November 2024 arXiv papers — page 2

Qi Yu, Ruitao Ma, Chen Qu, Riccardo Conte

Most widely used machine learned (ML) potentials for condensed phase applications rely on many-body permutationally invariant polynomial (PIP) or atom-centered neural networks (NN). However, these approaches often lack chemical interpretability in atomistic energy decomposition and the computational efficiency of traditional force fields has not been fully a

Wei-Ting Chen

The increasing complexity and energy demands of deep learning models have highlighted the limitations of traditional computing architectures, especially for edge devices with constrained resources. Spiking Neural Networks (SNNs) offer a promising alternative by mimicking biological neural networks, enabling energy-efficient computation through event-driven p

Francesco Ferrini, Antonio Longa, Andrea Passerini, Manfred Jaeger

Recently, significant attention has been given to the idea of viewing relational databases as heterogeneous graphs, enabling the application of graph neural network (GNN) technology for predictive tasks. However, existing GNN methods struggle with the complexity of the heterogeneous graphs induced by databases with numerous tables and relations. Traditional

Yutian Bu, Chenyu He, Min Fang, Chengyuan Li

Many young clusters possess extended main sequences, a phenomenon commonly ascribed to stellar rotation. However, the mechanism behind their very wide stellar rotation distributions remains unclear. A proposed explanation is that magnetic star-disk interaction can regulate stellar rotation, i.e., protostars with longer disk lifetimes will eventually evolve i

Shubhonkar Paramanick, Eric G. Blackman, John A. Tarduno, Jonathan Carroll-Nellenback

Light volatile elements in lunar regolith are thought to be a mixture of the solar wind and Earth's atmosphere, the latter sourced in the absence of geomagnetic field. However, the extent to which both the current and primitive geodynamo influence the transport of terrestrial ions still remains unclear, and this uncertainty is further complicated by the enig

Amir Barda, Matheus Gadelha, Vladimir G. Kim, Noam Aigerman

We propose a generative technique to edit 3D shapes, represented as meshes, NeRFs, or Gaussian Splats, in approximately 3 seconds, without the need for running an SDS type of optimization. Our key insight is to cast 3D editing as a multiview image inpainting problem, as this representation is generic and can be mapped back to any 3D representation using the

Xinzheng Wu, Junyi Chen, Xingyu Xing, Jian Sun

Scenario-based virtual testing is one of the most significant methods to test and evaluate the safety of automated driving systems (ADSs). However, it is impractical to enumerate all concrete scenarios in a logical scenario space and test them exhaustively. Recently, Black-Box Optimization (BBO) was introduced to accelerate the scenario-based test of ADSs by

Karol Bołbotowski, Guy Bouchitté

The classical Kantorovich-Rubinstein duality theorem establishes a significant connection between Monge optimal transport and maximization of a linear form on the set of 1-Lipschitz functions. This result has been widely used in various research areas. In particular, it unlocks the optimal transport methods in some of the optimal design problems. This paper

Tyrone Justin Sta. Maria, Jordan Aiko Deja

Smart Home Assistants (SHAs) have become ubiquitous in modern households, offering convenience and efficiency through its voice interface. However, for Deaf and Hard-of-Hearing (DHH) individuals, the reliance on auditory and textual feedback through a screen poses significant challenges. Existing solutions primarily focus on sign language input but overlook

Haochen Li, Xidong Mu, Yuanwei Liu, Yue Chen

A simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided integrated sensing, computing, and communication (ISCC) Internet of Robotic Things (IoRT) framework is proposed. Specifically, the full-duplex (FD) base station (BS) simultaneously receives the offloading signals from decision robots (DRs) and carries out target

Hongzhi Liao

The Circumcentered Reflection Method (CRM) is a recently developed projection method for solving convex feasibility problems. It offers preferable convergence properties compared to classic methods such as the Douglas-Rachford and the alternating projections method. In this study, our first main theorem establishes that CRM can identify a feasible point in t

Yanke Wang, Yolanne Y. R. Lee, Aurelio Dolfini, Markus Reischl

Lumbar spine problems are ubiquitous, motivating research into targeted imaging for treatment planning and guided interventions. While high resolution and high contrast CT has been the modality of choice, MRI can capture both bone and soft tissue without the ionizing radiation of CT albeit longer acquisition time. The critical trade-off between contrast qual

