May 2024 arXiv papers — page 138

Caixing Wang, Ziliang Shen

In this paper, we focus on distributed estimation and support recovery for high-dimensional linear quantile regression. Quantile regression is a popular alternative tool to the least squares regression for robustness against outliers and data heterogeneity. However, the non-smoothness of the check loss function poses big challenges to both computation and th

Shuo Yin, Weihao You, Zhilong Ji, Guoqiang Zhong

The tool-use Large Language Models (LLMs) that integrate with external Python interpreters have significantly enhanced mathematical reasoning capabilities for open-source LLMs, while tool-free methods chose another track: augmenting math reasoning data. However, a great method to integrate the above two research paths and combine their advantages remains to

Luigi Riz, Sergio Povoli, Andrea Caraffa, Davide Boscaini

Berry picking has long-standing traditions in Finland, yet it is challenging and can potentially be dangerous. The integration of drones equipped with advanced imaging techniques represents a transformative leap forward, optimising harvests and promising sustainable practices. We propose WildBe, the first image dataset of wild berries captured in peatlands a

Tong Pu, Yifei Zhang, Yiying Zhang

Systemic risk is the risk that a company- or industry-level risk could trigger a huge collapse of another or even the whole institution. Various systemic risk measures have been proposed in the literature to quantify the domino and (relative) spillover effects induced by systemic risks such as the well-known CoVaR, CoES, MES and CoD risk measures, and associ

Yilu Xu, Shouxin Chen

Vortices produce locally concentrated field configurations and are solutions to the nonlinear partial differential equations systems of complicated structures. In this paper, we establish the existence and uniqueness for solutions of the gauged non-Abelian vortices in a coupled 4D-2D quantum field theory by researching the nonlinear elliptic equations system

Mohammad Rowshan, Min Qiu, Yixuan Xie, Xinyi Gu

Channel coding plays a pivotal role in ensuring reliable communication over wireless channels. With the growing need for ultra-reliable communication in emerging wireless use cases, the significance of channel coding has amplified. Furthermore, minimizing decoding latency is crucial for critical-mission applications, while optimizing energy efficiency is par

Ashutosh Singh

In this paper, we derive the field equations of modified Gauss-Bonnet gravity termed as $f(R,G)$ gravity for the non-flat Friedmann-Robertson-Walker (FRW) spacetime. We utilize the dynamical system approach to study the cosmic dynamics of two different class of $f(R,G)$ models composed of radiation and matter (cold dark matter and baryonic matter). The linea

Tabea Herrmann, Roland Brandau, Arnd Bäcker

It is commonly expected that for quantum chaotic many body systems, the statistical properties approach those of random matrices when increasing the system size. We demonstrate for various kicked spin-1/2 chain models that the average eigenstate entanglement indeed approaches the random matrix result. However, the distribution of the eigenstate entanglement

Leon Eisemann, Johannes Maucher

High-resolution road representations are a key factor for the success of (highly) automated driving functions. These representations, for example, high-definition (HD) maps, contain accurate information on a multitude of factors, among others: road geometry, lane information, and traffic signs. Through the growing complexity and functionality of automated dr

Jia Hu, Mingyue Lei, Haoran Wang, Zeyu Liu

Personalization is crucial for the widespread adoption of advanced driver assistance system. To match up with each user's preference, the online evolution capability is a must. However, conventional evolution methods learn from naturalistic driving data, which requires a lot computing power and cannot be applied online. To address this challenge, this paper

Yunsheng Ni, Chuanjian Liu, Yehui Tang, Kai Han

Speculative decoding emerges as a pivotal technique for enhancing the inference speed of Large Language Models (LLMs). Despite recent research aiming to improve prediction efficiency, multi-sample speculative decoding has been overlooked due to varying numbers of accepted tokens within a batch in the verification phase. Vanilla method adds padding tokens in

Shuji Shinohara, Daiki Morita, Hayato Hirai, Ryosuke Kuribayashi

The L\'evy walk, a type of random walk characterized by linear step lengths that follow a power-law distribution, is observed in the migratory behaviors of various organisms, ranging from bacteria to humans. Notably, L\'evy walks with power exponents close to two, also known as Cauchy walks, are frequently observed, though their underlying causes remain elus

Quentin Vagne, Guillaume Salbreux

We present a generic framework for describing interacting, spinning, active polar particles, aimed at modelling dense cell aggregates, where cells are treated as polar, rotating objects that interact mechanically with one another and their surrounding environment. Using principles from non-equilibrium thermodynamics, we derive constitutive equations for inte

