December 2024 arXiv papers — page 145

Jialin Wang, Zhihua Duan

This study explores the integration of Agent AI with LangGraph to enhance real-time data analysis systems in big data environments. The proposed framework overcomes limitations of static workflows, inefficient stateful computations, and lack of human intervention by leveraging LangGraph's graph-based workflow construction and dynamic decision-making capabili

Yingchu Wang, Ji He, Shijie Yu

Structural Health Monitoring (SHM) is a sustainable and essential approach for infrastructure maintenance, enabling the early detection of structural defects. Leveraging computer vision (CV) methods for automated infrastructure monitoring can significantly enhance monitoring efficiency and precision. However, these methods often face challenges in efficiency

Hangyan Zhu, Ming Liu, Chao Zhou, Zifei Yan

Image colorization methods have shown prominent performance on natural images. However, since humans are more sensitive to faces, existing methods are insufficient to meet the demands when applied to facial images, typically showing unnatural and uneven colorization results. In this paper, we investigate the facial image colorization task and find that the p

Bruce C. Berndt, Raghavendra N. Bhat, Jeffrey L. Meyer, Likun Xie

The sum $S(h,k):=\sum_{j=1}^{k-1}(-1)^{j+1+[hj/k]}$ appears in the modular transformation formulae of the classical theta function $\vartheta_3(z)$. The double sum $S(k) := \sum_{h=1}^{k-1}S(h,k)$ has a remarkable distribution of values. Although properties for $S(k)$ and a related sum can be established, several interesting conjectures are open.

Hansle Gwon, Imjin Ahn, Young-Hak Kim, Sanghyun Park

Recent advancements in Large Language Models (LLMs) have been remarkable, with new models consistently surpassing their predecessors. These advancements are underpinned by extensive research on various training mechanisms. Among these, Preference Optimization has played a significant role in improving the performance of LLMs by incorporating human preference

Huawei Huang, Zhaokang Yin, Qinde Chen, Guang Ye

State-of-the-art blockchain sharding solutions such as Monoxide, can cause severely imbalanced distribution of transaction (TX) workloads across all blockchain shards due to the deployment policy of their accounts. Imbalanced TX distributions then produce hot shards, in which the cross-shard TXs may experience an unlimited confirmation latency. Thus, how to

Nimisha Ghosh, Daniele Santoni, Indrajit Saha, Giovanni Felici

In recent times, Transformer-based language models are making quite an impact in the field of natural language processing. As relevant parallels can be drawn between biological sequences and natural languages, the models used in NLP can be easily extended and adapted for various applications in bioinformatics. In this regard, this paper introduces the major

Kaixun Yang, Mladen Raković, Zhiping Liang, Lixiang Yan

Students are increasingly relying on Generative AI (GAI) to support their writing-a key pedagogical practice in education. In GAI-assisted writing, students can delegate core cognitive tasks (e.g., generating ideas and turning them into sentences) to GAI while still producing high-quality essays. This creates new challenges for teachers in assessing and supp

Debasmita Pal, Redwan Sony, Arun Ross

Iris-based biometric systems are vulnerable to presentation attacks (PAs), where adversaries present physical artifacts (e.g., printed iris images, textured contact lenses) to defeat the system. This has led to the development of various presentation attack detection (PAD) algorithms, which typically perform well in intra-domain settings. However, they often

Tian Lan

Ocneanu's tube algebra provides a finite algorithm to compute the Drinfeld center of a fusion category. In this work we reveal the universal property underlying the tube algebra. Take a base category $\mathcal V$ which is strongly concrete, bicomplete, and closed symmetric monoidal. For physical applications one takes $\mathcal V=\mathbf{Vect}$ the category

Maxwell Kaye, Graham K. MacDonald, Eric Galbraith

Integrated global food system analysis is hampered by the fragmentation of data among food types, processes, and scales. Studies also often neglect the connection to human metabolism -- the ultimate driver of food demand. Here we use a common energetic framework to harmonize data on 95 individual food commodities across food system processes, including produ

Zheng Lin, Wei Wei, Zhe Chen, Chan-Tong Lam

As AI models expand in size, it has become increasingly challenging to deploy federated learning (FL) on resource-constrained edge devices. To tackle this issue, split federated learning (SFL) has emerged as an FL framework with reduced workload on edge devices via model splitting; it has received extensive attention from the research community in recent yea

