March 2024 arXiv papers — page 159

Xuhui Zhou, Zhe Su, Tiwalayo Eisape, Hyunwoo Kim

Recent advances in large language models (LLM) have enabled richer social simulations, allowing for the study of various social phenomena. However, most recent work has used a more omniscient perspective on these simulations (e.g., single LLM to generate all interlocutors), which is fundamentally at odds with the non-omniscient, information asymmetric intera

Jiabao Zhang, Yu Zhang

Mapping plays a crucial role in location and navigation within automatic systems. However, the presence of dynamic objects in 3D point cloud maps generated from scan sensors can introduce map distortion and long traces, thereby posing challenges for accurate mapping and navigation. To address this issue, we propose ERASOR++, an enhanced approach based on the

Zichong Meng, Changdi Yang, Jun Liu, Hao Tang

Recent advances in image editing have been driven by the development of denoising diffusion models, marking a significant leap forward in this field. Despite these advances, the generalization capabilities of recent image editing approaches remain constrained. In response to this challenge, our study introduces a novel image editing framework with enhanced g

Donghoon Ha, Jeong San Kim

We provide multi-player quantum data hiding based on nonlocal quantum state ensembles arising from multi-party quantum state discrimination. Using bounds on local minimum-error discrimination of multi-party quantum states, we construct a multi-player quantum data-hiding scheme. Our data-hiding scheme can be used to hide multiple bits, asymptotically, unless

Shengwen Gan, Shukun Wu

We prove some weighted $L^p\ell^p$-decoupling estimates when $p=2n/(n-1)$. As an application, we give a result beyond the real interpolation exponents for the maximal Bochner-Riesz operator in $\mathbb{R}^3$. We also make an improvement in the planar case.

Zichong Meng, Jie Zhang, Changdi Yang, Zheng Zhan

Class Incremental Learning (CIL) is challenging due to catastrophic forgetting. On top of that, Exemplar-free Class Incremental Learning is even more challenging due to forbidden access to previous task data. Recent exemplar-free CIL methods attempt to mitigate catastrophic forgetting by synthesizing previous task data. However, they fail to overcome the cat

Jia-Wei Wang, Xiang-Fa Zhou, Guang-Can Guo, Zheng-Wei Zhou

The phenomenon of quantum many-body scars has received widespread attention both in theoretical and experimental physics in recent years due to its unique physical properties. In this paper, based on the $su(2)$ algebraic relations, we propose a general method for constructing scar models by combining simple modules.This allows us to investigate many-body sc

Danyang Wu, Xinjie Shen, Jitao Lu, Jin Xu

Existing multigraph convolution methods either ignore the cross-view interaction among multiple graphs, or induce extremely high computational cost due to standard cross-view polynomial operators. To alleviate this problem, this paper proposes a Simple MultiGraph Convolution Networks (SMGCN) which first extracts consistent cross-view topology from multigraph

Wen-Ai Jackson, Peter Wild

An O'Nan configuration in a unital is a set of four lines forming a quadrilateral, where the six intersections of pairs of lines are points of the unital. In 2019 Feng and Li elegantly construct O'Nan configurations in orthogonal and Tits Buekenhout-Metz unitals. We extend their work by extending their construction to a Fano plane embedded in the orthogonal

Y. D. Li, Y. T. Cao, L. Y. Liu, P. Peng

In addition to the pressurized high-temperature superconductivity, bilayer and trilayer nickelate superconductors Lan+1NinO3n+1 (n = 2 and 3) exhibit many intriguing properties at ambient pressure, such as orbital-dependent electronic correlation, non-Fermi liquid behavior, and density-wave transitions. Here, using ultrafast reflectivity measurement, we obse

Yu-Jie Zhao, Zhen-Hua Zhang, Xin-Heng Guo

$C\!P$ violation in baryon decay processes is still undiscovered to date. We present a general analysis of the decay-angular-distributions and the corresponding $C\!P$ asymmetries in cascade decays of the type $\mathbb{H}\to R(\to ab) c$, where $\mathbb{H}$ is a heavy hadron that decays through weak interactions $\mathbb{H}\to R c$, and the resonance $R$ dec

Peng Liu, Dongyang Dai, Zhiyong Wu

Recent advancements in generative modeling have significantly enhanced the reconstruction of audio waveforms from various representations. While diffusion models are adept at this task, they are hindered by latency issues due to their operation at the individual sample point level and the need for numerous sampling steps. In this study, we introduce RFWave,

