November 2024 arXiv papers — page 135

Dimitar Stanev, Rafał Klimas, Marta Płonka, Natan Napiórkowski

Validating smartphone sensor-based tests to study gait and balance against reference measurement systems in a laboratory setting poses several technical challenges related to data quality and data processing. One challenge is to guarantee the correct annotation of the data, which is required to ensure that only data collected during the same test execution a

Yuzhen Qin, Marcel van Gerven

Epilepsy is a common neurological disorder characterized by abrupt seizures. Although seizures may appear random, they are often preceded by early warning signs in neural signals, notably, critical slowing down, a phenomenon in which the system's recovery rate from perturbations declines when it approaches a critical point. Detecting these markers could enab

Reo Yoneyama, Atsushi Miyashita, Ryuichi Yamamoto, Tomoki Toda

Neural vocoders often struggle with aliasing in latent feature spaces, caused by time-domain nonlinear operations and resampling layers. Aliasing folds high-frequency components into the low-frequency range, making aliased and original frequency components indistinguishable and introducing two practical issues. First, aliasing complicates the waveform genera

Attila Egri-Nagy, Miklós Hoffmann

Morphisms, structure preserving maps, are everywhere in Mathematics as useful tools for thinking and problem solving, or as objects to study. Here, we argue that the idea of operations being compatible across two domains goes beyond its mathematical use: it is a fundamental mechanism of any intelligence. We precisely define morphisms, distinguish between dyn

Yujia Zhou, Zheng Liu, Zhicheng Dou

The emergence of Large Language Models (LLMs) has significantly advanced natural language processing, but these models often generate factually incorrect information, known as "hallucination". Initial retrieval-augmented generation (RAG) methods like the "Retrieve-Read" framework was inadequate for complex reasoning tasks. Subsequent prompt-based RAG strateg

Artem Maevskiy, Alexandra Carvalho, Emil Sataev, Volha Turchyna

Discovering new superionic materials is essential for advancing solid-state batteries, which offer improved energy density and safety compared to the traditional lithium-ion batteries with liquid electrolytes. Conventional computational methods for identifying such materials are resource-intensive and not easily scalable. Recently, universal interatomic pote

Qi-Huan Zeng, Wen-Ping Liao, Sheng-Bang Qian, Lin-Jia Li

Eclipsing Algol-type systems containing a $\delta$ Scuti (hereafter $\delta$ Sct) star enable precise determination of physical parameters and the investigation of stellar internal structure and evolution. We present the absolute parameters of CZ Aquarius (hereafter CZ Aqr) based on TESS data. CZ Aqr has an orbital period of 0.86275209 d, a mass ratio of 0.4

Yuxiu Shao, David Dahmen, Stefano Recanatesi, Eric Shea-Brown

Networks of excitatory and inhibitory (EI) neurons form a canonical circuit in the brain. Seminal theoretical results on dynamics of such networks are based on the assumption that synaptic strengths depend on the type of neurons they connect, but are otherwise statistically independent. Recent synaptic physiology datasets however highlight the prominence of

Jinwei Rao, Yi-Pu Wang, Zhijian Chen, Bimu Yao

Time-varying media break the temporal translation symmetry of wave propagation in materials, enabling advanced wave manipulations. However, this novel phenomenon has been rarely explored in magnonic systems due to the significant challenge of achieving a sudden and prominent change in magnon dispersion within materials. Here, we construct a time-varying stro

Boxue Zheng, Tak Shing Chan

We study the deformation of soft solid layers in the presence of sessile droplets or capillary bridges. By incorporating the surface tension balance at the contact line, we examine the rotation of the wetting ridge and the corresponding change in the contact angle. Our findings reveal that the rotation direction of the wetting ridge aligns with the sign of t

Joanna Komorniczak, Paweł Ksieniewicz, Paweł Zyblewski

The following work addresses the problem of frameworks for data stream processing that can be used to evaluate the solutions in an environment that resembles real-world applications. The definition of structured frameworks stems from a need to reliably evaluate the data stream classification methods, considering the constraints of delayed and limited label a

David R. Nelson, Ashish Kumar Jaiswal, Noha Ismail, Alexandra Mystikou

AI language models (LMs) show promise for biological sequence analysis. We re-engineered open-source LMs (GPT-2, BLOOM, DistilRoBERTa, ELECTRA, and Mamba, ranging from 70M to 12B parameters) for microbial sequence classification. The models achieved F1 scores up to 95 and operated 16,580x faster and at 2.9x the recall of BLASTP. They effectively classified t

