November 2024 arXiv papers — page 50

Helffer Bernard, Nicoleau François

Inspired by a paper by T. Chakradhar, K. Gittins, G. Habib and N. Peyerimhoff, we analyze their conjecture that the ground state energy of the magnetic Dirichlet-to-Neumann operator on the disk tends to $+\infty$ as the magnetic field tends to $+\infty$. This is an important step towards the analysis of the curvature effect in the case of general domains in

Bartomeu Alorda, Cristian Carmona, Gabriel Torrens, Sebastia Bota

Increased process variability and reliability issues present a major challenge for future SRAM trends. Non-intrusive and accurate SRAM stability measurement is crucial for estimating yield in large SRAM arrays. Conventional SRAM variability metrics require including test structures that cannot be used to investigate cell bit fails in functional SRAM arrays.

Chris Bowman, Maud De Visscher, Alice Dell'Arciprete, Amit Hazi

We provide an ${\rm Ext}$-quiver and relations presentation of the Khovanov arc algebras and prove a precise analogue of the Kleshchev--Martin conjecture in this setting.

Ling Chen

This paper investigates the application of Feature-Enriched Generative Adversarial Networks (FE-GAN) in financial risk management, with a focus on improving the estimation of Value at Risk (VaR) and Expected Shortfall (ES). FE-GAN enhances existing GANs architectures by incorporating an additional input sequence derived from preceding data to improve model p

Manoj K Mishra, Shameela S F, Pradyuman Singh Rathore

The AErosol RObotic NETwork (AERONET), established in 1993 with limited global sites, has grown to over 900 locations, providing three decades of continuous aerosol data. While earlier studies based on shorter time periods (10-12 years) and fewer sites (approximately 250) made significant contributions to aerosol research, the vast AERONET dataset (1993-2023

Longfei Guo, Shaowen Xu, Qilong Cui, Qingmin Hu

Weyl semimetals, such as $TaIrTe_{4}$, characterized by their unique band structures and exotic transport phenomena, have become a central focus in modern electronics. Despite extensive research, a systematic understanding of the impact of heterogeneous integration on the electronic and optical properties of TaIrTe4 device remains elusive. We have carried ou

Matthias Schneider, Thilo Hagendorff

Text-to-image models are increasingly popular and impactful, yet concerns regarding their safety and fairness remain. This study investigates the ability of ten popular Stable Diffusion models to generate harmful images, including NSFW, violent, and personally sensitive material. We demonstrate that these models respond to harmful prompts by generating inapp

Varun J Kaushik, Ekta, Parveen, Jitender Kumar

Given a graph $A$ on a group $G$ and an equivalence relation $B$ on $G$, the $B$ super$A$ graph, whose vertex set is $G$ and two vertices $g$, $h$ are adjacent if and only if there exist $g^{\prime} \in[g]$ and $h^{\prime} \in[h]$ such that $g^{\prime}$ and $h^{\prime}$ are adjacent in $A$. Recently, Dalal \emph{et al.} (Spectrum of super commuting graphs of

Jiawei Li, Yuhao Li, Hanlin Ren

We study the *refuter* problems for proof complexity lower bounds. Suppose $\varphi$ is a hard tautology that does not admit any length-$s$ proof in some proof system $P$. In the corresponding refuter problem, we are given (query access to) a purported length-$s$ proof $\pi$ in $P$ that claims to have proved $\varphi$, and our goal is to find an invalid deri

Philipp Endres, Valentin Koch, Julia A. Schnabel, Carsten Marr

Histopathology, the microscopic study of diseased tissue, is increasingly digitized, enabling improved visualization and streamlined workflows. An important task in histopathology is the segmentation of cells and glands, essential for determining shape and frequencies that can serve as indicators of disease. Deep learning tools are widely used in histopathol

Jiayuan Zhu, Junde Wu, Cheng Ouyang, Konstantinos Kamnitsas

Medical image segmentation data inherently contain uncertainty. This can stem from both imperfect image quality and variability in labeling preferences on ambiguous pixels, which depend on annotator expertise and the clinical context of the annotations. For instance, a boundary pixel might be labeled as tumor in diagnosis to avoid under-estimation of severit

Pan Yin, Kaiyu Li, Xiangyong Cao, Jing Yao

Recently, road graph extraction has garnered increasing attention due to its crucial role in autonomous driving, navigation, etc. However, accurately and efficiently extracting road graphs remains a persistent challenge, primarily due to the severe scarcity of labeled data. To address this limitation, we collect a global-scale satellite road graph extraction

