March 2024 arXiv papers — page 55

Nan Zhang, Connor Heaton, Sean Timothy Okonsky, Prasenjit Mitra

Optical Character Recognition (OCR) is an established task with the objective of identifying the text present in an image. While many off-the-shelf OCR models exist, they are often trained for either scientific (e.g., formulae) or generic printed English text. Extracting text from chemistry publications requires an OCR model that is capable in both realms. N

Haizhou Wang, Zhilong Wang, Peng Liu

Many programs involves operations and logic manipulating user privileges, which is essential for the security of an organization. Therefore, one common malicious goal of attackers is to obtain or escalate the privileges, causing privilege leakage. To protect the program and the organization against privilege leakage attacks, it is important to eliminate the

Malek Hanounah, Lilia Mehidi

We consider a compact manifold $(M,\mathfrak{F})$ with a foliation $\mathfrak{F}$, and a smooth affine connection $\nabla$ on the tangent bundle of the foliation $T\mathfrak{F}$. We introduce and study a foliated completeness problem. Namely, under which conditions on $\nabla$ the leaves are complete? We consider different natural geometric settings: the fir

Arup Kumar Sarker, Aymen Alsaadi, Niranda Perera, Mills Staylor

Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficulties, impacting scientific fields like genomics, climate modeling, and astronomy. A large-scale solution like Google Pathways with a distributed execution environment for deep learn

Narumasa Tsutsumida, Akira Kato

Land cover classification faces persistent challenges with inter-investigator variability and salt-and-pepper noise. Although cloud platforms such as Google Earth Engine have made land cover classification more accessible, these issues persist, particularly when multiple investigators contribute to the process. This study developed a robust classification ap

Takashi Ono

We study harmonic bundles with an additional structure called symplectic structure. We study them for the case of the base manifold is compact and non-compact. For the compact case, we show that a harmonic bundle with a symplectic structure is equivalent to principle $Sp(2n,C)$-bundle with a reductive flat connection. For the non-compact case, we show that a

Yoshikazu Giga, Zhongyang Gu

A dryout point is recognized as the position where the phase transition from liquid to vapor occurs. In the one-dimensional case, by solving the stationary incompressible Navier-Stokes-Fourier equations with phase transition, we derive a necessary and sufficient condition for a dryout point to exist when the temperature at the liquid-vapor interface is given

Shrihari Sridharan, Surya Selvam, Kaushik Roy, Anand Raghunathan

Event cameras have emerged as a promising sensing modality for autonomous navigation systems, owing to their high temporal resolution, high dynamic range and negligible motion blur. To process the asynchronous temporal event streams from such sensors, recent research has shown that a mix of Artificial Neural Networks (ANNs), Spiking Neural Networks (SNNs) as

Zhe Xu, Tao Yan, Simon X. Yang, S. Andrew Gadsden

This paper addresses the challenges of distributed formation control in multiple mobile robots, introducing a novel approach that enhances real-world practicability. We first introduce a distributed estimator using a variable structure and cascaded design technique, eliminating the need for derivative information to improve the real time performance. Then, a

Daijun Ding, Li Dong, Zhichao Huang, Guangning Xu

Stance detection aims to determine the attitude expressed in text towards a given target. Zero-shot stance detection (ZSSD) has emerged to classify stances towards unseen targets during inference. Recent data augmentation techniques for ZSSD increase transferable knowledge between targets through text or target augmentation. However, these methods exhibit li

Daehee Cho, Doosung Choi, Mikyoung Lim

We investigate the effective elastic properties of periodic dilute two-phase composites consisting of an homogeneous isotropic matrix and a periodic array of rigid inclusions. We assume the rigid inclusion in a unit cell is a simply connected, bounded domain so that there exists an exterior conformal mapping corresponding the inclusion. Recently, an analytic

Daehee Cho, Doosung Choi, Mikyoung Lim

We investigate the two-dimensional elastostatic inclusion problem in an unbounded medium. Building on the recent developments for rigid inclusions \cite{Mattei:2021:EAS} and conductivity inclusions \cite{Jung:2021:SEL}, we extend these methodologies to the more general case of elastic inclusions with arbitrary Lam\'{e} constants. Our approach integrates laye

Chensheng Peng, Zhaoyu Zeng, Jinling Gao, Jundong Zhou

Multiple object tracking is a critical task in autonomous driving. Existing works primarily focus on the heuristic design of neural networks to obtain high accuracy. As tracking accuracy improves, however, neural networks become increasingly complex, posing challenges for their practical application in real driving scenarios due to the high level of latency.

