November 2022 arXiv papers — page 108

Yu-Hsi Chen, Chien-Yao Wang, Cheng-Yun Yang, Hung-Shuo Chang

We propose a post-processor, called NeighborTrack, that leverages neighbor information of the tracking target to validate and improve single-object tracking (SOT) results. It requires no additional data or retraining. Instead, it uses the confidence score predicted by the backbone SOT network to automatically derive neighbor information and then uses this in

Jumon Nozaki, Yugo Murawaki

Previous studies on neural linguistic steganography, except Ueoka et al. (2021), overlook the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper's suspicion. In this paper, we demonstrate that segmentation ambiguity indeed causes occasional decoding failures at the receiver's side. With the near-ubiquity of subwords, this pro

Nour Elhouda Djaa, Fethi Latti, Abderrahim Zagane

This paper, we define the Mus-Gradient metric on tangent bundle $TM$ by a deformation non-conform of Sasaki metric over an n-dimensional Riemannian manifold $(M, g)$. First we investigate the geometry of the Mus-Gradient metric and we characterize a new class of proper biharmonic maps. Examples of proper biharmonic maps are constructed when all of the factor

Silvio Galesso, Max Argus, Thomas Brox

The key to out-of-distribution detection is density estimation of the in-distribution data or of its feature representations. This is particularly challenging for dense anomaly detection in domains where the in-distribution data has a complex underlying structure. Nearest-Neighbors approaches have been shown to work well in object-centric data domains, such

Abdullah Hayajneh, Mohammad Shaqfeh, Erchin Serpedin, Mitchell A. Stotland

This paper presents a novel machine learning framework to consistently detect, localize and rate congenital cleft lip anomalies in human faces. The goal is to provide a universal, objective measure of facial differences and reconstructive surgical outcomes that matches human judgments. The proposed method employs the StyleGAN2 generative adversarial network

Chen Zhao, Jialu Zhang, Xiaoyue Hou, Chi Ho Yeung

As infected and vaccinated population increases, some countries decided not to impose non-pharmaceutical intervention measures anymore and to coexist with COVID-19. However, we do not have a comprehensive understanding of its consequence , especially for China where most population has not been infected and most Omicron transmissions are silent. This paper s

Lucas Guerreiro, Filipi N. Silva, Diego R. Amancio

Many real-world systems give rise to a time series of symbols. The elements in a sequence can be generated by agents walking over a networked space so that whenever a node is visited the corresponding symbol is generated. In many situations the underlying network is hidden, and one aims to recover its original structure and/or properties. For example, when a

G. I. Gakh, M. I. Konchatnij, N. P. Merenkov, E. Tomasi-Gustafsson

The dependence of the nucleon polarization in the reaction $e^+ + e^- \to N + \bar{N} +\pi^0 $ over different invariant variables in frame of the non-resonant mechanism, has been derived. The nucleon polarization is expressed in terms of six invariant complex amplitudes, assuming the conservation of the hadron electromagnetic currents and the P-invariance of

Kaicheng Yang, Ruxuan Zhang, Hua Xu, Kai Gao

Inter-modal interaction plays an indispensable role in multimodal sentiment analysis. Due to different modalities sequences are usually non-alignment, how to integrate relevant information of each modality to learn fusion representations has been one of the central challenges in multimodal learning. In this paper, a Self-Adjusting Fusion Representation Learn

Ramesh Sharma

We study umbilically synchronized space-times $M$ that have vanishing electric part of the Weyl tensor and show that (i) If $dim(M) = 4$, then $M$ is conformally flat, (ii) If $dim(M) > 4$, $M$ is conformally flat if and only if the spatial slices are conformally flat. Next, we characterize umbilically synchronized spacetimes that are vacuum and have zero el

Jie Li, Xiaohu Tang, Hanxu Hou, Yunghsiang S. Han

Partial maximum distance separable (PMDS) codes are a kind of erasure codes where the nodes are divided into multiple groups with each forming an MDS code with a smaller code length, thus they allow repairing a failed node with only a few helper nodes and can correct all erasure patterns that are information-theoretically correctable. However, the repair of

Shafqat Ul Islam, Jitendra Kumar, Rahul Kumar Walia, Sushant G. Ghosh

A mathematically consistent rotating black hole model in loop quantum gravity (LQG) is yet lacking. The scarcity of rotating black hole solutions in LQG substantially hampers the development of testing LQG from observations, e.g., from the Event Horizon Telescope (EHT) observations. The EHT observation revealed event horizon-scale images of the supermassive

