April 2023 arXiv papers — page 75

Carlo Branchina, Vincenzo Branchina, Filippo Contino

More than twenty years ago a paradigm emerged according to which a UV-insensitive Higgs mass $m_H$ and (more generally) a UV-insensitive Higgs effective potential $V_{1l}(\phi)$ are obtained from higher-dimensional theories with compact extra dimensions and Scherk-Schwarz supersymmetry breaking. Since then, these ideas have been applied to different models o

Alexandre Baraviera, Renaud Leplaideur

We prove that the Jacaranda tree obtained as a fixed point for a substreetution in previous work of the authors is strongly aperiodic and that the number of patches increases linearly with respect to the size of the patch. As a consequence we get that the tree has zero entropy.

Yiyao Cheng, Lei Liu, Shansuo Liang, Jonathan. H. Manton

Approximate message passing (AMP) algorithms break a (high-dimensional) statistical problem into parts then repeatedly solve each part in turn, akin to alternating projections. A distinguishing feature is their asymptotic behaviours can be accurately predicted via their associated state evolution equations. Orthogonal AMP (OAMP) was recently developed to avo

Oumar Wone

We give a historical presentation of the Grothendieck theorem on the splitting of vector bundles over the Riemann sphere, and explore some of its links with the Riemann-Hilbert-Birkhoff problems and the Birkhoff factorization theorem.

Raphaël Danchin

The present paper is devoted to the proof of time decay estimates for derivatives at any order of finite energy global solutions of the Navier-Stokes equations in general two-dimensional domains. These estimates only depend on the order of derivation and on the L2 norm of the initial data. The same elementary method just based on energy estimates and Ladyzhe

Subhankar Mondal, M. Thamban Nair

This paper is concerned with identification of a spatial source function from final time observation in a bi-parabolic equation, where the full source function is assumed to be a product of time dependent and a space dependent function. Due to the ill-posedness of the problem, recently some authors have employed different regularization method and analysed t

Hassan Al-Hamzawi, Alessandro Principi, Leone Di Mauro Villari

We discuss the numerical implementation of two related representations of fermionic density matrices which have been introduced in Annals of Physics 370, 12 (2016). In both of them, the density matrix is expanded in a basis of Bargmann coherent states with weights given by the two phase space distributions. We derive the equations of motion for the distribut

Hechao Liu, Zenan Du, Yufei Huang, Hanlin Chen

Let $G$ be a connected graph with $n$ vertices and $m$ edges. The vertex-degree-based topological index (VDB) (or graphical function-index) $TI(G)$ of $G$ with edge-weight function $I(x,y)$ is defined as $$TI(G)=\sum\limits_{uv\in E(G)}I(d_{u},d_{v}),$$ where $I(x,y)>0$ is a symmetric real function with $x\geq 1$ and $y\geq 1$, $d_{u}$ is the degree of verte

Suvendu Jana, Pintu Bhunia, Kallol Paul

In this paper, we develop several Euclidean operator radius inequalities of $d$-tuple operators, as well as the sum and the product of $d$-tuple operators. Also, we obtain a power inequality for the Euclidean operator radius. Further, we develop Euclidean operator radius inequalities of $2\times 2$ operator matrices whose entries are $d$-tuple operators.

Vinay Singh, Debasis Bhowmick, D. N. Basu

One of the three testaments in favor of the big bang theory is the prediction of the primordial elemental abundances in the big-bang nucleosynthesis (BBN). The Standard BBN is a parameter-free theory due to the precise knowledge of the baryon-to-photon ratio of the Universe obtained from studies of the anisotropies of cosmic microwave background radiation. A

Jean Pierre. -P. Gazeau, Mariano A. del Olmo

We revisit the Perelomov SU(1,1) displaced coherent states states as possible quantum states of light. We disclose interesting statistical aspects of these states in relation with photon counting and squeezing. In the non-displaced case we discuss the efficiency of the photodetector as inversely proportional to the parameter k of the discrete series of unita

Xiaowen Shi, Ze Wang, Yuanying Cai, Xiaoxu Wu

Nowadays, the mainstream approach in position allocation system is to utilize a reinforcement learning model to allocate appropriate locations for items in various channels and then mix them into the feed. There are two types of data employed to train reinforcement learning (RL) model for position allocation, named strategy data and random data. Strategy dat

Zhan-Feng Mai, Rui Xu, Dicong Liang, Lijing Shao

As a vector-tensor theory including nonminimal coupling between the Ricci tensor and a vector field, the bumblebee gravity is a potential theory to test Lorentz symmetry violation. Recently, a new class of numerical spherical black holes in the bumblebee theory was constructed. In this paper, we investigate the associated local thermodynamic properties. By i

