April 2024 arXiv papers — page 74

Junjie Li, Guanshuo Wang, Fufu Yu, Yichao Yan

Clothes-changing person re-identification (CC-ReID) aims to retrieve images of the same person wearing different outfits. Mainstream researches focus on designing advanced model structures and strategies to capture identity information independent of clothing. However, the same-clothes discrimination as the standard ReID learning objective in CC-ReID is pers

Yi Ding, Chun Yan

The advent of the era of big data provides new ideas for financial distress prediction. In order to evaluate the financial status of listed companies more accurately, this study establishes a financial distress prediction indicator system based on multi-source data by integrating three data sources: the company's internal management, the external market and

Lu Tang, Zhen-Hua Zhang

The extended kernel ridge regression (EKRR) method with odd-even effects was adopted to improve the description of the nuclear charge radius using five commonly used nuclear models. These are: (i) the isospin dependent $A^{1/3}$ formula, (ii) relativistic continuum Hartree-Bogoliubov (RCHB) theory, (iii) Hartree-Fock-Bogoliubov (HFB) model HFB25, (iv) the We

Sibei Chen, Yeye He, Weiwei Cui, Ju Fan

Spreadsheets are widely recognized as the most popular end-user programming tools, which blend the power of formula-based computation, with an intuitive table-based interface. Today, spreadsheets are used by billions of users to manipulate tables, most of whom are neither database experts nor professional programmers. Despite the success of spreadsheets, aut

Hannah Larson

Let $\mathscr{J}^d_g \to \mathscr{M}_g$ be the universal Picard stack parametrizing degree $d$ line bundles on genus $g$ curves, and let $\mathscr{J}^d_{2,g}$ be its restriction to locus of hyperelliptic curves $\mathscr{H}_{2,g} \subset \mathscr{M}_g$. We determine the rational Chow ring of $\mathscr{J}^d_{2,g}$ for all $d$ and $g$. In particular, we prove

Yu-Hsuan Ho, Longxiang Li, Ali Mostafavi

Street view imagery, aided by advancements in image quality and accessibility, has emerged as a valuable resource for urban analytics research. Recent studies have explored its potential for estimating lowest floor elevation (LFE), offering a scalable alternative to traditional on-site measurements, crucial for assessing properties' flood risk and damage ext

Ziyi Zhou, Ming Cheng, Xingjian Diao, Yanjun Cui

The escalating prevalence of diabetes globally underscores the need for diabetes management. Recent research highlights the growing focus on digital biomarkers in diabetes management, with innovations in computational frameworks and noninvasive monitoring techniques using personalized glucose metrics. However, they predominantly focus on insulin dosing and s

Kexin Chen, Yijie Mao

This work initiates the study of a beyond-diagonal reconfigurable intelligent surface (BD-RIS)-aided transmitter architecture for integrated sensing and communication (ISAC) in the millimeter-wave (mmWave) frequency band. Deploying BD-RIS at the transmitter side not only alleviates the need for extensive fully digital radio frequency (RF) chains but also enh

Austin J. Adams, Sharjeel Khan, Arjun S. Bhamra, Ryan R. Abusaada

Quantum computers have leaped from the theoretical realm into a race to large-scale implementations. This is due to the promise of revolutionary speedups, where achieving such speedup requires designing an algorithm that harnesses the structure of a problem using quantum mechanics. Yet many quantum programming languages today require programmers to reason at

Minzhe Huang, Changwei Nie, Weihong Zhong

In recent years, Face Anti-Spoofing (FAS) has played a crucial role in preserving the security of face recognition technology. With the rise of counterfeit face generation techniques, the challenge posed by digitally edited faces to face anti-spoofing is escalating. Existing FAS technologies primarily focus on intercepting physically forged faces and lack a

Wen-Jing Xing, Shu-Qing Li, Shanshan Cao, Guang-You Qin

We study bottom quark energy loss via the nuclear modification factor ($R_\mathrm{AA}$) and elliptic flow ($v_2$) of non-prompt $D^0$ and $J/\psi$ in relativistic heavy-ion collisions at the LHC. The space-time profile of quark-gluon plasma is obtained from the CLVisc hydrodynamics simulation, the dynamical evolution of heavy quarks inside the color deconfin

Matthew S. Winnel, Ziqing Wang, Robert Malaney, Ryan Aguinaldo

In typical laser communications classical information is encoded by modulating the amplitude of the laser beam and measured via direct detection. We add a layer of security using quantum physics to this standard scheme, applicable to free-space channels. We consider a simultaneous classical-quantum communication scheme where the classical information is enco

