January 2022 arXiv papers — page 10

Chenghao Huang, Weilong Chen, Yuxi Chen, Shunji Yang

In federated learning (FL), model aggregation has been widely adopted for data privacy. In recent years, assigning different weights to local models has been used to alleviate the FL performance degradation caused by differences between local datasets. However, when various defects make the FL process unreliable, most existing FL approaches expose weak robus

Jeongyeol Kwon, Yonathan Efroni, Constantine Caramanis, Shie Mannor

Motivated by online recommendation systems, we propose the problem of finding the optimal policy in multitask contextual bandits when a small fraction $\alpha < 1/2$ of tasks (users) are arbitrary and adversarial. The remaining fraction of good users share the same instance of contextual bandits with $S$ contexts and $A$ actions (items). Naturally, whether a

P. L. Robinson

We show how the elliptic function ${\rm dn}_2$ of Shen leads to the signature four transfer principle of Berndt, Bhargava and Garvan.

Zhizhi Zhang, Zhenyu Wang, Huanhuan Yang, Z. -X. Li

It is a conventional wisdom that a left-hand microwave cannot efficiently excite the spin wave (SW) in ferromagnets, due to the constraint of angular momentum conservation. In this work, we show that the left-hand microwave can drive nonreciprocal SWs in the presence of a strong ellipticity-mismatch between the microwave and precessing magnetization. A criti

Takashi Hamana, Chiaki Hikage, Masamune Oguri, Masato Shirasaki

We perform a cosmic shear analysis of HSC survey first-year data (HSC-Y1) using Complete Orthogonal Sets of E/B-Integrals (COSEBIs) to derive cosmological constraints. We compute E/B-mode COSEBIs from cosmic shear two-point correlation functions measured on an angular range of $4\arcmin<\theta<180\arcmin$. We perform the standard Bayesian likelihood analysis

Changwoo J. Lee, Huiyan Sang

Random partition models are widely used in Bayesian methods for various clustering tasks, such as mixture models, topic models, and community detection problems. While the number of clusters induced by random partition models has been studied extensively, another important model property regarding the balancedness of partition has been largely neglected. We

Morteza Taiebat, Elham Amini, Ming Xu

Ride-hailing is rapidly changing urban and personal transportation. Ride sharing or pooling is important to mitigate negative externalities of ride-hailing such as increased congestion and environmental impacts. However, there lacks empirical evidence on what affect trip-level sharing behavior in ride-hailing. Using a novel dataset from all ride-hailing trip

Otto Cranwell Schaeper, Ziwei Yang, Mehran Kianinia, Johannes E. Fröch

Silicon carbide has emerged as a promising material platform for quantum photonics and nonlinear optics. These properties make the development of integrated photonic components in high-quality silicon carbide a critical aspect for the advancement of scalable on-chip networks. In this work, we numerically design, fabricate and demonstrate the performance of m

Bahati Mukeru, Jesus Lubian, Lauro Tomio

A detailed study of total, Coulomb and nuclear breakup cross sections dependence on the projectile ground-state binding energy $\varepsilon_b$ is presented, by considering the $^8$Li+$^{12}$C and $^8$Li+$^{208}$Pb breakup reactions. To this end, apart from the experimental one-neutron separation energy of $^8$Li nucleus ($\varepsilon_b=2.03$~MeV), lower valu

Meiqing Li, Hao Sheng

This is an extended abstract for a presentation at The 17th International Conference on CUPUM - Computational Urban Planning and Urban Management in June 2021. This study presents an interdisciplinary synthesis of the state-of-the-art approaches in computer vision technologies to extract built environment features that could improve the robustness of empiric

Gabriel Okasa

Estimation of causal effects using machine learning methods has become an active research field in econometrics. In this paper, we study the finite sample performance of meta-learners for estimation of heterogeneous treatment effects under the usage of sample-splitting and cross-fitting to reduce the overfitting bias. In both synthetic and semi-synthetic sim

Ganzhao Yuan

We consider a class of structured fractional minimization problems, in which the numerator part of the objective is the sum of a differentiable convex function and a convex non-smooth function, while the denominator part is a convex or concave function. This problem is difficult to solve since it is non-convex. By exploiting the structure of the problem, we

M. A. Mojarro, R. Carrillo-Bastos, Jesús A. Maytorena

We explore topological transitions in the type of propagation of surface electromagnetic modes in massive anisotropic tilted Dirac systems. The presence of tilting and mass gives rise to an indirect band gap that strongly modifies the joint density of states compared to the gapless system. New van Hove singularities appear, and the interplay between intra an

