December 2024 arXiv papers — page 154

Zhen Qi, Jiajing Chen, Shuo Wang, Bingying Liu

This study aims to explore the performance improvement method of large language models based on GPT-4 under the multi-task learning framework and conducts experiments on two tasks: text classification and automatic summary generation. Through the combined design of shared feature extractors and task-specific modules, we achieve knowledge-sharing and optimiza

Kartik Patwari, David Schneider, Xiaoxiao Sun, Chen-Nee Chuah

Growing privacy concerns and regulations like GDPR and CCPA necessitate pseudonymization techniques that protect identity in image datasets. However, retaining utility is also essential. Traditional methods like masking and blurring degrade quality and obscure critical context, especially in human-centric images. We introduce Rendering-Refined Stable Diffusi

Zijian Zhao, Fanyi Meng, Zhonghao Lyu, Hang Li

Channel state information (CSI) is a fundamental component in both wireless communication and sensing systems, enabling critical functions such as radio resource optimization and environmental perception. In wireless sensing, data scarcity and packet loss hinder efficient model training, while in wireless communication, high-dimensional CSI matrices and shor

Yi Han

Let $X=(x_{ij})\in\mathbb{R}^{N\times n}$ be a rectangular random matrix with i.i.d. entries (we assume $N/n\to\mathbf{a}>1$), and denote by $\sigma_{min}(X)$ its smallest singular value. When entries have mean zero and unit second moment, the celebrated work of Bai-Yin and Tikhomirov show that $n^{-\frac{1}{2}}\sigma_{min}(X)$ converges almost surely to $\s

Saahith Janapati, Yangfeng Ji

The performance of Large Language Models (LLMs) on natural language tasks can be improved through both supervised fine-tuning (SFT) and in-context learning (ICL), which operate via distinct mechanisms. Supervised fine-tuning updates the model's weights by minimizing loss on training data, whereas in-context learning leverages task demonstrations embedded in

Yunheng Li, Yuxuan Li, Quansheng Zeng, Wenhai Wang

Pre-trained vision-language models (VLMs), such as CLIP, have demonstrated impressive zero-shot recognition capability, but still underperform in dense prediction tasks. Self-distillation recently is emerging as a promising approach for fine-tuning VLMs to better adapt to local regions without requiring extensive annotations. However, previous state-of-the-a

Sungpyo Kim, Jeonghyeok Do, Jaehyup Lee, Munchurl Kim

Conventional methods for PAN-sharpening often struggle to restore fine details due to limitations in leveraging high-frequency information. Moreover, diffusion-based approaches lack sufficient conditioning to fully utilize Panchromatic (PAN) images and low-resolution multispectral (LRMS) inputs effectively. To address these challenges, we propose an uncertai

Jan Blechta, Vít Průša, Ladislav Trnka, Karel Tůma

We consider linear second order differential equation y''= f with zero Dirichlet boundary conditions. At the continuous level this problem is solvable using the Green function, and this technique has a counterpart on the discrete level. The discrete solution is represented via an application of a matrix -- the Green matrix -- to the discretised right-hand si

Omraj Kamat, Tridib Ghosh, Kalaivani J, Angayarkanni V

Plagiarism is an act of using someone else's work without proper acknowledgment, and this sin is seen to cut across various arenas including the academy, publishing, and other similar arenas. The traditional methods of plagiarism detection through keyword matching and review by humans usually fail to cope with increasingly sophisticated techniques used to ma

Xiao-Qiong Wang, Rui-Lang Zeng, Zi-Yao Zhang, Chushun Tian

We report on the experimental observation of classical Brownian motion in momentum space by a Bose-Einstein condensate (BEC) of Rubidium atoms prepared in a hexagonal optical lattice. Upon suddenly increasing the effective atomic mass, the BEC as a whole behaves as a classical rigid body with its center-of-mass receiving random momentum kicks by a Langevin f

Mohammed N. Swileh, Shengli Zhang

Software defined networking (SDN) represents a transformative shift in network architecture by decoupling the control plane from the data plane, enabling centralized and flexible management of network resources. However, this architectural shift introduces significant security challenges, as SDN's centralized control becomes an attractive target for various

Yue Fu

This paper presents an autoethnography of my recent trip to China, during which I engaged in using various apps and discovered the cultural and social norms embedded in everyday mobile app use. Navigating between Western and Chinese cultures, my experience was simultaneously exhilarating, embarrassing, and bewildering. Through this autoethnography, I aim to

