November 2024 arXiv papers — page 158

Dongfeng Gao, Wei-Tou Ni

Analogous to magnetism in electrodynamics, it is gravitomagnetism in relativistic gravity. Since gravity determines locally inertial frames, in general relativity (GR) and other relativistic theories of gravity, frame-dragging with source motion plays a key role in gravitomagnetism. Recently, Wu has put forward a gauge theory of gravity, called the gravitati

Prashank Kadam

With the increasing number of financial services available online, the rate of financial fraud has also been increasing. The traffic and transaction rates on the internet have increased considerably, leading to a need for fast decision-making. Financial institutions also have stringent regulations that often require transparency and explainability of the dec

Anand Brahmbhatt, Kartikeya Rai, Amitabha Tripathi

A graph $G$ is cordial if there exists a function $f$ from the vertices of $G$ to $\{0,1\}$ such that the number of vertices labelled $0$ and the number of vertices labelled $1$ differ by at most $1$, and if we assign to each edge $xy$ the label $|f(x)-f(y)|$, the number of edges labelled $0$ and the number of edges labelled $1$ also differ at most by $1$. W

Yohann Tendero, Jerome Gilles, Stephane Landeau, Jean-Michel Morel

This paper introduces a new way to correct the non-uniformity (NU) in uncooled infrared-type images. The main defect of these uncooled images is the lack of a column (resp. line) time-dependent cross-calibration, resulting in a strong column (resp. line) and time dependent noise. This problem can be considered as a 1D flicker of the columns inside each frame

Jerome Gilles, Yves Meyer

In this paper we present some theoretical results about a structures-textures image decomposition model which was proposed by the second author. We prove a theorem which gives the behavior of this model in different cases. Finally, as a consequence of the theorem we derive an algorithm for the detection of long and thin objects applied to a road networks det

Naveen K. Singh, Gopal Kashyap

We derive the equation for pressure within a neutron star, taking into account a non-zero cosmological constant ($\Lambda$). We then examine the stability of the neutron star's equilibrium state in the presence of cosmological constant. Our analysis shows that the theorem used to assess the stability of stellar structures at equilibrium remains applicable to

Akshar Ramkumar, Mehdi Soleimanifar

Providing evidence that quantum computers can efficiently prepare low-energy or thermal states of physically relevant interacting quantum systems is a major challenge in quantum information science. A newly developed quantum Gibbs sampling algorithm by Chen, Kastoryano, and Gily\'en provides an efficient simulation of the detailed-balanced dissipative dynami

Daniel Wang, Jack McFarland, Afra Mashhadi, Ekin Ugurel

This work examines the fairness of generative mobility models, addressing the often overlooked dimension of equity in model performance across geographic regions. Predictive models built on crowd flow data are instrumental in understanding urban structures and movement patterns; however, they risk embedding biases, particularly in spatiotemporal contexts whe

Zhen Qin, Casey Jameson, Zhexuan Gong, Michael B. Wakin

The process of reconstructing quantum states from experimental measurements, accomplished through quantum state tomography (QST), plays a crucial role in verifying and benchmarking quantum devices. A key challenge of QST is to find out how the accuracy of the reconstruction depends on the number of state copies used in the measurements. When multiple measure

Takashi Shimonishi

Understanding the interstellar chemistry in low-metallicity environments is crucial to unveil physical and chemical processes in the past Galaxy or those in high-redshift galaxies, where the metallicity was significantly lower compared to the present-day solar neighborhood. This is also important for the understanding of the diversity of the chemical evoluti

Heshani Madigasekara, D. S. Poskitt, Lina Zhang, Xueyan Zhao

This paper aims to partially identify the distributional treatment effects (DTEs) that depend on the unknown joint distribution of treated and untreated potential outcomes. We construct the DTE bounds using panel data and allow individuals to switch between the treated and untreated states more than once over time. Individuals are grouped based on their past

Benjamin Móricz, Zoltán Lóránt Nagy

What is the maximum number of $r$-term sums admitting rational values in $n$-element sets of irrational numbers? We determine the maximum when $r<4$ or $r\geq n/2$ and also in case when we drop the condition on the number of summands. It turns out that the $r$-term sum problem is equivalent to determine the maximum number of $r$-term zero-sum subsequences in

Jared Fernandez, Yonatan Bisk, Emma Strubell

Large Language Models (LLMs) trained on web-scale text corpora have been shown to capture world knowledge in their parameters. However, the mechanism by which language models store different types of knowledge is poorly understood. In this work, we examine two types of knowledge relating to temporally sensitive entities and demonstrate that each type is loca