Nikhil P Ghanathe, Steven J E Wilton

Uncertainty quantification (UQ) provides a resource-efficient solution for on-device monitoring of tinyML models deployed without access to true labels. However, existing UQ methods impose significant memory and compute demands, making them impractical for ultra-low-power, KB-sized TinyML devices. Prior work has attempted to reduce overhead by using early-ex

Yanwei Jia

This paper studies continuous-time risk-sensitive reinforcement learning (RL) under the entropy-regularized, exploratory diffusion process formulation with the exponential-form objective. The risk-sensitive objective arises either as the agent's risk attitude or as a distributionally robust approach against the model uncertainty. Owing to the martingale pers

Haobo Zhang, Weihao Lu, Qian Lin

The generalization ability of kernel interpolation in large dimensions (i.e., $n \asymp d^{\gamma}$ for some $\gamma>0$) might be one of the most interesting problems in the recent renaissance of kernel regression, since it may help us understand the 'benign overfitting phenomenon' reported in the neural networks literature. Focusing on the inner product ker

Lasal Jayawardena, Prasan Yapa

Over the past year, the field of Natural Language Generation (NLG) has experienced an exponential surge, largely due to the introduction of Large Language Models (LLMs). These models have exhibited the most effective performance in a range of domains within the Natural Language Processing and Generation domains. However, their application in domain-specific

Zhengpeng Wang, Yanqun Tang, Yingzhe Mao, Tao Wang

This letter presents a deep reinforcement learning (DRL) approach for transmission design to optimize the energy efficiency in vehicle-to-vehicle (V2V) communication links. Considering the dynamic environment of vehicular communications, the optimization problem is non-convex and mathematically difficult to solve. Hence, we propose scenario identification-ba

Huilin Yin, Shengkai Su, Yinjia Lin, Pengju Zhen

With the flourishing development of intelligent warehousing systems, the technology of Automated Guided Vehicle (AGV) has experienced rapid growth. Within intelligent warehousing environments, AGV is required to safely and rapidly plan an optimal path in complex and dynamic environments. Most research has studied deep reinforcement learning to address this c

Tim Gould

Typical density functional theory (DFT) and approximations thereto solve the many-electron ground state problem by working from a numerically efficient non-interacting Kohn-Sham reference system; and benefit from useful minimization conditions that allow iteration (i.e. self-consistency) to the optimal energy and density. Ensembles of ground and excited stat

Tong Xu, Armeen Taeb, Simge Küçükyavuz, Ali Shojaie

We study the problem of learning directed acyclic graphs from continuous observational data, generated according to a linear Gaussian structural equation model. State-of-the-art structure learning methods for this setting have at least one of the following shortcomings: i) they cannot provide optimality guarantees and can suffer from learning sub-optimal mod

Albert Escrivà, Ryoto Inui, Yuichiro Tada, Chul-Moon Yoo

Supposing the Laser Interferometer Space Antenna (LISA) gravitational wave (GW) detector, we exhibit the detectability of a hypothetical smooth crossover in the early universe beyond the Standard Model of particle physics through the scalar-induced gravitational wave (SIGW) in terms of the Fisher forecast. A crossover at $\sim100\,\mathrm{TeV}$ can leave a s

A. Feder Cooper, James Grimmelmann

The New York Times's copyright lawsuit against OpenAI and Microsoft alleges OpenAI's GPT models have "memorized" NYT articles. Other lawsuits make similar claims. But parties, courts, and scholars disagree on what memorization is, whether it is taking place, and what its copyright implications are. These debates are clouded by ambiguities over the nature of

Juncheng Yang, Zuchao Li, Shuai Xie, Weiping Zhu

Adapter-based parameter-efficient transfer learning has achieved exciting results in vision-language models. Traditional adapter methods often require training or fine-tuning, facing challenges such as insufficient samples or resource limitations. While some methods overcome the need for training by leveraging image modality cache and retrieval, they overloo

Ngoc Quach, Qi Wang, Zijun Gao, Qifeng Sun

The widespread use of knowledge graphs in various fields has brought about a challenge in effectively integrating and updating information within them. When it comes to incorporating contexts, conventional methods often rely on rules or basic machine learning models, which may not fully grasp the complexity and fluidity of context information. This research

Mark Chiu Chong, Hien Duy Nguyen, TrungTin Nguyen

We consider the problem of estimating probability density functions based on sample data, using a finite mixture of densities from some component class. To this end, we introduce the $h$-lifted Kullback--Leibler (KL) divergence as a generalization of the standard KL divergence and a criterion for conducting risk minimization. Under a compact support assumpti