Allison M. Campbell, Soumya Kundu, Andrew P. Reiman, Orestis Vasios

Forecasting load at the feeder level has become increasingly challenging with the penetration of behind-the-meter solar, as this self-generation (also called total generation) is only visible to the utility as aggregated net-load. This work proposes a methodology for creation of scenarios of solar penetration at the feeder level for use by forecasters to tes

Wenjie Hao, Qingcai Zhang

Two purposes will be shown in this paper. The first one is to extend the classic Tumura-Clunie type theorem for meromorphic functions of one complex variable to meromorphic functions of several complex variables by using Clunie lemma. The second one is to characterize entire solutions of certain partial differential equations in $\mathbb{C}^{m}$. Our results

Xingran Chen, Yi Zhuang, Kun Yang

In communication and computation cooperative networks (3CNs), timely computation is crucial but not always guaranteed. There is a strong demand for a computational task to be completed within a given deadline. The time taken involves both processing time, communication time, and the impact of the deadline. However, a measure of such timeliness in 3CNs is lac

Huiying Zhong, Tianwei Gao, Zhiwei Steven Wu, Linjun Zhang

Pluralistic alignment requires learning from feedback that reflects persistent and potentially conflicting stakeholder preferences while ultimately selecting a single collective policy. We study this problem in offline reinforcement learning from human feedback (RLHF), where the party associated with each comparison is observed. Under a shared low-rank linea

Jaehyeok Shim, Kyungdon Joo

We propose a novel concept of dual and integrated latent topologies (DITTO in short) for implicit 3D reconstruction from noisy and sparse point clouds. Most existing methods predominantly focus on single latent type, such as point or grid latents. In contrast, the proposed DITTO leverages both point and grid latents (i.e., dual latent) to enhance their stren

Devanshu Agrawal, Shang Gao, Martin Gajek

Long-context large language models (LLMs) hold promise for tasks such as question-answering (QA) over long documents, but they tend to miss important information in the middle of context documents (arXiv:2307.03172v3). Here, we introduce $\textit{R&R}$ -- a combination of two novel prompt-based methods called $\textit{reprompting}$ and $\textit{in-context re

Teeraparb Chantavat, Suraphong Yuma, Punnapha Malelohit, Tirawut Worrakitpoonpon

We investigate the morphological and structural evolutions of disk galaxies in simulations for a wide range of Toomre's $Q$ parameter. In addition to the inspection of conventional bar modes, we compute the concentration, asymmetry and clumpiness (CAS) parameters to enlarge the understanding of the galaxy evolution. These parameters are widely employed to an

Xinrui Wu, Jianbo Xu, Puyuan Hu, Guangming Wang

Localization using a monocular camera in the pre-built LiDAR point cloud map has drawn increasing attention in the field of autonomous driving and mobile robotics. However, there are still many challenges (e.g. difficulties of map storage, poor localization robustness in large scenes) in accurately and efficiently implementing cross-modal localization. To so

Hong Du, Yu Zheng, Cuiying Pei, Chi-Ming Yim

Layered materials with kagome lattice have attracted a lot of attention due to the presence of nontrivial topological bands and correlated electronic states with tunability. In this work, we investigate a unique van der Waals (vdW) material system, $A_{2}M_{3}X_{4}$ ($A$ = K, Rb, Cs; $M$ = Ni, Pd; $X$ = S, Se), where transition metal kagome lattices, chalcog

Jianzong Wang, Pengcheng Li, Xulong Zhang, Ning Cheng

Intent is defined for understanding spoken language in existing works. Both textual features and acoustic features involved in medical speech contain intent, which is important for symptomatic diagnosis. In this paper, we propose a medical speech classification model named DRSC that automatically learns to disentangle intent and content representations from

Eldar Fischer

An $\epsilon$-test for any non-trivial property (one for which there are both satisfying inputs and inputs of large distance from the property) should use a number of queries that is at least inversely proportional in $\epsilon$. However, to the best of our knowledge there is no reference proof for this intuition. Such a proof is provided here. It is written

Daniel H. Pak, Minliang Liu, Theodore Kim, Caglar Ozturk

Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcified heart meshes for physics-driven simulations are still often reconstructed using manual operations. This poses a major bottleneck for large-scale adoption of computational sim

Jiapeng Wang, Chengyu Wang, Tingfeng Cao, Jun Huang

We present DiffChat, a novel method to align Large Language Models (LLMs) to "chat" with prompt-as-input Text-to-Image Synthesis (TIS) models (e.g., Stable Diffusion) for interactive image creation. Given a raw prompt/image and a user-specified instruction, DiffChat can effectively make appropriate modifications and generate the target prompt, which can be l