Balázs Németh, Ronojoy Adhikari

The stochastic dynamics of a rigid inclusion constrained to move on a curved surface has many applications in biological and soft matter physics, ranging from the diffusion of passive or active membrane proteins to the motion of phoretic particles on liquid-liquid interfaces. Here we construct intrinsic Langevin equations for an oriented rigid inclusion on a

Jia Hu, Yongwei Feng, Shuoyuan Li, Haoran Wang

Automated Parking Assist (APA) systems are now facing great challenges of low adoption in applications, due to users' concerns about parking capability, reliability, and completion efficiency. To upgrade the conventional APA planners and enhance user's acceptance, this research proposes an optimal-control-based parking motion planner. Its highlight lies in i

Yiming Fang, Li Chen

The conventional rounding error analysis provides worst-case bounds with an associated failure probability and ignores the statistical property of the rounding errors. In this paper, we develop a new statistical rounding error analysis for random matrix computations. Such computations have numerous applications in the field of wireless communications, signal

Xin Li, Wenyang Gan, Pang Wen, Daqi Zhu

To deal with the task assignment problem of multi-AUV systems under kinematic constraints, which means steering capability constraints for underactuated AUVs or other vehicles likely, an improved task assignment algorithm is proposed combining the Dubins Path algorithm with improved SOM neural network algorithm. At first, the aimed tasks are assigned to the

Chi-Hsien Tai, Wen-Yu Wen

We have investigated a holographic model of a multi-layered superconductor in (2+1)-dimensions using the AdS/CFT correspondence. This correspondence allows us to study strongly interacting condensed matter systems through a weakly interacting gravitational theory. Our study focused on the effects of a finite system size on the superconductor's properties. We

H. M. Nagesh, Muhammad Kamran Siddiqui

A numerical parameter, known as a topological index, is employed to represent the molecular structure of a compound by considering its graph-theoretical properties. In the study of quantitative structure-activity relationships (QSAR) and quantitative structure-property relationships (QSPR), topological indices are used to predict the physicochemical properti

Animesh Choudhury, Jagabandhu Panda, Asmita Mukherjee

Advanced weather and climate models use numerical techniques on grided meshes to simulate atmospheric and ocean dynamics, which are computationally expensive. Data-driven approaches are gaining popularity in weather and climate modeling, with a broad scope of applications. Although Machine Learning (ML) has been employed in this domain, significant progress

Sandro Rodriguez Garzon, Dennis Natusch, Artur Philipp, Axel Küpper

Authentication in TLS is predominately carried out with X.509 digital certificates issued by certificate authorities (CA). The centralized nature of current public key infrastructures, however, comes along with severe risks, such as single points of failure and susceptibility to cyber-attacks, potentially undermining the security and trustworthiness of the e

Hema Kharayat, Jagdev Singh, Muthu Priyal, B. Ravindra

Differential rotation is one of the basic characteristics of the Sun, and it plays an important role in generating the magnetic fields and its activities. We investigated rotation rate using chromospheric features such as plages, enhanced network, active network, and quiet network separately (for the first time). The digitized Ca-K images from Kodaikanal Obs

Reggie C. Pantig

Inspired by the two different dark matter frameworks that were studied recently: one that arises from the non-local effects of entanglement entropy as emergent gravity (characterized by the parameter $\xi(M)$, and zero-point scale length $l$), and one from dark energy viewed as a superconducting medium (characterized by $\eta(M)$, and screening length parame

Hanzhuo Tan, Qi Luo, Ling Jiang, Zizheng Zhan

Automated code completion, aiming at generating subsequent tokens from unfinished code, has been significantly benefited from recent progress in pre-trained Large Language Models (LLMs). However, these models often suffer from coherence issues and hallucinations when dealing with complex code logic or extrapolating beyond their training data. Existing Retrie

Mingxiang Pan, Hui Zeng, Erqing Wang, Huaqing Huang

Topological semimetals in chiral crystals, which possess both structural handedness and band crossings (or nodes) with topological chiral charge, exhibit many exotic physical properties. Here we demonstrate that the structural and electronic chirality of these systems can endow them with another fascinating phenomenon -- the intrinsic nonlinear planar Hall e

Justin Edwards, Philip R. Doyle, Holly P. Branigan, Benjamin R. Cowan

A growing literature on speech interruptions describes how people interrupt one another with speech, but these behaviours have not yet been implemented in the design of artificial agents which interrupt. Perceptions of a prototype proactive speech agent which adapts its speech to both urgency and to the difficulty of the ongoing task it interrupts are compar