Bradley Greig, Andrei Mesinger, Eduardo Bañados, George D. Becker

The attenuation of Ly$\alpha$ photons by neutral hydrogen in the intergalactic medium (IGM) at $z\gtrsim5$ continues to be a powerful probe for studying the epoch of reionisation. Given a framework to estimate the intrinsic (true) Ly$\alpha$ emission of high-$z$ sources, one can infer the ionisation state of the IGM during reionisation. In this work, we use

Frezer Guteta Wakgra, Binayak Kar, Seifu Birhanu Tadele, Shan-Hsiang Shen

The emergence of 5G networks has enabled the deployment of a two-tier edge and vehicular-fog network. It comprises Multi-access Edge Computing (MEC) and Vehicular-Fogs (VFs), strategically positioned closer to Internet of Things (IoT) devices, reducing propagation latency compared to cloud-based solutions and ensuring satisfactory quality of service (QoS). H

Bokgyeong Kang, Erin M. Schliep, Alan E. Gelfand, Tina M. Yack

Sound is assumed to be the primary modality of communication among marine mammal species. Analyzing acoustic recordings helps to understand the function of the acoustic signals as well as the possible impact of anthropogenic noise on acoustic behavior. Motivated by a dataset from a network of hydrophones in Cape Cod Bay, Massachusetts, utilizing automaticall

Zhengyang Mao, Haigang Liu, Xianfeng Chen

Mode sorter is the crucial component of the communication systems based on orbital angular momentum (OAM). However, schemes proposed so far can only effectively sort integer OAM (IOAM) modes. Here, we demonstrate the effective sorting of fractional OAM (FOAM) modes by utilizing the coordinate transformation method, which can convert FOAM modes to IOAM modes.

Shaomin Wu

In this paper, I characterize the network formation process as a static game of incomplete information, where the latent payoff of forming a link between two individuals depends on the structure of the network, as well as private information on agents' attributes. I allow agents' private unobserved attributes to be correlated with observed attributes through

Anirudh Sundar, Christopher Richardson, Adar Avsian, Larry Heck

This paper introduces Interactive Tables (iTBLS), a dataset of interactive conversations that focuses on natural-language manipulation of tabular information sourced from academic pre-prints on ArXiv. The iTBLS dataset consists of three types of tabular tasks -- interpretation, modification, and generation. Interpretation focuses on tabular understanding, mo

Renai Chen, Tammie Gibson, Galen T. Craven

The time-periodic modulation of a temperature gradient can alter the heat transport properties of a physical system. Oscillating thermal gradients give rise to behaviors such as modified thermal conductivity and controllable time-delayed energy storage that are not present in a system with static temperatures. Here, we examine how the heat transport properti

Maria Hilse, Justin Rodriguez, Jennifer Gray, Jinyuan Yao

The top-down synthesis of inherently ferroelectric semiconductors and their integration with traditional material platforms have the potential to enable new low power logic devices, and to harness the bulk photoelectric effect for more efficient photovoltaic cells. InSe is a layered van der Waals compound exhibiting multiple polytypes, with semiconducting ga

Jianxin Yang, Tianle Pan, Zhenming Xie, Wu Yuan

Electrokinetic force has been the major choice for driving the translocation of molecules through a nanopore. However, the use of this approach is limited by an uncontrollable translocation speed, resulting in non-uniform conductance signals with low conformational sensitivity, which hinders the accurate discrimination of the molecules. Here, we show the fir

Sarah Santos, Travis Breaux, Thomas Norton, Sara Haghighi

Language models that can learn a task at inference time, called in-context learning (ICL), show increasing promise in natural language inference tasks. In ICL, a model user constructs a prompt to describe a task with a natural language instruction and zero or more examples, called demonstrations. The prompt is then input to the language model to generate a c

Yanwen Wang, Mahdi Khodadadzadeh, Raul Zurita-Milla

Recent geospatial machine learning studies have shown that the results of model evaluation via cross-validation (CV) are strongly affected by the dissimilarity between the sample data and the prediction locations. In this paper, we propose a method to quantify such a dissimilarity in the interval 0 to 100% and from the perspective of the data feature space.