Peng Wang, Ziling Heng

Self-orthogonal codes are of interest as they have important applications in quantum codes, lattices and many areas. In this paper, based on the weakly regular plateaued functions or plateaued Boolean functions, we construct a family of linear codes with four nonzero weights. This family of linear codes is proved to be not only self-orthogonal but also optim

Chang-Hao Chen, Luis C. Ho, Ruancun Li, Ming-Yang Zhuang

We investigate the host galaxy properties of eight little red dots (LRDs) selected from the JWST UNCOVER survey, applying a new technique ({\tt\string GalfitS}) to simultaneously fit the morphology and spectral energy distribution using multi-band NIRCam images covering $\sim 1-4\,\mu {\rm m}$. We detect the host galaxy in only one LRD, MSAID38108 at $z = 4.

Ziling Heng, Peng Wang, Chunlei Xie, Haiyan Zhou

Perfect complementary sequence sets (PCSSs) are widely used in multi-carrier code-division multiple-access (MC-CDMA) communication systems. However, the set size of a PCSS is upper bounded by the number of row sequences of each two-dimensional matrix in the PCSS. Then quasi-complementary sequence sets (QCSSs) were proposed to support more users in MC-CDMA co

Bo Liu, Yanjie Jiang, Yuxia Zhang, Nan Niu

Recent advances in large language models (LLMs), make it potentially feasible to automatically refactor source code with LLMs. However, it remains unclear how well LLMs perform compared to human experts in conducting refactorings automatically and accurately. To fill this gap, in this paper, we conduct an empirical study to investigate the potential of LLMs

Whanhee Cho

The attention to table understanding using recent natural language models has been growing. However, most related works tend to focus on learning the structure of the table directly. Just as humans improve their understanding of sentences by comparing them, they can also enhance their understanding by comparing tables. With this idea, in this paper, we intro

Bingcheng Qing, Ahmed Hajr, Ke Wang, Gerwin Koolstra

Ubiquitous noises in quantum systems remain a key obstacle to building quantum computers, necessitating the use of quantum error correction codes. Recently, error-correcting codes tailored for noise-biased systems have been shown to offer high fault-tolerance thresholds and reduced hardware overhead, positioning noise-biased qubits as promising candidates fo

Yang Luo, Shaoxiang Sheng, Andrea Schirato, Alberto Martin-Jimenez

Probing and manipulating the spatiotemporal dynamics of hot carriers in nanoscale metals is crucial to a plethora of applications ranging from nonlinear nanophotonics to single molecule photochemistry. The direct investigation of these highly non-equilibrium carriers requires the experimental capability of high energy resolution (~ meV) broadband femtosecond

Prashank Kadam

Human-in-the-loop (HITL) feedback mechanisms can significantly enhance machine learning models, particularly in financial fraud detection, where fraud patterns change rapidly, and fraudulent nodes are sparse. Even small amounts of feedback from Subject Matter Experts (SMEs) can notably boost model performance. This paper examines the impact of HITL feedback

Godwill Mbiti Kanyolo, Titus Masese

Motivated by a recent pseudo-spin model for monolayer-bilayer phase transitions in silver-based honeycomb layered materials, we propose that the critical pseudo-magnetic fields in such systems correspond to both the infinite-channel Feshbach resonance widths of a (Fermi-Dirac/Bose-Einstein/etc.) condensate in 2 dimensions, and equivalently to the Lee-Yang ze

Elmira Mousa Rezabeyk, Salar Beigzad, Yasin Hamzavi, Mohsen Bagheritabar

Deep learning methods have established a significant place in image classification. While prior research has focused on enhancing final outcomes, the opaque nature of the decision-making process in these models remains a concern for experts. Additionally, the deployment of these methods can be problematic in resource-limited environments. This paper tackles

Prashank Kadam

As the availability of financial services online continues to grow, the incidence of fraud has surged correspondingly. Fraudsters continually seek new and innovative ways to circumvent the detection algorithms in place. Traditionally, fraud detection relied on rule-based methods, where rules were manually created based on transaction data features. However,

Nihat Sadik Deger, Ceren Ayse Deral

We study timelike supersymmetric solutions of a $D=3, N=4$ gauged supergravity using Killing spinor bilinears method and prove that AdS$_3$ is the only solution within this class. We then consider the ungauged version of this model. It is found that for this type of solutions, the ungauged theory effectively truncates to a supergravity coupled to a sigma mod