Yuhang Liu, Zhen Zhang, Dong Gong, Erdun Gao

Causal representation learning (CRL) offers the promise of uncovering the underlying causal model by which observed data was generated, but the practical applicability of existing methods remains limited by the strong assumptions required for identifiability and by challenges in applying them to real-world settings. Most current approaches are applicable onl

Lawan Bulama Mohammed, Adem Kilicman

It is generally known that in order to solve the split equality fixed-point problem (SEFPP), it is necessary to compute the norm of bounded and linear operators, which is a challenging task in real life, to address this issue, we studied the SEFPP involving the class of quasi-pseudocontractive mappings in Hilbert spaces and constructed novel algorithms in th

Guangdong Jing

This article is concerned with the second order necessary conditions for the stochastic optimal control problem of stochastic evolution equation with model uncertainty when the traditional Pontryagin-type maximum principle holds trivially and do not provide any information depicting the optimal control. The diffusion term of the state equation is allowed to

Sihan Ma, Qiong Cao, Jing Zhang, Dacheng Tao

This paper addresses the problem of generating 3D interactive human motion from text. Given a textual description depicting the actions of different body parts in contact with static objects, we synthesize sequences of 3D body poses that are visually natural and physically plausible. Yet, this task poses a significant challenge due to the inadequate consider

Chi-Ho Cheng, Pik-Yin Lai

The recently proposed Ehrenfest M-urn model with interactions on a ring is considered as a paradigm model which can exhibit a variety of distinct nonequilibrium steady states. Unlike the previous three-urn model on a ring which consists of a uniform steady state and a nonuniform nonequilibrium steady state, it is found that for even M>=4, an additional noneq

Aakash Lahoti, Stefani Karp, Ezra Winston, Aarti Singh

Vision tasks are characterized by the properties of locality and translation invariance. The superior performance of convolutional neural networks (CNNs) on these tasks is widely attributed to the inductive bias of locality and weight sharing baked into their architecture. Existing attempts to quantify the statistical benefits of these biases in CNNs over lo

Huiping Zhuang, Yizhu Chen, Di Fang, Run He

Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose previously learned knowledge when acquiring new tasks. The generalized CIL (GCIL) aims to address the CIL problem in a more real-world scenario, where incoming data have mixed data ca

Yuliang Guo, Abhinav Kumar, Cheng Zhao, Ruoyu Wang

Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose. While gradient-based optimization in a NeRF framework updates the initial pose, this paper highlights that scale-depth ambiguity in monocular object reconstruction causes failures when the initial pose deviates moderately from the true pose. Conseq

Dongbin Zhang, Chuming Wang, Weitao Wang, Peihao Li

Novel view synthesis from unconstrained in-the-wild images remains a meaningful but challenging task. The photometric variation and transient occluders in those unconstrained images make it difficult to reconstruct the original scene accurately. Previous approaches tackle the problem by introducing a global appearance feature in Neural Radiance Fields (NeRF)

Guangdong Jing

We study the singular stochastic optimal control problem with model uncertainty, where the necessary conditions determined by the corresponding maximum principle are trivial. Robust integral form and pointwise second order necessary optimality conditions under certain compactness conditions are derived. Both the drift and diffusion terms are control dependen

Max Emerick, Jared Jonas, Bassam Bamieh

We consider a problem of optimal swarm tracking which can be formulated as a tracking problem for distributions in the Wasserstein space. Optimal solutions to this problem are non-causal and require knowing the time-trajectory of the reference distribution in advance. We propose a scheme where these non-causal solutions can be used together with a predictive

Xueting Pan, Ziqian Luo, Lisang Zhou

Distributed File Systems (DFS) have emerged as sophisticated solutions for efficient file storage and management across interconnected computer nodes. The main objective of DFS is to achieve flexible, scalable, and resilient file storage management by dispersing file data across multiple interconnected computer nodes, enabling users to seamlessly access and

Botao Zhu, Ebrahim Bedeer, Ha H. Nguyen, Robert Barton

Energy load balancing is an essential issue in designing wireless sensor networks (WSNs). Clustering techniques are utilized as energy-efficient methods to balance the network energy and prolong its lifetime. In this paper, we propose an improved soft-k-means (IS-k-means) clustering algorithm to balance the energy consumption of nodes in WSNs. First, we use