Marsha Mariya Kappan, Joby George

A kind of pancreatic cancer called Pancreatic Ductal Adenocarcinoma (PDAC) is anticipated to be one of the main causes of mortality during past years. Evidence from several researches supported the concept that the oncogenic KRAS (Ki-ras2 Kirsten rat sarcoma viral oncogene) mutation is the major cause of pancreatic cancer. KRAS acts as an on-off switch that

Qihang Ai, Hanxiao Feng, Xinyu Yang, Mengxi Tan

The simultaneous progress of integrated optical frequency comb (OFC) and radio frequency (RF) photonic signal processing technique have promoted the rapid development of real-time signal processing. Integrated optical frequency comb offer multiple wavelengths as a powerful source for RF photonic signal transversal filter. Here, we review development of real-

Michael Yeung, Toya Teramoto, Songtao Wu, Tatsuo Fujiwara

The use of large-scale, web-scraped datasets to train face recognition models has raised significant privacy and bias concerns. Synthetic methods mitigate these concerns and provide scalable and controllable face generation to enable fair and accurate face recognition. However, existing synthetic datasets display limited intraclass and interclass diversity a

Seungtae Nam, Xiangyu Sun, Gyeongjin Kang, Younggeun Lee

Generalized feed-forward Gaussian models have achieved significant progress in sparse-view 3D reconstruction by leveraging prior knowledge from large multi-view datasets. However, these models often struggle to represent high-frequency details due to the limited number of Gaussians. While the densification strategy used in per-scene 3D Gaussian splatting (3D

Yong He, Zeyu Li, Dong Liu, Kangxiang Qin

We propose to transfer representational knowledge from multiple sources to a target noisy matrix completion task by aggregating singular subspaces information. Under our representational similarity framework, we first integrate linear representation information by solving a two-way principal component analysis problem based on a properly debiased matrix-valu

Wei Wang, Xulong Wang, Guancong Ma

The presence of disorder can severely impede wave transport, resulting in the famous Anderson localization. Previous theoretical studies found that Anderson transition can exist in one-dimensional (1D) non-Hermitian disordered rings with chiral hopping, defying the scaling theory of localization for Hermitian systems. In these systems, localized (extended) m

Leon Fernando, Billy Pik Lik Lau, Chau Yuen, U-Xuan Tan

The rapid advancements in unmanned aerial vehicles (UAVs) have unlocked numerous applications, including environmental monitoring, disaster response, and agricultural surveying. Enhancing the collective behavior of multiple decentralized UAVs can significantly improve these applications through more efficient and coordinated operations. In this study, we exp

Elad Paran

We construct an example of a division ring $D$ and a maximal left ideal $M$ in the polynomial ring $D[x,y]$ in two central variables over $D$, such that the intersection $M \cap D[x]$ is not a maximal left ideal in $D[x]$. This resolves a ring-theoretic problem of Amitsur and Small raised in 1978.

Prakash Aryan

This paper introduces DebateBrawl, an innovative AI-powered debate platform that integrates Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS) to create an adaptive and engaging debating experience. DebateBrawl addresses the limitations of traditional LLMs in strategic planning by incorporating evolutionary optimization and ga

Ming Li, Sheng Fang, Jingfang Fan, Youjin Deng

Finite-size scaling (FSS) for a critical phase transition ($t=0$) states that within a window of size $|t|\sim L^{-1/\nu}$, the scaling behavior of any observable $Q$ in a system of linear size $L$ asymptotically follows a scaling form as $Q(t,L)=L^{Y_Q}\tilde{Q}(tL^{1/\nu})$, where $\nu$ is the correlation-length exponent, $Y_Q$ is an FSS exponent and ${\ti

Marsha Mariya Kappan, Eduardo Benitez Sandoval, Erik Meijering, Francisco Cruz

Pose estimation is a critical task in computer vision with a wide range of applications from activity monitoring to human-robot interaction. However,most of the existing methods are computationally expensive or have complex architecture. Here we propose a lightweight attention based pose estimation network that utilizes depthwise separable convolution and Co

Sen Lin, Ao Kong, Robert Azencott

The Generalized Extreme Value (GEV) distribution plays a critical role in risk assessment across various domains, such as hydrology, climate science, and finance. In this study, we investigate its application in analyzing intraday trading risks within the Chinese stock market, focusing on abrupt price movements influenced by unique trading regulations. To ad

Hansub Hwang, Sunhwa Hwang, Jaewook Ahn, Shuhei Yoshida

Cold collisions between two Rydberg rubidium atoms ($^{87}$Rb) are investigated by controlling the impact parameter and collision energy. Optical tweezers are employed to hold one atom stationary while propelling the other to a constant velocity. After the tweezers are deactivated, both atoms are excited to a Rydberg state by a $\pi$-pulse. After a collision

