March 2024 arXiv papers — page 77

Nanda Kishor Panda, Simon H. Tindemans

Electric vehicles (EVs) play a crucial role in the transition towards sustainable modes of transportation and thus are critical to the energy transition. As their number grows, managing the aggregate power of EV charging is crucial to maintain grid stability and mitigate congestion. This study analyses more than 500 thousand real charging transactions in the

Shahram Khorshidi, Murad Dawood, Maren Bennewitz

In this paper, we introduce a novel approach to centroidal state estimation, which plays a crucial role in predictive model-based control strategies for dynamic legged locomotion. Our approach uses the Koopman operator theory to transform the robot's complex nonlinear dynamics into a linear system, by employing dynamic mode decomposition and deep learning fo

Wei Wang, Naike Du, Yuchao Guo, Chao Sun

The radar signal processing algorithm is one of the core components in through-wall radar human detection technology. Traditional algorithms (e.g., DFT and matched filtering) struggle to adaptively handle low signal-to-noise ratio echo signals in challenging and dynamic real-world through-wall application environments, which becomes a major bottleneck in the

Qiaojun Yu, Ce Hao, Junbo Wang, Wenhai Liu

Robotic manipulation in everyday scenarios, especially in unstructured environments, requires skills in pose-aware object manipulation (POM), which adapts robots' grasping and handling according to an object's 6D pose. Recognizing an object's position and orientation is crucial for effective manipulation. For example, if a mug is lying on its side, it's more

G. Moza, C. Lazureanu, F. Munteanu, C. Sterbeti

A two-dimensional Kolmogorov system with two parameters and having a degenerate condition is studied in this work. We obtain local analytical properties of the system when the parameters vary in a sufficiently small neighborhood of the origin. The behavior of the system is described by bifurcation diagrams. Applications of Kolmogorov systems can be found par

Muhammad Karam Shehzad, Luca Rose, Mohamad Assaad

In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, calle

Hadi Askari, Anshuman Chhabra, Bernhard Clemm von Hohenberg, Michael Heseltine

Polarization, declining trust, and wavering support for democratic norms are pressing threats to U.S. democracy. Exposure to verified and quality news may lower individual susceptibility to these threats and make citizens more resilient to misinformation, populism, and hyperpartisan rhetoric. This project examines how to enhance users' exposure to and engage

Mohamed Elshazli A. Zidan, Anouar Ben Mabrouk, Nidhal Ben Abdallah, Tawfeeq M. Alanazi

Marketing is the way we ensure our sales are the best in the market, our prices the most accessible, and our clients satisfied, thus ensuring our brand has the widest distribution. This requires sophisticated and advanced understanding of the whole related network. Indeed, marketing data may exist in different forms such as qualitative and quantitative data.

A. V. Kotikov, B. G. Shaikhatdenov, N. S. Korchagin, P. Zhang

The deep inelastic scattering data on the nucleon F_2 structure function, accumulated by BCDMS, SLAC and NMC collaborations in fixed-target experiments, are analyzed in the non-singlet approximation within the frameworks of both conventional \overline{\mathbf{MS}} scheme as well as those with resummations of logarithms at large Bjorken x values. The use of t

Stephanie Chan, Peter Koymans, Carlo Pagano, Efthymios Sofos

We prove matching upper and lower bounds for the average of the 6-torsion of class groups of quadratic fields. Furthermore, we count the number of integer solutions on an affine quartic threefold.

Wenxuan Song, Han Zhao, Pengxiang Ding, Can Cui

Multi-task robot learning holds significant importance in tackling diverse and complex scenarios. However, current approaches are hindered by performance issues and difficulties in collecting training datasets. In this paper, we propose GeRM (Generalist Robotic Model). We utilize offline reinforcement learning to optimize data utilization strategies to learn

Nishan Parvez, Jacob S. Merson

We develop a new neural network architecture that strictly enforces constitutive constraints such as polyconvexity, frame-indifference, and the symmetry of the stress and material stiffness. Additionally, we show that the accuracy of the stress and material stiffness predictions is significantly improved for this neural network by using a Sobolev minimizatio

Huali Zhou, Yuke Lin, Dong Liu, Ming Li

This work aims to promote Chinese opera research in both musical and speech domains, with a primary focus on overcoming the data limitations. We introduce KunquDB, a relatively large-scale, well-annotated audio-visual dataset comprising 339 speakers and 128 hours of content. Originating from the Kunqu Opera Art Canon (Kunqu yishu dadian), KunquDB is meticulo

