December 2024 arXiv papers — page 209

Wei Zhu, Binbin Li, Zuo Zhu

The current Bayesian FFT algorithm relies on direct differentiation to obtain the posterior covariance matrix (PCM), which is time-consuming, memory-intensive, and hard to code, especially for the multi-setup operational modal analysis (OMA). Aiming at accelerating the uncertainty quantification in multi-setup OMA, an expectation-maximization (EM)-based algo

Zilin Du, Haoxin Li, Jianfei Yu, Boyang Li

Visual grounding aims to localize the image regions based on a textual query. Given the difficulty of large-scale data curation, we investigate how to effectively learn visual grounding under data-scarce settings in this paper. To address the data scarcity, we propose a novel framework, POBF (Paint Outside the Box and Filter). POBF synthesizes images by inpa

Tongshun Zhang, Pingping Liu, Ming Zhao, Haotian Lv

In the Fourier frequency domain, luminance information is primarily encoded in the amplitude component, while spatial structure information is significantly contained within the phase component. Existing low-light image enhancement techniques using Fourier transform have mainly focused on amplifying the amplitude component and simply replicating the phase co

Phu Pham, Damon Conover, Aniket Bera

We present FlashSLAM, a novel SLAM approach that leverages 3D Gaussian Splatting for efficient and robust 3D scene reconstruction. Existing 3DGS-based SLAM methods often fall short in sparse view settings and during large camera movements due to their reliance on gradient descent-based optimization, which is both slow and inaccurate. FlashSLAM addresses thes

S M Jishanul Islam, Sahid Hossain Mustakim, Sadia Ahmmed, Md. Faiyaz Abdullah Sayeedi

Anti-Muslim hate speech has emerged within memes, characterized by context-dependent and rhetorical messages using text and images that seemingly mimic humor but convey Islamophobic sentiments. This work presents a novel dataset and proposes a classifier based on the Vision-and-Language Transformer (ViLT) specifically tailored to identify anti-Muslim hate wi

Tahrim Hossain, Faisal Haque Bappy, Tarannum Shaila Zaman, Tariqul Islam

This work addresses the critical challenges of upgrading smart contracts, which are vital for trust in automated transactions but difficult to modify once deployed. To address this issue, we propose SEAM, a novel framework that automates the conversion of standard Solidity contracts into upgradable versions using the diamond pattern. SEAM simplifies the upgr

Atahan Dokme, Raj Kiriti Velicheti, Melih Bastopcu, Tamer Başar

Remote estimation is a crucial element of real time monitoring of a stochastic process. While most of the existing works have concentrated on obtaining optimal sampling strategies, motivated by malicious attacks on cyber-physical systems, we model sensing under surveillance as a game between an attacker and a defender. This introduces strategic elements to c

Jingwei Zhang, Anh Tien Nguyen, Xi Han, Vincent Quoc-Huy Trinh

Efficiently modeling large 2D contexts is essential for various fields including Giga-Pixel Whole Slide Imaging (WSI) and remote sensing. Transformer-based models offer high parallelism but face challenges due to their quadratic complexity for handling long sequences. Recently, Mamba introduced a selective State Space Model (SSM) with linear complexity and h

Faisal Haque Bappy, Joon S. Park, Kamrul Hasan, Tariqul Islam

As blockchain technology gains traction for enhancing data security and operational efficiency, traditional centralized authentication systems remain a significant bottleneck. This paper addresses the challenge of integrating decentralized authentication and access control within distributed networks. We propose a novel solution named ChainGuard, a fully dec

XRISM Collaboration

Sagittarius A East is a supernova remnant with a unique surrounding environment, as it is located in the immediate vicinity of the supermassive black hole at the Galactic center, Sagittarius A*. The X-ray emission of the remnant is suspected to show features of overionized plasma, which would require peculiar evolutionary paths. We report on the first observ

Hyo Seok Jang, Ki-Ahm Lee

We establish the Alexandroff-Bakelman-Pucci estimate, the Harnack inequality, the H\"older regularity and the Schauder estimates to a class of degenerate parabolic equations of non-divergence form in all dimensions \begin{equation} \mathcal{L}u:= u_t -Lu= u_t -(x a_{11} u_{xx} +2\sqrt{x} \sum_{j=2}^n a_{1j} u_{x y_j} + \sum_{i,j=2}^n a_{ij} u_{y_i y_j} + b_1

