October 2023 arXiv papers — page 37

Xhulja Shahini, Domenic Bubel, Andreas Metzger

Software quality assurance activities become increasingly difficult as software systems become more and more complex and continuously grow in size. Moreover, testing becomes even more expensive when dealing with large-scale systems. Thus, to effectively allocate quality assurance resources, researchers have proposed fault prediction (FP) which utilizes machi

Kazuhiro Kuboki

We study phase separation in the $t-t'-J$ model in which the constraint of no double occupancy due to strong electron correlations leads to effective narrowing of the band width. Using a mean-field approximation, we calculate the compressibility in the normal state and show that the phase separation can be induced by the variation of the band width.

N. Makris, A. Papageorgopoulos, P. Konteli, I. Tsoni

We experimentally evaluate the SKR generation for unoptimized QKD pairs in switched QKD and compare the performance of the switched-QKD with relayed-QKD networks to reveal they perform better for short distances and at large networks.

Dongkyun Kim, Mingi Kwon, Youngjung Uh

When the training dataset comprises a 1:1 proportion of dogs to cats, a generative model that produces 1:1 dogs and cats better resembles the training species distribution than another model with 3:1 dogs and cats. Can we capture this phenomenon using existing metrics? Unfortunately, we cannot, because these metrics do not provide any interpretability beyond

Xiaolong Xu, Xuanhong Zhou, Muhammad Bilal, Sherali Zeadally

In recent years, the maturation of emerging technologies such as Virtual Reality, Digital twins, and Blockchain has accelerated the realization of the metaverse. As a virtual world independent of the real world, the metaverse will provide users with a variety of virtual activities that bring great convenience to society. In addition, the metaverse can facili

N. Makris, A. Ntanos, A. Papageorgopoulos, A. Stathis

We have successfully integrated an O-band commercial Quantum-Key-Distribution (QKD) system over a lit GPON testbed that replicates a carrier-grade Fiber-to-the-Home (FTTH) optical access network with multiple ONTs to emulate real-life FTTH operational deployments.

M. W. Healy-Kalesh, M. J. Darnley, M. M. Shara, K. M. Lanzetta

A nova super-remnant (NSR) is an immense structure associated with a nova that forms when frequent and recurrent nova eruptions sweep up surrounding interstellar material (ISM) into a high density and distant shell. The prototypical NSR, measuring over 100 pc across, was discovered in 2014 around the annually erupting nova M31N 2008-12a. Hydrodynamical simul

Hrutuj Raut

A shell and tube heat exchanger design with respect to the total heat transfer rate and temperature profile has been invstigated by numerical modelling. The HE comprises of a single tube and has been resolved by the two equation models namely, the k epsilon and k omega. The low RMS residual levels calculated showed that the convergence is better in the k ome

Maarten Buyl, MaryBeth Defrance, Tijl De Bie

Current fairness toolkits in machine learning only admit a limited range of fairness definitions and have seen little integration with automatic differentiation libraries, despite the central role these libraries play in modern machine learning pipelines. We introduce a framework of fairness regularization terms (fairrets) which quantify bias as modular, fle

Chamuditha Jayanga Galappaththige, Gayal Kuruppu, Muhammad Haris Khan

Diabetic retinopathy (DR) is caused by long-standing diabetes and is among the fifth leading cause for visual impairments. The process of early diagnosis and treatments could be helpful in curing the disease, however, the detection procedure is rather challenging and mostly tedious. Therefore, automated diabetic retinopathy classification using deep learning

N. V. Ustinov

New type of localized solutions for the two-dimensional multicomponent Yajima-Oikawa system is presented. The dynamics of solutions of this type occurs on the zero background and is similar to that of rogue waves.

Joost Croonen, Luca Pezzini, Fabio Bacchini, Giovanni Lapenta

In this paper, we introduce and discuss an exactly energy-conserving Particle-in-Cell method for arbitrary curvilinear coordinates. The flexibility provided by curvilinear coordinates enables the study of plasmas in complex-shaped domains by aligning the grid to the given geometry, or by focusing grid resolution on regions of interest without overresolving t

Yongqing Zhao, Wenjun Liu, Guangying Lv, Yuepeng Wang

In this paper, the problem of continuous data assimilation of three dimensional primitive equations with magnetic field in thin domain is studied. We establish the well-posedness of the assimilation system and prove that the $H^2$-strong solution of the assimilation system converges exponentially to the reference solution in the sense of $L^2$ as $t\rightarr

