January 2022 arXiv papers — page 67

Masataka Matsumoto, Shin Nakamura

We study critical phenomena associated with a spontaneous chiral symmetry breaking in current-driven non-equilibrium steady states by using holography. We find that the critical exponents $(\gamma, \nu)$ at the tricritical point are asymmetric between the chiral symmetry restored phase and the broken phase. Their values in the broken phase are different from

Florian Lehmann, Daniel Buschek

Autocompletion is an approach that extends and continues partial user input. We propose to interpret autocompletion as a basic interaction concept in human-AI interaction. We first describe the concept of autocompletion and dissect its user interface and interaction elements, using the well-established textual autocompletion in search engines as an example.

Maryna V. Ishchenko, Margarita O. Sobolenko, Mukhagali T. Kalambay, Bekdaulet T. Shukirgaliyev

Using the data from Gaia (ESA) Data Release 2 we performed the orbital calculations of globular clusters (GCs) of the Milky Way. To explore possible collisions between the GCs, using our developed highorder {\phi}-GRAPE code, we integrated (backwards and forward) the orbits of 119 objects with reliable positions and proper motions. In calculations, we adopte

Kingman Cheung, Adil Jueid, Jinheung Kim, Soojin Lee

In the type-I two-Higgs-doublet model, existing theoretical and experimental constraints still permit the light charged Higgs boson with a mass below the top quark mass. We present a complete roadmap for the light charged Higgs boson at the LHC through the comprehensive phenomenology study, focusing on the normal scenario where the lighter \textit{CP}-even H

Yutong Dai, Brian Price, He Zhang, Chunhua Shen

Deep image matting methods have achieved increasingly better results on benchmarks (e.g., Composition-1k/alphamatting.com). However, the robustness, including robustness to trimaps and generalization to images from different domains, is still under-explored. Although some works propose to either refine the trimaps or adapt the algorithms to real-world images

Sai Hemanth Kasaraneni

Given the three dimensional complexity of a video signal, training a robust and diverse GAN based video generative model is onerous due to large stochasticity involved in data space. Learning disentangled representations of the data help to improve robustness and provide control in the sampling process. For video generation, there is a recent progress in thi

Hiroshi Yamauchi

We review 3-transposition groups arising in vertex operator algebra theory. One can construct a commutative algebra called the Matsuo algebra out of a 3-transposition group. Some 3-transposition groups arise as automorphism groups of vertex operator algebras via Matsuo algebras but there exist some 3-transposition groups which do not arise through Matsuo alg

Ke Hu, Yi Qi, Jianqiang Huang, Jia Cheng

Click-through rate(CTR) prediction is a core task in cost-per-click(CPC) advertising systems and has been studied extensively by machine learning practitioners. While many existing methods have been successfully deployed in practice, most of them are built upon i.i.d.(independent and identically distributed) assumption, ignoring that the click data used for

Weizhi Xu, Junfei Wu, Qiang Liu, Shu Wu

The prevalence and perniciousness of fake news has been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper, we focus on the evidence-based fake news detection, where several evidences are utilized to probe the veracity of news (i.e., a claim). Most previous methods first employ sequentia

Chen Wang, Qin Hu, Dongxiao Yu, Xiuzhen Cheng

Virtual network functions (VNFs) have been widely deployed in mobile edge computing (MEC) to flexibly and efficiently serve end users running resource-intensive applications, which can be further serialized to form service function chains (SFCs), providing customized networking services. To ensure the availability of SFCs, it turns out to be effective to pla

Michail Spitieris, Ingelin Steinsland, Emma Ingestrom

This work is motivated by personalized digital twins based on observations and physical models for treatment and prevention of Hypertension. The models commonly used are simplification of the real process and the aim is to make inference about physically interpretable parameters. To account for model discrepancy we propose to set up the estimation problem in

V. G. Gurzadyan, N. N. Fimin, V. M. Chechetkin

The emergence of one and two-dimensional configurations -- Zeldovich pancakes -- progenitors of the observed filaments and clusters and groups of galaxies, is predicted by means of a developed kinetic approach in analyzing the evolution of initial density perturbations. The self-consistent gravitational interaction described by Vlasov-Poisson set of equation

Ai-Jun Ma, Wen-Fei Wang

The contributions for the kaon pair from the intermediate states $\rho(1450)^+$ and $\rho(1700)^+$ in the decays $B^+ \to \bar{D}^0 K^+ \bar{K}^0$, $B^0 \to D^- K^+ \bar{K}^0$, and $B_s^0 \to D_s^-K^+ \bar{K}^0$ are analyzed within the perturbative QCD factorization approach. The decay amplitudes for all concerned decays in this work are dominated by the fac