Elliot Kienzle, Steven Rayan

Hyperbolic lattices underlie a new form of quantum matter with potential applications to quantum computing and simulation and which, to date, have been engineered artificially. A corresponding hyperbolic band theory has emerged, extending 2-dimensional Euclidean band theory in a natural way to higher-genus configuration spaces. Attempts to develop the hyperb

Till J. W. Wagner, Ian Eisenman, Amanda M. Ceroli, Navid C. Constantinou

Arctic icebergs, unconstrained sea ice floes, oil slicks, mangrove drifters, lost cargo containers, and other flotsam are known to move at 2-4% of the prevailing wind velocity relative to the water, despite vast differences in the material properties, shapes, and sizes of objects. Here, we revisit the roles of density, aspect ratio, and skin and form drag in

Mariah G. MacDonald, Leonard Feil, Tyler Quinn, David Rice

The study of orbital resonances allows for the constraint of planetary properties of compact systems. K2-138 is an early K-type star with six planets, five of which have been proposed to be in the longest chain of 3:2 mean motion resonances. To observe and potentially verify the resonant behavior of K2-138's planets, we run N-body simulations using previousl

Sejoon Oh, Berk Ustun, Julian McAuley, Srijan Kumar

Prediction models can exhibit sensitivity with respect to training data: small changes in the training data can produce models that assign conflicting predictions to individual data points during test time. In this work, we study this sensitivity in recommender systems, where users' recommendations are drastically altered by minor perturbations in other unre

Scott Pratt, Karina Martirosova

Correlations driven by the constraints of local charge conservation have been shown to provide insight into the chemical evolution and diffusivity of the high-temperature matter created in ultra-relativistic heavy ion collisions. Two-particle correlations driven by final-state interactions have allowed the extraction of critical femtoscopic space-time inform

Temirlan Kurbanov, Marek Cuchý, Jiří Vokřínek

This paper introduces a novel algorithm combination designed for fast one-to-many multicriteria shortest path search. A preprocessing algorithm excludes irrelevant vertices by building a smaller cover graph. A modified version of multicriteria label-setting algorithm operates on the cover graph and employs a dimensionality reduction technique for swifter dom

Alexandre Cortiella, Kwang-Chun Park, Alireza Doostan

In recent years, identification of nonlinear dynamical systems from data has become increasingly popular. Sparse regression approaches, such as Sparse Identification of Nonlinear Dynamics (SINDy), fostered the development of novel governing equation identification algorithms assuming the state variables are known a priori and the governing equations lend the

Jake S. Rhodes, Adele Cutler, Kevin R. Moon

Random forests are considered one of the best out-of-the-box classification and regression algorithms due to their high level of predictive performance with relatively little tuning. Pairwise proximities can be computed from a trained random forest and measure the similarity between data points relative to the supervised task. Random forest proximities have

Barış Serim, Michiel Spapé, Giulio Jacucci

Researchers increasingly explore deploying brain-computer interfaces (BCIs) for able-bodied users, with the motivation of accessing mental states more directly than allowed by existing body-mediated interaction. This motivation seems to contradict the long-standing HCI emphasis on embodiment, namely the general claim that the body is crucial for cognition. T

Yuandong Tian

We show that Contrastive Learning (CL) under a broad family of loss functions (including InfoNCE) has a unified formulation of coordinate-wise optimization on the network parameter $\boldsymbol{\theta}$ and pairwise importance $\alpha$, where the \emph{max player} $\boldsymbol{\theta}$ learns representation for contrastiveness, and the \emph{min player} $\al

Alfredo Zaragoza

In a previuos paper the author asked if there exists a one-dimensional space $X$ that is not almost zero-dimensional, such that the dimension of the hyperspace of compact subsets of $X$ is one-dimensional. In this short note we give examples of spaces $X$ that are not almost zero-dimensional such that $X$ is one-dimensional and their hyperspace of compacta o

Alasdair Paren, Leonard Berrada, Rudra P. K. Poudel, M. Pawan Kumar

We propose a novel method for training deep neural networks that are capable of interpolation, that is, driving the empirical loss to zero. At each iteration, our method constructs a stochastic approximation of the learning objective. The approximation, known as a bundle, is a pointwise maximum of linear functions. Our bundle contains a constant function tha

Ryan McKenna, Brett Mullins, Daniel Sheldon, Gerome Miklau

We propose AIM, a new algorithm for differentially private synthetic data generation. AIM is a workload-adaptive algorithm within the paradigm of algorithms that first selects a set of queries, then privately measures those queries, and finally generates synthetic data from the noisy measurements. It uses a set of innovative features to iteratively select th

