January 2022 arXiv papers — page 88

Xuefeng Peng, Fei Chen, Hang Cheng, Meiqing Wang

In recent years, graph signal processing (GSP) technology has become popular in various fields, and graph Laplacian regularizers have also been introduced into convolutional sparse representation. This paper proposes a convolutional sparse representation model based on the dual graph Laplacian regularizer to ensure effective application of a dual graph signa

Zachiri McKenzie

A result of Kaufmann shows that if $L_\alpha$ is countable, admissible and satisfies $\Pi_n\textsf{-Collection}$, then $\langle L_\alpha, \in \rangle$ has a proper $\Sigma_{n+1}$-elementary end extension. This paper investigates to what extent the theory that holds in $\langle L_\alpha, \in \rangle$ can be transferred to the partially-elementary end extensio

Hendrik F. R. Schmidt, Jörg Schlötterer, Marcel Bargull, Enrico Nasca

AI/Computing at scale is a difficult problem, especially in a health care setting. We outline the requirements, planning and implementation choices as well as the guiding principles that led to the implementation of our secure research computing enclave, the Essen Medical Computing Platform (EMCP), affiliated with a major German hospital. Compliance, data pr

Tarek Faycal, Claudio Zito

Neuroevolution has recently been shown to be quite competitive in reinforcement learning (RL) settings, and is able to alleviate some of the drawbacks of gradient-based approaches. This paper will focus on applying neuroevolution using a simple genetic algorithm (GA) to find the weights of a neural network that produce optimally behaving agents. In addition,

Beom-Seok Han, Kunwoo Kim, Jaeyun Yi

We study the compact support property for solutions of the following stochastic partial differential equations: $$\partial_t u = a^{ij}u_{x^ix^j}(t,x)+b^{i}u_{x^i}(t,x)+cu+h(t,x,u(t,x))\dot{F}(t,x),\quad (t,x)\in (0,\infty)\times{\bf{R}}^d,$$ where $\dot{F}$ is a spatially homogeneous Gaussian noise that is white in time and colored in space, and $h(t, x, u)

Tianzong Yu, Chunyuan Zhang, Yuan Wang, Meng Ma

Convolutional neural networks (CNNs) have succeeded in many practical applications. However, their high computation and storage requirements often make them difficult to deploy on resource-constrained devices. In order to tackle this issue, many pruning algorithms have been proposed for CNNs, but most of them can't prune CNNs to a reasonable level. In this p

Nicolas A. Mauchamp, Kazumasa Ikuse, Michiro Isobe, Satoshi Hamaguchi

The self-sputtering yield of the (100) face-centered cubic (fcc) crystal surface consisting of particles interacting with the Lennard-Jones (LJ) potential is presented as a function of the normalized incident particle kinetic energy for normal incidence. Because the self-sputtering yield depends only on the normalized incident energy, the yield curve present

Giovanni Giuffrida, Calogero G. Zarba

We study the concept of free will by defining a mathematical model that extends Newton's laws of motion, in such a way that bodies are replaced with agents endowed with free will. In our model the free will of agents is not entirely free, but is bound by the Golden Rule, an ethic found in almost all cultures, which states that one should wish upon others as

Linkai Peng, Li Lin, Pujin Cheng, Ziqi Huang

Various deep learning models have been developed to segment anatomical structures from medical images, but they typically have poor performance when tested on another target domain with different data distribution. Recently, unsupervised domain adaptation methods have been proposed to alleviate this so-called domain shift issue, but most of them are designed

Dakyung Seong

This paper considers an endogenous binary response model with many weak instruments. We employ a control function approach and a regularization scheme to obtain better estimation results for the endogenous binary response model in the presence of many weak instruments. Two consistent and asymptotically normally distributed estimators are provided, each of wh

Jeena Kleenankandy, K A Abdul Nazeer

Recursive neural networks (Tree-RNNs) based on dependency trees are ubiquitous in modeling sentence meanings as they effectively capture semantic relationships between non-neighborhood words. However, recognizing semantically dissimilar sentences with the same words and syntax is still a challenge to Tree-RNNs. This work proposes an improvement to Dependency

Yuchong Yao, Xiaohui Wangr, Yuanbang Ma, Han Fang

Class imbalance occurs in many real-world applications, including image classification, where the number of images in each class differs significantly. With imbalanced data, the generative adversarial networks (GANs) leans to majority class samples. The two recent methods, Balancing GAN (BAGAN) and improved BAGAN (BAGAN-GP), are proposed as an augmentation t

