November 2024 arXiv papers — page 174

Simon Kloker, Alex Cedric Luyima, Matthew Bazanya

This paper introduces WASHtsApp, a WhatsApp-based chatbot designed to educate rural African communities on clean water access, sanitation, and hygiene (WASH) principles. WASHtsApp leverages a Retrieval-Augmented Generation (RAG) approach to address the limitations of previous approaches with limited reach or missing contextualization. The paper details the d

Arnab Mukherjee, Sunandan Gangopadhyay, A. S. Majumdar

We investigate relativistic effects on the performance of a quantum battery in an open quantum framework. We consider an Unruh-DeWitt detector driven by a coherent classical pulse as a quantum battery that is interacting with a massless scalar field through a quadratic coupling. The battery follows a trajectory composed of uniform acceleration along one dire

Yuan Xie, Ji Xu, Jiawei Ren, Junfeng Li

Underwater acoustic target recognition based on passive sonar faces numerous challenges in practical maritime applications. One of the main challenges lies in the susceptibility of signal characteristics to diverse environmental conditions and data acquisition configurations, which can lead to instability in recognition systems. While significant efforts hav

Qixun Wang, Yifei Wang, Yisen Wang, Xianghua Ying

Enhancing node-level Out-Of-Distribution (OOD) generalization on graphs remains a crucial area of research. In this paper, we develop a Structural Causal Model (SCM) to theoretically dissect the performance of two prominent invariant learning methods -- Invariant Risk Minimization (IRM) and Variance-Risk Extrapolation (VREx) -- in node-level OOD settings. Ou

Sun-Sig Byun, Hongsoo Kim, Jehan Oh

We establish interior $W^{2,\delta}$ type estimates for a class of degenerate fully nonlinear elliptic equations with $L^n$ data. The main idea of our approach is to slide $C^{1,\alpha}$ cones, instead of paraboloids, vertically to touch the solution, and estimate the contact set in terms of the measure of the vertex set. This shows that the solution has tan

Soh Kumabe

Let $\mathcal{D}$ be a set family that is the solution domain of some combinatorial problem. The \emph{max-min diversification problem on $\mathcal{D}$} is the problem to select $k$ sets from $\mathcal{D}$ such that the Hamming distance between any two selected sets is at least $d$. FPT algorithms parameterized by $k+\ell $, where $\ell=\max_{D\in \mathcal{D

Matthias Bartolo, Dylan Seychell

As object detection techniques continue to evolve, understanding their relationships with complementary visual tasks becomes crucial for optimising model architectures and computational resources. This paper investigates the correlations between object detection accuracy and two fundamental visual tasks: depth prediction and visual saliency prediction. Throu

Lei Wang, Weiming Zeng, Kai Long, Hongyu Chen

Photoacoustic imaging (PAI) represents an innovative biomedical imaging modality that harnesses the advantages of optical resolution and acoustic penetration depth while ensuring enhanced safety. Despite its promising potential across a diverse array of preclinical and clinical applications, the clinical implementation of PAI faces significant challenges, in

David Rodríguez Rueda, Carlos Cotta, Antonio J. Fernández-Leiva

The template design problem (TDP) is a hard combinatorial problem with a high number of symmetries which makes solving it more complicated. A number of techniques have been proposed in the literature to optimise its resolution, ranging from complete methods to stochastic ones. However, although metaheuristics are considered efficient methods that can find en

Jiří Rosický

We show that, under certain assumptions, strongly finitary enriched monads are given by discrete enriched Lawvere theories. On the other hand, monads given by discrete enriched Lawvere theories preserve surjections.

Bing Cao, Yinan Xia, Yi Ding, Changqing Zhang

The inherent challenge of image fusion lies in capturing the correlation of multi-source images and comprehensively integrating effective information from different sources. Most existing techniques fail to perform dynamic image fusion while notably lacking theoretical guarantees, leading to potential deployment risks in this field. Is it possible to conduct

Chenhui Wang, Yongping Zhang, V. V. Konotop

We show that transfer of matter-wave solitons and half-vortex solitons in a spin-orbit coupled Bose-Einstein condensate between two (or more) arbitrarily chosen sites of an optical lattice can be implemented using the adiabatic passage. The underlying linear Hamiltonian has a flat band in its spectrum, so that even sufficiently weak inter-atomic interactions