Xu Ouyang, Ying Chen, Kaiyue Zhu, Gady Agam

Fine-grained text to image synthesis involves generating images from texts that belong to different categories. In contrast to general text to image synthesis, in fine-grained synthesis there is high similarity between images of different subclasses, and there may be linguistic discrepancy among texts describing the same image. Recent Generative Adversarial

Yujie Feng, Yin Yang, Xiaohong Fan, Zhengpeng Zhang

Recently, deep learning methods have gained remarkable achievements in the field of image restoration for remote sensing (RS). However, most existing RS image restoration methods focus mainly on conventional first-order degradation models, which may not effectively capture the imaging mechanisms of remote sensing images. Furthermore, many RS image restoratio

Genshiro Kitagawa

This study evaluated probability distributions for modeling time series with abrupt structural changes. The Pearson type VII distribution, with an adjustable shape parameter $b$, proved versatile. The generalized Laplace distribution performed similarly to the Pearson model, occasionally surpassing it in terms of likelihood and AIC. Mixture models, including

Puhua Niu, Byung-Jun Yoon, Xiaoning Qian

In this study, we focus on developing efficient calibration methods via Bayesian decision-making for the family of compartmental epidemiological models. The existing calibration methods usually assume that the compartmental model is cheap in terms of its output and gradient evaluation, which may not hold in practice when extending them to more general settin

Zachary Coalson, Jeonghyun Woo, Chris S. Lin, Joyce Qu

We study a new vulnerability in commercial-scale safety-aligned large language models (LLMs): their refusal to generate harmful responses can be broken by flipping only a few bits in model parameters. Our attack jailbreaks billion-parameter language models with just 5 to 25 bit-flips, requiring up to 40$\times$ fewer bit flips than prior attacks on much smal

David G Hobson, Majid Komeili

Blindness and visual impairments affect many people worldwide. For help with navigation, people with visual impairments often rely on tactile maps that utilize raised surfaces and edges to convey information through touch. Although these maps are helpful, they are often not widely available and current tools to automate their production have similar limitati

Chongze Wang, Shichang Yao, Shuyuan Liu, Bing Wang

A family of V-based kagome metals AV$_3$Sb$_5$ (A = Cs, Rb, K) presents an intriguing platform for exploring the interplay of time-reversal symmetry breaking, nontrivial topological bands, and electron correlations, resulting in a range of exotic quantum states, including the anomalous Hall effect, unconventional charge density waves, and superconductivity.

Yang Xiong, Ruichen Zhang, Yinqiu Liu, Dusit Niyato

The rapid development of next-generation networking technologies underscores their transformative role in revolutionizing modern communication systems, enabling faster, more reliable, and highly interconnected solutions. However, such development has also brought challenges to network optimizations. Thanks to the emergence of Large Language Models (LLMs) in

Yushun Dong, Patrick Soga, Yinhan He, Song Wang

Graph Neural Networks (GNNs) have achieved remarkable success in various graph-based learning tasks. While their performance is often attributed to the powerful neighborhood aggregation mechanism, recent studies suggest that other components such as non-linear layers may also significantly affecting how GNNs process the input graph data in the spectral domai

Pengxin Guo, Shuang Zeng, Wenhao Chen, Xiaodan Zhang

Federated Learning (FL) aims to protect data privacy by enabling clients to collectively train machine learning models without sharing their raw data. However, recent studies demonstrate that information exchanged during FL is subject to Gradient Inversion Attacks (GIA) and, consequently, a variety of privacy-preserving methods have been integrated into FL t

Shukuan Wang, Ke Xue, Lei Song, Xiaobin Huang

Bayesian optimization (BO) is a popular method for computationally expensive black-box optimization. However, traditional BO methods need to solve new problems from scratch, leading to slow convergence. Recent studies try to extend BO to a transfer learning setup to speed up the optimization, where search space transfer is one of the most promising approache

Zain Mehdi, Varun D. Vaidya, Isabelle Savill-Brown, Phoebe Grosser

Quantum logic operations between physically distinct qubits is an essential aspect of large-scale quantum information processing. We propose an approach to high-speed mixed-species entangling operations in trapped-ion quantum computers, based on mechanical excitation of spin-dependent ion motion by ultrafast pulsed lasers. We develop the theory and machine-d

Zhaomeng Chen, Junting Duan, Victor Chernozhukov, Vasilis Syrgkanis

This paper proposes the automatic Doubly Robust Random Forest (DRRF) algorithm for estimating the conditional expectation of a moment functional in the presence of high-dimensional nuisance functions. DRRF extends the automatic debiasing framework based on the Riesz representer to the conditional setting and enables nonparametric, forest-based estimation (At