Mitsuyoshi Adachi

In 2022 Baraglia and Konno showed the following: for a smooth family of a homotopy $K3$ surface $X \to \mathbb{X} \stackrel{\pi}{\to} B$, if the tangent bundle along the fibers $T_B \mathbb{X}$ admits a spin structure, then $\mathcal{H}^+(\mathbb{X})$ also admits a spin structure, where $\mathcal{H}^+(\mathbb{X})$ is the vector bundle consisting of self-dual

Fabian Jin, Samuel Lanthaler, Milton C. Lopes Filho, Helena J. Nussenzveig Lopes

Smooth solutions of the forced incompressible Euler equations satisfy an energy balance, where the rate-of-change in time of the kinetic energy equals the work done by the force per unit time. Interesting phenomena such as turbulence are closely linked with rough solutions which may exhibit {\it inviscid dissipation}, or, in other words, for which energy bal

Alan M. Knee, Jess McIver, Smadar Naoz, Isobel M. Romero-Shaw

Stellar-mass black hole binaries (BHBs) in galactic nuclei are gravitationally perturbed by the central supermassive black hole (SMBH) of the host galaxy, potentially inducing strong eccentricity oscillations through the eccentric Kozai-Lidov (EKL) mechanism. These highly eccentric binaries emit a train of gravitational-wave (GW) bursts detectable by the Las

Yuhan Zhao, Lan Shi, Quanyan Zhu

As assembly tasks grow in complexity, collaboration among multiple robots becomes essential for task completion. However, centralized task planning has become inadequate for adapting to the increasing intelligence and versatility of robots, along with rising customized orders. There is a need for efficient and automated planning mechanisms capable of coordin

Zhenzhong Wang, Qingyuan Zeng, Wanyu Lin, Min Jiang

While graph neural networks (GNNs) have become the de-facto standard for graph-based node classification, they impose a strong assumption on the availability of sufficient labeled samples. This assumption restricts the classification performance of prevailing GNNs on many real-world applications suffering from low-data regimes. Specifically, features extract

Jinzhi Huang, Zhongxiao Jia

In a Jacobi--Davidson (JD) type method for singular value decomposition (SVD) problems, called JDSVD, a large symmetric and generally indefinite correction equation is solved iteratively at each outer iteration, which constitutes the inner iterations and dominates the overall efficiency of JDSVD. In this paper, by fully exploiting useful information from cur

Tyler Clark

In this study, we develop a novel evolutionary model that incorporates Mendelian genetics, continuous strategies, and the potential for multiple genes to contribute to a single phenotypic trait. The evolution of altruistic behavior, which confers benefits to others at a cost to the individual, remains a fundamental question in evolutionary biology. While pre

Danyang Peng, Tanner Person, Ximing Shen, Yun Suen Pai

Watching Autonomous Sensory Meridian Response (ASMR) videos is a popular approach to support mental well-being, as the triggered ASMR tingling sensation supports de-stressing and regulating emotions. Therefore, there is increasing research on how to efficiently trigger ASMR tingling sensation. Tactile sensation remains unexplored because current popular ASMR

Yuanfei Huang, Adrian Röllin

We investigate the SIR epidemic on a dynamic inhomogeneous Erd\H{o}s-R\'enyi random graph, in which vertices are of one of $k$ types and in which edges appear and disappear independently of each other. We establish a functional law of large numbers for the susceptible, infected, and recovered ratio curves after a random time shift, and demonstrate that, unde

James Henderson, Yuta Nagano, Martina Milighetti, Andreas Tiffeau-Mayer

A key challenge in molecular biology is to decipher the mapping of protein sequence to function. To perform this mapping requires the identification of sequence features most informative about function. Here, we quantify the amount of information (in bits) that T-cell receptor (TCR) sequence features provide about antigen specificity. We identify informative

Kango Matsushima, Shuichi Tsukuda

We show that a connected finite topological space with $12$ or less points has a weak homotopy type of a wedge of spheres. In other words, we show that the order complex of a connected finite poset with $12$ or less points has a homotopy type of a wedge of spheres.

Ashvini Varatharaj, Abigail Welch, Mary Bucholtz, Jin Sook Lee

This position paper presents the AR Language Map, a speculative artifact designed to enhance understanding of linguistic justice among middle and high school students through augmented reality (AR) that allows students to map their linguistic experiences. Through a social justice-oriented academic outreach program aimed at linguistically, economically, and r

Nian Liu, Xue Liu

In this paper, we study the irregular set of any continuous observable for a class of skew product transformations, which is driven by a uniquely ergodic homeomorphism system $(\Omega,\mathbb{P},\theta)$ and satisfies Anosov and toplogical mixing on fibers property. We prove that if the irregular set of any continuous observable is nonempty on a fiber, then