Ko Arimatsu

The exploration of kilometre-sized trans-Neptunian objects (TNOs) is one of the ultimate goals in the search for the origin and evolution of the Solar System. However, such exploration is challenging because these small bodies are too faint to be directly detected. One potential avenue for detecting and investigating small and faint TNOs is the monitoring of

Sandra Robles, Stephan A. Meighen-Berger

We propose the first method for water Cherenkov detectors to constrain GeV-scale dark matter (DM) below the solar evaporation mass. While previous efforts have highlighted the Sun and Earth as DM capture targets, we demonstrate that Jupiter is a viable target. Jupiter's unique characteristics, such as its lower core temperature and significant gravitational

Tim Pearce, Tabish Rashid, Dave Bignell, Raluca Georgescu

The performance of embodied agents has been shown to improve by increasing model parameters, dataset size, and compute. This has been demonstrated in domains from robotics to video games, when generative learning objectives on offline datasets (pre-training) are used to model an agent's behavior (imitation learning) or their environment (world modeling). Thi

Sang J. Park, Hojun Lee, Jongjun M. Lee, Jangwoo Ha

Transverse electron transport in magnetic materials - manifested in effects such as the anomalous Hall and Nernst effects - holds promise for spintronic and thermoelectric applications. While recent advances have focused on enhancing such transport through topological single crystals via intrinsic mechanisms linked to Berry curvature, practical limitations r

Ali Haidar, Oded Yacobi

We prove that the Kazhdan-Lusztig basis of Specht modules is upper triangular with respect to all generalized Gelfand-Tsetlin bases constructed from any multiplicity-free tower of standard parabolic subgroups.

Charles Daly

In this paper we provide a means of certifying infinitesimal projective rigidity relative to the cusp for hyperbolic once punctured torus bundles in terms of twisted Alexander polynomials of representations associated to the holonomy. We also relate this polynomial to an induced action on the tangent space of the character variety of the free group of rank 2

Usha Bhalla, Suraj Srinivas, Asma Ghandeharioun, Himabindu Lakkaraju

With the growing complexity and capability of large language models, a need to understand model reasoning has emerged, often motivated by an underlying goal of controlling and aligning models. While numerous interpretability and steering methods have been proposed as solutions, they are typically designed either for understanding or for control, seldom addre

Yue Meng, Xu Wang, Jianbei Liu, Ming Shao

Working gas components significantly impact the performance of gaseous detectors. A fast-timing Micromegas detector with two-stage amplification is prone to notable deterioration of uniformity when scaled up. This paper presents a simulation study based on Garfield++ that aims to enhance the performance of such detectors by exploring different gas mixtures.

Shuqi Zhao, Xinghao Zhu, Yuxin Chen, Chenran Li

Dexterous manipulation is a critical aspect of human capability, enabling interaction with a wide variety of objects. Recent advancements in learning from human demonstrations and teleoperation have enabled progress for robots in such ability. However, these approaches either require complex data collection such as costly human effort for eye-robot contact,

Sonia K. Murthy, Tomer Ullman, Jennifer Hu

Researchers in social science and psychology have recently proposed using large language models (LLMs) as replacements for humans in behavioral research. In addition to arguments about whether LLMs accurately capture population-level patterns, this has raised questions about whether LLMs capture human-like conceptual diversity. Separately, it is debated whet

Yang Ding, Yi Bu

This paper contributes a new idea for exploring research funding effects on scholar performance. By collecting details of 9,501 research grants received by principal investigators from universities in the U.S. social sciences from 2000 to 2019 and data on their publications and citations in the Microsoft Academic Graph and Web of Science bibliographic collec

Ishika Agarwal, Krishnateja Killamsetty, Lucian Popa, Marina Danilevksy

Fine-tuning large language models (LLMs) is essential for enhancing their performance on specific tasks but is often resource-intensive due to redundant or uninformative data. To address this inefficiency, we introduce DELIFT (Data Efficient Language model Instruction Fine-Tuning), a novel algorithm that systematically optimizes data selection across the thr

Yicheng Gao, Gonghan Xu, Zhe Wang, Arman Cohan

Recent advances in large language models (LLMs) show the potential of using LLMs as evaluators for assessing the quality of text generations from LLMs. However, applying LLM evaluators naively to compare or judge between different systems can lead to unreliable results due to the intrinsic win rate estimation bias of LLM evaluators. In order to mitigate this