Yixuan Jin

The yields and ratios of light nuclei in heavy-ion collisions offer a method to distinguish between the thermal and coalescence models. Ratios such as $\rm N_t \times N_p/N_d^2$ and $\rm N_{^3He} \times N_p/N_d^2$ are suggested as potential probes to investigate critical phenomena within the QCD phase diagram. The significantly larger datasets from STAR BES-

Haochen Li, Yuanwei Liu, Xidong Mu, Yue Chen

The development of sixth-generation (6G) communication technologies is confronted with the significant challenge of spectrum resource shortage. To alleviate this issue, we propose a novel simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) aided multiple-input multiple-output (MIMO) cognitive radio (CR) system. Specifical

Ivan Zanardi, Alejandro N. Diaz, Seung Whan Chung, Marco Panesi

The domain decomposition (DD) nonlinear-manifold reduced-order model (NM-ROM) represents a computationally efficient method for integrating underlying physics principles into a neural network-based, data-driven approach. Compared to linear subspace methods, NM-ROMs offer superior expressivity and enhanced reconstruction capabilities, while DD enables cost-ef

Faith Griffin, Kevin Abelgas, Kriz Royce Tahimic, Andrei Kevin Chua

Expanding Fitts' Law into a 3D context, we analyze PointARs, a mixed reality system that teaches pointer skills through an object manipulation task. Nine distinct configurations, varying in object sizes and distances, were explored to evaluate task complexity using metrics such as completion time, error rate, and throughput. Our results support Fitts' Law, s

Annette Bieniusa, Markus Degen, Stefan Wehr

This Festschrift is dedicated to Peter Thiemann on the occasion of his sixtieth birthday, celebrating his significant contributions to the field of programming languages. Over the span of more than three decades, Peter has worked on a wide array of topics. This collection of five articles reflects the diversity of his work. The articles cover areas such as p

Jona Ballé, Luca Versari, Emilien Dupont, Hyunjik Kim

Inspired by the success of generative image models, recent work on learned image compression increasingly focuses on better probabilistic models of the natural image distribution, leading to excellent image quality. This, however, comes at the expense of a computational complexity that is several orders of magnitude higher than today's commercial codecs, and

Maicon Pierre Lourenço, Mosayeb Naseri, Lizandra Barrios Herrera, Hadi Zadeh-Haghighi

Active learning (AL) has been widely applied in chemistry and materials science. In this work we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by DFT or DFTB and this new d

Leonid Kotyuzanskiy, Artem Klimov

The transformer architecture has become an integral part of the field of modern neural networks, playing a crucial role in a variety of tasks, such as text generation, machine translation, image and audio processing, among others. There is also an alternative approach to building intelligent systems, proposed by Jeff Hawkins and inspired by the processes occ

Xinxin Zhang, S. Hassan HosseinNia

The Sinusoidal Input Describing Function (SIDF) is an effective tool for control system analysis and design, with its reliability directly impacting the performance of the designed control systems. This study enhances the reliability of SIDF analysis and the performance of closed-loop reset feedback control systems, presenting two main contributions. First,

Sealtiel B. Dy, Robert Joachim O. Encinas, Daphne Janelyn L. Go, Kyle Carlo C. Lasala

In-person presentations commonly depend on projectors or screens, requiring input devices for slide transitions and laser pointing. This paper introduces a glove-based pointer device that integrates these functions, offering an alternative to conventional tools. The device leverages accelerometer and gyroscope technology to enhance precision and usability. W

Thanh Tam Nguyen, Kaveh Edalati

Photoreforming is a sustainable photocatalytic process that degrades plastic waste while simultaneously producing hydrogen (H2) from water. However, this process has received limited attention due to the scarcity of effective catalysts capable of both plastic degradation and H2 production, such as titanium dioxide (TiO2). In this study, an active catalyst is

Thanh Tam Nguyen, Kaveh Edalati

Bismuth (III) oxide (Bi2O3) has been highly studied as a photocatalyst for green hydrogen production due to its low band gap, yet its efficiency requires enhancement. This study synthesizes a defective and strained black Bi2O3 by severe straining under high pressure, via a high-pressure torsion method, to improve its photocatalytic hydrogen production. The m

Xiang Chen, Mingkang Liu, Shiqiu Yin, Yu-Chen Gao

Electrolyte is a very important part of rechargeable batteries such as lithium batteries. However, the electrolyte innovation is facing grand challenges due to the complicated solution chemistry and infinite molecular space (>1060 for small molecules). This work reported an artificial intelligence (AI) platform, namely Uni-Electrolyte, for designing advanced