Xu Liu, Wei Peng, Zhiqiang Gong, Weien Zhou

Temperature field inversion of heat-source systems (TFI-HSS) with limited observations is essential to monitor the system health. Although some methods such as interpolation have been proposed to solve TFI-HSS, those existing methods ignore correlations between data constraints and physics constraints, causing the low precision. In this work, we develop a ph

Thomas Hansen, Simon Vendelbo Bylling Jensen, Lars Bojer Madsen

Using the Hubbard model we study how the process of high-order harmonic generation (HHG) is modified by beyond mean-field electron-electron correlation for both finite and bulk systems. A finite-size enhancement of the HHG signal is found and attributed to electrons backscattering off the lattice edges. Additionally, with increasing strength of the electron-

Azzam Alhussain, Mingjie Lin

Generative Adversarial Networks (GAN) are cutting-edge algorithms for generating new data samples based on the learned data distribution. However, its performance comes at a significant cost in terms of computation and memory requirements. In this paper, we proposed an HW/SW co-design approach for training quantized deconvolution GAN (QDCGAN) implemented on

Yangming Zhou, Xiaze Zhang, Na Geng, Zhibin Jiang

Finding an optimal set of critical nodes in a complex network has been a long-standing problem in the fields of both artificial intelligence and operations research. Potential applications include epidemic control, network security, carbon emission monitoring, emergence response, drug design, and vulnerability assessment. In this work, we consider the proble

Attila Nagy, Patrick Nanys, Balázs Frey Konrád, Bence Bial

We train Transformer-based neural machine translation models for Hungarian-English and English-Hungarian using the Hunglish2 corpus. Our best models achieve a BLEU score of 40.0 on HungarianEnglish and 33.4 on English-Hungarian. Furthermore, we present results on an ongoing work about syntax-based augmentation for neural machine translation. Both our code an

Alexia M. Lopez, Roger G. Clowes, Gerard M. Williger

We present the serendipitous discovery of a `Giant Arc on the Sky' at $z \sim 0.8$. The Giant Arc (GA) spans $\sim 1$ Gpc (proper size, present epoch), and appears to be almost symmetrical on the sky. It was discovered via intervening MgII absorbers in the spectra of background quasars, using the catalogues of Zhu \& M\'enard. The use of MgII absorbers repre

Ali H. Chamseddine, Peter West

The 1.5 formalism played a key role in the discovery of supergravity and it has been used to prove the invariance of essentially all supergravity theories under local supersymmetry. It emerged from the gauging of the super Poincare group to find supergravity. We review both of these developments as well as the auxiliary fields for simple supergravity and its

Yudai Okishio, Hiroaki Ito, Hiroyuki Kitahata

The system that consists of a ball bouncing off a sawtooth-shaped table vibrating vertically is considered. The horizontal motion in this system is caused by the table shape, and we plotted the mean horizontal velocity as a function of the asymmetry of the table shape. The ball is transported in the direction which the gentler slopes face. To give a descript

Shrimon Mukherjee, Madhusudan Ghosh, Partha Basuchowdhuri

Development of new drugs is an expensive and time-consuming process. Due to the world-wide SARS-CoV-2 outbreak, it is essential that new drugs for SARS-CoV-2 are developed as soon as possible. Drug repurposing techniques can reduce the time span needed to develop new drugs by probing the list of existing FDA-approved drugs and their properties to reuse them

Asato Tsuchiya, Kazushi Yamashiro

We study the target space entanglement entropy in a complex matrix model that describes the chiral primary sector in $\mathcal{N}=4$ super Yang-Mills theory, which is associated with the bubbling AdS geometry. The target space for the matrix model is a two-dimensional plane where the eigenvalues of the complex matrix distribute. The eigenvalues are viewed as

Alexander Kleshchev, Michael Livesey

We define and study RoCK blocks for double covers of symmetric groups. We prove that RoCK blocks of double covers are Morita equivalent to standard `local' blocks. The analogous result for blocks of symmetric groups, a theorem of Chuang and Kessar, was an important step in Chuang and Rouquier ultimately proving Brou\'e's abelian defect group conjecture for s

