December 2024 arXiv papers — page 136

G. R. Boroun, Yanbing Cai

The longitudinal structure function is considered at the next-to-leading order approximation using the expansion method, as defined by M.B.Gay Ducati and P.B.Goncalves [Phys.Lett.B {\bf390}, 401 (1997)] and further developed by Jingxuan Chen et al., [Chin.Phys.C {\bf48}, 063104 (2024)]. This method provides results for a wide range of $x$ and $Q^2$ values. I

Ren Fujiwara, Yasuko Matsubara, Yasushi Sakurai

We study the problem of modeling a non-linear dynamical system when given a time series by deriving equations directly from the data. Despite the fact that time series data are given as input, models for dynamics and estimation algorithms that incorporate long-term temporal dependencies are largely absent from existing studies. In this paper, we introduce a

D. M. Gokhfeld, M. I. Petrov, S. V. Semenov, A. D. Balaev

Solid-state synthesis is widely used in exploratory research to study various structural modifications that affect the properties (critical temperature, critical current density, irreversibility field, etc.) of superconductors. The popularity of this method is due to its relative simplicity and availability of the necessary equipment. Combining solid-state s

Li-Chun Huang

In this study, we applied the NEAT (NeuroEvolution of Augmenting Topologies) algorithm to stock trading using multiple technical indicators. Our approach focused on maximizing earning, avoiding risk, and outperforming the Buy & Hold strategy. We used progressive training data and a multi-objective fitness function to guide the evolution of the population tow

Haowei Lou, Helen Paik, Wen Hu, Lina Yao

Recent advancements in text-to-speech (TTS) systems, such as FastSpeech and StyleSpeech, have significantly improved speech generation quality. However, these models often rely on duration generated by external tools like the Montreal Forced Aligner, which can be time-consuming and lack flexibility. The importance of accurate duration is often underestimated

Sri Harsha Dumpala, David Arps, Sageev Oore, Laura Kallmeyer

Vision-language models (VLMs), serve as foundation models for multi-modal applications such as image captioning and text-to-image generation. Recent studies have highlighted limitations in VLM text encoders, particularly in areas like compositionality and semantic understanding, though the underlying reasons for these limitations remain unclear. In this work

Jiayun Luo, Mir Rayat Imtiaz Hossain, Pritam Sarkar, Boyang Li

Vision-Language Models (VLMs) have achieved strong performance on implicit and explicit visual grounding and related tasks. However, such abilities are generally tested on simple, single-object phrases. We find that grounding performance degrades for complex, multi-object references. These limitations largely arise from training objectives that leverage imag

Yuanliang Zhang, Yifan Xie, Shanshan Li, Ke Liu

Recently, large language models (LLMs) have shown strong potential in code generation tasks. However, there are still gaps before they can be fully applied in actual software development processes. Accurately assessing the code generation capabilities of large language models has become an important basis for evaluating and improving the models. Some existin

Lingdong Shen, Qigqi, Kun Ding, Gaofeng Meng

Scientific Literature charts often contain complex visual elements, including multi-plot figures, flowcharts, structural diagrams and etc. Evaluating multimodal models using these authentic and intricate charts provides a more accurate assessment of their understanding abilities. However, existing benchmarks face limitations: a narrow range of chart types, o

Jack Allsop, Ian M. Wanless

A quasigroup is a pair $(Q, *)$ where $Q$ is a non-empty set and $*$ is a binary operation on $Q$ such that for every $(a, b) \in Q^2$ there exists a unique $(x, y) \in Q^2$ such that $a*x=b=y*a$. Let $(Q, *)$ be a quasigroup. A pair $(x, y) \in Q^2$ is a commuting pair of $(Q, *)$ if $x * y = y * x$. Recently, it has been shown that every rational number in

Ian Weng Chan, Andrei L. Badescu, X. Sheldon Lin

Vehicle telematics provides granular data for dynamic driving risk assessment, but current methods often rely on aggregated metrics (e.g., harsh braking counts) and do not fully exploit the rich time-series structure of telematics data. In this paper, we introduce a flexible framework using continuous-time hidden Markov model (CTHMM) to model and analyze tri

Florian Lehner, Farzad Maghsoudi, Babak Miraftab

In 1982, Durnberger proved that every connected Cayley graph of a finite group with a commutator subgroup of prime order contains a hamiltonian cycle. In this paper, we extend this result to the infinite case. Additionally, we generalize this result to a broader class of infinite graphs $X$, where the automorphism group of $X$ contains a transitive subgroup