Hikaru Takeda, Masataka Kawano, Kyo Tamura, Masatoshi Akazawa

Complexity of quantum phases of matter is often understood by the underlying gauge structures, as was recognized by the $\mathbb{Z}_2$ and U(1) gauge theory description of spin liquid in frustrated magnets. Anomalous Hall effect of conducting electrons can intrisically arise from U(1) gauges expressing the spatial modulation of ferromagnetic moments or from

Shicai Wei, Yang Luo, Chunbo Luo

Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to assist incomplete multimodal learning. It consists of three com

Danial Safaei, Ali Sobhani, Ali Akbar Kiaei

In recent years, the intelligence of various parts of the home has become one of the essential features of any modern home. One of these parts is the intelligence lighting system that personalizes the light for each person. This paper proposes an intelligent system based on machine learning that personalizes lighting in the instant future location of a recog

Norman H. Christ, Xu Feng, Lu-Chang Jin, Christopher T. Sachrajda

Lattice QCD calculations of leptonic decay constants have now reached sub-percent precision so that isospin-breaking corrections, including QED effects, must be included to fully exploit this precision in determining fundamental quantities, in particular the elements of the Cabibbo-Kobayashi-Maskawa (CKM) matrix, from experimental measurements. A number of c

Long Lian, Zhirong Wu, Stella X. Yu

We study learning object segmentation from unlabeled videos. Humans can easily segment moving objects without knowing what they are. The Gestalt law of common fate, i.e., what move at the same speed belong together, has inspired unsupervised object discovery based on motion segmentation. However, common fate is not a reliable indicator of objectness: Parts o

Kawsalyaa Manivannan, Bharathi Sankar

In general. automated farming systems make decisions based on static models built from the properties of the plant. in the contrast, irrigation decisions in our suggested method are dynamically changing environmental conditions. the model"s learning process reveals the mathematical links between the environmental factors employed in the determining the irrig

Michel Hayoz, Christopher Hahne, Mathias Gallardo, Daniel Candinas

Purpose: Surgical scene understanding plays a critical role in the technology stack of tomorrow's intervention-assisting systems in endoscopic surgeries. For this, tracking the endoscope pose is a key component, but remains challenging due to illumination conditions, deforming tissues and the breathing motion of organs. Method: We propose a solution for ster

Nicolae Cindea, Geoffrey Lacour

The aim of this paper is to study the null controllability of a class of quasilinear parabolic equations. In a first step we prove that the associated linear parabolic equations with non-constant diffusion coefficients are approximately null controllable by the means of regular controls and that these controls depend continuously to the diffusion coefficient

Sagar Ghosh, Gadadhar Misra

In this semi-expository short note, we prove that the only homogeneous \textit{pure} hyponormal operator $T$ with $\operatorname{rank} (T^*T-TT^*) =1$, modulo unitary equivalence, is the unilateral shift.

Sunpeng Duan, Guo Yu, Juntao Duan, Yuedong Wang

Repeated measurements are common in many fields, where random variables are observed repeatedly across different subjects. Such data have an underlying hierarchical structure, and it is of interest to learn covariance/correlation at different levels. Most existing methods for sparse covariance/correlation matrix estimation assume independent samples. Ignorin

Tian-Xiang Ma, Chen Liang, Jie Yang, Yong-Qiang Wang

In this paper, we construct a hybrid boson star model that contains a complex scalar field and a Proca field. The scalar field is in the ground state, while the Proca field is in the first excited state. We numerically solve the model and obtain solution families of different coexisting states by considering both synchronized and nonsynchronized cases. By ex

Tao Zhang

We introduce the concept of Hom-associative algebra structures in Loday-Pirashvili category.The cohomology theory of Hom-associative algebras in this category is studied.Some applications on deformation and abelian extension theory are given. We also introduce the notion of Nijenhuis operators to describe trivial deformations. It is proved that equivalent cl

Huqiang Cheng, Mengying Xie, Xiaowei Yang, Qingguo Lü

In the intricate dance of multi-agent systems, achieving average consensus is not just vital--it is the backbone of their functionality. In conventional average consensus algorithms, all agents reach an agreement by individual calculations and sharing information with their respective neighbors. Nevertheless, the information interactions that occur in the co

