December 2020 arXiv papers — page 61

Hai-Yang Ma, Dandan Guan, Shiyong Wang, Yaoyi Li

To realize the braiding operations of Majorana zero mode in the vortex cores of a topological superconductor (TSC), a novel approach is proposed in this letter to replace the common tip (or tip-like) method. Instead of on top of the TSC thin film, arrays of electrically controllable pining centers are built beneath the film, hence detecting can proceed along

Xingjian Zhen, Rudrasis Chakraborty, Liu Yang, Vikas Singh

Many measurements or observations in computer vision and machine learning manifest as non-Euclidean data. While recent proposals (like spherical CNN) have extended a number of deep neural network architectures to manifold-valued data, and this has often provided strong improvements in performance, the literature on generative models for manifold data is quit

Myoungwon Jeon, Volker Bromm, Gurtina Besla, Jinmi Yoon

CEMP-no stars, a subset of carbon enhanced metal poor (CEMP) stars ($\rm [C/Fe]\geq0.7$ and $\rm [Fe/H]\lesssim-1$) have been discovered in ultra-faint dwarf (UFD) galaxies, with $M_{\rm vir} \sim 10^8$ Msun and $M_{\ast}\sim10^3-10^4$ Msun at $z=0$, as well as in the halo of the Milky Way (MW). These CEMP-no stars are local fossils that may reflect the prop

Anichur Rahman, Umme Sara, Dipanjali Kundu, Saiful Islam

The concept of Industry 4.0 is a newly emerging focus of research throughout the world. However, it has lots of challenges to control data, and it can be addressed with various technologies like Internet of Things (IoT), Big Data, Artificial Intelligence (AI), Software Defined Networking (SDN), and Blockchain (BC) for managing data securely. Further, the com

Wenkun Zhang, Ningning Liang, Linyuan Wang, Ailong Cai

Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-angle projection data at different X-ray spectra. Relaxing the requirement of data acquisition is a particularly attractive research to promote the applications of DECT in a wide ran

Atefeh Fazel-Najafabadi, Sebastian Schuster, Baptiste Auguié

Artificial nanostructures enable fine control of electromagnetic fields at the nanoscale, a possibility that has recently been extended to the interaction between polarised light and chiral matter. The theoretical description of such interactions, and its application to the design of optimised structures for chiroptical spectroscopies, brings new challenges

Jiaming Qiu, Xiongtao Dai, Zhengyuan Zhu

We consider the estimation of densities in multiple subpopulations, where the available sample size in each subpopulation greatly varies. This problem occurs in epidemiology, for example, where different diseases may share similar pathogenic mechanism but differ in their prevalence. Without specifying a parametric form, our proposed method pools information

Chendi Lin, Wenhao Luo, Katia Sycara

In this paper, we consider the dynamic multi-robot distribution problem where a heterogeneous group of networked robots is tasked to spread out and simultaneously move towards multiple moving task areas while maintaining connectivity. The heterogeneity of the system is characterized by various categories of units and each robot carries different numbers of u

Paulo F. Jarschel, Erick Lamilla, Yovanny A. V. Espinel, Ivan Aldaya

We investigate intermodal forward Brillouin scattering in a solid-core PCF, demonstrating efficient power conversion between the HE11 and HE21 modes, with a maximum gain coefficient of 21.4/W/km. By exploring mechanical modes of different symmetries, we observe both polarization-dependent and polarization-independent intermodal Brillouin interaction. Finally

Xianghao Zhan, Yiheng Li, Yuzhe Liu, August G. Domel

Multiple brain injury criteria (BIC) are developed to quickly quantify brain injury risks after head impacts. These BIC originated from different types of head impacts (e.g., sports and car crashes) are widely used in risk evaluation. However, the accuracy of using the BIC on brain injury risk estimation across different types of head impacts has not been ev

Adrián Lozano-Durán, Hyunji Jane Bae

The prediction of aircraft aerodynamic quantities of interest remains among the most pressing challenges for computational fluid dynamics. The aircraft aerodynamics are inherently turbulent with mean-flow three-dimensionality, often accompanied by laminar-to-turbulent transition, flow separation, secondary flow motions at corners, and shock wave formation, t

Jingyu Shao, Qing Wang, Asiri Wijesinghe, Erhard Rahm

Entity resolution targets at identifying records that represent the same real-world entity from one or more datasets. A major challenge in learning-based entity resolution is how to reduce the label cost for training. Due to the quadratic nature of record pair comparison, labeling is a costly task that often requires a significant effort from human experts.