Steven J. Kuntz, James B. Rawlings

We present the first general stability results for nonlinear offset-free model predictive control (MPC). Despite over twenty years of active research, the offset-free MPC literature has not shaken the assumption of closed-loop stability for establishing offset-free performance. In this paper, we present a nonlinear offset-free MPC design that is robustly sta

Changhong Li, Zhiqiang Guo

Sequential recommendations have drawn significant attention in modeling the user's historical behaviors to predict the next item. With the booming development of multimodal data (e.g., image, text) on internet platforms, sequential recommendation also benefits from the incorporation of multimodal data. Most methods introduce modal features of items as side i

Ayoosh Bansal, Duo Wang, Mikael Yeghiazaryan, Yangge Li

Autonomous air taxis are poised to revolutionize urban mass transportation, however, ensuring their safety and reliability remains an open challenge. Validating autonomy solutions on air taxis in the real world presents complexities, risks, and costs that further convolute this challenge. Verification and Validation (V&V) frameworks play a crucial role in th

Tomasz Niewiadomski, Anastasios Yiannakidis, Hanz Cuevas-Velasquez, Soubhik Sanyal

The model-based estimation of 3D animal pose and shape from images enables computational modeling of animal behavior. Training models for this purpose requires large amounts of labeled image data with precise pose and shape annotations. However, capturing such data requires the use of multi-view or marker-based motion-capture systems, which are impractical t

Saket Upadhyay

Fuzz testing is a fundamental technique employed to identify vulnerabilities within software systems. However, the process can be protracted and resource-intensive, especially when confronted with extensive codebases. In this work, I present FuzzDistill, an approach that harnesses compile-time data and machine learning to refine fuzzing targets. By analyzing

Fuqiang Liu, Sicong Jiang, Luis Miranda-Moreno, Seongjin Choi

Large Language Models (LLMs) have recently demonstrated significant potential in time series forecasting, offering impressive capabilities in handling complex temporal data. However, their robustness and reliability in real-world applications remain under-explored, particularly concerning their susceptibility to adversarial attacks. In this paper, we introdu

Jiawen Wen, Bangshuo Zhu, Huaming Chen

Recent studies have demonstrated outstanding capabilities of large language models (LLMs) in software engineering tasks, including code generation and comprehension. While LLMs have shown significant potential in assisting with coding, LLMs are vulnerable to adversarial attacks. In this paper, we investigate the vulnerability of LLMs to imperceptible attacks

Weicheng Fu, Yisen Wang

This study analyzes the Collatz map through nonlinear dynamics. By embedding integers in Sharkovsky's ordering, we show that odd initial values suffice for full dynamical characterization. We introduce ``direction phases'' to partition iterations into upward and downward phases, and derive a recursive function family parameterized by upward phase counts. Con

Zeshun Li, Fuhao Li, Wanting Zhang, Zijie Zheng

Most existing mobile robotic datasets primarily capture static scenes, limiting their utility for evaluating robotic performance in dynamic environments. To address this, we present a mobile robot oriented large-scale indoor dataset, denoted as THUD++ (TsingHua University Dynamic) robotic dataset, for dynamic scene understanding. Our current dataset includes

Shengheng Liu, Hao Wang, Mengguan Pan, Peng Liu

In this article, we present an intelligent framework for 5G new radio (NR) indoor positioning under a monostatic configuration. The primary objective is to estimate both the angle of arrival and time of arrival simultaneously. This requires capturing the pertinent information from both the antenna and subcarrier dimensions of the receive signals. To tackle t

Alexandru Chirvasitu

We prove that subhomogeneous continuous Banach bundles over compact metrizable spaces are equivalent to Hilbert bundles, while examples show that the metrizability assumption cannot be dropped completely. This complements the parallel statement for homogeneous bundles without the metrizability assumption, and generalizes the analogous result to the effect th

Jing Yu, Gongxiang Liu

We try to classify Hopf algebras with the Chevalley property according to their derived representation type. We show that a finite-dimensional indecomposable non-semisimple Hopf algebra $H$ with the Chevalley property is derived discrete if and only if it is isomorphic to $(A(n, 2, \mu, -1))^*$. Besides, we give a description for the indecomposable objects i

Oh-Hyun Kwon, Jisung Yoon, Lav R. Varshney, Woo-Sung Jung

We envision future technologies through science fiction, strategic planning, or academic research. Yet, our expectations do not always match with what actually unfolds, much like navigating a story where some events align with expectations while others surprise us. This gap indicates the inherent uncertainty of innovation-how technologies emerge and evolve i