David Holmes, Giulio Orecchia

We use the theory of logarithmic line bundles to construct compactifications of spaces of roots of a line bundle on a family of curves, generalising work of a number of authors. This runs via a study of the torsion in the tropical and logarithmic jacobians (recently constructed by Molcho and Wise). Our moduli space carries a `double ramification cycle' measu

Michael Gref, Nike Matthiesen, Sreenivasa Hikkal Venugopala, Shalaka Satheesh

For research in audiovisual interview archives often it is not only of interest what is said but also how. Sentiment analysis and emotion recognition can help capture, categorize and make these different facets searchable. In particular, for oral history archives, such indexing technologies can be of great interest. These technologies can help understand the

E. Kirstein, D. R. Yakovlev, E. A. Zhukov, J. Höcker

Methylammonium lead triiodine (MAPbI$_3$) is a material representative of the hybrid organic-inorganic lead halide perovskites which attract currently great attention due to their photovoltaic efficiency and bright optoelectronic properties. Here, the coherent spin dynamics of charge carriers and spin dependent phenomena induced by the carrier interaction wi

Fataneh Salahedin, Reza Pazhouhesh, Mohammad Malekjani

We use different combinations of data samples to investigate the new generalized Chaplygin gas (NGCG) model in the context of dark energy (DE) cosmology. Using the available cosmological data, we put constraints on the the free parameters of NGCG model based on the statistical Markov chain Monte Carlo method. We then find the best fit values of cosmological

Rosalia Tufano, Simone Scalabrino, Luca Pascarella, Emad Aghajani

Different from what happens for most types of software systems, testing video games has largely remained a manual activity performed by human testers. This is mostly due to the continuous and intelligent user interaction video games require. Recently, reinforcement learning (RL) has been exploited to partially automate functional testing. RL enables training

Nicolas Bélicard, Marc Junior Niémet-Mabiala, Jean-Noël Tourvieille, Pierre Lidon

Because they are sensitive to mechanical properties of materials and can propagate even in opaque systems, acoustic waves provides us with a powerful characterization tool in numerous fields. Common techniques mostly rely on time-of-flight measurements and do not exploit the spectral content: however, sound speed and attenuation spectra contain rich informat

Rasmus Larsen, Mikkel Nørgaard Schmidt

Reinforcement learning policies are often represented by neural networks, but programmatic policies are preferred in some cases because they are more interpretable, amenable to formal verification, or generalize better. While efficient algorithms for learning neural policies exist, learning programmatic policies is challenging. Combining imitation-projection

A. Tosato, B. M. Ferrari, A. Sammak, A. R. Hamilton

We design, fabricate, and study a hole bilayer in a strained germanium double quantum well. Magnetotransport characterisation of double quantum well field-effect transistors as a function of gate voltage reveals the population of two hole channels with a high combined mobility of 3.34$\times$10$^5$ cm$^2$/Vs and a low percolation density of 2.38$\times$10$^{

Feng-Yu Wang

To characterize Navier-Stokes type equations where the Laplacian is extended to a singular second order differential operator, we propose a class of SDEs depending on the distribution in future. The well-posedness and regularity estimates are derived for these SDEs.

Andrej Liptaj

Having a function $f$ and a set of functionals $\{\mathcal{C}_{n}\}$, $c_n^f \equiv \mathcal{C}_n \left(f\right)$, one can interpret function approximation very generally as a construction of some function $\mathcal{A}_{N}^{f}$ such that $c_n^f = \mathcal{C}_n \left(\mathcal{A}_N^f \right)$. All known approximations can be interpreted in this way and we revi

Michele Missikoff

Business process (BP) analysis represents a first key phase of information system development. It consists in the gathering of domain knowledge and its organization to be later used in the software development, and beyond (e.g., for Business Process Reengineering). The quality of the developed information system largely depends on how the BP analysis has bee

Simone Di Marino, Mathieu Lewin, Luca Nenna

We study a generalization of the multi-marginal optimal transport problem, which has no fixed number of marginals $N$ and is inspired of statistical mechanics. It consists in optimizing a linear combination of the costs for all the possible $N$'s, while fixing a certain linear combination of the corresponding marginals.