Yanzhou Li, Tianlin Li, Kangjie Chen, Jian Zhang

Mainstream backdoor attack methods typically demand substantial tuning data for poisoning, limiting their practicality and potentially degrading the overall performance when applied to Large Language Models (LLMs). To address these issues, for the first time, we formulate backdoor injection as a lightweight knowledge editing problem, and introduce the BadEdi

Dennis Wuhrer, Niklas Rohling, Wolfgang Belzig

We investigate the viability of dipole-dipole interaction as a means of entangling two distant ferromagnets. To this end we make use of the Bogoliubov transformation as a symplectic transformation. We show that the coupling of the uniform magnon modes can be expressed using four squeezing parameters which we interpret in terms of hybridization, one-mode and

Aya Watanabe, Shinnosuke Takamichi, Yuki Saito, Wataru Nakata

In text-to-speech synthesis, the ability to control voice characteristics is vital for various applications. By leveraging thriving text prompt-based generation techniques, it should be possible to enhance the nuanced control of voice characteristics. While previous research has explored the prompt-based manipulation of voice characteristics, most studies ha

Jingkun An, Yinghao Zhu, Zongjian Li, Enshen Zhou

Text-to-Image (T2I) diffusion models have achieved remarkable success in image generation. Despite their progress, challenges remain in both prompt-following ability, image quality and lack of high-quality datasets, which are essential for refining these models. As acquiring labeled data is costly, we introduce AGFSync, a framework that enhances T2I diffusio

Xinyu Geng, Jiaming Wang, Jiawei Gong, Yuerong Xue

Redundancy is a persistent challenge in Capsule Networks (CapsNet),leading to high computational costs and parameter counts. Although previous works have introduced pruning after the initial capsule layer, dynamic routing's fully connected nature and non-orthogonal weight matrices reintroduce redundancy in deeper layers. Besides, dynamic routing requires ite

Wajid M. Shaikh, Rupali S. Jain, B. Surendranath Reddy, Bhagyashri S. Patil

In this paper, we will give the generic construction of a binary linear code of dimension $n+3$ and derive the necessary and sufficient conditions for the constructed code to be minimal. Using generic construction, a new family of minimal binary linear code will be constructed from a special class of Boolean functions violating the Ashikhmin-Barg condition.

Xincheng Yao, Ruoqi Li, Zefeng Qian, Lu Wang

Unified anomaly detection (AD) is one of the most challenges for anomaly detection, where one unified model is trained with normal samples from multiple classes with the objective to detect anomalies in these classes. For such a challenging task, popular normalizing flow (NF) based AD methods may fall into a "homogeneous mapping" issue,where the NF-based AD

Weiying Wang, Victor Cai, Stephanie Gil

This paper presents MULAN-WC, a novel multi-robot 3D reconstruction framework that leverages wireless signal-based coordination between robots and Neural Radiance Fields (NeRF). Our approach addresses key challenges in multi-robot 3D reconstruction, including inter-robot pose estimation, localization uncertainty quantification, and active best-next-view sele

Joonmyung Choi, Sanghyeok Lee, Jaewon Chu, Minhyuk Choi

Video Transformers have become the prevalent solution for various video downstream tasks with superior expressive power and flexibility. However, these video transformers suffer from heavy computational costs induced by the massive number of tokens across the entire video frames, which has been the major barrier to training the model. Further, the patches ir

Xuening Tang, Xianghui Cao, Wei Xing Zheng

As networked control systems continue to evolve, ensuring the privacy of sensitive data becomes an increasingly pressing concern, especially in situations where the controller is physically separated from the plant. In this paper, we propose a secure control scheme for computing linear quadratic control in a networked control system utilizing two networked c

Emmanouil M. Athanasakos, Nicholas Kalouptsidis, Hariprasad Manjunath

This paper uses Euclidean Information Theory (EIT) to analyze the wiretap channel. We investigate a scenario of efficiently transmitting a small amount of information subject to compression rate and secrecy constraints. We transform the information-theoretic problem into a linear algebra problem and obtain the perturbed probability distributions such that se

Zhihan Zhou, Qixiang Fang, Leonardo Neves, Francesco Barbieri

User embeddings play a crucial role in user engagement forecasting and personalized services. Recent advances in sequence modeling have sparked interest in learning user embeddings from behavioral data. Yet behavior-based user embedding learning faces the unique challenge of dynamic user modeling. As users continuously interact with the apps, user embeddings

