November 2024 arXiv papers — page 65

Stéphane Mathis

Internal gravity waves (hereafter IGWs) are one of the mechanisms that can play a key role to redistribute efficiently angular momentum in stars along their evolution. The study of IGWs is thus of major importance since space-based asteroseismology reveals a transport of angular momentum in stars, which is stronger by two orders of magnitude than the one pre

Shuai Li, Chaoyi Chen, Haotian Zheng, Jiawei Wang

Controlling mixed platoons, which consist of both connected and automated vehicles (CAVs) and human-driven vehicles (HDVs), poses significant challenges due to the uncertain and unknown human driving behaviors. Data-driven control methods offer promising solutions by leveraging available trajectory data, but their performance can be compromised by noise and

Zhaofeng Lin, Yanqi Qiu, Mingjie Tan

In this paper, we establish the exact Fourier dimensions of all standard sub-critical Gaussian multiplicative chaos on the unit interval, thereby confirming the Garban-Vargas conjecture. The proof relies on a significant improvement of the vector-valued martingale method, initially developed by Chen-Han-Qiu-Wang in the studies of the Fourier dimensions of Ma

Bhupender Singh, Ananth Ram Rajagopalan, Srikrishna Bhashyam

In this paper, we consider nonparametric clustering of $M$ independent and identically distributed (i.i.d.) data sequences generated from {\em unknown} distributions. The distributions of the $M$ data sequences belong to $K$ underlying distribution clusters. Existing results on exponentially consistent nonparametric clustering algorithms, like single linkage

Alessandro Brusaferri, Danial Ramin, Andrea Ballarino

Forecasters using flexible neural networks (NN) in multi-horizon distributional regression setups often struggle to gain detailed insights into the underlying mechanisms that lead to the predicted feature-conditioned distribution parameters. In this work, we deploy a Neural Basis Model for Location, Scale and Shape, that blends the principled interpretabilit

Xue Yang, Rigui Zhou, ShiZheng Jia, YaoChong Li

Leveraging quantum computing's intrinsic properties to enhance machine learning has shown promise, with quantum generative adversarial networks (QGANs) demonstrating benefits in data generation. However, the application of QGANs to complex unsupervised image-to-image (I2I) translation remains unexplored. Moreover, classical neural networks often suffer from

Alessandro Costa, Emilio Mastriani, Federico Incardona, Kevin Munari

This study introduces a predictive maintenance strategy for high pressure industrial compressors using sensor data and features derived from unsupervised clustering integrated into classification models. The goal is to enhance model accuracy and efficiency in detecting compressor failures. After data pre processing, sensitive clustering parameters were tuned

Minghao Fu, Hao Yu, Jie Shao, Junjie Zhou

Deep neural networks, while achieving remarkable success across diverse tasks, demand significant resources, including computation, GPU memory, bandwidth, storage, and energy. Network quantization, as a standard compression and acceleration technique, reduces storage costs and enables potential inference acceleration by discretizing network weights and activ

Xiaomin Yu, Feiyang Wang, Ziyue Qiao

In affective computing, the task of Emotion Recognition in Conversations (ERC) has emerged as a focal area of research. The primary objective of this task is to predict emotional states within conversations by analyzing multimodal data including text, audio, and video. While existing studies have progressed in extracting and fusing representations from multi

Ayato Kanada, Ryo Takahashi, Keito Hayashi, Ryusuke Hosaka

Bio-inspired multi-joint snake robots offer the advantages of terrain adaptability due to their limbless structure and high flexibility. However, a series of dozens of motor units in typical multiple-joint snake robots results in a heavy body structure and hundreds of watts of high power consumption. This paper presents a joint-repositionable, inner-wireless

S. Ahyoune, K. Altenmueller, I. Antolin, S. Basso

BabyIAXO is the intermediate stage of the International Axion Observatory (IAXO) to be hosted at DESY. Its primary goal is the detection of solar axions following the axion helioscope technique. Axions are converted into photons in a large magnet that is pointing to the sun. The resulting X-rays are focused by appropriate X-ray optics and detected by sensiti

Zhaoyi Li, Wenjie Mei, Ke Yu, Yang Bai

Learning models of dynamical systems with external inputs, which may be, for example, nonsmooth or piecewise, is crucial for studying complex phenomena and predicting future state evolution, which is essential for applications such as safety guarantees and decision-making. In this work, we introduce \emph{Input Concomitant Neural ODEs (ICODEs)}, which incorp