Patrik Christen

Having a model and being able to implement open-ended evolutionary systems is important for advancing our understanding of open-endedness. Complex systems science and newest generation high-level programming languages provide intriguing possibilities to do so. First, some recent advances in modelling and implementing open-ended evolutionary systems are revie

Luya Wang, Feng Liang, Yangguang Li, Honggang Zhang

Recently, self-supervised vision transformers have attracted unprecedented attention for their impressive representation learning ability. However, the dominant method, contrastive learning, mainly relies on an instance discrimination pretext task, which learns a global understanding of the image. This paper incorporates local feature learning into self-supe

Shu-Yu Ho, Pyungwon Ko, Chih-Ting Lu

In this paper, we construct for the first time a two-component strongly interacting massive particles (SIMP) dark matter (DM) model, where a complex scalar and a vector-like fermion play the role of the SIMP DM candidates. These two particles are stable due to an accidental $\mathbb{Z}^{}_4$ symmetry after the breaking of a $\text{U}(1)^{}_\textsf{D}$ gauge

Qi Li, Lei Wu

A kinetic model is proposed for rarefied flows of molecular gas with rotational and temperature-dependent vibrational degrees of freedom. The model reduces to the Boltzmann equation for monatomic gas when the energy exchange between the translational and internal modes is absent, thus the influence of intermolecular potential can be captured. Moreover, not o

Kristoffer Andersson, Adam Andersson, Cornelis W. Oosterlee

In this paper, we propose a deep learning based numerical scheme for strongly coupled FBSDEs, stemming from stochastic control. It is a modification of the deep BSDE method in which the initial value to the backward equation is not a free parameter, and with a new loss function being the weighted sum of the cost of the control problem, and a variance term wh

Kaustav Goswami, Hemanta Kumar Mondal, Shirshendu Das, Dip Sankar Banerjee

Dynamic Random Access Memory (DRAM) is the de-facto choice for main memory devices due to its cost-effectiveness. It offers a larger capacity and higher bandwidth compared to SRAM but is slower than the latter. With each passing generation, DRAMs are becoming denser. One of its side-effects is the deviation of nominal parameters: process, voltage, and temper

Khaled Ben Amara, Maria Isabel Berenguer, Aref Jeribi

In this paper, the existence and uniqueness of the fixed point for the product of two nonlinear operator in Banach algebra is discussed. In addition, an approximation method of the fixed point of hybrid nonlinear equations in Banach algebras is established. This method is applied to two interesting different types of functional equations. In addition, to ill

Jawher Hbil, Mohamed Zaway

In this paper we study the class $\mathcal{E}_{m}(\Omega)$ of $m-$subharmonic functions introduced by Lu in \cite{L1}. We prove that the convergence in $m-$capacity implies the convergence of the associated Hessian measure for functions that belong to $\mathcal{E}_{m}(\Omega)$. Then we extend those results to the class $\mathcal{E}_{m,\chi}(\Omega)$ that dep

Rosalia Tufano, Simone Masiero, Antonio Mastropaolo, Luca Pascarella

Code review is a practice widely adopted in open source and industrial projects. Given the non-negligible cost of such a process, researchers started investigating the possibility of automating specific code review tasks. We recently proposed Deep Learning (DL) models targeting the automation of two tasks: the first model takes as input a code submitted for

Xiaoying Zhang, Baolin Peng, Jianfeng Gao, Helen Meng

End-to-end task bots are typically learned over a static and usually limited-size corpus. However, when deployed in dynamic, changing, and open environments to interact with users, task bots tend to fail when confronted with data that deviate from the training corpus, i.e., out-of-distribution samples. In this paper, we study the problem of automatically ada

Nan Wu, Hang Yang, Yuan Xie, Pan Li

Agile hardware development requires fast and accurate circuit quality evaluation from early design stages. Existing work of high-level synthesis (HLS) performance prediction usually needs extensive feature engineering after the synthesis process. To expedite circuit evaluation from as earlier design stage as possible, we propose a rapid and accurate performa

Jay Armas, Filippo Camilloni

We formulate the theory of first-order dissipative magnetohydrodynamics in an arbitrary hydrodynamic frame under the assumption of parity-invariance and discrete charge symmetry. We study the mode spectrum of Alfv\'en and magnetosonic waves as well as the spectrum of gapped excitations and derive constraints on the transport coefficients such that generic eq

Stefano Della Fiore, Alessandro Gnutti, Sven Polak

A code $\mathcal{C} \subseteq \{0, 1, 2\}^n$ is said to be trifferent with length $n$ when for any three distinct elements of $\mathcal{C}$ there exists a coordinate in which they all differ. Defining $\mathcal{T}(n)$ as the maximum cardinality of trifferent codes with length $n$, $\mathcal{T}(n)$ is unknown for $n \ge 5$. In this note, we use an optimized s

