May 2024 arXiv papers — page 36

Michael Bruner, Atish Mitra, Heidi Steiger

We introduce the notion of coarse bottlenecking in graphs and coarse skeletons of graphs and show how bottlenecking guarantees that a skeleton resembles (up to quasi-isometry) the original graph. We show how these tools can be used to simplify the structure of graphs upto quasi-isometry that have an excluded asymptotic minor, reducing it to a skeleton of the

Erika Lynet Salvador

This study assessed the effectiveness of machine learning models in predicting poverty levels in the Philippines using five boosting algorithms: Adaptive Boosting (AdaBoost), CatBoosting (CatBoost), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LightGBM), and Extreme Gradient Boosting (XGBoost). CatBoost emerged as the superior model and

Ignat Georgiev, Krishnan Srinivasan, Jie Xu, Eric Heiden

Model-Free Reinforcement Learning (MFRL), leveraging the policy gradient theorem, has demonstrated considerable success in continuous control tasks. However, these approaches are plagued by high gradient variance due to zeroth-order gradient estimation, resulting in suboptimal policies. Conversely, First-Order Model-Based Reinforcement Learning (FO-MBRL) met

Guowei Yu

In the $N$-body problem, a motion is called hyperbolic, when the mutual distances between the bodies go to infinity with non-zero limiting velocities as time goes to infinity. For Newtonian potential, in \cite{MV20} Maderna and Venturelli proved that starting from any initial position there is a hyperbolic motion with any prescribed limiting velocities at in

Yuying Duan, Yijun Tian, Nitesh Chawla, Michael Lemmon

Federated Learning (FL) is a distributed machine learning framework in which a set of local communities collaboratively learn a shared global model while retaining all training data locally within each community. Two notions of fairness have recently emerged as important issues for federated learning: group fairness and community fairness. Group fairness req

Hong Peng Zhang, Zhi Song

Potential wells are employed to constrain quantum particles into forming discrete energy levels, acting as artificial few-level systems. In contrast, an anti-parity-time ($\mathcal{PT}$) symmetric system can have a single pair of real energy levels, while all the remaining levels are unstable due to the negative imaginary part of the energy. In this work, we

Sara Ahmadian, Edith Cohen

Cardinality sketches are popular data structures that enhance the efficiency of working with large data sets. The sketches are randomized representations of sets that are only of logarithmic size but can support set merges and approximate cardinality (i.e., distinct count) queries. When queries are not adaptive, that is, they do not depend on preceding query

Huiping Zhuang, Di Fang, Kai Tong, Yuchen Liu

In autonomous driving, even a meticulously trained model can encounter failures when facing unfamiliar scenarios. One of these scenarios can be formulated as an online continual learning (OCL) problem. That is, data come in an online fashion, and models are updated according to these streaming data. Two major OCL challenges are catastrophic forgetting and da

Qi-Dong Wang, Yan-Qing Zhu, Shi-Liang Zhu, Zhen Zheng

Topological phases associated with non-Abelian charges can exhibit a distinguished bulk-edge correspondence compared with Abelian phases, although elucidating this relationship remains challenging in traditional solid-state systems. In this paper, we propose a theoretical framework for synthesizing non-Abelian quaternion charges in ultracold atomic gases. By

Jianzong Wang, Haoxiang Shi, Kaiyi Luo, Xulong Zhang

Known for efficient computation and easy storage, hashing has been extensively explored in cross-modal retrieval. The majority of current hashing models are predicated on the premise of a direct one-to-one mapping between data points. However, in real practice, data correspondence across modalities may be partially provided. In this research, we introduce an

Xingyu Ding, Lianlei Shan, Guiqin Zhao, Meiqi Wu

Deep learning-based information processing consumes long time and requires huge computing resources, especially for dense prediction tasks which require an output for each pixel, like semantic segmentation and salient object detection. There are mainly two challenges for quantization of dense prediction tasks. Firstly, directly applying the upsampling operat

V. V. Flambaum, I. B. Samsonov, G. K. Vong

Antiquark nuggets are hypothetical compact composite objects conjectured to account for a significant fraction of dark matter in the Universe. In contrast to quark nuggets, these objects consist of antimatter. They may remain undetected if they possess a sufficiently small cross section relative to their mass. In this paper, we investigate the allowed region

Yavuz Selim Inan

WHO has declared that more than 2.2 billion people worldwide are suffering from visual disorders, such as media haze, glaucoma, and drusen. At least 1 billion of these cases could have been either prevented or successfully treated, yet they remain unaddressed due to poverty, a lack of specialists, inaccurate ocular fundus diagnoses by ophthalmologists, or th