Luca Gamberi, Alessia Annibale, Pierpaolo Vivo

Information in the form of data, which can be stored and transferred between users, can be viewed as an intangible commodity, which can be traded in exchange for money. Determining the fair price at which a string of data should be traded is an important and open problem in many settings. In this work we develop a statistical physics framework that allows to

Anh T. Hoang, Zsolt J. Viharos

Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional machine learning algorithms rely on well-defined input and output variables; however, there are scenarios where the separation between the input and output variables and the underlying, associated input and output layers o

Ethan Tregidga, James Steiner, Cecilia Garraffo, Carter Rhea

Black hole X-ray binaries (BHBs) offer insights into extreme gravitational environments and the testing of general relativity. The X-ray spectrum collected by NICER offers valuable information on the properties and behaviour of BHBs through spectral fitting. However, traditional spectral fitting methods are slow and scale poorly with model complexity. This p

Isaac Meilijson

The Shepp & Vardi (1982) implementation of the EM algorithm for PET scan tumor estimation provides a point estimate of the tumor. The current study presents a closed-form formula of the observed Fisher information for Shepp & Vardi PET scan tumor estimation. Keywords: PET scan, EM algorithm, Fisher information matrix, standard errors.

Jack Miller, Charles O'Neill, Thang Bui

In some settings neural networks exhibit a phenomenon known as \textit{grokking}, where they achieve perfect or near-perfect accuracy on the validation set long after the same performance has been achieved on the training set. In this paper, we discover that grokking is not limited to neural networks but occurs in other settings such as Gaussian process (GP)

Shengna Liu, Liancun Zheng

In this paper, considering the variation of the viscous dissipative heat in the transfer direction, a new theoretical formula for thermal conductivity measurement was proposed based on the energy equation of the rotational Couette flow field. This theoretical formula shows that thermal conductivity and rheology have a synergistic effect. Based on this theore

Hao Li, Xiao-Hu Zhou, Shu-Hai Li, Mei-Jiang Gui

Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing offline RL challenges because of their capability to mitigate the limitations of data coverage through data generation using models. Nonetheless, a prevalent issue in offline RL is th

Han Bi, James Jun He

The search for evidence of Majorana states on the edges of topological superconductors (TSCs) is challenging due to the difficulty of detecting such charge-neutral electronic quasiparticles. Local microwave spectroscopy has been shown to be a possible method to detect propagating Majorana modes, where a spatially focused light beam must be used. Here, we sho

Zhi-Hai Liu, Wenkai Lou, Kai Chang, H. Q. Xu

Narrow bandgap semiconductor nanostructures have been explored for realization of topological superconducting quantum devices in which Majorana states can be created and employed for constructing topological qubits. However, a prerequisite to achieve the topological phase transition in these nanostructures is application of a magnetic field, which could comp

Tatjana Thomas, Yassine Agarmani, Steffi Hartmann, Mark Kartsovnik

Using a combination of resistance fluctuation (noise) and dielectric spectroscopy we investigate the nature of relaxor-type electronic ferroelectricity in the organic conductor $\kappa$-(BETS)$_2$Mn[N(CN)$_2$]$_3$, a system representative for a wider class of materials, where strong correlations of electrons on a lattice of dimerized molecules results in an

Silvère Gangloff, Pierre Guillon, Piotr Oprocha

It is well-known that when a positively expansive dynamical system is invertible then its underlying space is finite. C.Morales has introduced a decade ago a natural way to generalize positive expansiveness, by introducing other properties that he called positive $n$-expansiveness, for all $n \ge 1$, positive $1$-expansiveness being identical to positive exp

Francesco Belardinelli, Wojciech Jamroga, Munyque Mittelmann, Aniello Murano

In this paper, we investigate the probabilistic variants of the strategy logics ATL and ATL* under imperfect information. Specifically, we present novel decidability and complexity results when the model transitions are stochastic and agents play uniform strategies. That is, the semantics of the logics are based on multi-agent, stochastic transition systems

L. Linan, A. Maharana, S. Poedts, B. Schmieder

The aim of this paper is to present the implementation of two new CME models in the space weather forecasting tool, EUHFORIA. We introduce the two toroidal CME models analytically, along with their numerical implementation in EUHFORIA. One model is based on the modified Miller-Turner (mMT) solution, while the other is derived from the Soloviev equilibrium, a