Yubin Shi, Yixuan Chen, Mingzhi Dong, Xiaochen Yang

Despite their prevalence in deep-learning communities, over-parameterized models convey high demands of computational costs for proper training. This work studies the fine-grained, modular-level learning dynamics of over-parameterized models to attain a more efficient and fruitful training strategy. Empirical evidence reveals that when scaling down into netw

Qi Chen, Xiubo Geng, Corby Rosset, Carolyn Buractaon

Recent breakthroughs in large models have highlighted the critical significance of data scale, labels and modals. In this paper, we introduce MS MARCO Web Search, the first large-scale information-rich web dataset, featuring millions of real clicked query-document labels. This dataset closely mimics real-world web document and query distribution, provides ri

Zi-Qing Chen, Shao-Wen Wei

Dyonic black holes with quasitopological electromagnetism exhibit an intriguing phase diagram with two separated first-order coexistence curves. In this paper, we aim to uncover its influence on the black hole thermodynamical topology. At first, we investigate the phase transition and phase diagram of the dyonic black holes. Comparing with previous study tha

Chao He, Hongxi Wei

Deep image hashing aims to map input images into simple binary hash codes via deep neural networks and thus enable effective large-scale image retrieval. Recently, hybrid networks that combine convolution and Transformer have achieved superior performance on various computer tasks and have attracted extensive attention from researchers. Nevertheless, the pot

Quang Vinh Nguyen, Van Thong Huynh, Soo-Hyung Kim

Colonoscopy is a common and practical method for detecting and treating polyps. Segmenting polyps from colonoscopy image is useful for diagnosis and surgery progress. Nevertheless, achieving excellent segmentation performance is still difficult because of polyp characteristics like shape, color, condition, and obvious non-distinction from the surrounding con

Luca Ferrari, Francesco Verciani

Naples parking functions were introduced as a generalization of classical parking functions, in which cars are allowed to park backwards, by checking up to a fixed number of previous spots, before proceeding forward as usual. In this work we introduce the notion of a complete parking preference, through which we are able to give some information on the combi

Felix Hitzelhammer, Anežka Dostálová, Ilia Zykov, Barbara Platzer

Interferometric scattering microscopy is a powerful technique that enables various applications, such as mass photometry and particle tracking. Here we present a numerical toolbox to simulate images obtained in interferometric scattering, coherent bright-field, and dark-field microscopy. The scattered fields are calculated using a boundary element method, fa

Gao Yu Lee, Jinkuan Chen, Tanmoy Dam, Md Meftahul Ferdaus

High-quality images are crucial in remote sensing and UAV applications, but atmospheric haze can severely degrade image quality, making image dehazing a critical research area. Since the introduction of deep convolutional neural networks, numerous approaches have been proposed, and even more have emerged with the development of vision transformers and contra

Wen Lu

In this paper, we investigate the stability equivalence problem for stochastic differential delay equations, the auxiliary stochastic differential equations and their corresponding Euler-Maruyama (EM) methods under $G$-framework. More precisely, for $p\geq 2$, we prove the equivalence of practical exponential stability in $p$-th moment sense among stochastic

Raghu Prabhakar, Ram Sivaramakrishnan, Darshan Gandhi, Yun Du

Monolithic large language models (LLMs) like GPT-4 have paved the way for modern generative AI applications. Training, serving, and maintaining monolithic LLMs at scale, however, remains prohibitively expensive and challenging. The disproportionate increase in compute-to-memory ratio of modern AI accelerators have created a memory wall, necessitating new met

Boris Perminov, Kirill Grebnev, Uwe Huebner, Maria Chernysheva

Side-pumping fibre combiners offer several advantages in fibre laser design, including distributed pump absorption, reduced heat load, and improved flexibility and reliability. These benefits are particularly important for all-fibre lasers and amplifiers operating in the mid-IR wavelength range and based on soft-glass optical fibres. However, conventional fa

Xiaoxiao Wu, Zhenguo Gao, Xiaowei Chen, Yakai Wang

In recent years, deep learning based on Convolutional Neural Networks (CNNs) has achieved remarkable success in many applications. However, their heavy reliance on extensive labeled data and limited generalization ability to unseen classes pose challenges to their suitability for medical image processing tasks. Few-shot learning, which utilizes a small amoun