Jiazhao Zhang, Kunyu Wang, Shaoan Wang, Minghan Li

A practical navigation agent must be capable of handling a wide range of interaction demands, such as following instructions, searching objects, answering questions, tracking people, and more. Existing models for embodied navigation fall short of serving as practical generalists in the real world, as they are often constrained by specific task configurations

Yuhao Zhao, Xiande Zhang

A low-power error-correcting cooling (LPECC) code was introduced as a coding scheme for communication over a bus by Chee et al. to control the peak temperature, the average power consumption of on-chip buses, and error-correction for the transmitted information, simultaneously. Specifically, an $(n, t, w, e)$-LPECC code is a coding scheme over $n$ wires that

Harsh Shah, Jayakrishnan Nair, D Manjunath, Narayan Mandayam

We consider the following Colonel Blotto game between parties $P_1$ and $P_A.$ $P_1$ deploys a non negative number of troops across $J$ battlefields, while $P_A$ chooses $K,$ $K < J,$ battlefields to remove all of $P_1$'s troops from the chosen battlefields. $P_1$ has the objective of maximizing the number of surviving troops while $P_A$ wants to minimize it

Yuhang Li, Tianyi Gan, Jingxi Li, Mona Jarrahi

Unidirectional optical systems enable selective control of light through asymmetric processing of radiation, effectively transmitting light in one direction while blocking unwanted propagation in the opposite direction. Here, we introduce a reciprocal diffractive unidirectional focusing design based on linear and isotropic diffractive layers that are structu

Alexandros E. Tzikas, Arec Jamgochian, Nazim Kemal Ure, Mykel J. Kochenderfer

Computing the similarity between two probability distributions is a recurring theme across control. We introduce a unified family of distances between the probability distributions of two random variables that is based on the discrepancy between the cumulative distribution functions of random linear one-dimensional projections of the random variables. Our pr

Bochuan Cao, Jinyuan Jia, Chuxuan Hu, Wenbo Guo

Backdoor attacks aim to inject a backdoor into a classifier such that it predicts any input with an attacker-chosen backdoor trigger as an attacker-chosen target class. Existing backdoor attacks require either retraining the classifier with some clean data or modifying the model's architecture. As a result, they are 1) not applicable when clean data is unava

Alexander Hulpke

We describe a generalization of the concept of a pc presentation that applies to groups with a nontrivial solvable radical. Such a representation can be much more efficient in terms of memory use and even of arithmetic, than permuattion and matrix representations. We illustrate the use of such representations by constructing a maximal subgroup of the sporadi

Dmitrii Belogolovskii, Md Masudur Rahman, Karl Johnson, Vladimir Fedorov

Phase-sensitive integrated photonic devices are highly susceptible to minor manufacturing deviations, resulting in significant performance inconsistencies. This variability has limited the scalability and widespread adoption of these devices. Here, a major advancement is achieved through continuous-wave (CW) visible light (405 nm and 520 nm) trimming of plas

Yanxin Zhang, Zhengyu Hua, Long Yuan, Zi Chen

Community search on bipartite graphs, especially influential community detection, has received significant attention. Existing studies use minimum vertex weights, inadequately reflecting true community influence when some vertices have low weights. To address this, we introduce the $(\alpha,\beta)$-influential community model based on the average vertex weig

Jaden Mu

Autonomous vehicles (AVs) increasingly use DNN-based object detection models in vision-based perception. Correct detection and classification of obstacles is critical to ensure safe, trustworthy driving decisions. Adversarial patches aim to fool a DNN with intentionally generated patterns concentrated in a localized region of an image. In particular, object

Julia Brand, Ksenia Maximova, Steve Madden, Andrei V. Rode

This study investigates the possibility of using a femtosecond pulse laser to remove iron ore slurry used to stabilise blast-hole structures by mining industries, intending to preserve the wall's stability and the chemical and compositional properties of the underlying rock. In situ minerals are often coated in other material deposits, such as dust or slurry

C. R. Das

A hypothetical particle known as the axion holds the potential to resolve both the cosmic dark matter riddle and particle physics' long-standing, strong CP dilemma. An unusually strong 21-cm absorption feature associated with the initial star formation era, i.e., the dark ages, may be due to ultralight axion dark matter ($\sim$10$^{-22}$ eV) at this time. Th