Shanshan Wang, Hao Zhou, Xun Yang, Zhenwei He

Unsupervised domain adaptation (UDA) is a critical problem for transfer learning, which aims to transfer the semantic information from labeled source domain to unlabeled target domain. Recent advancements in UDA models have demonstrated significant generalization capabilities on the target domain. However, the generalization boundary of UDA models remains un

Yuedong Tan, Zongwei Wu, Yuqian Fu, Zhuyun Zhou

Multimodal sensing has proven valuable for visual tracking, as different sensor types offer unique strengths in handling one specific challenging scene where object appearance varies. While a generalist model capable of leveraging all modalities would be ideal, development is hindered by data sparsity, typically in practice, only one modality is available at

Alejandro Torres-Orjuela, Veronica Vazquez-Aceves, Rui Xu, Jin-Hong Chen

GWnext 2024 was a meeting held in the Kavli Institute for Astronomy and Astrophysics at Peking University in March $4^\text{th} - 8^\text{th}$, 2024. In the meeting researchers at different career stages -- with a particular focus on early career scientists -- working on the different aspects of gravitational wave (GW) astronomy gathered to discuss the curre

Tyrone N. O'Doherty, Arash Bahramian, Adelle J. Goodwin, James C. A. Miller-Jones

Identifying sources exhibiting ellipsoidal variability in large photometric surveys is becoming a promising method to search for candidate detached black holes in binaries. This technique aims to exploit the orbital-phase dependent modulation in optical photometry caused by the black hole distorting the shape of the luminous star to constrain the mass ratio

Yanghai Yu, Fang Liu

It is shown in \cite[Adv. Differ. Equ(2017)]{HT} that the Cauchy problem for the generalized Camassa-Holm equation is well-posed in $C^1$ and the data-to-solution map is H\"{o}lder continuous from $C^\alpha$ to $\mathcal{C}([0,T];C^\alpha)$ with $\alpha\in[0,1)$. In this paper, we further show that the data-to-solution map of the generalized Camassa-Holm equ

Botao He, Ze Wang, Yuan Zhou, Jingxi Chen

Neuromorphic vision sensors or event cameras have made the visual perception of extremely low reaction time possible, opening new avenues for high-dynamic robotics applications. These event cameras' output is dependent on both motion and texture. However, the event camera fails to capture object edges that are parallel to the camera motion. This is a problem

Zhuonan Zheng, Yuanchen Bei, Sheng Zhou, Yao Ma

Graph Neural Networks (GNNs) have demonstrated strong performance in graph mining tasks due to their message-passing mechanism, which is aligned with the homophily assumption that adjacent nodes exhibit similar behaviors. However, in many real-world graphs, connected nodes may display contrasting behaviors, termed as heterophilous patterns, which has attract

Robert Wu, Vardan Papyan

Neural collapse ($\mathcal{NC}$) is a phenomenon observed in classification tasks where top-layer representations collapse into their class means, which become equinorm, equiangular and aligned with the classifiers. These behaviours -- associated with generalization and robustness -- would manifest under specific conditions: models are trained towards zero l

Rahul Thapa, Bryan He, Magnus Ruud Kjaer, Hyatt Moore

Sleep is a complex physiological process evaluated through various modalities recording electrical brain, cardiac, and respiratory activities. We curate a large polysomnography dataset from over 14,000 participants comprising over 100,000 hours of multi-modal sleep recordings. Leveraging this extensive dataset, we developed SleepFM, the first multi-modal fou

Kun Yuan, Hongbo Liu, Mading Li, Muyi Sun

Video quality assessment (VQA) is a challenging problem due to the numerous factors that can affect the perceptual quality of a video, \eg, content attractiveness, distortion type, motion pattern, and level. However, annotating the Mean opinion score (MOS) for videos is expensive and time-consuming, which limits the scale of VQA datasets, and poses a signifi

Tianhao Zhang, Zhecheng Sheng, Zhexiao Lin, Chen Jiang

Autoregressive generative models play a key role in various language tasks, especially for modeling and evaluating long text sequences. While recent methods leverage stochastic representations to better capture sequence dynamics, encoding both temporal and structural dependencies and utilizing such information for evaluation remains challenging. In this work