Miguel Martinez, Isaac Ohavi

The main purpose of this work is to provide an existence and uniqueness result for the solution of a linear parabolic system posed on a star-shaped network, which presents a new type of Kirchhoff's boundary transmission condition at the junction. This new type of Kirchhoff's condition-that we decide to call here local-time Kirchhoff 's condition-induces a dy

Xiaoming Xue, Cuie Yang, Liang Feng, Kai Zhang

Sequential transfer optimization (STO), which aims to improve the optimization performance on a task of interest by exploiting the knowledge captured from several previously-solved optimization tasks stored in a database, has been gaining increasing research attention over the years. However, despite the remarkable advances in algorithm design, the developme

Shiqing Xu

Stress drop $Δτ$ and rupture speed $V_r$ are two important earthquake source parameters that control the characteristics of rupture process and the associated ground motion. However, how the two parameters correlate with one another is currently still under debate. Here I use an energy-based approach from fracture mechanics to understand the correlation betw

Giulio Maria Bianco, Emanuele Raso, Luca Fiore, Vincenzo Mazzaracchio

Points-of-care (PoCs) augment healthcare systems by performing care whenever needed and are becoming increasingly crucial for the well-being of the worldwide population. Personalized medicine, chronic illness management, and cost reduction can be achieved thanks to the widespread adoption of PoCs. Significant incentives for PoCs deployment are nowadays given

Viktoras Pyragas, Kestutis Pyragas

We analyze the dynamics of large networks of pulse-coupled quadratic integrate-and-fire neurons driven by Cauchy noise and non-Cauchy heterogeneous inputs. Two types of heterogeneities defined by families of $q$-Gaussian and flat distributions are considered. Both families are parametrized by an integer $n$, so that as $n$ increases, the first family tends t

Shuyu Miao, Lin Zheng, Jingjing Liu, and Hong Jin

The label-free model evaluation aims to predict the model performance on various test sets without relying on ground truths. The main challenge of this task is the absence of labels in the test data, unlike in classical supervised model evaluation. This paper presents our solutions for the 1st DataCV Challenge of the Visual Dataset Understanding workshop at

Taeho Kim, Jong-Min Lee

Most invariance-based self-supervised methods rely on single object-centric images (e.g., ImageNet images) for pretraining, learning features that invariant to geometric transformation. However, when images are not object-centric, the semantics of the image can be significantly altered due to cropping. Furthermore, as the model becomes insensitive to geometr

Yiming Lei, Zilong Li, Yan Shen, Junping Zhang

Lung nodule malignancy prediction has been enhanced by advanced deep-learning techniques and effective tricks. Nevertheless, current methods are mainly trained with cross-entropy loss using one-hot categorical labels, which results in difficulty in distinguishing those nodules with closer progression labels. Interestingly, we observe that clinical text infor

Meng-Yun Lai, De-Cheng Zou, Rui-Hong Yue, Yun Soo Myung

In this paper, we discuss a fully nonlinear mechanism for the formation of scalarized rotating black holes in Einstein-scalar-Gauss-Bonnet gravity, where Kerr black holes are linearly stable, but unstable against nonlinear scalar perturbations. With the help of the pseudospectral method, we obtain a spectrum of nonlinearly scalarized rotating black hole solu

Takuma Aihara, Takahiro Honma

We explore when the silting-discreteness is inherited. As a result, one obtains that taking idempotent truncations and homological epimorphisms of algebras transmit the silting-discreteness. We also study classification of silting-discrete simply-connected tensor algebras and silting-indiscrete selfinjective Nakayama algebras. This paper contains two appendi

Wenxin Zhong, Jian Fu, Shiyin Shen, Feng Yuan

We create mock X-ray observations of hot gas in galaxy clusters with a new extension of L-Galaxies semi-analytic model of galaxy formation, which includes the radial distribution of hot gas in each halo. Based on the model outputs, we first build some mock light cones, then generate mock spectra with SOXS package and derive the mock images in the light cones

Sudarshan Santra, Ratikanta Behera

This work aims to construct an efficient and highly accurate numerical method to address the time singularity at $t=0$ involved in a class of time-fractional parabolic integro-partial differential equations in one and two dimensions. The $L2$-$1_\sigma$ scheme is used to discretize the time-fractional operator, whereas a modified version of the composite tra

Ali Lazrak, Jianfeng Zhang

We consider a committee voting on whether to adopt a reform under a quota rule, where members differ in how much they value the reform some supporting it, others opposing it. We examine how members can influence each other's votes through coordinated non-negative transfer promises, made prior to voting and contingent on the vote outcome. In equilibrium, thes