Matthias Müller, Samarth Brahmbhatt, Ankur Deka, Quentin Leboutet

We introduce OpenBot-Fleet, a comprehensive open-source cloud robotics system for navigation. OpenBot-Fleet uses smartphones for sensing, local compute and communication, Google Firebase for secure cloud storage and off-board compute, and a robust yet low-cost wheeled robot toact in real-world environments. The robots collect task data and upload it to the c

Cheongho Han, Ian A. Bond, Andrzej Udalski, Chung-Uk Lee

We investigate the nature of the short-term anomaly that appears in the lensing light curve of KMT-2023-BLG-1866. The anomaly was only partly covered due to its short duration, less than a day, coupled with cloudy weather conditions and restricted nighttime duration. Considering intricacy of interpreting partially covered signals, we thoroughly explore all p

Juri Grosjean, Jannis Vamvas

Encoder models trained for the embedding of sentences or short documents have proven useful for tasks such as semantic search and topic modeling. In this paper, we present a version of the SwissBERT encoder model that we specifically fine-tuned for this purpose. SwissBERT contains language adapters for the four national languages of Switzerland -- German, Fr

Victor Chepoi

Semispaces of a convexity space $(X,C)$ are maximal convex sets missing a point. The separation axiom $S_3$ asserts that any point $x_0\in X$ and any convex set $A$ not containing $x_0$ can be separated by complementary halfspaces (convex sets with convex complements) or, equivalently, that all semispaces are halfspaces. In this paper, we study $S_3$ for geo

Alexander Kilin, Elena Pivovarova

This paper is concerned with the problem of an ellipsoid of revolution rolling on a horizontal plane under the assumption that there is no slipping at the point of contact and no spinning about the vertical. A reduction of the equations of motion to a fixed level set of first integrals is performed. Permanent rotations corresponding to the rolling of an elli

Hanshu Yan, Xingchao Liu, Jiachun Pan, Jun Hao Liew

We present Piecewise Rectified Flow (PeRFlow), a flow-based method for accelerating diffusion models. PeRFlow divides the sampling process of generative flows into several time windows and straightens the trajectories in each interval via the reflow operation, thereby approaching piecewise linear flows. PeRFlow achieves superior performance in a few-step gen

Ramin Ghorbani, Marcel J. T. Reinders, David M. J. Tax

Anomaly detection in time series data is crucial across various domains. The scarcity of labeled data for such tasks has increased the attention towards unsupervised learning methods. These approaches, often relying solely on reconstruction error, typically fail to detect subtle anomalies in complex datasets. To address this, we introduce RESTAD, an adaptati

Runzhi He, Hengzhi Ye, Minghui Zhou

Background: Open Source Software is the building block of modern software. However, the prevalence of project deprecation in the open source world weakens the integrity of the downstream systems and the broad ecosystem. Therefore it calls for efforts in monitoring and predicting project deprecations, empowering stakeholders to take proactive measures. Challe

Shuonan Chen, Xuhong Zhou, Yongtao Bai

Fatigue damages and failure widely exist in engineering structures. However, predicting fatigue life for various structural materials subjected to multiaxial loading paths remains a challenging problem. A novel multi-view deep learning model incorporating frequency-domain analysis for fatigue life prediction is proposed. The model consists of two main analyt

Quentin Lobbé, Camille Roth, Lena Mangold

We introduce Chronoblox, a system for visualizing dynamic graphs. Chronoblox consists of a chronophotography of a sequence of graph snapshots based on a single embedding space common to all time periods. The goal of Chronoblox is to project all snapshots onto a common visualization space so as to represent both local and global dynamics at a glance. In this

Jan Rooduijn, Dexter Kozen, Alexandra Silva

Guarded Kleene Algebra with Tests (GKAT for short) is an efficient fragment of Kleene Algebra with Tests, suitable for reasoning about simple imperative while-programs. Following earlier work by Das and Pous on Kleene Algebra, we study GKAT from a proof-theoretical perspective. The deterministic nature of GKAT allows for a non-well-founded sequent system who

Quentin Ferdinand, Gilles Le Chenadec, Benoit Clement, Panagiotis Papadakis

Class incremental learning consists in training discriminative models to classify an increasing number of classes over time. However, doing so using only the newly added class data leads to the known problem of catastrophic forgetting of the previous classes. Recently, dynamic deep learning architectures have been shown to exhibit a better stability-plastici

Stefano Rinaldi, Gabriele Demasi, Walter Del Pozzo, Otto A. Hannuksela

The Bayesian evidence, crucial ingredient for model selection, is arguably the most important quantity in Bayesian data analysis: at the same time, however, it is also one of the most difficult to compute. In this paper we present a hierarchical method that leverages on a multivariate normalised approximant for the posterior probability density to infer the