Santosh Sanjeev, Fadillah Adamsyah Maani, Arsen Abzhanov, Vijay Ram Papineni

With the emergence of vision language models in the medical imaging domain, numerous studies have focused on two dominant research activities: (1) report generation from Chest X-rays (CXR), and (2) synthetic scan generation from text or reports. Despite some research incorporating multi-view CXRs into the generative process, prior patient scans and reports h

Santosh Sanjeev, Nuren Zhaksylyk, Ibrahim Almakky, Anees Ur Rehman Hashmi

The scarcity of well-annotated medical datasets requires leveraging transfer learning from broader datasets like ImageNet or pre-trained models like CLIP. Model soups averages multiple fine-tuned models aiming to improve performance on In-Domain (ID) tasks and enhance robustness against Out-of-Distribution (OOD) datasets. However, applying these methods to t

Cristian F. Jiménez-Varón, Ying Sun, Han Lin Shang

We introduce a statistical method for modeling and forecasting functional panel data represented by multiple densities. Density functions are nonnegative and have a constrained integral and thus do not constitute a linear vector space. We implement a center log-ratio transformation to transform densities into unconstrained functions. These functions exhibit

Christian Hauschke, Serhii Nazarovets

The proliferation of scholarly publications underscores the necessity for reliable tools to navigate scientific literature. OpenAlex, an emerging platform amalgamating data from diverse academic sources, holds promise in meeting these evolving demands. Nonetheless, our investigation uncovered a flaw in OpenAlex's portrayal of publication status, particularly

Yilin Leng, Wenju Cui, Bai Chen, Xi Jiang

Prediction the conversion to early-stage dementia is critical for mitigating its progression but remains challenging due to subtle cognitive impairments and structural brain changes. Traditional T1-weighted magnetic resonance imaging (T1-MRI) research focus on identifying brain atrophy regions but often fails to address the intricate connectivity between the

Quan Zheng, Hao Sun, Huiyao Xu, Fanjiang Xu

Neural radiance field has achieved fundamental success in novel view synthesis from input views with the same brightness level captured under fixed normal lighting. Unfortunately, synthesizing novel views remains to be a challenge for input views with heterogeneous brightness level captured under low-light condition. The condition is pretty common in the rea

Jiadong Zhou, Yadan Zeng, Huixu Dong, I-Ming Chen

Robotic kitting has attracted considerable attention in logistics and industrial settings. However, existing kitting methods encounter challenges such as low precision and poor efficiency, limiting their widespread applications. To address these issues, we present a novel kitting framework that improves both the precision and computational efficiency of comp

Zhixin Lai, Xuesheng Zhang, Suiyao Chen

Large language models (LLMs) have reached human-like proficiency in generating diverse textual content, underscoring the necessity for effective fake text detection to avoid potential risks such as fake news in social media. Previous research has mostly tested single models on in-distribution datasets, limiting our understanding of how these models perform o

Chih-Wei Song, Yin-Te Tsai

This research's primary motivation of this study is to address the high hardware and computational demands typically associated with LLMs.Therefore,our goal is to find a balance between model lightness and performance,striving to maximize performance while using a comparatively lightweight model. Hyacinth6B was developed with this objective in mind,aiming to

Abdusattar Kurban, Xia Zhou, Na Wang, Yong-Feng Huang

Repeating X-ray bursts from the Galactic magnetar SGR 1806-20 have been observed with a period of 398 days. Similarly, periodic X-ray bursts from SGR 1935+2154 with a period of 238 days have also been observed. Here we argue that these X-ray bursts could be produced by the interaction of a neutron star (NS) with its planet in a highly elliptical orbit. The p

Seunguk Yu, Juhwan Choi, Youngbin Kim

Offensive language detection is an important task for filtering out abusive expressions and improving online user experiences. However, malicious users often attempt to avoid filtering systems through the involvement of textual noises. In this paper, we propose these evasions as user-intended adversarial attacks that insert special symbols or leverage the di

Yu Xi, Hao Li, Baochen Yang, Haoyu Li

Designing an efficient keyword spotting (KWS) system that delivers exceptional performance on resource-constrained edge devices has long been a subject of significant attention. Existing KWS search algorithms typically follow a frame-synchronous approach, where search decisions are made repeatedly at each frame despite the fact that most frames are keyword-i

Xiaosong Jia, Shaoshuai Shi, Zijun Chen, Li Jiang

As an essential task in autonomous driving (AD), motion prediction aims to predict the future states of surround objects for navigation. One natural solution is to estimate the position of other agents in a step-by-step manner where each predicted time-step is conditioned on both observed time-steps and previously predicted time-steps, i.e., autoregressive p