Chengyuan Li, Jun Liu, Hailong Hong, Wenju Mao

Taking the deep learning-based algorithms into account has become a crucial way to boost object detection performance in aerial images. While various neural network representations have been developed, previous works are still inefficient to investigate the noise-resilient performance, especially on aerial images with noise taken by the cameras with telephot

Yinhe Peng, Liang Yu

Assuming $\mathrm{ZF}$, we prove that Turing determinacy ($\mathrm{TD}$) implies countable choice axiom for sets of reals ($\mathrm{CCR}$).

Yanran Ding, Abhishek Pandala, Chuanzheng Li, Young-Ha Shin

This paper presents a novel Representation-Free Model Predictive Control (RF-MPC) framework for controlling various dynamic motions of a quadrupedal robot in three dimensional (3D) space. Our formulation directly represents the rotational dynamics using the rotation matrix, which liberates us from the issues associated with the use of Euler angles and quater

R. J. Kinnear, R. R. Mazumdar, P. Marbach

We study a real-time bidding problem resulting from a set of contractual obligations stipulating that a firm win a specified number of heterogeneous impressions or ad placements over a defined duration in a real-time auction. The contracts specify item targeting criteria (which may be overlapping), and a supply requirement. Using the Pontryagin maximum princ

Emiel H. Por, Rémi Soummer, James Noss, Kathryn St. Laurent

Modern coronagraph design relies on advanced, large-scale optimization processes that require an ever increasing amount of computational resources. In this paper, we restrict ourselves to the design of Apodized Pupil Lyot Coronagraphs (APLCs). To produce APLC designs for future giant space telescopes, we require a fine sampling for the apodizer to resolve al

Eric Dunipace, Lorenzo Trippa

Statistical models often include thousands of parameters. However, large models decrease the investigator's ability to interpret and communicate the estimated parameters. Reducing the dimensionality of the parameter space in the estimation phase is a commonly used approach, but less work has focused on selecting subsets of the parameters for interpreting the

Qi Zhang, Diego Garlaschelli

The asymptotic (non)equivalence of canonical and microcanonical ensembles, describing systems with soft and hard constraints respectively, is a central concept in statistical physics. Traditionally, the breakdown of ensemble equivalence (EE) has been associated with nonvanishing relative canonical fluctuations of the constraints in the thermodynamic limit. R

Dmitri Burago, Sergei Ivanov, Yaroslav Kurylev, Jinpeng Lu

We consider a convolution-type operator on vector bundles over metric-measure spaces. This operator extends the analogous convolution Laplacian on functions in our earlier work to vector bundles, and is a natural extension of the graph connection Laplacian. We prove that for Euclidean or Hermitian connections on closed Riemannian manifolds, the spectrum of t

Rungang Han, Yuetian Luo, Miaoyan Wang, Anru R. Zhang

High-order clustering aims to identify heterogeneous substructures in multiway datasets that arise commonly in neuroimaging, genomics, social network studies, etc. The non-convex and discontinuous nature of this problem pose significant challenges in both statistics and computation. In this paper, we propose a tensor block model and the computationally effic

Nicholas Vincent, Hanlin Li, Nicole Tilly, Stevie Chancellor

Many powerful computing technologies rely on implicit and explicit data contributions from the public. This dependency suggests a potential source of leverage for the public in its relationship with technology companies: by reducing, stopping, redirecting, or otherwise manipulating data contributions, the public can reduce the effectiveness of many lucrative