Ziyao Liu, Jiecheng Chen, Dashan Fan

This is a continuation of our previous research about an oscillatory integral operator $T_{\alpha, \beta}$ on compact manifolds $\mathbb{M}$. We prove the sharp $H^{p}$-$L^{p,\infty}$ boundedness on the maximal operator $T^{*}_{\alpha, \beta}$ for all $0<p<1$. As applications, we first prove the sharp $H^{p}$-$L^{p,\infty}$ boundedness on the maximal operato

John D. Clayton

A continuum mixture theory is formulated for large deformations, thermal effects, phase interactions, and degradation of soft biologic tissues. Such tissues consist of one or more solid and fluid phases and can demonstrate nonlinear anisotropic elastic, viscoelastic, thermoelastic, and poroelastic physics. Under extremely large or rapid deformations, for exa

Chao Zhou, Liyang Ma, Yanpeng Feng, Chang-Yang Kuo

In the realm of ferroelectric memories, HfO2-based ferroelectrics stand out because of their exceptional CMOS compatibility and scalability. Nevertheless, their switchable polarization and switching speed are not on par with those of perovskite ferroelectrics. It is widely acknowledged that defects play a crucial role in stabilizing the metastable polar phas

Zewen Chen, Haina Qin, Juan Wang, Chunfeng Yuan

Due to the diversity of assessment requirements in various application scenarios for the IQA task, existing IQA methods struggle to directly adapt to these varied requirements after training. Thus, when facing new requirements, a typical approach is fine-tuning these models on datasets specifically created for those requirements. However, it is time-consumin

A. D. S. Souza, C. R. Muniz, R. M. P. Neves, M. B. Cruz

This study investigates the potential of the Sagnac Effect for detecting dark matter in the Solar System, particularly within the Sun. Originating from the relative delay and interference of light beams traveling in opposite directions on rotating platforms, the effect can account for how varying gravitational conditions affect its manifestation. We analyze

Mako Bates, Joseph P. Near

Formal methods for guaranteeing that a protocol satisfies a cryptographic security definition have advanced substantially, but such methods are still labor intensive and the need remains for an automated tool that can positively identify an insecure protocol. In this work, we demonstrate that property-based testing, "run it a bunch of times and see if it bre

Yi-An Chen, Kai-Feng Chen

Machine learning, particularly deep neural networks, has been widely used in high-energy physics, demonstrating remarkable results in various applications. Furthermore, the extension of machine learning to quantum computers has given rise to the emerging field of quantum machine learning. In this paper, we propose the Quantum Complete Graph Neural Network (Q

Fernando Vera, Palina Pauliuchenka, Ethan Oh, Bai Chien Kao

This research introduces graph analysis methods and a modified Graph Attention Convolutional Neural Network (GAT) to the critical challenge of open source package vulnerability remediation by analyzing control flow graphs to profile breaking changes in applications occurring from dependency upgrades intended to remediate vulnerabilities. Our approach uniquel

Bing-Yu Su, Xu Pan, Guan-Sen Wang, Lei Zu

Primordial black holes (PBHs) are the plausible candidates for the cosmological dark matter. Theoretically, PBHs with masses $M_{\rm PBH}$ in the range of $4\times10^{14}\sim 10^{17}\,{\rm g}$ can emit sub-GeV electrons and positrons through Hawking radiation. Some of these particles could undergo diffusive reacceleration during propagation in the Milky Way,

Jia-Zhi Huang, Yu-Feng Zhou

Cosmic-ray (CR) positrons are relatively rare due to their secondary origin and thus sensitive to exotic contributions. Primordial black holes (PBHs) with masses above $\sim 5\times10^{14}\,\mathrm{g}$ can be stable sources of CR positrons due to Hawking radiation. The energies of the evaporated positrons can increase significantly through scattering with th

R. Evans, F. Lemmermeyer, Z. -H. Sun, M. van Veen

For squarefree $d>1$, let $M$ denote the ring class field for the order $Z[\sqrt{-3d}]$ in $F=Q(\sqrt{-3d})$. Hasse proved that $3$ divides the class number of $F$ if and only if there exists a cubic extension $E$ of $Q$ such that $E$ and $F$ have the same discriminant. Define the real cube roots $v=(a+b\sqrt{d})^{1/3}$ and $v'=(a-b\sqrt{d})^{1/3}$, where $a