Joey Rivkin, Gregory Valiant, Paul Valiant

We introduce the following natural generalization of trace reconstruction, parameterized by a deletion probability $\delta \in (0,1)$ and length $n$: There is a length $n$ string of probabilities, $S=p_1,\ldots,p_n,$ and each "trace" is obtained by 1) sampling a length $n$ binary string whose $i$th coordinate is independently set to 1 with probability $p_i$

Xinshou Zheng

We propose a novel algorithm, TR-SVR, for solving unconstrained stochastic optimization problems. This method builds on the trust-region framework, which effectively balances local and global exploration in optimization tasks. TR-SVR incorporates variance reduction techniques to improve both computational efficiency and stability when addressing stochastic o

Carson Kohlbrenner, Caleb Escobedo, Nataliya Nechyporenko, Alessandro Roncone

Tactile sensing is used in robotics to obtain real-time feedback during physical interactions. Fine object manipulation is a robotic application that benefits from a high density of sensors to accurately estimate object pose, whereas a low sensing resolution is sufficient for collision detection. Introducing variable sensing resolution into a single tactile

Yunpeng Bai, Qixing Huang

Monocular Depth Estimation (MDE) is a fundamental 3D vision problem with numerous applications such as 3D scene reconstruction, autonomous navigation, and AI content creation. However, robust and generalizable MDE remains challenging due to limited real-world labeled data and distribution gaps between synthetic datasets and real data. Existing methods often

Anton Pribytok

We find the novel class of the supersymmetric deformation of the $\mathbb{CP}^{1}$ $\sigma$-model and its equivalence with the generalised chiral Gross-Neveu. This construction allows the use of field-theoretic techniques and particularly the study of renormalisability and $\beta$-function. Provided approach is useful in finding conformal limits and establis

Li Jiakun, Wang Qingqing, Dong Hongbin, Li Kexin

Current state-of-the-art vision models often utilize feature pyramids to extract multi-scale information, with the Feature Pyramid Network (FPN) being one of the most widely used classic architectures. However, traditional FPNs and their variants (e.g., AUGFPN, PAFPN) fail to fully address spatial misalignment on a global scale, leading to suboptimal perform

Ryo Takahashi

We define a uniformly dominant local ring as a commutative noetherian local ring with an integer r such that the residue field is built from any nonzero object in the singularity category by direct summands, shifts and at most r mapping cones. We find sufficient conditions for uniform dominance, by which we show Burch rings and local rings with quasi-decompo

Xiaomei Chen

We calculate the number of humps and peaks in Motzkin paths with a given height, and calculate the number of standard Young tableaux (SYTs) in a $(2,1)$-hook with the difference of the first two parts fixed, which refine Regev's results in 2009. We also give new combinatorial proofs of Regev's results, and reveal some new recurrence relations related to hump

Lucy Ham, Taylor E. Woodford, Megan A. Coomer, Michael P. H. Stumpf

Many cellular processes involve information processing and decision making. We can probe these processes at increasing molecular detail. The analysis of heterogeneous data remains a challenge that requires new ways of thinking about cells in quantitative, predictive, and mechanistic ways. We discuss the role of mathematical models in the context of cell-fate

Yingjian Chen, Lei Zhang, Yakun Niu, Lei Tan

Diffusion Models enable realistic image generation, raising the risk of misinformation and eroding public trust. Currently, detecting images generated by unseen diffusion models remains challenging due to the limited generalization capabilities of existing methods. To address this issue, we rethink the effectiveness of pre-trained models trained on large-sca

Zhi Qi, Shihong Yuan, Yulin Yuan, Linling Kuang

Diffusion models have shown strong performances in solving inverse problems through posterior sampling while they suffer from errors during earlier steps. To mitigate this issue, several Decoupled Posterior Sampling methods have been recently proposed. However, the reverse process in these methods ignores measurement information, leading to errors that imped