Daisuke Kishimoto, Yichen Tong

The fundamental group of a directed graph admits a natural sequence of quotient groups called $r$-fundamental groups, and the $r$-fundamental groups can capture properties of a directed graph that the fundamental group cannot capture. The fundamental group of a directed graph is related to path homology through the Hurewicz theorem. The magnitude-path spectr

Wei Huang, Andi Han, Yongqiang Chen, Yuan Cao

Multi-modal contrastive learning with language supervision has presented a paradigm shift in modern machine learning. By pre-training on a web-scale dataset, multi-modal contrastive learning can learn high-quality representations that exhibit impressive robustness and transferability. Despite its empirical success, the theoretical understanding is still in i

Yun-Han Gui, Mao-Zhi Yang

We study the decay processes of $B\rightarrow \rho\eta$, $\rho\eta^{\prime}$ in the perturbative quantum chromodynamics (QCD) approach with a few improvements incorporated in it, where the contributions with large momentum transfer are calculated perturbatively, and the contributions with lower energy scale are treated by introducing soft transition form fac

Ludovic Stephan, Yizhe Zhu

The Bethe-Hessian matrix, introduced by Saade, Krzakala, and Zdeborov\'a (2014), is a Hermitian matrix designed for applying spectral clustering algorithms to sparse networks. Rather than employing a non-symmetric and high-dimensional non-backtracking operator, a spectral method based on the Bethe-Hessian matrix is conjectured to also reach the Kesten-Stigum

Jiahui Liu, Jesper Byggmastar, Zheyong Fan, Bing Bai

High-entropy alloys (HEAs) based on tungsten (W) have emerged as promising candidates for plasma-facing components in future fusion reactors, owing to their excellent irradiation resistance. In this study, we construct an efficient machine-learned interatomic potential for the MoNbTaVW quinary system. This potential achieves computational speeds comparable t

Sayanta Adhikari, Rishav Kumar, Konda Reddy Mopuri, Rajalakshmi Pachamuthu

Contextual information plays a critical role in object recognition models within computer vision, where changes in context can significantly affect accuracy, underscoring models' dependence on contextual cues. This study investigates how context manipulation influences both model accuracy and feature attribution, providing insights into the reliance of objec

Hossein Hosseini, Mohammad Sobhan Zare, Amir Hossein Mohammadi, Arefeh Kazemi

Retrieval augmented generation (RAG) models, which integrate large-scale pre-trained generative models with external retrieval mechanisms, have shown significant success in various natural language processing (NLP) tasks. However, applying RAG models in Persian language as a low-resource language, poses distinct challenges. These challenges primarily involve

Taseen Mubassira, Mehedi Hasan, A. B. M. Alim Al Iislam

The research community has traditionally shown a keen interest in emotion modeling, with a notable emphasis on the detection aspect. In contrast, the exploration of emotion generation has received less attention.This study delves into an existing state-of-the-art emotional chatbot, EmoBot, designed for generating emotions in general-purpose conversations. Th

Giwon Hong, Emile van Krieken, Edoardo Ponti, Nikolay Malkin

In-context learning (ICL) adapts LLMs by providing demonstrations without fine-tuning the model parameters; however, it does not differentiate between demonstrations and quadratically increases the complexity of Transformer LLMs, exhausting the memory. As a solution, we propose Mixtures of In-Context Learners (MoICL), a novel approach to treat subsets of dem

Hongpeng Jin, Yanzhao Wu

Large Language Models (LLMs) exhibit remarkable human-like predictive capabilities. However, it is challenging to deploy LLMs to provide efficient and adaptive inference services at the edge. This paper proposes a novel Cloud-Edge Collaboration framework for LLMs (CE-CoLLM) to tackle these challenges. First, we identify the transmission of LLM contextual dat

Fattah Sakuldee, Mehdi Abdi

We develop a method for entangling operations on nuclear spins surrounding a negatively charged boron vacancy (VB-center) point defect in hexagonal boron nitride (hBN). To this end, we propose to employ the electron spin of a VB-center as a control qubit. We show that in the presence of a background magnetic field and by applying control pulses, one can coll

Ahmet M. Elbir, Abdulkadir Celik, Ahmed M. Eltawil

Integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize spectrum and thereby save cost and power. At the higher end of the spectrum, ISAC systems operate at wideband using large antenna arrays to meet the stringent demands for high-resolution sensing and enhanced communications capacity. On the other hand, the overall desig

Yuheng Liang, Lvchang Li, Haichou Li

The Korenblum space, often referred to as a growth space, is a special type of analytic function space. This paper investigates the properties of the difference of composition operators on the Korenblum space over the product of upper half planes, characterizing their boundedness and compactness. Using the result on boundedness, we show that all bounded diff