Meihui Zhang, Yaxue Liu, Mengmeng Liu, Wenlin Qiu

This work generalizes the subdiffusive Black-Scholes model by introducing the variable exponent in order to provide adequate descriptions for the option pricing, where the variable exponent may account for the variation of the memory property. In addition to standard nonlinear-to-linear transformation, we apply a further spatial-temporal transformation to co

Haiqing Cheng, Kui Wang

We demonstrate that $n$-dimension closed Einstein manifolds, whose smallest eigenvalue of the curvature operator of the second kind of $\mathring{R}$ satisfies $\lambda_1 \ge -\theta(n) \bar\lambda$, are either flat or round spheres, where $\bar \lambda$ is the average of the eigenvalues of $\mathring{R}$, and $\theta(n)$ is defined as in equation (1.2). Our

Dai Imaike

We study the boundary behavior of the invariant of $K3^{[2]}$-type manifolds with antisymplectic involution, which we obtained using equivariant analytic torsion. We show the algebraicity of the singularity of the invariant by using the asymptotic of equivariant Quillen metrics and equivariant $L^2$-metrics. We prove that, in some cases, the invariant coinci

Zhi Wang

The Generalized Uncertainty Principle (GUP) extends the Heisenberg Uncertainty Principle (HUP) by suggesting a minimum observable scale that includes the effects of quantum gravity, which is supposed to potentially result in observable effects far below the Planck energy scale, providing us the opportunity to explore the theory of quantum gravity through phy

Honglin Li, Yuting Gao, Chenglu Zhu, Jingdong Chen

Multimodal large language models (MLLMs) are closing the gap to human visual perception capability rapidly, while, still lag behind on attending to subtle images details or locating small objects precisely, etc. Common schemes to tackle these issues include deploying multiple vision encoders or operating on original high-resolution images. Few studies have c

Zihao Wang, Jian Cheng, Liang Xu, Lizhu Hao

A hybrid physics-machine learning modeling framework is proposed for the surface vehicles' maneuvering motions to address the modeling capability and stability in the presence of environmental disturbances. From a deep learning perspective, the framework is based on a variant version of residual networks with additional feature extraction. Initially, an impe

Yunrui Sun, Gang Hu, Yinglei Teng, Dunbo Cai

Split Learning (SL) is a promising collaborative machine learning approach, enabling resource-constrained devices to train models without sharing raw data, while reducing computational load and preserving privacy simultaneously. However, current SL algorithms face limitations in training efficiency and suffer from prolonged latency, particularly in sequentia

F. K. J. Niggl

Evolutionary partial differential equations play a crucial role in many areas of science and engineering. Spatial discretization of these equations leads to a system of ordinary differential equations which can then be solved by numerical time integration. Such a system is often of very high dimension, making the simulation very time consuming. One way to re

Zi-Yu Xiong, Yong-Jun Xiao, Ye-Qi Zhang, Qi-Liang He

We investigate the effect of counter-rotating-wave terms on nonlocality and entanglement for three qubits coupled with a common bath for strong and ultrastrong coupling regimes beyond the traditional treatment of Born-Markovian, perturbative and rotating wave approximations by employing the numerical hierarchical equations of motion approach. Our findings ar

Song Jiang, Da JU, Andrew Cohen, Sasha Mitts

How are LLM-based agents used in the future? While many of the existing work on agents has focused on improving the performance of a specific family of objective and challenging tasks, in this work, we take a different perspective by thinking about full delegation: agents take over humans' routine decision-making processes and are trusted by humans to find s

Shreya Srivastava

The urgent need to promptly detect cardiac disorders from 12-lead Electrocardiograms using limited computations is motivated by the heart's fast and complex electrical activity and restricted computational power of portable devices. Timely and precise diagnoses are crucial since delays might significantly impact patient health outcomes. This research present

Zhijie Bao, Qingyun Liu, Ying Guo, Zhengqiang Ye

In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting their time and attention for each patient and ultimately reducing service quality. In this paper, we present the Personalized Intelligent Outpatient Reception System (PIORS). This system integrates an LLM-based reception nurse and a collaboration between LLM and hospit

Gayatri Deshmukh, Somsubhra De, Chirag Sehgal, Jishu Sen Gupta

Specialized datasets that capture the fashion industry's rich language and styling elements can boost progress in AI-driven fashion design. We present FLORA, (Fashion Language Outfit Representation for Apparel Generation), the first comprehensive dataset containing 4,330 curated pairs of fashion outfits and corresponding textual descriptions. Each descriptio