Namasivayam Kalithasan, Himanshu Singh, Vishal Bindal, Arnav Tuli

Given a natural language instruction and an input scene, our goal is to train a model to output a manipulation program that can be executed by the robot. Prior approaches for this task possess one of the following limitations: (i) rely on hand-coded symbols for concepts limiting generalization beyond those seen during training [1] (ii) infer action sequences

Nils Dycke, Ilia Kuznetsov, Iryna Gurevych

Peer review constitutes a core component of scholarly publishing; yet it demands substantial expertise and training, and is susceptible to errors and biases. Various applications of NLP for peer reviewing assistance aim to support reviewers in this complex process, but the lack of clearly licensed datasets and multi-domain corpora prevent the systematic stud

Jiawei Guo, Yanzhong Yao, Han Wang, Tongxiang Gu

The physics informed neural network (PINN) is a promising method for solving time-evolution partial differential equations (PDEs). However, the standard PINN method may fail to solve the PDEs with strongly nonlinear characteristics or those with high-frequency solutions. The physics informed neural network (PINN) is a promising method for solving time-evolut

Qiang Zou

The quality of tool paths is a dominant factor in CNC machining, determining its efficiency and accuracy. This paper provides a brief review (in Chinese) on tool path planning methods reported in the literature, focusing on their categorization, motivating problems/issues, and historical development. Some promising research trends have also been stressed, es

Xiaoming Tan, Genqian Liu

For the Lam\'{e} operator $\mathcal{L}_{\lambda,\mu}$ with variable coefficients $\lambda$ and $\mu$ on a smooth compact Riemannian manifold $(M,g)$ with smooth boundary $\partial M$, we give an explicit expression for full symbol of the elastic Dirichlet-to-Neumann map $\Lambda_{\lambda,\mu}$. We show that $\Lambda_{\lambda,\mu}$ uniquely determines partial

Luxi Li, Qin Zou, Fan Zhang, Hongkai Yu

Mural image inpainting is far less explored compared to its natural image counterpart and remains largely unsolved. Most existing image-inpainting methods tend to take the target image as the only input and directly repair the damage to generate a visually plausible result. These methods obtain high performance in restoration or completion of some pre-define

Hyebin Kwon, Joungbin An, Dongwoo Lee, Won-Yong Shin

Considerable research attention has been paid to table detection by developing not only rule-based approaches reliant on hand-crafted heuristics but also deep learning approaches. Although recent studies successfully perform table detection with enhanced results, they often experience performance degradation when they are used for transferred domains whose t

Abhik Chakraborty, Parijat Barman, Ankit Kumar Singh, Xiaofei Wu

Plasmonic enhancement of nonlinear light-matter interaction can be achieved via dedicated optimization of resonant plasmonic modes that are spectrally matched to the different wavelengths involved in the particular nonlinear optical process. In this work, we investigate the generation and enhancement of broadband four-wave mixing (FWM) in a plasmonic azimuth

Karl El Hajal, Zihan Wu, Neil Scheidwasser-Clow, Gasser Elbanna

Automatic speech quality assessment is essential for audio researchers, developers, speech and language pathologists, and system quality engineers. The current state-of-the-art systems are based on framewise speech features (hand-engineered or learnable) combined with time dependency modeling. This paper proposes an efficient system with results comparable t

Arezoo Zohrabi, Pasha Zusmanovich

We prove that $\delta$-derivations of a simple finite-dimensional Lie algebra over a field of characteristic zero, with values in a finite-dimensional module, are either inner derivations, or, in the case of adjoint module, multiplications by a scalar, or some exceptional cases related to $\mathfrak{sl}(2)$. This can be viewed as an extension of the classica

Da Xu, Xu-Ke Gu, He-Kang Li, Yuan-Chao Weng

Non-classical quantum states are the pivotal features of a quantum system that differs from its classical counterpart. However, the generation and coherent control of quantum states in a macroscopic spin system remain an outstanding challenge. Here we experimentally demonstrate the quantum control of a single magnon in a macroscopic spin system (i.e., 1~mm-d

Abdelrahman Alkhodary, Berke Gur

Soft robotic manipulators provide numerous advantages over conventional rigid manipulators in fragile environments such as the marine environment. However, developing analytic inverse models necessary for shape, motion, and force control of such robots remains a challenging problem. As an alternative to analytic models, numerical models can be learned using