Isabel Astrid Goos, Xavier Bertou, Tanguy Pierog

We propose a method to extract high-energy hadronic interaction properties from the distributions of two of the main observables of proton extensive air showers: the depth of maximum shower development, $X_\mathrm{max}$, and the number of muons at the ground, $N_\mu$. We determine relevant parameters of the first and subsequent interactions of the cascade an

Robert Peters, Jannis Neuhaus-Steinmetz, Thore Posske

The flow of electric current through a two-dimensional material in a magnetic field gives rise to the family of Hall effects. The quantum versions of these effects accommodate robust electronic edge channels and fractional charges. Recently, the Hall effect of skyrmions, classical magnetic quasiparticles with a quantized topological charge, has been theoreti

Tianjian Lv, Bing Han, Ming Yan, Zhaoyang Wen

Coherent anti-Stokes Raman scattering (CARS) spectroscopy with time-delayed ultrashort pulses and a single-pixel photodetector has shown great potential for spectroscopic imaging and transient studies in chemistry and biological research. However, those systems rely on mechanical delay lines or two asynchronous optical combs with inflexible repetition freque

Thang Pham, Semin Yoo

Let $A$ and $B$ be sets in a finite vector space. In this paper, we study the magnitude of the set $A\cap f(B)$, where $f$ runs through a set of transformations. More precisely, we will focus on the cases that the set of transformations is given by orthogonal matrices or orthogonal projections. We prove that if $A, B\subset \mathbb{F}_q^d$ satisfy some natur

Yiran Zhang, Yuejian Peng

For graphs $G_0$, $G_1$ and $G_2$, write $G_0\longmapsto(G_1, G_2)$ if each red-blue-edge-coloring of $G_0$ yields a red $G_1$ or a blue $G_2$. The Ramsey number $r(G_1, G_2)$ is the minimum number $n$ such that the complete graph $K_n\longmapsto(G_1, G_2)$. In [Discrete Math. 312(2012)], Schelp formulated the following question: for which graphs $H$ there i

Sjoerd Broekhuijsen, Naureen Ghafoor, Matthias Schwartzkopf, Anton Zubayer

Multilayer neutron optics require precise control of interface morphology for optimal performance. In this work, we investigate the effects of different growth conditions on the interface morphology of Ni/Ti based multilayers, with a focus on incorporating low-neutron-absorbing 11B4C and using different ion assistance schemes. Grazing incidence small angle X

Wenjing Zhu, XiaoLong Wang

We determine the resonant parameters of the vector states $\phi(1680)$ and $\phi(2170)$, by doing a combined fit to the $e^{+}e^{-}\to \eta\phi$ cross sections from threshold to $2.85~\rm GeV$ measured by BaBar, Belle, BESIII and CMD-3 experiments. The mass $(1678^{+5}_{-3} \pm 7)~\rm MeV/c^2$ and the width $(156\pm 5 \pm 9)~\rm MeV$ are obtained for the $\p

Houshan Fu, Chunming Tang, Suijie Wang

We show that an adjoint of a loopless matroid is connected if and only if it itself is connected. Our first goal is to study the adjoint of modular matroids. We prove that a modular matroid has only one adjoint (up to isomorphism) which can be given by its opposite lattice, and proceed to present some alternative characterizations of modular matroids associa

Yilin Ye, Rong Huang, Kang Zhang, Wei Zeng

The recent advances of AI technology, particularly in AI-Generated Content (AIGC), have enabled everyone to easily generate beautiful paintings with simple text description. With the stunning quality of AI paintings, it is widely questioned whether there still exists difference between human and AI paintings and whether human artists will be replaced by AI.

Congcong Liu, Fei Teng, Xiwei Zhao, Zhangang Lin

Click-through rate (CTR) prediction is of great importance in recommendation systems and online advertising platforms. When served in industrial scenarios, the user-generated data observed by the CTR model typically arrives as a stream. Streaming data has the characteristic that the underlying distribution drifts over time and may recur. This can lead to cat

Qihui Liu, Hao Chen, Fei Xie, Yuqiang Hu

Negatively charged nitrogen-vacancy (NV) centers in diamond have been extensively studied as a promising high sensitivity solid-state magnetic field sensor at room temperature. However, their use for current sensing applications is limited due to the challenge of integration and miniaturization of the diamond NV sensor. Here, we demonstrate an integrated NV

Aradhana Kumari, Rahul Marathe, Sourabh Lahiri

Recent works on the concatenation of two simple heat engines have shown that it may lead to non-monotonic variations in the efficiency and power with parameters like driving amplitudes and asymmetries in cycle periods. Motivated by this study, we investigate the effect of the concatenation between two stochastic heat engines where colloidal particles have be