Jiajie Chen, Peng Qu, Youhui Zhang

Branch predictor (BP) is a critical component of modern processors, and its accurate modeling is essential for compilers and applications. However, processor vendors have disclosed limited details about their BP implementations. Recent advancements in reverse engineering the BP of general-purpose processors have enabled the creation of more accurate BP model

Ryoga Matsuo, Stefan Uhlich, Arun Venkitaraman, Andrea Bonetti

Machine learning models are advancing circuit design, particularly in analog circuits. They typically generate netlists that lack human interpretability. This is a problem as human designers heavily rely on the interpretability of circuit diagrams or schematics to intuitively understand, troubleshoot, and develop designs. Hence, to integrate domain knowledge

D. F. Liu, Y. F. Xu, H. Y. Hu, J. Y. Liu

The symbiosis of strong interactions, flat bands, topology and symmetry has led to the discovery of exotic phases of matter, including fractional Chern insulators, correlated moir\'e topological superconductors, and Dirac and Weyl semimetals. Correlated metals, such as those present in Kondo lattices, rely on the screening of local moments by a sea of non-ma

Carlos A. Benavides-Gallego, Swarnim Shashank, Haiguang Xu

According to the celebrated singularity theorems, space-time singularities in general relativity are inevitable. However, it is generally believed that singularities do not exist in nature, and their existence suggests the necessity of a new theory of gravity. In this paper, we investigated a regular astrophysically viable space-time (regular in the sense th

Tingyu Fan, Yueyu Hu, Ran Gong, Yao Wang

Point cloud video (PCV) is a versatile 3D representation of dynamic scenes with emerging applications. This paper introduces U-Motion, a learning-based compression scheme for both PCV geometry and attributes. We propose a U-Structured inter-frame prediction framework, U-Inter, which performs explicit motion estimation and compensation (ME/MC) at different sc

Qi S. Zhang

A forced solution $v$ of the Navier-Stokes equation in any open domain with no slip boundary condition is constructed. The scaling factor of the forcing term is the critical order $-2$. The velocity, which is smooth until its final blow up moment, is in the energy space through out. Since most physical forces from a point source in nature are regarded as ord

Gourab Pathak, Pritam Das, Mrinal Kumar Das

We propose a hybrid scotogenic inverse seesaw framework in which the Majorana mass term is generated at the one-loop level through the inclusion of a singlet fermion. This singlet Majorana fermion also serves as a viable thermal relic dark matter candidate due to its limited interactions with other fields. To construct the model, we adopt an $A_4$ flavour sy

Cong Wang, Jiabao Su

In this paper, we focus on (no)existence and asymptotic behavior of solutions for the double critical Maxwell equation involving with the Hardy, Hardy-Sobolev, Sobolev critical exponents. The existence and noexistence of solutions completely depend on the power exponents and coefficients of equation. On one hand, based on the concentration-compactness ideas,

Xinwei Cai, Kuiliang Wang, Gaojin Li, Xin Bian

Microswimmers play an important role in shaping the world around us. The squirmer is a simple model for microswimmer whose cilia oscillations on its spherical surface induce an effective slip velocity to propel itself. The rapid development of computational fluid dynamics methods has markedly enhanced our capacity to study the behavior of squirmers in aqueou

Yanbiao Ji, Yue Ding, Dan Luo, Chang Liu

Graph-based recommender systems have achieved remarkable effectiveness by modeling high-order interactions between users and items. However, such approaches are significantly undermined by popularity bias, which distorts the interaction graph's structure, referred to as topology bias. This leads to overrepresentation of popular items, thereby reinforcing bia

Lei Wang, Huanyuan Shan, Lin Nie, Cheng Cheng

We present the application of the image coaddition algorithm, Up-sampling and PSF Deconvolution Coaddition (UPDC), for stacking multiple exposure images captured by the James Webb Space Telescope (JWST) Near-Infrared Camera (NIRCam). By addressing the point spread function (PSF) effect, UPDC provides visually enhanced and sharper images. Furthermore, the ant

Stefan Uhlich, Andrea Bonetti, Arun Venkitaraman, Ali Momeni

Designing integrated circuits involves substantial complexity, posing challenges in revealing its potential applications - from custom digital cells to analog circuits. Despite extensive research over the past decades in building versatile and automated frameworks, there remains open room to explore more computationally efficient AI-based solutions. This pap

E. Barberio, T. Baroncelli, V. U. Bashu, L. J. Bignell

In this technical design report (TDR) executive summary we describe the SABRE South detector to be built at the Stawell Underground Physics Laboratory (SUPL). The SABRE South detector is designed to test the long-standing DAMA/LIBRA signal of an annually modulating rate consistent with dark matter by using the same target material. Located in the Southern He