LeoWu TomyEnrique, Xiangcheng Du, Kangliang Liu, Han Yuan

Scene text image super-resolution has significantly improved the accuracy of scene text recognition. However, many existing methods emphasize performance over efficiency and ignore the practical need for lightweight solutions in deployment scenarios. Faced with the issues, our work proposes an efficient framework called SGENet to facilitate deployment on res

Raffaella Morganti

The recurrent activity of radio AGN, with phases of activity alternating with periods of quiescence, has been known since the early studies of these objects. The full relevance of this cycle is emphasised by the requirement, from the AGN feedback scenario, of a recurrent impact of the energy released by the SMBH during the lifetime of the host galaxy: only i

Rui-Hong Yue, Kai-Qiang Qian, Bo Liu, De-Cheng Zou

Considering the nonminimal coupling of the dilaton field to the massive graviton field in Maxwell-dilaton-massive gravity, we obtain a class of analytical solutions of charged black holes, which are neither asymptotically flat nor (A)dS. The calculated thermodynamic quantities, such as mass, temperature and entropy, verify the validity of the first law of bl

Otto Seiskari, Jerry Ylilammi, Valtteri Kaatrasalo, Pekka Rantalankila

High-quality scene reconstruction and novel view synthesis based on Gaussian Splatting (3DGS) typically require steady, high-quality photographs, often impractical to capture with handheld cameras. We present a method that adapts to camera motion and allows high-quality scene reconstruction with handheld video data suffering from motion blur and rolling shut

Lorena Armas-Sanabria, Mario Eudave-Muñoz, Juan Pablo Díaz-González, Gabriela Hinojosa-Palafox

An integral framed, closed pure n-braid B' in the 3-sphere describes a positive Artin presentation, if the braid B can be put on a disk with holes such that each relation describes a positive path and these paths are disjoint. In the present paper we classify the closed, pure n-braids B' in the 3-sphere, such that B represents a positive Artin presentation.

Zhi Zheng, Wenshuo Chao, Zhaopeng Qiu, Hengshu Zhu

Recent advances in Large Language Models (LLMs) have been changing the paradigm of Recommender Systems (RS). However, when items in the recommendation scenarios contain rich textual information, such as product descriptions in online shopping or news headlines on social media, LLMs require longer texts to comprehensively depict the historical user behavior s

K Huang, G Song, Hanwen Su, Jiyan Wang

Out-of-distribution (OOD) detection is a critical task to ensure the reliability and security of machine learning models deployed in real-world applications. Conventional methods for OOD detection that rely on single-modal information, often struggle to capture the rich variety of OOD instances. The primary difficulty in OOD detection arises when an input im

Rikuya Ishikawa, Kyohei Takae, Yoshikazu Mizuguchi, Rei Kurita

High-entropy alloys (HEAs) are attracting attention due to their exceptional properties, such as enhanced mechanical toughness, superconducting robustness, and thermoelectric performance. Numerous HEAs have been developed for diverse applications, ranging from self-healing in fusion reactors to addressing environmental concerns with thermoelectric materials.

Yifan Wu, Jiawei Du, Ping Liu, Yuewei Lin

Dataset distillation is an advanced technique aimed at compressing datasets into significantly smaller counterparts, while preserving formidable training performance. Significant efforts have been devoted to promote evaluation accuracy under limited compression ratio while overlooked the robustness of distilled dataset. In this work, we introduce a comprehen

Leonard Bauersfeld, Koen Muller, Dominic Ziegler, Filippo Coletti

The widespread adoption of quadrotors for diverse applications, from agriculture to public safety, necessitates an understanding of the aerodynamic disturbances they create. This paper introduces a computationally lightweight model for estimating the time-averaged magnitude of the induced flow below quadrotors in hover. Unlike related approaches that rely on

Lei Hu, Zihao Zhang, Yongjing Ye, Yiwen Xu

3D Human motion style transfer is a fundamental problem in computer graphic and animation processing. Existing AdaIN- based methods necessitate datasets with balanced style distribution and content/style labels to train the clustered latent space. However, we may encounter a single unseen style example in practical scenarios, but not in sufficient quantity t

Daniel Duenias, Brennan Nichyporuk, Tal Arbel, Tammy Riklin Raviv

The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a comprehensive understanding of the clinical condition of a patient, improving diagnosis and treatment decision. D

