October 2023 arXiv papers — page 174

Yinxiang Ma, Wanting Cheng, Shidi Huang, François G. Schmitt

Turbulent skies have often inspired artists, particularly in the iconic swirls of Vincent van Gogh's \textbf{\textit{The Starry Night}}. For an extended period, debate has raged over whether the flow pattern in this masterpiece adheres to Kolmogorov's theory of turbulence. In contrast to previous studies that examined only part of this painting, {\textit{all

Litton J Kurisinkel, Nancy F. Chen

Multi-document summarization is a challenging task due to its inherent subjective bias, highlighted by the low inter-annotator ROUGE-1 score of 0.4 among DUC-2004 reference summaries. In this work, we aim to enhance the objectivity of news summarization by focusing on the main event of a group of related news documents and presenting it coherently with suffi

S. V. Tezlaf

The negative binomial distribution (NBD) has been theorized to express a scale-invariant property of many-body systems and has been consistently shown to outperform other statistical models in both describing the multiplicity of quantum-scale events in particle collision experiments and predicting the prevalence of cosmological observables, such as the numbe

Linda J. Smith, M. S. Oey, Svea Hernandez, Jenna Ryon

Mrk 71 is a low metallicity (Z = 0.16 Z_sun) starburst region in the local dwarf galaxy NGC 2366, hosting two super star clusters (SSCs A and B), and is recognized as a Green Pea (GP) analog with SSC A responsible for the GP properties. We present STIS and FOS far-ultraviolet (FUV) spectra of the embedded SSC Mrk 71-A obtained with the Hubble Space Telescope

Jee Seok Yoon, Kwanseok Oh, Yooseung Shin, Maciej A. Mazurowski

Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. However, deploying DL models for MedIA in real-world situations remains challenging due to their failure to gene

Daniil Kochergin, Vasilii Tiselko, Arsenii Onuchin

We studied the single-particle Anderson localization problem for non-Hermitian systems on directed graphs. Random regular graph and various undirected standard random graph models were modified by controlling reciprocity and hopping asymmetry parameters. We found the emergence of left, biorthogonal and right localized states depending on both parameters and

Rolf Bader

Musical large-scale form is investigated using an Electronic Dance Music (EDM) piece fed into a Finite-Difference Time Domain (FDTD) physical model of the cochlear which again inputs into an Impulse-Pattern Formulation (IPF) brain model. In previous studies, experimental EEG data showed an enhanced correlation between brain synchronization and the musical pi

Adrian Edin, Zheng Chen

Over-the-Air (OtA) Federated Learning (FL) refers to an FL system where multiple agents apply OtA computation for transmitting model updates to a common edge server. Two important features of OtA computation, namely linear processing and signal-level superposition, motivate the use of linear compression with compressed sensing (CS) methods to reduce the numb

Mateus G. Masteghin, Toussaint Gervais, Steven K. Clowes, David C. Cox

We report X-ray fluorescence (XRF) imaging of nanoscale inclusions of impurities for quantum technology. A very bright diffraction-limited focus of the X-ray beam produces very high sensitivity and resolution. We investigated gallium (Ga) dopants in silicon (Si) produced by a focused ion beam (FIB). These dopants might provide 3/2-spin qubits or p-type elect

Sebastián Caicedo-Dávila, Adi Cohen, Silvia G. Motti, Masahiko Isobe

Metal halide perovskites have shown great performance as solar energy materials, but their outstanding optoelectronic properties are paired with unusually strong anharmonic effects. It has been proposed that this intriguing combination of properties derives from the "lone pair" 6$s^2$ electron configuration of the Pb$^{2+}$ cations, and associated weak pseud

Amjad Yousef Majid, Eduard Marin

In the rapidly evolving field of serverless computing, efficient function scheduling and resource scaling are critical for optimizing performance and cost. This paper presents a comprehensive review of the application of Deep Reinforcement Learning (DRL) techniques in these areas. We begin by providing an overview of serverless computing, highlighting its be

Tianqi Ma, Chao Sun

Wind-wave interactions impose wind forcing on wave surface and wave effects on turbulent wind structures, which essentially influences the wind-wave loading on structures. Existing research treats the wind and wave loading separately and ignores their interactions. The present study aims to characterize the turbulent airflow over wave surfaces and analyze th

Deepak Raina, Abhishek Mathur, Richard M. Voyles, Juan Wachs

The one of the significant challenges faced by autonomous robotic ultrasound systems is acquiring high-quality images across different patients. The proper orientation of the robotized probe plays a crucial role in governing the quality of ultrasound images. To address this challenge, we propose a sample-efficient method to automatically adjust the orientati