Huaiwen Zhang, Yu Chen, Ming Wang, Shi Feng

Emotional Support Conversation (ESC) is a typical dialogue that can effectively assist the user in mitigating emotional pressures. However, owing to the inherent subjectivity involved in analyzing emotions, current non-artificial methodologies face challenges in effectively appraising the emotional support capability. These metrics exhibit a low correlation

Mengqi Zhou, Yuxi Wang, Jun Hou, Shougao Zhang

Developing comprehensive explicit world models is crucial for understanding and simulating real-world scenarios. Recently, Procedural Controllable Generation (PCG) has gained significant attention in large-scale scene generation by enabling the creation of scalable, high-quality assets. However, PCG faces challenges such as limited modular diversity, high ex

Pushkal Purohit, Anoop Jain

This paper addresses the problem of output consensus in linear passive multi-agent systems under a False Data Injection (FDI) attack, considering the unavailability of complete state information. Our formulation relies on an event-based cryptographic authentication scheme for sensor integrity and considers FDI attacks at the actuator end, inspired by their p

Lingxing Kong, Yougang Chu, Zheng Ma, Jianbing Zhang

Relation extraction is a critical task in the field of natural language processing with numerous real-world applications. Existing research primarily focuses on monolingual relation extraction or cross-lingual enhancement for relation extraction. Yet, there remains a significant gap in understanding relation extraction in the mix-lingual (or code-switching)

Guangdong Jing, Penghui Wang, Shan Wang

The main purpose of this paper is to obtain the existence and uniqueness of $L^p$-solution to quantum stochastic differential equation driven by Fermion fields with nonlocal conditions in the case of non-Lipschitz coefficients for $p>2$. The key to our technique is to make use of the Burkholder-Gundy inequality given by Pisier and Xu and Minkowski-type inequ

Zhenhuang Cai, Chuanyi Zhang, Dan Huang, Yuanbo Chen

Manually annotating datasets for training deep models is very labor-intensive and time-consuming. To overcome such inferiority, directly leveraging web images to conduct training data becomes a natural choice. Nevertheless, the presence of label noise in web data usually degrades the model performance. Existing methods for combating label noise are typically

Lanxin Xu, Shuo Wang

In this report, we introduce a novel self-supervised learning method for extracting latent embeddings from behaviors of larval zebrafish. Drawing inspiration from Masked Modeling techniquesutilized in image processing with Masked Autoencoders (MAE) \cite{he2022masked} and in natural language processing with Generative Pre-trained Transformer (GPT) \cite{radf

Donghwa Han, Bowhyung Lee, Min Jang, Donghun Lee

Block orthogonal sparse superposition (BOSS) code is a class of joint coded modulation methods, which can closely achieve the finite-blocklength capacity with a low-complexity decoder at a few coding rates under Gaussian channels. However, for fading channels, the code performance degrades considerably because coded symbols experience different channel fadin

Hong-Ming Liu, Jin-Biao Wei, Zeng-Hua Li, G. F. Burgio

We systematically study the observable properties of dark-matter admixed neutron stars, employing a realistic nuclear EOS in combination with self-interacting fermionic dark matter respecting constraints on the self-interaction cross section. Deviations from universal relations valid for nucleonic neutron stars are analyzed over the whole parameter space of

Xiaoqiang Yan, Yingtao Gan, Yiqiao Mao, Yangdong Ye

Multi-view action clustering leverages the complementary information from different camera views to enhance the clustering performance. Although existing approaches have achieved significant progress, they assume all camera views are available in advance, which is impractical when the camera view is incremental over time. Besides, learning the invariant info

Bowen Huang, Yanwei Zheng, Chuanlin Lan, Xinpeng Zhao

Vision-and-Language Navigation (VLN) is a challenging task where an agent is required to navigate to a natural language described location via vision observations. The navigation abilities of the agent can be enhanced by the relations between objects, which are usually learned using internal objects or external datasets. The relationships between internal ob

Amrita Bhattacharjee, Raha Moraffah, Joshua Garland, Huan Liu

With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these models. While supervised AI-generated text detectors perform well on text generated by older LLMs, with the frequent release of new LLMs, building supervised detectors for identifyin

Libin Yang, Teng Long, Lixiang Yang

Hyperelastic models have been widely used to model polymers and soft tissues. However, most hyperelastic models are phenomenological material models. Based on statistical mechanics and molecular chain configuration, 8 chain model or Arruda-Boyce model is a physical model which can be used to understand how microstructures of chains affect macroscopic mechani