Liam Nolan, Mira Mechtley, Rogier Windhorst, Karen Knierman

We have developed a Java-based teaching tool, "Appreciating Hubble at Hyper-speed" ($\textit{AHaH}$), intended for use by students and instructors in beginning astronomy and cosmology courses, which we have made available online. This tool lets the user hypothetically traverse the Hubble Ultra Deep Field (HUDF) in three dimensions at over $\sim500\!\times\!1

Yimu Yin

We continue the effort of grokking the structure of power-bounded $T$-convex valued fields, whose theory is in general referred to as TCVF. In the present paper our focus is on certain expansion of it that is equipped with a tempered exponential function beyond the valuation ring. In order to construct such a tempered exponential function, the signed value g

S. E. Gibson, A. Malanushenko, G. de Toma, S. Tomczyk

Magnetism defines the complex and dynamic solar corona. Coronal mass ejections (CMEs) are thought to be caused by stresses, twists, and tangles in coronal magnetic fields that build up energy and ultimately erupt, hurling plasma into interplanetary space. Even the ever-present solar wind possesses a three-dimensional morphology shaped by the global coronal m

Riccardo Fantoni, John R. Klauder

Affine quantization, which is a parallel procedure with canonical quantization, needs to use its principal quantum operators, most simply $D=(PQ+QP)/2$ and $Q\neq0$, to represent appropriate kinetic factors, normally $P^2$, which involve only one canonical quantum operator. The need for this requirement stems from the quantization of selected problems that r

Cong Tran, Dung D. Vu, Won-Yong Shin

It has been insufficiently explored how to perform density-based clustering by exploiting textual attributes on social media. In this paper, we aim at discovering a social point-of-interest (POI) boundary, formed as a convex polygon. More specifically, we present a new approach and algorithm, built upon our earlier work on social POI boundary estimation (SoB

Bing Hu, Hengdi Zhao, Yu Zhang, Pedro Schlottmann

Sr2IrO4 is an archetypal spin-orbit-coupled Mott insulator with an antiferromagnetic state below 240 K. Here we report results of our study on single crystals of Sr2Ir1-xFexO4 (0<x<0.32) and Sr2Ir1-xCoxO4 (0<x<0.22). Fe doping retains the antiferromagnetic state but simultaneously precipitates an emergent metallic state whereas Co doping causes a rapid colla

Adel Ahmadyan, Liangkai Zhang, Jianing Wei, Artsiom Ablavatski

3D object detection has recently become popular due to many applications in robotics, augmented reality, autonomy, and image retrieval. We introduce the Objectron dataset to advance the state of the art in 3D object detection and foster new research and applications, such as 3D object tracking, view synthesis, and improved 3D shape representation. The datase

Anichur Rahman, Md. Jahidul Islam, Ziaur Rahman, Md. Mahfuz Reza

Condominium network refers to intra-organization networks, where smart buildings or apartments are connected and share resources over the network. Secured communication platform or channel has been highlighted as a key requirement for a reliable condominium which can be ensured by the utilization of the advanced techniques and platforms like Software-Defined

Joshua Taylor

Since the 1960s Supervisory Control and Data Acquisition (SCADA) systems have been used within industry. Referred to as critical infrastructure (CI), key installations such as power stations, water treatment and energy grids are controlled using SCADA. Existing literature reveals inherent security risks to CI and suggests this stems from the rise of intercon

Liang Ma, Ting He, Kin K. Leung, Don Towsley

This is a technical report, containing all the lemma and proposition proofs in paper "Topological Constraints on Identifying Additive Link Metrics via End-to-end Paths Measurements" by Liang Ma, Ting He, Kin K. Leung, Don Towsley, and Ananthram Swami, published in Annual Conference of The International Technology Alliance (ACITA), 2012.