Josh Bhagat Smith, Julie A. Adams

Successful human-robot teaming will require robots to adapt autonomously to a human teammate's internal state, where a critical element of such adaptation is the ability to estimate the human's workload in unknown situations. Existing workload models use machine learning to model the relationship between physiological signals and workload. These methods ofte

Haoyu Liu, Yaoxian Song, Xuwu Wang, Zhu Xiangru

With the explosive growth of multi-modal information on the Internet, unimodal search cannot satisfy the requirement of Internet applications. Text-image retrieval research is needed to realize high-quality and efficient retrieval between different modalities. Existing text-image retrieval research is mostly based on general vision-language datasets (e.g. MS

Rajesh Ranjan Patra, Sarit Maitra

The importance of the Allee effect in studying extinction vulnerability is widely recognized by researchers, and neglecting it could adversely impact the management of threatened or exploited populations [1]. In this article, we examine a discrete predator-prey model where the prey population is associated with two component Allee effects. We derive sufficie

Yew Ken Chia, Vernon Toh Yan Han, Deepanway Ghosal, Lidong Bing

Large multimodal models extend the impressive capabilities of large language models by integrating multimodal understanding abilities. However, it is not clear how they can emulate the general intelligence and reasoning ability of humans. As recognizing patterns and abstracting concepts are key to general intelligence, we introduce PuzzleVQA, a collection of

Zonghui Yang, Shijian Gao, Xiang Cheng, Liuqing Yang

Integrated sensing and communications (ISAC) is a critical enabler for emerging 6G applications, and at its core lies in the dual-functional waveform design. While orthogonal frequency division multiplexing (OFDM) has been a popular basic waveform, its primitive version falls short in sensing due to the inherent unregulated auto-correlation properties. Furth

Subhabrata Mukherjee, Paul Gamble, Markel Sanz Ausin, Neel Kant

We develop Polaris, the first safety-focused LLM constellation for real-time patient-AI healthcare conversations. Unlike prior LLM works in healthcare focusing on tasks like question answering, our work specifically focuses on long multi-turn voice conversations. Our one-trillion parameter constellation system is composed of several multibillion parameter LL

Dongwei Jiang, Marcio Fonseca, Shay B. Cohen

Large language models (LLMs) often struggle with complex logical reasoning due to logical inconsistencies and the inherent difficulty of such reasoning. We use Lean, a theorem proving framework, to address these challenges. By formalizing logical reasoning problems into theorems within Lean, we can solve them by proving or disproving the corresponding theore

Jingtao Tang, Hang Ma

We introduce the Multi-Robot Connected Fermat Spiral (MCFS), a novel algorithmic framework for Multi-Robot Coverage Path Planning (MCPP) that adapts Connected Fermat Spiral (CFS) from the computer graphics community to multi-robot coordination for the first time. MCFS uniquely enables the orchestration of multiple robots to generate coverage paths that conto

Guoxiong Gao, Haocheng Ju, Jiedong Jiang, Zihan Qin

The interactive theorem prover Lean enables the verification of formal mathematical proofs and is backed by an expanding community. Central to this ecosystem is its mathematical library, mathlib4, which lays the groundwork for the formalization of an expanding range of mathematical theories. However, searching for theorems in mathlib4 can be challenging. To

Christy Dunlap, Changgen Li, Hari Pandey, Ngan Le

This paper presents BubbleID, a sophisticated deep learning architecture designed to comprehensively identify both static and dynamic attributes of bubbles within sequences of boiling images. By amalgamating segmentation powered by Mask R-CNN with SORT-based tracking techniques, the framework is capable of analyzing each bubble's location, dimensions, interf

Rahul Pankajakshan, Sumitra Biswal, Yuvaraj Govindarajulu, Gilad Gressel

The rapid integration of Large Language Models (LLMs) across diverse sectors has marked a transformative era, showcasing remarkable capabilities in text generation and problem-solving tasks. However, this technological advancement is accompanied by significant risks and vulnerabilities. Despite ongoing security enhancements, attackers persistently exploit th

Luciano Combi, Sean M. Ressler

We present a semi-analytical binary black hole (BBH) metric approximation that models the entire evolution of the system from inspiral to merger. The metric is constructed as a boosted Kerr-Schild superposition following post-Newtonian (PN) trajectories at the fourth PN order in the inspiral phase. During merger, we interpolate the binary metric in time to a

Peishan Cong, Ziyi Wang, Zhiyang Dou, Yiming Ren

Language-guided scene-aware human motion generation has great significance for entertainment and robotics. In response to the limitations of existing datasets, we introduce LaserHuman, a pioneering dataset engineered to revolutionize Scene-Text-to-Motion research. LaserHuman stands out with its inclusion of genuine human motions within 3D environments, unbou