Nian Liu, Xue Liu

In this paper, we establish a variational principle, between the fiber Bowen's topological entropy on conditional level sets of Birkhoff average and fiber measure-theoretical entropy, for the skew product transformation driven by a uniquely ergodic homeomorphism system satisfying Anosov and topological mixing on fibers property. We prove it by utilizing a fi

Srinivasa Rao Konda, Puspendu Barik, Subshash Singh, Venkatesh Mottamchetty

The studies of the nonlinear optical (NLO) properties of the transition metal dichalcogenides (TMDs) coupled with photoactive particles, plasmonic nanocavities, waveguides, and metamaterials remain in their infancy. This study investigates the third-order NLO properties of MoS$_2$ nanosheets in the presence of a semiconductor photoactive medium. Our extensiv

Dayton G. Thorpe, Andrew J. Duberstein, Ian A. Kinsey

The current state-of-the-art (SOTA) for automated text-to-SQL still falls well short of expert human performance as measured by execution accuracy (EX) on the BIRD-SQL benchmark. The most accurate methods are also slow and expensive. To advance the SOTA for text-to-SQL while reducing cost and improving speed, we explore the combination of low-cost fine tunin

Chenglong Xiao, Chengyong Mao, Shanshan Wang

The problem of enumerating connected subgraphs of a given size in a graph has been extensively studied in recent years. In this paper, we propose an algorithm with a delay of $O(k\Delta)$ for enumerating all connected induced subgraphs of size $k$ in an undirected graph $G=(V, E)$, where $k$ and $\Delta$ are respectively the size of subgraphs and the maximum

Sharon Ferguson, Paula Akemi Aoyagui, Young-Ho Kim, Anastasia Kuzminykh

As large language models (LLMs) advance to produce human-like arguments in some contexts, the number of settings applicable for human-AI collaboration broadens. Specifically, we focus on subjective decision-making, where a decision is contextual, open to interpretation, and based on one's beliefs and values. In such cases, having multiple arguments and persp

Daosheng Tang

Understanding the phonon behavior in semiconductors from a topological physics perspective provides more opportunities to uncover extraordinary physics related to phonon transport and electron-phonon interactions. While various kinds of topological phonons have been reported in different crystalline solids, their microscopic origin has not been quantitativel

Alexander Scarlatos, Wanyong Feng, Digory Smith, Simon Woodhead

Multiple-choice questions (MCQs) are commonly used across all levels of math education since they can be deployed and graded at a large scale. A critical component of MCQs is the distractors, i.e., incorrect answers crafted to reflect student errors or misconceptions. Automatically generating them in math MCQs, e.g., with large language models, has been chal

Sahil Bhola, Karthik Duraisamy

Probabilistic rounding error analysis can yield much sharper bounds than classical worst-case theory, but existing results typically rely on zero-mean rounding errors and often leave the confidence parameter implicit. This work revisits probabilistic rounding error analysis in a moment-aware setting. We first derive a confidence-calibrated reformulation of t

Tiffany T. Nguyen, Cinthya Jauregui, Sarah H. Sallee, Mohan R. Chandrasekar

In this position paper, we explore the power of storytelling and its connection to place through the use of Augmented Reality (AR) technology, particularly within the context of Th\'amien Ohlone history on the Santa Clara University campus. To do this, we utilized SLAM and 8th Wall to create virtual, location-based experiences that geolocate tribal stories a

Jing Cheng, Ruigang Wang, Ian R. Manchester

In this paper, we introduce a novel class of neural differential equation, which are intrinsically Lyapunov stable, exponentially stable or passive. We take a recently proposed Polyak Lojasiewicz network (PLNet) as an Lyapunov function and then parameterize the vector field as the descent directions of the Lyapunov function. The resulting models have a same

Wei Jia, Roman Jandarov

This manuscript presents a novel Bayesian varying coefficient quantile regression (BVCQR) model designed to assess the longitudinal effects of chemical exposure mixtures on children's neurodevelopment. Recognizing the complexity and high-dimensionality of environmental exposures, the proposed approach addresses critical gaps in existing research by offering

Zezhou Huang, Eugene Wu

Data profilers play a crucial role in the preprocessing phase of data analysis by identifying quality issues such as missing, extreme, or erroneous values. Traditionally, profilers have relied solely on statistical methods, which lead to high false positives and false negatives. For example, they may incorrectly flag missing values where such absences are ex

Sabry G. Moustafa, Andrew J. Schultz

Imaginary-time path integral (PI) is a rigorous tool to compute static properties at finite temperatures. However, the stiff PI internal modes poses a sampling challenge. This is commonly tackled using staging coordinates, in which the free particle (FP) term of the PI action is diagonalized. We introduce novel and simple staging coordinates that diagonalize