Hang Du, Guoshun Nan, Jiawen Qian, Wangchenhui Wu

Recent advancements in video anomaly understanding (VAU) have opened the door to groundbreaking applications in various fields, such as traffic monitoring and industrial automation. While the current benchmarks in VAU predominantly emphasize the detection and localization of anomalies. Here, we endeavor to delve deeper into the practical aspects of VAU by ad

Kayne Uriel K. Rodrigo, Jerriane Hillary Heart S. Marcial, Samuel C. Brillo, Khatalyn E. Mata

This study focuses on enhancing rice leaf disease image classification algorithms, which have traditionally relied on Convolutional Neural Network (CNN) models. We employed transfer learning with MobileViTV2_050 using ImageNet-1k weights, a lightweight model that integrates CNN's local feature extraction with Vision Transformers' global context learning thro

Jason Zev Ludmir, Yuqian Huo, Nicholas S. DiBrita, Tirthak Patel

Quantum computing has the potential to accelerate various domains: scientific computation, machine learning, and optimization. Recently, Rydberg atom quantum computing has emerged as a promising quantum computing technology, especially with the demonstration of the zonal addressing architecture. However, this demonstration is only compatible with one type of

Shuaifeng Jiang, Ahmed Alkhateeb

This paper explores a novel research direction where a digital twin is leveraged to assist the beamforming design for an integrated sensing and communication (ISAC) system. In this setup, a base station designs joint communication and sensing beamforming to serve the communication user and detect the sensing target concurrently. Utilizing the electromagnetic

Quoc Chuong Nguyen

Boids, developed by Craig W. Reynolds in 1986, is one of the earliest emergent models where the global pattern emerges from the interaction between many individuals within the local scale. In the original model, Boids follow three rules: separation, alignment, and cohesion; which allow them to move around and create a flock without intention in the empty env

R. Connor Greene

A method is presented for forming polynomial interpolants on squares and cubes, which are more efficient in the so-called Euclidean degree than other commonly used methods with the same number of collocation points. These methods have several additional desirable properties. The interpolants can be formed and evaluated via the FFT and have a minimally growin

Yin Yuan, Wu Mei, Ding Xiang, He Peiyi

The infinite-layer nickelates, proposed as analogs to superconducting cuprates, provide a promising platform for exploring the mechanisms of unconventional superconductivity. However, the superconductivity under atmospheric pressure has only been observed in thin films, indicating the heterointerface is essential. Here, we employed the advanced Scanning Tran

Julien Roy

In the last decade, Deep Reinforcement Learning has evolved into a powerful tool for complex sequential decision-making problems. It combines deep learning's proficiency in processing rich input signals with reinforcement learning's adaptability across diverse control tasks. At its core, an RL agent seeks to maximize its cumulative reward, enabling AI algori

Yang Gao, Enci Wang, Qing-Hua Tan, Timothy A. Davis

We present the analysis of a comprehensive sample of 352 early-type galaxies using public data, to investigate the correlations between CO luminosities and mid-infrared luminosities observed by \textit{Wide-field Infrared Survey Explorer} (\textit{WISE}). We find strong correlations between both CO (1-0) and CO (2-1) luminosities and 12 \micron\ luminosity,

Syed Saim Gardezi, Soyiba Jawed, Mahnoor Khan, Muneeba Bukhari

A multitude of individuals across the globe grapple with motor disabilities. Neural prosthetics utilizing Brain-Computer Interface (BCI) technology exhibit promise for improving motor rehabilitation outcomes. The intricate nature of EEG data poses a significant hurdle for current BCI systems. Recently, a qualitative repository of EEG signals tied to both upp

Vui Seng Chua, Yujie Pan, Nilesh Jain

We present Statistical Calibrated Activation Pruning (SCAP), a post-training activation pruning framework that (1) generalizes sparsification by input activations of Fully-Connected layers for generic and flexible application across Transformers, and (2) features a simple Mode-Centering technique to pre-calibrate activation distributions for maximizing post-

Zhigang Yan, Dong Li

Most of current semantic communication (SemCom) frameworks focus on the image transmission, which, however, do not address the problem on how to deliver digital signals without any semantic features. This paper proposes a novel SemCom approach to transmit digital signals by using the image as the carrier signal. Specifically, the proposed approach encodes th

Jie Liang, Dong Liu, Hao-Jie Lin, Zheng-Wen Long

This study delves into the existence of dark matter around supermassive black holes in galactic cores using a novel gravitational model. By analyzing gravitational waves emitted during the ringdown phase of black holes under different field perturbations, we explore the potential for detecting dark matter. The model hypothesizes that the dark matter distribu