Qingkai Meng, Xiaokang Li, Jie Liu, Lingxiao Zhao

The underlying mechanism of the thermal Hall effect (THE) generated by phonons in a variety of insulators is yet to be identified. Here, we report on a sizeable thermal Hall conductivity in NiPS$_3$, a van der Waals stack of honeycomb layers with a zigzag antiferromagnetic order below $T_N$ = 155 K. The longitudinal ($\kappa_{aa}$) and the transverse ($\kapp

Tian-Yu Tu, Yang Chen, Ping Zhou, Samar Safi-Harb

Supernova remnants (SNRs) exert strong influence on the physics and chemistry of the nearby molecular clouds (MCs) through shock waves and the cosmic rays (CRs) they accelerate. To investigate the SNR-cloud interaction in the prototype interacting SNR W28 (G6.4$-$0.1), we present new observations of $\rm HCO^+$, HCN and HNC $J=1\text{--}0$ lines, supplemente

Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou

Current perceptive models heavily depend on resource-intensive datasets, prompting the need for innovative solutions. Leveraging recent advances in diffusion models, synthetic data, by constructing image inputs from various annotations, proves beneficial for downstream tasks. While prior methods have separately addressed generative and perceptive models, Det

Y. Bisabr

We introduce a theoretical framework to alleviate the Hubble tension. This framework is based on dynamics of a minimally coupled scalar field which either belongs to the Brans-Dicke theory with a self-interacting potential or is the scalar partner of f(R) gravity. These two theories are dynamically equivalent when the Brans-Dicke parameter is zero. We will u

Vasiliki Bitsouni, Nikolaos Gialelis, Ioannis G. Stratis, Vasilis Tsilidis

Cervical intraepithelial neoplasia (CIN) is the development of abnormal cells on the surface of the cervix, caused by a human papillomavirus (HPV) infection. Although in most of the cases it is resolved by the immune system, a small percentage of people might develop a more serious CIN which, if left untreated, can develop into cervical cancer. Cervical canc

Qihao Zhu, Leah Chong, Maria Yang, Jianxi Luo

In human-centered design, developing a comprehensive and in-depth understanding of user experiences, i.e., empathic understanding, is paramount for designing products that truly meet human needs. Nevertheless, accurately comprehending the real underlying mental states of a large human population remains a significant challenge today. This difficulty mainly a

Lu Zou, Liang Ding

Additive Gaussian Processes (GPs) are popular approaches for nonparametric feature selection. The common training method for these models is Bayesian Back-fitting. However, the convergence rate of Back-fitting in training additive GPs is still an open problem. By utilizing a technique called Kernel Packets (KP), we prove that the convergence rate of Back-fit

Minglei Lu, Chensen Lin, Martian Maxey, George Karniadakis

The intricate process of bubble growth dynamics involves a broad spectrum of physical phenomena from microscale mechanics of bubble formation to macroscale interplay between bubbles and surrounding thermo-hydrodynamics. Traditional bubble dynamics models including atomistic approaches and continuum-based methods segment the bubble dynamics into distinct scal

Byeongho Heo, Song Park, Dongyoon Han, Sangdoo Yun

Rotary Position Embedding (RoPE) performs remarkably on language models, especially for length extrapolation of Transformers. However, the impacts of RoPE on computer vision domains have been underexplored, even though RoPE appears capable of enhancing Vision Transformer (ViT) performance in a way similar to the language domain. This study provides a compreh

Jean-Baptiste Bouvier, Kartik Nagpal, Negar Mehr

In this paper, we seek to learn a robot policy guaranteed to satisfy state constraints. To encourage constraint satisfaction, existing RL algorithms typically rely on Constrained Markov Decision Processes and discourage constraint violations through reward shaping. However, such soft constraints cannot offer verifiable safety guarantees. To address this gap,

Syed Mahbub Hafiz, Chitrabhanu Gupta, Warren Wnuck, Brijesh Vora

Private information retrieval (PIR), a privacy-preserving cryptographic tool, solves a simplified version of this problem by hiding the database item that a client accesses. Most PIR protocols require the client to know the exact row index of the intended database item, which cannot support the complicated aggregation-based statistical query in a similar set

Tao Yu, Xiaoran Ye, Zhaoxin Liang

The exploration of dimensional crossover carries profound fundamental significance, serving as a crucial bridge in comprehending the remarkable disparities observed in transitional phenomena across the two distinct dimensions of a physical system. The prevalent strategy for manipulating the dimensionality involves meticulously controlling the external trappi