Ashutosh Shukla, Rahul Chand, Sneha Boby, G. V. Pavan Kumar

Optical tweezers have revolutionized particle manipulation at the micro- and nanoscale, playing a critical role in fields such as plasmonics, biophysics, and nanotechnology. While traditional optical trapping methods primarily rely on optical forces to manipulate and organize particles, recent studies suggest that optothermal traps in surfactant solutions ca

Ryan Cotsakis, Erwan Koch, Christian-Yann Robert

Hourly maxima of 3-second wind gust speeds are prominent indicators of the severity of wind storms, and accurately forecasting them is thus essential for populations, civil authorities and insurance companies. Space-time max-stable models appear as natural candidates for this, but those explored so far are not suited for forecasting and, more generally, the

Hiroshi Watanabe, Hiroaki Ikeda

The realization of BCS-BEC crossover in superconductors, which smoothly connects Bardeen-Cooper-Schrieffer (BCS) theory with Bose-Einstein Condensation (BEC) in fermion systems, is an intriguing recent topic in strongly correlated electron systems. The organic superconductor $\kappa$-(BEDT-TTF)$_4$Hg$_{2.89}$Br$_8$ ($\kappa$-HgBr) under pressure is one of th

Xiaoyue Mi, Fan Tang, Juan Cao, Qiang Sheng

Visual generation models have achieved remarkable progress in computer graphics applications but still face significant challenges in real-world deployment. Current assessment approaches for visual generation tasks typically follow an isolated three-phase framework: test input collection, model output generation, and user assessment. These fashions suffer fr

U. Özdem

Investigating the electromagnetic characteristics of unconventional states may offer new insights into their internal structures. In particular, the magnetic moment attributes may serve as a crucial physical observable for differentiating exotic states with disparate configurations or spin-parity quantum numbers. As a promising avenue for research, encompass

Yuncong Yang, Han Yang, Jiachen Zhou, Peihao Chen

Constructing compact and informative 3D scene representations is essential for effective embodied exploration and reasoning, especially in complex environments over extended periods. Existing representations, such as object-centric 3D scene graphs, oversimplify spatial relationships by modeling scenes as isolated objects with restrictive textual relationship

Vsevolod Kleshchenko, Khristina Albitskaya, Mihail Petrov

In this work, we study the acoustic forces acting on particles due to sound scattering at the interface with an elastic substrate. Utilizing the Green's function formalism, we predict that excitation of leaking Rayleigh wave results in strong modification of the acoustic pressure force acting on a monopole scatterer and changes the equilibrium position of pa

Joohyun Lee, Minji Roh

As Large Language Models (LLMs) increasingly address domain-specific problems, their application in the financial sector has expanded rapidly. Tasks that are both highly valuable and time-consuming, such as analyzing financial statements, disclosures, and related documents, are now being effectively tackled using LLMs. This paper details the development of a

Harsh Narola, Thibeau Wouters, Luca Negri, Melissa Lopez

Gravitational Wave (GW) detectors routinely encounter transient noise bursts, known as glitches, which are caused by either instrumental or environmental factors. Due to their high occurrence rate, glitches can overlap with GW signals, as in the notable case of GW170817, the first detection of a binary neutron star merger. Accurate reconstruction and subtrac

Abhi Savaliya, Ayush Bidlan

We present a theoretical framework on non-local classical field theory using fractional integrodifferential operators. Due to the lack of easily manageable symmetries in traditional fractional calculus and the difficulties that arise in the formalism of multi-fractional calculus over $\mathbb{R}^{\text{D}}$ space, we introduce a set of new fractional operato

Joao P. Ferreira, Vinicius C. Ferreira, Sergio L. Nogueira, Joao M. Faria

The sharing of mobile network infrastructure has become a key topic with the introduction of 5G due to the high costs of deploying such infrastructures, with neutral host models coupled with features such as network function virtualization (NFV) and network slicing emerging as viable solutions for the challenges in this area. With this in mind, this work pre

Jin Tian, Baichun Wei, Chifu Yang, Suo Luo

Objective: Shoulder exoskeletons can effectively assist with overhead work. However, their impacts on muscle synergy remain unclear. The objective is to systematically investigate the effects of the shoulder exoskeleton on muscle synergies during overhead work.Methods: Eight male participants were recruited to perform a screwing task both with (Intervention)

Michael Baake, Franz Gähler, Jan Mazáč, Lorenzo Sadun

The Spectre is an aperiodic monotile for the Euclidean plane that is truly chiral in the sense that it tiles the plane without any need for a reflected tile. The topological and dynamical properties of the Spectre tilings are very similar to those of the Hat tilings. Specifically, the Spectre sits within a complex $2$-dimensional family of tilings, most of w