Xiaorui Qi, Yanlong Wen, Xiaojie Yuan

Graph generation is one of the most challenging tasks in recent years, and its core is to learn the ground truth distribution hiding in the training data. However, training data may not be available due to security concerns or unaffordable costs, which severely blows the learning models, especially the deep generative models. The dilemma leads us to rethink

JunJie Wee, Xue Gong, Wilderich Tuschmann, Kelin Xia

We introduce, for the first time, a cohomology-based Gromov-Hausdorff ultrametric method to analyze 1-dimensional and higher-dimensional (co)homology groups, focusing on loops, voids, and higher-dimensional cavity structures in simplicial complexes, to address typical clustering questions arising in molecular data analysis. The Gromov-Hausdorff distance quan

Md Mahedi Hasan, Shoaib Meraj Sami, Nasser Nasrabadi

An important aspect of deploying face recognition (FR) algorithms in real-world applications is their ability to learn new face identities from a continuous data stream. However, the online training of existing deep neural network-based FR algorithms, which are pre-trained offline on large-scale stationary datasets, encounter two major challenges: (I) catast

Tongzhou Jiang, Lipeng Liu, Junyue Jiang, Tianyao Zheng

This paper studies the application of the DDPG algorithm in trajectory-tracking tasks and proposes a trajectorytracking control method combined with Frenet coordinate system. By converting the vehicle's position and velocity information from the Cartesian coordinate system to Frenet coordinate system, this method can more accurately describe the vehicle's de

Evelyn Hubbard, Liam Cregg, Serdar Yüksel

We study the problem of joint optimization involving coding and control policies for a controlled Markovian sytem over a finite-rate noiseless communication channel. While structural results on the optimal encoding and control have been obtained in the literature, their implementation has been prohibitive in general, except for linear models. We develop regu

Prabhat Lankireddy, Jayakrishnan Nair, D Manjunath

We analyze the unintended effects that recommender systems have on the preferences of users that they are learning. We consider a contextual multi-armed bandit recommendation algorithm that learns optimal product recommendations based on user and product attributes. It is well known that the sequence of recommendations affects user preferences. However, typi

Wee Han Lim, Tuomo Tanttu, Tony Youn, Jonathan Yue Huang

Recent advances in semiconductor spin qubits have achieved linear arrays exceeding ten qubits. Moving to two-dimensional (2D) qubit arrays is a critical next step to advance towards fault-tolerant implementations, but it poses substantial fabrication challenges, particularly because enabling control of nearest-neighbor entanglement requires the incorporation

Dazhi Huang, Pengcheng Xu, Xiaocheng Huang, Jiayi Chen

Topological Data Analysis (TDA) has recently gained significant attention in the field of financial prediction. However, the choice of point cloud construction methods, topological feature representations, and classification models has a substantial impact on prediction results. This paper addresses the classification problem of stock index movement. First,

Ferit Gurbuz

In harmonic analysis, studies of inequalities of Riesz potential in various function spaces have a very important place. Variable exponent Morrey type spaces and the examines of the boundedness of such operators on these spaces have an important place in harmonic analysis and have become an interesting field. In this work, we obtain the boundedness of Riesz

Mansi Bhati, Zhen Tao, Xuezhi Bian, Jonathan Rawlinson

In a companion paper, we have developed a phase-space electronic structure theory of molecules in magnetic fields, whereby the electronic energy levels arise from diagonalizing a phase-space Hamiltonian $\hat H_{PS}(\bf{X},\bf{\Pi})$ that depends parametrically on nuclear position and momentum. The resulting eigenvalues are translationally invariant; moreove

Cheng-Yu Pai, Zilong Liu, Chao-Yu Chen

This paper presents a novel training matrix design for spatial modulation (SM) systems, by introducing a new class of two-dimensional (2D) arrays called sparse zero correlation zone (SZCZ) arrays. An SZCZ array is characterized by a majority of zero entries and exhibits the zero periodic auto- and cross-correlation zone properties across any two rows. With t

Tetsu Toyoda

We establish a family of inequalities that hold true on any $6$ points in any $\mathrm{CAT}(0)$ space. We prove that the validity of these inequalities does not follow from any properties of $5$-point subsets of $\mathrm{CAT}(0)$ spaces. In particular, the validity of these inequalities does not follow from the $\mathrm{CAT}(0)$ $4$-point condition.