Jakob Burkhardt, Hannah Keller, Claudio Orlandi, Chris Schwiegelshohn

We introduce the linear-transformation model, a distributed model of differentially private data analysis. Clients have access to a trusted platform capable of applying a public matrix to their inputs. Such computations can be securely distributed across multiple servers using simple and efficient secure multiparty computation techniques. The linear-transfor

Thanh Tam Nguyen, Kaveh Edalati

Simultaneous catalytic hydrogen (H2) production and plastic waste degradation under light, known as photoreforming, is a novel approach to green fuel production and efficient waste management. Here, we use a high-entropy oxide (HEO), a new family of catalysts with five or more principal cations in their structure, for plastic degradation and simultaneous H2

Ismail Lotfi, Nouf Alabbasi, Omar Alhussein

This paper explores the application of large language models (LLMs) in designing strategic mechanisms -- including auctions, contracts, and games -- for specific purposes in communication networks. Traditionally, strategic mechanism design in telecommunications has relied on human expertise to craft solutions based on game theory, auction theory, and contrac

Roland Becker, Malte Braack

This paper addresses the challenge of proving the existence of solutions for nonlinear equations in Banach spaces, focusing on the Navier-Stokes equations and discretizations of thom. Traditional methods, such as monotonicity-based approaches and fixed-point theorems, often face limitations in handling general nonlinear operators or finite element discretiza

Duo Zheng, Shijia Huang, Liwei Wang

The rapid advancement of Multimodal Large Language Models (MLLMs) has significantly impacted various multimodal tasks. However, these models face challenges in tasks that require spatial understanding within 3D environments. Efforts to enhance MLLMs, such as incorporating point cloud features, have been made, yet a considerable gap remains between the models

Yuchen Zhao

Robotic surface consisting of many actuators can change shape to perform tasks, such as facilitating human-machine interactions and transporting objects. Increasing the number of actuators can enhance the robot's capacity, but controlling them requires communication bandwidth to increase equally in order to avoid time delays. We propose a novel control metho

Yan Wang, Jimin Huang, Huan He, Vincent Zhang

Common Data Elements (CDEs) standardize data collection and sharing across studies, enhancing data interoperability and improving research reproducibility. However, implementing CDEs presents challenges due to the broad range and variety of data elements. This study aims to develop an effective and efficient mapping tool to bridge the gap between local data

Samuel Mallick, Azita Dabiri, Bart De Schutter

Online model predictive control (MPC) for piecewise affine (PWA) systems requires the online solution to an optimization problem that implicitly optimizes over the switching sequence of PWA regions, for which the computational burden can be prohibitive. Alternatively, the computation can be moved offline using explicit MPC; however, the online memory require

Chade Li, Pengju Zhang, Jiaming Zhang, Yihong Wu

3D point cloud segmentation has a wide range of applications in areas such as autonomous driving, augmented reality, virtual reality and digital twins. The point cloud data collected in real scenes often contain small objects and categories with small sample sizes, which are difficult to handle by existing networks. In this regard, we propose a point cloud s

Alicia Bremer, Jeff Orchard

High-dimensional vectors have been proposed as a neural method for representing information in the brain using Vector Symbolic Algebras (VSAs). While previous work has explored decoding and cleaning up these vectors under the noise that arises during computation, existing methods are limited. Cleanup methods are essential for robust computation within a VSA.

Jiasi Zhou, Cong Zhou, Yijie Mao, Chintha Tellambura

Supporting immense throughput and ubiquitous connectivity holds paramount importance for future wireless networks. To this end, this letter focuses on how the spatial beams configured for legacy near-field (NF) users can be leveraged to serve extra NF or far-field users while ensuring the rate requirements of legacy NF users. In particular, a flexible rate s

Feng Liu, Haipeng Li, Guangyuan Zou, Junlun Li

Full waveform inversion (FWI) is able to construct high-resolution subsurface models by iteratively minimizing discrepancies between observed and simulated seismic data. However, its implementation can be rather involved for complex wave equations, objective functions, or regularization. Recently, automatic differentiation (AD) has proven to be effective in

Boyang Fu

Understanding the strong interactions between baryons, especially hyperon-nucleon ($Y$-$N$) interactions, is crucial for comprehending the equation-of-state (EoS) of the nuclear matter and inner structure of neutron star. In these proceedings, we present the measurements of $p$-$\Xi^{-}$ ($\bar{p}$-$\bar{\Xi}^{+}$) correlation functions with high statistics