Q. Y. Cai, W. Z. Jia

We investigate the single-photon scattering spectra of a giant atom coupled to a one dimensional waveguide via multiple connection points or a continuous coupling region. Using a full quantum mechanical method, we obtain the general analytic expressions for the single-photon scattering coefficients, which are valid in both the Markovian and the non-arkovian

Hongzhen Tian, Reuven Zev Cohen, Chuck Zhang, Yajun Mei

Multistage sequential decision-making scenarios are commonly seen in the healthcare diagnosis process. In this paper, an active learning-based method is developed to actively collect only the necessary patient data in a sequential manner. There are two novelties in the proposed method. First, unlike the existing ordinal logistic regression model which only m

Shaoxiong Zhang, Yunhong Wang, Tianrui Chai, Annan Li

Human gait is considered a unique biometric identifier which can be acquired in a covert manner at a distance. However, models trained on existing public domain gait datasets which are captured in controlled scenarios lead to drastic performance decline when applied to real-world unconstrained gait data. On the other hand, video person re-identification tech

Yuzhen Qin, Tommaso Menara, Samet Oymak, ShiNung Ching

In this paper, we study representation learning for multi-task decision-making in non-stationary environments. We consider the framework of sequential linear bandits, where the agent performs a series of tasks drawn from distinct sets associated with different environments. The embeddings of tasks in each set share a low-dimensional feature extractor called

Min-Yih Hsu, Felicitas Hetzelt, David Gens, Michael Maitland

Estimating instruction-level throughput is critical for many applications: multimedia, low-latency networking, medical, automotive, avionic, and industrial control systems all rely on tightly calculable and accurate timing bounds of their software. Unfortunately, how long a program may run - or if it may indeed stop at all - cannot be answered in the general

Tejasvi Alladi, Vinay Chamola, Nishad Sahu, Vishnu Venkatesh

Vehicular networks promise features such as traffic management, route scheduling, data exchange, entertainment, and much more. With any large-scale technological integration comes the challenge of providing security. Blockchain technology has been a popular choice of many studies for making the vehicular network more secure. Its characteristics meet some of

M. Land

The Standard Model (SM) ascribes the observed mass of elementary particles to an effective interaction between basis states defined without mass terms and a scalar potential associated with the Higgs boson. In the relativistic field theory that underlies the SM, mass itself, understood as the Lorentz-invariant squared 4-momentum of a particle or field, is fi

Shi-Hai Wei, Bo Jing, Xue-Ying Zhang, Jin-Yu Liao

Quantum networks play an extremely important role in quantum information science, with application to quantum communication, computation, metrology and fundamental tests. One of the key challenges for implementing a quantum network is to distribute entangled flying qubits to spatially separated nodes, at which quantum interfaces or transducers map the entang

Yìlè Yīng, Ulrich Zülicke

Since the initial isolation of few-layer graphene, a plethora of two-dimensional atomic crystals has become available, covering almost all known materials types including metals, semiconductors, superconductors, ferro- and antiferromagnets. These advances have augmented the already existing variety of two-dimensional materials that are routinely realized by

Yunfeng Hou, Yunfeng Ji, Gang Wang, Ching-Yen Weng

In the context of networked discrete-event systems (DESs), communication delays and losses exist between the plant and the supervisor for observation and between the supervisor and the actuator for control. In this paper, we first introduce a new framework for supervisory control of networked DESs. Under the introduced framework, we address the state estimat

Reynaldo Gil-Pons, Max Ward, Loïc Miller

We consider the problem of computing an $(s,d)$-hypernetwork in an acyclic F-hypergraph. This is a fundamental computational problem arising in directed hypergraphs, and is a foundational step in tackling problems of reachability and redundancy. This problem was previously explored in the context of general directed hypergraphs (containing cycles), where it

Brendan C. Mulkerin, Xing-Can Yao, Yoji Ohashi, Xia-Ji Liu

We theoretically study the thermodynamic properties of a strongly interacting Fermi gas at the crossover from a Bardeen-Cooper-Schrieffer (BCS) superfluid to a Bose-Einstein condensate (BEC), by applying a recently outlined strong-coupling theory that includes pair fluctuations beyond the commonly-used many-body $T$-matrix or ladder approximation at the Gaus

Yunpeng Shi, Shaohan Li, Tyler Maunu, Gilad Lerman

We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline. The statistics are based on consistency constraints that arise within the clustered structure of the graph of keypoint matches. The statistics are designed to give smaller values to corrupted matches and than uncorrupted matches. These new stati