Aaditya Prasad, Kevin Lin, Jimmy Wu, Linqi Zhou

Many robotic systems, such as mobile manipulators or quadrotors, cannot be equipped with high-end GPUs due to space, weight, and power constraints. These constraints prevent these systems from leveraging recent developments in visuomotor policy architectures that require high-end GPUs to achieve fast policy inference. In this paper, we propose Consistency Po

Naoki Koshimoto, Norita Kawanaka, Daichi Tsuna

The natal kick velocity distribution for black holes (BHs) is unknown regardless of its importance for understanding the BH formation process. Gravitational microlensing is a unique tool for studying the distribution of BHs in our Galaxy, and the first isolated stellar-mass BH event, OGLE-2011-BLG-0462/MOA-2011-BLG-191 (OB110462), was recently identified by

Ranjani G Sundaram, Himanshu Gupta

Long-distance quantum communication in quantum networks faces significant challenges due to the constraints imposed by the no-cloning theorem. Most existing quantum communication protocols rely on the a priori distribution of entanglement pairs (EPs), a process known to incur considerable latency due to its stochastic nature. In this work, we consider the pr

Yuzhang Xie, Jiaying Lu, Joyce Ho, Fadi Nahab

Linking (aligning) biomedical concepts across diverse data sources enables various integrative analyses, but it is challenging due to the discrepancies in concept naming conventions. Various strategies have been developed to overcome this challenge, such as those based on string-matching rules, manually crafted thesauri, and machine learning models. However,

Ranjani G Sundaram, Himanshu Gupta, C. R. Ramakrishnan

Present quantum computers are constrained by limited qubit capacity and restricted physical connectivity, leading to challenges in large-scale quantum computations. Distributing quantum computations across a network of quantum computers is a promising way to circumvent these challenges and facilitate large quantum computations. However, distributed quantum c

Sridhar Majety, Victoria A. Norman, Pranta Saha, Alex H. Rubin

Color center platforms have been at the forefront of quantum nanophotonics for applications in quantum networking, computing, and sensing. However, large-scale deployment of this technology has been stifled by a lack of ability to integrate photonic devices at scale while maintaining the properties of quantum emitters. We address this challenge in silicon ca

Xin Liu, Weiqi Wang, Jiaxin Bai, Yangqiu Song

Subgraph isomorphism counting is known as #P-complete and requires exponential time to find the accurate solution. Utilizing representation learning has been shown as a promising direction to represent substructures and approximate the solution. Graph kernels that implicitly capture the correlations among substructures in diverse graphs have exhibited great

Gelei Deng, Haoran Ou, Yi Liu, Jie Zhang

CAPTCHAs have become a ubiquitous tool in safeguarding applications from automated bots. Over time, the arms race between CAPTCHA development and evasion techniques has led to increasingly sophisticated and diverse designs. The latest iteration, reasoning CAPTCHAs, exploits tasks that are intuitively simple for humans but challenging for conventional AI tech

Xufeng Duan, Shixuan Li, Zhenguang G. Cai1

There has been increasing interest in investigating the behaviours of large language models (LLMs) and LLM-powered chatbots by treating an LLM as a participant in a psychological experiment. We therefore developed an R package called "MacBehaviour" that aims to interact with more than 60 language models in one package (e.g., OpenAI's GPT family, the Claude f

Yerong Xu, Ciro Pinto, Daniele Rogantini, Didier Barret

The extreme velocities and high ionization states of ultra-fast outflows (UFOs) make them a promising candidate for AGN feedback on the evolution of the host galaxy. However, their exact underlying driving mechanism is not yet fully understood. Given that the variability of UFOs may be used to distinguish among different launching mechanisms, we aim to searc

Yuichi Sasazawa, Yasuhiro Sogawa

In recent years, there has been an increasing demand for customizable 3D virtual spaces. Due to the significant human effort required to create these virtual spaces, there is a need for efficiency in virtual space creation. While existing studies have proposed methods for automatically generating layouts such as floor plans and furniture arrangements, these

Junda Zhou, Cheuk Ting Li

We consider a key agreement setting where two parties observe correlated random sources, and want to agree on a secret key via public discussions. In order to allow the key length to adapt to the realizations of the random sources, we allow the key to be of variable length, subject to a novel variable-length version of the uniformity constraint based on rand

Yan Li

In this paper, we establish the existence of three-dimensional axisymmetric compressible jet flows for steady Euler system with large vorticity by using the variational method. More precisely, for given axial velocity of the flow at the upstream, if the mass flux is sufficiently large, we can find a unique outer pressure such that a smooth subsonic three-dim