Yu Cao, Jiantao Wang, Mingfeng Liu, Yan Liu

We revisit the long-standing question of whether water molecules dissociate on the Ru(0001) surface through nanosecond-scale path-integral molecular dynamics simulations on a sizable supercell. This is made possible through the development of an efficient and reliable machine-learning potential with near first-principles accuracy, overcoming the limitations

Mario Castro, David Gálvez, Sebastián Castillo, Vagson L. Carvalho-Santos

Magnetic bimerons are potential information carriers in spintronic devices. Bimerons, topologically equivalent to skyrmions, manifest in chiral magnetic systems with in-plane magnetization due to anisotropies or external magnetic fields. Applications demanding their current-driven motion face significant challenges, notably the bimeron Hall effect, which cau

Elena E. Berdysheva, Mita D. Ramabulana, Szilárd Gy. Révész

A unifying framework for some extremal problems on locally compact Abelian groups is considered, special cases of which include the Delsarte and Tur\'an extremal problems. A slight variation of the extremal problem is introduced and the different formulations are studied for equivalence. Extending previous work, a general result on existence of extremal func

Hongliang He, Jinfeng Huang, Qi Li, Xu Wang

In recent years, large language models have made significant advancements in the field of natural language processing, yet there are still inadequacies in specific domain knowledge and applications. This paper Proposes MaintAGT, a professional large model for intelligent operations and maintenance, aimed at addressing this issue. The system comprises three k

R. Julian R. Abel, Ingo Janiszczak, Reiner Staszewski

We will show that there are at least 8, 10 and 9 mutually orthogonal Latin squares (MOLS) of orders $n=54$, $96$ and $108$. The cases $n=54$ and $96$ are obtained by constructing separable permutation codes consisting of $8 \times 54$ and $10 \times 96$ codeword respectively; in addition, these codes respectively have lengths $54$, $96$ and minimum distances

Roberto Ulloa, Frank Mangold, Felix Schmidt, Judith Gilsbach

As the exploration of digital behavioral data revolutionizes communication research, understanding the nuances of data collection methodologies becomes increasingly pertinent. This study focuses on one prominent data collection approach, web scraping, and more specifically, its application in the growing field of research relying on web browsing data. We inv

Xinyu Lin, Tianyu Zhang, Chengbin Hou, Jinbao Wang

Node Importance Estimation (NIE) is a task that quantifies the importance of node in a graph. Recent research has investigated to exploit various information from Knowledge Graphs (KGs) to estimate node importance scores. However, the semantic information in KGs could be insufficient, missing, and inaccurate, which would limit the performance of existing NIE

Chenggang Yang, Yuang Shi

Abstract representations of 3D scenes play a crucial role in computer vision, enabling a wide range of applications such as mapping, localization, surface reconstruction, and even advanced tasks like SLAM and rendering. Among these representations, line segments are widely used because of their ability to succinctly capture the structural features of a scene

Chen Jiang, Peng Ren

We give a criterion for slope-stability of the syzygy bundle of a globally generated ample line bundle on a smooth projective variety of Picard number $1$ in terms of Hilbert polynomial. As applications, we prove the stability of syzygy bundles on many varieties, such as smooth Fano or Calabi--Yau complete intersections, hyperk\"ahler varieties of Picard num

Yi-Wen Wu, Jun-Qing Xia

This paper leverages the first released catalog from the FAST All Sky \textsc{Hi} Survey (FASHI) to examine the hypothesis of cosmic isotropy in the local Universe. Given the design of the overall FAST survey, the inhomogeneous detection sensitivity of FASHI is likely to introduce significant biases in the statistical properties of the catalog. To mitigate t

Tanmoy Mondal, Stefano Moretti, Prasenjit Sanyal

Under the assumption that the various evidences of a `95 GeV' excess, seen in data at the Large Electron Positron (LEP) collider as well as the Large Hadron Collider (LHC), correspond to actual signals of new physics Beyond the Standard Model (BSM), we characterise the underlying particle explaining these anomalies in terms of its Charge/Parity (CP) quantum

Yu Wang, Xiaofei Zhou, Yichen Wang, Geyuan Zhang

With the significant advancement of Large Vision-Language Models (VLMs), concerns about their potential misuse and abuse have grown rapidly. Previous studies have highlighted VLMs' vulnerability to jailbreak attacks, where carefully crafted inputs can lead the model to produce content that violates ethical and legal standards. However, existing methods strug