Yifeng Chen, Wenqing Chu, Fangfang Wang, Ying Tai

Recently, there is growing attention on one-stage panoptic segmentation methods which aim to segment instances and stuff jointly within a fully convolutional pipeline efficiently. However, most of the existing works directly feed the backbone features to various segmentation heads ignoring the demands for semantic and instance segmentation are different: The

Jiaqiao Shi, Aleksandar Vakanski, Min Xian, Jianrui Ding

Deep learning-based computer-aided diagnosis has achieved unprecedented performance in breast cancer detection. However, most approaches are computationally intensive, which impedes their broader dissemination in real-world applications. In this work, we propose an efficient and light-weighted multitask learning architecture to classify and segment breast tu

Suvarna Saumya Chandrashekhar, Mashrin Srivastava, B. Jaganathan, Pankaj Shukla

The purpose of the research is to find a centrality measure that can be used in place of PageRank and to find out the conditions where we can use it in place of PageRank. After analysis and comparison of graphs with a large number of nodes using Spearman's Rank Coefficient Correlation, the conclusion is evident that Eigenvector can be safely used in place of

Andrew Wilkey, Joseph Suelzer, Yogesh N. Joglekar, Gautam Vemuri

Open systems with anti parity-time (anti $\mathcal{PT}$-) or $\mathcal{PT}$ symmetry exhibit a rich phenomenology absent in their Hermitian counterparts. To date all model systems and their diverse realizations across classical and quantum platforms have been local in time, i.e. Markovian. Here we propose a non-Markovian system with anti-$\mathcal{PT}$-symme

Amin Bahmanian

Let $L$ be an $n\times n$ array whose top left $r\times s$ subarray is filled with $k$ different symbols, each occurring at most once in each row and at most once in each column. We find necessary and sufficient conditions that ensure the remaining cells of $L$ can be filled such that each symbol occurs at most once in each row and at most once in each colum

Lan Wang, Yusan Lin, Yuhang Wu, Huiyuan Chen

Today's cyber-world is vastly multivariate. Metrics collected at extreme varieties demand multivariate algorithms to properly detect anomalies. However, forecast-based algorithms, as widely proven approaches, often perform sub-optimally or inconsistently across datasets. A key common issue is they strive to be one-size-fits-all but anomalies are distinctive

Shinya Kanemura, Masanori Tanaka

We discuss first-order electroweak phase transition in models with extended Higgs sectors for the case with relatively heavy additional scalar bosons. We first show that, by the combination of the sphaleron decoupling condition, perturbative unitarity and vacuum stability, mass upper bounds on additional scalar bosons can be obtained at the TeV scale even at

Rikizo Ikuta

We show a wave-particle duality of light and its complementary relation in the context of the intensity interference measured by intensity correlation measurement, especially for the case of the second-order intensity interference observed in the Hong-Ou-Mandel interferometer. Different from the complementary relation appearing in the interference based on t

Peter Moeck, Lukas von Koch

The recently developed information-theoretic approach to crystallographic symmetry classifications and quantifications in two dimensions (2D) from digital transmission electron and scanning probe microscope images is adapted for the analysis of an experimental electron diffraction spot pattern, for the first time. Digital input data are considered in this ap

Weiling Chen, Sheng Lun Benjamin Chua, Stefan Winkler, See-Kiong Ng

The development of powerful deep learning technologies has brought about some negative effects to both society and individuals. One such issue is the emergence of fake media. To tackle the issue, we have organized the Trusted Media Challenge (TMC) to explore how Artificial Intelligence (AI) technologies could be leveraged to combat fake media. To enable furt

Mao-Jie Geng, Tian-Jie Xu, Ying Chen, Tian-Yu Ye

In this paper, we propose a novel semiquantum private comparison (SQPC) protocol of size relationship based on d-level single-particle states. The designed protocol can compare the size relationship of different privacy messages from two classical users with the help of a semi-honest third party (TP), who is permitted to misbehave on her own but cannot be in

Guangyu Wu, Anders Lindquist

Probability density estimation is a core problem of statistics and signal processing. Moment methods are an important means of density estimation, but they are generally strongly dependent on the choice of feasible functions, which severely affects the performance. In this paper, we propose a non-classical parametrization for density estimation using sample

Xin Tong

In this dissertation, we discuss mainly the corresponding geometric and representation theoretic aspects of relative $p$-adic Hodge theory and $p$-adic motives. To be more precise, we study the corresponding analytic geometry of the corresponding spaces over and attached to period rings in the relative $p$-adic Hodge theory, including derived topological de

