be fully three-dimensional,

Marcella Contini

We present an analysis of the metal-poor galaxy spectra in the redshift range 0.00574$\leq$z$\leq$0.05368 which were reported by Nakajima et al (2022) in their EMPG (extreme metal poor galaxy) sample. The models account for the active galactic nuclei (AGN) and the starburst (SB) galaxies, for accretion and ejection, for the physical parameters and the elemen

Xu Yang, Chenhui Lin, Haotian Liu, Wenchuan Wu

As large-scale distributed energy resources are integrated into the active distribution networks (ADNs), effective energy management in ADNs becomes increasingly prominent compared to traditional distribution networks. Although advanced reinforcement learning (RL) methods, which alleviate the burden of complicated modelling and optimization, have greatly imp

Rémi Boutin, Pierre Latouche, Charles Bouveyron

The increased quantity of data has led to a soaring use of networks to model relationships between different objects, represented as nodes. Since the number of nodes can be particularly large, the network information must be summarised through node clustering methods. In order to make the results interpretable, a relevant visualisation of the network is also

Ze-Yu Xing, Shu Chen, Haiping Hu

Non-Hermitian systems exhibit a distinctive type of wave propagation, due to the intricate interplay of non-Hermiticity and disorder. Here, we investigate the spreading dynamics in the archetypal non-Hermitian Aubry-Andr\'e model with quasiperiodic disorder. We uncover counter-intuitive transport behaviors: subdiffusion with a spreading exponent $\delta=1/3$

Han Han, Wei Zhai, Yang Cao, Bin Li

Tracking Any Point (TAP) plays a crucial role in motion analysis. Video-based approaches rely on iterative local matching for tracking, but they assume linear motion during the blind time between frames, which leads to point loss under large displacements or nonlinear motion. The high temporal resolution and motion blur-free characteristics of event cameras

Qiyuan Shen, Hengwang Zhao, Weihao Yan, Chunxiang Wang

Cross-modal localization has drawn increasing attention in recent years, while the visual relocalization in prior LiDAR maps is less studied. Related methods usually suffer from inconsistency between the 2D texture and 3D geometry, neglecting the intensity features in the LiDAR point cloud. In this paper, we propose a cross-modal visual relocalization system

Zi-Han Yu, Shuo Cao, Rui Yang, Qing-Miao Hu

Most recently, some substitutional solute atoms in {\alpha}-Ti have been predicted to occupy unexpectedly the low-symmetry (LS) positions away from the high-symmetry (HS) lattice site, which was speculated to result in enhanced solid solution hardening (SSH). In the present work, the SSH induced by the LS off-center solute atom is evaluated within the framew

Fengze Xie, Sizhe Wei, Yue Song, Yisong Yue

We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning, that integrates robotic kinematic structures and morphological symmetries into a single graph network. These structural priors are embedded into the learning architecture as constraints, ensuring high generalizability, sample and

Katharina Prasse, Isaac Bravo, Stefanie Walter, Margret Keuper

Visual framing analysis is a key method in social sciences for determining common themes and concepts in a given discourse. To reduce manual effort, image clustering can significantly speed up the annotation process. In this work, we phrase the clustering task as a Minimum Cost Multicut Problem [MP]. Solutions to the MP have been shown to provide clusterings

Pengzhan Zhou, Yuepeng He, Yijun Zhai, Kaixin Gao

Recently, Federated Learning (FL) has gained popularity for its privacy-preserving and collaborative learning capabilities. Personalized Federated Learning (PFL), building upon FL, aims to address the issue of statistical heterogeneity and achieve personalization. Personalized-head-based PFL is a common and effective PFL method that splits the model into a f

Rui Gao, Heguo Liu, Xingzhong Xu, Sheng Yang

Let $p$ be an odd prime and $S$ a nonabelian finite $p$-group. In [9, 10], they proposed the following conjecture: if $\mathcal{F}$ be a transitive fusion system over a finite $p$-group $S$, then $S$ is either extraspecial of order $p^{3}$ or elementary abelian. In this note, we use an easy method to prove that this conjecture holds when the $p$-rank of $S$

Surangika Ranathunga, Rumesh Sirithunga, Himashi Rathnayake, Lahiru De Silva

Text Simplification is a task that has been minimally explored for low-resource languages. Consequently, there are only a few manually curated datasets. In this paper, we present a human curated sentence-level text simplification dataset for the Sinhala language. Our evaluation dataset contains 1,000 complex sentences and corresponding 3,000 simplified sente