Yiming Meng, Ruikun Zhou, Melkior Ornik, Jun Liu

The Koopman operator has gained significant attention in recent years for its ability to verify evolutionary properties of continuous-time nonlinear systems by lifting state variables into an infinite-dimensional linear vector space. The challenge remains in providing estimations for transitional properties pertaining to the system's vector fields based on d

Aneesh Raghavan, Karl Henrik Johansson

A given region in 2-D Euclidean space is divided by a unknown linear classifier in to two sets each carrying a label. The objective of an agent with known dynamics traversing the region is to identify the true classifier while paying a control cost across its trajectory. We consider two scenarios: (i) the agent is able to measure the true label perfectly; (i

Karl Christ, Xiang He, Ilya Tyomkin

Given a family of parameterized algebraic curves over a strictly semistable pair, we show that the simultaneous tropicalization of the curves in the family forms a family of parameterized tropical curves over the skeleton of the strictly semistable pair. We show that the induced tropical moduli map satisfies a certain balancing condition, which allows us to

David Nwachukwu, Edith Nnenna Oketah, Chineze Helen Ugwu, Hope Chioma Innocent-Adiele

Despite its endemic nature as well as the recent outbreaks, information on the opportunistic DENV in Anambra state has been sparse. This study thus aimed to give seroepidemiological evidence of past dengue virus infection among HIV-infected patients in Onitsha, Anambra State, Nigeria. Plasma from 94 HIV-infected patients who were attending Saint Charles Borr

Anna Stubbin, Thompson Chyrikov, Jim Zhao, Christina Chajo

Explainable artificial intelligence (XAI) plays an indispensable role in demystifying the decision-making processes of AI, especially within the healthcare industry. Clinicians rely heavily on detailed reasoning when making a diagnosis, often CT scans for specific features that distinguish between benign and malignant lesions. A comprehensive diagnostic appr

Yifei Zhang, Marcos M. Vasconcelos

Many socioeconomic phenomena, such as technology adoption, collaborative problem-solving, and content engagement, involve a collection of agents coordinating to take a common action, aligning their decisions to maximize their individual goals. We consider a model for networked interactions where agents learn to coordinate their binary actions under a strict

Malak Lafi, Artem Zvavitch

We study a version of the Busemann-Petty problem for $\log$-concave measures with an additional assumption on the dilates of convex, symmetric bodies. One of our main tools is an analog of the classical large deviation principle applied to $\log$-concave measures, depending on the norm of a convex body. We hope this will be of independent interest.

Xiaoqiang Yan, Zhixiang Jin, Fengshou Han, Yangdong Ye

In recent several years, the information bottleneck (IB) principle provides an information-theoretic framework for deep multi-view clustering (MVC) by compressing multi-view observations while preserving the relevant information of multiple views. Although existing IB-based deep MVC methods have achieved huge success, they rely on variational approximation a

Hikaru Watanabe, Youichi Yanase

Parity-time-reversal symmetry ($\mathcal{PT}$ symmetry), a symmetry for the combined operations of space inversion ($\mathcal{P}$) and time reversal ($\mathcal{T}$), is a fundamental concept of physics and characterizes the functionality of materials as well as $\mathcal{P}$ and $\mathcal{T}$ symmetries. In particular, the $\mathcal{PT}$-symmetric systems ca

Hao Yan, Zhihui Ke, Xiaobo Zhou, Tie Qiu

Implicit neural representations for video (NeRV) have recently become a novel way for high-quality video representation. However, existing works employ a single network to represent the entire video, which implicitly confuse static and dynamic information. This leads to an inability to effectively compress the redundant static information and lack the explic

Philippe-André Luneau

We are interested in building low-dimensional surrogate models to reduce optimization costs, while having theoretical guarantees that the optimum will satisfy the constraints of the full-size model, by making conservative approximations. The surrogate model is constructed using a Gaussian process regression (GPR). To ensure conservativeness, two new approach

Rajat Gupta, Noah Lebowitz-Lockard

In this article, we introduce the notion of almost consecutive partitions. A partition is almost consecutive if every term is consecutive, with the possible exception of the smallest one. We find formulas relating to the smallest parts of consecutive and almost consecutive partitions. We also find an alternate combinatorial interpretation of the number of al