Guo-Li Liu, Fei Wang, Wenyu Wang

Mirror Twin Higgs(MTH) models always contain heavy gauge bosons and extra Higgses. Besides, to accommodate tiny neutrino masses via seesaw mechanism, new heavy neutrinos can also be introduced in MTH extension models. Such new particles and interactions may lead to new contributions to the lepton flavor violating (LFV) processes, including $\ell_i \to \ell_j

Yao Duan, Xingwang Zhang, Yimin Ding, Xingjie Ni

The exploration of quantum-inspired symmetries in optical systems has spawned promising physics and provided fertile ground for developing devices exhibiting exotic functionalities. Founded on the anti-parity-time (APT) symmetry that is enabled by both spatial and temporal interplay between gain and loss, we demonstrate theoretically and numerically bi-color

Liang Ma, Ting He, Kin K. Leung, Ananthram Swami

This is a technical report, containing all the theorem proofs in the following two papers: (1) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Identifiability of Link Metrics Based on End-to-end Path Measurements," in ACM IMC, 2013. (2) Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, "Inferring Link Metrics from End-to-

Li Wang, Zhenya Yan

The physics-informed neural networks (PINNs) can be used to deep learn the nonlinear partial differential equations and other types of physical models. In this paper, we use the multi-layer PINN deep learning method to study the data-driven rogue wave solutions of the defocusing nonlinear Schr\"odinger (NLS) equation with the time-dependent potential by cons

H. K. Avetissian, B. R. Avchyan, G. F. Mkrtchian, H. H. Matevosyan

In this paper, a possible way to achieve lasing from THz to extreme UV domain due to stimulated scattering of graphene plasmons on the free electrons is considered. The analytical-quantitative description of the proposed FEL scheme is based on the self-consistent set of the Maxwell--Vlasov equations. We study the downconversion as well as the upconversion. I

Li Wang, Zhenya Yan

The defocusing nonlinear Schr\"odinger (NLS) equation has no the modulational instability, and was not found to possess the rogue wave (RW) phenomenon up to now. In this paper, we firstly investigate some novel nonlinear wave structures in the defocusing NLS equation with real-valued time-dependent and time-independent potentials such that the stable new RWs

Majid E. Abbasov

In the present work we study a problem of finding a global minimum of a piecewise affine function. We employ optimality conditions for the problem in terms of coexhausters and use them to state and prove necessary and sufficient conditions for a piecewise affine function to be bounded from below. We construct a simple method based on these conditions which a

Ziyuan Gu, Meead Saberi

In this paper, we characterize the effects of turning and merging maneuvers of connected and/or automated vehicles (CAVs or AVs) on network traffic instability using the macroscopic or network fundamental diagram (MFD or NFD). We revisit the two-ring system from a theoretical perspective and develop an integrated modeling framework consisting of different mi

Liang Ma, Ting He, Kin K. Leung, Don Towsley

This is a technical report, containing all the theorem proofs and additional evaluations in paper "Efficient Identification of Additive Link Metrics via Network Tomography" by Liang Ma, Ting He, Kin K. Leung, Don Towsley, and Ananthram Swami, published in IEEE ICDCS, 2013.

Emma Ozanich, Aaron Thode, Peter Gerstoft, Lauren A. Freeman

Deep clustering was applied to unlabeled, automatically detected signals in a coral reef soundscape to distinguish fish pulse calls from segments of whale song. Deep embedded clustering (DEC) learned latent features and formed classification clusters using fixed-length power spectrograms of the signals. Handpicked spectral and temporal features were also ext

O. Maryeva, K. Bicz, C. Xia, M. Baratella

The study is devoted to search for flare stars among confirmed members of Galactic open clusters using high-cadence photometry from {\it TESS} mission. We analyzed 957 high-cadence light curves of members from 136 open clusters. As a result, 56 flare stars were found, among them 8 hot B-A type objects. Of all flares, 63\% were detected in a sample of cool st

Garv Chauhan, Xun-Jie Xu

We consider a generic dark photon that arises from a hidden $U(1)$ gauge symmetry imposed on right-handed neutrinos ($\nu_{R}$). Such a $\nu_{R}$-philic dark photon is naturally dark due to the absence of tree-level couplings to normal matter. However, loop-induced couplings to charged leptons and quarks are inevitable, provided that $\nu_{R}$ mix with left-

V. B. Verma, B. Korzh, A. B. Walter, A. E. Lita

We developed superconducting nanowire single-photon detectors (SNSPDs) based on tungsten silicide (WSi) that show saturated internal detection efficiency up to a wavelength of 10 um. These detectors are promising for applications in the mid-infrared requiring ultra-high gain stability, low dark counts, and high efficiency such as chemical sensing, LIDAR, dar