Fuhao Lou

China has been developing very fast since the beginning of the 21st century. The net income of households has been increased a lot as well. Nonetheless, migration from rural areas to urban sectors tends to keep a high saving rate instead of consumption. This essay tries to use the conventional Ordinary Least Square regression, along with the method of Instru

Thanh-Luan Nguyen, Tri Nhu Do, Georges Kaddoum

In this paper, we aim to improve the connectivity, scalability, and energy efficiency of machine-type communication (MTC) networks with different types of MTC devices (MTCDs), namely Type-I and Type-II MTCDs, which have different communication purposes. To this end, we propose two transmission schemes called connectivity-oriented machine-type communication (

Yury Geints, Olga Minina, Alexander Zemlyanov

The propagation of high-power femtosecond laser pulses in air under conditions of superposed spatial phase modulation is considered theoretically. The numerical simulations are carried out on the basis of the reduced form of nonlinear Schrodinger equation (NLSE) for time-averaged electric field envelope. Initial spatial modulations are applied to pulse wavef

Jiang Zhang, Lillian Clark, Matthew Clark, Konstantinos Psounis

Cellular providers and data aggregating companies crowdsource celluar signal strength measurements from user devices to generate signal maps, which can be used to improve network performance. Recognizing that this data collection may be at odds with growing awareness of privacy concerns, we consider obfuscating such data before the data leaves the mobile dev

Chunyuan Zhang, Chao Liu, Qi Song, Jie Zhao

The echo state network (ESN) is a special type of recurrent neural networks for processing the time-series dataset. However, limited by the strong correlation among sequential samples of the agent, ESN-based policy control algorithms are difficult to use the recursive least squares (RLS) algorithm to update the ESN's parameters. To solve this problem, we pro

Viet Ha Hoang, Chen Hui Pang, Wee Chin Tan

We develop an essentially optimal finite element approach for solving ergodic stochastic two-scale elliptic equations whose two-scale coefficient may depend also on the slow variable. We solve the limiting stochastic two-scale homogenized equation obtained from the stochastic two-scale convergence in the mean (A. Bourgeat, A. Mikelic and S. Wright, J. reine

Asya Dipkaya, Sakine Batun, Bahar Çavdar

Advancements in service sector and growing online platforms are intensifying the information exchange between customers through (electronic) word-of-mouth (WoM). The information obtained by WoM has shown to be a dominant factor in customers' purchase decisions creating an endogenous demand structure. Service providers can monitor how their service is perceiv

Jiayu Shang, Xubo Tang, Ruocheng Guo, Yanni Sun

Motivation: Bacteriophages are viruses infecting bacteria. Being key players in microbial communities, they can regulate the composition/function of microbiome by infecting their bacterial hosts and mediating gene transfer. Recently, metagenomic sequencing, which can sequence all genetic materials from various microbiome, has become a popular means for new p

Bingshu Wang, Jiangbin Zheng, C. L. Philip Chen

Coronavirus disease 2019 (COVID-19) continues to pose a great challenge to the world since its outbreak. To fight against the disease, a series of artificial intelligence (AI) techniques are developed and applied to real-world scenarios such as safety monitoring, disease diagnosis, infection risk assessment, lesion segmentation of COVID-19 CT scans,etc. The

Yi Cai, Wenxia Li

For a positive integer $m$ let $\Omega _m=\{0,1, \cdots , m\}$ and \begin{align*} \mathcal B_2(m)=&\left \{q\in(1,m+1]: \text{$\exists\; x\in [0, m/(q-1)]$ has exactly }\right. \\ &\left. \text{two different $q$-expansions w.r.t. $\Omega _m$}\right \}. \end{align*} Sidorov \cite{S} firstly studied the set $\mathcal B_2(1)$ and raised some questions. Komornik

Koji Ohkitani

First, we discuss the non-Gaussian type of self-similar solutions to the Navier-Stokes equations. We revisit a class of self-similar solutions which was studied in Canonne-Planchon (1996). In order to shed some light on it, we study self-similar solutions to the 1D Burgers equation in detail, completing the most general form of similarity profiles that it ca

Lai Chen, Li-Gang Wang

We report experimental generation and manipulation of optical tornado waves (ToWs). By controlling the self-focusing length, total angular momentum, and foci deviation of ToWs, the propagation properties of optical ToWs, especially their angular velocity of the main intensity lobes, can be manipulated. We achieve controlling the accumulated rotation angle of