Haoran Lian, Junmin Chen, Wei Huang, Yizhe Xiong

Recently, Large language models (LLMs) have revolutionized Natural Language Processing (NLP). Pretrained LLMs, due to limited training context size, struggle with handling long token sequences, limiting their performance on various downstream tasks. Current solutions toward long context modeling often employ multi-stage continual pertaining, which progressiv

Hau-Hung Yang, Chia-Min Wei, Yu-Chang Chen

This study establishes the consistency of Bayesian adaptive testing methods under the Rasch model, addressing a gap in the literature on their large-sample guarantees. Although Bayesian approaches are recognized for their finite-sample performance and capability to circumvent issues such as the cold-start problem; however, rigorous proofs of their asymptotic

Tal Zeevi, Ravid Shwartz-Ziv, Yann LeCun, Lawrence H. Staib

Accurate uncertainty estimation is crucial for deploying neural networks in risk-sensitive applications such as medical diagnosis. Monte Carlo Dropout is a widely used technique for approximating predictive uncertainty by performing stochastic forward passes with dropout during inference. However, using static dropout rates across all layers and inputs can l

Xuecheng Wu, Junxiao Xue, Liangyu Fu, Jiayu Nie

Recent research on real-time object detectors (e.g., YOLO series) has demonstrated the effectiveness of attention mechanisms for elevating model performance. Nevertheless, existing methods neglect to unifiedly deploy hierarchical attention mechanisms to construct a more discriminative YOLO head which is enriched with more useful intermediate features. To tac

Ke Xue, Ruo-Tong Chen, Xi Lin, Yunqi Shi

In modern chip design, placement aims at placing millions of circuit modules, which is an essential step that significantly influences power, performance, and area (PPA) metrics. Recently, reinforcement learning (RL) has emerged as a promising technique for improving placement quality, especially macro placement. However, current RL-based placement methods s

Nicholas N. Gibbons

equilibrium-c (eqc) is a program for computing the composition of gas mixtures in chemical equilibrium. In typical usage, the program is given a known thermodynamic state, such as fixed temperature and pressure, as well as an initial composition of gaseous species, and computes the final composition in the limit of a large amount of time relative to the reac

Jacob Adkins, Michael Bowling, Adam White

The performance of modern reinforcement learning algorithms critically relies on tuning ever-increasing numbers of hyperparameters. Often, small changes in a hyperparameter can lead to drastic changes in performance, and different environments require very different hyperparameter settings to achieve state-of-the-art performance reported in the literature. W

Feihu Liu, Guoce Xin, Zihao Zhang

The order polytopes arising from the finite poset were first introduced and studied by Stanley. For any positive integer $d\geq 14$, Liu and Tsuchiya proved that there exists a non-Ehrhart positive order polytope of dimension $d$. They also proved that any order polytope of dimension $d\leq 11$ is Ehrhart positive. We confirm that any order polytope of dimen

Dustin Enyeart, Guang Lin

DeepONets and Koopman autoencoders are two prevalent neural operator architectures. These architectures are autoencoders. An adversarial addition to an autoencoder have improved performance of autoencoders in various areas of machine learning. In this paper, the use an adversarial addition for these two neural operator architectures is studied.

Mingjie Lu, Yuanxian Huang, Ji Liu, Xingliang Huang

Occupancy Network has recently attracted much attention in autonomous driving. Instead of monocular 3D detection and recent bird's eye view(BEV) models predicting 3D bounding box of obstacles, Occupancy Network predicts the category of voxel in specified 3D space around the ego vehicle via transforming 3D detection task into 3D voxel segmentation task, which

N. Chalus, A. W. D. Leishman, R. M. Menezes, G. Longbons

The skyrmion lattice (SkL) in MnSi was studied using small-angle neutron scattering and under the influence of a radial electric current in a Corbino geometry. In response to the applied current, the SkL undergoes an angular reorientation with respect to the MnSi crystal lattice. The reorientation is non-monotonic with increasing current, with the SkL rotati

Yun Li, Zhe Liu, Lina Yao

Compositional Zero-Shot Learning (CZSL) aims to recognize unseen combinations of seen attributes and objects. Current CLIP-based methods in CZSL, despite their advancements, often fail to effectively understand and link the attributes and objects due to inherent limitations in CLIP's pretraining mechanisms. To address these shortcomings, this paper introduce