Mohammad Zamani, Jochen Trumpf, Chris Manzie

We consider the problem of collaborative bearing estimation using a method with historic roots in set theoretic estimation techniques. We refer to this method as the Convex Combination Ellipsoid (CCE) method and show that it provides a less conservative covariance estimate than the well known Covariance Intersection (CI) method. The CCE method does not intro

Qian Liu, Fan Zhou, Zhengbao Jiang, Longxu Dou

Fine-tuning language models on tasks with instructions has demonstrated potential in facilitating zero-shot generalization to unseen tasks. In this paper, we introduce a straightforward yet effective method for enhancing instruction tuning by employing symbolic tasks. Compared to crowdsourced human tasks or model-generated tasks, symbolic tasks present a uni

H. R. Strauss

Locked modes are precursors to major disruptions. During locked modes, the temperature decreases in the plasma edge region. This causes the current to contract. A model is given to analyze the MHD stability of contracted current equilibria. If there is sufficient current contraction, resistive wall tearing modes are destabilized. This requires that the q = 2

Liu Yang, Siting Liu, Tingwei Meng, Stanley J. Osher

This paper introduces a new neural-network-based approach, namely In-Context Operator Networks (ICON), to simultaneously learn operators from the prompted data and apply it to new questions during the inference stage, without any weight update. Existing methods are limited to using a neural network to approximate a specific equation solution or a specific op

Devika Venkuzhy Sudhakaran, Dinesh Kumar Sahu, Osamu Haba, Surajit Dhara

Rational control over the periodic arrangement of particles by means of external stimuli is a technologically important aspect of colloidal science with important physical underpinnings. Here, a robust structural control of particle assemblies in a nematic liquid crystal (NLC) is demonstrated by dissolving trace amounts of light-responsive azo-dendrimer mole

Sota Kato, Kazuhiro Hotta

Semantic segmentation of microscopic cell images using deep learning is an important technique, however, it requires a large number of images and ground truth labels for training. To address the above problem, we consider an efficient learning framework with as little data as possible, and we propose two types of learning strategies: One-shot segmentation wh

Mikhail A. Bragin, Farhan Hyder, Bing Yan, Peter B. Luh

Electricity prices determined by economic dispatch that do not consider fixed costs may lead to significant uplift payments. However, when fixed costs are included, prices become non-monotonic with respect to demand, which can adversely impact market transparency. To overcome this issue, convex hull (CH) pricing has been introduced for unit commitment with f

Zac Pullar-Strecker, Xinglong Chang, Liam Brydon, Ioannis Ziogas

Running complex sets of machine learning experiments is challenging and time-consuming due to the lack of a unified framework. This leaves researchers forced to spend time implementing necessary features such as parallelization, caching, and checkpointing themselves instead of focussing on their project. To simplify the process, in this paper, we introduce M

Ran Liu, Charles Nicholas

The popularity of dynamic malware analysis has grown significantly, as it enables analysts to observe the behavior of executing samples, thereby enhancing malware detection and classification decisions. With the continuous increase in new malware variants, there is an urgent need for an automated malware analysis engine capable of accurately identifying malw

Ziang Xiao, Xingdi Yuan, Q. Vera Liao, Rania Abdelghani

Qualitative analysis of textual contents unpacks rich and valuable information by assigning labels to the data. However, this process is often labor-intensive, particularly when working with large datasets. While recent AI-based tools demonstrate utility, researchers may not have readily available AI resources and expertise, let alone be challenged by the li

Zhenduo Wang, Zhichao Xu, Qingyao Ai

Existing conversational search studies mainly focused on asking better clarifying questions and/or improving search result quality. These works aim at retrieving better responses according to the search context, and their performances are evaluated on either single-turn tasks or multi-turn tasks under naive conversation policy settings. This leaves some ques

Ge Zhang, Yemin Shi, Ruibo Liu, Ruibin Yuan

Instruction tuning is widely recognized as a key technique for building generalist language models, which has attracted the attention of researchers and the public with the release of InstructGPT~\citep{ouyang2022training} and ChatGPT\footnote{\url{https://chat.openai.com/}}. Despite impressive progress in English-oriented large-scale language models (LLMs),