Xuzhuo Wang, Guoan Yan, Zhengshuo Li

A low-observable distribution system has insufficient measurements for conventional weighted least square state estimators. Matrix completion state estimators have been suggested, but their computational times could be prohibitive. To resolve this problem, a novel and efficient power-flow-embedded projection conic matrix completion method customized for low-

Shuhong Xiao, Yunnong Chen, Yaxuan Song, Liuqing Chen

Texts, widgets, and images on a UI page do not work separately. Instead, they are partitioned into groups to achieve certain interaction functions or visual information. Existing studies on UI elements grouping mainly focus on a specific single UI-related software engineering task, and their groups vary in appearance and function. In this case, we propose ou

Jian-Guo He, Yong Shao, Xiao-Jie Xu, Xiang-Dong Li

Neutron star$-$white dwarf (NSWD) binaries are one of the most abundant sources of gravitational waves (GW) in the Milky Way. These GW sources are the evolutionary products of primordial binaries that experienced many processes of binary interaction. We employ a binary population synthesis method to investigate the properties of Galactic NSWD binaries detect

Gilchan Park, Sean McCorkle, Carlos Soto, Ian Blaby

Because protein-protein interactions (PPIs) are crucial to understand living systems, harvesting these data is essential to probe disease development and discern gene/protein functions and biological processes. Some curated datasets contain PPI data derived from the literature and other sources (e.g., IntAct, BioGrid, DIP, and HPRD). However, they are far fr

Jiwon Choi, Wooyoung Jo, Seongyon Hong, Beomseok Kwon

This paper presents an energy-efficient stable diffusion processor for text-to-image generation. While stable diffusion attained attention for high-quality image synthesis results, its inherent characteristics hinder its deployment on mobile platforms. The proposed processor achieves high throughput and energy efficiency with three key features as solutions:

Zijian Zhao, Sola Woo, Khandker Akif Aabrar, Sharadindu Gopal Kirtania

In this work, we propose a dual-port cell design to address the pass disturb in vertical NAND storage, which can pass signals through a dedicated and string-compatible pass gate. We demonstrate that: i) the pass disturb-free feature originates from weakening of the depolarization field by the pass bias at the high-${V}_{TH}$ (HVT) state and the screening of

Jia-Kun Li, Kai Sun, Ze-Yan Hao, Jia-He Liang

Jones polynomials were introduced as a tool to distinguish between topologically different links. Recently, they emerged as the central building block of topological quantum computation: by braiding non-Abelian anyons it is possible to realise quantum algorithms through the computation of Jones polynomials. So far, it has been a formidable task to evaluate J

Tal August, Kyle Lo, Noah A. Smith, Katharina Reinecke

Language models (LMs) show promise as tools for communicating science to the general public by simplifying and summarizing complex language. Because models can be prompted to generate text for a specific audience (e.g., college-educated adults), LMs might be used to create multiple versions of plain language summaries for people with different familiarities

Naman Agarwal, Pranjal Awasthi, Satyen Kale, Eric Zhao

Stacking, a heuristic technique for training deep residual networks by progressively increasing the number of layers and initializing new layers by copying parameters from older layers, has proven quite successful in improving the efficiency of training deep neural networks. In this paper, we propose a theoretical explanation for the efficacy of stacking: vi

Yiwei Zou, Ting Li, Zong-fu Luo

Closeness Centrality (CC) and Betweenness Centrality (BC) are crucial metrics in network analysis, providing essential reference for discerning the significance of nodes within complex networks. These measures find wide applications in critical tasks, such as community detection and network dismantling. However, their practical implementation on extensive ne

Asaf Cohen, Mathieu Laurière, Ethan Zell

This paper proposes and analyzes two neural network methods to solve the master equation for finite-state mean field games (MFGs). Solving MFGs provides approximate Nash equilibria for stochastic, differential games with finite but large populations of agents. The master equation is a partial differential equation (PDE) whose solution characterizes MFG equil

Sharmita Dey, Arndt F. Schilling

This article presents a vision for the future of prosthetic devices, leveraging the advancements in large language models (LLMs) and Large Multimodal Models (LMMs) to revolutionize the interaction between humans and assistive technologies. Unlike traditional prostheses, which rely on limited and predefined commands, this approach aims to develop intelligent

Khalil Besrour, Abdellah Sebbar

In this paper we study the modular differential equation $y''+s\,E_4\, y=0$ where $E_4$ is the weight 4 Eisenstein series and $s=\pi^2r^2$ with $r=n/m$ being a rational number in reduced form such that $m\geq 7$. This study is carried out by solving the associated Schwarzian equation $\{h,\tau\}=2\,s\,E_4$ and using the theory of equivariant functions on the