François Le Gall, Oran Nadler, Harumichi Nishimura, Rotem Oshman

In this paper we study a quantum version of the multiparty simultaneous message-passing (SMP) model, and we show that in some cases, quantum communication can replace public randomness, even with no entanglement between the parties. This was already known for two players, but not for more than two players, and indeed, so far all that was known was a negative

Ashutosh Bajpai, Tanmoy Chakraborty

The unwavering disparity in labeled resources between resource-rich languages and those considered low-resource remains a significant impediment for Large Language Models (LLMs). Recent strides in cross-lingual in-context learning (X-ICL), mainly through semantically aligned examples retrieved from multilingual pre-trained transformers, have shown promise in

Hiromasa Suzuki, Naomi Tsuji, Yoshiaki Kanemaru, Megumi Shidatsu

A recent report on the detection of very-high-energy gamma rays from V4641 Sagittarii (V4641 Sgr) up to ~0.8 peta-electronvolt has made it the second confirmed "PeVatron" microquasar. Here we report on the observation of V4641 Sgr with X-Ray Imaging and Spectroscopy Mission (XRISM) in September 2024. Thanks to the large field of view and low background, the

Zexi Cai, Donglin Zeng, Karen S. Marder, Lawrence S. Honig

Disease progression prediction based on patients' evolving health information is challenging when true disease states are unknown due to diagnostic capabilities or high costs. For example, the absence of gold-standard neurological diagnoses hinders distinguishing Alzheimer's disease (AD) from related conditions such as AD-related dementias (ADRDs), including

Yuanyuan Liang, Tingyu Xie, Gan Peng, Zihao Huang

The emergence of Large Language Models (LLMs) has revolutionized many fields, not only traditional natural language processing (NLP) tasks. Recently, research on applying LLMs to the database field has been booming, and as a typical non-relational database, the use of LLMs in graph database research has naturally gained significant attention. Recent efforts

Honglin Bao, Jiawei Zhang, Mingxuan Cao, James A. Evans

We present a comprehensive study on the emergence of Computational Social Science (CSS) - an interdisciplinary field leveraging computational methods to address social science questions - and its impact on adjacent social sciences. We trained a robust CSS classifier using papers from CSS-focused venues and applied it to 11 million papers spanning 1990 to 202

Zi-Qian Cheng, Xiao-Shuang Yin, Liu-Xiang Yang, Hui Dong

Dynamics of materials under high-pressure conditions has been an important focus of materials science, especially in the timescale of pico- and femto-second of electronic and vibrational motion, which is typically probed by ultrafast laser pulses. To probe such dynamics, it requires an integration of high-pressure devices with the ultrafast laser system. In

Syrine Belakaria, Alaleh Ahmadianshalchi, Barbara Engelhardt, Stefano Ermon

We consider the problem of finite-horizon sequential experimental design to solve multi-objective optimization (MOO) of expensive black-box objective functions. This problem arises in many real-world applications, including materials design, where we have a small resource budget to make and evaluate candidate materials in the lab. We solve this problem using

Zhongyi Zhang, Jie Zhang, Wenbo Zhou, Xinghui Zhou

Face-swapping techniques have advanced rapidly with the evolution of deep learning, leading to widespread use and growing concerns about potential misuse, especially in cases of fraud. While many efforts have focused on detecting swapped face images or videos, these methods are insufficient for tracing the malicious users behind fraudulent activities. Intrus

Yixin Zhang, Kevin Kramer, Maciej A. Mazurowski

Automated segmentation of medical images heavily relies on the availability of precise manual annotations. However, generating these annotations is often time-consuming, expensive, and sometimes requires specialized expertise (especially for cross-sectional medical images). Therefore, it is essential to optimize the use of annotation resources to ensure effi

Xin Zhang, Yan Wang, Haijiang Zhang

Seismic tomography is a methodology to image subsurface properties of the Earth. In order to better interpret the resulting images, it is important to assess uncertainty in the results. Mixture density networks (MDNs) provide an efficient way to estimate Bayesian posterior probability density functions (pdfs) that describe the uncertainty of tomographic imag

Xihua Zhu, Yiqian Yang, Fan Zhang

With the rapid development of gravitational wave astronomy, the increasing number of detected events necessitates efficient methods for parameter estimation and model updates. This study presents a novel approach using knowledge distillation techniques to enhance computational efficiency in gravitational wave analysis. We develop a framework combining ResNet