Zhendong Li, Chang Su, Zhou Su, Haixia Peng

In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horiz

Joshua Newey, Jared P Whitehead, Elizabeth Carlson

We review an algorithm developed for parameter estimation within the Continuous Data Assimilation (CDA) approach. We present an alternative derivation for the algorithm presented in a paper by Carlson, Hudson, and Larios (CHL, 2021). This derivation relies on the same assumptions as the previous derivation but frames the problem as a finite dimensional root-

Jiaxin Deng, Junbiao Pang, Jiayu Xu, Haitao Yu

In our urban life, long distance coaches supply a convenient yet economic approach to the transportation of the public. One notable problem is to discover the abnormal stop of the coaches due to the important reason, i.e., illegal pick up on the way which possibly endangers the safety of passengers. It has become a pressing issue to detect the coach abnormal

Bai Cong, Nico Daheim, Yuesong Shen, Daniel Cremers

We show that variational learning can significantly improve the accuracy and calibration of Low-Rank Adaptation (LoRA) without a substantial increase in the cost. We replace AdamW by the Improved Variational Online Newton (IVON) algorithm to finetune large language models. For Llama-2 with 7 billion parameters, IVON improves the accuracy over AdamW by 2.8% a

Walter Gerych, Haoran Zhang, Kimia Hamidieh, Eileen Pan

Vision-language model (VLM) embeddings have been shown to encode biases present in their training data, such as societal biases that prescribe negative characteristics to members of various racial and gender identities. VLMs are being quickly adopted for a variety of tasks ranging from few-shot classification to text-guided image generation, making debiasing

Zhendong Li, Jianle Ba, Zhou Su, Haixia Peng

In this paper, we propose a full-duplex integrated sensing and communication (ISAC) system enabled by a movable antenna (MA). By leveraging the characteristic of MA that can increase the spatial diversity gain, the performance of the system can be enhanced. We formulate a problem of minimizing the total transmit power consumption via jointly optimizing the d

Soheil Behnezhad, Rajmohan Rajaraman, Omer Wasim

Over the years, there has been extensive work on fully dynamic algorithms for classic graph problems that admit greedy solutions. Examples include $(\Delta+1)$ vertex coloring, maximal independent set, and maximal matching. For all three problems, there are randomized algorithms that maintain a valid solution after each edge insertion or deletion to the $n$-

Kyuho Kim, Kunwoo Park, Hyungchul Park, Sunkyu Yu

Unitarity serves as a fundamental concept for characterizing linear and conservative wave phenomena in both classical and quantum systems. Developing platforms that perform unitary operations on light waves in a uni-versal and programmable manner enables the emulation of complex light-matter interactions and the execution of general-purpose functionalities f

Hiroyoshi Nakano, Kazuma Yokota

In fluids under temperature gradients, long-range correlations (LRCs) emerge generically, leading to enhanced density fluctuations. This phenomenon, characterized by the $\boldsymbol{q}^{-4}$ divergence in the static structure factor (where $\boldsymbol{q}$ is the wavenumber), has been extensively studied both theoretically and experimentally. However, they

Yu Meng, Chuan Liu, Teng Wang, Haobo Yan

We present a model-independent method to calculate the radiative transition without the momentum extrapolation for the off-shell transition factors. The on-shell transition factor is directly obtained from the lattice hadronic function. We apply the method to calculate the charmonium radiative transition $J/\psi \rightarrow \gamma\eta_c$. After a continuous

Rômulo Damasclin Chaves dos Santos

This paper advances the stochastic regularity theory for the Navier-Stokes equations by introducing a variable-intensity noise model within the Sobolev and Besov spaces. Traditional models usually assume constant-intensity noise, but many real-world turbulent systems exhibit fluctuations of varying intensities, which can critically affect flow regularity and

Yu Hu, Yuang Zhang, Yunlong Song, Yang Deng

Optical flow captures the motion of pixels in an image sequence over time, providing information about movement, depth, and environmental structure. Flying insects utilize this information to navigate and avoid obstacles, allowing them to execute highly agile maneuvers even in complex environments. Despite its potential, autonomous flying robots have yet to

Qi Huang, Swarnabha Chattaraj, Lucas Jordao, Jiefei Zhang

Fault tolerant on-chip photonic quantum computation is enormously helped by (a) deterministic generation of the needed thousands to millions of photon qubits from (b) quantum emitters in designed spatially ordered arrays to enable networks for implementing many-qubit logic circuits. Scaling up photonic quantum information processing systems has, however, bee