Yuting Xiao, Jiale Xu, Shenghua Gao

Benefiting from the continuous representation ability, deep implicit functions can represent a shape at infinite resolution. However, extracting high-resolution iso-surface from an implicit function requires forward-propagating a network with a large number of parameters for numerous query points, thus preventing the generation speed. Inspired by the Taylor

Ying Hu, Xiaomin Shi, Zuo Quan Xu

In this paper, we study a stochastic linear-quadratic control problem with random coefficients and regime switching on a horizon $[0,T\wedge\tau]$, where $\tau$ is a given random jump time for the underlying state process and $T$ is a constant. We obtain an explicit optimal state feedback control and explicit optimal cost value by solving a system of stochas

Rohitash Chandra, Yash Vardhan Sharma

Evolutionary algorithms provide gradient-free optimisation which is beneficial for models that have difficulty in obtaining gradients; for instance, geoscientific landscape evolution models. However, such models are at times computationally expensive and even distributed swarm-based optimisation with parallel computing struggles. We can incorporate efficient

Zhiheng Jiang, Hoai Nguyen Huynh

Using a dataset of more than 90,000 metal music reviews written by over 9,000 users in a period of 15 years, we analyse the genre structure of metal music with the aid of review text information. We model the relationships between genres using a user-oriented network, based on the written reviews. We then perform community detection and employ a network "ave

Michael Gref, Nike Matthiesen, Christoph Schmidt, Sven Behnke

Automatic speech recognition systems have accomplished remarkable improvements in transcription accuracy in recent years. On some domains, models now achieve near-human performance. However, transcription performance on oral history has not yet reached human accuracy. In the present work, we investigate how large this gap between human and machine transcript

Ryoya Arimoto

The Neretin group $\mathcal{N}_{d,k}$ is the totally disconnected locally compact group consisting of almost automorphisms of the tree $\mathcal{T}_{d,k}$. This group has a distinguished open subgroup $\mathcal{O}_{d,k}$. We prove that this open subgroup is not of type I. This gives an alternative proof of the recent result of P.-E. Caprace, A. Le Boudec and

Osama Khlaif, Taro Kimura

We study the root of unity limit of $(\textbf{q}, \textbf{t})$-deformed Virasoro matrix models, for which we call the resulting model Uglov matrix model. We derive the associated Virasoro constraints on the partition function and find agreement of the central charge with the expression obtained from the level-rank duality associated with the parafermion CFT.

Peihao Sun, Giulio Monaco, Peter Zalden, Klaus Sokolowski-Tinten

Liquid polymorphism is an intriguing phenomenon which has been found in a few single-component systems, the most famous being water. By supercooling liquid Te to more than 130 K below its melting point and performing simultaneous small-angle and wide-angle X-ray scattering measurements, we observe clear maxima in its thermodynamic response functions around 6

Khaled Youssef, Kevin Shao, Seulgi Moon, Louis-Serge Bouchard

Landslides are notoriously difficult to predict because numerous spatially and temporally varying factors contribute to slope stability. Artificial neural networks (ANN) have been shown to improve prediction accuracy but are largely uninterpretable. Here we introduce an additive ANN optimization framework to assess landslide susceptibility, as well as datase

Ziyuan Gao, Sanjay Jain, Li Zeyong, Ammar Fathin Sabili

Prior work of Hartmanis and Simon (Hartmanis and Simon, 1974) and Floyd and Knuth (Floyd and Knuth, 1990) investigated what happens if a device uses primitive steps more natural than single updates of a Turing tape. One finding was that in the numerical setting, addition, subtraction, comparisons and bit-wise Boolean operations of numbers preserve polynomial

Derek Xu

My project tackles the question of whether Ray can be used to quickly train autonomous vehicles using a simulator (Carla), and whether a platform robust enough for further research purposes can be built around it. Ray is an open-source framework that enables distributed machine learning applications. Distributed computing is a technique which parallelizes co

Yang Li, Yu Shen, Huaijun Jiang, Wentao Zhang

The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the scalability of state-of-the-arts starts to become a crucial bottleneck. In this paper, inspired by our experience when deploying hyper-parameter tuning in a real-world application in

Matteo Acciai, Preden Roulleau, Imen Taktak, D. Christian Glattli

We consider an electronic Hong-Ou-Mandel interferometer in the integer quantum Hall regime, where the colliding electronic states are generated by applying voltage pulses (creating for instance Levitons) to ohmic contacts. The aim of this work is to investigate possible mechanisms leading to a reduced visibility of the Pauli dip, i.e., the noise suppression