Gao-xiang Deng, Zhe He, Yu Liu, Wei Shao

This research employs the Kraus representation and Sz.-Nagy dilation theorem to model a three-level quantum heat on quantum circuits, investigating its dynamic evolution and thermodynamic performance. The feasibility of the dynamic model is validated by tracking the changes of population. On the basis of reinforcement learning algorithm, the optimal cycle of

Oleg V. Pavlov, Evangelos Katsamakas

We develop a feedback theory that includes reinforcing and balancing feedback effects that emerge when colleges compete for reputation, applicants, and tuition revenue. The feedback theory is replicated in a formal duopoly model consisting of two competing colleges. An independent ranking entity determines the relative order of the colleges. College applican

Hao Di, Haishan Ye, Yueling Zhang, Xiangyu Chang

Variance reduction techniques are designed to decrease the sampling variance, thereby accelerating convergence rates of first-order (FO) and zeroth-order (ZO) optimization methods. However, in composite optimization problems, ZO methods encounter an additional variance called the coordinate-wise variance, which stems from the random gradient estimation. To r

N. Sasao, M. Yoshimura, M. Tanaka

We confront measurable neutrino degrees of freedom $N_{\rm eff}$ and summed neutrino mass in the early universe to particle physics at the energy scale beyond the standard model (BSM), in particular including the issue of neutrino mass type distinction. The Majorana-type of massive neutrino is perfectly acceptable by Planck observations, while the Dirac-type

Jiacheng Yao, Wei Xu, Zhaohui Yang, Xiaohu You

To enable wireless federated learning (FL) in communication resource-constrained networks, two communication schemes, i.e., digital and analog ones, are effective solutions. In this paper, we quantitatively compare these two techniques, highlighting their essential differences as well as respectively suitable scenarios. We first examine both digital and anal

David M T Kuo

This comprehensive study investigates charge transport through the multiple end zigzag edge states of finite-size armchair graphene nanoribbons/boron nitride nanoribbons (n-AGNR/w-BNNR) junctions under a longitudinal electric field, where n and w denote the widths of the AGNRs and the BNNRs, respectively. In 13-atom wide AGNR segments, the edge states exhibi

Penghui Ruan, Divya Saxena, Jiannong Cao, Xiaoyun Liu

Accurate surface roughness prediction is critical for ensuring high product quality, especially in areas like manufacturing and aerospace, where the smallest imperfections can compromise performance or safety. However, this is challenging due to complex, non-linear interactions among variables, which is further exacerbated with limited and imbalanced dataset

Zhifeng Chen, Kamlesh Pawar, Kh Tohidul Islam, Himashi Peiris

Motion artifacts in Magnetic Resonance Imaging (MRI) are one of the frequently occurring artifacts due to patient movements during scanning. Motion is estimated to be present in approximately 30% of clinical MRI scans; however, motion has not been explicitly modeled within deep learning image reconstruction models. Deep learning (DL) algorithms have been dem

Shengnan Wang, Youhui Bai, Lin Zhang, Pingyi Zhou

Length generalization failure problem, namely the large language model (LLM) fails to generalize to texts longer than its maximum training length, greatly restricts the application of LLM in the scenarios with streaming long inputs. To address this problem, the existing methods either require substantial costs or introduce precision loss. In this paper, we e

Clement Wong, Andrew Weng, Sravan Pannala, Jeesoon Choi

Diagnosing imbalances in capacity and resistance within parallel-connected cells in battery packs is critical for battery management and fault detection, but it is challenging given that individual currents flowing into each cell are often unmeasured. This work introduces a novel method useful for identifying imbalances in capacity and resistance within a pa

Vladimir Dvorkin

In two-stage electricity markets, renewable power producers enter the day-ahead market with a forecast of future power generation and then reconcile any forecast deviation in the real-time market at a penalty. The choice of the forecast model is thus an important strategy decision for renewable power producers as it affects financial performance. In electric

R. A. Tinguely, P. G. Puglia, S. Dowson, M. Porkolab

While much about Alfven eigenmode (AE) stability has been explored in previous and current tokamaks, open questions remain for future burning plasma experiments, especially regarding exact stability threshold conditions and related isotope effects; the latter, of course, requiring good knowledge of the plasma ion composition. In the JET tokamak, eight in-ves

Atul C. Thakur, Richard C. Remsing

The surface of Saturn's moon Titan is coated with small molecule organic solids termed cryominerals. Cryominerals play an analogous role to minerals on Earth in Titan's surface geology and geochemistry. To develop a predictive understanding of Titan's surface geochemistry, we need to characterize the structure and dynamics of cryominerals at the molecular sc