Xin Tie, Weijie Chen, Zachary Huemann, Brayden Schott

Accurate segmentation of gross tumor volume (GTV) is essential for effective MRI-guided adaptive radiotherapy (MRgART) in head and neck cancer. However, manual segmentation of the GTV over the course of therapy is time-consuming and prone to interobserver variability. Deep learning (DL) has the potential to overcome these challenges by automatically delineat

Shile Chen, Li Yan, Shuzhe Shi

The thermalization of quark gluon plasma created in relativistic heavy-ion collisions is a crucial theoretical question in understanding the onset of hydrodynamics, and in a broad sense, a key step to the exploration of thermalization in isolated quantum systems. Addressing this problem theoretically, in a first principle manner, requires a real-time, non-pe

Emile Anand, Ishani Karmarkar, Guannan Qu

Designing efficient algorithms for multi-agent reinforcement learning (MARL) is fundamentally challenging because the size of the joint state and action spaces grows exponentially in the number of agents. These difficulties are exacerbated when balancing sequential global decision-making with local agent interactions. In this work, we propose a new algorithm

David W. Facemyer, Sergio E. Ulloa

Understanding and quantifying entanglement entropy is crucial to characterize the quantum behaviors that drive phenomena in a variety of systems. Rare-earth spin complexes, with their unique magnetic properties, provide fertile ground for exploring these nonlocal correlations. In this work, we study Eu$^{2+}$ ions deposited on a Au(111) substrate, modeling f

Jianhui Li, Shi Pu, Jianqi Chen, Junfeng Wu

Bilevel optimization has gained considerable attention due to its broad applicability across various fields. While several studies have investigated the convergence rates in the strongly-convex-strongly-convex (SC-SC) setting, no prior work has proven that a single-loop algorithm can achieve linear convergence. This paper employs a small-gain theorem in {rob

Worapree Maneesoonthorn, David T. Frazier, Gael M. Martin

A new approximate Bayesian inferential framework is proposed that exploits multiple information sources -- daily spot returns, high-frequency spot data and option prices -- and enables fast calculation of probabilistic predictions of future option prices. This approach operates directly from the theoretical option pricing model, and does not require an expli

Madhav Neupane

This comprehensive survey paper provides an in-depth analysis of Dynamic Software Updating (DSU) techniques in the Internet of Things (IoT). This study critically examines eight significant research papers that employ diverse methodologies to address the challenges of DSU in IoT devices. The primary objectives include comparative analysis to identify the app

Son Pham Tien, Hieu Nguyen Doan, An Nguyen Dai, Sang Dinh Viet

In the field of legal information retrieval, effective embedding-based models are essential for accurate question-answering systems. However, the scarcity of large annotated datasets poses a significant challenge, particularly for Vietnamese legal texts. To address this issue, we propose a novel approach that leverages large language models to generate high-

Hongrui Lu, Yuxiong Huang, Tong He, Gengfeng Li

Unit maintenance and unit commitment are two critical and interrelated aspects of electric power system operation, both of which face the challenge of coordinating efforts to enhance reliability and economic performance. This challenge becomes increasingly pronounced in the context of increased integration of renewable energy and flexible loads, such as wind

Mario Ghossoub, Qinghua Ren, Ruodu Wang

We study risk sharing among agents with preferences modeled by heterogeneous distortion risk measures, who are not necessarily risk averse. Pareto optimality for agents using risk measures is often studied through the lens of inf-convolutions, because allocations that attain the inf-convolution are Pareto optimal, and the converse holds true under translatio

Özge Sürer, Stefan M. Wild

The unknown parameters of simulation models often need to be calibrated using observed data. When simulation models are expensive, calibration is usually carried out with an emulator. The effectiveness of the calibration process can be significantly improved by using a sequential selection of parameters to build an emulator. The expansion of parallel computi

Zihao Tang, Boyuan Wang, Chuan Wen, Jiaye Teng

Conformal prediction is widely adopted in uncertainty quantification, due to its post-hoc, distribution-free, and model-agnostic properties. In the realm of modern deep learning, researchers have proposed Feature Conformal Prediction (FCP), which deploys conformal prediction in a feature space, yielding reduced band lengths. However, the practical utility of