R. Alinaghipour, H. Mohammadi

Feasibility study is done for the possibility of universal set of quantum gate implementation based on phononic state via 4th order Duffing nonlinearity in an optomechanical system. The optomechanical system consists of N doubly clamped coupled nanobeam array drived by local static and radio frequency electrical potentials, coupled to a single-mode high fine

Jun Liu, Yuanyuan Xie, Jiwei Yan, Jinhao Huang

With the rising demand for code quality assurance, developers are not only utilizing existing static code checkers but also seeking custom checkers to satisfy their specific needs. Nowadays, various code-checking frameworks provide extensive checker customization interfaces to meet this need. However, both the abstract checking logic and the complex API usag

Oliver Müller, Francine R. Marleau, Nick Heesters, Pierre-Alain Duc

A recent study of the distribution of dwarf galaxies in the MATLAS sample in galaxy groups revealed an excess of flattened satellite structures, reminiscent of the co-rotating planes of dwarf galaxies discovered in the local Universe. If confirmed, this lends credence to the plane-of-satellite problem and further challenges the standard model of hierarchical

Pengfei Zhang, Yu Gao, Xiansong Xu, Ning Wang

Non-equilibrium quantum transport is crucial to technological advances ranging from nanoelectronics to thermal management. In essence, it deals with the coherent transfer of energy and (quasi-)particles through quantum channels between thermodynamic baths. A complete understanding of quantum transport thus requires the ability to simulate and probe macroscop

Lijian Lu, Xin Weng, Li Xiao

The recent advancements in mobile/data technology have fostered a widespread adoption of on-demand or gig service platforms. The increasingly available data and independent contractors have enabled these platforms to design customized services and a cost-efficient workforce to effectively match demand and supply. In practice, a diverse landscape of the workf

Wenxuan Pan, Feifei Zhao, Bing Han, Haibo Tong

By exploiting discrete signal processing and simulating brain neuron communication, Spiking Neural Networks (SNNs) offer a low-energy alternative to Artificial Neural Networks (ANNs). However, existing SNN models, still face high computational costs due to the numerous time steps as well as network depth and scale. The tens of billions of neurons and trillio

Denis Ilin, Alexander V. Poshakinskiy, Alexander S. Solntsev

We present a theoretical framework for investigating the non-classical collective relaxation in arrays of {\Lambda}-type three-level atoms, where the two optical transitions coupled to a pair of orthogonal chiral or achiral waveguide modes. We demonstrate that the atomic entanglement emerges in the course of relaxation and persists in the final steady state

Muhammad Shahrul Zaim bin Ahmad, Kazuhiro Takemoto

The rapid advancement of Large Language Models (LLMs) and their potential integration into autonomous driving systems necessitates understanding their moral decision-making capabilities. While our previous study examined four prominent LLMs using the Moral Machine experimental framework, the dynamic landscape of LLM development demands a more comprehensive a

Yizhuo Yang, Shenghai Yuan, Muqing Cao, Jianfei Yang

In this study, we introduce AV-PedAware, a self-supervised audio-visual fusion system designed to improve dynamic pedestrian awareness for robotics applications. Pedestrian awareness is a critical requirement in many robotics applications. However, traditional approaches that rely on cameras and LIDARs to cover multiple views can be expensive and susceptible

Felix Brändle, Frank Allgöwer

In this paper, we present a new parametrization to perform direct data-driven analysis and controller synthesis for the error-in-variables case. To achieve this, we employ the Sherman-Morrison-Woodbury formula to transform the problem into a linear fractional transformation (LFT) with unknown measurement errors and disturbances as uncertainties. For bounded

Jiawei Fan, Chao Li, Xiaolong Liu, Anbang Yao

In this paper, we question if well pre-trained vision transformer (ViT) models could be used as teachers that exhibit scalable properties to advance cross architecture knowledge distillation (KD) research, in the context of using large-scale datasets for evaluation. To make this possible, our analysis underlines the importance of seeking effective strategies

Zhuorui Cui, Shengze Dong, Ding Liu

As a powerful tool for characterizing cellular subpopulations and cellular heterogeneity, single cell RNA sequencing (scRNA-seq) technology offers advantages of high throughput and multidimensional analysis. However, the process of data acquisition is often constrained by high cost and limited sample availability. To overcome these limitations, we propose a

Shanjun Xu, Linghui Li, Kaiguo Yuan, Bingyu Li

Deep neural networks can be vulnerable to adversarially crafted examples, presenting significant risks to practical applications. A prevalent approach for adversarial attacks relies on the transferability of adversarial examples, which are generated from a substitute model and leveraged to attack unknown black-box models. Despite various proposals aimed at i

Kyle Bradford, James Taylor, George Volz

In this paper we create a definition for prime gaps in the Gaussian integers using a boxcar metric. From this we used numerical methods to derive an asymptotic upper bound for the gaps in this scenario, namely O(log^2|p_{n}|).