Zhaohui Yan, Songlin Yang, Wei Liu, Kewei Tu

Entity and Relation Extraction (ERE) is an important task in information extraction. Recent marker-based pipeline models achieve state-of-the-art performance, but still suffer from the error propagation issue. Also, most of current ERE models do not take into account higher-order interactions between multiple entities and relations, while higher-order modeli

Rufeng Xiao, Yuze Ge, Rujun Jiang, Yifan Yan

The empirical loss, commonly referred to as the average loss, is extensively utilized for training machine learning models. However, in order to address the diverse performance requirements of machine learning models, the use of the rank-based loss is prevalent, replacing the empirical loss in many cases. The rank-based loss comprises a weighted sum of sorte

Sergey Kitaev, Philip B. Zhang

The Eulerian polynomials $A_n(x)$ give the distribution of descents over permutations. It is also known that the distribution of descents over stack-sortable permutations (i.e. permutations sortable by a certain algorithm whose internal storage is limited to a single stack data structure) is given by the Narayana numbers $\frac{1}{n}{n \choose k}{n \choose k

Puneet Sharma, Stephan Herminghaus, Knut M. Heidemann

Bi-modla public transport system consisting of a rail-bound line service and a fleet of on-demand shuttles providing connections to the line service stops is studied for the states of Berlin and Brandenburg for user adoption of x = 1% and x = 10% of the total population.

Catalin Dima, Wojciech Jamroga

Real-life agents seldom have unlimited reasoning power. In this paper, we propose and study a new formal notion of computationally bounded strategic ability in multi-agent systems. The notion characterizes the ability of a set of agents to synthesize an executable strategy in the form of a Turing machine within a given complexity class, that ensures the sati

Ping Guo, Xiangpeng Wei, Yue Hu, Baosong Yang

Expressing universal semantics common to all languages is helpful in understanding the meanings of complex and culture-specific sentences. The research theme underlying this scenario focuses on learning universal representations across languages with the usage of massive parallel corpora. However, due to the sparsity and scarcity of parallel data, there is s

Igor G. Vladimirov, Ian R. Petersen

This paper is concerned with open quantum harmonic oscillators (OQHOs) described by linear quantum stochastic differential equations. This framework includes isolated oscillators with zero Hamiltonian, whose system variables remain unchanged (in the Heisenberg picture of quantum dynamics) over the course of time, making such systems potentially applicable as

Aneta Maslowska, Dominika M. Kochanowska, Adrian Sulich, Jaroslaw Z. Domagala

This study explores the suitability of semi-insulating compounds, specifically (Cd,Mn)Te and (Cd,Mn)(Te,Se), as materials for room temperature X-ray and gamma-ray detectors. These compounds were grown using the Bridgman method, known for its efficient growth rate. The investigation aims to compare their crystal structure, mechanical properties, optical chara

Alex Tamkin, Mohammad Taufeeque, Noah D. Goodman

Understanding neural networks is challenging in part because of the dense, continuous nature of their hidden states. We explore whether we can train neural networks to have hidden states that are sparse, discrete, and more interpretable by quantizing their continuous features into what we call codebook features. Codebook features are produced by finetuning n

Didier Henrion

The moment-SOS (sum of squares) hierarchy is a powerful approach for solving globally non-convex polynomial optimization problems (POPs) at the price of solving a family of convex semidefinite optimization problems (called moment-SOS relaxations) of increasing size, controlled by an integer, the relaxation order. We say that a relaxation of a given order is

Anirudh Khatry, Sumit Gulwani, Priyanshu Gupta, Vu Le

Target similarity tuning (TST) is a method of selecting relevant examples in natural language (NL) to code generation through large language models (LLMs) to improve performance. Its goal is to adapt a sentence embedding model to have the similarity between two NL inputs match the similarity between their associated code outputs. In this paper, we propose di

Lynne A. Hillenbrand, Adolfo Carvalho, Jan van Roestel, Kishalay De

We present evidence in support of the hypothesis that the young stellar object RNO 54 is a mature-stage FU Ori type source. The star was first cataloged as a ``red nebulous object" in the 1980s but appears to have undergone its outburst prior to the 1890s. Present-day optical and near-infrared spectra are consistent with those of other FU Ori type stars, bot

Maja Napieraj, Annie Brûlet, Javier Perez, François Boué

We address the issue of structure changes of a canola protein gel (as a solid food model) during gastrointestinal digestion. We present a method for synchrotron Small-Angle X-ray Scattering analysis of the digestion of a gel in a capillary. Scanning the capillary allows tracking the digestion under diffusion of enzymatic juices. The fitting parameters charac