Nan Li, Haoyu Jiang, Ping Yi

Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment. Recent research reveals that backdoors can be erased from infected DNNs by pruning a specific group of neurons, while how to effectively identify and remove these backdoor-associated neurons remains an open challenge. In this pa

Jung-Wan Ryu, Jae-Ho Han, Chang-Hwan Yi, Hee Chul Park

The complex eigenenergies and non-orthogonal eigenstates of non-Hermitian systems exhibit unique topological phenomena that cannot appear in Hermitian systems. Representative examples are the non-Hermitian skin effect and exceptional points. In a two-dimensional parameter space, topological classifications of non-separable bands in multiband non-Hermitian sy

Matías Menni

The radically synthetic foundation for smooth geometry formulated in [Law11] postulates a space T with the property that it has a unique point and, out of the monoid T^T of endomorphisms, it extracts a submonoid R which, in many cases, is the (commutative) multiplication of a rig structure. The rig R is said to be bi-directional if its subobject of invertibl

Congyi Zhong, Jie jiang, Zebin Zhang

The details of the dynamo process in the Sun are an important aspect of research in solar-terrestrial physics and astrophysics. The surface part of the dynamo can be constrained by direct observations, but the subsurface part lacks direct observational constraints. The torsional oscillations, a small periodic variation of the Sun's rotation with the solar cy

Nan Li, Haiyang Yu, Ping Yi

Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attacks, posing concerning threats to their reliable deployment. Recent research reveals that backdoors can be erased from infected DNNs by pruning a specific group of neurons, while how to effectively identify and remove these backdoor-associated neurons remains an open challenge. Most of th

Andrew Ducharme

Special functions like the polygamma, Hurwitz zeta, and Lerch zeta functions have sporadically been connected with the nth derivatives of trigonometric functions. We show the polylogarithm $\text{Li}_s(z)$, a function of complex argument and order $z$ and $s$, encodes the nth derivatives of the cotangent, tangent, cosecant and secant functions, and their hyp

David Lipshutz, Eero P. Simoncelli

Efficient coding theory posits that sensory circuits transform natural signals into neural representations that maximize information transmission subject to resource constraints. Local interneurons are thought to play an important role in these transformations, dynamically shaping patterns of local circuit activity to facilitate and direct information flow.

Elynn Chen, Jianqing Fan, Xiaonan Zhu

We introduce \underline{F}actor-\underline{A}ugmented \underline{Ma}trix \underline{R}egression (FAMAR) to address the growing applications of matrix-variate data and their associated challenges, particularly with high-dimensionality and covariate correlations. FAMAR encompasses two key algorithms. The first is a novel non-iterative approach that efficiently

Chenyu Huang, Zhengyang Tang, Shixi Hu, Ruoqing Jiang

Optimization modeling plays a critical role in the application of Operations Research (OR) tools to address real-world problems, yet they pose challenges and require extensive expertise from OR experts. With the advent of large language models (LLMs), new opportunities have emerged to streamline and automate such task. However, current research predominantly

Shijie Sun, Eloy de Lera Acedo, Fengquan Wu, Bin Yue

The redshifted 21 cm line signal is a powerful probe of the cosmic dawn and the epoch of reionization. The global spectrum can potentially be detected with a single antenna and spectrometer. However, this measurement requires an extremely accurate calibration of the instrument to facilitate the separation of the 21 cm signal from the much brighter foreground

Rui Kong, Qiyang Li, Xinyu Fang, Qingtian Feng

Recent literature has found that an effective method to customize or further improve large language models (LLMs) is to add dynamic adapters, such as low-rank adapters (LoRA) with Mixture-of-Experts (MoE) structures. Though such dynamic adapters incur modest computational complexity, they surprisingly lead to huge inference latency overhead, slowing down the

Srijata Maji, Moghis Fereidouni, Vinaik Chhetri, Umar Farooq

Existing recommendation systems have focused on two paradigms: 1- historical user-item interaction-based recommendations and 2- conversational recommendations. Conversational recommendation systems facilitate natural language dialogues between users and the system, allowing the system to solicit users' explicit needs while enabling users to inquire about rec

James Prather, Brent Reeves, Juho Leinonen, Stephen MacNeil

Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies. Previous research has shown that novices can encounter multiple metacognitive difficulties while programming. Novices are typically unaware of how these difficulties are hindering their progress. Meanwhile, many novices are now progr