Komal Tanwar, Viney Kumar, Jai Prakash Tripathi

Misinformation about vaccination poses a significant public health threat by reducing vaccination rates and increasing disease burden. Understanding population heterogeneity can aid in recognizing and mitigating the effects of such misinformation, especially when vaccine effectiveness is low. Our research quantifies the impact of misinformation on vaccinatio

Jilong Wang, Javokhirbek Rajabov, Chaoyi Xu, Yiming Zheng

Legged robots with advanced manipulation capabilities have the potential to significantly improve household duties and urban maintenance. Despite considerable progress in developing robust locomotion and precise manipulation methods, seamlessly integrating these into cohesive whole-body control for real-world applications remains challenging. In this paper,

Thang Nguyen, Dung Nguyen, Kha Pham, Truyen Tran

Forecasting temporal processes such as virus spreading in epidemics often requires more than just observed time-series data, especially at the beginning of a wave when data is limited. Traditional methods employ mechanistic models like the SIR family, which make strong assumptions about the underlying spreading process, often represented as a small set of co

Shubo Lin, Yutong Kou, Zirui Wu, Shaoru Wang

While existing query-based 3D end-to-end visual trackers integrate detection and tracking via the tracking-by-attention paradigm, these two chicken-and-egg tasks encounter optimization difficulties when sharing the same parameters. Our findings reveal that these difficulties arise due to two inherent constraints on the self-attention mechanism, i.e., over-de

Xiao-Chuan Xu, Yi-Jun Pan

In this work, we study the inverse spectral problem, using the Weyl matrix as the input data, for the matrix Schrodinger operator on the half-line with the boundary condition being the form of the most general self-adjoint. We prove the uniqueness theorem, and derive the main equation and prove its solvability, which yields a theoretical reconstruction algor

S. I. Ipatov

Migration of bodies under the gravitational influence of almost formed planets was studied, and probabilities of their collisions with the Earth and other terrestrial planets were calculated. Based on the probabilities, several conclusions on the accumulation of the terrestrial planets have been made. The outer layers of the Earth and Venus could accumulate

Mikhail Dremin, Konstantin Kozhemyakov, Ivan Molodetskikh, Malakhov Kirill

A main goal in developing video-compression algorithms is to enhance human-perceived visual quality while maintaining file size. But modern video-analysis efforts such as detection and recognition, which are integral to video surveillance and autonomous vehicles, involve so much data that they necessitate machine-vision processing with minimal human interven

Yu-Meng Ren, Xue-Feng Pan, Xiao-Yu Yao, Xiao-Wen Huo

Nonreciprocal interaction between two spatially separated subsystems plays a crucial role in signal processing and quantum networks. Here, we propose an efficient scheme to achieve nonreciprocal interaction and entanglement between two qubits by combining coherent and dissipative couplings in a superconducting platform, where two coherently coupled transmon

Yingjie Fu, Bozhou Li, Linyi Li, Wentao Zhang

The capabilities of Large Language Models (LLMs) in code generation have been extensively studied, particularly for implementing target functionalities from natural-language descriptions. Alternatively, input-output (I/O) examples provide an accessible, unambiguous, and flexible way to describe functionalities. However, their inherent diversity, opaqueness,

Lu Yu, Zheng Chang, Yunjian Jia, Geyong Min

The integration of autonomous driving technologies with vehicular networks presents significant challenges in privacy preservation, communication efficiency, and resource allocation. This paper proposes a novel U-shaped split federated learning (U-SFL) framework to address these challenges on the way of realizing in vehicular edge networks. U-SFL is able to

Liu Zhuoxian, Shi Tuo, Hu Xiaofeng

Front-line police officers often categorize all police call reported cases of Telecom Fraud into 14 subcategories to facilitate targeted prevention measures, such as precise public education. However, the associated data is characterized by its large volume, diverse information content, and variations in expression. Currently, there is a lack of efficient an

Dániel Garamvölgyi, Ryuhei Mizutani, Taihei Oki, Tamás Schwarcz

Consider a matroid $M$ whose ground set is equipped with a labeling to an abelian group. A basis of $M$ is called $F$-avoiding if the sum of the labels of its elements is not in a forbidden label set $F$. H\"orsch, Imolay, Mizutani, Oki, and Schwarcz (2024) conjectured that if an $F$-avoiding basis exists, then any basis can be transformed into an $F$-avoidi