Zhong Yi Wan, Ignacio Lopez-Gomez, Robert Carver, Tapio Schneider

Effective climate risk assessment is hindered by the resolution gap between coarse global climate models and the fine-scale information needed for regional decisions. We introduce GenFocal, an AI framework that generates statistically accurate, fine-scale weather from coarse climate projections, without requiring paired simulated and observed events during t

Satoru Ohgata, Satoru Hayami

We theoretically investigate the intrinsic anomalous Hall effect in two magnetically ordered systems: One is the ferromagnetic dipole system, and the other is the anisotropic magnetic dipole system, the latter of which has been proposed as a microscopic indicator of the anomalous Hall effect in antiferromagnets with the negligibly small magnetization. We sho

Zi-Dan Huang, Wei Zhang, Shuang-Quan Zhang, Ting-Ting Sun

The studies of transuranium nuclei are of vital significance in exploring the existence of the ``island of superheavy nuclei". This work presents the systematic investigations for the ground-state properties and structure evolutions of odd-$A$ transuranium Bk isotopes taking the deformed relativistic Hartree-Bogoliubov theory in continuum~(DRHBc) with PC-PK1

Zhen Wang, Yun Liu, Chen Cui, Shi Shu

Recently, designing neural solvers for large-scale linear systems of equations has emerged as a promising approach in scientific and engineering computing. This paper first introduce the Richardson(m) neural solver by employing a meta network to predict the weights of the long-step Richardson iterative method. Next, by incorporating momentum and precondition

Ting-Wei Chao, Hung-Hsun Hans Yu

In this paper, we provide a new proof of a density version of Tur\'an's theorem. We also rephrase both the theorem and the proof using entropy. With the entropic formulation, we show that some naturally defined entropic quantity is closely connected to other common quantities such as Lagrangian and spectral radius. In addition, we also determine the Tur\'an

Zhang Cheng, Yanxia Wang, Guoyu Xia

In recent years, the accuracy of gaze estimation techniques has gradually improved, but existing methods often rely on large datasets or large models to improve performance, which leads to high demands on computational resources. In terms of this issue, this paper proposes a lightweight gaze estimation model EM-Net based on deep learning and traditional mach

Ferhat Can Ataman, Gözde Bozdaği Akar

The aim of multispectral image fusion is to combine object or scene features of images with different spectral characteristics to increase the perceptual quality. In this paper, we present a novel learning-based solution to image fusion problem focusing on infrared and visible spectrum images. The proposed solution utilizes only convolution and pooling layer

Zhiyan Wang, Deyin Liu, Lin Yuanbo Wu, Song Wang

Semantic segmentation is a fundamental task in multimedia processing, which can be used for analyzing, understanding, editing contents of images and videos, among others. To accelerate the analysis of multimedia data, existing segmentation researches tend to extract semantic information by progressively reducing the spatial resolutions of feature maps. Howev

Xinxin Zhang, Zhuoqun Xu, Guangpu Zhu, Chien Ming Jonathan Tay

Recent advanced large language models (LLMs) have showcased their emergent capability of in-context learning, facilitating intelligent decision-making through natural language prompts without retraining. This new machine learning paradigm has shown promise in various fields, including general control and optimization problems. Inspired by these advancements,

Jing Jiang, Chunxu Zhang, Honglei Zhang, Zhiwei Li

Personalization stands as the cornerstone of recommender systems (RecSys), striving to sift out redundant information and offer tailor-made services for users. However, the conventional cloud-based RecSys necessitates centralized data collection, posing significant risks of user privacy breaches. In response to this challenge, federated recommender systems (

Brian Knight, Naoki Saito

The monogenic signal (MS) was introduced by Felsberg and Sommer, and independently by Larkin under the name vortex operator. It is a two-dimensional (2D) analog of the well-known analytic signal, and allows for direct amplitude and phase demodulation of (amplitude and phase) modulated images so long as the signal is intrinsically one-dimensional (i1D). Felsb

Xiaoyun Liang, Jingyi Ren, Jiayi Qi, Chao Peng

Large Language Models (LLMs) have become increasingly integral to enhancing developer productivity, particularly in code generation, comprehension, and repair tasks. However, fine-tuning these models with high-quality, real-world data is challenging due to privacy concerns and the lack of accessible, labeled datasets. In this paper, we present DialogAgent, a