Mariano Bonifacio, Francesco Piazza, Tobias Donner

Experimental platforms based on ultracold atomic gases have significantly advanced the quantum simulation of complex systems, yet the exploration of phenomena driven by long-range interactions remains a formidable challenge. Currently available methods utilizing dipolar quantum gases or multi-mode cavities allow to implement long-range interactions with a $1

M. Priyanka, P. Muthukumar

The reduction in coral reef densities, characterized by the proliferation of macroalgae, has emerged as a global threat. In this paper, we present a discrete-time coral reef dynamical model that incorporates macroalgae. We explore all ecologically possible equilibrium points for the proposed model. The conditions for the local stability of the interior equil

Jisu Kim, Juhwan Lee

The rapid advancement of Large Language Models (LLMs) has improved text understanding and generation but poses challenges in computational resources. This study proposes a curriculum learning-inspired, data-centric training strategy that begins with simpler tasks and progresses to more complex ones, using criteria such as prompt length, attention scores, and

Jingwei Zhang, Farzan Farnia

Transportation of samples across different domains is a central task in several machine learning problems. A sensible requirement for domain transfer tasks in computer vision and language domains is the sparsity of the transportation map, i.e., the transfer algorithm aims to modify the least number of input features while transporting samples across the sour

Yanhong Peng, Yuxin Wang, Fangchao Hu, Miao He

We present a novel approach to predicting the pressure and flow rate of flexible electrohydrodynamic pumps using the Kolmogorov-Arnold Network. Inspired by the Kolmogorov-Arnold representation theorem, KAN replaces fixed activation functions with learnable spline-based activation functions, enabling it to approximate complex nonlinear functions more effectiv

Chihiro Tsutake, Keita Takahashi, Toshiaki Fujii

Compression of the sign information of discrete cosine transform coefficients is an intractable problem in image compression schemes due to the equiprobable occurrence of the sign bits. To overcome this difficulty, we propose an efficient compression method for such sign information based on phase retrieval, which is a classical signal restoration problem at

Tom Day, Maya Isarov, William J. Pappas, Brett C. Johnson

Masers once represented the state-of-the-art in low noise microwave amplification technology, but eventually became obsolete due to their need for cryogenic cooling. Masers based on solid-state spin systems perform most effectively as amplifiers, since they provide a large density of spins and can therefore operate at relatively high powers. Whilst solid-sta

Deng-Wang Shi, Shan-Qin Wang, Wen-Pei Gan, En-Wei Liang

We study the energy sources, the physical properties of the ejecta and the circumstellar medium (CSM), as well as the mass-loss history of the progenitor of SN 2017dio which is a broad-lined Ic (Ic-BL) supernova (SN) having unusual light curves (LCs) and signatures of hydrogen-rich CSM in its early spectrum. We find that the temperature of SN 2017dio began t

Aaqid Bhat, Raja Solanki, P. K. Sahoo

In this article, we attempt to explore the dark sector of the universe i.e. dark matter and dark energy, where the dark energy components are related to the modified $f(Q)$ Lagrangian, particularly a power law function $f(Q)= \gamma \left(\frac{Q}{Q_0}\right)^n$, while the dark matter component is described by the Extended Bose-Einstein Condensate (EBEC) equ

Mingxia Ding, Wenxiao Zhao, Tianshi Chen

Focusing on identification, this paper develops a class of convex optimization-based criteria and correspondingly the recursive algorithms to estimate the parameter vector $\theta^{*}$ of a stochastic dynamic system. Not only do the criteria include the classical least-squares estimator but also the $L_l=|\cdot|^l, l\geq 1$, the Huber, the Log-cosh, and the

Khai Nguyen, Hai Nguyen, Nhat Ho

The sliced Wasserstein barycenter (SWB) is a widely acknowledged method for efficiently generalizing the averaging operation within probability measure spaces. However, achieving marginal fairness SWB, ensuring approximately equal distances from the barycenter to marginals, remains unexplored. The uniform weighted SWB is not necessarily the optimal choice to

Tianci Bi, Xiaoyi Zhang, Zhizheng Zhang, Wenxuan Xie

Significant progress has been made in scene text detection models since the rise of deep learning, but scene text layout analysis, which aims to group detected text instances as paragraphs, has not kept pace. Previous works either treated text detection and grouping using separate models, or train a model from scratch while using a unified one. All of them h