Lael Shin, Jubee Sohn, Young Ju, Inkyu Park

We present a new simulated galaxy cluster catalog based on the IllustrisTNG simulation. We use the Mulguisin (MGS) algorithm to identify galaxy overdensities. Our cluster identification differs from the previous FoF cluster identification in two aspects; 1) we identify cluster halos based on the galaxy subhalos instead of unobservable dark matter particles,

Ben Kallus, Prashant Anantharaman, Michael Locasto, Sean W. Smith

HTTP/1.1 parsing discrepancies have been the basis for numerous classes of attacks against web servers. Previous techniques for discovering HTTP parsing discrepancies have focused on blackbox differential testing of HTTP gateway servers, despite evidence that the most significant parsing anomalies occur within origin servers. While these techniques can detec

Xie-Qian Li, Chun-Wang Wu, Ping-Xing Chen

Measuring the phonon number of the laser-cooled ions is an indispensable step in evaluating whether an ion is in ground state. At present, commonly used methods in the experiments are red-to-blue sideband ratios and adiabatic evolution red-sideband methods. We theoretically propose a method using composite pulses which does not need a fit of state evolution

Leonardo R. S. Rodrigues, Felipe Gabrielli

This paper presents a study on a compartmental epidemic model for COVID-19, examining the stability of its equilibrium points upon the introduction of vaccination as a strategy to mitigate the spread of the disease. Initially, the SIQR (Susceptible-Infectious-Quarantine-Recovered) mathematical model and its technical aspects are introduced. Subsequently, vac

Yanbing Bai, Xinyi Wu, Lai Xu, Jihan Pei

With the rapid development of earth observation technology, we have entered an era of massively available satellite remote-sensing data. However, a large amount of satellite remote sensing data lacks a label or the label cost is too high to hinder the potential of AI technology mining satellite data. Especially in such an emergency response scenario that use

Blake C. Stacey

Adrian Kent has recently criticized Masanes, Galley and M\"uller's work on postulates for quantum mechanics. MGM claim to find two contradictions in Kent's criticism. I argue that neither is a true contradiction unless some other premise is added.

Jiahuan Cao, Yongxin Shi, Dezhi Peng, Yang Liu

Classical Chinese Understanding (CCU) holds significant value in preserving and exploration of the outstanding traditional Chinese culture. Recently, researchers have attempted to leverage the potential of Large Language Models (LLMs) for CCU by capitalizing on their remarkable comprehension and semantic capabilities. However, no comprehensive benchmark is a

Rishi Kesav Mohan, Risheek Rakshit Sukumar Kanmani, Krishna Anandan Ganesan, Nisha Ramasubramanian

In the era of big data, conventional RDBMS models have become impractical for handling colossal workloads. Consequently, NoSQL databases have emerged as the preferred storage solutions for executing processing-intensive Online Analytical Processing (OLAP) tasks. Within the realm of NoSQL databases, various classifications exist based on their data storage me

Yake Wei, Di Hu

Multimodal learning methods with targeted unimodal learning objectives have exhibited their superior efficacy in alleviating the imbalanced multimodal learning problem. However, in this paper, we identify the previously ignored gradient conflict between multimodal and unimodal learning objectives, potentially misleading the unimodal encoder optimization. To

Rui Zhang, Shuailong Li, Junxiao Xue, Feng Lin

Video recognition remains an open challenge, requiring the identification of diverse content categories within videos. Mainstream approaches often perform flat classification, overlooking the intrinsic hierarchical structure relating categories. To address this, we formalize the novel task of hierarchical video recognition, and propose a video-language learn

Boammani Aser Lompo, Thanh-Dung Le

The processing of numerical values is a rapidly developing area in the field of Language Models (LLMs). Despite numerous advancements achieved by previous research, significant challenges persist, particularly within the healthcare domain. This paper investigates the limitations of Transformer models in understanding numerical values. \textit{Objective:} thi

Toru Ishida, Tongxi Liu, Hailong Wang, William K. Cheunga

Workshop courses designed to foster creativity are gaining popularity. However, even experienced faculty teams find it challenging to realize a holistic evaluation that accommodates diverse perspectives. Adequate deliberation is essential to integrate varied assessments, but faculty often lack the time for such exchanges. Deriving an average score without di

Lu Chen, Guozhen Lu, Hanli Tang

Recently, Dolbeault-Esteban-Figalli-Frank-Loss [20] established the optimal stability of the first-order $L^2$-Sobolev inequality with dimension-dependent constant. Subsequently, Chen-Lu-Tang [18] obtained the optimal stability for the $L^2$ fractional Sobolev inequality of order $s$ when $0<s<1$.This paper considers the remaining case $1<s<\frac{n}{2}$. Our