Puja De, Joyanti Halder, Chinmayee Chowde Gowda, Sakshi Kansal

Porous electrodes are fast emerging as essential components for next generation supercapacitors. Using porous structures of Co3O4, Mn3O4, alpha Fe2O3, and carbon, their advantages over the solid counterpart is unequivocally established. The improved performance in porous architecture is linked to the enhanced active specific surface and direct channels leadi

Sakshi Kansal, Paulomi Singh, Sudipta Biswas, Ananya Chowdhury

Layered double-hydroxides (LDHs) are superior to the conventional (Transition Metal Oxides) TMOs as their lamellar morphology accommodates higher active sites contributing to the facile electron transfer towards the material degradation relations, thus attracts an immense attention for catalysis application. Moreover, the high catalytic activity of LDHs is l

Xinyi Liu, Dongyan Zhang, Zhimin Li, Maolin Zhang

For understanding the stability of Co-free positive electrode material, LiNi0.5Mn0.5O2 was synthesized with different addition amount of lithium during calcination. The valence states of transition metal in the prepared samples were determined by combining accurate stoichiometry analysis via ICP, magnetic moment measurement via SQUID, and element valence ana

Sherrie Wang, Francois Waldner, David B. Lobell

Crop field boundaries aid in mapping crop types, predicting yields, and delivering field-scale analytics to farmers. Recent years have seen the successful application of deep learning to delineating field boundaries in industrial agricultural systems, but field boundary datasets remain missing in smallholder systems due to (1) small fields that require high

Austen Z. Fan, Paraschos Koutris

ML models are typically trained using large datasets of high quality. However, training datasets often contain inconsistent or incomplete data. To tackle this issue, one solution is to develop algorithms that can check whether a prediction of a model is certifiably robust. Given a learning algorithm that produces a classifier and given an example at test tim

Kaifeng Pang, Zuhayr Asad, Shilin Zhao, Yuankai Huo

The prediction of microsatellite instability (MSI) and microsatellite stability (MSS) is essential in predicting both the treatment response and prognosis of gastrointestinal cancer. In clinical practice, a universal MSI testing is recommended, but the accessibility of such a test is limited. Thus, a more cost-efficient and broadly accessible tool is desired

Biplav Srivastava, Tarmo Koppel, Sai Teja Paladi, Siva Likitha Valluru

We introduce an emerging AI-based approach and prototype system for assisting team formation when researchers respond to calls for proposals from funding agencies. This is an instance of the general problem of building teams when demand opportunities come periodically and potential members may vary over time. The novelties of our approach are that we: (a) ex

Noveen Sachdeva, Carole-Jean Wu, Julian McAuley

We study the practical consequences of dataset sampling strategies on the ranking performance of recommendation algorithms. Recommender systems are generally trained and evaluated on samples of larger datasets. Samples are often taken in a naive or ad-hoc fashion: e.g. by sampling a dataset randomly or by selecting users or items with many interactions. As w

Haris Aziz

In the sport of cricket, the side that wins the toss and has the first choice to bat or bowl can have an unfair or a critical advantage. The issue has been discussed by International Cricket Council committees, as well as several cricket experts. In this article, I outline a method to make the toss fair in cricket. The method is based on ideas from the acade

Aloukik Aditya, Liudu Zhou, Hrishika Vachhani, Dhivya Chandrasekaran

In recent days, with increased population and traffic on roadways, vehicle collision is one of the leading causes of death worldwide. The automotive industry is motivated on developing techniques to use sensors and advancements in the field of computer vision to build collision detection and collision prevention systems to assist drivers. In this article, a

Jiming Ma, Baohua Xie

Let $$G_{6,3}=\langle a_0, \cdots, a_5| a_{i}^{3}=id, a_{i} a_{i+1}= a_{i+1} a_{i}, i \in \mathbb{Z}/6\mathbb{Z}\rangle$$ be a hyperbolic group with boundary the Menger curve. J. Granier \cite{Granier} constructed a discrete, convex cocompact and faithful representation $\rho$ of $G_{6,3}$ into $\mathbf{PU}(2,1)$. We show the 3-orbifold at infinity of $\rho(

Koji Ohkitani

This is an idiosyncratic survey of statistical fluid mechanics centering on the Hopf functional differential equation. Using the Burgers equation for illustration we review several functional integration approaches to theory of turbulence. We notice in particular that some important contributions have been brought about by researchers working on wave propaga