Zhijie Chen, Qiaobo Li, Arindam Banerjee

Combining gradient compression methods (e.g., CountSketch, quantization) and adaptive optimizers (e.g., Adam, AMSGrad) is a desirable goal in federated learning (FL), with potential benefits on both fewer communication rounds and less per-round communication. In spite of the preliminary empirical success of sketched adaptive methods, existing convergence ana

Riccardo Stucchi, Philipp Lauber

Landau damping is one of the cornerstones of plasma physics. In the context of the mathematical framework developed by Landau in his original derivation of Landau damping, we examine the solutions of the linear Vlasov-Poisson system for different equilibrium velocity distribution functions, such as the Maxwellian distribution, kappa distributions, and cut-of

Genqiang Liu, Mingjie Li

Let $U_S$ be the localization of $U(\mathfrak{sp}_{2n})$ with respect to the Ore subset $S$ generated by the root vectors $X_{\epsilon_1-\epsilon_2},\dots,X_{\epsilon_1-\epsilon_n}, X_{2\epsilon_1}$. We show that the minimal nilpotent finite $W$-algebra $W(\mathfrak{sp}_{2n}, e)$ is isomorphic to the centralizer $C_{U_S}(B)$ of some subalgebra $B$ in $U_S$,

Yiwen Duan, Yonghong Yu, Xiaoming Zhao, Yichang Wu

Code Large Language Models (Code LLMs), such as Code llama and DeepSeek-Coder, have demonstrated exceptional performance in the code generation tasks. However, most existing models focus on the abilities of generating correct code, but often struggle with bug repair. We introduce a suit of methods to enhance LLM's SQL bug-fixing abilities. The methods are ma

Daehan Lee, Hyungtae Lim, Soohee Han

Light detection and ranging (LiDAR)-based odometry has been widely utilized for pose estimation due to its use of high-accuracy range measurements and immunity to ambient light conditions. However, the performance of LiDAR odometry varies depending on the environment and deteriorates in degenerative environments such as long corridors. This issue stems from

Jiayan Fang, Siwei Li, Yichun Wu

Utilizing fault diagnosis methods is crucial for nuclear power professionals to achieve efficient and accurate fault diagnosis for nuclear power plants (NPPs). The performance of traditional methods is limited by their dependence on complex feature extraction and skilled expert knowledge, which can be time-consuming and subjective. This paper proposes a nove

Hongsheng Zhang, Zhong Ji, Jingren Liu, Yanwei Pang

Vision Language Models (VLMs), pre-trained on large-scale image-text datasets, enable zero-shot predictions for unseen data but may underperform on specific unseen tasks. Continual learning (CL) can help VLMs effectively adapt to new data distributions without joint training, but faces challenges of catastrophic forgetting and generalization forgetting. Alth

Gene S. Kopp

We give a new interpretation of Stark units associated to real quadratic fields as real multiplication values of a modular cocycle. The cocycle of interest is a meromorphic factor describing the modular transformations of the $q$-Pochhammer symbol and is related to the Shintani--Barnes double sine function and the Faddeev quantum dilogarithm. We prove a refi

Yuzhou Zhang, Mohan Hua, Jinan Liu, Haihui Ruan

Many glass products require thermoformed geometry with high precision. However, the traditional approach of developing a thermoforming process through trials and errors can cause large waste of time and resources and often end up with unsuccessfulness. Hence, there is a need to develop an efficient predictive model, replacing the costly simulations or experi

Jinsol Seo

We present a general $L_p$-solvability framework for both the classical and time-fractional heat equations in non-smooth domains under the zero Dirichlet boundary condition. We consider domains $\Omega$ admitting the Hardy inequality: There exists a constant $N>0$ such that $$ \int_{\Omega}\Big|\frac{f(x)}{d(x,\partial\Omega)}\Big|^2\,\mathrm{d} x\leq N\int_

Cheng Xue, Xiao-Fan Xu, Xi-Ning Zhuang, Tai-Ping Sun

Nonlinear partial differential equations (PDEs) are crucial for modeling complex fluid dynamics and are foundational to many computational fluid dynamics (CFD) applications. However, solving these nonlinear PDEs is challenging due to the vast computational resources they demand, highlighting the pressing need for more efficient computational methods. Quantum