Thong Thanh Nguyen, Xiaobao Wu, Yi Bin, Cong-Duy T Nguyen

To equip artificial intelligence with a comprehensive understanding towards a temporal world, video and 4D panoptic scene graph generation abstracts visual data into nodes to represent entities and edges to capture temporal relations. Existing methods encode entity masks tracked across temporal dimensions (mask tubes), then predict their relations with tempo

Valeriy G. Bardakov, Igor M. Nikonov, Viktor N. Zhelaybin

We know definition of Rota--Baxter operators on different algebraic systems. For examples, on groups, on algebras, on Hopf algebras. On some algebraic systems it is possible to define different types of Rota--Baxter operators. For example, on group algebra it is possible to define Rota--Baxter operator as on associative algebra, group Rota--Baxter operator a

Thong Thanh Nguyen, Yi Bin, Xiaobao Wu, Zhiyuan Hu

Temporal grounding, which localizes video moments related to a natural language query, is a core problem of vision-language learning and video understanding. To encode video moments of varying lengths, recent methods employ a multi-level structure known as a feature pyramid. In this structure, lower levels concentrate on short-range video moments, while high

Peijie Qiu, Satrajit Chakrabarty, Phuc Nguyen, Soumyendu Sekhar Ghosh

Deep learning has made significant strides in automated brain tumor segmentation from magnetic resonance imaging (MRI) scans in recent years. However, the reliability of these tools is hampered by the presence of poor-quality segmentation outliers, particularly in out-of-distribution samples, making their implementation in clinical practice difficult. Theref

Anthony Miyaguchi, Jed Moutahir, Tanmay Sutar

This paper presents a semi-supervised approach to extracting and analyzing combat phases in judo tournaments using live-streamed footage. The objective is to automate the annotation and summarization of live streamed judo matches. We train models that extract relevant entities and classify combat phases from fixed-perspective judo recordings. We employ semi-

Hao Liang, Zhipeng Dong, Hao Li, Yufeng Yue

Tractor-trailer wheeled robots need to perform comprehensive perception tasks to enhance their operations in areas such as logistics parks and long-haul transportation. The perception of these robots faces three major challenges: the asynchronous vibrations between the tractor and trailer, the relative pose change between the tractor and trailer, and the sig

Daizhan Cheng, Zhengping Ji

Motivated by the study of dynamic control systems, this paper proposes novel algebraic operations on cubic matrices to construct both linear and nonlinear controlled dynamics. The standard t-product of cubic matrices imposes strict dimensional constraints; to resolve this, we first introduce the dimension-keeping semi-tensor product (DK-STP), which generaliz

Jiangang Wang, Qingnan Fan, Qi Zhang, Haigen Liu

Owing to the robust priors of diffusion models, recent approaches have shown promise in addressing real-world super-resolution (Real-SR). However, achieving semantic consistency and perceptual naturalness to meet human perception demands remains difficult, especially under conditions of heavy degradation and varied input complexities. To tackle this, we prop

Jiahe Yan, Pratik Chaudhari, Leonard Kleinrock

Distributed model training needs to be adapted to challenges such as the straggler effect and Byzantine attacks. When coordinating the training process with multiple computing nodes, ensuring timely and reliable gradient aggregation amidst network and system malfunctions is essential. To tackle these issues, we propose \textit{dSTAR}, a lightweight and effic

Liguo Ma, Raghav Chaturvedi, Phuong X. Nguyen, Kenji Watanabe

The realization of graphene has provided a bench-top laboratory for quantum electrodynamics. The low-energy excitations of graphene are two-dimensional massless Dirac fermions with opposite chiralities at the $\pm$K valleys of the graphene Brillouin zone. It has been speculated that the electron-electron interactions in graphene could spontaneously break the

Jiangang Wang, Qingnan Fan, Jinwei Chen, Hong Gu

Benefiting from their powerful generative capabilities, pretrained diffusion models have garnered significant attention for real-world image super-resolution (Real-SR). Existing diffusion-based SR approaches typically utilize semantic information from degraded images and restoration prompts to activate prior for producing realistic high-resolution images. Ho

Sayak Chakrabarty, Souradip Pal

This paper introduces Multiple Choice Reasoning via. Process of Elimination using Multi-Modal models, herein referred to as Multi-Modal Process of Elimination (MM-PoE). This novel methodology is engineered to augment the efficacy of Vision-Language Models (VLMs) in multiple-choice visual reasoning tasks. Diverging from conventional approaches that evaluate e