Wonsik Jung, Eunjin Jeon, Eunsong Kang, Heung-Il Suk

Deep learning models based on resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used to diagnose brain diseases, particularly autism spectrum disorder (ASD). Existing studies have leveraged the functional connectivity (FC) of rs-fMRI, achieving notable classification performance. However, they have significant limitations, includ

Ali Suri

This paper outlines the study of the curvature of the quantomorphism group and its central extension, as well as the quasi-geostrophic equation. By utilizing spherical harmonics and structure constants, a formula for computing the curvature of the $L^2$ metric on the central extension $\hat{\cg}=\cg\ltimes_\Omega\mathbb{R}$ is derived, where $\cg$ represents

Zhenyu Bu, Kai-Ni Wang, Fuxing Zhao, Shengxiao Li

Ultrasound imaging serves as an effective and non-invasive diagnostic tool commonly employed in clinical examinations. However, the presence of speckle noise in ultrasound images invariably degrades image quality, impeding the performance of subsequent tasks, such as segmentation and classification. Existing methods for speckle noise reduction frequently ind

Antonio Boiano, Alessandro Enrico Cesare Redondi, Matteo Cesana

The widespread deployment of Consumer Internet of Things devices in proximity to human activities makes them digital observers of our daily actions. This has led to a new field of digital forensics, known as IoT Forensics, where digital traces generated by IoT devices can serve as key evidence for forensic investigations. Thus, there is a need to develop too

Huan Ma, Changqing Zhang, Huazhu Fu, Peilin Zhao

Nowadays, billions of people engage in communication and express their opinions on the internet daily. Unfortunately, not all of these expressions are friendly or compliant, making content moderation an indispensable task. A common approach is to use a discriminative model to classify the content, but this method often requires strict data engineering, other

Taraneh Younesian, Daniel Daza, Emile van Krieken, Thiviyan Thanapalasingam

Graph neural networks (GNNs) learn to represent nodes by aggregating information from their neighbors. As GNNs increase in depth, their receptive field grows exponentially, leading to high memory costs. Several existing methods address this by sampling a small subset of nodes, scaling GNNs to much larger graphs. These methods are primarily evaluated on homop

Hugues Van Assel, Cédric Vincent-Cuaz, Titouan Vayer, Rémi Flamary

We present a versatile adaptation of existing dimensionality reduction (DR) objectives, enabling the simultaneous reduction of both sample and feature sizes. Correspondances between input and embedding samples are computed through a semi-relaxed Gromov-Wasserstein optimal transport (OT) problem. When the embedding sample size matches that of the input, our m

N. Castro Segura, C. Knigge, J. H. Matthews, F. M. Vincentelli

The evolution of accreting X-ray binary systems is closely coupled to the properties of their donor stars. As a result, we can constrain the evolutionary track a system is by establishing the nature of its donor. Here, we present far-UV spectroscopy of the transient neutron-star low-mass X-ray binary Swift J1858 in three different accretion states (low-hard,

Adrian Cosma, Emilian Radoi

Gait analysis leverages unique walking patterns for person identification and assessment across multiple domains. Among the methods used for gait analysis, skeleton-based approaches have shown promise due to their robust and interpretable features. However, these methods often rely on hand-crafted spatial-temporal graphs that are based on human anatomy disre

Claude Godrèche, Jean-Marc Luck

We consider the simple random walk (or P\'olya walk) on the one-dimensional lattice subject to stochastic resetting to the origin with probability $r$ at each time step. The focus is on the joint statistics of the numbers ${\mathcal{N}}_t^{\times}$ of spontaneous returns of the walker to the origin and ${\mathcal{N}}_t^{\bullet}$ of resetting events up to so

Khaled Wahba, Joaquim Ortiz-Haro, Marc Toussaint, Wolfgang Hönig

We propose a motion planner for cable-driven payload transportation using multiple unmanned aerial vehicles (UAVs) in an environment cluttered with obstacles. Our planner is kinodynamic, i.e., it considers the full dynamics model of the transporting system including actuation constraints. Due to the high dimensionality of the planning problem, we use a hiera

Lorenc Kapllani, Long Teng, Matthias Rottmann

Deep learning-based numerical schemes for solving high-dimensional backward stochastic differential equations (BSDEs) have recently raised plenty of scientific interest. While they enable numerical methods to approximate very high-dimensional BSDEs, their reliability has not been studied and is thus not understood. In this work, we study uncertainty quantifi

Tita A. Bach, Jenny K. Kristiansen, Aleksandar Babic, Alon Jacovi

Ensuring quality human-AI interaction (HAII) in safety-critical industries is essential. Failure to do so can lead to catastrophic and deadly consequences. Despite this urgency, existing research on HAII is limited, fragmented, and inconsistent. We present here a survey of that literature and recommendations for research best practices that should improve th