Wenqin Wang, Zixin Zhang, Zhangliang Chen, Yonghao Wang

Many hot Jupiters may experience orbital decays, which are manifested as long-term transit timing variations. We have analyzed 7068 transits from the Transiting Exoplanet Survey Satellite (TESS) for a sample of 326 hot Jupiters. These new mid-transit time data allow us to update ephemerides for these systems. By combining the new TESS transit timing data wit

Paul-Andrei Dragan, Andreas Metzger, Klaus Pohl

When multiple self-adaptive systems share the same environment and have common goals, they may coordinate their adaptations at runtime to avoid conflicts and to satisfy their goals. There are two approaches to coordination. (1) Logically centralized, where a supervisor has complete control over the individual self-adaptive systems. Such approach is infeasibl

Venugopal V. Veeravalli, Georgios Fellouris, George V. Moustakides

In the problem of quickest change detection, a change occurs at some unknown time in the distribution of a sequence of random vectors that are monitored in real time, and the goal is to detect this change as quickly as possible subject to a certain false alarm constraint. In this work we consider this problem in the presence of parametric uncertainty in the

Yuchao Hua, Lingai Luo, Zeng-Yuan Guo

Here, we investigate the maximum power and corresponding efficiency of thermoelectric generators through devising a set of protocols for the isothermal and adiabatic processes of thermoelectricity to build a Carnot-like thermoelectric cycle, with the analysis based on fluctuation theorem (FT). First of all, the Carnot efficiency can be readily obtained for t

Yamina Benamra, Laurent Auvray, Jérôme Andrieux, François Cauwet

In this work, the successful heteroepitaxial growth of boron carbide (B x C) on 4HSiC(0001) 4{\textdegree} off substrate using chemical vapor deposition (CVD) is reported. Towards this end, a two-step procedure was developed, involving the 4H-SiC substrate boridation under BCl 3 precursor at 1200{\textdegree}C, followed by conventional CVD under BCl 3 + C 3

M. Fuat Kına, Erdem Yörük, Ali Hürriyetoğlu, Melih Can Yardı

This paper tests the validity of a digital trace database (Politus) obtained from Twitter, with a recently conducted representative social survey, focusing on the use case of religiosity in Turkey. Religiosity scores in the research are extracted using supervised machine learning under the Politus project. The validation analysis depends on two steps. First,

Francesco Belardinelli, Angelo Ferrando, Wojciech Jamroga, Vadim Malvone

The model checking problem for multi-agent systems against Strategy Logic specifications is known to be non-elementary. On this logic several fragments have been defined to tackle this issue but at the expense of expressiveness. In this paper, we propose a three-valued semantics for Strategy Logic upon which we define an abstraction method. We show that the

Jiachen Li, Xiaojin Gong

This work aims to adapt large-scale pre-trained vision-language models, such as contrastive language-image pretraining (CLIP), to enhance the performance of object reidentification (Re-ID) across various supervision settings. Although prompt learning has enabled a recent work named CLIP-ReID to achieve promising performance, the underlying mechanisms and the

Chenze Shao, Zhengrui Ma, Min Zhang, Yang Feng

Maximum likelihood estimation (MLE) is a statistical method used to estimate the parameters of a probability distribution that best explain the observed data. In the context of text generation, MLE is often used to train generative language models, which can then be used to generate new text. However, we argue that MLE is not always necessary and optimal, es

Christoph Angermann, Johannes Bereiter-Payr, Kerstin Stock, Markus Haltmeier

Medical image processing has been highlighted as an area where deep learning-based models have the greatest potential. However, in the medical field in particular, problems of data availability and privacy are hampering research progress and thus rapid implementation in clinical routine. The generation of synthetic data not only ensures privacy, but also all

Takuya Kawada, Isamu Yasuda, Masashi Kawaguchi, Masamitsu Hayashi

We have developed an approach to determine the magnetoelastic coupling constant of magnetic layers in thin film heterostructures. The film is formed on a piezoelectric substrate between two interdigital transducers (IDT), a platform often used to construct a surface acoustic wave device. With the substrate piezoelectricity, strain is induced into the film by