Zhiyan Wang, Xin Guo, Song Wang, Peixiao Zheng

Low computational complexity and high segmentation accuracy are both essential to the real-world semantic segmentation tasks. However, to speed up the model inference, most existing approaches tend to design light-weight networks with a very limited number of parameters, leading to a considerable degradation in accuracy due to the decrease of the representat

Xin-Cheng Wen, Zirui Lin, Cuiyun Gao, Hongyu Zhang

Software vendors often silently release security patches without providing sufficient advisories (e.g., Common Vulnerabilities and Exposures) or delayed updates via resources (e.g., National Vulnerability Database). Therefore, it has become crucial to detect these security patches to ensure secure software maintenance. However, existing methods face the foll

Xiao-Ming Wang, Shan-Zhong Li, Zhi Li

We study the topological properties of the one-dimensional generalized quasiperiodic modulated Su-Schrieffer-Heeger model. The results reveal that topological re-entrant phase transition emerges. Through the analysis of a real-space winding number , we divide the emergent topological re-entrant phase transitions into two types. The first is the re-entrant ph

Haiyan Wang, Ye Yuan

Personal interaction data can be effectively modeled as individual graphs for each user in recommender systems.Graph Neural Networks (GNNs)-based recommendation techniques have become extremely popular since they can capture high-order collaborative signals between users and items by aggregating the individual graph into a global interactive graph.However, t

Hongning Ruan, Yulin Shao, Qianqian Yang, Liang Zhao

Point clouds have gained prominence across numerous applications due to their ability to accurately represent 3D objects and scenes. However, efficiently compressing unstructured, high-precision point cloud data remains a significant challenge. In this paper, we propose NeRC$^3$, a novel point cloud compression framework that leverages implicit neural repres

Tong Su, Jiangkai Peng, Alaa Selim, Junbo Zhao

The emergence of grid-forming (GFM) inverter technology and the increasing role of machine learning in power systems highlight the need for evaluating the latest dynamic simulators. Open-source simulators offer distinct advantages in this field, being both free and highly customizable, which makes them well-suited for scientific research and validation of th

Yizhe Ding, Runze Li, Lingzhou Xue

This paper studies the convergence rates of optimal transport (OT) map estimators, a topic of growing interest in statistics, machine learning, and various scientific fields. Despite recent advancements, existing results rely on regularity assumptions that are very restrictive in practice and much stricter than those in Brenier's Theorem, including the compa

Jiaqi Chen, Xiaoye Zhu, Tianyang Liu, Ying Chen

Large Language Models (LLMs) have revolutionized text generation, making detecting machine-generated text increasingly challenging. Although past methods have achieved good performance on detecting pure machine-generated text, those detectors have poor performance on distinguishing machine-revised text (rewriting, expansion, and polishing), which can have on

Zhanming Guan, Junlin Liu, Jierui Liu, Chao Peng

Large Language Models (LLMs) have demonstrated impressive capabilities in code completion tasks, where they assist developers by predicting and generating new code in real-time. However, existing LLM-based code completion systems primarily rely on the immediate context of the file being edited, often missing valuable repository-level information, user behavi

Nishchay Karle, Ben Clifford, Yadu Babuji, Ryan Chard

The Common Workflow Language (CWL) is a widely adopted language for defining and sharing computational workflows. It is designed to be independent of the execution engine on which workflows are executed. In this paper, we describe our experiences integrating CWL with Parsl, a Python-based parallel programming library designed to manage execution of workflows

Harry Zhang, Luca Carlone

We introduce CUPS, a novel method for learning sequence-to-sequence 3D human shapes and poses from RGB videos with uncertainty quantification. To improve on top of prior work, we develop a method to generate and score multiple hypotheses during training, effectively integrating uncertainty quantification into the learning process. This process results in a d

Foivos Tsimpourlas, Chao Peng, Carlos Rosuero, Ping Yang

The Go programming language has gained significant traction for developing software, especially in various infrastructure systems. Nonetheless, concurrency bugs have become a prevalent issue within Go, presenting a unique challenge due to the language's dual concurrency mechanisms-communicating sequential processes and shared memory. Detecting concurrency bu

Jordan Lekeufack, Michael I. Jordan

We study Online Convex Optimization (OCO) with adversarial constraints, where an online algorithm must make sequential decisions to minimize both convex loss functions and cumulative constraint violations. We focus on a setting where the algorithm has access to predictions of the loss and constraint functions. Our results show that we can improve the current