Laura Valencia Molina, Rocio Camacho Morales, Jihua Zhang, Roland Schiek

The ability to detect and image short-wave infrared light has important applications in surveillance, autonomous navigation, and biological imaging. However, the current infrared imaging technologies often pose challenges due to their large footprints, large thermal noise, and the inability to augment infrared and visible imaging. Here, we demonstrate infrar

Evelina Dubovski

We investigate the density properties of generalized divisor functions $\displaystyle f_s(n)=\frac{\sum_{d|n}d^s}{n^s}$ and extend the analysis from the already-proven density of $s=1$ to $s\geq0$. We demonstrate that for every $s>0$, $f_s$ is locally dense, revealing the structure of $f_s$ as the union of infinitely many $trains$ -- specially organized coll

Yiyu Li, Ke Xu, Gerhard Petrus Hancke, Rynson W. H. Lau

Images captured under sub-optimal illumination conditions may contain both over- and under-exposures. Current approaches mainly focus on adjusting image brightness, which may exacerbate the color tone distortion in under-exposed areas and fail to restore accurate colors in over-exposed regions. We observe that over- and under-exposed regions display opposite

Haoran Han, Jian Cheng, Maolong Lv

This paper proposes a three-layer unmanned combat aerial vehicle (UCAV) dogfight frame where Deep reinforcement learning (DRL) is responsible for high-level maneuver decision. A four-channel low-level control law is firstly constructed, followed by a library containing eight basic flight maneuvers (BFMs). Double deep Q network (DDQN) is applied for BFM selec

Wei Pang, Masoumeh Shafieinejad, Lucy Liu, Stephanie Hazlewood

Recent research in tabular data synthesis has focused on single tables, whereas real-world applications often involve complex data with tens or hundreds of interconnected tables. Previous approaches to synthesizing multi-relational (multi-table) data fall short in two key aspects: scalability for larger datasets and capturing long-range dependencies, such as

Hafiz Tayyab Rauf, Andre Freitas, Norman W. Paton

Deep clustering (DC), a fusion of deep representation learning and clustering, has recently demonstrated positive results in data science, particularly text processing and computer vision. However, joint optimization of feature learning and data distribution in the multi-dimensional space is domain-specific, so existing DC methods struggle to generalize to o

Glenn Barnich, Luca Ciambelli, Hernán A. González

Chiral shift symmetries of the massless free bosons in two dimensions are global symmetries that are somewhat similar to asymptotic symmetries. They are most transparent in double-null coordinates where they are parametrized by two functions of one variable. In BMS-type coordinates, half of them appear as an infinite tower of sub-leading super-shift symmetri

Charles Badger, Rahul Srinivasan, Alejandro Torres-Forné, Marie Anne Bizouard

Current gravitational wave (GW) detection pipelines for compact binary coalescence based on matched-filtering have reported over 90 confident detections during the first three observing runs of the LIGO-Virgo-KAGRA (LVK) detector network. Decreasing the latency of detection, in particular for future detectors anticipated to have high detection rates, remains

Inhwa Han, Jaayeon Lee, Jong Chul Ye

Research efforts for visual decoding from fMRI signals have attracted considerable attention in research community. Still multi-subject fMRI decoding with one model has been considered intractable due to the drastic variations in fMRI signals between subjects and even within the same subject across different trials. To address current limitations in multi-su

Boshen Xu, Ziheng Wang, Yang Du, Zhinan Song

Egocentric video-language pretraining is a crucial step in advancing the understanding of hand-object interactions in first-person scenarios. Despite successes on existing testbeds, we find that current EgoVLMs can be easily misled by simple modifications, such as changing the verbs or nouns in interaction descriptions, with models struggling to distinguish

Sihe Zhang, Qingdong He, Jinlong Peng, Yuxi Li

Image retrieval aims to identify visually similar images within a database using a given query image. Traditional methods typically employ both global and local features extracted from images for matching, and may also apply re-ranking techniques to enhance accuracy. However, these methods often fail to account for the noise present in query images, which ca

Stephen L. Skinner, Manuel Guedel

We present new Chandra X-ray observations of TAP 26, a ~17 Myr old magnetically-active weak-lined T Tauri star that has been reported to host a massive planet in a 10.8 day orbit. At a separation of a = 0.097 AU the planet will be exposed to intense X-ray and UV radiation from the star. The first observation caught the star in a state of elevated X-ray emiss