Abhik Bhattacharjee, Joydeep Naskar

In this paper we investigate the code properties of holographic fractal geometries initiated in \cite{Pastawski:2016qrs}. We study reconstruction wedges in $AdS_3/CFT_2$ for black hole backgrounds, which are in qualitative agreement with the vacuum-AdS approximation using generalized entanglement entropy in \cite{Bao:2022tgv}. In higher dimensions, we study

Minseon Gwak, Seongrok Moon, Joohwan Ko, PooGyeon Park

Due to the lack of state dimension optimization methods, deep state space models (SSMs) have sacrificed model capacity, training search space, or stability to alleviate computational costs caused by high state dimensions. In this work, we provide a structured pruning method for SSMs, Layer-Adaptive STate pruning (LAST), which reduces the state dimension of e

Zhihao Zhu, Yi Yang, Defu Lian

Training Data Detection (TDD) is a task aimed at determining whether a specific data instance is used to train a machine learning model. In the computer security literature, TDD is also referred to as Membership Inference Attack (MIA). Given its potential to assess the risks of training data breaches, ensure copyright authentication, and verify model unlearn

Mehrdad Najafpour

We construct new examples of constant scalar curvature K\"{a}hler metrics on suitable resolutions of certain constant scalar curvature K\"{a}hler orbifolds with type I singularities, in the sense of Apostolov--Rollin, along a suborbifold of complex codimension greater than 2.

Somnath Buriuly, Leena Vachhani, Sivapragasam Ravitharan, Arpita Sinha

The Rural Postman Problem with Temporal Unavailability (RPP-TU) is a variant of the Rural Postman Problem (RPP) specified for multi-agent planning over directed graphs with temporal constraints. These temporal constraints represent the unavailable time intervals for each arc during which agents cannot traverse the arc. Such arc unavailability scenarios occur

Zhanwei Zhang, Shizhao Sun, Wenxiao Wang, Deng Cai

Recently, there is a growing interest in creating computer-aided design (CAD) models based on user intent, known as controllable CAD generation. Existing work offers limited controllability and needs separate models for different types of control, reducing efficiency and practicality. To achieve controllable generation across all CAD construction hierarchies

Mithilesh Kumar, Daniel Lokshtanov

A {\em bipartite tournament} is a directed graph $T:=(A \cup B, E)$ such that every pair of vertices $(a,b), a\in A,b\in B$ are connected by an arc, and no arc connects two vertices of $A$ or two vertices of $B$. A {\em feedback vertex set} is a set $S$ of vertices in $T$ such that $T - S$ is acyclic. In this article we consider the {\sc Feedback Vertex Set}

Yuhan Liu, Yuyang Huang, Jiayi Yao, Shaoting Feng

Compound AI systems, such as agentic systems, are an emerging trend in large-scale enterprise settings, with multiple LLMs specialized for different users, tasks, and/or roles working together. In these scenarios, different models often process inputs that share the same context prefix. Although much work was done in the past to enable the reuse of prefix KV

Tali Kaufman, Izhar Oppenheim, Shmuel Weinberger

Coboundary expansion is a high dimensional generalization of the Cheeger constant to simplicial complexes. Originally, this notion was motivated by the fact that it implies topological expansion, but nowadays a significant part of the motivation stems from its deep connection to problems in theoretical computer science such as agreement expansion in the low

Qin Liu, Jianfeng Wang, Zhengyuan Yang, Linjie Li

Semi-supervised video object segmentation (VOS) has been largely driven by space-time memory (STM) networks, which store past frame features in a spatiotemporal memory to segment the current frame via softmax attention. However, STM networks face memory limitations due to the quadratic complexity of softmax matching, restricting their applicability as video

Abu Saleh Musa Miah, Md Mahbubur Rahman Tusher, Md. Moazzem Hossain, Md Mamun Hossain

In recent years, advanced artificial intelligence technologies, such as ChatGPT, have significantly impacted various fields, including education and research. Developed by OpenAI, ChatGPT is a powerful language model that presents numerous opportunities for students and educators. It offers personalized feedback, enhances accessibility, enables interactive c

Liang Qiu, Wenhao Chi, Xiaohan Xing, Praveenbalaji Rajendran

Precision therapy for liver cancer necessitates accurately delineating liver sub-regions to protect healthy tissue while targeting tumors, which is essential for reducing recurrence and improving survival rates. However, the segmentation of hepatic segments, known as Couinaud segmentation, is challenging due to indistinct sub-region boundaries and the need f