Reza Namdar, Mohammad Norouzi, Fathollah Varnik

The present study provides a systematic derivation of a phase-field version of the momentum, mass and heat transport equations, while accounting for chemical reactions in the fluid phase. To achieve this goal, the volume averaging technique is used to reformulate the conservation equations in the presence of multiple phases and their respective diffuse inter

Juliette Veuillez--Mainard, Simon Modeste

This article deals with peer-assessment in the context of higher education teaching in mathematics, and examines the nature of student activity when assessing work produced by peers. After an overview of research on peer assessment, we propose an experiment with students in a specific post-secondary scientific class, and analyze the activity of the students

Junxiao Xue, Jie Wang, Xuecheng Wu, Qian Zhang

Video content is rich in semantics and has the ability to evoke various emotions in viewers. In recent years, with the rapid development of affective computing and the explosive growth of visual data, affective video content analysis (AVCA) as an essential branch of affective computing has become a widely researched topic. In this study, we comprehensively r

Daniel Seifried, Stefanie Walch, Thorsten Balduin

We extend previous theoretical works to gain a better understanding of the origin of observed polarisation degree spectra of molecular clouds, which show a so-called V-shape, i.e. a pronounced minimum around 350~$\mu$m. For this purpose, we present results of two-phase dust models investigated with POLARIS. We also provide a guideline to calculate individual

Petar Mali, Sonja Gombar, Slobodan Radoševi\' c, Milica Rutonjski

We demonstrate that certain classes of Schl\" omilch-like infinite series and series that include generalized hypergeometric functions can be calculated in closed form starting from a simple quantum model of a particle trapped inside an infinite potential well and using principles of quantum mechanics. We provide a general framework based on the Hilbert spac

Roy Hirsch, Regev Cohen, Mathilde Caron, Tomer Golany

A key element of computer-assisted surgery systems is phase recognition of surgical videos. Existing phase recognition algorithms require frame-wise annotation of a large number of videos, which is time and money consuming. In this work we join concepts of graph segmentation with self-supervised learning to derive a random-walk solution for per-frame phase p

Konstantinos Boutivas, Dimitrios Katsinis, Georgios Pastras, Nikolaos Tetradis

We compute the evolution of the entanglement entropy for a massless field within a spherical region throughout the inflationary period and the subsequent era of radiation domination, starting from the Bunch-Davies vacuum. In order to focus on the entanglement of modes that are directly accessible to observations, we impose an ultraviolet cutoff set by the wa

Rupsa Saha, Vladimir I. Zadorozhny, Ole-Christoffer Granmo

We propose a novel way of assessing and fusing noisy dynamic data using a Tsetlin Machine. Our approach consists in monitoring how explanations in form of logical clauses that a TM learns changes with possible noise in dynamic data. This way TM can recognize the noise by lowering weights of previously learned clauses, or reflect it in the form of new clauses

Isamu Yasuda, Takuya Kawada, Hiroki Matsumoto, Masashi Kawaguchi

We studied helicity resolved Raman scattering in PtSe$_2$ flakes with different thicknesses. The peak amplitude of helicity-switched Raman scattering is significantly larger than that of helicity-conserved scattering for the in-plane $E_{g}$ mode, consistent with the Raman tensor analyses and conservation law of angular momentum. The peak amplitude of the he

Clément Weinreich, Louis de Oliveira, Antoine Houdard, Georges Nader

Neural materials typically consist of a collection of neural features along with a decoder network. The main challenge in integrating such models in real-time rendering pipelines lies in the large size required to store their features in GPU memory and the complexity of evaluating the network efficiently. We present a neural material model whose features and

Damiano Ricciarelli, Ioannis Deretzis, Gaetano Calogero, Giuseppe Fisicaro

The properties of liquid Si-Ge binary systems at melting conditions deviate from those expected by the ideal alloy approximation. Particularly, a non-linear dependence of the dielectric functions occurs with the reflectivity of liquid Si-Ge reaching a maximum at 50\% Ge content, being 10\% higher than in pure Si or Ge. Using \textit{ab initio} methodologies,

Francoise Combes, Neeraj Gupta

Absorption lines at high redshift in front of quasars are rare in the mm domain. Only five associated and five intervening systems have been reported in the literature. These bring very useful information complementary to emission lines, for instance, to distinguish between inflows and outflows. They are also good candidates to study the variations of the fu

Ting-Ting Sun, Bing-Xin Li, Kun Liu

(Pseudo)spin symmetries play vital roles in nuclear physics and have been studied extensively in spherical nuclei. In this work, possible spin and pseudospin symmetries in deformed nuclei are examined by solving a coupled-channel Dirac equation with quadruple deformation. The Green's function method is taken which provides a novel way to exactly determine th