Takuro Kutsuna

In this paper, we first identify activation shift, a simple but remarkable phenomenon in a neural network in which the preactivation value of a neuron has non-zero mean that depends on the angle between the weight vector of the neuron and the mean of the activation vector in the previous layer. We then propose linearly constrained weights (LCW) to reduce the

Daniel Katz, Prashanth Sridhar

A theorem of Paul Roberts states that the integral closure of a regular local ring in a generically abelian extension is Cohen-Macaulay, provided the characteristic of the residue field does not divide the order of the Galois group. An example of Koh shows the conclusion is false in the modular case. After a modification to the statement concerning ramificat

Christopher D'Ambrosia, Florian Richter, Zih-Yun Chiu, Nikhil Shinde

Controlling robotic manipulators via visual feedback requires a known coordinate frame transformation between the robot and the camera. Uncertainties in mechanical systems as well as camera calibration create errors in this coordinate frame transformation. These errors result in poor localization of robotic manipulators and create a significant challenge for

J. S. T. de Souza, G. S. Vicente, L. L. Graef

We revisit the proposal that an energy transfer from dark energy into dark matter can be described in field theory by a first order phase transition. We analyze the model proposed in Ref. Abdalla et al. (2013), using updated constraints on the decay time of a metastable dark energy from the work of Ref. Shafieloo et al. (2018). The results of our analysis sh

Wanwen Chen, Adam Schmidt, Eitan Prisman, Septimiu E Salcudean

Finding point-level correspondences is a fundamental problem in ultrasound (US), since it can enable US landmark tracking for intraoperative image guidance in different surgeries, including head and neck. Most existing US tracking methods, e.g., those based on optical flow or feature matching, were initially designed for RGB images before being applied to US

Hesam Bakhtiary Yekta, Andrew N. Norris

The interaction of an acoustic plane wave with a pair of plates connected by periodically spaced stiffeners in water is considered. The rib-stiffened structure is called a "flex-layer" because its low frequency response is dominated by bending stiffness. The quasi-static behavior is equivalent a homogeneous layer of compressible fluid, which we identify as a

Suozhi Huang, Juexiao Zhang, Yiming Li, Chen Feng

Collaborative perception leverages rich visual observations from multiple robots to extend a single robot's perception ability beyond its field of view. Many prior works receive messages broadcast from all collaborators, leading to a scalability challenge when dealing with a large number of robots and sensors. In this work, we aim to address \textit{scalable

Steven E. Anderson, Luis Chacón, Andrei N. Simakov, Brian M. Haines

We report on simulations of counter-propagating laser-produced plasmas in an inertial confinement fusion (ICF) hohlraum surrogate, aiming to replicate observations reported by Le Pape et. al in recent work. The conditions of the colliding plasmas are relevant to ICF hohlraums used for indirect-drive ignition, and are obtained both with and without low-densit

Wen-Bin Chang, De-fu Hou

In this paper, we use a five-dimensional Einstein-dilaton-two-Maxwell holographic QCD model to investigate the dissociation effects of $J/\Psi$ and $\Upsilon(1S)$ states in an anisotropic medium by calculating their spectral functions. First, we present the holographic quarkonium masses at zero temperature via Physics-Informed Neural Networks. Then, at finit

Lezhong Wang, Jeppe Revall Frisvad, Mark Bo Jensen, Siavash Arjomand Bigdeli

The demand for stereo images increases as manufacturers launch more XR devices. To meet this demand, we introduce StereoDiffusion, a method that, unlike traditional inpainting pipelines, is trainning free, remarkably straightforward to use, and it seamlessly integrates into the original Stable Diffusion model. Our method modifies the latent variable to provi

Carlo Lipizzi

Large Language Models (LLM) have taken the front seat in most of the news since November 2022, when ChatGPT was introduced. After more than one year, one of the major reasons companies are resistant to adopting them is the limited confidence they have in the trustworthiness of those systems. In a study by (Baymard, 2023), ChatGPT-4 showed an 80.1% false-posi

Xuanxin Wu, Yuki Arase

Recent studies have used both automatic metrics and human evaluations to assess the simplification abilities of LLMs. However, the suitability of existing evaluation methodologies for LLMs remains in question. First, the suitability of current automatic metrics on LLMs' simplification evaluation is still uncertain. Second, current human evaluation approaches

Sudipta Paul, Bulent Yener, Amanda W. Lund

Graph-based learning approaches, due to their ability to encode tissue/organ structure information, are increasingly favored for grading colorectal cancer histology images. Recent graph-based techniques involve dividing whole slide images (WSIs) into smaller or medium-sized patches, and then building graphs on each patch for direct use in training. This meth