Sujan Subedi, Wenhao Liu, Wuzhang Fang, Carter Fox

Terahertz (THz) technology is critical for quantum material physics, biomedical imaging, ultrafast electronics, and next-generation wireless communications. However, standing in the way of widespread applications is the scarcity of efficient ultrafast THz sources with on-demand fast modulation and easy on-chip integration capability. Here we report the disco

Xiao-Bin Sui, Jing Liu, Rong-Gen Cai

We investigate density fluctuations and scalar-induced gravitational waves (GWs) arising from the production of long-lived solitons and oscillons, which can dominate the early Universe and drive reheating prior to the standard radiation-dominated era. Curvature perturbations are generated not only by the Poisson distribution of these solitons/oscillons but a

Hiroki Wada, Satoshi Yamaguchi

We consider D-branes in the Dabholkar-Park (DP) background, a $9$d orientifold theory obtained by gauging symmetry in the type IIB string theory compactified on a circle. Using anomalies in the world-sheet theory, we provide physical insights into the classification of stable D-branes by relative KR-theory. The nature, such as stability, of D-branes wrapping

Xiao-Jin Liu, Rahul Sengar, Matthew Bailes, Ralph P. Eatough

More than 300 pulsars have been discovered in Galactic globular clusters; however, none have been found in open clusters. Here we present results from 20-hour pulsar searching observations in seven open clusters with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). Our first discovery is a 1.9-second pulsar (J1922+37) found in the direction

Panlong Wu, Kangshuo Li, Junbao Nan, Fangxin Wang

Large Language Models (LLMs) have revolutionized intelligent services by enabling logical reasoning, tool use, and interaction with external systems as agents. The advancement of LLMs is frequently hindered by the scarcity of high-quality data, much of which is inherently sensitive. Federated learning (FL) offers a potential solution by facilitating the coll

Jin Hu, Xianglong Liu, Jiakai Wang, Junkai Zhang

Physical adversarial examples (PAEs) are regarded as whistle-blowers of real-world risks in deep-learning applications, thus worth further investigation. However, current PAE generation studies show limited adaptive attacking ability to diverse and varying scenes, revealing the urgent requirement of dynamic PAEs that are generated in real time and conditione

Manman Yuan, Weiming Jia, Xiong Luo, Jiazhen Ye

The precise detection of mild cognitive impairment (MCI) is of significant importance in preventing the deterioration of patients in a timely manner. Although hypergraphs have enhanced performance by learning and analyzing brain networks, they often only depend on vector distances between features at a single scale to infer interactions. In this paper, we de

Bing-Yi Jing, Ting Li, Jiangzhou Wang, Ya Wang

There has been extensive research on community detection in directed and bipartite networks. However, these studies often fail to consider the popularity of nodes in different communities, which is a common phenomenon in real-world networks. To address this issue, we propose a new probabilistic framework called the Two-Way Node Popularity Model (TNPM). The T

Huafeng Li, Dayong Su, Qing Cai, Yafei Zhang

If unaligned multimodal medical images can be simultaneously aligned and fused using a single-stage approach within a unified processing framework, it will not only achieve mutual promotion of dual tasks but also help reduce the complexity of the model. However, the design of this model faces the challenge of incompatible requirements for feature fusion and

Ao Li, Longwei Xu, Chen Ling, Jinghui Zhang

Sentiment and emotion understanding are essential to applications such as human-computer interaction and depression detection. While Multimodal Large Language Models (MLLMs) demonstrate robust general capabilities, they face considerable challenges in the field of affective computing, particularly in detecting subtle facial expressions and handling complex e

Pranav Pant, Niharika Dadu, Harsh V. Singh, Anshul Thakur

Facial recognition technology has made significant advances, yet its effectiveness across diverse ethnic backgrounds, particularly in specific Indian demographics, is less explored. This paper presents a detailed evaluation of both traditional and deep learning-based facial recognition models using the established LFW dataset and our newly developed IITJ Fac

Ning Dai, Bin Zhou

A finite equilibrium current density arises in the anomalous Hall effect (AHE) as a result of time-reversal symmetry breaking, affecting both the differential current density and total current. This study illustrates the equilibrium current density pattern in a ribbon-shaped system within the AHE regime, consisting of two sets of counterpropagating channels

Daniel Ovalle, Joshua L. Pulsipher, Yixin Ye, Kyle Harshbarger

Supply and manufacturing networks in the chemical industry involve diverse processing steps across different locations, rendering their operation vulnerable to disruptions from unplanned events. Optimal responses should consider factors such as product allocation, delayed shipments, and price renegotiation , among other factors. In such context, we propose a