Maria Ferrara, Marco Trombetti

Let $\sigma=\{\sigma_j\,:\, j\in J\}$ be a partition of the set $\mathbb{P}$ of all prime numbers. A subgroup $X$ of a finite group $G$ is~\textit{$\sigma$-subnormal} in $G$ if there exists a chain of subgroups $$X=X_0\leq X_1\leq\ldots\leq X_n=G$$ such that, for each $1\leq i\leq n-1$, $X_{i-1}\trianglelefteq X_i$ or $X_i/(X_{i-1})_{X_i}$ is a $\sigma_{j_i}

Natalia Sergienko, Elisabeth Cuervo Lumbaque, Nick Duinslaeger, Jelena Radjenovic

Electrooxidation is an attractive technique that can be effectively applied for the treatment of persistent organic contaminants, but its implementation in practice is limited by the lack of efficient and low cost anode material that would not lead to the generation of chlorinated by products. Herein, we developed a novel anode based on TiO2 nanotube array N

Yong Zhao, Zhengqiu Zhu, Bin Chen, Sihang Qiu

The growing complexity of real-world systems necessitates interdisciplinary solutions to confront myriad challenges in modeling, analysis, management, and control. To meet these demands, the parallel systems method rooted in Artificial systems, Computational experiments, and Parallel execution (ACP) approach has been developed. The method cultivates a cycle,

Jaak Peetre, Per G. Nilsson

This note is an (exact) copy of the report of Jaak Peetre, "Generalizing Ovchinnikov's Theorem". Published as Technical Report, Lund (1981). Some more recent general references have been added, some references updated though (in italics) and some misprints corrected.

Paolo Rabino, Antonio Alliegro, Francesco Cappio Borlino, Tatiana Tommasi

Moving deep learning models from the laboratory setting to the open world entails preparing them to handle unforeseen conditions. In several applications the occurrence of novel classes during deployment poses a significant threat, thus it is crucial to effectively detect them. Ideally, this skill should be used when needed without requiring any further comp

Boris Bilich

We study the gauge-invariant ideal structure of the Nica-Toeplitz algebra $\mathcal{NT}(X)$ of a product system $(A, X)$ over $\mathbb{N}^n$. We obtain a clear description of $X$-invariant ideals in $A$, that is, restrictions of gauge-invariant ideals in $\mathcal{NT}(X)$ to $A$. The main result is a classification of gauge-invariant ideals in $\mathcal{NT}(

Viola Valentina Vogler-Neuling, Ülle-Linda Talts, Rebecca Ferraro, Helena Weigand

Nonlinear optical effects are used to generate coherent light at wavelengths difficult to reach with lasers. Materials periodically poled or nanostructured in the nonlinear susceptibility in three spatial directions are called 3D nonlinear photonic crystals (NPhCs). They enable enhanced nonlinear optical conversion efficiencies, emission control, and simulta

S. Jaiswal, Connor Belt, Anton Kananovich, E. M. Aguirre

We report the experimental study of the structural transition of a stable complex plasma crystal to a solid-liquid phase coexistence by the controlled adjustment of the confinement potential, while keeping all other parameters constant. The experiments are carried out in a tabletop Linear Dusty Plasma Experimental (LDPEx) device which consists of a circular

Xilin Yang, Md Sadman Sakib Rahman, Bijie Bai, Jingxi Li

As an optical processor, a Diffractive Deep Neural Network (D2NN) utilizes engineered diffractive surfaces designed through machine learning to perform all-optical information processing, completing its tasks at the speed of light propagation through thin optical layers. With sufficient degrees-of-freedom, D2NNs can perform arbitrary complex-valued linear tr

Xiaoying Wang, Yong Li, Yuecai Han

The main focus of this paper is to explore how much similarity between two dynamical systems. Analogous to the classical Hartman-Grobman theorem, the relationship between two systems can be linked by a homeomorphic map $K$, and the core is to study the minimizer $K^*$ to measure the degree of similarity. We prove the sufficient conditions and necessary condi

J. M. J. van Leeuwen

The analogy between the quantum evolution and that of the master equation is explored. By stressing the stochastic nature of quantum evolution a number of conceptual difficulties in the interpretation of quantum mechanics are avoided.