Nathan Fradet, Jean-Pierre Briot, Fabien Chhel, Amal El Fallah Seghrouchni

Recent progress in natural language processing has been adapted to the symbolic music modality. Language models, such as Transformers, have been used with symbolic music for a variety of tasks among which music generation, modeling or transcription, with state-of-the-art performances. These models are beginning to be used in production products. To encode an

Amirsadegh Roshanzamir

In recent years and with introduction of 5G cellular network and communication, researchers have shown great interest in Multiple Input Multiple Output (MIMO) communication, an advanced technology. Many studies have examined the problem of designing the beampattern for MIMO communication using uniform arrays and the covariance-based method to concentrate the

Xingyan Chen, Yaling Liu, Huaming Du, Mu Wang

Federated learning, a decentralized approach to machine learning, faces significant challenges such as extensive communication overheads, slow convergence, and unstable improvements. These challenges primarily stem from the gradient variance due to heterogeneous client data distributions. To address this, we introduce a novel Networked Control Variates (FedN

Qin Fei

The recent stochastic signal observed jointly by NANOGrav, PPTA, EPTA, and CPTA can be accounted for by scalar-induced gravitational waves (SIGWs). The source of the SIGWs is from the primordial curvature perturbations, and the main contribution to the SIGWs is from the peak of the primordial curvature power spectrum. To effectively model this peak, we apply

Niklas Lettner, Lukas Antoniuk, Anna P. Ovvyan, Helge Gehring

Nanophotonic quantum devices can significantly boost light-matter interaction which is important for applications such as quantum networks. Reaching a high interaction strength between an optical transition of a spin system and a single mode of light is an essential step which demands precise control over all degrees of freedom of the optical coupling. While

Toshihiro Nishimura, Takeshi Takaki, Yosuke Suzuki, Tokuo Tsuji

This paper presents a novel industrial robotic gripper with a high grasping speed (maximum: 1396 mm/s), high tip force (maximum: 80 N) for grasping, large motion range, and lightweight design (0.3 kg). To realize these features, the high-speed section of the quick-return mechanism and load-sensitive continuously variable transmission mechanism are installed

Hai-Feng Wang, Giovanni Carraro, Xin Li, Qi-Da Li

In this study we estimate the stellar ages of LAMOST DR8 Red Giant Branch (RGB) stars based on the Gradient Boosting Decision Tree algorithm (GBDT). We used 2,643 RGB stars extracted from the APOKASC-2 astero-seismological catalog as training data-set. After selecting the parameterses ([$\alpha$/Fe], [C/Fe], T$_{eff}$, [N/Fe], [C/H], log g) highly correlated

Rui An, Vera Gluscevic

We present a model-independent reconstruction of the early expansion and thermal histories of the universe, obtained from light element abundance measurements. The expansion history is tightly constrained around the onset of the Big Bang Nucleosynthesis (BBN). The temperature of photons is additionally constrained around the time of neutrino decoupling. Allo

Minh Tran, Mohammad Soleymani

Existing privacy-preserving speech representation learning methods target a single application domain. In this paper, we present a novel framework to anonymize utterance-level speech embeddings generated by pre-trained encoders and show its effectiveness for a range of speech classification tasks. Specifically, given the representations from a pre-trained en

Ryota Tanaka, Tomohiro Suzuki, Kazuya Takeda, Keisuke Fujii

Automatic evaluating systems are fundamental issues in sports technologies. In many sports, such as figure skating, automated evaluating methods based on pose estimation have been proposed. However, previous studies have evaluated skaters' skills in 2D analysis. In this paper, we propose an automatic edge error judgment system with a monocular smartphone cam

Toshihiro Nishimura, Tetsuyou Watanabe

This study proposes a novel robotic gripper driven by a single motor. The main task is to pick up objects in confined spaces. For this purpose, the developed gripper has three operating modes: grasping, finger-bending, and pull-in modes. Using these three modes, the developed gripper can rotate and translate a grasped object, i.e., can perform in-hand manipu

Jiahai Feng, Jacob Steinhardt

To correctly use in-context information, language models (LMs) must bind entities to their attributes. For example, given a context describing a "green square" and a "blue circle", LMs must bind the shapes to their respective colors. We analyze LM representations and identify the binding ID mechanism: a general mechanism for solving the binding problem, whic