T. C. Scott, L. Cortese, P. Lagos, E. Brinks

We present HI and radio continuum, narrow-band H$\alpha$ imaging, IFU spectroscopy, and X-ray observations of the FGC 1287 triplet projected $\sim$ 1.8 Mpc west of the galaxy cluster Abell 1367. One triplet member, FGC 1287, displays an exceptionally long, 250 kpc HI tail and an unperturbed stellar disk which are the typical signatures of ram pressure stripp

Tatsuki Koga, Casey Meehan, Kamalika Chaudhuri

Aggregate time-series data like traffic flow and site occupancy repeatedly sample statistics from a population across time. Such data can be profoundly useful for understanding trends within a given population, but also pose a significant privacy risk, potentially revealing e.g., who spends time where. Producing a private version of a time-series satisfying

Ian Adelstein, Elijah Fromm, Rajiv Nelakanti, Faren Roth

We consider geodesic nets (critical points of a length functional on the space of embedded graphs) on doubled polygons (topological 2-spheres endowed with a flat metric away from finitely many cone singularities). We use the theorem of Gauss-Bonnet to demonstrate the existence and non-existence of specific geodesic nets on regular doubled polygons.

Marius Tărnăuceanu

Let $o(G)$ be the average order of a finite group $G$. In this paper, we prove that if $o(G)<\frac{31}{12}$\,, then $G$ is supersolvable. Moreover, we have $o(G)=\frac{31}{12}$ if and only if $G\cong A_4$. We also classify finite groups $G$ satisfying $o(G)<\frac{31}{12}$\,.

Wenyu Xing, Ranran Cai, Kodai Moriyama, Kensuke Nara

Spin Seebeck effect (SSE), the generation of spin current from heat, has been extensively studied in a large variety of magnetic materials, including ferromagnets, antiferromagnets, paramagnets, and quantum spin liquids. In this paper, we report the study of the SSE in the single crystalline Pb2V3O9, a spin-gapped quantum magnet candidate with quasi-one-dime

Haruya Mizutani, Zijun Wan, Xiaohua Yao

This paper is devoted to establishing several types of $L^p$-boundedness of wave operators $W_\pm=W_\pm(H, \Delta^2)$ associated with the bi-Schr\"odinger operators $H=\Delta^{2}+V(x)$ on the line $\mathbb{R}$. Given suitable decay potentials $V$, we firstly prove that the wave and dual wave operators are bounded on $L^p(\mathbb{R})$ for all $1<p<\infty$: $$

Wei-Chi Chiu, Guoqing Chang, Gennevieve Macam, Ilya Belopolski

The intersections of topology, geometry and strong correlations offer many opportunities for exotic quantum phases to emerge in condensed matter systems. Weyl fermions, in particular, provide an ideal platform for exploring the dynamical instabilities of single-particle physics under interactions. Despite its fundamental role in relativistic field theory, th

Tianya Zhang, Peter J. Jin

In this paper, we developed the solution of roadside LiDAR object detection using a combination of two unsupervised learning algorithms. The 3D point clouds are firstly converted into spherical coordinates and filled into the elevation-azimuth matrix using a hash function. After that, the raw LiDAR data were rearranged into new data structures to store the i

Tianya T. Zhang, Peter J. Jin

This paper presents a machine-learning-enhanced longitudinal scanline method to extract vehicle trajectories from high-angle traffic cameras. The Dynamic Mode Decomposition (DMD) method is applied to extract vehicle strands by decomposing the Spatial-Temporal Map (STMap) into the sparse foreground and low-rank background. A deep neural network named Res-UNet

Alakananda Mitra, Sukrutha L. T. Vangipuram, Anand K. Bapatla, Venkata K. V. V. Bathalapalli

The world population is anticipated to increase by close to 2 billion by 2050 causing a rapid escalation of food demand. A recent projection shows that the world is lagging behind accomplishing the "Zero Hunger" goal, in spite of some advancements. Socio-economic and well being fallout will affect the food security. Vulnerable groups of people will suffer ma

Lucas Benigni, Sandrine Péché

This paper is concerned with the asymptotic distribution of the largest eigenvalues for some nonlinear random matrix ensemble stemming from the study of neural networks. More precisely we consider $M= \frac{1}{m} YY^\top$ with $Y=f(WX)$ where $W$ and $X$ are random rectangular matrices with i.i.d. centered entries. This models the data covariance matrix or t

Mark Pollicott, Polina Vytnova

In this short note we describe a simple but remarkably effective method for rigorously estimating Lyapunov exponents for expanding maps of the interval. We illustrate the applicability of this method with some standard examples.