Valerica Raicu

The inhomogeneous wave equations for the scalar, vector, and Hertz potentials are derived starting from retarded charge, current, and polarization densities and then solved in the reciprocal (or k-) space to obtain general solutions, which are formulated as nested integrals of such densities over the source volume, k-space, and time. The solutions thus obtai

Zixuan Wang, Suyash Mahar, Luyi Li, Jangseon Park

We present a thorough analysis of the use of modern heterogeneous systems interconnected by various cachecoherent links, including CXL, NVLink-C2C, and Infinity Fabric. We studied a wide range of server systems that combined CPUs from different vendors and various types of coherent memory devices, including CXL memory expander, CXL pool, CXL shared memory, G

Fangfei Lan, Malin Ejdbo, Joachim Moeyens, Bei Wang

We introduce NEOviz, an interactive visualization system designed to assist planetary defense experts in the visual analysis of the movements of near-Earth objects in the Solar System that might prove hazardous to Earth. Asteroids are often discovered using optical telescopes and their trajectories are calculated from images, resulting in an inherent asymmet

Shuo-Chieh Huang, Tengyuan Liang, Ruey S. Tsay

Missing data can significantly hamper standard time series analysis, yet they occur frequently in applications. In this paper, we introduce temporal Wasserstein imputation, a novel method for imputing missing data in time series. Unlike most existing techniques, our approach is fully nonparametric, circumventing the need for model specification prior to impu

Muhammad Tayyab Khan, Lequn Chen, Ye Han Ng, Wenhe Feng

Automatic feature recognition (AFR) is essential for transforming design knowledge into actionable manufacturing information. Traditional AFR methods, which rely on predefined geometric rules and large datasets, are often time-consuming and lack generalizability across various manufacturing features. To address these challenges, this study investigates visio

Jinyin Chen, Wenbo Mu, Luxin Zhang, Guohan Huang

Graph neural network (GNN) has captured wide attention due to its capability of graph representation learning for graph-structured data. However, the distributed data silos limit the performance of GNN. Vertical federated learning (VFL), an emerging technique to process distributed data, successfully makes GNN possible to handle the distributed graph-structu

Nobuhito Maru, Akio Nago

We study the electroweak symmetry breaking in a five dimensional $Sp(6)$ gauge-Higgs unification model where the weak mixing angle is predicted to be $\sin^2 \theta_W=1/4$ at the compactification scale. We find that the correct pattern of electroweak symmetry breaking and a viable Higgs mass are realized by introducing a 4-rank totally symmetric representati

Yansong David Wang, Tao Louie Xu, Cheng Yuan

This research, employing the Alkire-Foster approach to uncover multidimensional poverty between 2012 and 2020 in China, models and examines the sustainable effects and mechanisms of the three-pillar pension system in household poverty mitigation with the China Family Panel Studies data. The results evince that more participation in the pension system mitigat

Warin Watson, Cash Cherry, Rachelle Lang

We demonstrate that automatic differentiation (AD), which has become commonly available in machine learning frameworks, is an efficient way to explore ideas that lead to algorithmic improvement in multi-scale affine image registration and affine super-resolution problems. In our first experiment on multi-scale registration, we implement an ODE predictor-corr

Daehwan Kim, Hyungmin Kim, Daun Jeong, Sungho Suh

In this paper, we propose SPACE, a novel anomaly detection methodology that integrates a Feature Encoder (FE) into the structure of the Student-Teacher method. The proposed method has two key elements: Spatial Consistency regularization Loss (SCL) and Feature converter Module (FM). SCL prevents overfitting in student models by avoiding excessive imitation of

Scott Seidenberger, Marc Beret, Raveen Wijewickrama, Murtuza Jadliwala

We introduce NinjaDoH, a novel DNS over HTTPS (DoH) protocol that leverages the InterPlanetary Name System (IPNS), along with public cloud infrastructure, to create a censorship-resistant moving target DoH service. NinjaDoH is specifically designed to evade traditional censorship methods that involve blocking DoH servers by IP addresses or domains by continu

Ayush Jha, Abootaleb Shirvani, Svetlozar T. Rachev, Frank J. Fabozzi

We introduce a new identification strategy for uncertainty shocks to explain macroeconomic volatility in financial markets. The Chicago Board Options Exchange Volatility Index (VIX) measures market expectations of future volatility, but traditional methods based on second-moment shocks and time-varying volatility of the VIX often fail to capture the non-Gaus

Surojit Ghosh, Bikramjit Kundu

This article explores equivariant localization in the category of $G$-spaces, where $G$ is a compact Lie group. We establish a commutation rule for the localization functor and the equivariant loop functor. Additionally, we introduce and classify certain good objects in this category up to their Bredon cohomology with coefficients in the constant rational Ma

Spyros Alexakis, Zhongshan An, Ahmed Ellithy, Lan-Hsuan Huang

We obtain existence and local uniqueness of asymptotically flat, static vacuum extensions for Bartnik data on a sphere near the data of a sphere of symmetry in a Schwarzschild manifold.