Junjie Qi, Chui-Zhen Chen, Juntao Song, Jie Liu

The interplay between novel topological states and superconductivity has garnered substantial interest due to its potential for topological quantum computing. The Josephson effect serves as a useful probe for edge superconductivity in these hybrid topological materials. In Josephson junctions based on topological materials, supercurrents exhibit unique quant

Zefan Qu, Ke Xu, Gerhard Petrus Hancke, Rynson W. H. Lau

Neural Radiance Fields (NeRFs) have shown remarkable performances in producing novel-view images from high-quality scene images. However, hand-held low-light photography challenges NeRFs as the captured images may simultaneously suffer from low visibility, noise, and camera shakes. While existing NeRF methods may handle either low light or motion, directly c

D. Blume, Q. Guan, J. Kruse, M. Kunitski

Short intense laser pulses are routinely used to induce rotational wave packet dynamics of molecules. Ro-vibrational wave packet dynamics has been explored comparatively infrequently, focusing predominantly on extremely light and rigid molecules such as H$_2^+$, H$_2$, and D$_2$. This work presents quantum mechanical calculations that account for the rotatio

Dhiman Das, Arnob Ray, Chittaranjan Hens, Dibakar Ghosh

Complexity is an important metric for appropriate characterization of different classes of irregular signals, observed in the laboratory or in nature. The literature is already rich in the description of such measures using a variety of entropy and disequilibrium measures, separately or in combination. Chaotic signal was given prime importance in such studie

Eerik Cockin, Xinhua Wang

Within the missile guidance and control system the autopilot must overcome an array of variables and uncertainties to maintain tracking trajectory. A large uncertainty explored in this paper is the difference between the assumed flight dynamics, the controller design relies on, and the true flight dynamics the missile experiences. To capture these difference

George Kirst, Xinhua Wang

Tiltrotors are an aircraft concept with the ability to rotate their rotors freely, achieving vertical take-off and fast forward flight. The combination of helicopter and fixed-wing flight into one aircraft provides versatility in mission selection, yet challenges persist in their construction and control. Tiltrotor aircraft can operate in three primary modes

Jungseok Hong, Ran Choi, John J. Leonard

Object Simultaneous Localization and Mapping (SLAM) systems struggle to correctly associate semantically similar objects in close proximity, especially in cluttered indoor environments and when scenes change. We present Semantic Enhancement for Object SLAM (SEO-SLAM), a novel framework that enhances semantic mapping by integrating heterogeneous multimodal la

Zhicheng Zhang

Autonomous driving technology is rapidly evolving and becoming a pivotal element of modern automation systems. Effective decision-making and planning are essential to ensuring autonomous vehicles operate safely and efficiently in complex environments. This paper introduces a decision-making and planning framework for autonomous vehicles, leveraging dynamic p

Xing Yao, Runxuan Yu, Dewei Hu, Hao Yang

Ultrasound (US) image stitching can expand the field-of-view (FOV) by combining multiple US images from varied probe positions. However, registering US images with only partially overlapping anatomical contents is a challenging task. In this work, we introduce SynStitch, a self-supervised framework designed for 2DUS stitching. SynStitch consists of a synthet

Liu Zhuoxian, Shi Tuo, Hu Xiaofeng

Police incident data is crucial for public security intelligence, yet grassroots agencies struggle with efficient classification due to manual inefficiency and automated system limitations, especially in telecom and online fraud cases. This research proposes a multichannel neural network model, KLCBL, integrating Kolmogorov-Arnold Networks (KAN), a linguisti

Qingtong Wu

This paper establishes the global well-posedness and long-time dynamics of the general Ericksen--Leslie system for isotropic nematic liquid crystals under a constant magnetic field. On the two-dimensional torus $\mathbb{T}^2$, a liquid crystal molecule coincides with itself under rotations by integer multiples of $\pi$, which results in special boundary cond

Nhan Thanh Nguyen, Tianyu Fang, Hien Quoc Ngo, Markku Juntti

In this work, we consider a cell-free massive multiple-input multiple-output (MIMO) integarted sensing and communications (ISAC) system with maximum-ratio transmission schemes combined with multistatic radar-type sensing. Our focus lies on deriving closed-form expressions for the achievable communications rate and the Cram\'er-Rao lower bound (CRLB), which s

Jingyao Wang, Huijie Guo, Wenwen Qiang, Jiangmeng Li

Humans excel at adapting perceptions and actions to diverse environments, enabling efficient interaction with the external world. This adaptive capability relies on the biological nervous system (BNS), which activates different brain regions for distinct tasks. Meta-learning similarly trains machines to handle multiple tasks but relies on a fixed network str

Robert L. Benedetto, Dragos Ghioca, Jamie Juul, Thomas J. Tucker

We provide an explicit construction of the arboreal Galois group for the postcritically finite polynomial $f(z) = z^2 +c$, where $c$ belongs to some arbitrary field of characteristic not equal to $2$. In this first of two papers, we consider the case that the critical point is periodic.