Maeve McGillycuddy, Gordana Popovic, Benjamin M. Bolker, David I. Warton

Multivariate random effects with unstructured variance-covariance matrices of large dimensions, $q$, can be a major challenge to estimate. In this paper, we introduce a new implementation of a reduced-rank approach to fit large dimensional multivariate random effects by writing them as a linear combination of $d < q$ latent variables. By adding reduced-rank

Shixin Chen, Hengyuan Zhang, Zichao Ling, Jianwang Zhai

Enhancing performance while reducing costs is the fundamental design philosophy of integrated circuits (ICs). With advancements in packaging technology, interposer-based chiplet architecture has emerged as a promising solution. Chiplet integration, often referred to as 2.5D IC, offers significant benefits, including cost-effectiveness, reusability, and impro

Jesse Yao, Saurav Acharya, Priyaranjan Parida, Srinivas Attipalli

Enterprise searches require users to have complex knowledge of queries, configurations, and metadata, rendering it difficult for them to access information as needed. Most go-to-market (GTM) platforms utilize advanced search, an interface that enables users to filter queries by various fields using categories or keywords, which, historically, however, has pr

Wenjun Wu

As AI and deep learning have become hot spots in the 21st century , they are widely used in the current quant market. In 2020, Huatai Securities constructed deep-learning-based AlphaNet for stock feature extraction and price prediction. At present, it has developed to the 3rd version and has formed a great influence in the market. However, the AlphaNet has s

Keyvan Majd, Geoffrey Clark, Georgios Fainekos, Heni Ben Amor

This paper introduces a new method for safety-aware robot learning, focusing on repairing policies using predictive models. Our method combines behavioral cloning with neural network repair in a two-step supervised learning framework. It first learns a policy from expert demonstrations and then applies repair subject to predictive models to enforce safety co

Bijuan Chen, Yuhao Gu, Dong Wang, Dexi Shao

We report pressure-induced superconductivity in the spinel CuIr$_{2}$S$_{4}$ with a transition temperature ($T_{\text{c}}$) reaching \textbf{18.2 K}, establishing a new record for this class of materials and surpassing the decades-old limit of 13.7 K. Our electrical transport and synchrotron X-ray diffraction studies up to 224 GPa reveal the emergence of \te

Luting Wang, Yang Zhao, Zijian Zhang, Jiashi Feng

Abstract Modern image generation (IG) models have been shown to capture rich semantics valuable for image understanding (IU) tasks. However, the potential of IU models to improve IG performance remains uncharted. We address this issue using a token-based IG framework, which relies on effective tokenizers to project images into token sequences. Currently, pix

Thiago Bergamaschi, Yunchao Liu

Preparing encoded logical states is the first step in a fault-tolerant quantum computation. Standard approaches based on concatenation or repeated measurement incur a significant time overhead. The Raussendorf-Bravyi-Harrington cluster state offers an alternative: a single-shot preparation of encoded states of the surface code, by means of a constant depth q

Qingyao Tian, Huai Liao, Xinyan Huang, Lujie Li

Monocular depth estimation has shown promise in general imaging tasks, aiding in localization and 3D reconstruction. While effective in various domains, its application to bronchoscopic images is hindered by the lack of labeled data, challenging the use of supervised learning methods. In this work, we propose a transfer learning framework that leverages synt

Zhichao Geng, Yiwen Wang, Dongyu Ru, Yang Yang

Learned sparse retrieval, which can efficiently perform retrieval through mature inverted-index engines, has garnered growing attention in recent years. Particularly, the inference-free sparse retrievers are attractive as they eliminate online model inference in the retrieval phase thereby avoids huge computational cost, offering reasonable throughput and la

D. Aguirre-Olivas, G. Mellado-Villaseñor, B. Perez-Garcia, B. M. Rodríguez-Lara

We explore a family of paraxial beams constructed by the linear superposition of Laguerre-Gaussian beams, representing an optical analogue to generalized $SU(2)$ Lie group coherent states. A single complex parameter controls a smooth transition between Laguerre-Gaussian and Hermite-Gaussian beams, with intermediate beams that merge characteristics of both fa