Christophe Ponsard, Gustavo Ospina, Denis Darquennes

Comparing the quality of software written in different computer languages is required in a variety of scenarios, e.g. multi-language projects or application selection process among candidates in different languages. We focus on the challenges related to comparing the maintainability quality typically through a maintainability index or technical debt approach

Tanghaoran Zhang, Yue Yu, Xinjun Mao, Shangwen Wang

Code snippet adaptation is a fundamental activity in the software development process. Unlike code generation, code snippet adaptation is not a "free creation", which requires developers to tailor a given code snippet in order to fit specific requirements and the code context. Recently, large language models (LLMs) have confirmed their effectiveness in the c

Libo Wang

As the application of large language models continues to expand in various fields, it poses higher challenges to the effectiveness of identifying harmful content generation and guardrail mechanisms. This research aims to evaluate the guardrail effectiveness of GPT-4o, Grok-2 Beta, Llama 3.1 (405B), Gemini 1.5, and Claude 3.5 Sonnet through black-box testing

Yifan Wu, Min Zeng, Yang Li, Yang Zhang

Most current molecular language models transfer the masked language model or image-text generation model from natural language processing to molecular field. However, molecules are not solely characterized by atom/bond symbols; they encapsulate important physical/chemical properties. Moreover, normal language models bring grammar rules that are irrelevant fo

Roger Barlow, Alessandra Brazzale, Igor Volobouev

We present a procedure for handling asymmetric errors. Many results in particle physics are presented as values with different positive and negative errors, and there is no consistent procedure for handling them. We consider the difference between errors quoted using pdfs and using likelihoods, and the difference between the rms spread of a measurement and t

Hongjie Dong, Haigang Li, Huaijun Teng, Peihao Zhang

We investigate higher derivative estimates for the Lam\'e system with hard inclusions embedded in a bounded domain in $\mathbb{R}^{d}$. As the distance $\varepsilon$ between two closely spaced hard inclusions approaches zero, the stress in the narrow regions between the inclusions increases significantly. This stress is captured by the gradient of the soluti

Datao Tang, Xiangyong Cao, Xuan Wu, Jialin Li

Remote sensing image object detection (RSIOD) aims to identify and locate specific objects within satellite or aerial imagery. However, there is a scarcity of labeled data in current RSIOD datasets, which significantly limits the performance of current detection algorithms. Although existing techniques, e.g., data augmentation and semi-supervised learning, c

Si-Qi Liu, Haonan Qu, Youjin Zhang

For two generalized Frobenius manifolds related by a Legendre-type transformation, we show that the associated integrable hierarchies of hydrodynamic type, which are called the Legendre-extended Principal Hierarchies, are related by a certain linear reciprocal transformation; we also show, under the semisimplicity condition, that the topological deformations

Hannu Vilpponen, Antti Piirainen, Miikka Kallberg, Tommi Mikkonen

The utilization of health data for secondary purposes, such as research, sta-tistics, and development, has become increasingly significant in advancing healthcare systems. To foster the above, Finland has established a framework for the secondary use of health and social data through legislative measures and the creation of specialized institutions, which ar

Hiroya Umeda, Masami Ouchi, Satoshi Kikuta, Yuichi Harikane

We present the luminosity functions (LFs) and angular correlation functions (ACFs) derived from 18,960 Ly$\alpha$ emitters (LAEs) at $z=2.2-7.3$ over a wide survey area of $\lesssim24 {\rm deg^2}$ that are identified in the narrowband data of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) and the Cosmic HydrOgen Reionization Unveiled with Subaru (C

Ronny Ko, Abdelkarim Kati, Robin Geelen, Rasoul Akhavan Mahdavi

Gradient boosting decision forests, used by XGBoost or AdaBoost, offer higher accuracy and lower training times than decision trees for large datasets. Protocols for private inference over decision trees can be used to preserve the privacy of the input data as well as the privacy of the trees. However, naively extending private inference over decision trees

M. A. Semina, F. Tabataba-Vakili, A. Rupp, A. S. Baimuratov

We study theoretically the neutral and charged excitons in two-dimensional semiconductors with anisotropic dispersion of charge carriers. Such a situation is realized in CrSBr-based van der Waals heterostructures. We calculate the binding energies of excitons and trions and explore their dependence on the mass ratio, dielectric screening, and interlayer dist

R. Y. Cheng, K. Godbey, Y. B. Niu, Y. G. Ma

Recent advances in both theoretical and computational methods have enabled large-scale, precision calculations of the properties of atomic nuclei. With the growing complexity of modern nuclear theory, however, also comes the need for novel methods to perform systematic studies and quantify the uncertainties of models when confronted with experimental data. T