Ahmed Ellithy

We establish the local well-posedness of the Bartnik static metric extension problem for arbitrary Bartnik data that perturb that of any sphere in a Schwarzschild $\{t=0\}$ slice. Our result in particular includes spheres with arbitrary small mean curvature. We introduce a new framework to this extension problem by formulating the governing equations in a ge

Viktoriya Petrakova, Olga Krivorotko

The paper describes and compares three approaches to modeling an epidemic spread. The first approach is a well-known system of SIR ordinary differential equations. The second is a mean-field model, in which an isolation strategy for each epidemiological group (Susceptible, Infected, and Removed) is chosen as an optimal control. The third is another meanfield

Yuka Ogino, Yuho Shoji, Takahiro Toizumi, Atsushi Ito

We propose an image-adaptive object detection method for adverse weather conditions such as fog and low-light. Our framework employs differentiable preprocessing filters to perform image enhancement suitable for later-stage object detections. Our framework introduces two differentiable filters: a B\'ezier curve-based pixel-wise (BPW) filter and a kernel-base

Muhtadi Choudhury, Minyan Gao, Avinash Varna, Elad Peer

Finite state machines (FSMs) regulate sequential circuits, including access to sensitive information and privileged CPU states. Courtesy of contemporary research on laser attacks, laser-based fault injection (LFI) is becoming even more precise where an adversary can thwart chip security by altering individual flip-flop (FF) values. Different laser models, e.

Qidong Zhao, Hao Wu, Yuming Hao, Zilingfeng Ye

Effective performance profiling and analysis are essential for optimizing training and inference of deep learning models, especially given the growing complexity of heterogeneous computing environments. However, existing tools often lack the capability to provide comprehensive program context information and performance optimization insights for sophisticate

Zongzhe Xu, Ritvik Gupta, Wenduo Cheng, Alexander Shen

Following its success for vision and text, the "foundation model" (FM) paradigm -- pretraining large models on massive data, then fine-tuning on target tasks -- has rapidly expanded to domains in the sciences, engineering, healthcare, and beyond. Has this achieved what the original FMs accomplished, i.e. the supplanting of traditional supervised learning in

Akul Datta

This paper reviews the development of the Receptance Weighted Key Value (RWKV) architecture, emphasizing its advancements in efficient language modeling. RWKV combines the training efficiency of Transformers with the inference efficiency of RNNs through a novel linear attention mechanism. We examine its core innovations, adaptations across various domains, a

Xu Han, Junyu Gao, Chuang Yang, Yuan Yuan

Texts on the intelligent transportation scene include mass information. Fully harnessing this information is one of the critical drivers for advancing intelligent transportation. Unlike the general scene, detecting text in transportation has extra demand, such as a fast inference speed, except for high accuracy. Most existing real-time text detection methods

Mingcheng Li, Dingkang Yang, Yang Liu, Shunli Wang

Multimodal Sentiment Analysis (MSA) is an important research area that aims to understand and recognize human sentiment through multiple modalities. The complementary information provided by multimodal fusion promotes better sentiment analysis compared to utilizing only a single modality. Nevertheless, in real-world applications, many unavoidable factors may

Nodoka Yamanaka

We propose a mechanism which explains the masses of $\eta$ and $\eta'$ mesons without invoking the explicit violation of $U(1)_A$ symmetry by the chiral anomaly. It is shown that the U(1) problem, the problem for which the prediction of $\eta$ and $\eta'$ masses in the simple chiral perturbation theory largely deviates from the experimental values, is actual

Jianqiao Wangni

This paper introduces a novel framework that leverages large language models (LLMs) for machine translation (MT). We start with one conjecture: an ideal translation should contain complete and accurate information for a strong enough LLM to recover the original sentence. We generate multiple translation candidates from a source language A to a target languag