Xiaoyi Zhu, Asil Koc, Robert Morawski, Tho Le-Ngoc

This paper presents a novel channel estimation technique for the multi-user massive multiple-input multiple-output (MU-mMIMO) systems using angular-based hybrid precoding (AB-HP). The proposed channel estimation technique generates group-wise channel state information (CSI) of user terminal (UT) zones in the service area by deep neural networks (DNN) and fuz

Liam Fowl, Jonas Geiping, Steven Reich, Yuxin Wen

A central tenet of Federated learning (FL), which trains models without centralizing user data, is privacy. However, previous work has shown that the gradient updates used in FL can leak user information. While the most industrial uses of FL are for text applications (e.g. keystroke prediction), nearly all attacks on FL privacy have focused on simple image c

Rickard Brüel-Gabrielsson, Mikhail Yurochkin, Justin Solomon

Several recent works use positional encodings to extend the receptive fields of graph neural network (GNN) layers equipped with attention mechanisms. These techniques, however, extend receptive fields to the complete graph, at substantial computational cost and risking a change in the inductive biases of conventional GNNs, or require complex architecture adj

Marco Rasetto, Qingzhou Wan, Himanshu Akolkar, Feng Xiong

Memristors have emerged as a promising technology for efficient neuromorphic architectures owing to their ability to act as programmable synapses, combining processing and memory into a single device. Although they are most commonly used for static encoding of synaptic weights, recent work has begun to investigate the use of their dynamical properties, such

Mathias Van Regemortel, Oles Shtanko, Luis Pedro Garcia-Pintos, Abhinav Deshpande

The dynamics of open quantum systems is generally described by a master equation, which describes the loss of information into the environment. By using a simple model of uncoupled emitters, we illustrate how the recovery of this information depends on the monitoring scheme applied to register the decay clicks. The dissipative dynamics, in this case, is desc

Jonas Golm, Mina Nahvi, Ryan Gabrys, Olgica Milenkovic

The $k$-deck problem is concerned with finding the smallest positive integer $S(k)$ such that there exist at least two strings of length $S(k)$ that share the same $k$-deck, i.e., the multiset of subsequences of length $k$. We introduce the new problem of gapped $k$-deck reconstruction: For a given gap parameter $s$, we seek the smallest positive integer $G_

Reza Namazi, Elahe Ghalebi, Sinead Williamson, Hamidreza Mahyar

Graph convolutional networks (GCNs) allow us to learn topologically-aware node embeddings, which can be useful for classification or link prediction. However, they are unable to capture long-range dependencies between nodes without adding additional layers -- which in turn leads to over-smoothing and increased time and space complexity. Further, the complex

Jan C. Schulze, Danimir T. Doncevic, Alexander Mitsos

We use Koopman theory to develop a data-driven nonlinear model reduction and identification strategy for multiple-input multiple-output (MIMO) input-affine dynamical systems. While the present literature has focused on linear and bilinear Koopman models, we derive and use a Wiener-type Koopman formulation. We discuss that the Wiener structure is particularly

Bo Zhu

In this paper, we study the interplay of geometry and positive scalar curvature on a complete, non-compact manifold with non-negative Ricci curvature. In three-dimensional manifold, we prove a minimal volume growth, an estimate of integral of scalar curvature and width. In higher dimensional manifold, we obtain a volume growth with a stronger condition.

Minghao Yan, Nicholas Meisburger, Tharun Medini, Anshumali Shrivastava

More than 70% of cloud computing is paid for but sits idle. A large fraction of these idle compute are cheap CPUs with few cores that are not utilized during the less busy hours. This paper aims to enable those CPU cycles to train heavyweight AI models. Our goal is against mainstream frameworks, which focus on leveraging expensive specialized ultra-high band

Conor O'Brien, Arvind Thiagarajan, Sourav Das, Rafael Barreto

Online advertising has typically been more personalized than offline advertising, through the use of machine learning models and real-time auctions for ad targeting. One specific task, predicting the likelihood of conversion (i.e.\ the probability a user will purchase the advertised product), is crucial to the advertising ecosystem for both targeting and pri

Zhijie Xu

By decomposing velocity dispersion into non-spin and spin-induced, mean flow and dispersion are analytically solved for axisymmetric rotating and growing halos. The polar flow can be neglected and azimuthal flow is directly related to dispersion. The fictitious ("Reynolds") stress acts on mean flow to enable energy transfer from mean flow to random motion an