Julian Barbour, Zaza Doborjginidze, Tim Koslowski, Hemant Shukla

Except for crystalline or random structures, an agreed definition of complexity for intermediate and hence interesting cases does not exist. We fill this gap with a notion of complexity that characterises shapes formed by any finite number of particles greater than or equal to the three needed to define triangle shapes. The resulting shape complexity is a si

Houze Liu, Chongqing Wang, Xiaoan Zhan, Haotian Zheng

Robust 3D object detection remains a pivotal concern in the domain of autonomous field robotics. Despite notable enhancements in detection accuracy across standard datasets, real-world urban environments, characterized by their unstructured and dynamic nature, frequently precipitate an elevated incidence of false positives, thereby undermining the reliabilit

Ruihang Ji, Shuzhi Sam Ge, Kai Zhao

This paper studies a Coded Event-triggered Control (CEC) for a class of nonlinear systems under any initial condition. To reduce communication burden, the CEC is designed from the encoding-decoding viewpoint by which only $m$-length string is transmitted for each communication between CEC and actuator. If a more general Entry Capture Problem is encountered,

Isshin Arai, Tomoaki Itano, Masako Sugihara-Seki

A pioneering study conducted by Egbers and Rath [Acta Mech. 111 pp. 125--140 (1995)] experimentally captured spiral waves to elucidate the transition in the wide-gap spherical Couette flow. However, the physical field quantities of the spiral waves corresponding to light patterns of various intensities, as obtained in the experiment, remain unclear, and we h

Damaris Meier, Kai Rajala

We explore the interplay between different definitions of distortion for mappings $f\colon X\to \mathbb{R}^2$, where $X$ is any metric surface, meaning that $X$ is homeomorphic to a domain in $\mathbb{R}^2$ and has locally finite 2-dimensional Hausdorff measure. We establish that finite distortion in terms of the familiar analytic definition always implies f

Yewon Kim, Thanh-Long V. Le, Donghwi Kim, Mina Lee

We investigate how non-native English speakers (NNESs) interact with diverse information aids to assess and select AI-generated paraphrases. We develop ParaScope, an AI paraphrasing assistant that integrates diverse information aids, such as back-translation, explanations, and usage examples, and logs user interaction data. Our in-lab study with 22 NNESs rev

Xinglin Chen, Yishuai Cai, Yunxin Mao, Minglong Li

Robots executing tasks following human instructions in domestic or industrial environments essentially require both adaptability and reliability. Behavior Tree (BT) emerges as an appropriate control architecture for these scenarios due to its modularity and reactivity. Existing BT generation methods, however, either do not involve interpreting natural langua

Theodore Jerome Tinker, Kenji Doya, Jun Tani

In Reinforcement Learning (RL), artificial agents are trained to maximize numerical rewards by performing tasks. Exploration is essential in RL because agents must discover information before exploiting it. Two rewards encouraging efficient exploration are the entropy of action policy and curiosity for information gain. Entropy is well-established in literat

Haodong Chen, Yongle Huang, Haojian Huang, Xiangsheng Ge

The increasing prominence of e-commerce has underscored the importance of Virtual Try-On (VTON). However, previous studies predominantly focus on the 2D realm and rely heavily on extensive data for training. Research on 3D VTON primarily centers on garment-body shape compatibility, a topic extensively covered in 2D VTON. Thanks to advances in 3D scene editin

Maria Chudnovsky, Nicolas Trotignon

We construct classes of graphs that are variants of the so-called layered wheel. One of their key properties is that while the treewidth is bounded by a function of the clique number, the construction can be adjusted to make the dependance grow arbitrarily. Some of these classes provide counter-examples to several conjectures. In particular, the construction

Soma Haraoka, Edmund Soji Otabe, Yasunori Mawatari

Studies of the magnetic interference of sandwich-type Josephson junctions in which perpendicular or oblique magnetic fields are applied to the junction plane have received less attention than those where the applied magnetic fields are parallel. Recently, it has been theoretically demonstrated that a variety of magnetic interferences of the critical currents

Xiaolan Chen, Jiayang Xiang, Shanfu Lu, Yexin Liu

Large language models (LLMs) have emerged as powerful tools with transformative potential across numerous domains, including healthcare and medicine. In the medical domain, LLMs hold promise for tasks ranging from clinical decision support to patient education. However, evaluating the performance of LLMs in medical contexts presents unique challenges due to