Ji Li, Chong-Wei Liang, Fred Yu-Hsiang Lin, Chun-Yen Shen

Fix $\lambda>-1/2$ and $\lambda \not=0$. Consider the Bessel operator (introduced by Muckenhoupt--Stein) $\triangle_\lambda:=-\frac{d^2}{dx^2}-\frac{2\lambda}{x} \frac d{dx}$ on $\mathbb{R_+}:=(0,\infty)$ with $dm_\lambda(x):=x^{2\lambda}dx$ and $dx$ the Lebesgue measure on $\mathbb{R_+}$. In this paper, we study the Muckenhoupt-type weights which reveal the

Yu. A. Budkov, N. N. Kalikin

We utilize the self-consistent field theory to explore the mechanical and electrical properties of charged surfaces immersed in polyelectrolyte solutions that could be potentially useful for electrochemical applications. Our research focuses on how the dielectric heterogeneity of the solution could affect the disjoining pressure and differential capacitance

Nan Li, Yutong Li, Ilya Kolmanovsky

In this paper, we propose a supervisory control scheme that unifies the abilities of safety protection and safety extension. It produces a control that is able to keep the system safe indefinitely when such a control exists. When such a control does not exist due to abnormal system states, it optimizes the control to maximize the time before any safety viola

Sofiane Tanji, Andrea Della Vecchia, François Glineur, Silvia Villa

Kernel methods provide a powerful framework for non parametric learning. They are based on kernel functions and allow learning in a rich functional space while applying linear statistical learning tools, such as Ridge Regression or Support Vector Machines. However, standard kernel methods suffer from a quadratic time and memory complexity in the number of da

Arpita Biswas, Yiduo Ke, Samir Khuller, Quanquan C. Liu

Massive surges of enrollments in courses have led to a crisis in several computer science departments - not only is the demand for certain courses extremely high from majors, but the demand from non-majors is also very high. Much of the time, this leads to significant frustration on the part of the students, and getting seats in desired courses is a rather a

Bing Luo, Yutong Feng, Shiqiang Wang, Jianwei Huang

Incentive mechanism is crucial for federated learning (FL) when rational clients do not have the same interests in the global model as the server. However, due to system heterogeneity and limited budget, it is generally impractical for the server to incentivize all clients to participate in all training rounds (known as full participation). The existing FL i

Yunruo Zhang, Tianyu Du, Shouling Ji, Peng Tang

It is well-known that recurrent neural networks (RNNs), although widely used, are vulnerable to adversarial attacks including one-frame attacks and multi-frame attacks. Though a few certified defenses exist to provide guaranteed robustness against one-frame attacks, we prove that defending against multi-frame attacks remains a challenging problem due to thei

Jianlin Liu, Qiang Nie, Yong Liu, Chengjie Wang

We propose a novel visual re-localization method based on direct matching between the implicit 3D descriptors and the 2D image with transformer. A conditional neural radiance field(NeRF) is chosen as the 3D scene representation in our pipeline, which supports continuous 3D descriptors generation and neural rendering. By unifying the feature matching and the

Di Hong, Jiangrong Shen, Yu Qi, Yueming Wang

Spiking Neural Networks (SNNs) are biologically realistic and practically promising in low-power computation because of their event-driven mechanism. Usually, the training of SNNs suffers accuracy loss on various tasks, yielding an inferior performance compared with ANNs. A conversion scheme is proposed to obtain competitive accuracy by mapping trained ANNs'

Jingqiu Zhou, Linjiang Huang, Liang Wang, Si Liu

The task of weakly supervised temporal action localization targets at generating temporal boundaries for actions of interest, meanwhile the action category should also be classified. Pseudo-label-based methods, which serve as an effective solution, have been widely studied recently. However, existing methods generate pseudo labels during training and make pr

Brianna S. Mills, Shane W. Davis, Yan-Fei Jiang, Matthew J. Middleton

We use the Athena++ Monte Carlo (MC) radiation transfer module to post-process simulation snapshots from non-relativistic Athena++ radiation magnetohydrodynamic (RMHD) simulations. These simulations were run using a gray (frequency integrated) approach but were also restarted and ran with a multi-group approach that accounts for Compton scattering with a Kom

Yuchao Chang, Wen Chen, Jun Li, Jianpo Liu

Network energy efficiency is a main pillar in the design and operation of wireless communication systems. In this paper, we investigate a dense radio access network (dense-RAN) capable of radiated power management at the base station (BS). Aiming to improve the long-term network energy efficiency, an optimization problem is formulated by collaboratively mana

Oleg V. Pavlov, Jason M. Sardell

This chapter develops a feedback economic model that explains the rise of the Sicilian mafia in the 19th century. Grounded in economic theory, the model incorporates causal relationships between the mafia activities, predation, law enforcement, and the profitability of local businesses. Using computational experiments with the model, we explore how different