Nader Masmoudi, Cuili Zhai, Weiren Zhao

In this paper, we study the asymptotic stability for the two-dimensional Navier-Stokes Boussinesq system around the Couette flow with small viscosity $\nu$ and small thermal diffusion $\mu$ in a finite channel. In particular, we prove that if the initial velocity and initial temperature $(v_{in},\rho_{in})$ satisfies $\|v_{in}-(y,0)\|_{H_{x,y}^2}\leq \e_0 \m

Neng-Chang Wei, Ai-Chao Wang, Fei Huang

A gauge-invariant model is constructed for the $\gamma p \to K^+\Lambda(1690)$ reaction within a tree-level effective Lagrangian approach with the purpose to understand the underlying production mechanisms and to study the resonance contributions in this reaction. In addition to the $t$-channel $K$ and $K^\ast$ exchanges, the $s$-channel nucleon exchange, an

V. Krumins, M. Tamanis, R. Ferber, A. V. Oleynichenko

Laser-induced fluorescence spectra of the $c^3\Sigma^+(v_{c},J_{c}=N_{c})\rightarrow a^3\Sigma^+(v_{a},N_{a} = J_{c} \pm 1)$ transitions excited from the ground $X^1\Sigma^+$ state of $^{39}$K$^{133}$Cs molecule were recorded with Fourier-transform spectrometer IFS125-HR (Bruker) at the highest achievable spectral resolution of 0.0063 cm${}^{-1}$. Systematic

Adam Doliwa, Artur Siemaszko

We show that solution to the Hermite-Pad\'{e} type I approximation problem leads in a natural way to a subclass of solutions of the Hirota (discrete Kadomtsev-Petviashvili) system and of its adjoint linear problem. Our result explains the appearence of various ingredients of the integrable systems theory in application to multiple orthogonal polynomials, num

Joao P. Moutinho, Bruno Coutinho, Lorenzo Buffoni

We report on a model built to predict links in a complex network of scientific concepts, in the context of the Science4Cast 2021 competition. We show that the network heavily favours linking nodes of high degree, indicating that new scientific connections are primarily made between popular concepts, which constitutes the main feature of our model. Besides th

Takahiro Odagawa, Tsutomu Fukuda, Ayami Hiramoto, Hiroaki Kawahara

Precise measurement of neutrino-nucleus interactions with an accelerator neutrino beam is highly important for current and future neutrino oscillation experiments. To measure muon-neutrino charged-current interactions with nuclear-emulsion-based hybrid detector, muon track matching among the detectors are essential. We describe the design and performance of

Giacomo Ascione, Salvatore Cuomo

In this paper we introduce a new approach to discrete-time semi-Markov decision processes based on the sojourn time process. Different characterizations of discrete-time semi-Markov processes are exploited and decision processes are constructed by their means. With this new approach, the agent is allowed to consider different actions depending also on the so

Thomas Place, Marc Zeitoun

We investigate two operators on classes of regular languages: polynomial closure (Pol) and Boolean closure (Bool). We apply these operators to classes of group languages G and to their well-suited extensions G+, which is the least Boolean algebra containing G and the singleton language containing the empty word. This yields the classes Bool(Pol(G)) and Bool(

Mojtaba Shahidi Zandi, Roozbeh Rajabi

License plate recognition systems have a very important role in many applications such as toll management, parking control, and traffic management. In this paper, a framework of deep convolutional neural networks is proposed for Iranian license plate recognition. The first CNN is the YOLOv3 network that detects the Iranian license plate in the input image wh

Haidong Wang, Zhiyong Li, Yaping Li, Ke Nai

Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each historical tracklet is composed of an o

Yuwen Li, Zhengguo Li, Chaobing Zheng, Shiqian Wu

Existing shape from focus (SFF) techniques cannot preserve depth edges and fine structural details from a sequence of multi-focus images. Moreover, noise in the sequence of multi-focus images affects the accuracy of the depth map. In this paper, a novel depth enhancement algorithm for the SFF based on an adaptive weighted guided image filtering (AWGIF) is pr

Timothée Crin-Barat, Raphaël Danchin

Here we investigate global strong solutions for a class of partially dissipative hyperbolic systems in the framework of critical homogeneous Besov spaces. Our primary goal is to extend the analysis of our previous paper [10] to a functional framework where the low frequencies of the solution are only bounded in L p-type spaces with p larger than 2. This enab

Xiaojun Mao, Liuhua Peng, Zhonglei Wang

Random forests are a widely used machine learning algorithm, but their computational efficiency is undermined when applied to large-scale datasets with numerous instances and useless features. Herein, we propose a nonparametric feature selection algorithm that incorporates random forests and deep neural networks, and its theoretical properties are also inves

Chong Chen, Fei Sun, Min Zhang, Bolin Ding

Recommender systems provide essential web services by learning users' personal preferences from collected data. However, in many cases, systems also need to forget some training data. From the perspective of privacy, several privacy regulations have recently been proposed, requiring systems to eliminate any impact of the data whose owner requests to forget.