Christian Elsholtz, Jakob Führer, Erik Füredi, Benedek Kovács

We study subsets of $\mathbb{F}_p^n$ that do not contain progressions of length $k$. We denote by $r_k(\mathbb{F}_p^n)$ the cardinality of such subsets containing a maximal number of elements. In this paper we focus on the case $k=p$ and therefore sets containing no full line. A~trivial lower bound $r_p(\mathbb{F}_p^n)\geq(p-1)^n$ is achieved by a hypercube

Jie Liang, Haoyi Tan, Jiaomei Liu, Huizhao Qi

The potential leakage of nuclear waste, especially radioiodine, is a major safety concerning issue around the world. To remove radioiodine from nuclear waste efficiently, there is an urgent demand for adsorbents that possess both high stability and strong adsorption affinity for environmental remediation. Herein, two Zr-based metal-organic frameworks (Zr-MOF

David Albandea, Luigi Del Debbio, Pilar Hernández, Richard Kenway

The so-called trivializing flows were proposed to speed up Hybrid Monte Carlo simulations, where the Wilson flow was used as an approximation of a trivializing map, a transformation of the gauge fields which trivializes the theory. It was shown that the scaling of the computational costs towards the continuum did not change with respect to HMC. The introduct

Xingdong Ren, Tianxing Zhang, Hanzhou Wu, Xinpeng Zhang

In this work, we propose StegGuard, a novel fingerprinting mechanism to verify the ownership of the suspect pre-trained encoder using steganography. A critical perspective in StegGuard is that the unique characteristic of the transformation from an image to an embedding, conducted by the pre-trained encoder, can be equivalently exposed how an embeder embeds

Zhaorun Chen, Zhuokai Zhao, Tairan He, Binhao Chen

Ensuring safety in Reinforcement Learning (RL), typically framed as a Constrained Markov Decision Process (CMDP), is crucial for real-world exploration applications. Current approaches in handling CMDP struggle to balance optimality and feasibility, as direct optimization methods cannot ensure state-wise in-training safety, and projection-based methods corre

Pawan R. Bhure, M. S. Santhanam

The study of interacting dynamical systems continues to attract research interest in various fields of science and engineering. In a collection of interacting particles, the interaction network contains information about how various components interact with one another. Inferring the information about the interaction network from the dynamics of agents is a

Luoying Hao, Yan Hu, Wenjun Lin, Qun Wang

Recognition and localization of surgical detailed actions is an essential component of developing a context-aware decision support system. However, most existing detection algorithms fail to provide high-accuracy action classes even having their locations, as they do not consider the surgery procedure's regularity in the whole video. This limitation hinders

Anisa Rula, Jennifer D'Souza

Recent advancements in the field of Natural Language Processing, particularly the development of large-scale language models that are pretrained on vast amounts of knowledge, are creating novel opportunities within the realm of Knowledge Engineering. In this paper, we investigate the usage of large language models (LLMs) in both zero-shot and in-context lear

Haitao Yu, Deheng Zhang, Peiyuan Xie, Tianyi Zhang

This paper proposes a novel controllable human motion synthesis method for fine-level deformation based on static point-based radiance fields. Although previous editable neural radiance field methods can generate impressive results on novel-view synthesis and allow naive deformation, few algorithms can achieve complex 3D human editing such as forward kinemat

Fabio Stroppa

Soft-growing robots are innovative devices that feature plant-inspired growth to navigate environments. Thanks to their embodied intelligence of adapting to their surroundings and the latest innovation in actuation and manufacturing, it is possible to employ them for specific manipulation tasks. The applications of these devices include exploration of delica

Danko Radić, Leonid I. Gorelik, Sergei I. Kulinich, Robert I. Shekhter

We suggest a nanoelectromechanical setup and corresponding time-protocol for controlling parameters in order to demonstrate nanomechanical manipulation of superconducting charge-qubit quantum network. We illustrate it on an example reflecting important task for quantum information processing - transmission of quantum information between two charge-qubits fac

Nils Friederich, Angelo Yamachui Sitcheu, Oliver Neumann, Süheyla Eroğlu-Kayıkçı

In the biomedical environment, experiments assessing dynamic processes are primarily performed by a human acquisition supervisor. Contemporary implementations of such experiments frequently aim to acquire a maximum number of relevant events from sometimes several hundred parallel, non-synchronous processes. Since in some high-throughput experiments, only one

Luigi Catello, Ludovica Ruggiero, Lucia Schiavone, Mario Valentino

The stock market presents a challenging environment for accurately predicting future stock prices due to its intricate and ever-changing nature. However, the utilization of advanced methodologies can significantly enhance the precision of stock price predictions. One such method is Hidden Markov Models (HMMs). HMMs are statistical models that can be used to