Firoj Alam, Fahim Dalvi, Nadir Durrani, Hassan Sajjad

The opacity of deep neural networks remains a challenge in deploying solutions where explanation is as important as precision. We present ConceptX, a human-in-the-loop framework for interpreting and annotating latent representational space in pre-trained Language Models (pLMs). We use an unsupervised method to discover concepts learned in these models and en

Xin Gao

In the field of medical image, deep convolutional neural networks(ConvNets) have achieved great success in the classification, segmentation, and registration tasks thanks to their unparalleled capacity to learn image features. However, these tasks often require large amounts of manually annotated data and are labor-intensive. Therefore, it is of significant

Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai, Song-Hai Zhang

3D single object tracking plays an essential role in many applications, such as autonomous driving. It remains a challenging problem due to the large appearance variation and the sparsity of points caused by occlusion and limited sensor capabilities. Therefore, contextual information across two consecutive frames is crucial for effective object tracking. How

Pasha Zusmanovich

We study so called regular Lie algebras, i.e. Lie algebras in which each nonzero element is regular. We make a connection with an open problem whether any element of reduced trace zero in a simple associative algebra is a commutator.

Nishant Shukla, Pawan Chetri, Ratan Boruah, Ankur Gogoi

Surface Plasmon Resonance (SPR) offers a powerful tool for label-free and non-invasive characterization of biomolecular interactions. To date, several experimental configurations, based on two fundamental physical phenomena, e.g., attenuated total reflection and diffraction, have been developed to measure the SPR signal generated due to the resonant interact

Pasha Zusmanovich

We study the behavior of contact brackets on the tensor product of two algebras, in particular, address the question of Mart\'inez and Zelmanov about extension of a contact bracket on the tensor product from the brackets on the factors.

Cécile Trottet, Thijs Vogels, Martin Jaggi, Mary-Anne Hartley

Data-driven Clinical Decision Support Systems (CDSS) have the potential to improve and standardise care with personalised probabilistic guidance. However, the size of data required necessitates collaborative learning from analogous CDSS's, which are often unsharable or imperfectly interoperable (IIO), meaning their feature sets are not perfectly overlapping.

Saswata Jana, Partha Sarathi Mandal

The coordination among drones and ground vehicles for last-mile delivery has gained significant interest in recent years. In this paper, we study \textit{multiple drone delivery scheduling problem(MDSP) \cite{Betti_ICDCN22} for last-mile delivery, where we have a set of drones with an identical battery budget and a set of delivery locations, along with rewar

Zanbin Xing, Minghui Ding, Lei Chang

We provide a novel approach to calculate the gravitational form factor of pion under the ladder approximation of the Bethe-Salpeter equation, with contact interactions. Central to this approach is a symmetry-preserving treatment of the dressed $\pi\pi$ amplitude, which shows explicitly the contributions from intrinsic quarks and bound states, the latter bein

Rakesh Arora, Phuoc-Tai Nguyen, Vicentiu D. Radulescu

In this work, we address the questions of existence, uniqueness, and boundary behavior of the positive weak-dual solution of equation $\mathbb{L}_\gamma^s u = \mathcal{F}(u)$, posed in a $C^2$ bounded domain $\Omega \subset \mathbb{R}^N$, with appropriate homogeneous boundary or exterior Dirichlet conditions. The operator $\mathbb{L}_\gamma^s$ belongs to a g

Pasha Zusmanovich

We give a new proof of a version of the main theorem of the previous paper in the series about embedding of an algebraic system into ultraproducts.

Biveen Shajilal, Oliver Thearle, Aaron Tranter, Yuerui Lu

Squeezed states are an interesting class of quantum states that have numerous applications. This work presents the design, characterisation, and operation of a bow-tie optical parametric amplifier (OPA) for squeezed vacuum generation. We report the high duty cycle operation and long-term stability of the system that makes it suitable for post-selection based

Pasha Zusmanovich

We are concerned with the question when Hom-Lie structures on a Lie algebra are closed with respect to the Jordan product. Somewhat unexpectedly, this leads us to certain questions connected with the Yang-Baxter equation, and with decomposition of a Lie algebra into the sum of subalgebras with given properties.