Qingyuan Jiang, Burak Susam, Jun-Jee Chao, Volkan Isler

In the robot follow-ahead task, a mobile robot is tasked to maintain its relative position in front of a moving human actor while keeping the actor in sight. To accomplish this task, it is important that the robot understand the full 3D pose of the human (since the head orientation can be different than the torso) and predict future human poses so as to plan

Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu

Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead makes it hard for an algorithm to sufficiently explore candidate networks. In this paper, we propose AutoBuild, a scheme which learns to align the latent embeddings of operations a

Timothy M. Chan, Isaac M. Hair

We revisit a standard polygon containment problem: given a convex $k$-gon $P$ and a convex $n$-gon $Q$ in the plane, find a placement of $P$ inside $Q$ under translation and rotation (if it exists), or more generally, find the largest copy of $P$ inside $Q$ under translation, rotation, and scaling. Previous algorithms by Chazelle (1983), Sharir and Toledo (1

Qi Liu, Gang Guo, Jiaxin Mao, Zhicheng Dou

With the development of pre-trained language models, the dense retrieval models have become promising alternatives to the traditional retrieval models that rely on exact match and sparse bag-of-words representations. Different from most dense retrieval models using a bi-encoder to encode each query or document into a dense vector, the recently proposed late-

Bingqi Liu, Dominic Liao-McPherson

In this paper, we propose an equilibrium-seeking algorithm for finding generalized Nash equilibria of non-cooperative monotone convex quadratic games. Specifically, we recast the Nash equilibrium-seeking problem as variational inequality problem that we solve using a log-domain interior point method and provide a general purpose solver based on this algorith

Han-Hung Lee, Manolis Savva, Angel X. Chang

Recent years have seen an explosion of work and interest in text-to-3D shape generation. Much of the progress is driven by advances in 3D representations, large-scale pretraining and representation learning for text and image data enabling generative AI models, and differentiable rendering. Computational systems that can perform text-to-3D shape generation h

Ying Shuai Quan, Jian Zhou, Erik Frisk, Chung Choo Chung

This paper proposes a safety-critical controller for dynamic and uncertain environments, leveraging a robust environment control barrier function (ECBF) to enhance the robustness against the measurement and prediction uncertainties associated with moving obstacles. The approach reduces conservatism, compared with a worst-case uncertainty approach, by incorpo

Sanath Salil, Nischay Ram Mamidi, Anil Nemili, Elliott Slaughter

Regent is an implicitly parallel programming language that allows the development of a single codebase for heterogeneous platforms targeting CPUs and GPUs. This paper presents the development of a parallel meshfree solver in Regent for two-dimensional inviscid compressible flows. The meshfree solver is based on the least squares kinetic upwind method. Exampl

Yun Wang, Chrysanthi Kosyfaki, Sihem Amer-Yahia, Reynold Cheng

Hypothesis testing is a statistical method used to draw conclusions about populations from sample data, typically represented in tables. With the prevalence of graph representations in real-life applications, hypothesis testing in graphs is gaining importance. In this work, we formalize node, edge, and path hypotheses in attributed graphs. We develop a sampl

Anshu Gupta, Cathryn M. Trott, Ravi Jaiswar, E. V. Ryan-Weber

We present the discovery of a unique Lyman-continuum (LyC) emitter at z=3.088. The LyC emission were detected using the Hubble Space Telescope (HST) WFC3/UVIS F336W filter, covering a rest-frame wavelength range of 760-900 Angstrom. The peak signal-to-noise ratio (SNR) of LyC emission is 3.9 in a r=0.24'' aperture and is spatially offset by 0.29''+/-0.04'' (

Lakshay Sharma, Jonathan P. How

To operate safely and efficiently, autonomous warehouse/delivery robots must be able to accomplish tasks while navigating in dynamic environments and handling the large uncertainties associated with the motions/behaviors of other robots and/or humans. A key scenario in such environments is the hallway problem, where robots must operate in the same narrow cor

Ning Bao, Joydeep Naskar

We present a deterministic way of finding contraction maps for candidate holographic entanglement entropy inequalities modulo choices due to actual degeneracy. We characterize its complexity and give an argument for the completeness of the contraction map proof method as a necessary and sufficient condition for the validity of an entropy inequality for holog