Yves Fomatati

Let $R=K[x_{1},x_{2},\cdots, x_{m}]$ where $K$ is a field. In this paper, we give some properties of $n$-matrix factorizations of polynomials in $R$. We also derive some results giving some lower bounds on the number of $n$-matrix factors of polynomials. In particular, we give a lower bound on the number of matrix factors of minimal size for the sums of squa

Shubham Jha

We consider distributed optimization over a $d$-dimensional space, where $K$ remote clients send coded gradient estimates over an {\em additive Gaussian Multiple Access Channel (MAC)} with noise variance $\sigma_z^2$. Furthermore, the codewords from the clients must satisfy the average power constraint $P$, resulting in a signal-to-noise ratio (SNR) of $KP/\

Shivank Garg, Aravinda Prasad, Debadatta Mishra, Sreenivas Subramoney

Software managed byte-addressable hybrid memory systems consisting of DRAMs and NVMMs offer a lot of flexibility to design efficient large scale data processing applications. Operating systems (OS) play an important role in enabling the applications to realize the integrated benefits of DRAMs' low access latency and NVMMs' large capacity along with its persi

Elena Mäder-Baumdicker, Jona Seidel

A sharp lower bound for the injectivity radius in noncompact nonnegatively curved Riemannian manifolds involving their soul goes back to \v{S}arafutdinov. We generalize this bound to the setting of Alexandrov spaces. Our main theorem reads as follows. If the injectivity radius of an Alexandrov space of nonnegative curvature does not coincide with the one of

Qinyuan Cheng, Tianxiang Sun, Wenwei Zhang, Siyin Wang

In this paper, we establish a benchmark named HalluQA (Chinese Hallucination Question-Answering) to measure the hallucination phenomenon in Chinese large language models. HalluQA contains 450 meticulously designed adversarial questions, spanning multiple domains, and takes into account Chinese historical culture, customs, and social phenomena. During the con

Teresa Rauscher

This paper presents a paraxial modeling approach for vibro-acoustography, a high-frequency ultrasound imaging technique that makes use of the excited low-frequency field to achieve a higher resolution while avoiding speckles. We start from a general second order wave equation, introduce a second order perturbation and make use of a paraxial change of variabl

J. B. Nation, Gianluca Paolini

We start a systematic analysis of the first-order model theory of free lattices. Firstly, we prove that the free lattices of finite rank are not positively indistinguishable, as there is a positive $\exists \forall$-sentence true in $\mathbf F_3$ and false in $\mathbf F_4$. Secondly, we show that every model of $\mathrm{Th}(\mathbf F_n)$ admits a canonical h

Hossein Jafari, Karim Faez, Hamidreza Amindavar

Lung cancer is highly lethal, emphasizing the critical need for early detection. However, identifying lung nodules poses significant challenges for radiologists, who rely heavily on their expertise for accurate diagnosis. To address this issue, computer-aided diagnosis (CAD) systems based on machine learning techniques have emerged to assist doctors in ident

Peter Werner, Alexander K. Hartmann

The Higgs RNA-Model is studied in regard to finite-time driving protocols with minimal-work requirement. In this paper, RNA sequences which at low temperature exhibits hairpins are considered, which are often cited as typical template systems in stochastic thermodynamics. The optimized work protocols for this glassy many-particle system are determined numeri

Jinting Wang, Li Liu, Jun Wang, Hei Victor Cheng

Speech-to-face generation is an intriguing area of research that focuses on generating realistic facial images based on a speaker's audio speech. However, state-of-the-art methods employing GAN-based architectures lack stability and cannot generate realistic face images. To fill this gap, we propose a novel speech-to-face generation framework, which leverage

Prerit Pramod

This paper provides a simple introduction to pulse width modulation control techniques used for the control of power converters in the context of electric motor drive systems. A summary of each technique is presented along with analytical models that provide intuitive insight and enable their rapid implementation for practical purposes.

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using data samples with an integrated luminosity of 22.42 fb$^{-1}$ collected by the BESIII detector operating at the BEPCII storage ring, we measure the cross sections of the $e^{+}e^{-}\rightarrow\etaJ/\psi$ process at center-of-mass energies from 3.808 to 4.951 GeV. Three structures are observed in the line shape of the measured cross sections. A maximum-

Zhuoyuan Wu, Jiachen Sun, Chaowei Xiao

Despite recent advancements in deep neural networks for point cloud recognition, real-world safety-critical applications present challenges due to unavoidable data corruption. Current models often fall short in generalizing to unforeseen distribution shifts. In this study, we harness the inherent set property of point cloud data to introduce a novel critical