Sheshera Mysore, Garima Dhanania, Kishor Patil, Surya Kallumadi

Personalized search is a problem where models benefit from learning user preferences from per-user historical interaction data. The inferred preferences enable personalized ranking models to improve the relevance of documents for users. However, personalization is also seen as opaque in its use of historical interactions and is not amenable to users' control

Chak Lam Shek, Kasra Torshizi, Troi Williams, Pratap Tokekar

In a standard navigation pipeline, a robot localizes at every time step to lower navigational errors. However, in some scenarios, a robot needs to selectively localize when it is expensive to obtain observations. For example, an underwater robot surfacing to localize too often hinders it from searching for critical items underwater, such as black boxes from

Yuan Xie, Jiawei Ren, Junfeng Li, Ji Xu

Underwater acoustic target recognition has emerged as a prominent research area within the field of underwater acoustics. However, the current availability of authentic underwater acoustic signal recordings remains limited, which hinders data-driven acoustic recognition models from learning robust patterns of targets from a limited set of intricate underwate

Amanda Gefter

This paper began as a set of notes introducing quantum physicists of the QBist persuasion to enactive theory. Unlike mainstream cognitive science, which views cognition as computations on internal representations of the external world (and thus the mind as in the head), the enactive approach sees cognition as adaptive, embodied action. Enaction can ground co

Hui Li

We derive a new version of the von K\'arm\'an energy and the corresponding Euler-Langrange equations, in the context of thin prestrained plates, under the condition of incompressibility relative to the given prestrain. Our derivation uses the theory of $\Gamma$-convergence in the calculus of variations, building on prior techniques in [Conti, Dolzmann (2009)

Jason Vega, Junsheng Huang, Gaokai Zhang, Hangoo Kang

Safety alignment of Large Language Models (LLMs) has recently become a critical objective of model developers. In response, a growing body of work has been investigating how safety alignment can be bypassed through various jailbreaking methods, such as adversarial attacks. However, these jailbreak methods can be rather costly or involve a non-trivial amount

Xuewei Cheng, Ke Huang, Shujie Ma

Recurrent Neural Networks (RNNs) have achieved great success in the prediction of sequential data. However, their theoretical studies are still lagging behind because of their complex interconnected structures. In this paper, we establish a new generalization error bound for vanilla RNNs, and provide a unified framework to calculate the Rademacher complexity

David Mayo, Christopher Wang, Asa Harbin, Abdulrahman Alabdulkareem

When evaluating stimuli reconstruction results it is tempting to assume that higher fidelity text and image generation is due to an improved understanding of the brain or more powerful signal extraction from neural recordings. However, in practice, new reconstruction methods could improve performance for at least three other reasons: learning more about the

Xiao-Ming Zhang

As standard data loading processes, quantum state preparation and block-encoding are critical and necessary processes for quantum computing applications, including quantum machine learning, Hamiltonian simulation, and many others. Yet, existing protocols suffer from poor robustness under device imperfection, thus limiting their practicality for real-world ap

Ao Zhang, Yanjie Zhang, Sanyang Zhai, Li Lin

This article discusses the weak pullback attractors for a damped stochastic fractional Schr\"odinger equation on $\mathbb{R}^n$ with $n\geq 2$. By utilizing the stochastic Strichartz estimates and a stopping time technique argument, the existence and uniqueness of a global solution for the systems with the nonlinear term $|u|^{2\sigma}u$ are proven. Furtherm

Giannis Daras, Yeshwanth Cherapanamjeri, Constantinos Daskalakis

The quality of generative models depends on the quality of the data they are trained on. Creating large-scale, high-quality datasets is often expensive and sometimes impossible, e.g. in certain scientific applications where there is no access to clean data due to physical or instrumentation constraints. Ambient Diffusion and related frameworks train diffusio

Zhanshan Qiao

With the ongoing increase in the worldwide population and escalating consumption habits,there's a surge in the amount of waste produced.The situation poses considerable challenges for waste management and the optimization of recycling operations.The research tackles the pressing issue of waste classification for recycling by analyzing various deep learning m

Siyuan Chen, Karan Jani

The coalescence of binary black holes and neutron stars increases the entropy in the universe. The release of entropy from the inspiral stage to the merger depends primarily on the mass and spin vectors of the compact binary. In this study, we report a novel application of entropy to study the demographics of the compact binaries reported by the LIGO-Virgo-K