Grigoris Panotopoulos

In the framework of the gauge/gravity duality, and in particular of the $AdS_3/CFT_2$ correspondence, we study one-dimensional superconductors analyzing the dual (1+2)-dimensional gravity in the presence of the Einstein-power-Maxwell non-linear Electrodynamics. In the probe limit we compute the critical temperature of the transition as a function of the mass

Liang Ma, Ting He, Kin K. Leung, Ananthram Swami

This is a technical report, containing all the theorem proofs and additional evaluations in paper "Monitor Placement for Maximal Identifiability in Network Tomography" by Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, Don Towsley, published in IEEE INFOCOM, 2014.

Yang Li, Mario Amado, Timo Hyart, Grzegorz P. Mazur

Graphene on hexagonal boron nitride (hBN) can exhibit a topological phase via mutual crystallographic alignment. Recent measurements of nonlocal resistance ($R_{nl}$) near the secondary Dirac point (SDP) in ballistic graphene/hBN superlattices have been interpreted as arising due to the quantum valley Hall state. We report hBN/graphene/hBN superlattices in w

Goce Ristanoski, Jon Emery, Javiera Martinez-Gutierrez, Damien Mccarthy

More than 144 000 Australians were diagnosed with cancer in 2019. The majority will first present to their GP symptomatically, even for cancer for which screening programs exist. Diagnosing cancer in primary care is challenging due to the non-specific nature of cancer symptoms and its low prevalence. Understanding the epidemiology of cancer symptoms and patt

Mai Gehrke, Tomáš Jakl, Luca Reggio

A systematic theory of structural limits for finite models has been developed by Nesetril and Ossona de Mendez. It is based on the insight that the collection of finite structures can be embedded, via a map they call the Stone pairing, in a space of measures, where the desired limits can be computed. We show that a closely related but finer grained space of

Christopher Moakler, Katherine A. Newhall

Granular materials are a ubiquitous yet ill-understood class of media. Many different approaches and techniques have been developed to understand the many complex behaviors they exhibit but none have been completely successful. We present a novel means to understand granular materials, the Hydra String Method (HSM). This is an efficient and autonomous way to

Huong Ha, Sunil Gupta, Santu Rana, Svetha Venkatesh

Level Set Estimation (LSE) is an important problem with applications in various fields such as material design, biotechnology, machine operational testing, etc. Existing techniques suffer from the scalability issue, that is, these methods do not work well with high dimensional inputs. This paper proposes novel methods to solve the high dimensional LSE proble

Liang Ma, Ting He, Kin K. Leung, Ananthram Swami

This is a technical report, containing all the theorem proofs in paper "Link Identifiability in Communication Networks with Two Monitors" by Liang Ma, Ting He, Kin K. Leung, Ananthram Swami, and Don Towsley, published in IEEE Globecom, 2013.

Jessica Dickson, Rachel Perrier

Dots-and-Boxes is a popular children's game whose winning strategies have been studied by Berlekamp, Conway, Guy, and others. In this article we consider two variations, Dots-and-Triangles and Dots-and-Polygons, both of which utilize the same lattice game board structure as Dots-and-Boxes. The nature of these variations along with this lattice structure lend

Andrei Afanasev, Jaseer Ahmed, Igor Akushevich, Jan C. Bernauer

Current precision scattering experiments and even more so many experiments planed for the Electron Ion Collider will be limited by systematics. From the theory side, a fundamental source of systematic uncertainty is the correct treatment of radiative effects. To gauge the current state of technique and knowledge, help the cross-pollination between different

Pax Kivimae

We study the characteristic polynomials of both the Gaussian Orthogonal and Symplectic Ensembles. We show that for both ensembles, powers of the absolute value of the characteristic polynomials converge in law to Gaussian multiplicative chaos measures after normalization for sufficiently small real powers. The main tool is a new asymptotic relation between t