Philip Dorpmüller, Thomas Schmitz, Naveen Bejagam, Torsten Bertram

Basis splines enable a time-continuous feasibility check with a finite number of constraints. Constraints apply to the whole trajectory for motion planning applications that require a collision-free and dynamically feasible trajectory. Existing motion planners that rely on gradient-based optimization apply time scaling to implement a shrinking planning horiz

Nuoyan Zhou, Nannan Wang, Decheng Liu, Dawei Zhou

Deep neural networks are vulnerable to adversarial noise. Adversarial Training (AT) has been demonstrated to be the most effective defense strategy to protect neural networks from being fooled. However, we find AT omits to learning robust features, resulting in poor performance of adversarial robustness. To address this issue, we highlight two criteria of ro

Francesc Salvat, Carlos Heredia

Electromagnetic interactions of protons and alpha particles are modeled in a form that is suitable for Monte Carlo simulation of the transport of charged particles. The differential cross section (DCS) for elastic collisions with neutral atoms is expressed as the product of the DCS for collisions with the bare nucleus and a correction factor that accounts fo

Boon Suan Ho

A vertex ordering of a graph $G$ is a bijection $\pi\colon\{1,\dots,|V(G)|\}\to V(G)$. It is successive if the induced subgraph $G[v_{\pi(1)},\dots,v_{\pi(k)}]$ is connected for each $k$. Lixing Fang, Hao Huang, J\'anos Pach, G\'abor Tardos, and Junchi Zuo [J. Comb. Theory A199 (2023), 105776] gave formulas for counting the number of successive vertex orderi

Vince Grolmusz

The LogRank conjecture of Lov\'asz and Saks from 1988 is the most famous open problem in the communication complexity theory. The statement is as follows: Suppose that two players intend to compute a Boolean function $f(x,y)$ when $x$ is known for the first and $y$ for the second player, and they may send and receive messages encoded with bits, then they can

Zelai Xu, Yancheng Liang, Chao Yu, Yu Wang

Self-play (SP) is a popular multi-agent reinforcement learning (MARL) framework for solving competitive games, where each agent optimizes policy by treating others as part of the environment. Despite the empirical successes, the theoretical properties of SP-based methods are limited to two-player zero-sum games. However, for mixed cooperative-competitive gam

Andy Xiao, Deep Doshi, Lihao Wang, Harsha Gorantla

Surround-view fisheye cameras are commonly used for near-field sensing in automated driving scenarios, including urban driving and auto valet parking. Four fisheye cameras, one on each side, are sufficient to cover 360{\deg} around the vehicle capturing the entire near-field region. Based on surround view cameras, there has been much research on parking slot

Seungwoo Jeong, Wonsik Jung, Junghyo Sohn, Heung-Il Suk

Alzheimer's disease (AD) is a devastating neurodegenerative condition that precedes progressive and irreversible dementia; thus, predicting its progression over time is vital for clinical diagnosis and treatment. Numerous studies have implemented structural magnetic resonance imaging (MRI) to model AD progression, focusing on three integral aspects: (i) temp

David Huber, Yizuo Chen, Alessandro Antonucci, Adnan Darwiche

We discuss the problem of bounding partially identifiable queries, such as counterfactuals, in Pearlian structural causal models. A recently proposed iterated EM scheme yields an inner approximation of those bounds by sampling the initialisation parameters. Such a method requires multiple (Bayesian network) queries over models sharing the same structural equ

P. Miranda Afonso, D. Rizopoulos, A. K. Palipana, G. C. Zhou

The joint modeling of longitudinal and time-to-event outcomes has become a popular tool in follow-up studies. However, fitting Bayesian joint models to large datasets, such as patient registries, can require extended computing times. To speed up sampling, we divided a patient registry dataset into subsamples, analyzed them in parallel, and combined the resul

Tianqi Ma, Chao Sun, Paul Miller

Recent extreme tropical cyclones caused extensive damages to critical civil infrastructure globally. To better capture the unique hurricane wind characteristics, a large eddy simulation (LES) Hurricane Boundary Layer (HBL) model is developed by considering the variation of meso-scale kinematic and thermodynamic conditions. An asymmetric model is adopted to o

Armin Ettenhofer, Jan-Philipp Schulze, Karla Pizzi

Audio adversarial examples are audio files that have been manipulated to fool an automatic speech recognition (ASR) system, while still sounding benign to a human listener. Most methods to generate such samples are based on a two-step algorithm: first, a viable adversarial audio file is produced, then, this is fine-tuned with respect to perceptibility and ro

Gabriel Lazrak, Börge Göbel, Agnès Barthélémy, Ingrid Mertig

Strontium titanate (SrTiO$_3$) two-dimensional electron gases (2DEGs) have broken spatial inversion symmetry and possess a finite Rashba spin-orbit coupling. This enables the interconversion of charge and spin currents through the direct and inverse Edelstein effects, with record efficiencies at low temperature, but more modest effects at room temperature. H