Debasis Das, Yunuo Cen, Jianze Wang, Xuanyao Fong

Topologically stable magnetic skyrmion has a much lower depinning current density that may be useful for memory as well as neuromorphic computing. However, skyrmion-based devices suffer from the Magnus force originating from the skyrmion Hall effect, which may result in unwanted skyrmion annihilation if the magnitude of the driving current gets too large. A

T. Liimets, I. Kolka, M. Kraus, T. Eenmäe

Context. V838 Monocerotis is a peculiar binary that underwent an immense stellar explosion in 2002, leaving behind an expanding cool supergiant and a hot B3V companion. Five years after the outburst, the B3V companion disappeared from view, and so far did not recover. Aims. We investigate the changes in the light curve and spectral features Methods. A monito

Subramanya Hegde, Madhu Mishra, Debangshu Mukherjee, Bindusar Sahoo

In this paper, we use the superconformal approach to derive the higher derivative action for N = 3 Poincare supergravity in four space-time dimensions. We first study the coupling of N = 3 vector multiplets to conformal supergravity. Thereafter we combine it with the pure N = 3 conformal supergravity action and use a minimum of three vector multiplets as com

Zhixi Cai, Shreya Ghosh, Kalin Stefanov, Abhinav Dhall

This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS). Our proposed framework, named MARLIN, is a facial video maske

Daniela Bubboloni, Michele Gori

We prove that, for any given set of networks satisfying suitable conditions, the net-oudegree network solution, the net-indegree network solution, and the total network solution are the unique network solutions on that set satisfying neutrality, consistency and cancellation. The generality of the result obtained allows to get an analogous result for social c

Gianluigi Grandesso, Elisa Alboni, Gastone P. Rosati Papini, Patrick M. Wensing

This paper presents a novel algorithm for the continuous control of dynamical systems that combines Trajectory Optimization (TO) and Reinforcement Learning (RL) in a single framework. The motivations behind this algorithm are the two main limitations of TO and RL when applied to continuous nonlinear systems to minimize a non-convex cost function. Specificall

Congpei An, Xin Xu

We develop a novel stepsize based on \BB method for solving some challenging optimization problems efficiently, named regularized \BB (RBB) stepsize. We indicate that RBB stepsize is the close solution to a $\ell_{2}^{2}$-regularized least squares problem. When the regularized item vanishes, the RBB stepsize reduces to the original \BB stepsize. RBB stepsize

Mamatha Thota, Dewei Yi, Georgios Leontidis

Humans and animals have the ability to continuously learn new information over their lifetime without losing previously acquired knowledge. However, artificial neural networks struggle with this due to new information conflicting with old knowledge, resulting in catastrophic forgetting. The complementary learning systems (CLS) theory suggests that the interp

Donato Scarcella

In 2015, M. Canadell and R. de la Llave consider a time-dependent perturbation of a vector field having an invariant torus supporting quasiperiodic solutions. Under a smallness assumption on the perturbation and assuming the perturbation decays (when t goes to infinity) exponentially fast in time, they proved the existence of motions converging in time (when

A Waqdim, M Agouri, A Abbassi, B. Elhadadi

Various physical properties (electronic, optical and thermoelectric) of cubic perovskite oxides $SrXO_3\; (X=Sc,\; Ge,\; Si)$ are investigated by using the density functional theory (DFT) within Wien2k code. This code is based on different approximations such as generalized gradient approximation GGA, PBEsol, LDA, WC and the modified Becke-Johnson exchange p

Qing Liu, Tiexiang Li, Shuo Zhang

In this paper, we study the Helmholtz transmission eigenvalue problem for inhomogeneous anisotropic media with the index of refraction $n(x)\equiv 1$ in two and three dimension. Starting with a nonlinear fourth order formulation established by Cakoni, Colton and Haddar [2009], by introducing some auxiliary variables, we present an equivalent mixed formulatio

Ivo Gollini Navarrete, Mohammad Yaqub

Lung cancer is a leading cause of death worldwide. Early-stage detection of lung cancer is essential for a more favorable prognosis. Radiogenomics is an emerging discipline that combines medical imaging and genomics features for modeling patient outcomes non-invasively. This study presents a radiogenomics pipeline that has: 1) a novel mixed architecture (RA-

Huibin Chang, Li Yang, Stefano Marchesini

In nanoscale imaging technique and ultrafast laser, the reconstruction procedure is normally formulated as a blind phase retrieval (BPR) problem, where one has to recover both the sample and the probe (pupil) jointly from phaseless data. This survey first presents the mathematical formula of BPR, related nonlinear optimization problems and then gives a brief

Tôru Sakai, Hiroki Nakano, Rito Furuchi, Kiyomi Okamoto

The magnetization process of the $S=1/2$ distorted diamond spin chain with the anisotropic ferromagnetic interaction is investigated using the numerical diagonalization of finite-size clusters. It is found that the spin nematic and SDW Tomonaga-Luttinger liquids can appear for sufficiently large easy axis anisotropy.