Jun Jiang, Shi-Yuan Li, Xiao Liang, Yan-Rui Liu

Within a phenomenological diquark fragmentation model, we study the production of doubly charmed tetraquark $T_{cc}$ via photon-photon fusion at electron-positron colliders. The production of $T_{cc}$ is divided into two steps: the perturbative production of heavy $(cc)$-diquark and its nonperturbative hadronization. Two diquark configurations of $(cc)[^3S_1

Hao Hu, Haijun Yu

Hermite polynomials and functions have extensive applications in scientific and engineering problems. Although it is recognized that employing the scaled Hermite functions rather than the standard ones can remarkably enhance the approximation performance, the understanding of the scaling factor remains insufficient. Due to the lack of theoretical analysis, r

Xiaoci Zhang, Te Zhang, Zhaotong Zhuang, Zixuan Leng

A comprehensive study of the low-temperature properties of YbNi$_4$Mg has revealed evidence of a superheavy-fermion state, characterized by a large electronic specific-heat coefficient $\gamma_0$ $\approx$ 5.65 J mol$^{-1}$ K$^{-2}$ and an elevated Wilson ratio $R_W$ = 32.1. No magnetic ordering was observed down to 70 mK; however, a broad maximum appears in

Nodoka Seya, Masataka Taguri, Takeo Ishii

Inverse probability (IP) weighting of marginal structural models (MSMs) can provide consistent estimators of time-varying treatment effects under correct model specifications and identifiability assumptions, even in the presence of time-varying confounding. However, this method has two problems: (i) inefficiency due to IP-weights cumulating all time points a

Cristina David, Pascal Kesseli, Daniel Kroening, Hanliang Zhang

When done manually, refactoring legacy code in order to eliminate uses of deprecated APIs is an error-prone and time-consuming process. In this paper, we investigate to which degree refactorings for deprecated Java APIs can be automated, and quantify the benefit of Javadoc code hints for this task. To this end, we build a symbolic and a neural engine for the

Brent Sordo, Ellen Rathje, Krishna Kumar

This paper presents a hybrid Finite Element Method (FEM) and Material Point Method (MPM) approach for modeling liquefaction-induced tailings dam failures from initiation through runout. We apply this method to simulate the 1978 Mochikoshi tailings dam failure, which occurred due to seismic loading and liquefaction during an earthquake. Our approach leverages

Wolfram Bauer, Yawei Wei, Xiaodong Zhou

In this paper, we study two kinds of nonlinear degenerate elliptic equations containing the Grushin operator. First, we prove radial symmetry and a decay rate at infinity of solutions to such a Grushin equation by using the moving plane method in combination with suitable integral inequalities. Applying similar methods, we obtain nonexistence results for sol

Hang Gao, Chenhao Zhang, Fengge Wu, Junsuo Zhao

Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. However, traditional methods face challenges when dealing with heterogeneous graphs that contain various types of nodes and edges due to the diverse sources and complex nature of the data

Phan Minh Hung, Nguyen Duy Phuoc, Tran Nguyen Thanh Son

Given a simple graph $G$, the artinian monomial algebra associated to $G$, denoted by $A(G)$, is defined by the edge ideal of $G$ and the squares of the variables. In this article, we classify some tadpole graphs $G$ for which $A(G)$ has or fails the weak Lefschetz property.

Jessie Sheflin

This work examines two ways of using proper orthogonal decomposition (POD) to enhance the prior work of EITPose, a device which uses electrical impedance tomography (EIT) to detect posture by way of a band of electrodes on the forearm. First, an electrode placement algorithm is described, which employs the sensitivity volume method and a POD basis to choose

Hanliang Zhang, Cristina David, Meng Wang, Brandon Paulsen

Large language models (LLMs) show promise in code translation due to their ability to generate idiomatic code. However, a significant limitation when using LLMs for code translation is scalability: existing works have shown a drop in translation success rates for code exceeding around 100 lines. We overcome this limitation by developing a modular approach to

Zhigang Cen, Ningyan Guo, Wenjing Xu, Zhiyong Feng

Video semantic segmentation(VSS) has been widely employed in lots of fields, such as simultaneous localization and mapping, autonomous driving and surveillance. Its core challenge is how to leverage temporal information to achieve better segmentation. Previous efforts have primarily focused on pixel-level static-dynamic contexts matching, utilizing technique