Chenglong Li, Zukun Lu, Long Huang, Shaojie Ni

In this paper, we investigate ultra-wideband (UWB) localization and tracking in cluttered environments. Instead of mitigating the multipath, we exploit the specular reflections to enhance the localizability and improve the positioning accuracy. With the assistance of the multipath, it is also possible to achieve localization purposes using fewer anchors or w

Milivoje Lukić, Xingya Wang

We study Jost solutions of Schr\"odinger operators with potentials which decay with respect to a local $H^{-1}$ Sobolev norm; in particular, we generalize to this setting the results of Christ--Kiselev for potentials between the integrable and square-integrable rates of decay, proving existence of solutions with WKB asymptotic behavior on a large set of posi

David Omogbhe

A unique inversion of the exponential X-ray transform of some class of symmetric 2-tensor field in a two dimensional strictly convex set is considered. The approach to inversion is based on the Cauchy problem for a Beltrami-like equation associated with $A$-analytic maps.

Micah Carroll, Davis Foote, Anand Siththaranjan, Stuart Russell

Existing AI alignment approaches assume that preferences are static, which is unrealistic: our preferences change, and may even be influenced by our interactions with AI systems themselves. To clarify the consequences of incorrectly assuming static preferences, we introduce Dynamic Reward Markov Decision Processes (DR-MDPs), which explicitly model preference

Ahatsham Hayat, Mohammad Rashedul Hasan

This paper presents a novel approach named \textbf{C}ontextually \textbf{R}elevant \textbf{I}mputation leveraging pre-trained \textbf{L}anguage \textbf{M}odels (\textbf{CRILM}) for handling missing data in tabular datasets. Instead of relying on traditional numerical estimations, CRILM uses pre-trained language models (LMs) to create contextually relevant de

Jian Liao, Kevin Van, Zhijie Xia, Ryo Suzuki

This paper introduces RealityEffects, a desktop authoring interface designed for editing and augmenting 3D volumetric videos with object-centric annotations and visual effects. RealityEffects enhances volumetric capture by introducing a novel method for augmenting captured physical motion with embedded, responsive visual effects, referred to as object-centri

Xu Wang, Yuan Wu

Adversarial training has been instrumental in advancing multi-domain text classification (MDTC). Traditionally, MDTC methods employ a shared-private paradigm, with a shared feature extractor for domain-invariant knowledge and individual private feature extractors for domain-specific knowledge. Despite achieving state-of-the-art results, these methods grapple

Paiheng Xu, Louiqa Raschid, Vanessa Frias-Martinez

Social media platforms like Twitter (now X) have been pivotal in information dissemination and public engagement. The objective of our research is to analyze the effect of localized engagement on social media conversations. This study examines the impact of geographic co-location, as a proxy for localized engagement. Our research is grounded in a COVID-19 da

Daisuke Miki, Akira Matsumura, Kazuhiro Yamamoto

We report the feasibility of detecting the gravity-induced entanglement (GIE) with optomechanical systems, which is the first investigation that clarifies the feasible experimental parameters to achieve a signal-to-noise ratio of S/N=1. Our proposal focuses on GIE generation between optomechanical mirrors, coupled via gravitational interactions, under contin

Pedro H. D. B. Hokama, Carla N. Lintzmayer, Mário C. San Felice

Given a set of customers, the Flying Sidekick Traveling Salesman Problem (FSTSP) consists of using one truck and one drone to perform deliveries to them. The drone is limited to delivering to one customer at a time, after which it returns to the truck, from where it can be launched again. The goal is to minimize the time required to service all customers and

Priyanka Iyer, Rajendra Singh Negi, Andreas Schadschneider, Gerhard Gompper

The emergent collective motion of active agents - in particular pedestrians - at a three-way intersection is studied by Langevin simulations of cognitive intelligent active Brownian particles (iABPs) with directed visual perception and self-steering avoidance. Depending on the maneuverability $Ω$, the goal fixation $K$, and the vision angle $ψ$, different ty

Lou E. Whitehead, James M. S. Wason, Oliver Sailer, Haiyan Zheng

Randomized controlled clinical trials provide the gold standard for evidence generation in relation to the efficacy of a new treatment in medical research. Relevant information from previous studies may be desirable to incorporate in the design and analysis of a new trial, with the Bayesian paradigm providing a coherent framework to formally incorporate prio