Feng Zhang, Ming Tian, Zhiqiang Li, Bin Xu

Tone mapping aims to convert high dynamic range (HDR) images to low dynamic range (LDR) representations, a critical task in the camera imaging pipeline. In recent years, 3-Dimensional LookUp Table (3D LUT) based methods have gained attention due to their ability to strike a favorable balance between enhancement performance and computational efficiency. Howev

Xiao Liang, Tao Shi, Yaoyuan Liang, Te Tao

Video grounding aims to localize the target moment in an untrimmed video corresponding to a given sentence query. Existing methods typically select the best prediction from a set of predefined proposals or directly regress the target span in a single-shot manner, resulting in the absence of a systematical prediction refinement process. In this paper, we prop

Rui Qin, Ming Sun, Fangyuan Zhang, Xing Wen

Blind super-resolution (BSR) methods based on high-resolution (HR) reconstruction codebooks have achieved promising results in recent years. However, we find that a codebook based on HR reconstruction may not effectively capture the complex correlations between low-resolution (LR) and HR images. In detail, multiple HR images may produce similar LR versions d

Shi Yan, Yan Liang, Le Zheng, Mingyang Fan

The optimality of Bayesian filtering relies on the completeness of prior models, while deep learning holds a distinct advantage in learning models from offline data. Nevertheless, the current fusion of these two methodologies remains largely ad hoc, lacking a theoretical foundation. This paper presents a novel solution, namely an explainable gated Bayesian r

Chenghao Li, Yifei Wu, Wenbo Shen, Zichen Zhao

Rust programming language is gaining popularity rapidly in building reliable and secure systems due to its security guarantees and outstanding performance. To provide extra functionalities, the Rust compiler introduces Rust unstable features (RUF) to extend compiler functionality, syntax, and standard library support. However, these features are unstable and

Xiaoliang Wan, Tao Zhou, Yuancheng Zhou

We introduce an adaptive sampling method for the Deep Ritz method aimed at solving partial differential equations (PDEs). Two deep neural networks are used. One network is employed to approximate the solution of PDEs, while the other one is a deep generative model used to generate new collocation points to refine the training set. The adaptive sampling proce

Jiale Chen, Zhao-Xian Chen, Jun-Long Kou, Yan-Qing Lu

For photonic crystal slab (PCS) structures, bound states in the continuum (BICs) and circularly polarized states (dubbed C-points) are important topological polarization singularities in momentum-space and have attracted burgeoning attention due to their novel topological and optical properties. In our work, the evolution of polarization singularities from B

Yudong Chen, Sen Wang, Jiajun Liu, Xuwei Xu

Conventionally, during the knowledge distillation process (e.g. feature distillation), an additional projector is often required to perform feature transformation due to the dimension mismatch between the teacher and the student networks. Interestingly, we discovered that even if the student and the teacher have the same feature dimensions, adding a projecto

Katharina Pohl, Martin Hutzenthaler

The combination of the It\^o formula and the Bismut-Elworthy-Li formula implies that suitable smooth solutions of semilinear Kolmogorov partial differential equations (PDEs) are also solutions to certain stochastic fixed point equations (SFPEs). In this paper we generalize known results on existence and uniqueness of solutions of SFPEs associated with PDEs w

Hualin Liu, Qi Li, Weifang Chen, Lei Wu

The shock wave and boundary layer interaction (SWBLI) plays an important role in the design of hypersonic vehicles. However, discrepancies between the numerical results of high-temperature gas dynamics and experiment data have not been fully addressed. It is believed that the rarefaction effects are important in SWBLI, but the systematic analysis of the temp

Jason J. Choi, Donggun Lee, Boyang Li, Jonathan P. How

Control invariant sets are crucial for various methods that aim to design safe control policies for systems whose state constraints must be satisfied over an indefinite time horizon. In this article, we explore the connections among reachability, control invariance, and Control Barrier Functions (CBFs). Unlike prior formulations based on backward reachabilit

Sebastián Espinel-Ríos, José L. Avalos

We outline a modeling and optimization strategy for investigating dynamic metabolic engineering interventions. Our framework is particularly useful at the early stages of research and development, often constrained by limited knowledge and experimental data. Elucidating a priori optimal trajectories of manipulatable intracellular fluxes can guide the design