Qizhe Yang, Boxuan Liang, Hao Chen, Guoqiang Li

Zero-knowledge proof (ZKP) systems have surged attention and held a fundamental role in contemporary cryptography. Zero-knowledge succinct non-interactive argument of knowledge (zk-SNARK) protocols dominate the ZKP usage, implemented through arithmetic circuit programming paradigm. However, underconstrained or overconstrained circuits may lead to bugs. The f

Gareth Lamb, Ching Hei Lo, Jin Wu, Calvin K. F. Lee

Camera traps are used by ecologists globally as an efficient and non-invasive method to monitor animals. While it is time-consuming to manually label the collected images, recent advances in deep learning and computer vision has made it possible to automating this process [1]. A major obstacle to this is the generalisability of these models when applying the

Jose A. Solano-Castellanos, Peter A. Fisher, Anuradha Annaswamy

This manuscript considers the problem of ensuring stability and safety during formation control with distributed multi-agent systems in the presence of parametric uncertainty in the dynamics and limited communication. We propose an integrative approach that combines Adaptive Control, Control Barrier Functions (CBFs), and connected graphs. The main elements e

Zhenyu Bi, Sajib Acharjee Dip, Daniel Hajialigol, Sindhura Kommu

The capabilities of AI for biomedicine span a wide spectrum, from the atomic level, where it solves partial differential equations for quantum systems, to the molecular level, predicting chemical or protein structures, and further extending to societal predictions like infectious disease outbreaks. Recent advancements in large language models, exemplified by

Hongying Dong, Yizhe Zhang, Hyeonmin Lee, Shumon Huque

The DNS HTTPS resource record is a new DNS record type designed for the delivery of configuration information and parameters required to initiate connections to HTTPS network services. In addition, it is a key enabler for TLS Encrypted ClientHello (ECH) by providing the cryptographic keying material needed to encrypt the initial exchange. To understand the a

Nhat Minh Nguyen, Stephen McIlvanna, Jack Close, Mien Van

Advancements in underwater vehicle technology have significantly expanded the potential scope for deploying autonomous or remotely operated underwater vehicles in novel practical applications. However, the efficiency and maneuverability of these vehicles remain critical challenges, particularly in the dynamic aquatic environment. In this work, we propose a n

Indranil Sahoo, Suman Majumder, Arnab Hazra, Ana G. Rappold

Observations of groundwater pollutants, such as arsenic or Perfluorooctane sulfonate (PFOS), are riddled with left censoring. These measurements have impact on the health and lifestyle of the populace. Left censoring of these spatially correlated observations are usually addressed by applying Gaussian processes (GPs), which have theoretical advantages. Howev

Weiwei Fan, L. Jeff Hong, Guangxin Jiang, Jun Luo

Large-scale simulation optimization (SO) problems encompass both large-scale ranking-and-selection problems and high-dimensional discrete or continuous SO problems, presenting significant challenges to existing SO theories and algorithms. This paper begins by providing illustrative examples that highlight the differences between large-scale SO problems and t

S. Vanderwoude, J. L. West, B. M. Gaensler, L. Rudnick

The Polarisation Sky Survey of the Universe's Magnetism (POSSUM) will conduct a sensitive $\sim$1 GHz radio polarization survey covering 20 000 square degrees of the Southern sky with the Australian Square Kilometre Array Pathfinder (ASKAP). In anticipation of the full survey, we analyze pilot observations of low-band (800-1087 MHz), mid-band (1316-1439 MHz)

Kaustubh D. Dhole, Shivam Bajaj, Ramraj Chandradevan, Eugene Agichtein

Formulating effective search queries remains a challenging task, particularly when users lack expertise in a specific domain or are not proficient in the language of the content. Providing example documents of interest might be easier for a user. However, such query-by-example scenarios are prone to concept drift, and the retrieval effectiveness is highly se

Sally Andria, Jacqueline Rojas, Wállace Mangueira

It is well-known that the Fermat surface of degree $d\geq 3$ has $3d^2$ lines. However, it has not yet been established what is the maximal number of pairwise disjoint lines that it can have if $d\geq 4$. In this article we show that the maximal number of skew lines on the Fermat surface of degree $d\geq 4$ is $3d$, either $d$ even or $d$ odd distinct of 5,

Caroline Rublein, Fidan Mehmeti, Mark Mahon, Thomas F. La Porta

Edge computing has become a very popular service that enables mobile devices to run complex tasks with the help of network-based computing resources. However, edge clouds are often resource-constrained, which makes resource allocation a challenging issue. In addition, edge cloud servers must make allocation decisions with only limited information available,