Jack Spielberg

We solve the isomorphism problem for essential unital $C^*$-algebra extensions of the form $0 \to \mathcal{K} \oplus \mathcal{K} \to E \xrightarrow{\pi} M_n \otimes C(\mathbb{T}) \to 0$. We then relate these to analogs of the Effros Shen AF algebras for rational numbers. This involves $C^*$-algebras constructed from categories of paths built from certain non

Allen Nie, Yash Chandak, Christina J. Yuan, Anirudhan Badrinath

Offline policy evaluation (OPE) allows us to evaluate and estimate a new sequential decision-making policy's performance by leveraging historical interaction data collected from other policies. Evaluating a new policy online without a confident estimate of its performance can lead to costly, unsafe, or hazardous outcomes, especially in education and healthca

Anni Hong, Nynke M. D. Niezink

Social actors are often embedded in multiple social networks, and there is a growing interest in studying social systems from a multiplex network perspective. In this paper, we propose a mixed-effects model for cross-sectional multiplex network data that assumes dyads to be conditionally independent. Building on the uniplex $p_2$ model, we incorporate depend

Kevin Dela Rosa

In this work, we propose the use of "aligned visual captions" as a mechanism for integrating information contained within videos into retrieval augmented generation (RAG) based chat assistant systems. These captions are able to describe the visual and audio content of videos in a large corpus while having the advantage of being in a textual format that is bo

Linhan Wang, Kai Cheng, Shuo Lei, Shengkun Wang

We present DC-Gaussian, a new method for generating novel views from in-vehicle dash cam videos. While neural rendering techniques have made significant strides in driving scenarios, existing methods are primarily designed for videos collected by autonomous vehicles. However, these videos are limited in both quantity and diversity compared to dash cam videos

Amir El-Ghoussani, Julia Hornauer, Gustavo Carneiro, Vasileios Belagiannis

In monocular depth estimation, unsupervised domain adaptation has recently been explored to relax the dependence on large annotated image-based depth datasets. However, this comes at the cost of training multiple models or requiring complex training protocols. We formulate unsupervised domain adaptation for monocular depth estimation as a consistency-based s

Jason Li

Recent research (arXiv:2310.11453, arXiv:2402.17764) has proposed binary and ternary transformer networks as a way to significantly reduce memory and improve inference speed in Large Language Models (LLMs) while maintaining accuracy. In this work, we apply techniques from mechanistic interpretability to investigate whether such networks learn distinctly diff

Haoxuan Ma, Brian Yueshuai He, Tomas Kaljevic, Jiaqi Ma

The diffusion of Electric Vehicles (EVs) plays a pivotal role in mitigating greenhouse gas emissions, particularly in the U.S., where ambitious zero-emission and carbon neutrality objectives have been set. In pursuit of these goals, many states have implemented a range of incentive policies aimed at stimulating EV adoption and charging infrastructure develop

Jinjin Zhao, Sanjay Krishnan

Tracking data lineage is important for data integrity, reproducibility, and debugging data science workflows. However, fine-grained lineage (i.e., at a cell level) is challenging to store, even for the smallest datasets. This paper introduces DSLog, a storage system that efficiently stores, indexes, and queries array data lineage, agnostic to capture methodo

Kanad Shrikar Pardeshi, Itai Shapira, Ariel D. Procaccia, Aarti Singh

Is it possible to understand or imitate a policy maker's rationale by looking at past decisions they made? We formalize this question as the problem of learning social welfare functions belonging to the well-studied family of power mean functions. We focus on two learning tasks; in the first, the input is vectors of utilities of an action (decision or policy

Zhengyu Mao, Chen Wan, Lei Zhang

In this paper, we give a complete list of strongly tempered hyperspherical Hamiltonian spaces. We show that the period integrals attached to the list contains many previously studied Rankin-Selberg integrals and period integrals, thus give a new conceptual understanding of these integrals. The list also proposes many new interesting period integrals to study

Isla Duporge, Maksim Kholiavchenko, Roi Harel, Scott Wolf

Using drones to track multiple individuals simultaneously in their natural environment is a powerful approach for better understanding group primate behavior. Previous studies have demonstrated that it is possible to automate the classification of primate behavior from video data, but these studies have been carried out in captivity or from ground-based came

Mohammad Mahdi Maheri, Sandra Siby, Sina Abdollahi, Anastasia Borovykh

Personalized learning is a proposed approach to address the problem of data heterogeneity in collaborative machine learning. In a decentralized setting, the two main challenges of personalization are client clustering and data privacy. In this paper, we address these challenges by developing P4 (Personalized Private Peer-to-Peer) a method that ensures that e