Leonid Ugadiarov, Vitaliy Vorobyov, Aleksandr I. Panov

The advances in unsupervised object-centric representation learning have significantly improved its application to downstream tasks. Recent works highlight that disentangled object representations can aid policy learning in image-based, object-centric reinforcement learning tasks. This paper proposes a novel object-centric reinforcement learning algorithm th

Fengyuan Shi, Limin Wang

Despite the success of transformers on various computer vision tasks, they suffer from excessive memory and computational cost. Some works present dynamic vision transformers to accelerate inference by pruning redundant tokens. A key to improving token pruning is using well-trained models as initialization for faster convergence and better performance. Howev

Mou Deb, Madhab Deb, Mrinal Kanti Dhar

Accurate teeth segmentation and orientation are fundamental in modern oral healthcare, enabling precise diagnosis, treatment planning, and dental implant design. In this study, we present a comprehensive approach to teeth segmentation and orientation from panoramic X-ray images, leveraging deep-learning techniques. We built an end-to-end instance segmentatio

Tim Binz, Felix Brandt, Matthias Hieber, Arnab Roy

This article investigates the interaction of nematic liquid crystals modeled by a simplified Ericksen-Leslie model with a rigid body. It is shown that this problem is locally strongly well-posed, and that it also admits a unique, global strong solution for initial data close to constant equilibria. The proof of the global strong solution relies on a new spli

F. H. Kamin, S. Salimi, M. B. Arjmandi

We investigate the steady-state charging process of a single-cell quantum battery embedded in an N-cell star network of qubits, each interacting with a fermion reservoir, collectively and individually in equilibrium and non-equilibrium scenarios, respectively. We find an optimal steady-state charging in both scenarios, which grows monotonically with the rese

Dexter Neo, Tsuhan Chen

We present a novel extension to the family of Soft Actor-Critic (SAC) algorithms. We argue that based on the Maximum Entropy Principle, discrete SAC can be further improved via additional statistical constraints derived from a surrogate critic policy. Furthermore, our findings suggests that these constraints provide an added robustness against potential doma

Bo Peng, Karol Kowalski

In this study, we introduce a novel approach to coupled-cluster Green's function (CCGF) embedding by seamlessly integrating conventional CCGF theory with the state-of-the-art sub-system embedding sub-algebras coupled cluster (SES-CC) formalism. This integration focuses primarily on delineating the characteristics of the sub-system and the corresponding segme

Jennifer Tang, Aviv Adler, Amir Ajorlou, Ali Jadbabaie

Social networks often exert social pressure, causing individuals to adapt their expressed opinions to conform to their peers. An agent in such systems can be modeled as having a (true and unchanging) inherent belief while broadcasting a declared opinion at each time step based on her inherent belief and the past declared opinions of her neighbors. An importa

Liao Pan, Yang Feng, Zhao Wenhui, Yua Jinwen

Decoder-only methods, such as GPT, have demonstrated superior performance in many areas compared to traditional encoder-decoder structure transformer methods. Over the years, end-to-end methods based on the traditional transformer structure, like MOTR, have achieved remarkable performance in multi-object tracking. However,the substantial computational resour

Ming-Jun Lai, Jinsil Lee

In this paper, we study a spline collocation method for a numerical solution to the optimal transport problem We mainly solve the \MAE with the second boundary condition numerically by proposing a center matching algorithm. We prove a pointwise convergence of our iterative algorithm under the assumption the boundedness of spline iterates. We use the \MAE wit

Sohrab Andaz, Carson Eisenach, Dhruv Madeka, Kari Torkkola

In this paper we address the problem of learning and backtesting inventory control policies in the presence of general arrival dynamics -- which we term as a quantity-over-time arrivals model (QOT). We also allow for order quantities to be modified as a post-processing step to meet vendor constraints such as order minimum and batch size constraints -- a comm

Zhenkai Zhang, Krista A. Ehinger, Tom Drummond

This paper introduces two key contributions aimed at improving the speed and quality of images generated through inverse diffusion processes. The first contribution involves reparameterizing the diffusion process in terms of the angle on a quarter-circular arc between the image and noise, specifically setting the conventional $\displaystyle \sqrt{\bar{\alpha

Taejun Yun, Jinhyeon Kim, Deokyeong Kang, Seong Hoon Lim

Cross-lingual transfer (XLT) is an emergent ability of multilingual language models that preserves their performance on a task to a significant extent when evaluated in languages that were not included in the fine-tuning process. While English, due to its widespread usage, is typically regarded as the primary language for model adaption in various tasks, rec