Jonathan A. Gross, Elie Genois, Dripto M. Debroy, Yaxing Zhang

Repeating a gate sequence multiple times amplifies systematic errors coherently, making it a useful tool for characterizing quantum gates. However, the precision of such an approach is limited by low-frequency noises, while its efficiency hindered by time-consuming scans required to match up the phases of the off-diagonal matrix elements being amplified. Her

Charlotte Kobiella, Yarhy Said Flores López, Fiona Draxler, Albrecht Schmidt

Large language models (LLMs) like ChatGPT have been widely adopted in work contexts. We explore the impact of ChatGPT on young professionals' perception of productivity and sense of accomplishment. We collected LLMs' main use cases in knowledge work through a preliminary study, which served as the basis for a two-week diary study with 21 young professionals

Ryo Yonetani, Jun Baba, Yasutaka Furukawa

We present RetailOpt, a novel opt-in, easy-to-deploy system for tracking customer movements offline in indoor retail environments. The system uses readily accessible information from customer smartphones and retail apps, including motion data, store maps, and purchase records. This eliminates the need for additional hardware installations/maintenance and ens

Ata Koklu, Yusuf Guven, Tufan Kumbasar

Type-1 and Interval Type-2 (IT2) Fuzzy Logic Systems (FLS) excel in handling uncertainty alongside their parsimonious rule-based structure. Yet, in learning large-scale data challenges arise, such as the curse of dimensionality and training complexity of FLSs. The complexity is due mainly to the constraints to be satisfied as the learnable parameters define

Kaipeng Zeng, Bo yang, Xin Zhao, Yu Zhang

Motivation: Retrosynthesis planning poses a formidable challenge in the organic chemical industry. Single-step retrosynthesis prediction, a crucial step in the planning process, has witnessed a surge in interest in recent years due to advancements in AI for science. Various deep learning-based methods have been proposed for this task in recent years, incorpo

Andi Gu, Salvatore F. E. Oliviero, Lorenzo Leone

Entanglement serves as a foundational pillar in quantum information theory, delineating the boundary between what is classical and what is quantum. The common assumption is that higher entanglement corresponds to a greater degree of `quantumness'. However, this folk belief is challenged by the fact that classically simulable operations, such as Clifford

Johnathan Koch, Beth Bjorkman

Phasor Measurement Units (PMUs) are placed at strategic vertices in an electrical power network to monitor the flow of power. Determining the minimum number and optimal placement of PMUs is modeled by the graph theoretic process called Power Domination. This paper describes the Power Domination Toolbox (PDT), which efficiently identifies a minimum number of

Yalda Foroutan, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi

3D Gaussian Splatting has recently been embraced as a versatile and effective method for scene reconstruction and novel view synthesis, owing to its high-quality results and compatibility with hardware rasterization. Despite its advantages, Gaussian Splatting's reliance on high-quality point cloud initialization by Structure-from-Motion (SFM) algorithms is a

Shivesh Pathak, Lucas Kocia Kovalsky

Known solutions to three-dimensional gravity with negative cosmological constant so far consist of either AdS$_3$ or its orbifolds (or orientifolds). We geometrically derive a novel non-orientable AdS$_3$ spacetime that is an orientifold of a spinor double cover of AdS$_3$, unlike existing solutions. This spacetime's universal cover contains a lightlike inst

Xuemin Yu, Fahim Dalvi, Nadir Durrani, Marzia Nouri

Interpreting and understanding the predictions made by deep learning models poses a formidable challenge due to their inherently opaque nature. Many previous efforts aimed at explaining these predictions rely on input features, specifically, the words within NLP models. However, such explanations are often less informative due to the discrete nature of these

Mohsen Zaker Esteghamati, Brennan Bean, Henry V. Burton, M. Z. Naser

Machine learning (ML)-based solutions are rapidly changing the landscape of many fields, including structural engineering. Despite their promising performance, these approaches are usually only demonstrated as proof-of-concept in structural engineering, and are rarely deployed for real-world applications. This paper aims to illustrate the challenges of devel

Lior Benizri, Jan Troost

We study two-dimensional topological gauge theories with gauge group equal to the symmetric group $S_n$ and their string theory duals. The simplest such theory is the topological quantum field theory of principal $S_n$ fiber bundles. Its correlators are equal to Hurwitz numbers. The operator products in the gauge theory for each finite value of $n$ are coded