Zefang Liu

Incident response (IR) is a critical aspect of cybersecurity, requiring rapid decision-making and coordinated efforts to address cyberattacks effectively. Leveraging large language models (LLMs) as intelligent agents offers a novel approach to enhancing collaboration and efficiency in IR scenarios. This paper explores the application of LLM-based multi-agent

Minghao Han, Dingkang Yang, Jiabei Cheng, Xukun Zhang

Recent advancements in multimodal pre-training models have significantly advanced computational pathology. However, current approaches predominantly rely on visual-language models, which may impose limitations from a molecular perspective and lead to performance bottlenecks. Here, we introduce a Unified Molecule-enhanced Pathology Image REpresentationn Learn

A. Ravlić, S. Giraud, N. Paar, R. G. T. Zegers

Calculations for electron capture rates on nuclei with atomic numbers between $Z=20$ and $Z=52$ are performed in a self-consistent finite-temperature covariant energy density functional theory within the relativistic quasiparticle random-phase approximation. Electron captures on these nuclei contribute most to reducing the electron fraction during the collap

Patrick Lahr, Axel Niemeyer

We characterize the extreme points of the set of incentive-compatible mechanisms for screening problems with linear utility. Our framework subsumes problems with and without transfers, such as monopoly pricing, principal-optimal bilateral trade and barter exchange, delegation and veto bargaining, or belief elicitation via proper scoring rules. In every probl

Jingyang Xiang, Sai Qian Zhang

Rotating the activation and weight matrices to reduce the influence of outliers in large language models (LLMs) has recently attracted significant attention, particularly in the context of model quantization. Prior studies have shown that in low-precision quantization scenarios, such as 4-bit weights and 4-bit activations (W4A4), randomized Hadamard transfor

Delong Kong, Yu Tian, Hongbao Zhang, Jinan Zhao

Recently Hollands, Wald and Zhang proposed a new formula for the entropy of a dynamical black hole for an arbitrary theory of gravity obtained from a diffeomorphism covariant Lagrangian via the Noether charge method. We present an alternative, pedagogical derivation of the dynamical black hole entropy for $f(R)$ gravity as well as canonical scalar-tensor the

Siddharth Sambamoorthy, Henry C. W. Chu

Current theories of diffusiophoresis in porous media are limited to a porous medium saturated with a valence symmetric electrolyte. A predictive model for diffusiophoresis in porous media saturated with a valence asymmetric electrolyte, or a general mixture of valence symmetric and asymmetric electrolytes, is lacking. To close this knowledge gap, in this wor

Kai Yu, Song Lin, Bin-Bin Cai

Convolutional neural network is a crucial tool for machine learning, especially in the field of computer vision. Its unique structure and characteristics provide significant advantages in feature extraction. However, with the exponential growth of data scale, classical computing architectures face serious challenges in terms of time efficiency and memory req

Chow-Choong Ngeow, Chia-Yu Cheng

We complement the $y$-band template light curves for RR Lyrae to the well-established template light curves in the $ugriz$-band, where the latter have been adopted in the astroML/gatspy python package as one of the period-search methods for RR Lyrae. These $y$-band template light curves were constructed based on the $z$-band time series data taken from the S

Christopher Alexander, Blake Temple, Zeke Vogler

We give a definitive characterization of the instability of the pressureless ($p=0$) critical ($k=0$) Friedmann spacetime to smooth radial perturbations. We use this to characterize the global accelerations away from $k\leq0$ Friedmann spacetimes induced by the instability in the underdense case. The analysis begins by incorporating the Friedmann spacetimes

Mahmoud Abo Khamis, Kyle Deeds, Dan Olteanu, Dan Suciu

Cardinality Estimation is to estimate the size of the output of a query without computing it, by using only statistics on the input relations. Existing estimators try to return an unbiased estimate of the cardinality: this is notoriously difficult. A new class of estimators have been proposed recently, called "pessimistic estimators", which compute a guarant