Guangwen Li, Xiao Yu

Effective iterative decoding of short BCH codes faces two primary challenges: identifying an appropriate parity-check matrix and accelerating decoder convergence. To address these issues, we propose a systematic scheme to derive an optimized parity-check matrix through a heuristic approach. This involves a series of binary sum and row shift operations, resul

Alexander Drewitz, Alejandro F. Ramírez, Santiago Saglietti, Zhicheng Zheng

We consider a random walk in an i.i.d. random environment on Zd and study properties of its large deviation rate function at the origin. It was proved by Comets, Gantert and Zeitouni in dimension d = 1 in 1999 and later by Varadhan in dimensions d >= 2 in 2003 that, for uniformly elliptic i.i.d. random environments, the quenched and the averaged large deviat

Khalifa Afane, Wenqi Wei, Ying Mao, Junaid Farooq

The escalating threat of phishing emails has become increasingly sophisticated with the rise of Large Language Models (LLMs). As attackers exploit LLMs to craft more convincing and evasive phishing emails, it is crucial to assess the resilience of current phishing defenses. In this study we conduct a comprehensive evaluation of traditional phishing detectors

Jiabin Luo, Hanzhe Pan

In response to the persistent safety challenges within coal mines, this study proposes a novel approach integrating a three-layer feedforward backpropagation artificial neural network with a genetic algorithm (GA-BP) for establishing a safety early warning system. Focused on a coal mine in Shandong, China, the model's effectiveness is evaluated using relevan

Delin An, Pengfei Gu, Milan Sonka, Chaoli Wang

Deep learning (DL) methods have shown remarkable successes in medical image segmentation, often using large amounts of annotated data for model training. However, acquiring a large number of diverse labeled 3D medical image datasets is highly difficult and expensive. Recently, mask propagation DL methods were developed to reduce the annotation burden on 3D m

Juan B. Roldan, David Maldonado, C. Aguilera-Pedregosa, F. J. Alonso

Variability in memristive devices based on h-BN dielectrics is studied in depth. Different numerical techniques to extract the reset voltage are described and the corresponding cycle-to-cycle variability is characterized by means of the coefficient of variance. The charge-flux domain was employed to develop one of the extraction techniques, the calculation o

Z. Yousaf, Bander Almutairi, S. Khan, Kazuharu Bamba

We investigate the impact of fuzzy dark matter (FDM) on supermassive black holes (SMBHs) characterized by a spherical charge distribution. This work introduces a new class of spherically symmetric, self-gravitational relativistic charged models for FDM haloes, using the Einasto density model. This study enables the dark matter (DM) to appear as the matter in

A. K. Maity, L. K. Dewangan, N. K. Bhadari, Y. Fukui

Hub-filament systems (HFSs) are potential sites of massive star formation (MSF). To understand the role of filaments in MSF and the origin of HFSs, we conducted a multi-scale and multi-wavelength observational investigation of the molecular cloud G321.93-0.01. The $^{13}$CO($J$ = 2-1) data reveal multiple HFSs, namely, HFS-1, HFS-2, and a candidate HFS (C-HF

Tomoya Matsuoka, Makoto Ohsaki, Kazuki Hayashi

This study proposes a methodology to utilize machine learning (ML) for topology optimization of periodic lattice structures. In particular, we investigate data representation of lattice structures used as input data for ML models to improve the performance of the models, focusing on the filtering process and feature selection. We use the filtering technique

Xiang Li, Feng Ruan, Huiyuan Wang, Qi Long

Watermarking has offered an effective approach to distinguishing text generated by large language models (LLMs) from human-written text. However, the pervasive presence of human edits on LLM-generated text dilutes watermark signals, thereby significantly degrading detection performance of existing methods. In this paper, by modeling human edits through mixtu

Hailong Yang, Zhaohong Deng, Wei Zhang, Zhuangzhuang Zhao

Generative Models (GMs), particularly Large Language Models (LLMs), have garnered significant attention in machine learning and artificial intelligence for their ability to generate new data by learning the statistical properties of training data and creating data that resemble the original. This capability offers a wide range of applications across various

Mansi Bhati, Zhen Tao, Xuezhi Bian, Jonathan Rawlinson

We develop a phase-space electronic structure theory of molecules in magnetic fields. For a system of electrons in a magnetic field with vector potential $\bf{A}(\hat{\bf{r}})$, the usual Born-Oppenheimer Hamiltonian is the sum of the nuclear kinetic energy and the electronic Hamiltonian, $\frac{(\bf{P} - q\bf{A}(\bf{X}) )^2}{2M} + \hat{H}_{e}(\bf{X})$ (wher