A. V. Guglielmi, B. I. Klain, A. D. Zavyalov, O. D. Zotov

Omori Law describes the evolution of the aftershocks of a strong earthquake. Established at the end of the century before last, it is characterized by the beauty of its form, quite definite clarity, as a result of which it still attracts considerable attention of the geophysical community. In recent years, we have accumulated considerable experience in study

Yuhe Zhang, Zixuan Zeng, Qian Liang, Wenhao Bu

We demonstrate one-dimensional Doppler cooling of a beam of buffer-gas cooled Barium monofluoride (BaF) molecules. The dependences of the cooling efficiency with the laser detuning, the bias filed and the laser intensity are carefully measured. We numerical simulate our experiment with a Monte Carlo method, and find the theoretic predictions consists with ou

Avah Banerjee

In this paper we look at the problem of adjacency labeling of graphs. Given a family of undirected graphs the problem is to determine an encoding-decoding scheme for each member of the family such that we can decode the adjacency information of any pair of vertices only from their encoded labels. Further, we want the length of each label to be short (logarit

G. Csörnyei, L. Szabados, L. Molnár, B. Cseh

Investigating period changes of classical Cepheids through the framework of $O-C$ diagrams provides a unique insight to the evolution and nature of these variable stars. In this work, the new or extended $O-C$ diagrams for 148 Galactic classical Cepheids are presented. By correlating the calculated period change rates with the Gaia EDR3 colours, we obtain ob

Dirk Helbing, Matjaž Perc

In a non-sustainable, "over-populated" world, what might the use of nanotechnology-based targeted, autonomous weapons mean for the future of humanity? In order to gain some insights, we make a simplified game-theoretical thought experiment. We consider a population where agents play the public goods game, and where in parallel an epidemic unfolds. Agents tha

Florian Juengermann, Maxime Laasri, Marius Merkle

To better understand the theoretical behavior of large neural networks, several works have analyzed the case where a network's width tends to infinity. In this regime, the effect of random initialization and the process of training a neural network can be formally expressed with analytical tools like Gaussian processes and neural tangent kernels. In this pap

Ao Cai, Pedro Duarte, Silvius Klein

We derive a criterion for the positivity of the maximal Lyapunov exponent of generic mixed random-quasiperiodic linear cocycles, a model introduced in a previous work. This result is applicable to cocycles corresponding to Schr\"odinger operators with randomly perturbed quasiperiodic potentials. Moreover, we establish an average uniform convergence to the Ly

Fumihito Oda, Yugen Takegahara, Tomoyuki Yoshida

Balmer and Dell'Ambrogio introduced the pseudo-functor $P$ from the bicategory of $k$-linear Mackey $2$-motives to the bicategory of $k$-linear cohomological Mackey $2$-motives over a commutative ring $k$. They showed that $P$ maps the general Mackey $2$-motives to the cohomological Mackey $2$-motives by using the ring homomorphism from the crossed Burnside

Damien Gobin, Niky Kamran

We compute a Green's function giving rise to the solution of the Cauchy problem for the source-free Maxwell's equations on a causal domain $\mathcal{D}$ contained in a geodesically normal domain of the Lorentzian manifold $AdS^5 \times \mathbb{S}^2 \times \mathbb{S}^3$, where $AdS^5$ denotes the simply connected $5$-dimensional anti-de-Sitter space-time. Our

G. P. Ranathunga, Y. M. Stokes, M. J. Chen

Mathematical modelling is used to examine the unsteady problem of heating and pulling an axisymmetric cylindrical glass tube with an over-pressure applied within the tube to form tapers with a near uniform bore and small wall thickness at the tip. To allow for the dependence of viscosity on temperature, a prescribed axially varying viscosity is assumed. Our

Paul Schmitt, Nicholas Britten, JiHyun Jeong, Amelia Coffey

We present nuReality, a virtual reality 'VR' environment designed to test the efficacy of vehicular behaviors to communicate intent during interactions between autonomous vehicles 'AVs' and pedestrians at urban intersections. In this project we focus on expressive behaviors as a means for pedestrians to readily recognize the underlying intent of the AV's mov

Antonia Kagkoura, Raul Arenal, Nikos Tagmatarchis

The realization of novel heterostructures arising from the combination of nanomaterials is an effective way to modify their physicochemical and electrocatalytic properties, giving them enhanced characteristics stemming from their individual constituents. Interfacing carbon nanohorns (CNHs) possessing high porosity, large specific surface area and good electr

Michael S. Turner

The past 50 years has seen cosmology go from a field known for the errors being in the exponents to precision science. The transformation, powered by ideas, technology, a paradigm shift and culture change, has revolutionized our understanding of the Universe, with the $\Lambda$CDM paradigm as its crowning achievement. I chronicle the journey of precision cos