Peng Xu, Hongjin Wu, Jinle Wang, Rongjia Lin

This paper details a technical plan for building a clinical case database for Traditional Chinese Medicine (TCM) using web scraping. Leveraging multiple platforms, including 360doc, we gathered over 5,000 TCM clinical cases, performed data cleaning, and structured the dataset with crucial fields such as patient details, pathogenesis, syndromes, and annotatio

Soonki Hong, Sanghoon Kwon

In this paper, we define the edge zeta function of weighted complex. We also present the formula for the edge zeta function of the standard non-uniform complex $\operatorname{PGL}(3,\mathbb{F}_q[t])\backslash\operatorname{PGL}(3,\mathbb{F}_q(\!(t^{-1})\!))/\operatorname{PGL}(3,\mathbb{F}_q[\![t^{-1}]\!])$, arising from the group $\operatorname{PGL}_3$, as a

Rong-Cheng Tu, Zi-Ao Ma, Tian Lan, Yuehao Zhao

Driven by the remarkable progress in diffusion models, text-to-image generation has made significant strides, creating a pressing demand for automatic quality evaluation of generated images. Current state-of-the-art automatic evaluation methods heavily rely on Multi-modal Large Language Models (MLLMs), particularly powerful commercial models like GPT-4o. Whi

Fan Zhang, Siyuan Zhao, Naye Ji, Zhaohan Wang

Speech-driven gesture generation using transformer-based generative models represents a rapidly advancing area within virtual human creation. However, existing models face significant challenges due to their quadratic time and space complexities, limiting scalability and efficiency. To address these limitations, we introduce DiM-Gestor, an innovative end-to-

Yizhen Guo, Tian Zhou, Wanyi Jiang, Bo Wu

Weather and climate forecasting is vital for sectors such as agriculture and disaster management. Although numerical weather prediction (NWP) systems have advanced, forecasting at the subseasonal-to-seasonal (S2S) scale, spanning 2 to 6 weeks, remains challenging due to the chaotic and sparse atmospheric signals at this interval. Even state-of-the-art deep l

Vicente Alvarez, Amin Esfahani

In this study, we investigate the Klein-Gordon-Zakharov system with a focus on identifying multi-soliton solutions. Specifically, for a given number $N$ of solitons, we demonstrate the existence of a multi-soliton solution that asymptotically converges, in the energy space, to the sum of these solitons. Our proof extends and builds upon the previous results

Mohammed Saqr, Sonsoles López-Pernas, Tiina Törmänen, Rogers Kaliisa

This paper presents a novel learning analytics method: Transition Network Analysis (TNA), a method that integrates Stochastic Process Mining and probabilistic graph representation to model, visualize, and identify transition patterns in the learning process data. Combining the relational and temporal aspects into a single lens offers capabilities beyond eith

Wei Xu

Following our previous work [68], this paper continues to investigate the evolution dynamics of local times of spectrally positive L\'evy processes with Gaussian components in the spatial direction. We prove that conditioned on the finiteness of the first time at which the local time at zero exceeds a given value, local times at positive line are equal in la

Parinthapat Pengpun, Can Udomcharoenchaikit, Weerayut Buaphet, Peerat Limkonchotiwat

We present a synthetic data approach for instruction-tuning large language models (LLMs) for low-resource languages in a data-efficient manner, specifically focusing on Thai. We identify three key properties that contribute to the effectiveness of instruction-tuning datasets: fluency, diversity, and cultural context. We propose a seed-data-free framework for

Hanyang Wang

The inhibition of Lysine-Specific Histone Demethylase 1 (LSD1) is a promising strategy for cancer treatment and targeting epigenetic mechanisms. This paper introduces a Probabilistic Conditional Generative Adversarial Network (Prob-cGAN), designed to predict the activity of LSD1 inhibitors. The Prob-cGAN was evaluated against state-of-the-art models using th

Su Sun, Cheng Zhao, Zhuoyang Sun, Yingjie Victor Chen

Most existing Dynamic Gaussian Splatting methods for complex dynamic urban scenarios rely on accurate object-level supervision from expensive manual labeling, limiting their scalability in real-world applications. In this paper, we introduce SplatFlow, a Self-Supervised Dynamic Gaussian Splatting within Neural Motion Flow Fields (NMFF) to learn 4D space-time