Yang Zhang, Mark D. Gould, Artem Pulemotov, Jorgen Rasmussen

This paper initiates the study of the Einstein equation on homogeneous supermanifolds. First, we produce explicit curvature formulas for graded Riemannian metrics on these spaces. Next, we present a construction of homogeneous supermanifolds by means of Dynkin diagrams, resembling the construction of generalised flag manifolds in classical (non-super) theory

Arjun Krishnan U M, Raul Puente, M. A. H. B. Md Yusoff, Herman Batelaan

A dip in coincidence peaks for an electron beam is an experimental signature to detect Coulomb repulsion and Pauli pressure. This paper discusses another effect that can produce a similar signature but that does not originate from the properties of the physical system under scrutiny. Instead, the detectors and electronics used to measure those coincidences s

Luyuan Peng, Mandar Chitre, Hari Vishnu, Yuen Min Too

Real-time visual feedback is essential for tetherless control of remotely operated vehicles, particularly during inspection and manipulation tasks. Though acoustic communication is the preferred choice for medium-range communication underwater, its limited bandwidth renders it impractical to transmit images or videos in real-time. To address this, we propose

Jaeyoung Song, Jun-Pyo Hong

In this paper, we consider asynchronous federated learning (FL) over time-division multiple access (TDMA)-based communication networks. Considering TDMA for transmitting local updates can introduce significant delays to conventional synchronous FL, where all devices start local training from a common global model. In the proposed asynchronous FL approach, we

Xiaoge Zhang, Zijie Wu, Mehwish Nasim, Mingtao Feng

Lossy compression relies on an autoencoder to transform a point cloud into latent points for storage, leaving the inherent redundancy of latent representations unexplored. To reduce redundancy in latent points, we propose a diffusion-based framework guided by sparse priors that achieves high reconstruction quality, especially at low bitrates. Our approach fe

Dexian Ma, Bo Zhou

Automating complex industrial robots requires precise nonlinear control and efficient energy management. This paper introduces a data-driven nonlinear model predictive control (NMPC) framework to optimize control under multiple objectives. To enhance the prediction accuracy of the dynamic model, we design a single-shot multi-step prediction (SSMP) model base

Jinpeng An, Aaron Brown, Zhiyuan Zhang

We prove many new cases of Zimmer's conjecture for actions by lattices in non-$\mathbb{R}$-split semisimple Lie groups $G$. By prior arguments, Zimmer's conjecture reduces to studying certain probability measures invariant under a minimal parabolic subgroup for the induced $G$-action. Two techniques are introduced to give lower bounds on the dimension of a m

A. V. Ivanov

The paper studies a regularization of the quantum (effective) action for a scalar field theory in a general position on a compact smooth Riemannian manifold. As the main method, we propose the use of a special averaging operator, which leads to a quasi-locality and is a natural generalization of a cutoff regularization in the coordinate representation in the

Dexian Ma, Yirong Liu, Wenbo Liu, Bo Zhou

This work proposes a data-driven modeling and the corresponding hybrid motion control framework for unmanned and automated operation of industrial heavy-load hydraulic manipulator. Rather than the direct use of a neural network black box, we construct a reversible nonlinear model by using multilayer perceptron to approximate dynamics in the physical integrat

Allen Yang, Edward Yang

According to PBS, nearly one-third of Americans lack access to primary care services, and another forty percent delay going to avoid medical costs. As a result, many diseases are left undiagnosed and untreated, even if the disease shows many physical symptoms on the skin. With the rise of AI, self-diagnosis and improved disease recognition have become more p

Abdur Razzak, Atanu Barai, Nandakishore Santhi, Abdel-Hameed A. Badawy

Reuse distance analysis is a widely recognized method for application characterization that illustrates cache locality. Although there are various techniques to calculate the reuse profile from dynamic memory traces, it is both time and space-consuming due to the requirement to collect dynamic memory traces at runtime. In contrast, static analysis reuse prof

Zhen Wei Wu, Jean-Louis Barrat, Walter Kob

Recent studies point out far-reaching connections between the topological characteristics of structural glasses and their material properties, paralleling results in quantum physics that highlight the relevance of the nature of the wavefunction. However, the structural arrangement of the topological defects in glasses has so far remained elusive. Here we inv

Maorong Wang, Nicolas Michel, Jiafeng Mao, Toshihiko Yamasaki

Image generation has shown remarkable results in generating high-fidelity realistic images, in particular with the advancement of diffusion-based models. However, the prevalence of AI-generated images may have side effects for the machine learning community that are not clearly identified. Meanwhile, the success of deep learning in computer vision is driven