Jing-Jing Zhang, Zhi-Long Han, Ang Liu, Feng-Lan Shao

The new gauge boson $Z'$ in $U(1)_{B-L}$ is widely considered as the mediator of dark matter. In this paper, we propose the conversion-driven dark matter in $U(1)_{B-L}$. The dark sector contains two Dirac fermions $\tilde{\chi}_1$ and $\tilde{\chi}_2$ with $U(1)_{B-L}$ charge 0 and $-1$, respectively. A $Z_2$ symmetry is also introduced to ensure the stabil

Behrad Samari, Amy Nejati, Abolfazl Lavaei

This paper offers a data-driven divide-and-conquer strategy to analyze large-scale interconnected networks, characterized by both unknown mathematical models and interconnection topologies. Our data-driven scheme treats an unknown network as an interconnection of individual agents (a.k.a. subsystems) and aims at constructing their symbolic models, referred t

Zhengxu Xia, Hanchen Li, Junchen Jiang

Because of reinforcement learning's (RL) ability to automatically create more adaptive controlling logics beyond the hand-crafted heuristics, numerous effort has been made to apply RL to congestion control (CC) design for real time video communication (RTC) applications and has successfully shown promising benefits over the rule-based RTC CCs. Online reinfor

Esha Saha, Oscar Wang, Amit K. Chakraborty, Pablo Venegas Garcia

Bitumen extraction for the production of synthetic crude oil in Canada's Athabasca Oil Sands industry has recently come under spotlight for being a significant source of greenhouse gas emission. A major cause of concern is methane, a greenhouse gas produced by the anaerobic biodegradation of hydrocarbons in oil sands residues, or tailings, stored in settle b

Zhangfan Yang, Junkai Ji, Shan He, Jianqiang Li

Molecular docking is a crucial step in drug development, which enables the virtual screening of compound libraries to identify potential ligands that target proteins of interest. However, the computational complexity of traditional docking models increases as the size of the compound library increases. Recently, deep learning algorithms can provide data-driv

Haolin Liu, Zakaria Mhammedi, Chen-Yu Wei, Julian Zimmert

We consider regret minimization in low-rank MDPs with fixed transition and adversarial losses. Previous work has investigated this problem under either full-information loss feedback with unknown transitions (Zhao et al., 2024), or bandit loss feedback with known transition (Foster et al., 2022). First, we improve the $poly(d, A, H)T^{5/6}$ regret bound of Z

Ahmed Telili, Wassim Hamidouche, Ibrahim Farhat, Hadi Amirpour

Omnidirectional (360-degree) video is rapidly gaining popularity due to advancements in immersive technologies like virtual reality (VR) and extended reality (XR). However, real-time streaming of such videos, especially in live mobile scenarios like unmanned aerial vehicles (UAVs), is challenged by limited bandwidth and strict latency constraints. Traditiona

Junyeong Park, Junmo Cho, Sungjin Ahn

Significant advances have been made in developing general-purpose embodied AI in environments like Minecraft through the adoption of LLM-augmented hierarchical approaches. While these approaches, which combine high-level planners with low-level controllers, show promise, low-level controllers frequently become performance bottlenecks due to repeated failures

Kai Kim, Howard Tsai, Rajat Sen, Abhimanyu Das

Current forecasting approaches are largely unimodal and ignore the rich textual data that often accompany the time series due to lack of well-curated multimodal benchmark dataset. In this work, we develop TimeText Corpus (TTC), a carefully curated, time-aligned text and time dataset for multimodal forecasting. Our dataset is composed of sequences of numbers

Bin Liu, Wuzhang Yang, Guang-Han Cao, Zhi Ren

We report the study of the cocktail effect on superconductivity in high-entropy alloys (HEAs),using hexagonal close-packed HEAs as a prototype system. Compared with the compositional averages of the constituent elements, the superconducting transition temperature Tc is enhanced by from a factor of about 2 to over one order of magnitude. This Tc enhancement c

Xiu Yuan

Generalization in Deep Reinforcement Learning (DRL) across unseen environment variations often requires training over a diverse set of scenarios. Many existing DRL algorithms struggle with efficiency when handling numerous variations. The Generalist-Specialist Learning (GSL) framework addresses this by first training a generalist model on all variations, the