Zhongtong Wang, Robert J. Wagner, Tianke Chen, Sagar P. Shah

Polymer matrix fiber composites often suffer from residual stresses due to differences in coefficients of thermal expansion between the fibers and resins, as well as contractile strain of the resins during curing. To address residual stress driven composite failure, we propose the use of vitrimers as composite resins, which can undergo thermally activated, s

Ailing Zheng, Wanli Ni, Wen Wang, Hui Tian

The robust beamforming design in multi-functional reconfigurable intelligent surface (MF-RIS) assisted wireless networks is investigated in this work, where the MF-RIS supports signal reflection, refraction, and amplification to address the double-fading attenuation and half-space coverage issues faced by traditional RISs. Specifically, we aim to maximize th

Sahil Dharod, Malyala Preethi Sravani, Sakshi Heda, Sharayu Moharir

We study a grouped bandit setting where each arm comprises multiple independent sub-arms referred to as attributes. Each attribute of each arm has an independent stochastic reward. We impose the constraint that for an arm to be deemed feasible, the mean reward of all its attributes should exceed a specified threshold. The goal is to find the arm with the hig

Sohan Kumar Jha

The linear-quadratic Generalized uncertainty principle (LQG) is consistent with predictions of a minimum measurable length and a maximum measurable momentum put forth by various theories of quantum gravity. The quantum gravity effect is incorporated into a black hole (BH) by modifying its ADM mass. In this article, we explore the impact of GUP on the optical

Qiang Qu, Hanxue Liang, Xiaoming Chen, Yuk Ying Chung

Neural View Synthesis (NVS) has demonstrated efficacy in generating high-fidelity dense viewpoint videos using a image set with sparse views. However, existing quality assessment methods like PSNR, SSIM, and LPIPS are not tailored for the scenes with dense viewpoints synthesized by NVS and NeRF variants, thus, they often fall short in capturing the perceptua

Jin Hong, Yong Wang

In this paper, we compute the spectral Einstein functional associated with the Dirac operator with torsion on even-dimensional spin manifolds without boundary.

Jin Huang, Xiao Li, Guanghua Ji

We present rigorous error estimates towards a first-order unconditionally energy stable scheme designed for 3D hydrodynamic Q-tensor model of nematic liquid crystals. This scheme combines the scalar auxiliary variable (SAV), stabilization and projection method together. The unique solvability and energy dissipation of the scheme are proved. We further derive

Peter Hayman

Notes prepared for the introductory general relativity course PHYSICS 748 at The University of Auckland. They are designed to introduce general relativity to upper-year undergraduate students directly using the modern language of differential geometry but in a physically motivated way, and throughout keeping a logical flow from section to section and chapter

Peiyuan Zhang, Amin Karbasi

Classical optimization theory requires a small step-size for gradient-based methods to converge. Nevertheless, recent findings challenge the traditional idea by empirically demonstrating Gradient Descent (GD) converges even when the step-size $\eta$ exceeds the threshold of $2/L$, where $L$ is the global smooth constant. This is usually known as the Edge of

Shengmin Piao, Sanghyun Park

Large Language Models exhibit impressive reasoning capabilities across diverse tasks, motivating efforts to distill these capabilities into smaller models through generated reasoning data. However, direct training on such synthesized reasoning data may lead to superficial imitation of reasoning process, rather than fostering a genuine integration of reasonin

Can Wu, Dong-Hui Li, Defeng Sun

Support matrix machine (SMM) is a successful supervised classification model for matrix-type samples. Unlike support vector machines, it employs low-rank regularization on the regression matrix to effectively capture the intrinsic structure embedded in each input matrix. When solving a large-scale SMM, a major challenge arises from the potential increase in

Mao-Sheng Li, Yi-Xi Tan

The imaginary in quantum theory plays a crucial role in describing quantum coherence and is widely applied in quantum information tasks such as state discrimination, pseudorandomness generation, and quantum metrology. A recent paper by Fernandes et al. [C. Fernandes, R. Wagner, L. Novo, and E. F. Galv\~ao, Phys. Rev. Lett. 133, 190201 (2024) ] showed how to

Chongyi Zheng, Jens Tuyls, Joanne Peng, Benjamin Eysenbach

Self-supervised learning has the potential of lifting several of the key challenges in reinforcement learning today, such as exploration, representation learning, and reward design. Recent work (METRA) has effectively argued that moving away from mutual information and instead optimizing a certain Wasserstein distance is important for good performance. In th