Tanmay Vachaspati, Axel Brandenburg

We characterize magnetic fields produced during electroweak symmetry breaking by non-dynamical numerical simulations based on the Kibble mechanism. The generated magnetic fields were thought to have an energy spectrum $\propto k^3$ for small wavenumbers $k$, but here we show that it is actually a spectrum $\propto k^4$ along with characteristic fluctuations

Lexiao Lai

Modern data science applications demand solving large-scale optimization problems. The prevalent approaches are first-order methods, valued for their scalability. These methods are implemented to tackle highly irregular problems where assumptions of convexity and smoothness are untenable. Seeking to deepen the understanding of these methods, we study first-o

Mahdi Erfanian, Mohsen Dehghankar, Abolfazl Asudeh

Multi-modal datasets, like those involving images, often miss the detailed descriptions that properly capture the rich information encoded in each item. This makes answering complex natural language queries a major challenge in this domain. In particular, unlike the traditional nearest neighbor search, where the tuples and the query are represented as points

Hao Jin, Hengyuan Chang, Xiaoxuan Xie, Zhengyang Wang

Designing stylized cinemagraphs is challenging due to the difficulty in customizing complex and expressive flow elements. To achieve intuitive and detailed control of the generated cinemagraphs, sketches provide a feasible solution to convey personalized design requirements beyond text inputs. In this paper, we propose Sketch2Cinemagraph, a sketch-guided fra

Julien Despres

We extend the quench spectroscopy method to dissipative and isolated non-Hermitian quantum lattice models via the case study of the open Bose-Hubbard chain and the non-Hermitian transverse-field Ising chain respectively. We first investigate theoretically the dynamics of the open Bose-Hubbard chain confined in the superfluid phase induced by a sudden global

Ke Li, Yaqin Zhang, Yunqing Huang, Chenyue Xie

This paper proposes an Adaptive Basis-inspired Deep Neural Network (ABI-DNN) for solving partial differential equations with localized phenomena such as sharp gradients and singularities. Like the adaptive finite element method, ABI-DNN incorporates an iteration of "solve, estimate, mark, enhancement", which automatically identifies challenging regions and a

Ishaan Bhadoo

In this article, we study the critical percolation threshold $p_c$ for $d$-regular graphs. It is well-known that $p_c \geq \frac{1}{d-1}$ for such graphs, with equality holding for the $d$-regular tree. We prove that among all quasi-transitive $d$-regular graphs, the equality $p_c(G) = \frac{1}{d-1}$ holds if and only if $G$ is a tree. Furthermore, we provid

Alice Harward, Junjie Lin, Yun Wang, Xiaoke Xie

With the development of the economy, fresh food e-commerce has experienced rapid growth. One of the core competitive advantages of fresh food e-commerce platforms lies in selecting an appropriate logistics distribution model. This study focuses on the front warehouse model, aiming to minimize distribution costs. Considering the perishable nature and short sh

Taicheng Liu, Yuanze Wu

In this paper, we consider the following Schr\"{o}dinger equation: \begin{equation*} \begin{cases} -\Delta u=\lambda u+\mu|u|^{q-2}u+|u|^{2^*-2}u\quad\text{in }\mathbb{R}^N,\\ \int_{\mathbb{R}^N}|u(x)|^2dx=a,\quad u\in H^1(\mathbb{R}^N),\\ \end{cases} \end{equation*} where $N\ge 3$, $2<q<2+\frac{4}{N}$, $a, \mu>0$, $2^*=\frac{2N}{N-2}$ is the critical Sobole

Jiasi Zhou, Cong Zhou, Cheng Zeng, Chintha Tellambura

This letter presents a flexible rate-splitting multiple access (RSMA) framework for near-field (NF) integrated sensing and communications (ISAC). The spatial beams configured to meet the communication rate requirements of NF users are simultaneously leveraged to sense an additional NF target. A key innovation lies in its flexibility to select a subset of use

Yang Wu, Huayi Zhang, Yizheng Jiao, Lin Ma

Instruction tuning has underscored the significant potential of large language models (LLMs) in producing more human controllable and effective outputs in various domains. In this work, we focus on the data selection problem for task-specific instruction tuning of LLMs. Prevailing methods primarily rely on the crafted similarity metrics to select training da