Mustafa Mhamed, Richard Sutcliffe, Xia Sun, Jun Feng

Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely-used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of datasets have been created to support such work. H

Venera Khoromskaia, Boris N. Khoromskij

Tensor numerical methods, based on the rank-structured tensor representation of $d$-variate functions and operators, are designed to provide $O(dn)$ complexity of numerical calculations on $n^{\otimes d }$ grids contrary to $O(n^d)$ scaling by conventional grid-based methods. However, multiple tensor operations may lead to enormous increase in the tensor ran

Gourab K Patro, Lorenzo Porcaro, Laura Mitchell, Qiuyue Zhang

Ranking, recommendation, and retrieval systems are widely used in online platforms and other societal systems, including e-commerce, media-streaming, admissions, gig platforms, and hiring. In the recent past, a large "fair ranking" research literature has been developed around making these systems fair to the individuals, providers, or content that are being

Steven Giacalone, Courtney D. Dressing, Christina Hedges, Veselin B. Kostov

The James Webb Space Telescope (JWST) will be able to probe the atmospheres and surface properties of hot, terrestrial planets via emission spectroscopy. We identify 18 potentially terrestrial planet candidates detected by the Transiting Exoplanet Survey Satellite (TESS) that would make ideal targets for these observations. These planet candidates cover a br

Asil Koc, Ahmed Masmoudi, Tho Le-Ngoc

In this paper, a novel full-duplex non-coherent (FD-NC) transmission scheme is developed for massive multiple-input multiple-output (mMIMO) systems using analog beamforming (ABF). We propose to use a structured Grassmannian constellation for the non-coherent communications that does not require channel estimation. Then, we design the transmit and receive ABF

Asil Koc, Mike Wang, Tho Le-Ngoc

This paper proposes a deep learning based power allocation (DL-PA) and hybrid precoding technique for multiuser massive multiple-input multiple-output (MU-mMIMO) systems. We first utilize an angular-based hybrid precoding technique for reducing the number of RF chains and channel estimation overhead. Then, we develop the DL-PA algorithm via a fully-connected

Mingwei Ma, Jizhou Liu, Samuel Sokota, Max Kleiman-Weiner

An unaddressed challenge in multi-agent coordination is to enable AI agents to exploit the semantic relationships between the features of actions and the features of observations. Humans take advantage of these relationships in highly intuitive ways. For instance, in the absence of a shared language, we might point to the object we desire or hold up our fing

Hossein Abedsoltan, Zeinab Zoghi, Amir H. Mohammadi

Aqueous hydrolysis is used to chemically recycle polyethylene terephthalate (PET) due to the production of high-quality terephthalic acid (TPA), the PET monomer. PET hydrolysis depends on various reaction conditions including PET size, catalyst concentration, reaction temperature, etc. So, modeling PET hydrolysis by considering the effective factors can prov

Bing-Jia Chen, Ronald Y. Chang

Device-free wireless indoor localization is an essential technology for the Internet of Things (IoT), and fingerprint-based methods are widely used. A common challenge to fingerprint-based methods is data collection and labeling. This paper proposes a few-shot transfer learning system that uses only a small amount of labeled data from the current environment

Hugo Cui, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

We consider the problem of kernel classification. While worst-case bounds on the decay rate of the prediction error with the number of samples are known for some classifiers, they often fail to accurately describe the learning curves of real data sets. In this work, we consider the important class of data sets satisfying the standard source and capacity cond

Jose N. V. Gomes, Joao F. B. Pereira, Dragomir M. Tsonev

Let $\overline{M}^{n+1}$ be a semi-Riemannian manifold of constant sectional curvature, and endowed with a conformal vector field . Consider a Riemannian manifold $M^n$, isometrically immersed into $\overline{M}^{n+1}$. With these hypotheses in mind, the ultimate goal of this paper is to investigate the intimate relationship between conformal vector fields o

Song-Bo Zhang, M. Michael Denner, Tomáš Bzdušek, Michael A. Sentef

We study the Hatano-Nelson model, i.e., a one-dimensional non-Hermitian chain of spinless fermions with nearest-neighbour nonreciprocal hopping, in the presence of repulsive nearest-neighbour interactions. At half filling, we find two $\mathcal{PT}$ transitions, as the interaction strength increases. The first transition is marked by an exceptional point bet