Bo Li, Shaolin Zhu, Lijie Wen

Image Translation (IT) holds immense potential across diverse domains, enabling the translation of textual content within images into various languages. However, existing datasets often suffer from limitations in scale, diversity, and quality, hindering the development and evaluation of IT models. To address this issue, we introduce MIT-10M, a large-scale pa

Roman Gonin, Andrei Ionov, Kostiantyn Tolmachov

Using hyperbolic localization, we identify the nearby cycles along the Vinberg degeneration with the composition of Radon and Harish-Chandra functors, both considered for the category of character sheaves. This provides a new, simple proof of the exactness of this composition, extending previously known results to arbitrary monodromy and more general sheaf-t

Qiong Qin, Congjun Wu

Motivated by the recent experimental discovery of superconductivity in rhombohedral tetralayer graphene, we investigate the pairing mechanism arising from the density-density interactions within the random-phase approximation. This approach successfully highlights the dominance of the chiral $p$-wave pairing between electrons with the same spin and valley in

Hao Li, Ruoyuan Gong, Hao Jiang

Predicting roll call votes through modeling political actors has emerged as a focus in quantitative political science and computer science. Widely used embedding-based methods generate vectors for legislators from diverse data sets to predict legislative behaviors. However, these methods often contend with challenges such as the need for manually predefined

Chunna Zeng, Yu Lan

All continuous, SL(n) covariant valuations on Orlicz spaces are completely classified without any symmetric assumptions. It is shown that the moment matrix is the only such valuation if n\geq3, while a new functional shows up in dimension two.

Ruihuan Mao, Guozhen Shen

A set $A$ is dually Dedekind finite if every surjection from $A$ onto $A$ is injective; otherwise, $A$ is dually Dedekind infinite. It is proved consistent with $\mathsf{ZF}$ (i.e., the Zermelo--Fraenkel set theory without the axiom of choice) that there exists a family $\langle A_n\rangle_{n\in\omega}$ of sets such that, for all $n\in\omega$, $A_n^n$ is dua

Qianhao Han, Junyi Liu, Zengchang Qin, Zheng Zheng

Automating radiology report generation can significantly reduce the workload of radiologists and enhance the accuracy, consistency, and efficiency of clinical documentation.We propose a novel cross-modal framework that uses MedCLIP as both a vision extractor and a retrieval mechanism to improve the process of medical report generation.By extracting retrieved

Beilin Chu, Xuan Xu, Xin Wang, Yufei Zhang

The rapid advancement of diffusion models has significantly improved high-quality image generation, making generated content increasingly challenging to distinguish from real images and raising concerns about potential misuse. In this paper, we observe that diffusion models struggle to accurately reconstruct mid-band frequency information in real images, sug

Chunna Zeng, Yu Lan

A representation theorem for continuous, SL(n) covariant vector-valued valuations on Orlicz spaces is established. Such valuations are uniquely characterized as moment vectors.

Yang Jiao, Kai Yang, Chengtao Jian

Trilevel learning (TLL) found diverse applications in numerous machine learning applications, ranging from robust hyperparameter optimization to domain adaptation. However, existing researches primarily focus on scenarios where TLL can be addressed with first order information available at each level, which is inadequate in many situations involving zeroth o

Qiyao Bao, Rui Liu, Jie Shen

A Banach space is said to have the ball-covering property (BCP) if its unit sphere can be covered by countably many closed or open balls off the origin. Let $X$ be a Banach space with a shrinking $1$-unconditional basis. In this paper, by constructing an equivalent norm on $B(X)$, we prove that the quotient Banach algebra $B(X)/K(X)$ fails the BCP. In partic

Meixu Chen, Kai Wang, Payal Kapur, James Brugarolas

Purpose: A reliable cancer prognosis model for clear cell renal cell carcinoma (ccRCC) can enhance personalized treatment. We developed a multi-modal ensemble model (MMEM) that integrates pretreatment clinical data, multi-omics data, and histopathology whole slide image (WSI) data to predict overall survival (OS) and disease-free survival (DFS) for ccRCC pat

Shixin Song, Joseph Zhang, Mengjia Yan

Address Space Layout Randomization (ASLR) is one of the most prominently deployed mitigations against memory corruption attacks. ASLR randomly shuffles program virtual addresses to prevent attackers from knowing the location of program contents in memory. Microarchitectural side channels have been shown to defeat ASLR through various hardware mechanisms. We