Yuanqiu Mo, Wenwu Yu, Huazhou Hou, Soura Dasgupta

This paper considers small gain theorems for the global asymptotic and exponential input-to-state stability for discrete time time-delay systems using Razumikhin-type Lyapunov function. Among other things, unlike the existing literature, it provides both necessary and sufficient conditions for exponential input-to-state stability in terms of the Razumikhin-t

Amruta Parulekar, Utkarsh Kanwat, Ravi Kant Gupta, Medha Chippa

Segmentation and classification of cell nuclei in histopathology images using deep neural networks (DNNs) can save pathologists' time for diagnosing various diseases, including cancers, by automating cell counting and morphometric assessments. It is now well-known that the accuracy of DNNs increases with the sizes of annotated datasets available for training

Maciej Lisicki, Marcos F. Velho Rodrigues, Eric Lauga

Eukaryotic swimming cells such as spermatozoa, algae or protozoa use flagella or cilia to move in viscous fluids. The motion of their flexible appendages in the surrounding fluid induces propulsive forces that balance with the viscous drag on the cells and lead to a directed swimming motion. Here, we use our recently built database of cell motility (BOSO-Mic

Xuan Lin

Hybrid model predictive control (MPC) with both continuous and discrete variables is widely applicable to robotic control tasks, especially those involving contact with the environment. Due to the combinatorial complexity, the solving speed of hybrid MPC can be insufficient for real-time applications. In this paper, we proposed a hybrid MPC solver based on G

Arvind Ayyer, Samarth Misra

We study an interacting particle process on a finite ring with $L$ sites with at most $K$ particles per site, in which particles hop to nearest neighbors with rates given in terms of $t$-deformed integers and asymmetry parameter $q$, where $t>0$ and $q \geq 0$ are parameters. This model, which we call the $(q, t)$~$K$-ASEP, reduces to the usual ASEP on the r

Woojun Kim, Jeonghye Kim, Youngchul Sung

In this paper, a unified framework for exploration in reinforcement learning (RL) is proposed based on an option-critic model. The proposed framework learns to integrate a set of diverse exploration strategies so that the agent can adaptively select the most effective exploration strategy over time to realize a relevant exploration-exploitation trade-off for

Gabriel Wlazlowski, Michael McNeil Forbes, Saptarshi Rajan Sarkar, Andreas Marek

Ultracold atoms provide a platform for analog quantum computer capable of simulating the quantum turbulence that underlies puzzling phenomena like pulsar glitches in rapidly spinning neutron stars. Unlike other platforms like liquid helium, ultracold atoms have a viable theoretical framework for dynamics, but simulations push the edge of current classical co

Ashish Gupta, Sugata Mandal

Let $L/K$ be a cyclic extension of degree $n = 2m$. It is known that the space $\text{Alt}_K(L)$ of alternating $K$-bilinear forms (skew-forms) on $L$ decomposes into a direct sum of $K$-subspaces $A^{\sigma^i}$ indexed by the elements of $\text{Gal}(L/K) = \langle \sigma \rangle$. It is also known that the components $A^{\sigma^i}$ can have nice constant-ra

Julius Trebbien, Sebastian Pütz, Benjamin Schäfer, Heidi S. Nygård

Accurate forecasts of electricity prices are crucial for the management of electric power systems and the development of smart applications. European electricity prices have risen substantially and became highly volatile after the Russian invasion of Ukraine, challenging established forecasting methods. Here, we present a Long Short-Term Memory (LSTM) model

Sedighe Sajadian, Hossein Fatheddin

Wavelets are waveform functions that describe transient and unstable variations, such as noises. In this work, we study the advantages of discrete and continuous wavelet transforms (DWT and CWT) of microlensing data to denoise them and extract their planetary signals and intrinsic pulsations hidden by noises. We first generate synthetic microlensing data and

Shuai Yang, Yukang Chen, Luozhou Wang, Shu Liu

Denoising Diffusion Probabilistic Models (DDPMs) have garnered popularity for data generation across various domains. However, a significant bottleneck is the necessity for whole-network computation during every step of the generative process, leading to high computational overheads. This paper presents a novel framework, Denoising Diffusion Step-aware Model

Arpita Mondal, Amruta Mishra

The in-medium properties of open strange ($K$, $\bar{K}$), open charm ($D$, $\bar{D}$), and open bottom ($B$, $\bar{B}$) mesons are investigated in asymmetric nuclear matter using Quark Meson Coupling (QMC) model. A direct coupling of scalar ($\sigma$, $\delta$) and vector ($\omega$, $\rho$) mesons to the light quarks and anti-quarks of these mesons give ris