Samir M. Perlaza, Gaetan Bisson, Iñaki Esnaola, Alain Jean-Marie

The empirical risk minimization (ERM) problem with relative entropy regularization (ERM-RER) is investigated under the assumption that the reference measure is a $\sigma$-finite measure, and not necessarily a probability measure. Under this assumption, which leads to a generalization of the ERM-RER problem allowing a larger degree of flexibility for incorpor

Xu Han, Zhonghua Hou

Let G be a n-dimensional Lie group (n>2) with a bi-invariant Riemannian metric. We prove that if a surface of constant Gaussian curvature in G can be expressed as the product of two curves, then it must be flat. In particular, we can essentially characterize all such surfaces locally in 3-dimensional case.

M. Khalid Hossain, K. Hashizume, Y. Hatano

Proton-conducting oxides have potential applications in hydrogen sensors, hydrogen pumps, and other electrochemical devices including the tritium purification and recovery systems of nuclear fusion reactors. Although the distribution of hydrogen (H) in such oxide materials is an important aspect, its precise measurement is difficult. In the present study, th

Yameng Liu, Jianhua Zhang, Yuxiang Zhang, Zhiqiang Yuan

Integrated Sensing And Communication (ISAC) has been recognized as a promising technology in the 6G communication. A realistic channel model is a prerequisite for designing ISAC systems. Most existing channel models independently generate the communication and sensing channels under the same framework. However, due to the multiplexing of hardware resources a

Siyi Qian, Haochao Ying, Renjun Hu, Jingbo Zhou

Graph Neural Networks (GNNs) have become widely-used models for semi-supervised learning. However, the robustness of GNNs in the presence of label noise remains a largely under-explored problem. In this paper, we consider an important yet challenging scenario where labels on nodes of graphs are not only noisy but also scarce. In this scenario, the performanc

Aparajita Dasgupta, Santosh Kumar Nayak

In this article, we study two different types of operators, the localization operator and Weyl transform, on the reduced Heisenberg group with multidimensional center $\mathcal{G}$. The group $\mathcal{G}$ is a quotient group of non-isotropic Heisenberg group with multidimensional center $\mathcal{H}^m$ by its center subgroup. Firstly, we define the localiza

Ziyi Zhang, Weikai Chen, Hui Cheng, Zhen Li

We investigate a practical domain adaptation task, called source-free domain adaptation (SFUDA), where the source-pretrained model is adapted to the target domain without access to the source data. Existing techniques mainly leverage self-supervised pseudo labeling to achieve class-wise global alignment [1] or rely on local structure extraction that encourag

Manuel Bello Hernández, Alejandro del Campo López

We prove mean convergence of the Fourier series in Akhiezer-Chebyshev polynomials in $L^p$, $p>1$, using a weighted inequality for the Hilbert transform in an arc of the unit circle.

Lizheng Wang, Junlin Xiong, Bin Cheng, Yudi Dai

The building block of in-memory computing with spintronic devices is mainly based on the magnetic tunnel junction with perpendicular interfacial anisotropy (p-MTJ). The resulting asymmetric write and read-out operations impose challenges in downscaling and direct cascadability of p-MTJ devices. Here, we propose that a new symmetric write and read-out mechani

Shuyi Mao, Xinpeng Li, Qingyang Wu, Xiaojiang Peng

Studies have proven that domain bias and label bias exist in different Facial Expression Recognition (FER) datasets, making it hard to improve the performance of a specific dataset by adding other datasets. For the FER bias issue, recent researches mainly focus on the cross-domain issue with advanced domain adaption algorithms. This paper addresses another p

BeiChen Cai, XiaoMing Kong, JianRong Shi, Qi Gao

A small fraction of giants possess photospheric lithium(Li) abundance higher than the value predicted by the standard stellar evolution models, and the detailed mechanisms of Li enhancement are complicated and lack a definite conclusion. In order to better understand the Li enhancement behaviors, a large and homogeneous Li-rich giants sample is needed. In th

Shuncheng Jia, Tielin Zhang, Ruichen Zuo, Bo Xu

Network architectures and learning principles are playing key in forming complex functions in artificial neural networks (ANNs) and spiking neural networks (SNNs). SNNs are considered the new-generation artificial networks by incorporating more biological features than ANNs, including dynamic spiking neurons, functionally specified architectures, and efficie