Peihu Duan, Lidong He, Lingying Huang, Guanrong Chen

This paper investigates sensor scheduling for state estimation of complex networks over shared transmission channels. For a complex network of dynamical systems, referred to as nodes, a sensor network is adopted to measure and estimate the system states in a distributed way, where a sensor is used to measure a node. The estimates are transmitted from sensors

Wim Hordijk

Zipf's law is well known in linguistics: the frequency of a word is inversely proportional to its rank. This is a special case of a more general power law, a common phenomenon in many kinds of real-world statistical data. Here, it is shown that snooker statistics also follow such a mathematical pattern, but with varying (estimated) parameter values. Two type

Rui Wang, Ying D. Liu, Shangbin Yang, Huidong Hu

Homologous coronal mass ejections (CMEs) are an interesting phenomenon, and it is possible to investigate the formation of CMEs by comparing multi-CMEs under a homologous physical condition. AR 11283 had been present on the solar surface for several days when a bipole emerged on 2011 September 4. Its positive polarity collided with the pre-existing negative

Guruprasad Kadam

In this work, we calculate the dynamic density correlations using Mori-Zwanzig-Nakajima projection operator method. With a judicious choice of slow variables we derive the evolution equations for these slow variables starting from generalised Langevin equation. We get the hydrodynamic form of density correlations function which consist of two acoustic peaks:

Lin Wang, Ye-Zhao Yu, Feifei Kou, Kuo Liu

We present simultaneous broad-band radio observations on the abnormal emission mode from PSR B1859$+$07 using the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This pulsar shows peculiar emission phenomena, which are occasional shifts of emission to an early rotational phase and mode change of emission at the normal phase. We confirm all thes

Qianqian Zhang, Xinru Liao, Quan Liu, Jian Xu

Advertisers play an essential role in many e-commerce platforms like Taobao and Amazon. Fulfilling their marketing needs and supporting their business growth is critical to the long-term prosperity of platform economies. However, compared with extensive studies on user modeling such as click-through rate predictions, much less attention has been drawn to adv

Ravi Kumar, Ranjan Modak

While global quantum quench has been extensively used in the literature to understand the localization-delocalization transition for the one-dimensional quantum spin chain, the effect of geometric quench (which corresponds to a sudden change of the geometry of the chain) in the context of such transitions is yet to be well understood. In this work, we invest

Boris Latosh

Package FeynGrav which provides a framework to deal with Feynman rules for gravity within FeynCalc is presented. We present a framework to deal with the corresponding Feynman rules for general relativity and non-supersymmetric matter minimally coupled to gravity. Applicability of the package is tested with 2 -> 2 on-shell tree level graviton scattering, pola

Mike Wu, Will McTighe, Kaili Wang, Istvan A. Seres

A common misconception among blockchain users is that pseudonymity guarantees privacy. The reality is almost the opposite. Every transaction one makes is recorded on a public ledger and reveals information about one's identity. Mixers, such as Tornado Cash, were developed to preserve privacy through "mixing" transactions with those of others in an anonymity

Yi-Xuan Wu, Zi-Yan Guan, Sai Li, Zheng-Yuan Xue

Quantum state transfer (QST) and entangled state generation (ESG) are important building blocks for modern quantum information processing. To achieve these tasks, convention wisdom is to consult the quantum adiabatic evolution, which is time-consuming, and thus is of low fidelity. Here, using the shortcut to adiabaticity technique, we propose a general metho

Laurits Tani, Christian Veelken

When using machine learning (ML) techniques, users typically need to choose a plethora of algorithm-specific parameters, referred to as hyperparameters. In this paper, we compare the performance of two algorithms, particle swarm optimisation (PSO) and Bayesian optimisation (BO), for the autonomous determination of these hyperparameters in applications to dif