Ramin Mousa, Saeed Chamani, Mohammad Morsali, Mohammad Kazzazi

Skin cancer (SC) stands out as one of the most life-threatening forms of cancer, with its danger amplified if not diagnosed and treated promptly. Early intervention is critical, as it allows for more effective treatment approaches. In recent years, Deep Learning (DL) has emerged as a powerful tool in the early detection and skin cancer diagnosis (SCD). Altho

Wang Liang

Building a general-purpose task model similar to ChatGPT has been an important research direction for gene large language models. Instruction fine-tuning is a key component in building ChatGPT, but existing instructions are primarily based on natural language. Natural language and gene sequences have significant differences in tokenization and encoding. Ther

Bo Zhang, Cui-Bai Luo, Lei Feng

In our galaxy, white dwarfs inevitably undergo scattering and capture processes with the interstellar diffuse dark matter. The captured dark matter forms a dark halo that eventually evaporates or annihilates. Theoretical pulsation modes and observations of pulsating white dwarfs provide predictions about their evolution. This motivates us to study the impact

Fang Liu, Xiao-Bin Ji, Sheng-Sen Sun, Huai-Min Liu

Using $(10087 \pm 44) \times 10^6$ $J/\psi$ events collected with the BESIII detector in 2009, 2012, 2018 and 2019, the tracking efficiency of charged pions is studied using the decay $J/\psi \rightarrow \pi^+ \pi^- \pi^0$. The systematic uncertainty of the tracking efficiency and the corresponding correction factors for charged pions are evaluated, in bins

Nick James, Max Menzies

This paper studies the time-varying structure of the equity market with respect to market capitalization. First, we analyze the distribution of the 100 largest companies' market capitalizations over time, in terms of inequality, concentration at the top, and overall discrepancies in the distribution between different times. In the next section, we introduce

Héctor del Castillo, Luis Lomelí

We prove Kim's Assumtion A for the split classical groups in positive characteristic. Actually, we work in the slightly more general setting of groups of classical kind, which includes quasi-split classical groups and generalized spinor groups. We establish our results whenever a local Ramanujan bound holds; a bound that is known for the split classical grou

Sameera Bharadwaja H., Chandra R. Murthy

We consider the problem of identifying the defectives from a population of items via a non-adaptive group testing framework with a random pooling-matrix design. We analyze the sufficient number of tests needed for approximate set identification, i.e., for identifying almost all the defective and non-defective items with high confidence. To this end, we view

Yutong Zhou, Masahiro Ryo

We introduce AgriBench, the first agriculture benchmark designed to evaluate MultiModal Large Language Models (MM-LLMs) for agriculture applications. To further address the agriculture knowledge-based dataset limitation problem, we propose MM-LUCAS, a multimodal agriculture dataset, that includes 1,784 landscape images, segmentation masks, depth maps, and de

Gabriel Pedroza

This work proposes a mathematical approach that (re)defines a property of Machine Learning models named stability and determines sufficient conditions to validate it. Machine Learning models are represented as functions, and the characteristics in scope depend upon the domain of the function, what allows us to adopt topological and metric spaces theory as a

Leonard Thiele, Fabian Batsch, Rama Calaga, Heiko Damerau

The International Muon Collider Collaboration (IMCC) is engaged in a design study for a future facility intended to collide muons. Subsequent to the initial linear acceleration, the counter-rotating muons and anti-muons are accelerated in a chain of rapid cycling synchrotrons (RCS) up to the multi-TeV collision energy. To maximise the number of muons availab

Yi Yan, Jiacheng Hou, Zhenjie Song, Ercan Engin Kuruoglu

As irregularly structured data representations, graphs have received a large amount of attention in recent years and have been widely applied to various real-world scenarios such as social, traffic, and energy settings. Compared to non-graph algorithms, numerous graph-based methodologies benefited from the strong power of graphs for representing high-dimensi

Muhammad Yunusa, Andrew K. Schulz, Tim Parker, Felix Schneider

Gallenene is a promising low-dimensional material with a structure down to the thickness of a single atom, similar to graphene. However, van der Waals stacking of two-dimensional (2D) gallenene under confinement remain poorly understood. In this study, we present evidence of the formation of parallel-stacked hexagonal gallenene (a100) structures in liquid ga

Weizhe Liu, Renshuai Tao, Hongguang Zhu, Yunda Sun

Current data-driven approaches for X-ray prohibited items detection remain under-explored, particularly in the design of effective data augmentations. Existing natural image augmentations for reflected light imaging neglect the data characteristics of X-ray security images. Moreover, prior X-ray augmentation methods have predominantly focused on foreground p

Fatemeh Shirmohammadzadeh Maleki

The paper is devoted to the introduction of natural deduction systems for some weak subintuitionistic logics, along with proofs of normalization theorems for these systems.