Cathelijne ter Burg, Kay Jörg Wiese

When driving a disordered elastic manifold through quenched disorder, the pinning forces exerted on the center of mass are fluctuating, with mean $f_c=-\overline{F_w} $ and variance $\Delta(w)=\overline{F_w F_0}^c$, where $w$ is the externally imposed control parameter for the preferred position of the center of mass. $\Delta(w)$ was obtained via the functio

Hanaa Achour, Sabri Bensid

In this article, the problems to be studied are the following \leqnomode \begin{equation*} \label{p} \left\{\begin{array}{ll} (-\Delta )_p^s u \pm \dfrac{|u|^{p-2}u}{|x|^{sp}} = \lambda f(x,u) & \quad \mbox{in }\ \Omega\\[0.3cm] u= 0 & \quad \mbox{on }\ \mathbb{R}^N \setminus \Omega,\tag{P$_{\pm}$} \end{array} \right. \end{equation*} \reqnomode where $\Omega

Delia Garijo, Alberto Márquez, Rafael Robles

Many variations of the classical graph coloring model have been intensively studied due to their multiple applications; scheduling problems and aircraft assignments, for instance, motivate the robust coloring problem. This model gets to capture natural constraints of those optimization problems by combining the information provided by two colorings: a vertex

Sina Khanagha

In this paper, a machine learning based approach is introduced to estimate pendubot angular position from its captured images. Initially, a baseline algorithm is introduced to estimate the angle using conventional image processing techniques. The baseline algorithm performs well for the cases that the pendubot is not moving fast. However, when moving quickly

Vahid R. Asadi, Marco L. Carmosino, Mohammadmahdi Jahanara, Akbar Rafiey

Protecting the privacy of people whose data is used by machine learning algorithms is important. Differential Privacy is the appropriate mathematical framework for formal guarantees of privacy, and boosted decision trees are a popular machine learning technique. So we propose and test a practical algorithm for boosting decision trees that guarantees differen

Domenico Lembo, Alessandra Limosani, Francesca Medda, Alessandra Monaco

In this paper we report on the initial activities carried out within a collaboration between Consob and Sapienza University. We focus on Information Extraction from documents describing financial instruments. We discuss how we automate this task, via both rule-based and machine learning-based methods and provide our first results.

Vladimir Gurvich, Mariya Naumova

Consider the following statement: $B(t, \Delta t)$: a $t$ years old person NN will survive another $\Delta t$ years, where $t, \Delta t\in \mathbb{R}$ are nonnegative real numbers. We know only that NN is $t$ years old and nothing about the health conditions, gender, race, nationality, etc. We bet that $B(t, \Delta t)$ holds. It seems that our odds are very

Loïc Pauletto, Massih-Reza Amini, Nicolas Winckler

In this paper, we propose a Neural Architecture Search strategy based on self supervision and semi-supervised learning for the task of semantic segmentation. Our approach builds an optimized neural network (NN) model for this task by jointly solving a jigsaw pretext task discovered with self-supervised learning over unlabeled training data, and, exploiting t

Serkan Kiranyaz, Ozer Can Devecioglu, Turker Ince, Junaid Malik

Continuous long-term monitoring of electrocardiography (ECG) signals is crucial for the early detection of cardiac abnormalities such as arrhythmia. Non-clinical ECG recordings acquired by Holter and wearable ECG sensors often suffer from severe artifacts such as baseline wander, signal cuts, motion artifacts, variations on QRS amplitude, noise, and other in

Serge Bouc, Deniz Yılmaz

Let $k$ be an algebraically closed field of characteristic $p>0$, let $R$ be a commutative ring, and let $\mathbb{F}$ be an algebraically closed field of characteristic 0. We consider the $R$-linear category $\mathcal{F}^\Delta_{Rpp_k}$ of diagonal $p$-permutation functors over $R$. We first show that the category $\mathcal{F}^\Delta_{\mathbb{F}pp_k}$ is sem

Yuezhou Zhang, Amos A Folarin, Shaoxiong Sun, Nicholas Cummins

Gait is an essential manifestation of depression. Laboratory gait characteristics have been found to be closely associated with depression. However, the gait characteristics of daily walking in real-world scenarios and their relationships with depression are yet to be fully explored. This study aimed to explore associations between depression symptom severit

Shiyu Fan, Sobhit Singh, Xianghan Xu, Kiman Park

Hafnia (HfO2) is a promising material for emerging chip applications due to its high-k dielectric behaviour, suitability for negative capacitance heterostructures, scalable ferroelectricity, and silicon compatibility. The lattice dynamics along with phononic properties such as thermal conductivity, contraction, and heat capacity are under-explored, primarily