Hongyan Zhi, Peihao Chen, Junyan Li, Shuailei Ma

Research on 3D Vision-Language Models (3D-VLMs) is gaining increasing attention, which is crucial for developing embodied AI within 3D scenes, such as visual navigation and embodied question answering. Due to the high density of visual features, especially in large 3D scenes, accurately locating task-relevant visual information is challenging. Existing works

Qing Yu, Kechuan Dong, Zhiling Guo, Jiaxing Li

This research tackles the challenges of estimating Building-Integrated Photovoltaics (BIPV) potential across various temporal and spatial scales, accounting for different geographical climates and urban morphology. We introduce a holistic methodology for evaluating BIPV potential, integrating 3D building footprint models with diverse meteorological data sour

Akash Kumar, Sanjoy Dasgupta

In this work, we investigate the problem of learning distance functions within the query-based learning framework, where a learner is able to pose triplet queries of the form: ``Is $x_i$ closer to $x_j$ or $x_k$?'' We establish formal guarantees on the query complexity required to learn smooth, but otherwise general, distance functions under two notions of a

Zhuokun Chen, Jinwu Hu, Zeshuai Deng, Yufeng Wang

Multimodal LLMs (MLLMs) equip language models with visual capabilities by aligning vision encoders with language models. Existing methods to enhance the visual perception of MLLMs often involve designing more powerful vision encoders, which requires exploring a vast design space and re-aligning each potential encoder with the language model, resulting in pro

Weiran Ding, Jianquan Ge, Fagui Li

In this paper, following the method of Cheng-Li-Yau, we first modify the coefficients in the constant $B_n$ to improve the volume gap. Further, we also enlarge our gap by applying an estimate of Cheng-Yang for eigenvalues of Laplacian.

Sergio López Ureña, Dionisio F. Yáñez

Inspired by recent developments in subdivision schemes founded on the Weighted Least Squares technique, we construct linear approximants for noisy data in which the weighting strategy minimizes the output variance, thereby establishing a direct correspondence with the Generalized Least Squares and the Minimum-Variance Formulas methodologies. By introducing a

Abdurrahman Javid Shaikh, Othman Sidek, Fauzi Packeer

Silicon is a nonlinear material and optics based on silicon makes use of these nonlinearities to realize various functionalities required for on-chip communications. This article describes foundations of these nonlinearities in silicon at length. Particularly, self phase modulation and cross phase modulation in the context of integrated on-board and on-chip

Pablo Gottheil, Saraswat Bhattacharyya, Kolya Lettl, Philip Friedrich

In invasive breast cancer, cell clusters of varying sizes and shapes are embedded in the fibrous extracellular matrix (ECM). Although the prevailing view attributes this structure to increasing disorder resulting from loss of function and dedifferentiation, our findings reveal that it arises through a process of active self-organization driven by cancer cell

Shan Yang

Text-to-image generation models have revolutionized content creation, but diffusion-based vision-language models still face challenges in precisely controlling the shape, appearance, and positional placement of objects in generated images using text guidance alone. Existing global image editing models rely on additional masks or images as guidance to achieve

Yuepeng He, Pengzhan Zhou, Yijun Zhai, Fang Qu

Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to variations in drivers' driving styles and vehicle types, speed predictions for different target vehicles may significantly differ. Existing methods may not realize personalized vehic

Wenqi Zhang, Jinyang Liu, Zixiang Zhou, Shuai Yang

The quantum circuit synthesis problem bridges quantum algorithm design and quantum hardware implementation in the Noisy Intermediate-Scale Quantum (NISQ) era. In quantum circuit synthesis problems, diagonal unitary synthesis plays a crucial role due to its fundamental and versatile nature. Meanwhile, experimental results have shown that moderately approximat

Houhao Wang, Licheng Zhang, Ruixi Pu, Xiangang Wan

The realization of Hopf-link structure in the Brillouin zone is rather rare hindering the comprehensive exploration and understanding of such exotic nodal loop geometry. Here we first tabulate 141 space groups hosting Hopf-link structure and then investigate Phonon Database at Kyoto University consisting of 10034 materials to search for phonon realization of