Musfer Adzhymambetov, Yuri Sinyukov

The present work is devoted to developing the integrated hydrokinetic model (iHKM) for relativistic nucleus-nucleus collisions. While the previous cycle of works on this topic focused on ultra-relativistic collisions at the top RHIC and different LHC energies, the current work addresses relativistic collisions at the lower energies, specifically ranging from

Mohammed Q. Shormani

This study sets out to answer one major question: Who thinks better, non-native speakers of English or ChatGPT?, providing evidence from processing and interpreting center-embedding English constructions that human brain surpasses ChatGPT, and that ChatGPT cannot be regarded as a theory of language. Fifteen non-native speakers of English were recruited as pa

Neil Jerome A. Egarguin, Daniel Onofrei

In this paper, we present a time domain extension of our strategy on manipulating radiated scalar Helmholtz fields and discuss two important applied scenarios, namely (1) creating personal sound zones inside a bounded domain and (2) shielded localized communication. Our strategy is based on the authors' previous works establishing the possibility and stabili

S. Morozov, V. Bocharnikov, D. Finogeev, M. Golubeva

A new Highly-Granular time-of-flight Neutron Detector (HGND) is being developed and constructed to measure azimuthal neutron flow and neutron yields in nucleus-nucleus interactions in heavy-ion collisions with energies up to 4A GeV in the fixed target experiment BM@N at JINR. Details of the detector design and results of performance studies for neutron ident

Carmelo Cisto, Raquel Tapia-Ramos

Let $\mathcal{C}$ be a positive integer cone and $k\in \mathcal{C}$. A $\mathcal{C}$-semigroup $S$ is $k$-positioned if for every $h\in \mathcal{C}\setminus S$ we have that $k-h$ belongs to $S$. In this work, we focus on this family of semigroups and introduce primary positioned $\mathcal{C}$-semigroups, characterizing a subfamily of them through the perspec

Atharva Deshpande, Kaushik Gopalan, Jeet Shah, Hrishikesh Simu

This study explores the application of deep learning for rainfall prediction, leveraging the Spinning Enhanced Visible and Infrared Imager (SEVIRI) High rate information transmission (HRIT) data as input and the Operational Program on the Exchange of weather RAdar information (OPERA) ground-radar reflectivity data as ground truth. We use the mean of 4 InfraR

Mariia Ivanchenkoa, Peter L. Walters, Fei Wang

In this work, we developed a rigorous procedure for mapping the exact non-Markovian propagator to the generalized Lindblad form. It allows us to extract the negative decay rate that is the indicator of the non-Markovian effect. As a consequence, we can investigate the influence of the non-Markovian bath on the system's properties such as coherence and equili

Cristian García Ruiz, Olga Muñoz, Antonio Pascual-Iserte

The mmWave bands, considered to support the forthcoming generation of mobile communications technologies, have a well-known vulnerability to blockages. Recent works in the literature analyze the blockage probability considering independence or correlation among the blocking elements of the different links. In this letter, we characterize the effect of blocka

Shuang Ji, Jing Lu

In this paper, we study the blow up and scattering result of the solution to the focusing nonlinear Hartree equation with potential $$i\partial_t u +\Delta u - Vu = - (|\cdot|^{-3} \ast |u|^2)u, \qquad (t, x) \in \mathbb{R} \times \mathbb{R}^5 $$ in the energy space ${H}^1(\mathbb{R}^5)$ below the mass-energy threshold. The potential $V$ we considered is an

Xubing Ye, Yukang Gan, Yixiao Ge, Xiao-Ping Zhang

Large Vision Language Models (LVLMs) have achieved significant success across multi-modal tasks. However, the computational cost of processing long visual tokens can be prohibitively expensive on resource-limited devices. Previous methods have identified redundancy in visual tokens within the Large Language Model (LLM) decoder layers and have mitigated this

Yongqi Zhai, Jiayu Yang, Wei Jiang, Chunhui Yang

In neural video codecs, current state-of-the-art methods typically adopt multi-scale motion compensation to handle diverse motions. These methods estimate and compress either optical flow or deformable offsets to reduce inter-frame redundancy. However, flow-based methods often suffer from inaccurate motion estimation in complicated scenes. Deformable convolu