Zhepeng Wang, Runxue Bao, Yawen Wu, Guodong Liu

Graph neural networks (GNNs) are powerful machine learning models designed to handle irregularly structured data. However, their generic design often proves inadequate for analyzing brain connectomes in Alzheimer's Disease (AD), highlighting the need to incorporate domain knowledge for optimal performance. Infusing AD-related knowledge into GNNs is a complic

Qinfeng Zhu, Yuan Fang, Lei Fan

Crack detection is a critical task in structural health monitoring, aimed at assessing the structural integrity of bridges, buildings, and roads to prevent potential failures. Vision-based crack detection has become the mainstream approach due to its ease of implementation and effectiveness. Fusing infrared (IR) channels with red, green and blue (RGB) channe

Jiechao Gao, Yuangang Li, Jie Wang, Yue Zhao

With the rapid development of the Internet of Things (IoT), federated learning (FL) has gained increasing attention for its privacy-preserving use of distributed data. However, conventional two-tier FL architectures are poorly suited to the hierarchical and heterogeneous nature of real-world IoT systems. Hierarchical Federated Learning (HFL) introduces multi

Kim Sung-Bin, Arda Senocak, Hyunwoo Ha, Tae-Hyun Oh

How does audio describe the world around us? In this work, we propose a method for generating images of visual scenes from diverse in-the-wild sounds. This cross-modal generation task is challenging due to the significant information gap between auditory and visual signals. We address this challenge by designing a model that aligns audio-visual modalities by

Fei Yu, Zhe Xiang, Nan Che, Zhuoran Zhang

Multimodal semantic communication, which integrates various data modalities such as text, images, and audio, significantly enhances communication efficiency and reliability. It has broad application prospects in fields such as artificial intelligence, autonomous driving, and smart homes. However, current research primarily relies on analog channels and assum

Hanping Zhang, Yuhong Guo

Learning from Demonstration (LfD) is a well-established problem in Reinforcement Learning (RL), which aims to facilitate rapid RL by leveraging expert demonstrations to pre-train the RL agent. However, the limited availability of expert demonstration data often hinders its ability to effectively aid downstream RL learning. To address this problem, we propose

Nan Zhang, Prafulla Kumar Choubey, Alexander Fabbri, Gabriel Bernadett-Shapiro

Indexing is an important step towards strong performance in retrieval-augmented generation (RAG) systems. However, existing methods organize data based on either semantic similarity (similarity) or related information (relatedness), but do not cover both perspectives comprehensively. Our analysis reveals that modeling only one perspective results in insuffic

Mahla Ardebili Pour, Mohammad B. Ghiasi, Ali Karkehabadi

Floods are among the most prevalent and destructive natural disasters, often leading to severe social and economic impacts in urban areas due to the high concentration of assets and population density. In Iran, particularly in Tehran, recurring flood events underscore the urgent need for robust urban resilience strategies. This paper explores flood resilienc

Yanqi Cheng, Carola-Bibiane Schönlieb, Angelica I Aviles-Rivero

The use of Plug-and-Play (PnP) methods has become a central approach for solving inverse problems, with denoisers serving as regularising priors that guide optimisation towards a clean solution. In this work, we introduce KAN-PnP, an optimisation framework that incorporates Kolmogorov-Arnold Networks (KANs) as denoisers within the Plug-and-Play (PnP) paradig

Robert Dilworth, Charan Gudla

This paper explores the relatively underexplored application of Positive Unlabeled (PU) Learning and Negative Unlabeled (NU) Learning in the cybersecurity domain. While these semi-supervised learning methods have been applied successfully in fields like medicine and marketing, their potential in cybersecurity remains largely untapped. The paper identifies ke

Guojian Wu, Fang Gao, Qing Gao, Yu Pan

Linear system solvers are widely used in scientific computing, with the primary goal of solving linear system problems. Classical iterative algorithms typically rely on the conjugate gradient method. The rise of quantum computing has spurred interest in quantum linear system problems (QLSP), particularly following the introduction of the HHL algorithm by Har

Yohei Yamashita, Chihiro Nakatani, Norimichi Ukita

This paper addresses a new virtual try-on problem of fitting any size of clothes to a reference person in the image domain. While previous image-based virtual try-on methods can produce highly natural try-on images, these methods fit the clothes on the person without considering the relative relationship between the physical sizes of the clothes and the pers

Kotaro Hisa, Kazuhiro Ishige

We study qualitative properties of initial traces of nonnegative solutions to a semilinear heat equation in a smooth domain under the Dirichlet boundary condition. Furthermore, for the corresponding Cauchy--Dirichlet problem, we obtain sharp necessary conditions and sufficient conditions on the existence of nonnegative solutions and identify optimal singular