Tom W. J. de Geus, Matthieu Wyart

Phenomena including friction and earthquakes are complicated by the joint presence of disorder and non-linear instabilites, such as those triggered by the presence of velocity weakening. In [de Geus and Wyart, Phys. Rev. E 106, 065001 (2022)], we provided a theory for the nucleation of flow and the magnitude of hysteresis, building on recent results on disor

Kevin Zelaya, Mohammad-Ali Miri

We introduce a photonic integrated circuit solution for the direction-of-arrival estimation in the optical frequency band. The proposed circuit is built on discrete sampling of the phasefront of an incident optical beam and its analog processing in a photonic matrix-vector multiplier that maps the angle of arrival into the intensity profile at the output por

Yuan Li, Gwenael Giacinti, Siming Liu, Yi Xing

We reanalyze 15 years of data recorded by the Fermi Large Area Telescope in a region around supernova remnant (SNR) $\gamma$-Cygni from 100 MeV to 1 TeV, and find that the spectra of two extended sources associated with the southeast radio SNR arc and the TeV VERITAS source can be described well by single power-laws with photon indices of $2.149\pm0.005$ and

Hanqing Li, Diego Klabjan

We explore a new language model inversion problem under strict black-box, zero-shot, and limited data conditions. We propose a novel training-free framework that reconstructs prompts using only a limited number of text outputs from a language model. Existing methods rely on the availability of a large number of outputs for both training and inference, an ass

Changcun Huang

A neural network with one hidden layer or a two-layer network (regardless of the input layer) is the simplest feedforward neural network, whose mechanism may be the basis of more general network architectures. However, even to this type of simple architecture, it is also a ``black box''; that is, it remains unclear how to interpret the mechanism of its solut

Huilin Wang, Weibing Deng

In this study, we employ a superstatistical approach to construct q exponential and q Maxwell Boltzmann complex networks, generalizing the concept of scale free networks. By adjusting the crossover parameter {\lambda}, we control the degree of the q exponential plateau at low node degrees, allowing a smooth transition to pure power law degree distributions.

Yueyang Cang, Yu hang liu, Li Shi

Kolmogorov-Arnold Networks(KANs), as a theoretically efficient neural network architecture, have garnered attention for their potential in capturing complex patterns. However, their application in computer vision remains relatively unexplored. This study first analyzes the potential of KAN in computer vision tasks, evaluating the performance of KAN and its c

Taewoo Jo, Ho Jung Lee, Sulim Chun, In-Kwon Lee

Eye gaze is considered a promising interaction modality in extende reality (XR) environments. However, determining selection intention from gaze data often requires additional manual selection techniques. We present a Bayesian-based machine learning (ML) model to predict user selection intention in real-time using only gaze data. Our model uses a Bayesian ap

Shizhe Yuan, Li Zhou

With the advancement of artificial intelligence, 3D human pose estimation-based systems for sports training and posture correction have gained significant attention in adolescent sports. However, existing methods face challenges in handling complex movements, providing real-time feedback, and accommodating diverse postures, particularly with occlusions, rapi

Chiranjit Mondal, Rasoul Ghadimi, Bohm-Jung Yang

In two-dimensional systems with space-time inversion symmetry, Dirac nodes (DNs) carry non-Abelian topological charges which induce intriguing momentum space braiding phenomenon. Although the original idea was proposed in condensed matter setup, the experimental verification of non-Abelian charge conversion has been limited to artificial metamaterials becaus

Xin Sun, Jan de Wit, Zhuying Li, Jiahuan Pei

Chatbots or conversational agents (CAs) are increasingly used to improve access to digital psychotherapy. Many current systems rely on rigid, rule-based designs, heavily dependent on expert-crafted dialogue scripts for guiding therapeutic conversations. Although advances in large language models (LLMs) offer potential for more flexible interactions, their la

Yeming Wen, Swarat Chaudhuri

Presenting users with diverse responses from foundation models is crucial for enhancing user experience and accommodating varying preferences. However, generating multiple high-quality and diverse responses without sacrificing accuracy remains a challenge, especially when using greedy sampling. In this work, we propose a novel framework, Synthesize-Partition

Yang Zhao, Yue Xiu, Minrui Xu, Ning Wei

Federated learning (FL) in wireless computing effectively utilizes communication bandwidth, yet it is vulnerable to errors during the analog aggregation process. While removing users with unfavorable channel conditions can mitigate these errors, it also reduces the available local training data for FL, which in turn hinders the convergence rate of the traini