M S Chaitanya Kumar, Sai Satya Narayana J, Yunkai Bao, Xin Wang

Federated Learning (FL) presents a paradigm shift towards distributed model training across isolated data repositories or edge devices without explicit data sharing. Despite of its advantages, FL is inherently less efficient than centralized training models, leading to increased energy consumption and, consequently, higher carbon emissions. In this paper, we

Arpan Pal, Ruta Kale, Qian H. S. Wang, Daniel R. Wik

Diffuse radio sources known as radio relics are direct tracers of shocks in the outskirts of merging galaxy clusters. PSZ2 G200.95-28.16, a low-mass merging cluster($\textrm{M}_{500} = (2.7 \pm 0.2) \times 10^{14}~\mathrm{M}_{\odot}$) features a prominent radio relic, first identified by Kale et al. 2017. We name this relic as the Seahorse. The MeerKAT Galax

Jared Miller, Jie Wang, Feng Guo

A polynomial matrix inequality is a formula asserting that a polynomial matrix is positive semidefinite. Polynomial matrix optimization concerns minimizing the smallest eigenvalue of a symmetric polynomial matrix subject to a tuple of polynomial matrix inequalities. This work explores the use of sparsity methods in reducing the complexity of sum-of-squares b

Dongdong Xu, Pierre Ricco, Elena Marensi

We study the generation, nonlinear development and secondary instability of unsteady G\"ortler vortices and streaks in compressible boundary layers exposed to free-stream vortical disturbances and evolving over concave, flat and convex walls. The formation and evolution of the disturbances are governed by the compressible nonlinear boundary-region equations,

Abhinav Joshi, Shaswati Saha, Divyaksh Shukla, Sriram Vema

Large Language Models (LLMs) have shown to be a great success in a wide range of applications ranging from regular NLP-based use cases to AI agents. LLMs have been trained on a vast corpus of texts from various sources; despite the best efforts during the data pre-processing stage while training the LLMs, they may pick some undesirable information such as pe

Gábor Domokos, Alain Goriely, Ákos G. Horváth, Krisztina Regős

Recently, we introduced a new class of shapes, called soft cells which fill space as soft tilings without gaps and overlaps while minimizing the number of sharp corners. We introduced the edge bending algorithm that deforms a polyhedral tiling into a soft tiling and we proved that an infinite class of polyhedral tilings can be smoothly deformed into standard

Long Wen, Shixin Li, Yu Zhang, Yuhong Huang

3D Gaussian Splatting (3DGS) allows flexible adjustments to scene representation, enabling continuous optimization of scene quality during dense visual simultaneous localization and mapping (SLAM) in static environments. However, 3DGS faces challenges in handling environmental disturbances from dynamic objects with irregular movement, leading to degradation

Danilo Costarelli, Mariarosaria Natale

In this paper, we introduce the nonlinear exponential Kantorovich sampling series. We establish pointwise and uniform convergence properties and a nonlinear asymptotic formula of the Voronovskaja-type given in terms of the limsup. Furthermore, we extend these convergence results to Mellin-Orlicz spaces with respect to the logarithmic (Haar) measure. Quantita

Dibakar Roychowdhury

We probe four dimensional accelerating black holes with D-brane and build up the notion of metallic holography for spacetime with negative cosmological constant. We explore various thermodynamic entities associated with the boundary QFT at low temperatures and finite chemical potential. The DC conductivity in the boundary QFT is enhanced due to the effects o

Yu Zhou

In extended hearts of bounded $t$-structures on a triangulated category, we provide a Happel-Reiten-Smalo tilting theorem and a characterization for $s$-torsion pairs. Applying these to $m$-extended module categories, we characterize torsion pairs induced by $(m+1)$-term silting complexes. After establishing Auslander-Reiten theory in extended module categor

Pengfei Zhang, Pinxin Liu, Pablo Garrido, Hyeongwoo Kim

Current human motion synthesis frameworks rely on global action descriptions, creating a modality gap that limits both motion understanding and generation capabilities. A single coarse description, such as run, fails to capture details such as variations in speed, limb positioning, and kinematic dynamics, leading to ambiguities between text and motion modali

Hao Chen, YanQing Zou

We prove that the mapping class groups of the genus 3 Heegaard splittings of the connected sum of two lens spaces are finitely generated, and the corresponding reducing sphere complexes are all connected.

Tofara Moyo

We present a novel method for learning hierarchical abstractions that prioritize competing objectives, leading to improved global expected rewards. Our approach employs a secondary rewarding agent with multiple scalar outputs, each associated with a distinct level of abstraction. The traditional agent then learns to maximize these outputs in a hierarchical m

Rohit Dandamudi, Gema Rodríguez-Pérez

Evaluating the performance of Code Language Models (CLMs) for software engineering tasks, especially in multilingual and low-resource programming language settings, poses significant challenges. These challenges are primarily due to the lack of high-quality benchmarks across various programming languages and the imbalanced nature of the CLMs training corpus.