Xiaocui Yang, Shi Feng, Daling Wang, Pengfei Hong

Multimodal sentiment analysis has gained significant attention due to the proliferation of multimodal content on social media. However, existing studies in this area rely heavily on large-scale supervised data, which is time-consuming and labor-intensive to collect. Thus, there is a need to address the challenge of few-shot multimodal sentiment analysis. To

Shu Liu, Ivan Tjuawinata, Chaoping Xing

For codes equipped with metrics such as Hamming metric, symbol pair metric or cover metric, the Johnson bound guarantees list-decodability of such codes. That is, the Johnson bound provides a lower bound on the list-decoding radius of a code in terms of its relative minimum distance $\delta$, list size $L$ and the alphabet size $q.$ For study of list-decodab

Weichen Zhao, Chenguang Wang, Congying Han, Tiande Guo

The over-smoothing problem is an obstacle of developing deep graph neural network (GNN). Although many approaches to improve the over-smoothing problem have been proposed, there is still a lack of comprehensive understanding and conclusion of this problem. In this work, we analyze the over-smoothing problem from the Markov chain perspective. We focus on mess

Keita Ushida, Tatsuya Yanagisawa, Ruo Hibino, Masato Matsuda

The elastic constants of an unconventional superconductor, UTe$_2$, were investigated using ultrasound. In this paper, we report the elastic response of the normal state at temperatures down to 2 K and up to 14 T for $H \parallel b$ at ambient pressure. The transverse ultrasonic mode $C_{55}$, which corresponds to the strain susceptibility of $\varepsilon_{z

Karim Mosani

What happens to a massive star at the end of its life cycle is one of the most important and intriguing questions in theoretical physics. An unhindered gravitational collapse of such a sufficiently massive star can give rise to a spacetime singularity. This thesis discusses the phenomenon of such unhindered gravitational collapse and the causal structure of

Umberto Michelucci

Training neural networks means solving a high-dimensional optimization problem. Normally the goal is to minimize a loss function that depends on what is called the network function, or in other words the function that gives the network output given a certain input. This function depends on a large number of parameters, also known as weights, that depends on

Siyao Liu, Yong Wang

In [22], the authors proved a Kastler-Kalau-Walze type theorem for the J-twist D_J of the Dirac operator on 3-dimensional and 4-dimensional almost product Riemannian spin manifold with boundary. In this paper, we develop the Kastler-Kalau-Walze type theorem for the J-twist D_J of the Dirac operator on a 6-dimensional almost product Riemannian spin manifold w

Xian Wu, Shuxin Yang, Zhaopeng Qiu, Shen Ge

Fast screening and diagnosis are critical in COVID-19 patient treatment. In addition to the gold standard RT-PCR, radiological imaging like X-ray and CT also works as an important means in patient screening and follow-up. However, due to the excessive number of patients, writing reports becomes a heavy burden for radiologists. To reduce the workload of radio

M. H. K. Rubel, M. A. Hossain, M. Khalid Hossain, K. M. Hossain

This study has explored numerous physical properties of CaPd$_3$Ti$_4$O$_{1}$2 (CPTO) and CaPd$_3$V$_4$O$_{12}$ (CPVO) quadruple perovskites employing the density functional theory (DFT) method. The mechanical permanence of these two compounds was observed by the Born stability criteria as well. The band structure of CPTO reveals a 0.88 and 0.46 eV direct na

Csaba Tóth

Let ${\cal M}(S; \Lambda; P)$ denote a Rees $I\times \Lambda$ matrix semigroup without zero over a semigroup $S$, where $I$ is a singleton. If $\theta _S$ denotes the kernel of the right regular representation of a semigroup $S$, then a triple $A, B, C$ of semigroups is said to be right regular, if there are mappings $A\stackrel{P}{\longleftarrow}B$ and $B\s

Valery V. Ryzhikov

Birkhoff's theorem states that for an ergodic automorphism, the time averages converge to the space average. Given sequence $\psi(n)\to+0$, U. Krengel proved that for any ergodic automorphism there is an indicator such that the corresponding time averages converged a.e. with a rate slower than $\psi$. We prove again similar statements answering a question of

Keyi Shi, Jingchao Wang, Hongyan Li, Kan Wang

Deterministic routing has emerged as a promising technology for future non-terrestrial networks (NTNs), offering the potential to enhance service performance and optimize resource utilization. However, the dynamic nature of network topology and resources poses challenges in establishing deterministic routing. These challenges encompass the intricacy of joint