Nizar El Idrissi, Samir Kabbaj, Brahim Moalige

If $H$ is a Hilbert space, the Stiefel manifold $St(n,H)$ is formed by all the independent $n$-tuples in $H$. In this article, we contribute to the topological study of Stiefel manifolds by proving a path-connectedness result. We prove that the intersection of translates of $St(n,H)$ is path-connected by polygonal paths under a condition on the codimension o

Theodore D. Drivas, Alexander Dunlap, Cole Graham, Joonhyun La

We consider a stochastic conservation law on the line with solution-dependent diffusivity, a super-linear, sub-quadratic Hamiltonian, and smooth, spatially-homogeneous kick-type random forcing. We show that this Markov process admits a unique ergodic spatially-homogeneous invariant measure for each mean in a non-explicit unbounded set. This generalizes previ

L. Ts. Adzhemyan, D. A. Evdokimov, M. Hnatič, E. V. Ivanova

We have calculated the five-loop RG expansions of the $n$-component A model of critical dynamics in dimensions $d=4-\varepsilon$ within the Minimal Subtraction scheme. This is made possible by using the advanced diagram reduction method and the Sector Decomposition technique adapted to the problems of critical dynamics. The $\varepsilon$ expansions for the c

Zifan Wang, Michael F Hyland, Younghun Bahk, Navjyoth JS Sarma

Shared-ride mobility services that incorporate traveler walking legs aim to reduce vehicle-kilometers-travelled (VKT), vehicle-hours-travelled (VHT), request rejections, fleet size, or some combination of these factors, compared to door-to-door (D2D) shared-ride services. This paper provides a review of shared-ride services with walking legs (SRSWL), particu

Raul Gomez, Dmitry Gourevitch, Siddhartha Sahi

The classical Stone-von Neuman theorem relates the irreducible unitary representations of the Heisenberg group $H_n$ to non-trivial unitary characters of its center $Z$, and plays a crucial role in the construction of the oscillator representation for the metaplectic group. In this paper we extend these ideas to non-unitary and non-irreducible representation

Pedro P. Ladeira, Leandro M. de Lima, Renato A. Krohling

Through the analysis of cultural, socioeconomic and academic performance aspects it is possible to map the profile of the students and their motivations to drop out. This article aims to create a computational tool for data visualization that allows drawing the profile of students to support educational institutions managers in the definition of dropout avoi

William Graham Hoover, Carol Griswold Hoover

Carnot's four-part ideal-gas cycle includes both isothermal and adiabatic expansions and compressions. Analyzing this cycle provides the fundamental basis for statistical thermodynamics. We explore the cycle here from a pedagogical view in order to promote understanding of the macroscopic thermodynamic entropy, the state function associated with thermal ener

R. Abbasi, M. Ackermann, J. Adams, J. A. Aguilar

We present a measurement of the density of GeV muons in near-vertical air showers using three years of data recorded by the IceTop array at the South Pole. Depending on the shower size, the muon densities have been measured at lateral distances between 200 m and 1000 m. From these lateral distributions, we derive the muon densities as functions of energy at

Nir Shlezinger, Ariel Amar, Ben Luijten, Ruud J. G. van Sloun

Analog-to-digital converters (ADCs) allow physical signals to be processed using digital hardware. Their conversion consists of two stages: Sampling, which maps a continuous-time signal into discrete-time, and quantization, i.e., representing the continuous-amplitude quantities using a finite number of bits. ADCs typically implement generic uniform conversio

Varun Vasudevan, Maxime Bassenne, Md Tauhidul Islam, Lei Xing

Prior studies using graph neural networks (GNNs) for image classification have focused on graphs generated from a regular grid of pixels or similar-sized superpixels. In the latter, a single target number of superpixels is defined for an entire dataset irrespective of differences across images and their intrinsic multiscale structure. On the contrary, this s

Simiao Ren, Yang Deng, Willie J. Padilla, Jordan Malof

Deep learning (DL) is revolutionizing the scientific computing community. To reduce the data gap, active learning has been identified as a promising solution for DL in the scientific computing community. However, the deep active learning (DAL) literature is dominated by image classification problems and pool-based methods. Here we investigate the robustness

Muhammad Ahsan Khan, Ameur Yagoub

Toeplitz matrices are ubiquitous and play important roles across many areas of mathematics. In this paper, we present some algebraic results concerning block Toeplitz matrices with block entries belonging to a commutative algebra $\AA$. The characterization of normal block Toeplitz matrices with entries from $\AA$ is also obtained.