Runfa Blark Li, Keito Suzuki, Bang Du, Ki Myung Brian Lee

Signed distance-radiance field (SDF-NeRF) is a promising environment representation that offers both photo-realistic rendering and geometric reasoning such as proximity queries for collision avoidance. However, the slow training speed and convergence of SDF-NeRF hinder their use in practical robotic systems. We propose SplatSDF, a novel SDF-NeRF architecture

Yingke Ma, Xuetong Zhao, Yaokai Jia, Zhenxian Han

The Genome Warehouse (GWH), accessible at https://ngdc.cncb.ac.cn/gwh, is an extensively utilized public repository dedicated to the deposition, management and sharing of genome assembly sequences, annotations, and metadata. This paper highlights noteworthy enhancements to the GWH since the 2021 version, emphasizing substantial advancements in web interfaces

Chaehun Shin, Jooyoung Choi, Heeseung Kim, Sungroh Yoon

Subject-driven text-to-image generation aims to produce images of a new subject within a desired context by accurately capturing both the visual characteristics of the subject and the semantic content of a text prompt. Traditional methods rely on time- and resource-intensive fine-tuning for subject alignment, while recent zero-shot approaches leverage on-the

Xulin Zhao

Absorption features Ca II NIR and Ca II H&K of type Ia supernovae (SNe Ia) are characterized by their strong high-velocity features (HVFs). We find that, for these two features of calcium there is a puzzling anti-correlation between the line strengths of HVF and photospheric (PHO) components, and an unexpected positive correlation between the velocity differ

Kelly Xiao, Bryce Wong, Jarod Meyer, Leland Nordin

PbSe is a narrow bandgap IV-VI compound semiconductor with application in mid-wave infrared optoelectronics, thermoelectrics, and quantum devices. Alkaline earth or rare earth elements such as Sr and Eu can substitute Pb to widen the bandgap of PbSe in heterostructure devices, but they come with challenges such as deteriorating optical and electronic propert

Koichi Fujii, Tomomi Matsui

Constructing a suitable schedule for sports competitions is a crucial issue in sports scheduling. The round-robin tournament is a competition adopted in many professional sports. For most round-robin tournaments, it is considered undesirable that a team plays consecutive away or home matches; such an occurrence is called a break. Accordingly, it is preferabl

Manuel Tonneau, Diyi Liu, Niyati Malhotra, Scott A. Hale

To address the global challenge of online hate speech, prior research has developed detection models to flag such content on social media. However, due to systematic biases in evaluation datasets, the real-world effectiveness of these models remains unclear, particularly across geographies. We introduce HateDay, the first global hate speech dataset represent

Alexey V. Veryaskin, Howard C. Golden, Khyl J. McMahon, Neil M. Provins

Project TAIPAN has been carried out jointly by Trinity Research Lab and the Frequency and Quantum Metrology Research Group located at the School of Physics, Mathematics and Computing of the University of Western Australia (UWA). Lockheed Martin Corporation (USA) has also been a partner in this joint collaboration providing financial backing to the project an

Shuqi Li, Shufang Xie, Hongda Sun, Yuhan Chen

Traditional drug discovery processes are both time-consuming and require extensive professional expertise. With the accumulation of drug-target interaction (DTI) data from experimental studies, leveraging modern machine-learning techniques to discern patterns between drugs and target proteins has become increasingly feasible. In this paper, we introduce the

Paul Denham, Alex Ody, Pietro Musumeci, Nathan Burger

We present an experimental demonstration of a single-shot, non-destructive electron beam diagnostic based on the ionization of a low-density pulsed gas jet. In our study, 7~MeV electron bunches from a radio frequency (RF) photoinjector, carrying up to 100 pC of charge, traversed a localized distribution of nitrogen gas (N$_2$). The interaction of the electro

Xinqi Liu, Li Zhou, Zikun Zhou, Jianqiu Chen

The vision-language tracking task aims to perform object tracking based on various modality references. Existing Transformer-based vision-language tracking methods have made remarkable progress by leveraging the global modeling ability of self-attention. However, current approaches still face challenges in effectively exploiting the temporal information and

Jiawei E, Yinglong Zhang, Xuewen Xia, Xing Xu

In the field of deep learning, Graph Neural Networks (GNNs) and Graph Transformer models, with their outstanding performance and flexible architectural designs, have become leading technologies for processing structured data, especially graph data. Traditional GNNs often face challenges in capturing information from distant vertices effectively. In contrast,