Hui Li

We derive the variational limiting theory of thin films, parallel to the F\"oppl-von K\'arm\'an theory in the nonlinear elasticity, for films that have been prestrained and whose thickness is a general non-constant function. Using $\Gamma$-convergence, we extend the existing results to the variable thickness setting, calculate the associated Euler-Lagrange e

Weiheng Wu, Wei Qiao, Wenhao Yan, Bo Jiang

Advanced Persistent Threats (APTs) are continuously evolving, leveraging their stealthiness and persistence to put increasing pressure on current provenance-based Intrusion Detection Systems (IDS). This evolution exposes several critical issues: (1) The dense interaction between malicious and benign nodes within provenance graphs introduces neighbor noise, h

Jan Hamann, Yuqi Kang

The lensing convergence field describing the weak lensing effect of the Cosmic Microwave Background (CMB) radiation is expected to be subject to mild deviations from Gaussianity. We perform a suite of full-sky lensing simulations using ray tracing through multiple lens planes - generated by combining $N$-body simulations on smaller scales and low-to-intermed

Duong H. Nguyen, Phi L. Nguyen, Truong T. Nguyen, Hieu H. Pham

Federated Learning (FL) is a machine learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Despite its promises, FL is prone to critical security risks. First, because FL depends on a central server to aggregate local training models, this is a single point of failure

Mohamed Samshad, Ketan Rajawat

This paper presents a communication and energy-aware multi-UAV Coverage Path Planning (mCPP) method for scenarios requiring continuous inter-UAV communication, such as cooperative search and rescue and surveillance missions. Unlike existing mCPP solutions that focus on energy, time, or coverage efficiency, the proposed method generates coverage paths that mi

Lars van der Laan, Alex Luedtke, Marco Carone

Doubly robust estimators are widely used for estimating average treatment effects and other linear summaries of regression functions. While consistency requires only one of two nuisance functions to be estimated consistently, asymptotic normality for linear functionals typically requires sufficiently fast convergence of both. We address this mismatch by show

Nicolas Langrené, Xavier Warin, Pierre Gruet

Rahimi and Recht (2007) introduced the idea of decomposing positive definite shift-invariant kernels by randomly sampling from their spectral distribution for machine learning applications. This famous technique, known as Random Fourier Features (RFF), is in principle applicable to any such kernel whose spectral distribution can be identified and simulated.

Reyhaneh Hosseinpourkhoshkbari, Richard M. Golden

If the probability model is correctly specified, then we can estimate the covariance matrix of the asymptotic maximum likelihood estimate distribution using either the first or second derivatives of the likelihood function. Therefore, if the determinants of these two different covariance matrix estimation formulas differ this indicates model misspecification

Siriwiwat Lata, Sirawan Phiphitphatphaisit, Emmanuel Okafor, Olarik Surinta

The Thai One-Stage Fingerspelling (One-Stage-TFS) dataset is a comprehensive resource designed to advance research in hand gesture recognition, explicitly focusing on the recognition of Thai sign language. This dataset comprises 7,200 images capturing 15 one-stage consonant gestures performed by undergraduate students from Rajabhat Maha Sarakham University,

Uday Kiran Reddy Tadipatri, Benjamin D. Haeffele, Joshua Agterberg, René Vidal

We propose a general framework for deriving generalization bounds for parallel positively homogeneous neural networks--a class of neural networks whose input-output map decomposes as the sum of positively homogeneous maps. Examples of such networks include matrix factorization and sensing, single-layer multi-head attention mechanisms, tensor factorization, d

Javad A. Asadzade, Nazim I. Mahmudov

Several dynamical systems in fields such as engineering, chemistry, biology, and physics show impulsive behavior by reason of unexpected changes at specific times. These behaviors are described by differential systems under impulse effects. The current paper examines approximate controllability for semi-linear impulsive differential and neutral differential

Lidia Angeleri Hügel, Marcelo Lanzilotta, Jifen Liu, Sonia Trepode

We consider endomorphism algebras of $n$-term silting complexes in derived categories of hereditary algebras, and we show that the module category of such an endomorphism algebra has a separated $n$-section. For $n=3$ we obtain a trisection in the sense of [2].