Carlos Mera Acosta, Elton Ogoshi, Jose Antonio Souza, Gustavo M. Dalpian

Magnetic materials have been applied in a large variety of technologies, from data storage to quantum devices. The development of 2D materials has opened new arenas for magnetic compounds, even when classical theories discourage their examination. Here we propose a machine-learning-based strategy to predict and understand magnetic ordering in 2D materials. T

Arjun Bagchi, Rudranil Basu, Minhajul Islam, Kedar S. Kolekar

The procedure of null reduction provides a concrete way of constructing field theories with Galilean invariance. We use this to examine Galilean gauge theories, viz. Galilean electrodynamics and Yang-Mills theories in spacetime dimensions 3 and 4. Different non-relativistic conformal symmetries arise in these contexts: Schr{\"o}dinger symmetry in $d=3$ and G

Xin Shen, Yan-Qing Zhu, Zhi Li

Topological quantum state described by the global invariant has been extensively studied in theory and experiment. In this letter, we investigate the relationship between \emph{Zitterbewegung} and the topology of systems that reflect the properties of the local and whole energy bands, respectively. We generalize the usual two-band effective Hamiltonian to ch

Tim Suter, Alessandro Tomasino, Matteo Savoini, Sarah Houver

We report the coherent generation and detection of terahertz (THz) pulses covering the bandwidth of 0.1-9 THz in a high repetition rate, low pulse energy laser system. In this work we demonstrate the application and evaluation of solid-state biased coherent detection in combination with a spintronic emitter. This combination was used to generate and detect T

Joseph Gesnouin, Steve Pechberti, Bogdan Stanciulescu, Fabien Moutarde

Pedestrian crossing prediction has been a topic of active research, resulting in many new algorithmic solutions. While measuring the overall progress of those solutions over time tends to be more and more established due to the new publicly available benchmark and standardized evaluation procedures, knowing how well existing predictors react to unseen data r

Shaiban Ahmed, David Le, Taeyoon Son, Tobiloba Adejumo

Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a redesigned UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially comp

Melvin Vaupel, Erik Hermansen, Paul Trygsland

We introduce Reeb complexes in order to capture how generators of homology flow along sections of a real valued continuous function. This intuition suggests a close relation of Reeb complexes to established methods in topological data analysis such as levelset zigzags and persistent homology. We make this relation precise and in particular explain how Reeb c

Zhihua Du, Ying Wang, Min Ku

In this article,we first give a modified Schwarz-Pompeiu formula in a general sector ring by proper conformal mappings, and obtain the solution of the Schwarz problem for the Cauchy-Riemann equation in explicit forms. Furthermore, a class of integral operators is introduced together with their properties. Finally, by virtue of these operators, Schwarz proble

Muhammad Asim Khan, Lan Hong, Sajjad Ahmed

We propose a new technique for recognition of dumb person hand gesture in real world environment. In this technique, the hand image containing the gesture is preprocessed and then hand region is segmented by convergent the RGB color image to L.a.b color space. Only few statistical features are used to classify the segmented image to different classes. Artifi

R. Bouet, J. Busto, V. Cecchini, C. Cerna

The possible use of a Spherical Proportional Counter for the search of neutrinoless double beta decay is investigated in the R2D2 R&D project. Dual charge and scintillation light readout may improve the detector performance. Tests were carried out with pure argon at 1.1 bar using a 6x6 mm2 silicon photomultiplier. Scintillation light was used for the first t

Alexandros Eskenazis

We survey various aspects of the theory of nonlinear spectral gaps. In particular, we present a self-contained proof of Naor's average John theorem.

Izat B. Baybusinov, Enrico Maria Fenoaltea, Yi-Cheng Zhang

We propose and solve a negotiation model of multiple players facing many alternative solutions. The model can be generalized to many relevant circumstances where stakeholders' interests partially overlap and partially oppose. We also show that the model can be mapped into the well-known directed percolation and directed polymers problems. Moreover, many stat

Gian Paolo Clemente, Rosanna Grassi, Giorgio Rizzini

The increasing complexity of interrelated systems has made the use of multiplex networks an important tool for explaining the nature of relations between elements in the system. In this paper, we aim at investigating various aspects of countries' behaviour during the coronavirus pandemic period. By means of a multiplex network we consider simultaneously stri

Melvin Vaupel, Erik Hermansen, Paul Trygsland

A theory of sections of simplicial height functions is developed. At the core of this theory lies the section complex, which is assembled from higher section spaces. The latter encode flow lines along the height, as well as their homotopies, in a combinatorial way. The section complex has an associated spectral sequence, which computes the homology of the he