Charles Averill

The ubiquity of networking infrastructure in modern life necessitates scrutiny into networking fundamentals to ensure the safety and security of that infrastructure. The formalization of concurrent algorithms, a cornerstone of networking, is a longstanding area of research in which models and frameworks describing distributed systems are established. Despite

Carmen B. Rodríguez, Stephanie M. Wu, Stephanie Alimena, Alecia J McGregor

Many studies have examined social determinants of health (SDoH) independently, overlooking their interconnected nature. Our study uses a multidimensional approach to construct a neighborhood-level measure that explores how multiple SDoH jointly impact care received for endometrial cancer (EC) patients in Massachusetts (MA). Using 2015-2019 American Community

Jessica S. Purcell, Lecheng Su

It has been known for several decades that classical alternating links in the 3-sphere have nice hyperbolic geometric properties. Recent work generalises such results to give hyperbolic geometry of links with alternating projections onto any surface in very general 3-manifolds. However, the most general results require an orientable projection surface. In th

Wei-Lun Huang, Minghao Xue, Zhiyou Liu, Davood Tashayyod

Melanoma is the most deadly form of skin cancer. Tracking the evolution of nevi and detecting new lesions across the body is essential for the early detection of melanoma. Despite prior work on longitudinal tracking of skin lesions in 3D total body photography, there are still several challenges, including 1) low accuracy for finding correct lesion pairs acr

Yuriy G. Pogorelov, Volodymyr Turkowski, Vadim M. Loktev

We consider electronic spectra of twisted carbon nanotubes and their perturbation by impurity atoms absorbed at different positions on nanotube surface within the framework of Anderson hybrid model. A special attention is given to the cases when 1D Weyl (massless Dirac) modes are present in the nanotube spectrum and their hybridization with localized impurit

Ryosuke Kobashi, Shiu-Hang Lee, Takaaki Tanaka, Keiichi Maeda

While the environment around Tycho's supernova remnant (SNR) has long been believed to be close to homogeneous, the latest analysis of Chandra data has identified a substantial deceleration of the forward shock which poses a major challenges to this picture. arXiv:2310.14841 showed that the existence of dense molecular cloud (MC) surrounding a rarefied wind-

Yunming Zhang, Dengpan Ye, Sipeng Shen, Jun Wang

Arbitrary Style Transfer (AST) achieves the rendering of real natural images into the painting styles of arbitrary art style images, promoting art communication. However, misuse of unauthorized art style images for AST may infringe on artists' copyrights. One countermeasure is robust watermarking, which tracks image propagation by embedding copyright waterma

Yibo Li, Fengming Dong, Xiaolan Hu, Huiqing Liu

For a connected graph $G$, a spanning tree $T$ of $G$ is called a homeomorphically irreducible spanning tree (HIST) if $T$ has no vertices of degree $2$. In this paper, we show that if $G$ is a graph of order $n\ge 270$ and $|N(u)\cup N(v)|\geq\frac{n-1}{2}$ holds for every pair of nonadjacent vertices $u$ and $v$ in $G$, then $G$ has a HIST, unless $G$ belo

Rui Li, Song Wang, Chen Wang

Preconditioning techniques are crucial for enhancing the efficiency of solving large-scale linear equation systems that arise from partial differential equation (PDE) discretization. These techniques, such as Incomplete Cholesky factorization (IC) and data-driven neural network methods, accelerate the convergence of iterative solvers like Conjugate Gradient

Pranav Sunil, Ryan B. Sills

We present a method whereby the finite element method is used to train physics-informed neural networks that are suitable for surrogate modeling. The method is based on a custom convolutional operation called stencil convolution which leverages the inverse isoparametric map of the finite element method. We demonstrate the performance of the method in several

Chong-Xing Yue, Yue-Qi Wang, Xiao-Chen Sun, Xin-Yang Li

Vector-like leptons (VLLs) as one kind of interesting new particles can produce rich phenomenology at low- and high-energy experiments. In the framework of the singlet vector-like leptons with scalar (VLS) model, we investigate the discovery potential of VLL via its single production at the International Linear Collider (ILC) with the center of mass energy $

Wenxuan Chen, Linjie Zhao

We study a weakly asymmetric exclusion process with long jumps and with infinitely many extended reservoirs. We prove that the stationary fluctuations of the process are governed by the generalized Ornstein-Uhlenbeck process or the stochastic Burgers equation with Dirichlet boundary conditions depending on the strength of the asymmetry of the dynamics.