Le Zhao, Zesong Fei, Xinyi Wang, Jingxuan Huang

This paper presents a novel two-stage method for constructing channel knowledge maps (CKMs) specifically for A2G (Aerial-to-Ground) channels in the presence of non-cooperative interfering nodes (INs). We first estimate the interfering signal strength (ISS) at sampling locations based on total received signal strength measurements and the desired communicatio

A. Barsode, S. Goyal, P. Ajith

A small fraction of the gravitational-wave (GW) signals from binary black holes observable by ground-based detectors will be strongly lensed by intervening objects such as galaxies and clusters. Strong lensing will produce nearly identical copies of the GW signals separated in time. These lensed signals must be identified against a background of unlensed pai

Joachim Dunkel

Multi Agent Path Finding (MAPF) is critical for coordinating multiple robots in shared environments, yet robust execution of generated plans remains challenging due to operational uncertainties. The Action Dependency Graph (ADG) framework offers a way to ensure correct action execution by establishing precedence-based dependencies between wait and move actio

Elliot Murphy

Natural language syntax can serve as a major test for how to integrate two infamously distinct frameworks: symbolic representations and connectionist neural networks. Building on a recent neurocomputational architecture for syntax (ROSE), I discuss the prospects of reconciling the neural code for hierarchical 'vertical' syntax with linear and predictive 'hor

Sascha Welten, Karl Kindermann, Ahmet Polat, Martin Görz

With the introduction of data protection regulations, the need for innovative privacy-preserving approaches to process and analyse sensitive data has become apparent. One approach is the Personal Health Train (PHT) that brings analysis code to the data and conducts the data processing at the data premises. However, despite its demonstrated success in various

Hugo Dornier, Olivier P Le Maître, Pietro M Congedo, Itham Salah El Din

When numerically solving partial differential equations, for a given problem and operating condition, adaptive mesh refinement (AMR) has proven its efficiency to automatically build a discretization achieving a prescribed accuracy at low cost. However, with continuously varying operating conditions, such as those encountered in uncertainty quantification, ad

Jiazhou Liu, Aravinda S. Rao, Fucai Ke, Tim Dwyer

Together with industry experts, we are exploring the potential of head-mounted augmented reality to facilitate safety inspections on high-rise construction sites. A particular concern in the industry is inspecting perimeter safety screens on higher levels of construction sites, intended to prevent falls of people and objects. We aim to support workers perfor

Subham Sahoo, Huai Wang, Frede Blaabjerg

This paper introduces a novel approach to quantify the uncertainties in fault diagnosis of motor drives using Bayesian neural networks (BNN). Conventional data-driven approaches used for fault diagnosis often rely on point-estimate neural networks, which merely provide deterministic outputs and fail to capture the uncertainty associated with the inference pr

Sen Xing, Muyan Zhong, Zeqiang Lai, Liangchen Li

In this work, we explore a cost-effective framework for multilingual image generation. We find that, unlike models tuned on high-quality images with multilingual annotations, leveraging text encoders pre-trained on widely available, noisy Internet image-text pairs significantly enhances data efficiency in text-to-image (T2I) generation across multiple langua

Xiaoming Shi, Xiaodan Shao, Beixiong Zheng, Rui Zhang

In this letter, we propose a six-dimensional movable antenna (6DMA)-aided cell-free massive multiple-input multiple-output (MIMO) system to fully exploit its macro spatial diversity, where a set of distributed access points (APs), each equipped with multiple 6DMA surfaces, cooperatively serve all users in a given area. Connected to a central processing unit

Kaixin Wu, Yixin Ji, Zeyuan Chen, Qiang Wang

Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements of large language models (LLMs) in various natural language processing (NLP) tasks, LLM-based relevance modeling is gradually being adopted within industrial search systems. Neverth

Yiqin Wang, Haoji Zhang, Jingqi Tian, Yansong Tang

Most existing GUI agents typically depend on non-vision inputs like HTML source code or accessibility trees, limiting their flexibility across diverse software environments and platforms. Current multimodal large language models (MLLMs), which excel at using vision to ground real-world objects, offer a potential alternative. However, they often struggle with

Wei Luo, Deyu Zhang, Ying Tang, Fan Wu

This paper addresses the challenges of Online Action Recognition (OAR), a framework that involves instantaneous analysis and classification of behaviors in video streams. OAR must operate under stringent latency constraints, making it an indispensable component for real-time feedback for edge computing. Existing methods, which typically rely on the processin