Manuel Weiß, Lukas Baumgärtner, Laura Weigl, Ronny Bergmann

We consider the problem of surface segmentation, where the goal is to partition a surface represented by a triangular mesh. The segmentation is based on the similarity of the normal vector field to a given set of label vectors. We propose a variational approach and compare two different regularizers, both based on a total variation measure. The first regular

Dhananjay Singh, Arvind Kumar

Tsallis nonextensive statistics is applied to study the transport coefficients of strongly interacting matter within the Polyakov chiral SU(3) quark mean field model (PCQMF). Nonextensivity is introduced within the PCQMF model through a dimensionless $q$ parameter to examine the viscous properties such as shear viscosity ($\eta$), bulk viscosity ($\zeta_b$),

Marco Favino

We formally derive interface conditions for modeling fractures in Darcy flow problems and, more generally, thin inclusions in heterogeneous diffusion problems expressed as the divergence of a flux. Through a formal integration of the governing equations within the inclusions, we establish that the resulting interface conditions are of Wentzell type for the f

Cristian García Ruiz, Antonio Pascual-Iserte, Olga Muñoz-Medina

In the millimeter waves (mmWave) bands considered for 5G and beyond, the use of very high frequencies results in the interruption of communication whenever there is no line of sight between the transmitter and the receiver. Blockages have been modeled in the literature so far using tools such as stochastic geometry and random shape theory. Using these tools,

Mohammad Taha Shah, Gourab Ghatak, Shobha Sundar Ram

Advanced driver assistance systems (ADAS) enabled by automotive radars have significantly enhanced vehicle safety and driver experience. However, the extensive use of radars in dense road conditions introduces mutual interference, which degrades detection accuracy and reliability. Traditional interference models are limited to simple highway scenarios and ca

Haicheng Wang, Chen Ju, Weixiong Lin, Shuai Xiao

In rapidly evolving field of vision-language models (VLMs), contrastive language-image pre-training (CLIP) has made significant strides, becoming foundation for various downstream tasks. However, relying on one-to-one (image, text) contrastive paradigm to learn alignment from large-scale messy web data, CLIP faces a serious myopic dilemma, resulting in biase

Armando Capasso

I prove ``Lefschetz principle''-type theorems for slope semistable and curve semistable Higgs sheaves on smooth projective varieties, defined over an algebraically closed field of characteristic $0$. These theorems are applied to reduce a conjecture, about curve semistable Higgs bundles, from the previous general setting to the complex case. This con

Vadim Alekseev, Hiroshi Ando, Friedrich Martin Schneider, Andreas Thom

We define and study notions of amenability and skew-amenability of continuous actions of topological groups on compact topological spaces. Our main motivation is the question under what conditions amenability of a topological group passes to a closed subgroup. Other applications include the understanding of the universal minimal flow of various non-amenable

Yongqi Zhai, Yi Ma, Luyang Tang, Wei Jiang

Scalable coding, which can adapt to channel bandwidth variation, performs well in today's complex network environment. However, most existing scalable compression methods face two challenges: reduced compression performance and insufficient scalability. To overcome the above problems, this paper proposes a learned fine-grained scalable image compression fram

Rinto Thomas, Praveen Ranganath Prabhakar, Douglas J. Tobias, Michael von Domaros

The oxidation of human sebum, a lipid mixture covering our skin, generates a range of volatile and semi-volatile carbonyl compounds that contribute largely to indoor air pollution in crowded environments. Kinetic models have been developed to gain a deeper understanding of this complex multiphase chemistry, but they rely partially on rough estimates of kinet

Shipeng Liu, Boshen Zhang, Zhehui Huang

Large Language Models (LLMs) have opened transformative possibilities for human-robot collaboration. However, enabling real-time collaboration requires both low latency and robust reasoning, and most LLMs suffer from high latency. To address this gap, we first propose a fine-grained benchmark that explicitly assesses agents' proactive adaptability and tempor

Jin Wang, Erio Tosatti

The physics of membranes, a classic subject, acquires new momentum from two-dimensional (2D) materials multilayers. This work reports the surprising results emerged during a theoretical study of equilibrium geometry of bilayers as freestanding membranes. While ordinary membranes are prone to buckle around compressive impurities, we predict that all 2D materi

Yuhai Wang, Maryam Pishgar

Aerial-Ground Person Re-identification (AGPReID) holds significant practical value but faces unique challenges due to pronounced variations in viewing angles, lighting conditions, and background interference. Traditional methods, often involving a global analysis of the entire image, frequently lead to inefficiencies and susceptibility to irrelevant data. In

Peter H. C. Pang

In this note we construct solutions to rough differential equations ${\rm d} Y = f(Y) \,{\rm d} X$ with a driver $X \in C^\alpha([0,T];\mathbb{R}^d)$, $\frac13 < \alpha \le \frac12$, using a splitting-up scheme. We show convergence of our scheme to solutions in the sense of Davie by a new argument and give a rate of convergence.