Chao Fan, Junhao Liang, Chuanfu Shen, Saihui Hou

Gait recognition is one of the most critical long-distance identification technologies and increasingly gains popularity in both research and industry communities. Despite the significant progress made in indoor datasets, much evidence shows that gait recognition techniques perform poorly in the wild. More importantly, we also find that some conclusions draw

Klichchupong Dabsamut, Intuon Chatratin, Thanasee Thanasarnsurapong, Tosapol Maluangnont

The theoretical discovery of new and stable 2D penta materials has stimulated the technological advancement due to the anticipated exotic properties of such structure, including the recent $\alpha$ phase and $\beta$ phase of penta-NiPS based on first-principles calculations. Inspired by the similarity between the theoretically proposed penta-NiPS and the exp

Shota Hirose, Shiori Maki, Naoki Wada, Heming Sun

Spectral Normalization is one of the best methods for stabilizing the training of Generative Adversarial Network. Spectral Normalization limits the gradient of discriminator between the distribution between real data and fake data. However, even with this normalization, GAN's training sometimes fails. In this paper, we reveal that more severe restriction is

Lei Zhan, Tuan M. Nguyen, Juntao Xiong, Feng Liu

The combustion instability is investigated computationally for a ten-injector rocket engine using the compressible flamelet progress variable (FPV) model and detached eddy simulation (DES). An C++ code is developed based on OpenFOAM 4.1 to apply the combustion model. Flamelet tables are generated for methane/oxygen combustion at the background pressure of 20

He Xiaoyang, Jin Shi, Yu Yue

We investigate time complexities of finite difference methods for solving the multiscale transport equation with quantum algorithms. We find that the time complexities of both the classical treatment and quantum treatment for a standard explicit scheme scale as $\mathcal{O}(1/\varepsilon)$, where $\varepsilon$ is the small scaling parameter, while the comple

D. Belomestny, E. Ivanova, T. Orlova

In this paper we study the problem of constructing bootstrap confidence intervals for the L\'evy density of the driving L\'evy process based on high-frequency observations of a L\'evy-driven moving average processes. Using a spectral estimator of the L\'evy density, we propose a novel implementations of multiplier and empirical bootstraps to construct confid

Michael O. Hanson, Dmitri M. Orlov, Stewart Zweben, Andreas Wingen

We report on a study of the structure of edge turbulence in NSTX H-mode discharges with applied n=1 and n=3 non-axisymmetric magnetic perturbations. The edge turbulence is diagnosed in NSTX using the gas puff imaging (GPI) system to understand how these 3D fields affect edge transport. The presented database study uses a selection of discharges that have a c

Jiayun Wu, Tao Jia, Yansong Wang, Li Tao

Temporal networks are suitable for modeling complex evolving systems. It has a wide range of applications, such as social network analysis, recommender systems, and epidemiology. Recently, modeling such dynamic systems has drawn great attention in many domains. However, most existing approaches resort to taking discrete snapshots of the temporal networks and

Nadeem ur Rehman, Shuliang Huang

Let $\mathscr{R}$ be a prime ring of Char$(\mathscr{R}) \neq 2$ and $m\neq 1$ be a positive integer. If $S$ is a nonzero skew derivation with an associated automorphism $\mathscr{T}$ of $\mathscr{R}$ such that $([S([a, b]), [a, b]])^{m} = [S([a, b]), [a, b]]$ for all $a, b \in \mathscr{R}$, then $\mathscr{R}$ is commutative.

Haodong Ouyang

Object detection is an important topic in computer vision, with post-processing, an essential part of the typical object detection pipeline, posing a significant bottleneck affecting the performance of traditional object detection models. The detection transformer (DETR), as the first end-to-end target detection model, discards the requirement of manual comp

Amnon Neeman

Small, finite entities are easier and simpler to manipulate than gigantic, infinite ones. Consequently huge chunks of mathematics are devoted to methods reducing the study of big, cumbersome objects to an analysis of their finite building blocks. The manifestation of this general pattern, in the study of derived and triangulated categories, dates back almost

Sanatan Digal, Vinod Mamale, Sabiar Shaikh

We study lattice cutoff effects on the confinement-deconfinement transition and the $Z_3$ symmetry in $SU(3)$-Higgs theory in $3+1$ dimensions. The Higgs in this study is a complex triplet with vanishing bare mass and quartic coupling. The lattice cutoff is regulated by varying the number of temporal lattice sites, $N_\tau$. Our results show that the nature

Therar Kadri, Amina Halat

Through viewing out the literature, many generated distributions took a new special form of probability density function (PDF) in which it is written as a linear combination of n other distributions. Therefore, we define in this paper a new type of distributions called "The New Mixed Distribution" form in which a distribution is written as a linear combinati