Rupam Barman, Sipra Mairty, Sulakashna

Let $\mathbb{F}_q$ be a finite field with $q$ elements. For $a,b,c,d,e,f \in \mathbb{F}_q^{\times}$, denote by $C_{a,b,c,d,e,f}$ the family of algebraic curves over $\mathbb{F}_q$ given by the affine equation \begin{align*} C_{a,b,c,d,e,f}:ay^2+bx^2+cxy=d+ex^2y^2+fx^3y. \end{align*} The family of generalized twisted Edwards curves is a subfamily of $C_{a,b,c

James Vo

As Large Language Models (LLMs) scale to longer context windows, the computational cost of attention mechanisms, which traditionally grows quadratically with input length, presents a critical challenge for real-time and memory-constrained deployments. Existing sparse attention techniques have sought to reduce this complexity, but they often incur significant

Spencer Folk

Small unmanned aerial vehicles (UAVs) have become standard tools in reconnaissance and surveying for both civilian and defense applications. In the future, UAVs will likely play a pivotal role in autonomous package delivery, but current multi-rotor candidates suffer from poor energy efficiency leading to insufficient endurance and range. In order to reduce t

Kun Yan, Wenping Ma, Shaohui Sun

5G networks provide secure and reliable information transmission services for the Internet of Everything, thus paving the way for 6G networks, which is anticipated to be an AI-based network, supporting unprecedented intelligence across applications. Abundant computing resources will establish the 6G Computing Power Network (CPN) to facilitate ubiquitous inte

Léa Demeule, Mahtab Sandhu, Glen Berseth

The majority of signal data captured in the real world uses numerous sensors with different resolutions. In practice, however, most deep learning architectures are fixed-resolution; they consider a single resolution at training time and inference time. This is convenient to implement but fails to fully take advantage of the diverse signal data that exists. I

Cassiano A. Daniel

By extending the six-dimensional hybrid formalism for the superstring to include $d=6$ $\mathcal{N}=1$ superspace variables along with unconstrained bosonic ghost fields, we construct a manifestly spacetime supersymmetric vertex operator $U$. We demonstrate that the BRST invariance of $U$ implies the $d=6$ $\mathcal{N}=1$ SYM equations of motion in superspac

Zong Ke, Yuchen Yin

As the increasing application of AI in finance, this paper will leverage AI algorithms to examine tail risk and develop a model to alter tail risk to promote the stability of US financial markets, and enhance the resilience of the US economy. Specifically, the paper constructs a multivariate multilevel CAViaR model, optimized by gradient descent and genetic

Jiwon Choi, Dongjin Cho, Gihyeon Lee, Hogyun Kim

Maritime environments often present hazardous situations due to factors such as moving ships or buoys, which become obstacles under the influence of waves. In such challenging conditions, the ability to detect and track potentially hazardous objects is critical for the safe navigation of marine robots. To address the scarcity of comprehensive datasets captur

Ziyuan Qu, Zihao Zou, Vivek Boominathan, Praneeth Chakravarthula

Event cameras, which feature pixels that independently respond to changes in brightness, are becoming increasingly popular in high-speed applications due to their lower latency, reduced bandwidth requirements, and enhanced dynamic range compared to traditional frame-based cameras. Numerous imaging and vision techniques have leveraged event cameras for high-s

Usman Ahmad

In this paper, we propose a hypothesis regarding the travel and movement of chemicals between locations. We introduce six distinct methods to explain this process. The chemicals referred to in this article are those that we detect by the nose and can travel through the air. These are generally known as odorants or volatile compounds. Method 1 involves decomp

Guozhen Hu, Zhengyi Shao, Erbil Gugercinoglu, Wenyuan Cui

We explore the metal-poor regime of the Galactic disk on the distribution of stars in the [$\alpha$/M]-$V_{\phi}$ plane, to identify the most metal-poor thin disk (MPTnD) stars belonging to the low-$\alpha$ sequence. Chemical abundances and velocities of sample stars are either taken or derived from APOGEE DR17 and Gaia DR3 catalogs. We find the existence of

Mushuang Liu, Ilya Kolmanovsky

We prove input-to-state stability (ISS) of perturbed Newton-type methods for generalized equations arising from Nash equilibrium (NE) and generalized NE (GNE) problems. This ISS property allows the use of inexact computations in equilibrium-seeking to enable fast solution tracking in dynamic systems such as in model predictive control (MPC). For NE problems,

Boris Muha, Srđan Trifunović

In this paper, we examine the dynamic behavior of a viscoelastic string oscillating above a rigid obstacle in a one-dimensional setting, accounting for inelastic contact between the string and the obstacle. We construct a global-in-time weak solution to this problem by using an approximation method that incorporates a penalizing repulsive force of the form $