Misha Ivkov, Tselil Schramm

We give a fast, spectral procedure for implementing approximate-message passing (AMP) algorithms robustly. For any quadratic optimization problem over symmetric matrices $X$ with independent subgaussian entries, and any separable AMP algorithm $\mathcal A$, our algorithm performs a spectral pre-processing step and then mildly modifies the iterates of $\mathc

Sangdaow Noppitak, Emmanuel Okafor, Olarik Surinta

The EcoCropsAID dataset is a comprehensive collection of 5,400 aerial images captured between 2014 and 2018 using the Google Earth application. This dataset focuses on five key economic crops in Thailand: rice, sugarcane, cassava, rubber, and longan. The images were collected at various crop growth stages: early cultivation, growth, and harvest, resulting in

Miguel Garrido, Pau Martín, Jaime Paradela

In the 70s McGehee introduced a compactification of the phase space of the restricted 3-body problem by gluing a manifold of periodic orbits "at infinity". Although from the dynamical point of view these periodic orbits are parabolic (the linearization of the Poincar\'{e} map is the identity matrix), one of them, denoted here by $O$, possesses stable and uns

Mahmood Etedadialiabadi, Su Gao, Feng Li, Ruiwen Li

In this paper we address the question: How many pairwise non-isomorphic extremely amenable groups are there which are separable metrizable or even Polish? We show that there are continuum many such groups. In fact we construct continuum many pairwise non-isomorphic extremely amenable groups as automorphism groups of countable structures. We also consider thi

C. Wolf, S. Lai, J. -J. Tang, J. Tonry

Characteristic time scales in the stochastic UV-optical variability of quasars may depend on the mass of their black holes, $M_{\rm BH}$, as much as physical timescales in their accretion discs do. We calculate emission-weighted mean radii, $R_{\rm mean}$, and orbital timescales, $t_{\rm mean}$, of standard thin disc models for emission wavelengths $\lambda$

Zhongling Huang, Xidan Zhang, Zuqian Tang, Feng Xu

SAR images possess unique attributes that present challenges for both human observers and vision AI models to interpret, owing to their electromagnetic characteristics. The interpretation of SAR images encounters various hurdles, with one of the primary obstacles being the data itself, which includes issues related to both the quantity and quality of the dat

Yuan Xie, Xiaowei Zhang, Jiawei Ren, Ji Xu

Building a robust underwater acoustic recognition system in real-world scenarios is challenging due to the complex underwater environment and the dynamic motion states of targets. A promising optimization approach is to leverage the intrinsic physical characteristics of targets, which remain invariable regardless of environmental conditions, to provide robus

Kuan Jia, Dingcheng Yang, Yapeng Wang, Tianyun Shui

This paper considers a patrol inspection scenario where multiple unmanned aerial vehicles (UAVs) are adopted to traverse multiple predetermined cruise points for data collection. The UAVs are connected to cellular networks and they would offload the collected data to the ground base stations (GBSs) for data processing within the constrained duration. This pa

Yue Qin, Gabriel G. Katul, Heping Liu, Dan Li

The attached-eddy model (AEM) predicts mean velocity and streamwise velocity variance profiles that follow a logarithmic shape in the overlap region of high Reynolds number wall-bounded turbulent flows. Moreover, the AEM coefficients are presumed to attain asymptotically constant values at very high Reynolds numbers. Here, the logarithmic behaviour of the AE

Michelle Leeberg, Xianghua Luo, Thomas A. Murray

We propose a restricted win probability estimand for comparing treatments in a randomized trial with a time-to-event outcome. We also propose Bayesian estimators for this summary measure as well as the unrestricted win probability. Bayesian estimation is scalable and facilitates seamless handling of censoring mechanisms as compared to related non-parametric

A. Bojtor, D. Krisztian, F. Korsos, S. Kollarics

The measurement and description of the charge-carrier lifetime (tauc) is crucial for the wide-ranging applications of lead-halide perovskites. We present time-resolved microwave-detected photoconductivity decay (TRMCD) measurements and a detailed analysis of the possible recombination mechanisms including trap-assisted, radiative, and Auger recombination. We

Pedro R. A. S. Bassi, Qilong Wu, Wenxuan Li, Sergio Decherchi

As medical datasets rapidly expand, creating detailed annotations of different body structures becomes increasingly expensive and time-consuming. We consider that requesting radiologists to create detailed annotations is unnecessarily burdensome and that pre-existing AI models can largely automate this process. Following the spirit don't use a sledgehammer o

Mingjing Chen, Yufei Zhang, Wenjuan Fang, Zhonglue Wen

With the advent of next-generation surveys, constraints on cosmological parameters are anticipated to become more stringent, particularly for the total neutrino mass. This study forecasts such constraints utilizing galaxy clusters from the Chinese Space Station Telescope (CSST). Employing Fisher matrix techniques, we derive the constraint $\sigma(M_\nu)$ fro