Jingxu Bai, Yuechun Jiao, Rong Song, Jiabei Fan

We present precise measurements of the quantum defects of cesium $n$F$_J$ Rydberg levels. We employ high-precision microwave spectroscopy of $(n+2)\mathrm{D}_{5/2}\rightarrow n\mathrm{F}_{5/2,7/2}$ transitions for $n=45$ to 50 in a cold-atom setup. Cold cesium $(n+2)$D$_{5/2}$ atoms, prepared via two-photon laser excitation, are probed by scanning weak micro

Chenqiu Zhao, Guanfang Dong, Shupei Zhang, Zijie Tan

Convolutional neural networks have demonstrated impressive results in many computer vision tasks. However, the increasing size of these networks raises concerns about the information overload resulting from the large number of network parameters. In this paper, we propose Frequency Regularization to restrict the non-zero elements of the network parameters in

Zhongyao Hu, Bo Chen, Rusheng Wang, Li Yu

This paper aims to study the state estimation problem under the stochastic event-triggered (SET) schedule. A posterior-based SET mechanism is proposed, which determines whether to transmit data by the effect of the measurement on the posterior estimate. Since this SET mechanism considers the whole posterior probability density function, it has better informa

Tianjun Wei, Jianghong Ma, Tommy W. S. Chow

In collaborative filtering, distance metric learning has been applied to matrix factorization techniques with promising results. However, matrix factorization lacks the ability of capturing collaborative information, which has been remarked by recent works and improved by interpreting user interactions as signals. This paper aims to find out how metric learn

Jiajia Liu, David Jess, Robert Erdélyi, Mihalis Mathioudakis

Swirls are ubiquitous in the solar atmosphere. They are believed to be related to the excitation of different modes of magnetohydrodynamic waves and pulses, as well as spicules. However, statistical studies of their collective behaviour are rare. In this paper, we aim to study the collective, as well as the behaviour of individual photospheric and chromosphe

Junzhang Chen, Xiangzhi Bai

The Segment Anything Model (SAM) is a promptable segmentation model recently introduced by Meta AI that has demonstrated its prowess across various fields beyond just image segmentation. SAM can accurately segment images across diverse fields, and generating various masks. We discovered that this ability of SAM can be leveraged to pretrain models for specifi

Sameer Chavan, Zenon Jan Jablonski, Il Bong Jung, Jan Stochel

In this paper, we study Brownian-type operators, which are upper triangular $2\times 2$ block matrix operators with entries satisfying some algebraic constraints. We establish a lifting theorem stating that any Brownian-type operator with subnormal $(2,2)$ entry lifts to a Brownian-type operator with normal $(2,2)$ entry, where lifting is understood in the s

Zidong Cao, Hao Ai, Athanasios V. Vasilakos, Lin Wang

To predict high-resolution (HR) omnidirectional depth map, existing methods typically leverage HR omnidirectional image (ODI) as the input via fully-supervised learning. However, in practice, taking HR ODI as input is undesired due to resource-constrained devices. In addition, depth maps are often with lower resolution than color images. Therefore, in this p

Peiyi Li, Jiachang Bi, Shunda Zhang, Rui Cai

With the recent report of near ambient superconductivity at room temperature in the N-doped lutetium hydride (Lu-H-N) system, the understanding of cubic Lu-H compounds has attracted worldwide attention. Generally, compared to polycrystal structures with non-negligible impurities, the single-crystalline form of materials with high purity can provide an opport

Fayez Abu-Ajamieh, Sudhir K. Vempati

We propose a renormalization scheme for non-local Quantum Field Theories (QFTs) with infinite derivatives inspired by string theory. Our Non-locality Renormalization Scheme (NRS) is inspired by Dimensional Regularization (DR) in local QFTs and is shown to significantly improve the UV behavior of non-local QFTs. We illustrate the scheme using simple examples

Xiao-Yun Wang, Chen Dong, Quanjin Wang

Under the framework of the vector meson dominance model (VMD), the absolute scattering lengths of light vector mesons ($\phi$, $\omega$, and $\rho$) with the deuteron are calculated from the cross sections of vector meson photoproduction off a deuteron. Additionally, a fitting function is used to predict the scattering lengths of the heavy vector mesons $J/\

Xiaoming Zheng, Kun Zhao, Jiahong Wu, Weiwei Hu

A new iterative projection method is proposed to solve the unsteady Navier-Stokes equations with high Reynolds numbers. The convectional projection method attempts to project the intermediate velocity to the divergence free space only once per time step. However, such a velocity is not genuinely divergence free in general practice, which can yield large erro