Theo J. O'Neill, Catherine Zucker, Alyssa A. Goodman, Gordian Edenhofer

Leveraging a high-resolution 3D dust map of the solar neighborhood from Edenhofer et al. (2024), we derive a new 3D model for the dust-traced surface of the Local Bubble, the supernova-driven cavity surrounding the Sun. We find that the surface of the Local Bubble is highly irregular in shape, with its peak extinction surface falling at an average distance o

André Kelm, Niels Hannemann, Bruno Heberle, Lucas Schmidt

This study introduces a novel expert generation method that dynamically reduces task and computational complexity without compromising predictive performance. It is based on a new hierarchical classification network topology that combines sequential processing of generic low-level features with parallelism and nesting of high-level features. This structure a

Yuhao Wu, Franziska Roesner, Tadayoshi Kohno, Ning Zhang

Large language models (LLMs) extended as systems, such as ChatGPT, have begun supporting third-party applications. These LLM apps leverage the de facto natural language-based automated execution paradigm of LLMs: that is, apps and their interactions are defined in natural language, provided access to user data, and allowed to freely interact with each other

Gonzalo Herrera, Andreas Rappelt

Astrophysical uncertainties in dark matter direct detection experiments are typically addressed by parametrizing the velocity distribution in terms of a few uncertain parameters that vary around some central values. Here we propose a method to optimize over all velocity distributions lying within a given distance measure from a central distribution. We discr

J. Takata, H. H Wang, L. C. -C. Lin, S. Kisaka

We report on the properties of pulsed X-ray emission from eight MeV pulsars using XMM-Newton, NICER, NuSTAR and HXMT data. For the five among eight MeV pulsars, the X-ray spectra can be fitted by a broken-power law model with a break energy of $\sim5-10$ keV. The photon index below and above break energy are $\sim 1$ and $\sim 1.5$, respectively. In comparis

Xiaogeng Liu, Zhiyuan Yu, Yizhe Zhang, Ning Zhang

Large Language Models (LLMs) excel in processing and generating human language, powered by their ability to interpret and follow instructions. However, their capabilities can be exploited through prompt injection attacks. These attacks manipulate LLM-integrated applications into producing responses aligned with the attacker's injected content, deviating from

James T. Garland, Karen L. Masters, Daniel Grin

We evaluate recent and upcoming low-redshift neutral hydrogen (HI) surveys as a cosmological probe of small scale structure with a goal of determining the survey criteria necessary to test ultra-light axion (ULA) dark matter models. Standard cold dark matter (CDM) models predict a large population of low-mass galactic halos, whereas ULA models demonstrate si

Kyle Burke, Matthew Ferland, Svenja Huntemann, Shang-Hua Teng

In this paper, we address a natural question at the intersection of combinatorial game theory and computational complexity: "Can a sum of simple tepid games in canonical form be intractable?" To resolve this fundamental question, we consider superstars, positions first introduced in Winning Ways where all options are nimbers. Extending Morris' classic result

Edgar Medina, Leyong Loh

Human motion prediction is still an open problem, which is extremely important for autonomous driving and safety applications. Although there are great advances in this area, the widely studied topic of adversarial attacks has not been applied to multi-regression models such as GCNs and MLP-based architectures in human motion prediction. This work intends to

D. Bazeia, M. A. Feitosa, R. Menezes, G. S. Santiago

This work deals with the presence of topological structures in models of two real scalar fields in the two-dimensional spacetime. The subject concerns the presence of a geometric constriction, which appears with a modification of the kinetic term of one of the two fields. We elaborate on the construction of a first-order framework, which directly contributes

Qingkai Kong, William R. Walter, Ruijia Wang, Brandon Schmandt

Combining physics with machine learning models has advanced the performance of machine learning models in many different applications. In this paper, we evaluate adding a weak physics constraint, i.e., a physics-based empirical relationship, to the loss function (the Physics Informed manner) in local distance explosion discrimination in the hope of improving

Hideo Bannai, Keisuke Goto, Shunsuke Kanda, Dominik Köppl

Indexing a set of strings for prefix search or membership queries is a fundamental task with many applications such as information retrieval or database systems. A classic abstract data type for modelling such an index is a trie. Due to the fundamental nature of this problem, it has sparked much interest, leading to a variety of trie implementations with dif