Junzhong Xu, Sean P. Devan, Diwei Shi, Adithya Pamulaparthi

MATI (Microstructural Analysis Toolbox for Imaging) is a versatile MATLAB-based toolbox that combines both simulation and data fitting capabilities for microstructural dMRI research. It provides a user-friendly, GUI-driven interface that enables researchers, including those without programming experience, to perform advanced MRI simulations and data analyses

Sungho Shin, Wallace Gian Yion Tan, Mihai Anitescu

We present improved approximation bounds for the Moore-Penrose inverses of banded matrices, where the bandedness is induced by a metric on the index set. We show that the pseudoinverse of a banded matrix can be approximated by another banded matrix, and the error of approximation is exponentially small in the ratio of the bandwidth of the approximation to th

Hongsheng Wang, Zehui Feng, Tong Xiao, Genfan Yang

Current 3D human motion reconstruction methods from monocular videos rely on features within the current reconstruction window, leading to distortion and deformations in the human structure under local occlusions or blurriness in video frames. To estimate realistic 3D human mesh sequences based on incomplete features, we propose Temporally-alignable Probabil

Ke Xu, Rui Zhang, He, Chen

In this paper, we propose a radio-based passive target tracking algorithm using multipath measurements, including the angle of arrival and relative distance. We focus on a scenario in which a mobile receiver continuously receives radio signals from a transmitter located at an unknown position. The receiver utilizes multipath measurements extracted from the r

Yiwei Dong, Shaoxin Ye, Yuwen Cao, Qiyu Han

Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this problem, but they often suffer from overfitting, leading to excessive cluster generation with a lack of diversity. To overcome these limitations, we propose a Bayesian mixture mode

K. Mahesh Krishna

Pfender \textit{[J. Combin. Theory Ser. A, 2007]} provided a one-line proof for a variant of the Delsarte-Goethals-Seidel-Kabatianskii-Levenshtein upper bound for spherical codes, which offers an upper bound for the celebrated (Newton-Gregory) kissing number problem. Motivated by this proof, we introduce the notion of codes in pointed metric spaces (in parti

Yijian Pan, Yongchang Ma, Bolin Shen, Linyang He

The goal of our project is to use satellite data (including nighttime light data and remote sensing images) to give us some statistical estimation of the economic development level of a selected area (Singapore). Findings from the project could inform policymakers about areas needing intervention or support for economic development initiatives. Insights gain

Yunxiang Song, Yaowen Hu, Xinrui Zhu, Keith Powell

The rapid growth in artificial intelligence and modern communication systems demands innovative solutions for increased computational power and advanced signaling capabilities. Integrated photonics, leveraging the analog nature of electromagnetic waves at the chip scale, offers a promising complement to approaches based on digital electronics. To fully unloc

Yan Shuo Tan, Jason M. Klusowski, Krishnakumar Balasubramanian

Models based on recursive adaptive partitioning such as decision trees and their ensembles are popular for high-dimensional regression as they can potentially avoid the curse of dimensionality. Because empirical risk minimization (ERM) is computationally infeasible, these models are typically trained using greedy algorithms. Although effective in many cases,

Xin Zhang, Victor S. Sheng

Neuro-symbolic AI is an effective method for improving the overall performance of AI models by combining the advantages of neural networks and symbolic learning. However, there are differences between the two in terms of how they process data, primarily because they often use different data representation methods, which is often an important factor limiting

Bruce M. Kapron, Koosha Samieefar

We present a computational formulation for the approximate version of several variational inequality problems, investigating their computational complexity and establishing PPAD-completeness. Examining applications in computational game theory, we specifically focus on two key concepts: resilient Nash equilibrium, and multi-leader-follower games -- domains t

Jugal Garg, Xin Huang, Erel Segal-Halevi

We study fair division of indivisible chores among $n$ agents with additive cost functions using the popular fairness notion of maximin share (MMS). Since MMS allocations do not always exist for more than two agents, the goal has been to improve its approximations and identify interesting special cases where MMS allocations exists. We show the existence of 1

A. M. Begun, A. A. Korneev, A. V. Zorina

Protein MJ0366 is a hypothetical protein from Methanocaldococcus jannaschii that has a rare and complex knot in its structure. The knot is a right-handed trefoil knot that involves about half of the protein's residues. In this article, we investigate the thermal stability of protein MJ0366 using numerical simulations based on molecular dynamics and Monte Car

Yijian Pan, Hongjiao Qiang

In this project, we reviewed a paper that deals graph-structured convex optimization (GSCO) problem with the approximate Frank-Wolfe (FW) algorithm. We analyzed and implemented the original algorithm and introduced some extensions based on that. Then we conducted experiments to compare the results and concluded that our backtracking line-search method effect