Siyou Pei, Alexander Chen, Ronak Kaoshik, Ruofei Du

Embodied interaction has been introduced to human-robot interaction (HRI) as a type of teleoperation, in which users control robot arms with bodily action via handheld controllers or haptic gloves. Embodied teleoperation has made robot control intuitive to non-technical users, but differences between humans' and robots' capabilities \eg ranges of motion and

Haibo Li, Xingxing Wu, Liping Liu, Lin-Wang Wang

Neural network force field models such as DeePMD have enabled highly efficient large-scale molecular dynamics simulations with ab initio accuracy. However, building such models heavily depends on the training data obtained by costly electronic structure calculations, thereby it is crucial to carefully select and label the most representative configurations d

Ming Cheng, Yuke Lin, Ming Li

This paper proposes a novel Sequence-to-Sequence Neural Diarization (S2SND) framework to perform online and offline speaker diarization. It is developed from the sequence-to-sequence architecture of our previous target-speaker voice activity detection system and then evolves into a new diarization paradigm by addressing two critical problems. 1) Speaker Dete

Augusto T. Chantada, Pavlos Protopapas, Luca Gomez Bachar, Susana J. Landau

The use of neural networks to solve differential equations, as an alternative to traditional numerical solvers, has increased recently. However, error bounds for the obtained solutions have only been developed for certain equations. In this work, we report important progress in calculating error bounds of physics-informed neural networks (PINNs) solutions of

Ming Zhao, Xin Zhang, André Kaup

Detecting ships in synthetic aperture radar (SAR) images is challenging due to strong speckle noise, complex surroundings, and varying scales. This paper proposes MLDet, a multitask learning framework for SAR ship detection, consisting of object detection, speckle suppression, and target segmentation tasks. An angle classification loss with aspect ratio weig

Nithya Shikarpur, Cheng-Zhi Anna Huang

This paper presents a study of participants interacting with and using GaMaDHaNi, a novel hierarchical generative model for Hindustani vocal contours. To explore possible use cases in human-AI interaction, we conducted a user study with three participants, each engaging with the model through three predefined interaction modes. Although this study was conduc

Xin-Kai Wen, Bin Yan, Zhite Yu, C. -P. Yuan

We propose to probe light-quark dipole interactions at lepton colliders using the azimuthal asymmetry of a collinear dihadron pair $(h_1h_2)$ produced in association with another hadron $h'$. This asymmetry, arising from quantum interference in the quark spin space, is exclusively sensitive to dipole interactions at the leading power of the new physics scale

Qing-Yu Lan, Yun-Song Piao

In this paper, we present a wavefunction of the universe, which correspond to an Euclidean charged wineglass (half)-wormholes semiclassically, as a possible creation for our inflationary universe. We calculate the Euclidean action of the charged wormhole, and find that the initial state of universe brought by such an Euclidean charged wormhole can exhibit a

Makoto Ohsaki, Kentaro Hayakawa, Jingyao Zhang

We propose a two-level structural optimization method for obtaining an approximate optimal shape of piecewise developable surface without specifying internal boundaries between surface patches. The condition for developability of a polyhedral surface onto a plane is formulated using the area of discrete Gauss map formed by unit normal vectors at the faces ad

Kaihong Wang, Lingzhi Zhang, Jianming Zhang

Despite recent advancements, text-to-image generation models often produce images containing artifacts, especially in human figures. These artifacts appear as poorly generated human bodies, including distorted, missing, or extra body parts, leading to visual inconsistencies with typical human anatomy and greatly impairing overall fidelity. In this study, we

Hsuan Lo, You Wang, Rimi Banerjee, Baile Zhang

Interacting or nonlinear lattices can host emergent particle-like modes, such as Bogoliubov quasiparticles, whose band topology and other properties are potentially highly tunable. Despite originating in the study of superconducting materials, Bogoliubov quasiparticles can also occur in synthetic metamaterials. Here, we implement a nonlinear driven-dissipati

Nikolai Goncharov, Donald G. Dansereau

Segmented light field images can serve as a powerful representation in many of computer vision tasks exploiting geometry and appearance of objects, such as object pose tracking. In the light field domain, segmentation presents an additional objective of recognizing the same segment through all the views. Segment Anything Model 2 (SAM 2) allows producing sema

Yuhao Chen, Jialiang Hu, Guanchong Cheng, Jing Ye

New emerging flux (NEF) has long been considered a mechanism for solar eruptions, but detailed process remains an open question. In this work, we explore how NEF drives a coronal magnetic configuration to erupt. This configuration is created by two magnetic sources of strengths $M$ and $S$ embedded in the photosphere, one electric-current-carrying flux rope