Lingjun Mao, Zineng Tang, Alane Suhr

We study the perception of color illusions by vision-language models. Color illusion, where a person's visual system perceives color differently from actual color, is well-studied in human vision. However, it remains underexplored whether vision-language models (VLMs), trained on large-scale human data, exhibit similar perceptual biases when confronted with

Leif Schaumann

Work by Ma and Holdener in 2005 revealed that using turtle graphics to visualize the Thue-Morse sequence can result in curves which approximate the Koch fractal curve. A 2007 paper by Allouche and Skordev pointed out that this phenomenon is connected to certain complex sums considered by F. M. Dekking in 1982. We make this connection explicit by showing that

Zeng You, Zhiquan Wen, Yaofo Chen, Xin Li

Long video understanding has become a critical task in computer vision, driving advancements across numerous applications from surveillance to content retrieval. Existing video understanding methods suffer from two challenges when dealing with long video understanding: intricate long-context relationship modeling and interference from redundancy. To tackle t

Raghav Ramji, Keshav Ramji

Evaluating large language models (LLMs) on their linguistic reasoning capabilities is an important task to understand the gaps in their skills that may surface during large-scale adoption. In this work, we investigate the abilities of such models to perform abstract multilingual reasoning through the lens of linguistic puzzles on extremely low-resource langu

Bryan Li, Sounak Bagchi, Zizhan Wang

The increasing integration of Large Language Models (LLMs) into society necessitates robust defenses against vulnerabilities from jailbreaking and adversarial prompts. This project proposes a recursive framework for enhancing the resistance of LLMs to manipulation through the use of prompt simplification techniques. By increasing the transparency of complex

Lian Chen, Jiayuan Zhao, Xiaohu Wei, Zhaohui Song

The igneous rocks in deep formation have the characteristics of hardness, poor drillability and high abrasiveness, which is a difficulty in speeding up drilling. The drilling efficiency of existing conventional bits is low in igneous rocks. Based on the characteristics of igneous rocks, rock mechanical parameters and drillability experiments of granite, sand

Matīss Rikters, Edison Marrese-Taylor, Rinalds Vīksna

This research builds upon the Latvian Twitter Eater Corpus (LTEC), which is focused on the narrow domain of tweets related to food, drinks, eating and drinking. LTEC has been collected for more than 12 years and reaching almost 3 million tweets with the basic information as well as extended automatically and manually annotated metadata. In this paper we supp

Bokai Xu, Jiayi Zhang, Qingfeng Lin, Huahua Xiao

The key technologies of sixth generation (6G), such as ultra-massive multiple-input multiple-output (MIMO), enable intricate interactions between antennas and wireless propagation environments. As a result, it becomes necessary to develop joint models that encompass both antennas and wireless propagation channels. To achieve this, we utilize the multi-port c

Sajad Fathi Hafshejani, Md Mohsin Uddin, David Neufeld, Daya Gaur

This paper explores the use of quantum computing, specifically the use of HHL and VQLS algorithms, to solve optimal power flow problem in electrical grids. We investigate the effectiveness of these quantum algorithms in comparison to classical methods. The simulation results presented here which substantially improve the results in [1] indicate that quantum

Pranjal Aggarwal, Bryan Parno, Sean Welleck

Automated code generation with large language models has gained significant traction, but there remains no guarantee on the correctness of generated code. We aim to use formal verification to provide mathematical guarantees that the generated code is correct. However, generating formally verified code with LLMs is hindered by the scarcity of training data an

Xinyi Gao, Xiaodian Chen, Shu Wang, Jifeng Liu

The number of known periodic variable stars has increased rapidly in recent years. As an all-sky transit survey, the Transiting Exoplanet Survey Satellite (TESS) plays an important role in detecting low-amplitude variable stars. Using 2-minute cadence data from the first 67 sectors of TESS, we find 72,505 periodic variable stars. We used 19 parameters includ

Yuzhu Ji, Chuanxia Zheng, Tat-Jen Cham

Human motion transfer aims at animating a static source image with a driving video. While recent advances in one-shot human motion transfer have led to significant improvement in results, it remains challenging for methods with 2D body landmarks, skeleton and semantic mask to accurately capture correspondences between source and driving poses due to the larg

Isay Katsman, Ethan Lou, Anna Gilbert

Graph machine learning has enjoyed a meteoric rise in popularity since the introduction of deep learning in graph contexts. This is no surprise due to the ubiquity of graph data in large scale industrial settings. Tacitly assumed in all graph learning tasks is the separation of the graph structure and node features: node features strictly encode individual d