Yihua Cheng, Yaning Zhu, Zongji Wang, Hongquan Hao

Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its significance, research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this paper, we present three novel elements to advance in-vehicle gaze research. Fi

Yi Peng, Xiaoding Shi, Yuhang Wu

In this paper, the large time behavior of the solutions for the Cauchy problem to the one-dimensional compressible Navier-Stokes system with the motion of a viscous heat-conducting perfect polytropic gas is investigated.Our result shows that the combination of a viscous contact wave with rarefaction waves is asymptotically stable, when the large initial dist

Atiqah Almuzaini, Jin Ma

In this paper, we mainly focus on the set-valued (stochastic) analysis on the space of convex, closed, but possibly unbounded sets, and try to establish a useful theoretical framework for studying the set-valued stochastic differential equations with unbounded coefficients. The space that we will be focusing on are convex, closed sets that are "generated" by

M. Oberguggenberger, T. Todorov

We present a solution of the problem of multiplication of Schwartz distributions by embedding the space of distributions into a differential algebra of generalized functions, called in the paper ``asymptotic function'', similar to but different from J. F. Colombeau's algebras of new generalized functions.

Spurthi Setty, Harsh Thakkar, Alyssa Lee, Eden Chung

The effectiveness of Large Language Models (LLMs) in generating accurate responses relies heavily on the quality of input provided, particularly when employing Retrieval Augmented Generation (RAG) techniques. RAG enhances LLMs by sourcing the most relevant text chunk(s) to base queries upon. Despite the significant advancements in LLMs' response quality in r

Špela Špenko, Michel Van den Bergh

Let $X$ be a projective crepant resolution of a Gorenstein affine toric variety and let $((\mathbb{C}^*)^k,f)$ be the LG-model which is the Hori-Vafa mirror dual of $X$. Let ${D}$ be a generic fiber of $f$ equipped with the restriction of the standard Liouville form on $(\mathbb{C}^*)^k$. Let $\mathcal{K}_A$ be the so-called "stringy K\"ahler moduli space" o

Tevfik Uyar

As Artificial General Intelligence edges closer to reality, Artificial Superintelligence does too. This paper argues that ASI's unparalleled capabilities might lead people to attribute godlike infallibility to it, resulting in a cognitive bias toward unquestioning acceptance of its decisions. By drawing parallels between ASI and divine attributes such as omn

Pablo G. Madoery, Juan A. Fraire, Jorge M. Finochietto, Halim Yanikomeroglu

The emergence of low Earth orbit (LEO) satellite mega-constellations is dynamically transforming the space sector. While free-space optical (FSO) links efficiently facilitate intersatellite data forwarding, they suffer from atmospheric/weather conditions in the space-to-ground link. This study delves into utilizing high-altitude platform stations (HAPS) as e

Kejun Li, Jeeseop Kim, Xiaobin Xiong, Kaveh Akbari Hamed

Exoskeleton locomotion must be robust while being adaptive to different users with and without payloads. To address these challenges, this work introduces a data-driven predictive control (DDPC) framework to synthesize walking gaits for lower-body exoskeletons, employing Hankel matrices and a state transition matrix for its data-driven model. The proposed ap

Stephon Alexander, Heliudson Bernardo, Cyril Creque-Sarbinowski

Given the growing interest in gravitational-wave and cosmological parity-violating effects in dynamical Chern-Simons (dCS) gravity, it is crucial to investigate whether the scalar-gravitational Pontryagin term in dCS persists when formulated in the context of the $\text{U(1)}_{\text{B}-\text{L}}$ anomaly in the Standard Model (SM). In particular, it has been

Govind M. Chari, Yue Yu, Behçet Açıkmeşe

Many techniques for real-time trajectory optimization and control require the solution of optimization problems at high frequencies. However, ill-conditioning in the optimization problem can significantly reduce the speed of first-order primal-dual optimization algorithms. We introduce a preconditioning technique and step-size heuristic for Proportional-Inte

Mario Gómez

Split-metric decompositions are an important tool in the theory of phylogenetics, particularly because of the link between the tight span and the class of totally decomposable spaces, a generalization of metric trees whose decomposition does not have a ``prime'' component. Their close relationship with trees makes totally decomposable spaces attractive in th

Tongle Wu, Zhize Li, Ying Sun

We revisit two fundamental decentralized optimization methods, Decentralized Gradient Tracking (DGT) and Decentralized Gradient Descent (DGD), with multiple local updates. We consider two settings and demonstrate that incorporating local update steps can reduce communication complexity. Specifically, for $\mu$-strongly convex and $L$-smooth loss functions, w

Yagmur Kati, Jonas Ranft, Benjamin Lindner

The Kuramoto model has provided deep insights into synchronization phenomena and remains an important paradigm to study the dynamics of coupled oscillators. Yet, despite its success, the asynchronous regime in the Kuramoto model has received limited attention. Here, we adapt and enhance the mean-field approach originally proposed by Stiller and Radons [Phys.