Xiaowei Tang, Bin Long, Li Zhou

This research focuses on real-time monitoring and analysis of track and field athletes, addressing the limitations of traditional monitoring systems in terms of real-time performance and accuracy. We propose an IoT-optimized system that integrates edge computing and deep learning algorithms. Traditional systems often experience delays and reduced accuracy wh

Osman Akar, Yushan Han, Yizhou Chen, Weixian Lan

We present learning-based implicit shape representations designed for real-time avatar collision queries arising in the simulation of clothing. Signed distance functions (SDFs) have been used for such queries for many years due to their computational efficiency. Recently deep neural networks have been used for implicit shape representations (DeepSDFs) due to

Shinsuke Kawai, Nobuchika Okada

We investigate the flavour sector of the supersymmetric $SU(5)$ Grand Unified Theory (GUT) model using machine learning techniques. The minimal $SU(5)$ model is known to predict fermion masses that disagree with observed values in nature. There are two well-known approaches to address this issue: one involves introducing a 45-representation Higgs field, whil

Yichun Zhao, Xiangxing Tao, Jiang Zhou

In this paper, our main purpose is to establish a weak factorization of the classical Hardy spaces in terms of a multilinear Calder\'on-Zygmund operator on the ball Banach function spaces. Furthermore, a new characterization of the BMO space via the boundedness of the commutator generated by the multilinear Calder\'on-Zygmund operator is also obtained. The r

Ajay Jasra, Mohamed Maama, Raul Tempone

In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of [5] we develop a new randomized multilevel Monte Carlo method for estimating the parameters, based upon Markovia

Kentaro Yamaguchi

In the previous work, we study the moment polytope of the closure of the complex subtorus orbit in a symplectic toric manifold associated to an affine subspace when the closure is a smooth complex submanifold. In this paper, we clarify the condition for nonsingularity of the closure of the codimension one complex subtorus orbit in terms of polytopes. The mai

Xuming He, Zhiwang Zhou, Wenlong Zhang, Xiangyu Zhao

Weather radar data synthesis can fill in data for areas where ground observations are missing. Existing methods often employ reconstruction-based approaches with MSE loss to reconstruct radar data from satellite observation. However, such methods lead to over-smoothing, which hinders the generation of high-frequency details or high-value observation areas as

Chanseo Lee, Sonu Kumar, Kimon A. Vogt, Sam Meraj

This study compares Sporo Health's AI Scribe, a proprietary model fine-tuned for medical scribing, with various LLMs (GPT-4o, GPT-3.5, Gemma-9B, and Llama-3.2-3B) in clinical documentation. We analyzed de-identified patient transcripts from partner clinics, using clinician-provided SOAP notes as the ground truth. Each model generated SOAP summaries using zer

Xilin Zhang

This work applies a reduced basis method to study the continuum physics of a finite quantum system -- either few or many-body. Specifically, I develop reduced-order models, or emulators, for the underlying inhomogeneous Schr\"{o}dinger equation and train the emulators against the equation's bound-state-like solutions at complex energies. The emulators rapidl

Andrey Zhitnikov, Vadim Indelman

Taking into account future risk is essential for an autonomously operating robot to find online not only the best but also a safe action to execute. In this paper, we build upon the recently introduced formulation of probabilistic belief-dependent constraints. We present an anytime approach employing the Monte Carlo Tree Search (MCTS) method in continuous do

D. Shang, Q. Chen, X. Guo, H. Jin

Massive open online courses (MOOC) have become important in the learning journey of college students and have been extensively implemented in higher education. However, there are few studies that investigated the willingness to continue using Massive open online courses (MOOC) in the field of business in higher education. Therefore, this paper proposes a com

Chaeyun Jang, Hyungi Lee, Jungtaek Kim, Juho Lee

Fine-tuning pre-trained models for downstream tasks is a widely adopted technique known for its adaptability and reliability across various domains. Despite its conceptual simplicity, fine-tuning entails several troublesome engineering choices, such as selecting hyperparameters and determining checkpoints from an optimization trajectory. To tackle the diffic

Toshihiro Yada, Pieter-Jan Stas, Aziza Suleymanzade, Erik N. Knall

Thermodynamic principles governing energy and information are important tools for a deeper understanding and better control of quantum systems. In this work, we experimentally investigate the interplay of the thermodynamic costs and information flow in a quantum system undergoing iterative quantum measurement and feedback. Our study employs a state stabiliza