Steven Rossland, Daniel Wik, Brian Grefenstette, Nico Cappelluti

By characterizing the contribution of stray light to large datasets from the NuSTAR X-ray observatory collected over 2012--2017, we report a measurement of the cosmic X-ray background in the 3--20 keV energy range. These data represent $\sim20\%$ sky coverage while avoiding Galactic Ridge X-ray emission and are less weighted by deep, survey fields than previ

Sasisekhar Mangalam Govind, John Sahaya Rani Alex, Gabriel A. Wainer

Discrete Event Modelling of Embedded Systems (DEMES) is a development methodology based on the Discrete Event Systems (DEVS) specification that improves the time -to-market by simplifying the development and testing of embedded systems. CADMIUM is a C++ header-only library developed at Carleton University that helps simulate models built using the DEVS speci

R. Stadel, R. DeRose, K. M. Taddei, M. J. Krogstad

A true understanding of the properties of pnictide superconductors require the development of high-quality materials and performing measurements designed to unravel their intrinsic properties and short-range nematic correlations which are often obscured by extrinsic effects such as poor crystallinity, inhomogeneity, domain formation and twinning. In this pap

W. Ma, X. L. Huang, S. L. Wu

We investigate the dynamics of the driven open double two-level system by deriving a driven Markovian master equation based on the Lewis-Riesenfeld invariant theory. The transitions induced by coupling to the heat reservoir occur between the instantaneous eigenstates of the Lewis-Riesenfeld invariant. Therefore, different driving protocols associated with co

R Gnana Praveen, Eric Granger, Patrick Cardinal

In video-based emotion recognition (ER), it is important to effectively leverage the complementary relationship among audio (A) and visual (V) modalities, while retaining the intra-modal characteristics of individual modalities. In this paper, a recursive joint attention model is proposed along with long short-term memory (LSTM) modules for the fusion of voc

Kai Hu, Zhuoyuan Wu, Zhuoyao Zhong, Weihong Lin

In this paper, we present a new question-answering (QA) based key-value pair extraction approach, called KVPFormer, to robustly extracting key-value relationships between entities from form-like document images. Specifically, KVPFormer first identifies key entities from all entities in an image with a Transformer encoder, then takes these key entities as \te

S. L. Wu, X. L. Huang, X. X. Yi

We derive a Markovian master equation for driven open quantum systems based on the Lewis-Riesenfeld invariants theory, which is available for arbitrary driving protocols.The role of the Lewis-Riesenfeld invariants is to help us bypass the time-ordering obstacle in expanding the propagator of the free dynamics, such that the Lindblad operators in our driven-M

Junki Mori, Ryo Furukawa, Isamu Teranishi, Jun Sakuma

Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and the target domain has only unlabeled data. Existing HUDA methods assume that both positive and negative examples are available in the source domain, which may not be satisfied in so

Jihao Huang, Jun Zeng, Xuemin Chi, Koushil Sreenath

Obstacle avoidance for multi-robot navigation with polytopic shapes is challenging. Existing works simplify the system dynamics or consider it as a convex or non-convex optimization problem with positive distance constraints between robots, which limits real-time performance and scalability. Additionally, generating collision-free behavior for polytopic-shap

Moubariz Z. Garaev, Igor E. Shparlinski

For a prime number $p$ and integer $x$ with $\gcd(x,p)=1$ let $\overline{x}$ denote the multiplicative inverse of $x$ modulo $p.$ In the present paper we are interested in the problem of distribution modulo $p$ of the sequence $$ \overline{x}, \qquad x =1, \ldots, N, $$ and in lower bound estimates for the corresponding exponential sums. As representative ex

Zening Yan, Xiaoji Zhang, Maoyuan Wan, Chen Wu

In this paper, we calculated the quasinormal modes (QNMs) of a charged non-commutative black hole in scalar, electromagnetic and gravitational fields by three methods. We gave the influence of non-commutative parameter $\theta$ and charge $Q$ on QNMs in different fields. Thereafter, we calculated the shadow radius of the black hole and provided the valid ran

Meghan Muldoon, Naimul Khan

Accurate LVEF measurement is important in clinical practice as it identifies patients who may be in need of life-prolonging treatments. This paper presents a deep learning based framework to automatically estimate left ventricular ejection fraction from an entire 4-chamber apical echocardiogram video. The aim of the proposed framework is to provide an interp