Martina L. Caussi, Andrew J. Dombard, Donald G. Korycansky, Oliver L. White

The icy Galilean satellites display impact crater morphologies that are rare in the Solar System. They deviate from the archetypal sequence of crater morphologies as a function of size found on rocky bodies and other icy satellites: they exhibit central pits in place of peaks, followed by central dome craters, anomalous dome craters, penepalimpsests, palimps

Mehdi Shishehbor, Shirin Hosseinmardi, Ramin Bostanabad

Deep neural networks (DNNs) are increasingly used to solve partial differential equations (PDEs) that naturally arise while modeling a wide range of systems and physical phenomena. However, the accuracy of such DNNs decreases as the PDE complexity increases and they also suffer from spectral bias as they tend to learn the low-frequency solution characteristi

Jiaye Wu, Saeed Hadadan, Geng Lin, Matthias Zwicker

In this paper, we present GaNI, a Global and Near-field Illumination-aware neural inverse rendering technique that can reconstruct geometry, albedo, and roughness parameters from images of a scene captured with co-located light and camera. Existing inverse rendering techniques with co-located light-camera focus on single objects only, without modeling global

Yanda Geng, Alan Tsidilkovski, Kevin Weber, Shouvik Mukherjee

Maintaining stable and precise alignment of a laser beam is crucial in many optical setups. In this work, we present a microcontroller-based rapid auto-alignment system that detects and corrects for drifts in a laser beam trajectory using a pair of two-dimensional duo-lateral position sensing detectors (PSDs) and a pair of mirror mounts with piezoelectric ac

Jinho Choi, Jihong Park, Eleonora Grassucci, Danilo Comminiello

Semantic communication, emerging as a promising paradigm for data transmission, offers an innovative departure from the constraints of Shannon theory, heralding significant advancements in future communication technologies. Despite the proliferation of proposed approaches, there are still numerous challenges. In this paper, we review current semantic communi

Weizheng Wang, Ike Obi, Aniket Bera, Byung-Cheol Min

Navigating human-filled spaces is crucial for the interactive social robots to support advanced services, such as cooperative carrying, which enables service provision in complex and crowded environments while adapting behavior based on real-time human language commands or feedback. However, existing social robot navigation planners face two major challenges

Ze Chen, Gongyu Zhang, Jiayu Huo, Joan Nunez do Rio

This study introduces a novel framework for enhancing domain generalization in medical imaging, specifically focusing on utilizing unlabelled multi-view colour fundus photographs. Unlike traditional approaches that rely on single-view imaging data and face challenges in generalizing across diverse clinical settings, our method leverages the rich information

Nandhini Swaminathan, David Danks

This study offers an in-depth analysis of the application and implications of the National Institute of Standards and Technology's AI Risk Management Framework (NIST AI RMF) within the domain of surveillance technologies, particularly facial recognition technology. Given the inherently high-risk and consequential nature of facial recognition systems, our res

Gülnaz Boruzanli Ekinci, Csilla Bujtás

Let $G$ be a connected graph and $\cal X \subseteq V(G)$. By definition, two vertices $u$ and $v$ are $\cal X$-visible in $G$ if there exists a shortest $u,v$-path with all internal vertices being outside of the set $\cal X$. The largest size of $\cal X$ such that any two vertices of $G$ (resp. any two vertices from $\cal X$) are $\cal X$-visible is the tota

James E. Robinson, Uri Malamud, Cyrielle Opitom, Hagai Perets

All cometary nuclei that formed in the early Solar System incorporated radionuclides and therefore were subject to internal radiogenic heating. Previous work predicts that if comets have a pebble-pile structure internal temperature build-up is enhanced due to very low thermal conductivity, leading to internal differentiation. An internal thermal gradient cau

Shijian Deng, Erin E. Kosloski, Siddhi Patel, Zeke A. Barnett

In this article, we introduce a novel problem of audio-visual autism behavior recognition, which includes social behavior recognition, an essential aspect previously omitted in AI-assisted autism screening research. We define the task at hand as one that is audio-visual autism behavior recognition, which uses audio and visual cues, including any speech prese