Seokil Ham, Hee-Seon Kim, Sangmin Woo, Changick Kim

Despite the growing interest in Mamba architecture as a potential replacement for Transformer architecture, parameter-efficient fine-tuning (PEFT) approaches for Mamba remain largely unexplored. In our study, we introduce two key insights-driven strategies for PEFT in Mamba architecture: (1) While state-space models (SSMs) have been regarded as the cornersto

Rômulo Damasclin Chaves dos Santos

This article presents a comprehensive mathematical framework for the study of regularity, bifurcations, and turbulence in fluid dynamics, leveraging the power of Sobolev and Besov function spaces. We delve into the detailed definitions, properties, and notations of these spaces, illustrating their relevance in the context of partial differential equations go

Umar Sohail Qureshi, Alfredo Gurrola, Andres Flórez

We present a phenomenology study probing pair production of supersymmetric charginos and neutralinos ("electroweakinos") with the vector boson fusion (VBF) topology in proton-proton collisions at CERN's Large Hadron Collider (LHC). In particular, we examine the compressed-mass spectrum phase space that has been traditionally challenging due to experimental c

Lin Sun, Jiale Cao, Jin Xie, Xiaoheng Jiang

Contrastive Language-Image Pre-training (CLIP) exhibits strong zero-shot classification ability on various image-level tasks, leading to the research to adapt CLIP for pixel-level open-vocabulary semantic segmentation without additional training. The key is to improve spatial representation of image-level CLIP, such as replacing self-attention map at last la

Nilanthi P. Haputhanthrige, Mojtaba Rajabi, Oleg D. Lavrentovich

Spatially varying alignment of liquid crystals is essential for research and applications. One widely used method is based on the photopatterning of thin layers of azo-dye molecules, such as Brilliant Yellow (BY), that serve as an aligning substrate for a liquid crystal. In this study, we examine how photopatterning conditions, such as BY layer thickness (b)

Ratnangshu Das, Ahan Basu, Pushpak Jagtap

The paper considers the controller synthesis problem for general MIMO systems with unknown dynamics, aiming to fulfill the temporal reach-avoid-stay task, where the unsafe regions are time-dependent, and the target must be reached within a specified time frame. The primary aim of the paper is to construct the spatiotemporal tube (STT) using a sampling-based

Toru T. Takahashi, Yoshiko Kanada-En'yo

We study the color correlation between static quark and antiquark ($q\bar q$) that is accompanied by gluonic excitations in the confined phase at $T=0$ by constructing reduced density matrices $\rho$ in color space. We perform quenched lattice QCD calculations with the Coulomb gauge adopting the standard Wilson gauge action, and the spatial volume is $L^3 =

Joshua A. Burrow, Rutendo Jakachira, Gannon Lemaster, Kimani C. Toussaint

Significance: Smartphones hold great potential in point-of-care settings due to their accessibility and computational capabilities. This is critical as clinicians increasingly seek to quantify skin-tone, a characteristic which has been shown to impact the accuracy of pulse oximetry readings, particularly for dark skin tones, and hence, disproportionately aff

Qingquan Zhang, Qiqi Duan, Bo Yuan, Yuhui Shi

Large Language Models (LLMs) have made significant strides in the field of artificial intelligence, showcasing their ability to interact with humans and influence human cognition through information dissemination. However, recent studies have brought to light instances of bias inherent within these LLMs, presenting a critical issue that demands attention. In

J. Mumford, H. -Y. Xie, R. J. Lewis-Swan

This work theoretically investigates the transition from topology to chaos in a periodically driven system consisting of a quantum top coupled to a spin-1/2 particle. The system is driven by two alternating interaction kicks per period. For small kick strengths, localized topologically protected bound states exist, and as the kick strengths increase, these s

Yong-Geun Oh

This is the first of a series of papers in preparation on the Fukaya-type $A_\infty$ category generated by tame Legendrian submanifolds, called the Legendrian contact instanton Fukaya category (abbreviated as the Legendrian CI Fukaya category) and its applications to contact dynamics and topology. In the present paper, we give the construction of an $A_\inft

Jiaxin Zhang, S. Ghazaleh Dashti, John B. Carlin, Katherine J. Lee

When using multiple imputation (MI) for missing data, maintaining compatibility between the imputation model and substantive analysis is important for avoiding bias. For example, some causal inference methods incorporate an outcome model with exposure-confounder interactions that must be reflected in the imputation model. Two approaches for compatible imputa