Bibhu Prasad Tripathy, Bijan Kumar Patel

For any integer $k \geq 2$, let $\{Q_{n}^{(k)} \}_{n \geq -(k-2)}$ denote the $k$-generalized Pell-Lucas sequence which starts with $0, \dots ,2,2$($k$ terms) where each next term is the sum of the $k$ preceding terms. In this paper, we find all the $k$-generalized Pell-Lucas numbers that are the product of two repdigits. This generalizes a result of Erduvan

Yu-Jhe Li, Tao Xu, Bichen Wu, Ningyuan Zheng

We tackle the task of NeRF inversion for style-based neural radiance fields, (e.g., StyleNeRF). In the task, we aim to learn an inversion function to project an input image to the latent space of a NeRF generator and then synthesize novel views of the original image based on the latent code. Compared with GAN inversion for 2D generative models, NeRF inversio

Zachary Izzo, Jinsung Yoon, Sercan O. Arik, James Zou

In applications involving sensitive data, such as finance and healthcare, the necessity for preserving data privacy can be a significant barrier to machine learning model development. Differential privacy (DP) has emerged as one canonical standard for provable privacy. However, DP's strong theoretical guarantees often come at the cost of a large drop in its

Yunshan Zhong, Gongrui Nan, Yuxin Zhang, Fei Chao

Quantization-aware training (QAT) receives extensive popularity as it well retains the performance of quantized networks. In QAT, the contemporary experience is that all quantized weights are updated for an entire training process. In this paper, this experience is challenged based on an interesting phenomenon we observed. Specifically, a large portion of qu

Avirup Dey, Nibaran Das, Mita Nasipuri

Historical Document Image Binarization is a well-known segmentation problem in image processing. Despite ubiquity, traditional thresholding algorithms achieved limited success on severely degraded document images. With the advent of deep learning, several segmentation models were proposed that made significant progress in the field but were limited by the un

Kai Tang, Weijie Wang, Xiao Pan, Boli Chen

The development of electric and connected vehicles as well as automated driving technologies are key towards the smart city, with convenient urban mobility and high energy economy performance. However, the global rise in electricity price provokes renewed interest on CAVs with hybrid electric powertrains rather than considering battery electric powertrains.

Peter ED Love, Weili Fang, Jane Matthews, Stuart Porter

Explainable artificial intelligence has received limited attention in construction despite its growing importance in various other industrial sectors. In this paper, we provide a narrative review of XAI to raise awareness about its potential in construction. Our review develops a taxonomy of the XAI literature comprising its precepts and approaches. Opportun

Tianyi Shi, Xiaohuan Ding, Wei Zhou, Feng Pan

Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. However, achieving high pixel-wise accuracy, complete topology structure and robustness to various contrast variations are critical and challenging, and most existing methods focus only on achieving one or two o

Xu Han, Zhonghua Hou

The symmetry group of the mean curvature flow in general ambient Riemannian manifolds is determined, based on which we define generalized solitons to the mean curvature flow. We also provide examples of homothetic solitons in non-Euclidean surfaces and prove that all the affine solutions to the mean curvature flow are self-similar solutions.

Huan-Yu Teng, Bun'ei Sato, Masanobu Kunitomo, Takuya Takarada

We report the detection of a giant planet orbiting a G-type giant star HD 167768 from radial velocity measurements using HIgh Dispersion Echelle Spectrograph (HIDES) at Okayama Astrophysical Observatory (OAO). HD 167768 has a mass of $1.08_{-0.12}^{+0.14} M_{\odot}$, a radius of $9.70_{-0.25}^{+0.25} R_{\odot}$, a metallicity of $\rm{[Fe/H]}=-0.67_{-0.08}^{+

Young-Hun Kim, Semin Yoo

Let $\lambda$ be a partition of a positive integer $n$. The genomic Schur function $U_\lambda$ was introduced by Pechenik--Yong in the context of the $K$-theory of Grassmannians. Recently, Pechenik provided a positive combinatorial formula for the fundamental quasisymmetric expansion of $U_\lambda$ in terms of increasing gapless tableaux. In this paper, for

Xu Shang, Azim Eskandarian

Although extensive research in emergency collision avoidance has been carried out for straight or curved roads in a highway scenario, a general method that could be implemented for all road environments has not been thoroughly explored. Moreover, most current algorithms don't consider collision mitigation in an emergency. This functionality is essential sinc