Fan Liu, Chenwei Dong, Chuanyi Zhang, Hualiang Zhou

Many researchers collect data from the internet through crowd-sourcing or web crawling to alleviate the data-hungry challenge associated with cross-modal matching. Although such practice does not require expensive annotations, it inevitably introduces mismatched pairs and results in a noisy correspondence problem. Current approaches leverage the memorization

Huaxin Zhang, Xiaohao Xu, Xiang Wang, Jialong Zuo

How can we enable models to comprehend video anomalies occurring over varying temporal scales and contexts? Traditional Video Anomaly Understanding (VAU) methods focus on frame-level anomaly prediction, often missing the interpretability of complex and diverse real-world anomalies. Recent multimodal approaches leverage visual and textual data but lack hierar

Joseph P. Ndenda, Michael G. Watson, Ashish Misra, Mary R. Myerscough

Smooth muscle cells (SMCs) play a fundamental role in the development of atherosclerotic plaques. They ingest lipids in a similar way to monocyte-derived macrophages (MDMs) in the plaque. This can stimulate SMCs to undergo a phenotypic switch to a macrophage-like phenotype. We formulate an ordinary differential equation (ODE) model for the populations of SMC

Yu Wang, Yunhu Zhu, Yixin Li, Maolin Bo

Understanding the interatomic bonding and electronic properties of two-dimensional (2D) materials is crucial for preparing high-performance 2D semiconductor materials. We have calculated the band structure, electronic properties, and bonding characteristics of SnO in 2D materials by using density functional theory (DFT) and combining bond energy and bond cha

Hao Fu, Prashanth Krishnamurthy, Siddharth Garg, Farshad Khorrami

Out-of-distribution (OOD) detection is crucial for the deployment of machine learning models in the open world. While existing OOD detectors are effective in identifying OOD samples that deviate significantly from in-distribution (ID) data, they often come with trade-offs. For instance, deep OOD detectors usually suffer from high computational costs, require

Ruizhi Wang, Yu Rong, Kai Liu, Bingjie Li

In digital advertising, demand-side platforms (DSPs) allow advertisers to create multiple ad creatives from a single photo for real-time bidding. While increasing the number of creatives can improve bidding opportunities, it cannot scale indefinitely, and the incremental advertising revenue typically exhibits diminishing returns as more creatives are generat

Boyu Zhang, Triet H. M. Le, M. Ali Babar

Software vulnerabilities can result in catastrophic cyberattacks that increasingly threaten business operations. Consequently, ensuring the safety of software systems has become a paramount concern for both private and public sectors. Recent literature has witnessed increasing exploration of learning-based approaches for software vulnerability detection. How

Rohan Deb, Mohammad Ghavamzadeh, Arindam Banerjee

Conservative Contextual Bandits (CCBs) address safety in sequential decision making by requiring that an agent's policy, along with minimizing regret, also satisfies a safety constraint: the performance is not worse than a baseline policy (e.g., the policy that the company has in production) by more than $(1+\alpha)$ factor. Prior work developed UCB-style al

Yusuke Miyashita, Patrick Kin Man Tung, Johan Barthélemy

High-Performance Computing (HPC) is crucial for performing advanced computational tasks, yet their complexity often challenges users, particularly those unfamiliar with HPC-specific commands and workflows. This paper introduces Hypothetical Command Embeddings (HyCE), a novel method that extends Retrieval-Augmented Generation (RAG) by integrating real-time, u

Junyu Liu, Daniele Panozzo, Mario Botsch, Teseo Schneider

We study the use of polyhedral discretizations for the solution of heat diffusion and elastodynamic problems in computer graphics. Polyhedral meshes are more natural for certain applications than pure triangular or quadrilateral meshes, which thus received significant interest as an alternative representation. We consider finite element methods using barycen

Rongfeng Su, Changqing Xu, Xinyi Wu, Feng Xu

Previous studies have demonstrated that emotional features from a single acoustic sentiment label can enhance depression diagnosis accuracy. Additionally, according to the Emotion Context-Insensitivity theory and our pilot study, individuals with depression might convey negative emotional content in an unexpectedly calm manner, showing a high degree of incon

Yuming Li, Peidong Jia, Daiwei Hong, Yueru Jia

Training-free high-resolution (HR) image generation has garnered significant attention due to the high costs of training large diffusion models. Most existing methods begin by reconstructing the overall structure and then proceed to refine the local details. Despite their advancements, they still face issues with repetitive patterns in HR image generation. B