Polly Y. Yu, Eduardo D. Sontag

Steady state nonmonotonic ("biphasic") dose responses are often observed in experimental biology, which raises the control-theoretic question of identifying which possible mechanisms might underlie such behaviors. It is well known that the presence of an incoherent feedforward loop (IFFL) in a network may give rise to a nonmonotonic response. It has been con

Mengyu Yang, Ye Tian, Lanshan Zhang, Xiao Liang

Recently, prompt-based methods have emerged as a new alternative `parameter-efficient fine-tuning' paradigm, which only fine-tunes a small number of additional parameters while keeping the original model frozen. However, despite achieving notable results, existing prompt methods mainly focus on `what to add', while overlooking the equally important aspect of

Ching-Hsiang Wang

With the robust development of technology, license plate recognition technology can now be properly applied in various scenarios, such as road monitoring, tracking of stolen vehicles, detection at parking lot entrances and exits, and so on. However, the precondition for these applications to function normally is that the license plate must be 'clear' enough

Shaunak A. Mehta, Soheil Habibian, Dylan P. Losey

Robot arms should be able to learn new tasks. One framework here is reinforcement learning, where the robot is given a reward function that encodes the task, and the robot autonomously learns actions to maximize its reward. Existing approaches to reinforcement learning often frame this problem as a Markov decision process, and learn a policy (or a hierarchy

Ruochen Ma, Alex Turzillo

The interplay of symmetry and topology in quantum many-body mixed states has recently garnered significant interest. In a phenomenon not seen in pure states, mixed states can exhibit average symmetries -- symmetries that act on component states while leaving the ensemble invariant. In this work, we systematically characterize symmetry protected topological (

Ye Liu, Yixuan Liu, Yi Li, Cyrille Artho

Smart contracts are computer programs running on blockchains to implement Decentralized Applications. The absence of contract specifications hinders routine tasks, such as contract understanding and testing. In this work, we propose a specification mining approach to infer contract specifications from past transaction histories. Our approach derives high-lev

Hai-Long Huang, Tian-Yi Song, Yun-Song Piao

The coalescence of stellar-mass primordial black holes (PBHs) might explain some of the gravitation waves (GWs) events detected by LIGO-Virgo-KAGRA. On the other hand, observational hints for supermassive PBHs (SMPBHs) have been accumulated. Thus it can be expected that stellar-mass PBHs might be gravitationally bounded to SMPBHs ($\sim10^{6}-10^9M_\odot$) i

Yu-Hao Shen, Jun-Ding Zheng, Wen-Yi Tong, Zhi-Qiang Bao

With low-buckled structure for each layer in graphene bilayer system, there breaks inversion symmetry (P-symmetry) for one stacking when both A and B sublattices in top layer are aligned with those in bottom layer. In consideration of spin-orbit coupling (SOC), there opens nontrivial topological gap in each monolayer system to achieve quantum spin Hall effec

Carol Wu

In 1999, Balog, Br\"udern, and Wooley (1999) showed there are infinitely many prime gaps $p-q$ that are $(\log p)^{\frac{3}{4}}$-smooth, and infinitely many consecutive prime gaps that are $(\log p)^\frac{7}{8}$-smooth. Advancements made since then by Zhang (2014), Maynard (2014), and Polymath8b (2014) towards resolving the twin prime conjecture have given u

Qi Wang, Roman Verba, Kristyna Davidkova, Bjorn Heinz

Bistability, a universal phenomenon found in diverse fields such as biology, chemistry, and physics, describes a scenario in which a system has two stable equilibrium states and resets to one of the two states. The ability to switch between these two states is the basis for a wide range of applications, particularly in memory and logic operations. Here, we p

Michelle Blom

Single Transferable Vote (STV) is used to elect candidates to the 76 seat Australian Senate across six states and two territories. These eight STV contests are counted using a combination of ballot scanners, manual data entry and tabulation software. On election night, some properties of the set of cast ballots are determined by hand. This includes the first

Gaotian Wang, Kejia Ren, Kaiyu Hang

Nonprehensile manipulation through precise pushing is an essential skill that has been commonly challenged by perception and physical uncertainties, such as those associated with contacts, object geometries, and physical properties. For this, we propose a unified framework that jointly addresses system modeling, action generation, and control. While most exi

Yu Rong, Jinzhi Shen, Zichen Hua

Leveraging the datasets of galaxy triplets and large-scale filaments obtained from the Sloan Digital Sky Survey, we scrutinize the alignment of the three sides of the triangles formed by galaxy triplets and the normal vectors of the triplet planes within observed large-scale filaments. Our statistical investigation reveals that the longest and median sides o