Teodora Baluta, Pascal Lamblin, Daniel Tarlow, Fabian Pedregosa

Machine unlearning aims to solve the problem of removing the influence of selected training examples from a learned model. Despite the increasing attention to this problem, it remains an open research question how to evaluate unlearning in large language models (LLMs), and what are the critical properties of the data to be unlearned that affect the quality a

Tarek Mahmud, Bin Duan, Meiru Che, Awatif Yasmin

Android apps rely on application programming interfaces (APIs) to access various functionalities of Android devices. These APIs however are regularly updated to incorporate new features while the old APIs get deprecated. Even though the importance of updating deprecated API usages with the recommended replacement APIs has been widely recognized, it is non-tr

Xun Tu, Karthik Desingh

Grasp planning and estimation have been a longstanding research problem in robotics, with two main approaches to find graspable poses on the objects: 1) geometric approach, which relies on 3D models of objects and the gripper to estimate valid grasp poses, and 2) data-driven, learning-based approach, with models trained to identify grasp poses from raw senso

Xiangnian Ou, Yueqiang Hu, Dian Yu, Shulin Liu

Liquid crystal on silicon (LCoS) panels, renowned for their high resolution and fill-factor, are integral to modern projection displays. However, their inherent polarization sensitivity constrains the upper limit of light utilization, increases system complexity and restricts broader applicability. Here, we demonstrate a dual-layer metasurface-integrated LCo

Tomoya Oura, Koji Fukagata

We propose a defiltering method of turbulent flow fields for Lagrangian particle tracking using machine learning techniques. Numerical simulation of Lagrangian particle tracking is commonly used in various fields. In general, practical applications require an affordable grid size due to the limitation of computational resources; for instance, a large-eddy si

Xin Zhang, Victor S. Sheng

Explainability is an essential reason limiting the application of neural networks in many vital fields. Although neuro-symbolic AI hopes to enhance the overall explainability by leveraging the transparency of symbolic learning, the results are less evident than imagined. This article proposes a classification for explainability by considering both model desi

Yichen Shi, Zhuofu Tao, Yuhao Gao, Tianjia Zhou

High-performance analog and mixed-signal (AMS) circuits are mainly full-custom designed, which is time-consuming and labor-intensive. A significant portion of the effort is experience-driven, which makes the automation of AMS circuit design a formidable challenge. Large language models (LLMs) have emerged as powerful tools for Electronic Design Automation (E

Shupei Zhang, Boya Di, Aryan Kaushik, Yonina C. Eldar

Reconfigurable holographic surfaces (RHSs) have been suggested as an energy-efficient solution for extremely large-scale arrays. By controlling the amplitude of RHS elements, high-gain directional holographic patterns can be achieved. However, the complexity of acquiring real-time channel state information (CSI) for beamforming is exceedingly high, particula

Shang-Ling Hsu, Emmanuel Tung, John Krumm, Cyrus Shahabi

Human mobility modeling from GPS-trajectories and synthetic trajectory generation are crucial for various applications, such as urban planning, disaster management and epidemiology. Both of these tasks often require filling gaps in a partially specified sequence of visits - a new problem that we call "controlled" synthetic trajectory generation. Existing met

Filip Obradović

Recent literature proposes combining short-term experimental and long-term observational data to provide alternatives to conventional observational studies for the identification of long-term average treatment effects (LTEs). This paper re-examines the identification problem and uncovers that assumptions restricting temporal link functions -- relationships b

Subrina Sultana, Donald S. Williamson

Self-supervised learning (SSL) has grown in interest within the speech processing community, since it produces representations that are useful for many downstream tasks. SSL uses global and contextual methods to produce robust representations, where SSL even outperforms supervised models. Most self-supervised approaches, however, are limited to embedding inf

Luis F. Alday, Xinan Zhou

We study AdS form factors, given by the Mellin representation for CFT correlators of local operators in the presence of extended defects. We propose a formula for taking (and expanding around) the flat-space limit. This formula relates the flat-space form factors for particles scattering off an extended object to the high-energy limit of the Mellin amplitude

Cong Chen, Hua Wang

Let us consider the Schr\"{o}dinger operator $\mathcal{L}=-\Delta+V$ on $\mathbb R^d$ with $d\geq3$, where $\Delta$ is the Laplacian operator on $\mathbb R^d$ and the nonnegative potential $V$ belongs to certain reverse H\"{o}lder class $RH_s$ with $s\geq d/2$. In this paper, the authors first introduce two kinds of function spaces related to the Schr\"{o}di

Rui Luo, Jie Bao, Zhixin Zhou, Chuangyin Dang

Adversarial attacks pose significant threats to the reliability and safety of deep learning models, especially in critical domains such as medical imaging. This paper introduces a novel framework that integrates conformal prediction with game-theoretic defensive strategies to enhance model robustness against both known and unknown adversarial perturbations.