Yingwen Zhang, Meng Wang, Xihua Sheng, Peilin Chen

Lossy image compression networks aim to minimize the latent entropy of images while adhering to specific distortion constraints. However, optimizing the neural network can be challenging due to its nature of learning quantized latent representations. In this paper, our key finding is that minimizing the latent entropy is, to some extent, equivalent to maximi

Sukhandeep Kaur, Mubashir Buhari, Naman Khandelwal, Priyansh Tyagi

Deepfakes offer great potential for innovation and creativity, but they also pose significant risks to privacy, trust, and security. With a vast Hindi-speaking population, India is particularly vulnerable to deepfake-driven misinformation campaigns. Fake videos or speeches in Hindi can have an enormous impact on rural and semi-urban communities, where digita

Tatsuya Yoshida, Naoki Terada, Kiyoshi Kuramoto

Radiative cooling by molecules is a crucial process for hydrodynamic escape, as it can efficiently remove the thermal energy driving the outflow, acquired through X-ray and extreme UV absorption. Carbon oxides, such as CO and CO2, and their photochemical products are anticipated to serve as vital radiative cooling sources not only in atmospheres dominated by

Jiacheng Lu, Weijian Wang, Mingyuan Xiao, Yang Hua

Accurately predicting the popularity of micro-videos is a critical but challenging task, characterized by volatile, `rollercoaster-like' engagement dynamics. Existing methods often fail to capture these complex temporal patterns, leading to inaccurate long-term forecasts. This failure stems from two fundamental limitations: \ding{172} a superficial under

Eric Hallman

This work derives extremal tail bounds for the Gaussian trace estimator applied to a real symmetric matrix. We define a partial ordering on the eigenvalues, so that when a matrix has greater spectrum under this ordering, its estimator will have worse tail bounds. This is done for two families of matrices: positive semidefinite matrices with bounded effective

Te Yang, Jian Jia, Xiangyu Zhu, Weisong Zhao

Large Language Models (LLMs) have strong instruction-following capability to interpret and execute tasks as directed by human commands. Multimodal Large Language Models (MLLMs) have inferior instruction-following ability compared to LLMs. However, there is a significant gap in the instruction-following capabilities between the MLLMs and LLMs. In this study,

Tomoyuki Kubota, Yusuke Imai, Sumito Tsunegi, Kohei Nakajima

A physical neural network (PNN) has both the strong potential to solve machine learning tasks and intrinsic physical properties, such as high-speed computation and energy efficiency. Reservoir computing (RC) is an excellent framework for implementing an information processing system with a dynamical system by attaching a trained readout, thus accelerating th

Andrei Neverov, Olga Krivorotko

This paper considers the problem of modeling epidemic outbreaks in different regions with a common model, that uses additional information about these regions to adjust its parameters and relieve us of mundanity of data collecting, and inverse problem solving for each region separately. To that end, we study tuberculosis and HIV dynamics in regions of Russia

Steven J. Kuntz, James B. Rawlings

In this technical report, we establish the asymptotic stability of MPC under plant-model mismatch for problems where the origin remains a steady state despite mismatch. This class of problems includes, but is not limited to, inventory management, path-planning, and control of systems in deviation variables. Our results differ from prior results on the inhere

Kiran Sharma, Akshat Nagori, Manya, Mehul Dubey

The Indian Institutes of Technology (IITs) are vital to India's research ecosystem, advancing technology and engineering for industrial and societal benefits. This study reviews the research performance of top IITs-Bombay, Delhi, Madras, Kharagpur, and Kanpur based on Scopus-indexed publications (1952-2024). Research output has grown exponentially, supported

Zhiying Li, Zhi Liu, Dongjie Liu, Shengda Zhuo

Backdoor watermarking has emerged as the predominant approach for protecting public datasets, enabling dataset ownership verification (DOV) through embedded triggers that induce predefined model behaviors. While existing works assume that DOV results can serve as reliable evidence for copyright infringement claims, we argue that this assumption is fundamenta

Haotian Wang, Yuzhe Weng, Yueyan Li, Zilu Guo

Diffusion models have revolutionized the field of talking head generation, yet still face challenges in expressiveness, controllability, and stability in long-time generation. In this research, we propose an EmotiveTalk framework to address these issues. Firstly, to realize better control over the generation of lip movement and facial expression, a Vision-gu