Liang-Gui Zhu, Hui-Min Fan, Xian Chen, Yi-Ming Hu

Extreme-mass-ratio inspirals (EMRIs) could be detected by space-borne gravitational-wave (GW) detectors, such as the Laser Interferometer Space Antenna (LISA), TianQin and Taiji. Localizing EMRIs by GW detectors can help us select candidate host galaxies, which can be used to infer the cosmic expansion history. In this paper, we demonstrate that the localiza

Andrew Hundt

Imagine activating new robots meant to aid staff in an elder care facility, only to discover the robots are counterproductive. They undermine the most meaningful moments of the jobs and increase staff workloads, because robots demand care too. Eventually, they're returned. This vignette captures key elements of James Adrian Wright's ethnography, "Robots Won'

Jonathan G. Hedley, Kush Coshic, Aleksei Aksimentiev, Alexei A. Kornyshev

In solution, DNA is a highly charged macromolecule which bears a unit of negative charge on each phosphate of its sugar-phosphate backbone. Although partially compensated by counterions adsorbed at or condensed near it, DNA still produces a substantial electric field in its vicinity, which is screened by buffer electrolyte at longer distances from the DNA. S

Jonathan S. Arnaud, Tyler Mark, Christopher J. McDevitt

A surrogate model of the runaway electron avalanche growth rate in a magnetic fusion plasma is developed. This is accomplished by employing a physics-informed neural network (PINN) to learn the parametric solution of the adjoint to the relativistic Fokker-Planck equation. The resulting PINN is able to evaluate the runaway probability function across a broad

Tong Bo, James C. McWilliams, Chao Yan, Marcelo Chamecki

This study investigates the influence of suspended kelp farms on ocean mixed layer hydrodynamics in the presence of currents and waves. We use the large eddy simulation method, where the wave effect is incorporated by solving the wave-averaged equations. Distinct Langmuir circulation patterns are generated within various suspended farm configurations, includ

Vinesha Peiris, Duy Khoa Pham, Nadezda Sukhorukova

The problem of fixed knot approximation is convex and there are several efficient approaches to solve this problem, yet, when the knots joining the affine parts are also variable, finding conditions for a best Chebyshev approximation remains an open problem. It was noticed before that piecewise linear approximation with free knots is equivalent to neural net

Abram Rodgers, Daniele Venturi

Functional Differential Equations (FDEs) play a fundamental role in many areas of mathematical physics, including fluid dynamics (Hopf characteristic functional equation), quantum field theory (Schwinger-Dyson equation), and statistical physics. Despite their significance, computing solutions to FDEs remains a longstanding challenge in mathematical physics.

Zhongwei Wan, Che Liu, Xin Wang, Chaofan Tao

Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on classifying cardiac conditions using ECG data but have overlooked ECG report generation, which is time-consuming and requires clinical expertise. To automate ECG report generation an

Ha Manh Bui, Anqi Liu

Morden deep ensembles technique achieves strong uncertainty estimation performance by going through multiple forward passes with different models. This is at the price of a high storage space and a slow speed in the inference (test) time. To address this issue, we propose Density-Regression, a method that leverages the density function in uncertainty estimat

Maja Petrovic, Branko Malesevic

In this paper, we give new Taylor approximative formulae for the area of the egg-shaped parts of H\"ugelsch\"affer curves. Based on a parametrization of the H\"ugelsch\"affer curve, a formula for the area of the egg-shaped part of such a curve is derived via elliptic integrals of the first and second kind. Furthermore, new approximative formulae for calculat

Adriano D'Alessandro, Ali Mahdavi-Amiri, Ghassan Hamarneh

Object counting methods typically rely on manually annotated datasets. The cost of creating such datasets has restricted the versatility of these networks to count objects from specific classes (such as humans or penguins), and counting objects from diverse categories remains a challenge. The availability of robust text-to-image latent diffusion models (LDMs

J. Anthony Tyson, Adam Snyder, Daniel Polin, Meredith L. Rawls

We examine the simple model put forth in a recent note by Loeb regarding the brightness of space debris in the size range of 1-10 cm and their impact on the Rubin Observatory Legacy Survey of Space and Time (LSST) transient object searches. Their main conclusion was that "image contamination by untracked space debris might pose a bigger challenge [than large

Noah Torgerson, Jeremy West

The surface Houghton groups $\mathcal{H}_{n}$ are a family of groups generalizing Houghton groups $H_n$, which are constructed as asymptotically rigid mapping class groups. We give a complete computation of the BNSR-invariants $\Sigma^{m}(P\mathcal{H}_{n})$ of their intersection with the pure mapping class group. To do so, we prove that the associated Stein-