Hyosun Kim, Mark R. Morris, Jongsoo Kim, Jinhua He

We develop a physical framework for interpreting complex circumstellar patterns whorled around asymptotic giant branch (AGB) stars by investigating stable, coplanar triple systems using hydrodynamic and particle simulations. The introduction of a close tertiary body causes an additional periodic variation in the orbital velocity and trajectory of the AGB sta

Sai Sree Harsha, Ambareesh Revanur, Dhwanit Agarwal, Shradha Agrawal

Video editing methods based on diffusion models that rely solely on a text prompt for the edit are hindered by the limited expressive power of text prompts. Thus, incorporating a reference target image as a visual guide becomes desirable for precise control over edit. Also, most existing methods struggle to accurately edit a video when the shape and size of

Suyanpeng Zhang, Sze-chuan Suen

Repeated decision-making problems under uncertainty may arise in the health policy context, such as infectious disease control for COVID-19 and other epidemics. These problems may sometimes be effectively solved using Markov decision processes (MDPs). However, the continuous or large state space of such problems for capturing infectious disease prevalence re

Xiao Liu, Fabian Weigend, Yifan Zhou, Heni Ben Amor

While imitation learning provides a simple and effective framework for policy learning, acquiring consistent actions during robot execution remains a challenging task. Existing approaches primarily focus on either modifying the action representation at data curation stage or altering the model itself, both of which do not fully address the scalability of con

Junrui Zhang, Mozhgan Pourkeshavarz, Amir Rasouli

As a safety critical task, autonomous driving requires accurate predictions of road users' future trajectories for safe motion planning, particularly under challenging conditions. Yet, many recent deep learning methods suffer from a degraded performance on the challenging scenarios, mainly because these scenarios appear less frequently in the training data.

Bruno S. V. Araújo, Reginaldo Demarque, Josiane C. O. Faria, Luiz Viana

This work is concerned with the obtainment of new Carleman estimates for linear parabolic equations, where the second-order differential operator brings a super strong degeneracy in a positive measure subset of the spatial domain. In order to prove our main result, the control domain is supposed to contain the set of degeneracies. As a well-known consequence

Dante S. Lauretta, Harold C. Connolly,, Joseph E. Aebersold, Conel M. O. D. Alexander

On 24 September 2023, the NASA OSIRIS-REx mission dropped a capsule to Earth containing approximately 120 g of pristine carbonaceous regolith from Bennu. We describe the delivery and initial allocation of this asteroid sample and introduce its bulk physical, chemical, and mineralogical properties from early analyses. The regolith is very dark overall, with h

William Watson, Nicole Cho, Nishan Srishankar

Hallucination continues to be one of the most critical challenges in the institutional adoption journey of Large Language Models (LLMs). While prior studies have primarily focused on the post-generation analysis and refinement of outputs, this paper centers on the effectiveness of queries in eliciting accurate responses from LLMs. We present HalluciBot, a mo

Peiyang Song, Kaiyu Yang, Anima Anandkumar

Neural theorem proving combines large language models (LLMs) with proof assistants such as Lean, where the correctness of formal proofs can be rigorously verified, leaving no room for hallucination. With existing neural theorem provers pretrained on a fixed collection of data and offering valuable suggestions at times, it is challenging for them to continual

Jonas August, Christophe Bailly, Thierry Dufour

In this article, we focus on the plasma seed interaction and more specifically-on the feedback exerted by the seeds on the plasma properties. Dormant Arabidopsis seeds with different water contents (WC), namely 3 %DW, 10 %DW and 30%DW were exposed to cold ambient air plasma (C2AP) generated in a dielectric barrier device (DBD). It is found that increasing WC

Nikunj Khetan, Jerome Mertz

Coherent Plane Wave Compounding (CPWC) is widely used for ultrasound imaging. This technique involves sending plane waves into a sample at different transmit angles and recording the resultant backscattered echo at different receive positions. The time-delayed signals from the different combinations of transmit angles and receive positions are then coherentl

Mitchell Harling, Varun A. Kelkar, Kimani C. Toussaint,, Ayman F. Abouraddy

For partially coherent optical fields in which a single binary degree of freedom (DoF) is relevant, such as polarization, entropy uniquely identifies the class of optical fields that can be converted into each other via unitary transformations. However, when multiple DoFs are taken into consideration, entropy no longer serves this purpose. We investigate the

Atsushi Inoue, Sean Ku, Jun Masamune, Radosław K. Wojciechowski

We give two characterizations for the essential self-adjointness of the weighted Laplacian on birth-death chains. The first involves the edge weights and vertex measure and is classically known; however, we give another proof using stability results, limit point-limit circle theory and the connection between essential self-adjointness and harmonic functions.