Nhat A. Nghiem

We propose and analyze a simple framework for estimating the amplitudes of a given $n$-qubit quantum state $\ket{\psi} = \sum_{i=0}^{2^n-1} a_i \ket{i}$ in computational basis, utilizing a single-qubit measurement only. Previously, it was a common procedure that one could measure all qubits in order to collect measurement outcomes, from which one can estimat

Lei Shi, Xiaoxiong Liu, C. M. Wang, Tianyu Liu

Since the discovery of the relation between the Chern number and quantum Hall effect, searching for observables of topological invariants has been an intriguing topic. Topological Hopf-link semimetals have attracted tremendous interest, in which the conduction and valence energy bands touch at linked nodal lines. However, it is challenging to identify this s

Zirun Guo, Tao Jin, Wenlong Xu, Wang Lin

Multimodal sentiment analysis (MSA) is an emerging research topic that aims to understand and recognize human sentiment or emotions through multiple modalities. However, in real-world dynamic scenarios, the distribution of target data is always changing and different from the source data used to train the model, which leads to performance degradation. Common

Taira Tsuchiya, Shinji Ito, Haipeng Luo

Learning in games refers to scenarios where multiple players interact in a shared environment, each aiming to minimize their regret. An equilibrium can be computed at a fast rate of $O(1/T)$ when all players follow the optimistic follow-the-regularized-leader (OFTRL). However, this acceleration is limited to the honest regime, in which all players adhere to

Jiaqing Zhang, Mingxiang Cao, Xue Yang, Kai Jiang

Visual language models like Contrastive Language-Image Pretraining (CLIP) have shown impressive performance in analyzing natural images with language information. However, these models often encounter challenges when applied to specialized domains such as remote sensing due to the limited availability of image-text pairs for training. To tackle this issue, w

Shuo Zhang

Finite element spaces by Whitney $k$-forms on cubical meshes in $\mathbb{R}^n$ are presented. Based on the spaces, compatible discretizations to $H\Lambda^k$ problems are provided, and discrete de Rham complexes and commutative diagrams are constructed.

Zixuan Chai, Si-Yuan Chen, Chenzheng Yu, Anton M. Graf

A Berry crystal is a random superposition of N plane waves of equal amplitude and fixed wavevector magnitude, propagating in different directions. Using numerical simulations of wavepacket dynamics, spectral analysis based on autocorrelation functions, and scaling of Inverse Participation Ratio, the nature of eigenstates across the energy spectrum of a two-d

Fei Ma, Yukan Li, Yifan Xie, Ying He

Human emotion synthesis is a crucial aspect of affective computing. It involves using computational methods to mimic and convey human emotions through various modalities, with the goal of enabling more natural and effective human-computer interactions. Recent advancements in generative models, such as Autoencoders, Generative Adversarial Networks, Diffusion

Qiye Guan, Kaiyang Wang, Jingjie Yeo, Yongqing Cai

The development of high-performance solid-state electrolytes (SSEs) has entered a critical stage, where entropy-driven strategies offer transformative potential for enhancing electrochemical properties. By engineering local environments for conductive ions alongside introducing disorder, these approaches can significantly improve conductivity. However, embra

Haihang Wu, Wei Wang, Tamasha Malepathirana, Sachith Seneviratne

Pruning can be an effective method of compressing large pre-trained models for inference speed acceleration. Previous pruning approaches rely on access to the original training dataset for both pruning and subsequent fine-tuning. However, access to the training data can be limited due to concerns such as data privacy and commercial confidentiality. Furthermo

Dongjun Kim, Minhyuk Kim, YongChan Chun, Chanjun Park

Large Language Models (LLMs) have demonstrated notable proficiency in both code generation and comprehension across multiple programming languages. However, the mechanisms underlying this proficiency remain underexplored, particularly with respect to whether distinct programming languages are processed independently or within a shared parametric region. Draw

Nahid Alam, Karthik Reddy Kanjula, Surya Guthikonda, Timothy Chung

The rapid development of large Vision-Language Models (VLMs) has led to impressive results on academic benchmarks, primarily in widely spoken languages. However, significant gaps remain in the ability of current VLMs to handle low-resource languages and varied cultural contexts, largely due to a lack of high-quality, diverse, and safety-vetted data. Conseque

Bo-Wen Zhang, Yan Yan, Boxiang Yang, Yifei Xue

While scaling laws optimize training configurations for large language models (LLMs) through experiments on smaller or early-stage models, they fail to predict emergent abilities due to the absence of such capabilities in these models. To address this, we propose a method that predicts emergent abilities by leveraging proxy tasks. We begin by establishing re