Yihao Zhou, Haowei Xu, Lili Zhang, Shengdong Zhao

Cosplay commission (cos-commission) is a new form of commodified intimate relationship within the Otome game community in China. To explore the motivations, practices, experiences, and challenges, we conducted semi-structured interviews with 15 participants in different roles. Our findings reveal that cos-commission, as a hybrid activity, provides participan

Peng Zhang, Baosen Zhang

Inverter-based distributed energy resources facilitate the advanced voltage control algorithms in the online setting with the flexibility in both active and reactive power injections. A key challenge is to continuously track the time-varying global optima with the robustness against dynamics inaccuracy and communication delay. In this paper, we introduce the

Eva-Maria Hekkelman, Edward A. McDonald

We propose a simple approximation of the noncommutative integral in noncommutative geometry for the Connes--Van Suijlekom paradigm of spectrally truncated spectral triples. A close connection between this approximation and the field of quantum ergodicity and work by Widom in particular immediately provides a Szeg\H{o} limit formula for noncommutative geometr

Rithik Vir, Parsa Madinei

Cooking meals can be difficult, causing many to resort to cookbooks and online recipes. However, relying on these traditional methods of cooking often results in missing ingredients, nutritional hazards, and unsatisfactory meals. Using Augmented Reality (AR) can address these issues; however, current AR cooking applications have poor user interfaces and limi

You Wu, Xiangyang Yang, Xucheng Wang, Hengzhou Ye

Harnessing low-light enhancement and domain adaptation, nighttime UAV tracking has made substantial strides. However, over-reliance on image enhancement, limited high-quality nighttime data, and a lack of integration between daytime and nighttime trackers hinder the development of an end-to-end trainable framework. Additionally, current ViT-based trackers de

Alexey Dubinsky

While grey-body factors for a test scalar field in stringy black holes described by the renowned Gibbons-Maeda-Garfinkle-Horowitz-Strominger (GMGHS) solution have been analyzed in the literature, no such analysis exists for gravitons, likely due to the complexity of the perturbation equations. In this study, we utilize known data on quasinormal modes and the

Yogesh Kulkarni, Pooyan Fazli

Recent advances in video-large language models (Video-LLMs) have led to significant progress in video understanding. Current preference optimization methods often rely on proprietary APIs or human-annotated captions to generate preference data (i.e., pairs of model outputs ranked by quality or alignment with human judgment), which is then used to train model

Chensheng Peng, Ido Sobol, Masayoshi Tomizuka, Kurt Keutzer

We present a novel framework for training 3D image-conditioned diffusion models using only 2D supervision. Recovering 3D structure from 2D images is inherently ill-posed due to the ambiguity of possible reconstructions, making generative models a natural choice. However, most existing 3D generative models rely on full 3D supervision, which is impractical due

Heitor R. Medeiros, Atif Belal, Srikanth Muralidharan, Eric Granger

The zero-shot performance of object detectors degrades when tested on different modalities, such as infrared and depth. While recent work has explored image translation techniques to adapt detectors to new modalities, these methods are limited to a single modality and apply only to traditional detectors. Recently, vision-language detectors, such as YOLO-Worl

Chen-Wei Chang, Shailik Sarkar, Shutonu Mitra, Qi Zhang

Can we trust Large Language Models (LLMs) to accurately predict scam? This paper investigates the vulnerabilities of LLMs when facing adversarial scam messages for the task of scam detection. We addressed this issue by creating a comprehensive dataset with fine-grained labels of scam messages, including both original and adversarial scam messages. The datase

Ye Zheng, Yidan Hu

Trajectory collection is essential for location-based services, yet it can reveal highly sensitive information about users, such as daily routines and activities, raising serious privacy concerns. Local Differential Privacy (LDP) offers strong privacy guarantees for users even when the data collector is untrusted. However, existing trajectory collection meth

Eunjung Yeo

Dysarthria, a motor speech disorder, severely impacts voice quality, pronunciation, and prosody, leading to diminished speech intelligibility and reduced quality of life. Accurate assessment is crucial for effective treatment, but traditional perceptual assessments are limited by their subjectivity and resource intensity. To mitigate the limitations, automat