Gonzalo Gonzalez-Pumariega, Wayne Chen, Kushal Kedia, Sanjiban Choudhury

Planning in complex environments requires an agent to efficiently query a world model to find a feasible sequence of actions from start to goal. Recent work has shown that Large Language Models (LLMs), with their rich prior knowledge and reasoning capabilities, can potentially help with planning by searching over promising states and adapting to feedback fro

Zhixin Zhao, Yitao Hu, Ziqi Gong, Guotao Yang

Advances in deep neural networks (DNNs) have significantly contributed to the development of real-time video processing applications. Efficient scheduling of DNN workloads in cloud-hosted inference systems is crucial to minimizing serving costs while meeting application latency constraints. However, existing systems suffer from excessive module latency durin

Gaurav Shrivastava

Obstacle-aware trajectory navigation is crucial for many systems. For example, in real-world navigation tasks, an agent must avoid obstacles, such as furniture in a room, while planning a trajectory. Gaussian Process (GP) regression, in its current form, fits a curve to a set of data pairs, with each pair consisting of an input point 'x' and its correspondin

Hanzheng Lyu, Shaokang Xie, Jianyu Niu, Ivan Beschastnikh

Multi-Byzantine Fault Tolerant (Multi-BFT) consensus allows multiple consensus instances to run in parallel, resolving the leader bottleneck problem inherent in classic BFT consensus. However, the global ordering of Multi-BFT consensus enforces a strict serialized sequence of transactions, imposing additional confirmation latency and also limiting concurrenc

Jun Tian, Yu Pan, Shuo Cao, Qing-Quan Jiang

Searching for Lorentz invariance violation (LIV) using astrophysical sources such as gamma-ray bursts (GRBs) is crucial for probing quantum gravity. However, the dependence of LIV constraints on assumed cosmological models has been largely overlooked. In this work, we present a model-independent reconstruction of the cosmic expansion history using artificial

Zijian Zhou, Zhenya Yan

In this paper, we study the neural tangent kernel (NTK) for general partial differential equations (PDEs) based on physics-informed neural networks (PINNs). As we all know, the training of an artificial neural network can be converted to the evolution of NTK. We analyze the initialization of NTK and the convergence conditions of NTK during training for gener

Li Bai, Haibo Hu, Qingqing Ye, Haoyang Li

Federated learning is a decentralized machine learning approach where clients train models locally and share model updates to develop a global model. This enables low-resource devices to collaboratively build a high-quality model without requiring direct access to the raw training data. However, despite only sharing model updates, federated learning still fa

Yufei Zhu, P. M. R. Brydon

In many unconventional superconductors the pairing interaction is believed to be mediated by a fluctuating order. Although this is typically taken to be magnetic in origin, the role of other fluctuating orders has recently been considered. In this work we examine the weak-coupling pairing interaction produced by a general fluctuating order, and seek to ident

Guoxiao Zhang, Yi Wei, Yadong Zhang, Huajian Feng

Click-Through Rate (CTR) prediction is essential in online advertising, where semantic information plays a pivotal role in shaping user decisions and enhancing CTR effectiveness. Capturing and modeling deep semantic information, such as a user's preference for "H\"aagen-Dazs' HEAVEN strawberry light ice cream" due to its health-conscious and premium attribut

Duncan A. Forbes, Maria Luisa Buzzo, Anna Ferre-Mateu, Aaron J. Romanowsky

Some ultra diffuse galaxies (UDGs) reveal many more globular clusters (GCs) than classical dwarf galaxies of the same stellar mass. These UDGs, with a mass in their GC system (M$_{GC}$) approaching 10\% of their host galaxy stellar mass (M$_{\ast}$), are also inferred to have high halo mass to stellar mass ratios (M$_{halo}$/M$_{\ast}$). They have been dubbe

Edward Chen, Natalie Dullerud, Thomas Niedermayr, Elizabeth Kidd

Countless science and engineering applications in multi-objective optimization (MOO) necessitate that decision-makers (DMs) select a Pareto-optimal (PO) solution which aligns with their preferences. Evaluating individual solutions is often expensive, and the high-dimensional trade-off space makes exhaustive exploration of the full Pareto frontier (PF) infeas

Connor Malone, Somayeh Hussaini, Tobias Fischer, Michael Milford

Visual Place Recognition (VPR) enables coarse localization by comparing query images to a reference database of geo-tagged images. Recent breakthroughs in deep learning architectures and training regimes have led to methods with improved robustness to factors like environment appearance change, but with the downside that the required training and/or matching