Yeon-Jae Jwa, Giuseppe Di Guglielmo, Luca P. Carloni, Georgia Karagiorgi

This paper presents the custom implementation, optimization, and performance evaluation of convolutional neural networks on field programmable gate arrays, for the purposes of accelerating deep neural network inference on large, two-dimensional image inputs. The targeted application is that of data selection for high-resolution particle imaging detectors, an

Alejandro Pardo Pintos, Diego E Shalom, Enzo Tagliazucchi, Gabriel Mindlin

The analysis of thousands of time series in different languages reveals that word usage presents oscillations with a prevalence of 16-year cycles, mounted on slowly varying trends. These components carry different information: while similar oscillatory patterns gather semantically related words, similar trends group together keywords representative of cultur

Benjamin Bowman, Guido Montufar

We study the dynamics of a neural network in function space when optimizing the mean squared error via gradient flow. We show that in the underparameterized regime the network learns eigenfunctions of an integral operator $T_{K^\infty}$ determined by the Neural Tangent Kernel (NTK) at rates corresponding to their eigenvalues. For example, for uniformly distr

Rémi Abgrall, Fatemeh Nassajian Mojarrad

We are interested in building schemes for the compressible Euler equations that are also locally conserving the angular momentum. We present a general framework, describe a few examples of schemes and show results. These schemes can be of arbitrary order.

Huaming Chen, M. Ali Babar

The rapid development of Machine Learning (ML) has demonstrated superior performance in many areas, such as computer vision, video and speech recognition. It has now been increasingly leveraged in software systems to automate the core tasks. However, how to securely develop the machine learning-based modern software systems (MLBSS) remains a big challenge, f

Noah Golowich, Ankur Moitra, Dhruv Rohatgi

Partially Observable Markov Decision Processes (POMDPs) are a natural and general model in reinforcement learning that take into account the agent's uncertainty about its current state. In the literature on POMDPs, it is customary to assume access to a planning oracle that computes an optimal policy when the parameters are known, even though the problem is k

Hans Christian von Baeyer

Two decades after its creation, the interpretation of quantum mechanics called QBism is entering a new phase. Since it shares a personalist, subjective world-view with phenomenology, the philosophical study of human experience, there is a growing interest in the relationship between the two subjects. I call attention to the little-known philosopher Samuel To

M. Malki, G. S. Uhrig

Topological properties play an increasingly important role in future research and technology. This also applies to the field of topological magnetic excitations which has recently become a very active and broad field. In this Perspective article, we give an insight into the current theoretical and experimental investigations and try an outlook on future line

Mitra Alirezaei, Tolga Tasdizen

Most adversarial attack defense methods rely on obfuscating gradients. These methods are successful in defending against gradient-based attacks; however, they are easily circumvented by attacks which either do not use the gradient or by attacks which approximate and use the corrected gradient. Defenses that do not obfuscate gradients such as adversarial trai

Pengxiang Zhang, Chung-Tao Chou, Hwanhui Yun, Brooke C. McGoldrick

Electrical manipulation of spin textures inside antiferromagnets represents a new opportunity for developing spintronics with superior speed and high device density. Injecting spin currents into antiferromagnets and realizing efficient spin-orbit-torque-induced switching is however still challenging due to the complicated interactions from different sublatti

Ruiyang Li, Jian-Xun Wang, Eungkyu Lee, Tengfei Luo

Phonon Boltzmann transport equation (BTE) is a key tool for modeling multiscale phonon transport, which is critical to the thermal management of miniaturized integrated circuits, but assumptions about the system temperatures (i.e., small temperature gradients) are usually made to ensure that it is computationally tractable. To include the effects of large te

Shantanu Shahane, Erman Guleryuz, Diab W Abueidda, Allen Lee

Surrogate neural network-based models have been lately trained and used in a variety of science and engineering applications where the number of evaluations of a target function is limited by execution time. In cell phone camera systems, various errors, such as interferences at the lens-barrel and lens-lens interfaces and axial, radial, and tilt misalignment

Michael D. Barrus, Nathan Haronian

The realization graph $\mathcal{G}(d)$ of a degree sequence $d$ is the graph whose vertices are labeled realizations of $d$, where edges join realizations that differ by swapping a single pair of edges. Barrus [On realization graphs of degree sequences, Discrete Mathematics, vol. 339 (2016), no. 8, pp. 2146-2152] characterized $d$ for which $\mathcal{G}(d)$