Antonino Greco, Markus Siegel

Understanding how visual information is encoded in biological and artificial systems often requires vision scientists to generate appropriate stimuli to test specific hypotheses. Although deep neural network models have revolutionized the field of image generation with methods such as image style transfer, available methods for video generation are scarce. H

Yue Cynthia Wu, Eric Kunze, Amit Tandon, Amala Mahadevan

Idealized numerical simulation is used to explore energy sinks for lee waves trapped in their bottom-intensified generating flow. In addition to the loss to explicit dissipation and reabsorption predicted by linear wave action conservation, indirect dissipation due to a nonlinear forward cascade by parametric subharmonic instability represents a significant

Solange Nunes, Gabriel Escrig, Osvaldo G. Freitas, José A. Font

We test deep-learning (DL) techniques for the analysis of rotational core-collapse supernovae (CCSN) gravitational-wave (GW) signals by performing classification and parameter inference of the maximum (peak) frequency and the GW strain amplitude ($\Delta h$) multiplied by the luminosity distance ($D$) attained at core bounce, respectively, $(f_{peak})$ and $

Ivana Collado-Gonzalez, John McConnell, Jinkun Wang, Paul Szenher

Reliable localization is an essential capability for marine robots navigating in GPS-denied environments. SLAM, commonly used to mitigate dead reckoning errors, still fails in feature-sparse environments or with limited-range sensors. Pose estimation can be improved by incorporating the uncertainty prediction of future poses into the planning process and cho

CQ Sun

Water is ubiquitously important but least known. This perspective features the latest finding of two exotic forms of water called quasisolid and supersolid phases due to the cooperativity and disparity of the O:H-O bond in its segmental length, energy, and specific heat when subjected to thermal, electric, and undercoordination perturbation. The quasisolid (

Michael Howard

The growing popularity of electric vehicles in the United States requires an ever-expanding infrastructure of commercial DC fast charging stations. The U.S. Department of Energy estimates 33,355 publicly available DC fast charging stations as of September 2023. Range anxiety is an important impediment to the adoption of electric vehicles and is even more rel

Zhengtong Xu, Yu She

Grasping is a crucial task in robotics, necessitating tactile feedback and reactive grasping adjustments for robust grasping of objects under various conditions and with differing physical properties. In this paper, we introduce LeTac-MPC, a learning-based model predictive control (MPC) for tactile-reactive grasping. Our approach enables the gripper to grasp

Yiyuan Wang, Luke Hespanhol, Stewart Worrall, Martin Tomitsch

Shared space reduces segregation between vehicles and pedestrians and encourages them to share roads without imposed traffic rules. The behaviour of road users (RUs) is then controlled by social norms, and interactions are more versatile than on traditional roads. Autonomous vehicles (AVs) will need to adapt to these norms to become socially acceptable RUs i

Blaž Rolih, Dick Ameln, Ashwin Vaidya, Samet Akcay

Industrial anomaly detection is an important task within computer vision with a wide range of practical use cases. The small size of anomalous regions in many real-world datasets necessitates processing the images at a high resolution. This frequently poses significant challenges concerning memory consumption during the model training and inference stages, l

Vanshika Vats, Marzia Binta Nizam, Minghao Liu, Ziyuan Wang

As the capabilities of artificial intelligence (AI) continue to expand rapidly, Human-AI (HAI) Collaboration, combining human intellect and AI systems, has become pivotal for advancing problem-solving and decision-making processes. The advent of Large Foundation Models (LFMs) has greatly expanded its potential, offering unprecedented capabilities by leveragi

Yiyuan Wang, Luke Hespanhol, Martin Tomitsch

In recent years, researchers and manufacturers have started to investigate ways to enable autonomous vehicles (AVs) to interact with nearby pedestrians in compensation for the absence of human drivers. The majority of these efforts focuses on external human-machine interfaces (eHMIs), using different modalities, such as light patterns or on-road projections,

Montgomery Bohde, Meng Liu, Alexandra Saxton, Shuiwang Ji

Neural algorithmic reasoning is an emerging research direction that endows neural networks with the ability to mimic algorithmic executions step-by-step. A common paradigm in existing designs involves the use of historical embeddings in predicting the results of future execution steps. Our observation in this work is that such historical dependence intrinsic

Martin Tomitsch, Marius Hoggenmueller

Technological progress paves the way to ever-increasing opportunities for automating city services. This spans from already existing concepts, such as automated shuttles at airports, to more speculative applications, such as fully autonomous delivery robots. As these services are being automated, it is critical that this process is underpinned by a human-cen