Zheyuan Li, Jiguo Cao

We proposed a new penalized B-splines estimator, the general P-spline, to accommodate non-uniform B-splines on unevenly spaced knots. It is a complement to Eilers and Marx's standard P-spline tailored for uniform B-splines on equidistant knots. At its core, we derived a novel general difference penalty that accounts for irregular knot spacing, while still be

Yuta Nakamura, Takashi Takahashi, Yoshiyuki Kabashima

Knapsack problem (KP) is a representative combinatorial optimization problem that aims to maximize the total profit by selecting a subset of items under given constraints on the total weights. In this study, we analyze a generalized version of KP, which is termed the generalized multidimensional knapsack problem (GMDKP). As opposed to the basic KP, GMDKP all

Kitty Li, Ninh Pham

In collaborative outlier detection, multiple participants exchange their local detectors trained on decentralized devices without exchanging their own data. A key problem of collaborative outlier detection is efficiently aggregating multiple local detectors to form a global detector without breaching the privacy of participants' data and degrading the detect

Sabrina Pasterski

Here we consider what happens when we lift a codimension-1 slice of the celestial sphere to a codimension-1 slice of the bulk spacetime in a manner that respects our ability to quotient by the null generators of $\mathcal{I}^\pm$ to get to our codimension-2 hologram. The contour integrals of the 2D currents for the celestial symmetries lift to the standard b

Luca Varotto, Marco Fabris, Giulia Michieletto, Angelo Cenedese

Visual sensor networks (VSNs) constitute a fundamental class of distributed sensing systems, with unique complexity and appealing performance features, which correspondingly bring in quite active lines of research. An important research direction consists in the identification and estimation of the VSN sensing features: these are practically useful when scal

Yaxing Ma, Gengsheng Wang, Huaiqiang Yu

We design a new feedback law to stabilize a linear infinite-dimensional control system, where the state operator generates a C0-group and the control operator is unbounded. Our feedback law is based on the integration of a mutated Gramian operator-valued function. In the structure of the aforementioned mutated Gramian operator, we utilize the weak observabil

Susobhan Bandopadhyay, Sasthi C. Ghosh, Subhasis Koley

For two given nonnegative integers $h$ and $k$, an $L(h,k)$-edge labeling of a graph $G$ is the assignment of labels $\{0,1, \cdots, n\}$ to the edges so that two edges having a common vertex are labeled with difference at least $h$ and two edges not having any common vertex but having a common edge connecting them are labeled with difference at least $k$. T

Pascal Azerad, Marien-Lorenzo Hanot

We are interested in the numerical reconstruction of a vector field with prescribed divergence and curl in a general domain of R 3 or R 2 , not necessarily contractible. To this aim, we introduce some basic concepts of finite element exterieur calculus and rely heavily on recent results of P. Leopardi and A. Stern. The goal of the paper is to take advantage

Wolfgang Fuhl, Daniel Weber, Shahram Eivazi

This paper describes a feature extraction and gaze estimation software, named \textit{Pistol} that can be used with Pupil Invisible projects and other eye trackers in the future. In offline mode, our software extracts multiple features from the eye including, the pupil and iris ellipse, eye aperture, pupil vector, iris vector, eye movement types from pupil a

Han-Mai Lin, Florence Merlevède, Dalibor Voln{ý}

The first aim of this paper is to wonder to what extent we can generalize the central limit theorem of Gordin [5] under the so-called L 1-projective criteria to ergodic stationary random fields when completely commuting filtrations are considered. Surprisingly it appears that this result cannot be extended to its full generality and that an additional condit

Moun Meenakshi, Dipanjan Mukherjee, Alexander Y. Wagner, Nicole P. H. Nesvadba

We use the results of relativistic hydrodynamic simulations of jet-ISM interactions in a galaxy with a radio-loud AGN to quantify the extent of ionization in the central few kpcs of the gaseous galactic disc. We perform post-process radiative transfer of AGN radiation through the simulated gaseous jet-perturbed disc to estimate the extent of photo-ionization

Mina Lee, Percy Liang, Qian Yang

Large language models (LMs) offer unprecedented language generation capabilities and exciting opportunities for interaction design. However, their highly context-dependent capabilities are difficult to grasp and are often subjectively interpreted. In this paper, we argue that by curating and analyzing large interaction datasets, the HCI community can foster

Bruno Cessac

The retina is the entrance of the visual system. Although based on common biophysical principles the dynamics of retinal neurons is quite different from their cortical counterparts, raising interesting problems for modellers. In this paper I address some mathematically stated questions in this spirit, discussing, in particular: (1) How could lateral amacrine