Yan Ma, Isnaldi R. Souza Filho, Yang Bai, Johannes Schenk

Fossil-free ironmaking is indispensable for reducing massive anthropogenic CO2 emissions in the steel industry. Hydrogen-based direct reduction (HyDR) is among the most attractive solutions for green ironmaking, with high technology readiness. The underlying mechanisms governing this process are characterized by a complex interaction of several chemical (pha

Hasan Akin, Farrukh Mukhamedov

In the present paper, the Ising model with mixed spin-(1,1/2) is considered on the second order Cayley tree. A construction of splitting Gibbs measures corresponding the model is given which allows to establish the existence of the phase transition (non-uniqueness of Gibbs measures). We point out that, in the phase transition region, the considered model has

Matteo Varvello, Kleomenis Katevas, Mihai Plesa, Hamed Haddadi

Advances in cloud computing have simplified the way that both software development and testing are performed. This is not true for battery testing for which state of the art test-beds simply consist of one phone attached to a power meter. These test-beds have limited resources, access, and are overall hard to maintain; for these reasons, they often sit idle

Cui-Xian Guo, Shu Chen

We study one-dimensional general non-Hermitian models with asymmetric long-range hopping and explore to analytically solve the systems under some specific boundary conditions. Although the introduction of long-range hopping terms prevents us from finding analytical solutions for arbitrary boundary parameters, we identify the existence of exact solutions when

K. G. Bakshi, S. Sinha

We show that an N-person non-cooperative semi-Markov game under limiting ratio average pay-off has a pure semi-stationary Nash equilibrium. In an earlier paper, the zero-sum two person case has been dealt with. The proof follows by reducing such perfect information games to an associated semi-Markov decision process (SMDP) and then using existence results fr

Zhan Gao, Elvin Isufi

Stochastic graph neural networks (SGNNs) are information processing architectures that learn representations from data over random graphs. SGNNs are trained with respect to the expected performance, which comes with no guarantee about deviations of particular output realizations around the optimal expectation. To overcome this issue, we propose a variance-co

Mitre C. Dourado, Luciano N. Grippo, Martín D. Safe

A family of sets is $(p,q)$-intersecting if every nonempty subfamily of $p$ or fewer sets has at least $q$ elements in its total intersection. A family of sets has the $(p,q)$-Helly property if every nonempty $(p,q)$-intersecting subfamily has total intersection of cardinality at least $q$. The $(2,1)$-Helly property is the usual Helly property. A hypergraph

Fei Gao, Peng Geng, Jiaqi Guo, Yuan Liu

We introduce ApolloRL, an open platform for research in reinforcement learning for autonomous driving. The platform provides a complete closed-loop pipeline with training, simulation, and evaluation components. It comes with 300 hours of real-world data in driving scenarios and popular baselines such as Proximal Policy Optimization (PPO) and Soft Actor-Criti

Yosuke Mizuno

Relativistic jets are collimated plasma outflows with relativistic speeds. Astrophysical objects involving relativistic jets are a system comprising a compact object such as a black hole, surrounded by rotating accretion flows, with the relativistic jets produced near the central compact object. The most accepted models explaining the origin of relativistic

Sitakanta Satapathy, Bin Liu, Prathmesh Deshmukh, Paul M. Molinaro

Fluorescent proteins (FPs) have recently emerged as a serious contender for realizing ultralow threshold room temperature exciton-polariton condensation and lasing. Our contribution investigates the thermalization of FP microcavity exciton-polaritons upon optical pumping under ambient conditions. We realize polariton cooling using a new FP molecule, called m

Juan B. Climent, J. C. Guirado, M. R. Zapatero-Osorio, O. V. Zakhozhay

The substellar triple system VHS J125601.92$-$125723.9 is composed by an equal-mass M7.5 brown dwarf binary and a L7 low-mass substellar object. In this work, we aim to identify the origin of the radio emission occurring in the central binary of VHS 1256$-$1257 while discussing the expected mechanisms involved in the radio emission of ultracool dwarfs (UCDs)

I-Hsi Kao, Jian-An Su, Jau-Woei Perng

This study presents the design of a six-wheeled outdoor autonomous mobile robot. The main design goal of our robot is to increase its adaptability and flexibility when moving outdoors. This six-wheeled robot platform was equipped with some sensors, such as a global positioning system (GPS), high definition (HD) webcam, light detection and ranging (LiDAR), an