Ales Bouhada, Min Huang, Zetao Lin, Shiping Liu

We discover a new connection between Koszul theory and representation theory. Let $\La$ be a quadratic algebra defined by a locally finite quiver with relations. Firstly, we give a combinatorial description of the local Koszul complexes and the quadratic dual $\La^!$, which enables us to describe the linear projective resolutions and the colinear injective c

Marco A. S. Netto

Cloud platforms are increasingly being used to run HPC workloads. Major cloud providers offer a wide variety of virtual machine (VM) types, enabling users to find the optimal balance between performance and cost. However, this extensive selection of VM types can also present challenges, as users must decide not only which VM types to use but also how many no

Wei Guo, Heng Wang, Jianbo Ma, Weidong Cai

Audio synthesis has broad applications in multimedia. Recent advancements have made it possible to generate relevant audios from inputs describing an audio scene, such as images or texts. However, the immersiveness and expressiveness of the generation are limited. One possible problem is that existing methods solely rely on the global scene and overlook deta

Bingxin Xu, Yuzhang Shang, Yunhao Ge, Qian Lou

Large Multimodal Models (LMMs) have demonstrated impressive capabilities in visual-language tasks but face significant deployment challenges due to their high computational demands. While recent token reduction methods show promise for accelerating LMMs, they typically require extensive retraining or fine-tuning, making them impractical for many state-of-the

Xinyuan Wang, Zhiqiang Meng, Chang Qing Chen

Haptic displays are crucial for facilitating an immersive experience within virtual reality. However, when displaying continuous movements of contact, such as stroking and exploration, pixel-based haptic devices suffer from losing haptic information between pixels, leading to discontinuity. The trade-off between the travel distance of haptic elements and the

Lan-Tian Feng, Ming Zhang, Di Liu, Yu-Jie Cheng

Quantum networks provide a novel framework for quantum information processing, significantly enhancing system capacity through the interconnection of modular quantum nodes. Beyond the capability to distribute quantum states, the ability to remotely control quantum gates is a pivotal step for quantum networks. In this Letter, we implement high fidelity quantu

Ying Liu, Wei-Yong Zhang, Zi-Hang Zhu, Ming-Gen He

String breaking is a fundamental concept in gauge theories, describing the decay of a flux string connecting two charges through the production of particle-antiparticle pairs. This phenomenon is particularly important in particle physics, notably in Quantum Chromodynamics, and plays a crucial role in condensed matter physics. However, achieving a theoretical

Dahye Kim, Xavier Thomas, Deepti Ghadiyaram

We study $\textit{how}$ rich visual semantic information is represented within various layers and denoising timesteps of different diffusion architectures. We uncover monosemantic interpretable features by leveraging k-sparse autoencoders (k-SAE). We substantiate our mechanistic interpretations via transfer learning using light-weight classifiers on off-the-

Mohammad Shahidzadeh, Behnam Ghavami, Steve Wilton, Lesley Shannon

Formal Property Verification (FPV), using SystemVerilog Assertions (SVA), is crucial for ensuring the completeness of design with respect to the specification. However, writing SVA is a laborious task and has a steep learning curve. In this work, we present a large language model (LLM) -based flow to automatically generate high-quality SVA from the design sp

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

A study of $\it{\Lambda}_{\it{b}}^\rm{0}$ and $\it{\Xi}_{\it{b}}^\rm{0}$ decays to $\it{\Lambda} h^{+} h^{\prime -}$ $(h^{(\prime)}=\pi, K)$ is performed using $pp$ collision data collected by the LHCb experiment during LHC Runs 1$-$2, corresponding to an integrated luminosity of $9~\rm{fb}^{-1}$. The branching fractions for these decays are measured using t

Aniruddha Saha, Sadaf Sobhani

The dynamics of capillary rise under different geometric and fluid conditions have the common signatures of rapid rise followed by an equilibrium state that describe the underlying competing forces. We present a new interpretation of capillary dynamics using a linear damped system where modulation of damping and forcing characteristics are achieved using axi

Zhiying Li, Zhi Liu, Guanggang Geng, Shreyank N Gowda

Object detectors, which are widely used in real-world applications, are vulnerable to backdoor attacks. This vulnerability arises because many users rely on datasets or pre-trained models provided by third parties due to constraints on data and resources. However, most research on backdoor attacks has focused on image classification, with limited investigati

Jiayi Chen, Chen Wu, Shaoqun Zhang, Nan Li

Embedding models have become essential tools in both natural language processing and computer vision, enabling efficient semantic search, recommendation, clustering, and more. However, the high memory and computational demands of full-precision embeddings pose challenges for deployment in resource-constrained environments, such as real-time recommendation sy