José A. Carrillo, Hailiang Liu, Hui Yu

This paper is concerned with structure-preserving numerical approximations for a class of nonlinear nonlocal Fokker-Planck equations, which admit a gradient flow structure and find application in diverse contexts. The solutions, representing density distributions, must be non-negative and satisfy a specific energy dissipation law. We design an arbitrary high

François Delarue, Mattia Martini

The purpose of this work is to provide a finite dimensional approximation of the solution to a mean field optimal control problem set on the $d$-dimensional torus. The approximation is obtained by means of a Fourier-Galerkin method, the main principle of which is to convolve probability measures on the torus by the Dirichlet kernel or, equivalently, to trunc

Claudio Corianò, Mario Cretì, Stefano Lionetti, Dario Melle

We discuss fundamental aspects of chiral anomaly-driven interactions in conformal field theory (CFT) in four spacetime dimensions. They find application in very general contexts, from early universe plasma to topological condensed matter. We outline the key shared characteristics of these interactions, specifically addressing the case of chiral anomalies, bo

Xin Chen, I-Hong Hou

This paper introduces a novel multi-armed bandits framework, termed Contextual Restless Bandits (CRB), for complex online decision-making. This CRB framework incorporates the core features of contextual bandits and restless bandits, so that it can model both the internal state transitions of each arm and the influence of external global environmental context

Francesco Giovanni Celiberto

We review the semi-inclusive hadroproduction of a neutral hidden-flavor tetraquark with light and heavy quark flavor at the HL-LHC, accompanied by another heavy hadron or a light-flavored jet. We make use of the novel TQHL1.0 determinations of leading-twist fragmentation functions to describe the formation mechanism of a tetraquark state within the next-to-l

James Flemings, Meisam Razaviyayn, Murali Annavaram

Ensuring the privacy of Large Language Models (LLMs) is becoming increasingly important. The most widely adopted technique to accomplish this is DP-SGD, which trains a model to guarantee Differential Privacy (DP). However, DP-SGD overestimates an adversary's capabilities in having white box access to the model and, as a result, causes longer training times a

Adarsh Jagan Sathyamoorthy, Kasun Weerakoon, Mohamed Elnoor, Anuj Zore

We present ConVOI, a novel method for autonomous robot navigation in real-world indoor and outdoor environments using Vision Language Models (VLMs). We employ VLMs in two ways: first, we leverage their zero-shot image classification capability to identify the context or scenario (e.g., indoor corridor, outdoor terrain, crosswalk, etc) of the robot's surround

Yunian Pan, Tao Li, Quanyan Zhu

Mirror play (MP) is a well-accepted primal-dual multi-agent learning algorithm where all agents simultaneously implement mirror descent in a distributed fashion. The advantage of MP over vanilla gradient play lies in its usage of mirror maps that better exploit the geometry of decision domains. Despite extensive literature dedicated to the asymptotic converg

Anatol E. Wegner, Sofia C. Olhede

We propose a method for obtaining parsimonious decompositions of networks into higher order interactions which can take the form of arbitrary motifs.The method is based on a class of analytically solvable generative models, where vertices are connected via explicit copies of motifs, which in combination with non-parametric priors allow us to infer higher ord

Marcin Kolakowski, Jozef Modelski

The paper describes an NLOS (Non-Line-of-Sight) mitigation method intended for use in a UWB positioning system. In the proposed method propagation conditions between the localized objects and the anchors forming system infrastructure are classified into one of three categories: LOS (Line-of-Sight), NLOS and severe NLOS. Non-Line-of-Sight detection is conduct

Felix Parker, Diego A. Martínez, James Scheulen, Kimia Ghobadi

Data-driven optimization models have the potential to significantly improve hospital capacity management, particularly during demand surges, when effective allocation of capacity is most critical and challenging. However, integrating models into existing processes in a way that provides value requires recognizing that hospital administrators are ultimately r

P. Rodriguez-Fernandez, N. T. Howard, A. Saltzman, L. Shoji

This work characterizes the core transport physics of SPARC early-campaign plasmas using the PORTALS-CGYRO framework. Empirical modeling of SPARC plasmas with L-mode confinement indicates an ample window of breakeven (Q>1) without the need of H-mode operation. Extensive modeling of multi-channel (electron energy, ion energy and electron particle) flux-matche