Wen-He Jiao, Hang-Qiang Qiu, Wuzhang Yang, Jin-Ke Bao

We report transport studies on the layered van der Waals topological crystalline insulator Ta$_2$Pd$_3$Te$_5$. The temperature-dependent resistance at high temperature is dominated by a bulk insulating gap and tend to saturate at low temperatures. Low temperature magnetotransport shows that Ta$_2$Pd$_3$Te$_5$ exhibits weak antilocatization (WAL) effect in bo

Rongjie Yi, Xiang Li, Weikai Xie, Zhenyan Lu

The interest in developing small language models (SLM) for on-device deployment is fast growing. However, the existing SLM design hardly considers the device hardware characteristics. Instead, this work presents a simple yet effective principle for SLM design: architecture searching for (near-)optimal runtime efficiency before pre-training. Guided by this pr

Lin Yang, Sri Harsha Turlapati, Chen Lv, Domenico Campolo

Contact-rich manipulation often requires strategic interactions with objects, such as pushing to accomplish specific tasks. We propose a novel scenario where a robot inserts a book into a crowded shelf by pushing aside neighboring books to create space before slotting the new book into place. Classical planning algorithms fail in this context due to limited

Xian-Song Zhao, Chao Yu, Chong Wang, Tianyi Li

Differential absorption Lidar (DIAL) in the ultraviolet (UV) region is an effective approach for monitoring tropospheric ozone. 4H-SiC single-photon detectors (SPDs) are emergent devices for UV single-photon detection. Here, we demonstrate a 4H-SiC SPD-based ozone DIAL. We design and fabricate the 4H-SiC single-photon avalanche diode with a beveled mesa stru

Daniel O'Malley, Manish Bhattarai, Nishath Rajiv Ranasinghe, Erick Draayer

We present a novel benchmark designed to rigorously evaluate the capabilities of large language models (LLMs) in mathematical reasoning and algorithmic code synthesis tasks. The benchmark comprises integer sequence generation tasks sourced from the Online Encyclopedia of Integer Sequences (OEIS), testing LLMs' abilities to accurately and efficiently generate

Yonas Sium, Qi Li

Graph Neural Networks (GNNs) have become the leading approach for addressing graph analytical problems in various real-world scenarios. However, GNNs may produce biased predictions against certain demographic subgroups due to node attributes and neighbors surrounding a node. Most current research on GNN fairness focuses predominantly on debiasing GNNs using

Hongyu Li, Bruno Clerckx

Beyond diagonal reconfigurable intelligent surfaces (BD-RIS) is a new advance in RIS techniques that introduces reconfigurable inter-element connections to generate scattering matrices not limited to being diagonal. BD-RIS has been recently proposed and proven to have benefits in enhancing channel gain and enlarging coverage in wireless communications. Uniqu

Tongyu Li, Jiabei Zhu, Yi Shen, Lei Tian

We introduce a novel reflection-mode diffraction tomography technique that enables simultaneous recovery of forward and backward scattering information for high-resolution 3D refractive index reconstruction. Our technique works by imaging a sample on a highly reflective substrate and employing a novel multiple-scattering model and reconstruction algorithm. I

Jason Wei, Nguyen Karina, Hyung Won Chung, Yunxin Joy Jiao

We present SimpleQA, a benchmark that evaluates the ability of language models to answer short, fact-seeking questions. We prioritized two properties in designing this eval. First, SimpleQA is challenging, as it is adversarially collected against GPT-4 responses. Second, responses are easy to grade, because questions are created such that there exists only a

Soaad Hossain, James Rasalingam, Arhum Waheed, Fatah Awil

With the growing interest in using AI and machine learning (ML) in medicine, there is an increasing number of literature covering the application and ethics of using AI and ML in areas of medicine such as clinical psychiatry. The problem is that there is little literature covering the economic aspects associated with using ML in clinical psychiatry. This stu