Dongjun Lee, Choongwon Park, Jaehyuk Kim, Heesoo Park

Recent advancements in large language models (LLMs) have enabled in-context learning (ICL)-based methods that significantly outperform fine-tuning approaches for text-to-SQL tasks. However, their performance is still considerably lower than that of human experts on benchmarks that include complex schemas and queries, such as BIRD. This study considers the se

Naoki Yasukawa, Taichiro Nishio, Yasunori Mawatari

Superconducting nanowire cryotrons (nTrons) are expected to be used as interfaces for super-high-performance hybrid devices in which superconductor and semiconductor circuits are combined. However, nTrons are still under development, and diverse analyses of these devices are needed. Accordingly, we have developed a numerical technique to simulate the three-t

Daigo Shishika, Alexander Von Moll, Dipankar Maity, Michael Dorothy

Can deception exist in differential games? We provide a case study for a Turret-Attacker differential game, where two Attackers seek to score points by reaching a target region while a Turret tries to minimize the score by aligning itself with the Attackers before they reach the target. In contrast to the original problem solved with complete information, we

Xiaowang Wang, Chaitanya A. Gadre, Wanjuan Zou, Runqing Yang

Grain boundaries (GBs) strongly influence thermal transport in crystalline solids by disrupting lattice periodicity and scattering phonons. Due to the atomic-level disorder and structural complexity, a fundamental understanding of how specific GB geometries regulate nanoscale phonon behavior and macroscopic thermal conductivity has remained elusive. Here, us

Nobuhito Maru, Ryujiro Nago

A five dimensional $SU(6)$ grand gauge-Higgs unification compactified on $S^1/Z_2$ is discussed. We propose new sets of the $SU(6)$ representations where the quarks and leptons in one generation are embedded and there is no extra massless exotic fermions absent in the Standard Model. The correct electroweak symmetry breaking pattern can be realized by introd

Zhi-Lei Zhang, Guo-Jian Qiao, C. P. Sun

We revisit the Feynman approach to the Josephson effect, which employs a pair of linear coupling equations for its modeling. It is found that while the exact solutions can account for the AC Josephson effect when the coupling strength is significantly less than the voltage, they fail to produce the DC Josephson effect in any practical scenario. To address th

Jyoti Prasad Deka, Amarendra K. Sarma

We investigate the temporal dynamics of the Ikeda Map with Balanced Gain and Loss and in the presence of feedback loops with saturation nonlinearity. From the bifurcation analysis, we find that the temporal evolution of optical power undergoes period quadrupling at the exceptional point (EP) of the system and beyond that, chaotic dynamics emerge in the syste

Tatsuki Shimizu, Taira Oogi, Takashi Okamoto, Masahiro Nagashima

We have investigated the redshift evolution of the relationship between supermassive black hole (SMBH) mass and host bulge mass using a semi-analytical galaxy formation model $\nu^2$GC. Our model reproduces the relation in the local universe well. We find that, at high redshift ($z \gtrsim 3$), two sequences appear in the SMBH mass--bulge mass plane. The eme

Aakash Tripathi, Asim Waqas, Matthew B. Schabath, Yasin Yilmaz

HONeYBEE (Harmonized ONcologY Biomedical Embedding Encoder) is an open-source framework that integrates multimodal biomedical data for oncology applications. It processes clinical data (structured and unstructured), whole-slide images, radiology scans, and molecular profiles to generate unified patient-level embeddings using domain-specific foundation models

Min-Hyeok Sun, Dong-Hee Paek, Seung-Hyun Song, Seung-Hyun Kong

Focusing on the strength of 4D (4-Dimensional) radar, research about robust 3D object detection networks in adverse weather conditions has gained attention. To train such networks, datasets that contain large amounts of 4D radar data and ground truth labels are essential. However, the existing 4D radar datasets (e.g., K-Radar) lack sufficient sensor data and

Smit Desai, Mateusz Dubiel, Luis A. Leiva

Voice User Interfaces (VUIs) increasingly leverage 'humanness' as a foundational design metaphor, adopting roles like 'assistants,' 'teachers,' and 'secretaries' to foster natural interactions. Yet, this approach can sometimes misalign user trust and reinforce societal stereotypes, leading to socio-technical challenges that might impede long-term engagement.

Marcela Giraldo, Arkadiy Simonov, Hasung Sim, Ahmed Samir Lotfy

The functionality of magnetoelectric multiferroics depends on the formation, size, and coupling of their magnetic and electric domains. Knowing the parameters guiding these criteria is a key effort in the emerging field of magnetoelectric domain engineering. Here we show, using a combination of piezoresponse-force microscopy, non-linear optics, and x-ray sca