Yanshuo Wang, Jie Hong, Ali Cheraghian, Shafin Rahman

The objective of Continual Test-time Domain Adaptation (CTDA) is to gradually adapt a pre-trained model to a sequence of target domains without accessing the source data. This paper proposes a Dynamic Sample Selection (DSS) method for CTDA. DSS consists of dynamic thresholding, positive learning, and negative learning processes. Traditionally, models learn f

Khushnaseeb Roshan, Aasim Zafar, Sheikh Burhan Ul Haque

Network Intrusion Detection System (NIDS) is a key component in securing the computer network from various cyber security threats and network attacks. However, consider an unfortunate situation where the NIDS is itself attacked and vulnerable more specifically, we can say, How to defend the defender?. In Adversarial Machine Learning (AML), the malicious acto

Jia Syuen Lim, Ziwei Wang, Jiajun Liu, Abdelwahed Khamis

Regulatory compliance auditing across diverse industrial domains requires heightened quality assurance and traceability. Present manual and intermittent approaches to such auditing yield significant challenges, potentially leading to oversights in the monitoring process. To address these issues, we introduce a real-time, multi-modal sensing system employing

Jingyuan Sun, Xiaohan Zhang, Marie-Francine Moens

To understand the algorithm that supports the human brain's language representation, previous research has attempted to predict neural responses to linguistic stimuli using embeddings generated by artificial neural networks (ANNs), a process known as neural encoding. However, most of these studies have focused on probing neural representations of Germanic la

Shota Fujiwara, Yosuke Imamura, Tatsuya Mori, Shuichi Murayama

We study giant graviton expansions of the superconformal index of 4d orbifold/orientifold theories. In general, a giant graviton expansion is given as a multiple sum over wrapping numbers. It has been known that the expansion can be reduced to a simple sum for the ${\cal N}=4$ $U(N)$ SYM by choosing appropriate expansion variables. We find such a reduction o

Timothy Chu, Zhao Song, Chiwun Yang

In-context learning (ICL) is an astonishing emergent ability of large language models (LLMs). By presenting a prompt that includes multiple input-output pairs as examples and introducing a new query input, models can generate the corresponding output. However, the performance of models heavily relies on the quality of the input prompt when implementing in-co

David Stenger, Tim Reuscher, Heike Vallery, Dirk Abel

Climate-controlled cabins have for decades been standard in vehicles. Model Predictive Controllers (MPCs) have shown promising results in achieving temperature tracking in vehicle cabins and may improve upon model-free control performance. However, for the multi-zone climate control case, proper controller tuning is challenging, as externally, e.g., passenge

Prasanta Chatterjee, Uday Narayan Ghosh, Snehalata Nasipuri, M. Ruhul Amin

In this paper, we use a very prominent technique, Hirota Bilinear Method (HBM) to survey the lump structures of the Kadomtsev-Petviashvili (KP) equation in the frame of a collisionless magnetized plasma system composed of dust grains, ions, and nonextensive electrons. Nonlinearity has worldwide applications, and soliton theory is a powerful appliance to illu

Hossein B. Jond

A social network population engages in collective actions as a direct result of forming a particular opinion. The strategic interactions among the individuals acting independently and selfishly naturally portray a noncooperative game. Nash equilibrium allows for self-enforcing strategic interactions between selfish and self-interested individuals. This paper

Kun Li, Yong Luo, Xiantao Cai, Wenbin Hu

Conventional deep learning methods typically employ supervised learning for drug response prediction (DRP). This entails dependence on labeled response data from drugs for model training. However, practical applications in the preclinical drug screening phase demand that DRP models predict responses for novel compounds, often with unknown drug responses. Thi

Zhen wan, Yating Zhang, Yexiang Wang, Fei Cheng

While large language models (LLMs) like GPT-4 have recently demonstrated astonishing zero-shot capabilities in general domain tasks, they often generate content with hallucinations in specific domains such as Chinese law, hindering their application in these areas. This is typically due to the absence of training data that encompasses such a specific domain,

Jeremy L. Smallwood, Rebecca Nealon, Nicolás Cuello, Ruobing Dong

Observations reveal protoplanetary discs being perturbed by flyby candidates. We simulate a scenario where an unbound perturber, i.e., a flyby, undergoes an inclined grazing encounter, capturing material and forming a second-generation protoplanetary disc. We run $N$--body and three-dimensional hydrodynamical simulations of a parabolic flyby grazing a partic