Andrew Laverick, Kristen Surrao, Inigo Zubeldia, Boris Bolliet

Multi-agent systems (MAS) utilizing multiple Large Language Model agents with Retrieval Augmented Generation and that can execute code locally may become beneficial in cosmological data analysis. Here, we illustrate a first small step towards AI-assisted analyses and a glimpse of the potential of MAS to automate and optimize scientific workflows in Cosmology

Tingjia Shen, Hao Wang, Chuhan Wu, Jin Yao Chin

Scaling Laws have emerged as a powerful framework for understanding how model performance evolves as they increase in size, providing valuable insights for optimizing computational resources. In the realm of Sequential Recommendation (SR), which is pivotal for predicting users' sequential preferences, these laws offer a lens through which to address the chal

Sharva Gogawale, Madhura Deshpande, Parteek Kumar, Irad Ben-Gal

In recent times, online education and the usage of video-conferencing platforms have experienced massive growth. Due to the limited scope of a virtual classroom, it may become difficult for instructors to analyze learners' attention and comprehension in real time while teaching. In the digital mode of education, it would be beneficial for instructors to have

Matteo Kevin Crisafio, Alessio Fontanarossa, Dario Martelli

We construct new infinite classes of Euclidean supersymmetric solutions of four dimensional minimal gauged supergravity comprising a $U (1) \times U (1)$-invariant asymptotically locally hyperbolic metric on the total space of orbifold line bundles over a spindle (bolt). The conformal boundary is generically a squashed, branched, lens space and the graviphot

Teng-Fang Hsiao, Bo-Kai Ruan, Sung-Lin Tsai, Yi-Lun Wu

In this study, we aim to determine and solve the deficiency of Stable Diffusion Inpainting (SDI) in following the instruction of both prompt and mask. Due to the training bias from masking, the inpainting quality is hindered when the prompt instruction and image condition are not related. Therefore, we conduct a detailed analysis of the internal representati

Ayoub Hammal, Benno Uthayasooriyar, Caio Corro

Named-entity recognition (NER) is a task that typically requires large annotated datasets, which limits its applicability across domains with varying entity definitions. This paper addresses few-shot NER, aiming to transfer knowledge to new domains with minimal supervision. Unlike previous approaches that rely solely on limited annotated data, we propose a w

Harleen Kaur Bagga, Jasmine Bernard, Sahil Shaheen, Sarthak Arora

Sarcasm is hard to interpret as human beings. Being able to interpret sarcasm is often termed as a sign of intelligence, given the complex nature of sarcasm. Hence, this is a field of Natural Language Processing which is still complex for computers to decipher. This Literature Survey delves into different aspects of sarcasm detection, to create an understand

Guoli Wu, Zhiyong Feng, Shizhan Chen, Hongyue Wu

Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favourite items. This user-centric approach may lead to unfair exposure distribution among the providers. On the contrary, a provider-centric design might become unfair to the users. Therefore, this paper proposes a re-ranking model FairSort to find a trade-off solut

Stefan Meisenbacher, Silas Aaron Selzer, Mehdi Dado, Maximilian Beichter

Renewable energies and their operation are becoming increasingly vital for the stability of electrical power grids since conventional power plants are progressively being displaced, and their contribution to redispatch interventions is thereby diminishing. In order to consider renewable energies like Wind Power (WP) for such interventions as a substitute, da

Guangji Chen, Jun Li, Qingqing Wu, Yiyang Ni

This paper investigates an intelligent reflecting surface (IRS) aided wireless federated learning (FL) system, where an access point (AP) coordinates multiple edge devices to train a machine leaning model without sharing their own raw data. During the training process, we exploit the joint channel reconfiguration via IRS and resource allocation design to red

Amal Aldarawsheh, Samir Lounis

The ability to control matter at the atomic scale has revolutionized our understanding of the physical world, opening doors to unprecedented technological advancements. Quantum technology, which harnesses the unique principles of quantum mechanics, enables us to construct and manipulate atomic structures with extraordinary precision. Here, we propose a botto