Chen Gong, Kecen Li, Jin Yao, Tianhao Wang

Reinforcement learning (RL) trains an agent from experiences interacting with the environment. In scenarios where online interactions are impractical, offline RL, which trains the agent using pre-collected datasets, has become popular. While this new paradigm presents remarkable effectiveness across various real-world domains, like healthcare and energy mana

Taolei Shi, Wei Gong

Nowadays, indoor localization has received extensive research interest due to more and more applications' needs for location information to provide a more precise and effective service [1], [2]. There are various wireless techniques and mechanisms that have been proposed; some of them have been studied in depth and come into use, such as Wi-Fi, RFID, and sen

Innocent Okwudili Eya, Evaristus Uzochukwu Iyida

Glitch activity refers to the mean increase in pulsar spin frequency per year due to rotational glitches. It is an important tool for studying super-nuclear matter using neutron star interiors as templates. Glitch events are typically observed in the spin frequency ($\nu$) and frequency derivative ($\dot{\nu}$) of pulsars. The rate of glitch recurrence decre

Abhinna Sundar Samantaray, H. K. Jassal, Kulinder Pal Singh, G. C. Dewangan

We carry out deep near-ultraviolet (NUV) and far-ultraviolet (FUV) imaging of an interacting galaxy system, comprised of a Seyfert type 1 galaxy NGC 7469 and its companion IC 5283. Our aim is to resolve and map the star-forming regions in the outer arms and look for signs of interaction between the two galaxies. We used AstroSat Ultra Violet Imaging Telescop

Mohamed Ali Benabderrahim, Imen Bettaieb, Hédia Hannachi, Mokhtar Rejili

This study investigates the impact of three cold plasma treatments on barley seed germination: direct treatment of dry seeds (DDS), direct treatment of water-soaked seeds (DWS), and indirect treatment of seeds using plasma-activated water (IPAW).

James Seale Smith, Lazar Valkov, Shaunak Halbe, Vyshnavi Gutta

Foundation Models (FMs) have become the hallmark of modern AI, however, these models are trained on massive data, leading to financially expensive training. Updating FMs as new data becomes available is important, however, can lead to `catastrophic forgetting', where models underperform on tasks related to data sub-populations observed too long ago. This con

Lisanne Gossel, Elisa Corbean, Sören Dübal, Paul Brand

Metal energy carriers recently gained growing interest in research as a promising storage and transport material for renewable electricity. Within the development of a metal-fueled circular energy economy, research involves a model hierarchy spanning from micro to macro scales, making the transfer of information among different levels of complexity a crucial

Bradley E. Schaefer

The evolution of Cataclysmic Variables (CVs) is driven by period-changes ($\dot{P}$), for which the long-venerable consensus is the Magnetic Braking Model (MBM). The MBM has its only distinctive assumption being a power-law `recipe' describing the angular momentum loss (AML) in the binary, producing a single unique evolutionary track with $\dot{P}$ as a func

Dominik Bauer, Zhenjia Xu, Shuran Song

Manipulation of elastoplastic objects like dough often involves topological changes such as splitting and merging. The ability to accurately predict these topological changes that a specific action might incur is critical for planning interactions with elastoplastic objects. We present DoughNet, a Transformer-based architecture for handling these challenges,

N. Alvarez-Baena, R. Carrera, H. Thompson, L. Balaguer-Nuñez

Context: Open clusters' dynamical evolution is driven by stellar evolution, internal dynamics and external forces, which according to dynamical simulations, will evaporate them in a timescale of about 1 Ga. However, about 10\% of the known open clusters are older. They are special systems whose detailed properties are related to their dynamical evolution and

Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu

We study both stream-based and pool-based active learning with neural network approximations. A recent line of works proposed bandit-based approaches that transformed active learning into a bandit problem, achieving both theoretical and empirical success. However, the performance and computational costs of these methods may be susceptible to the number of cl

Hidekazu Tanaka, Kohei Okada

We performed linear calculations to determine the Type I planetary migration rate for three-dimensional locally isothermal disks with radial temperature gradients. For 3D disks with radial temperature gradients, the linear wave equation has a divergent term of the third pole, which makes corotation a non-removal singularity. We suppressed the divergence with

Amin Shojaeighadikolaei, Zsolt Talata, Morteza Hashemi

The widespread adoption of electric vehicles (EVs) poses several challenges to power distribution networks and smart grid infrastructure due to the possibility of significantly increasing electricity demands, especially during peak hours. Furthermore, when EVs participate in demand-side management programs, charging expenses can be reduced by using optimal c