Omid Madani, Thanh Ngo, Weifei Zeng, Sai Ankith Averine

An important task of community discovery in networks is assessing significance of the results and robust ranking of the generated candidate groups. Often in practice, numerous candidate communities are discovered, and focusing the analyst's time on the most salient and promising findings is crucial. We develop simple efficient group scoring functions derived

V. Lebedev, J. Jarvis, H. Piekarz, A. Romanov

The paper presents a journal version of the Design Report on the Optical Stochastic Cooling experiment to be carried out at IOTA ring in Fermilab later this year. It discusses the theory which experiment is based on, beam parameters, major requirements to the storage ring systems and technical details of the experiment implementation.

Reut Apel, Ido Erev, Roi Reichart, Moshe Tennenholtz

Sender-receiver interactions, and specifically persuasion games, are widely researched in economic modeling and artificial intelligence. However, in the classic persuasion games setting, the messages sent from the expert to the decision-maker (DM) are abstract or well-structured signals rather than natural language messages. This paper addresses the use of n

Victor Turchin, Thomas Willwacher

We study the spaces of embeddings $S^m\hookrightarrow R^n$ and those of long embeddings $R^m\hookrightarrow R^n$, i.e. embeddings of a fixed behavior outside a compact set. More precisely we look at the homotopy fiber of the inclusion of these spaces to the spaces of immersions. We find a natural fiber sequence relating these spaces. We also compare the $L_\

Liang Ma, Ting He, Ananthram Swami, Don Towsley

This is a technical report, containing all the theorem proofs in paper "Node Failure Localization in Communication Networks via Network Tomography" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, Kin K. Leung, and Jessica Lowe, published in ITA Annual Fall Meeting, 2014.

Artem Sevastopolsky, Savva Ignatiev, Gonzalo Ferrer, Evgeny Burnaev

In this work, a system for creating a relightable 3D portrait of a human head is presented. Our neural pipeline operates on a sequence of frames captured by a smartphone camera with the flash blinking (flash-no flash sequence). A coarse point cloud reconstructed via structure-from-motion software and multi-view denoising is then used as a geometric proxy. Af

Tianhong Li, Lijie Fan, Yuan Yuan, Hao He

Contrastive learning is one of the fastest growing research areas in machine learning due to its ability to learn useful representations without labeled data. However, contrastive learning is susceptible to feature suppression, i.e., it may discard important information relevant to the task of interest, and learn irrelevant features. Past work has addressed

Alicia Rouco Escorial, Wen-fai Fong, Peter Veres, Tanmoy Laskar

We present X-ray and multi-band optical observations of the afterglow and host galaxy of GRB 180418A, discovered by ${\it Swift}$/BAT and ${\it Fermi}$/GBM. We present a reanalysis of the GBM and BAT data deriving durations of the prompt emission of $T_{90}\approx$2.56s and $\approx$1.90s, respectively. Modeling the ${\it Fermi}$/GBM catalog of 1405 bursts (

Matthew Bach-Nutman

Understanding the common vulnerabilities in web applications help businesses be better prepared in protecting their data against such attacks. With the knowledge gained from research users and developers can be better equipped to deal with the most common attacks and form solutions to prevent future attacks against their web applications. Vulnerabilities exi

Liang Ma, Ting He, Ananthram Swami, Don Towsley

This is a technical report, containing all the theorem proofs and additional evaluations in paper "Network Capability in Localizing Node Failures via End-to-end Path Measurements" by Liang Ma, Ting He, Ananthram Swami, Don Towsley, and Kin K. Leung, published in IEEE/ACM Transactions on Networking, vol. 25, no. 1, pp. 434-450, 2017.

Josh Beal, Eric Kim, Eric Tzeng, Dong Huk Park

Transformers have become the dominant model in natural language processing, owing to their ability to pretrain on massive amounts of data, then transfer to smaller, more specific tasks via fine-tuning. The Vision Transformer was the first major attempt to apply a pure transformer model directly to images as input, demonstrating that as compared to convolutio

Ulf Pedersen, Ian Douglass, Peter Harrowell

Using computer simulations, we establish that the structure of a supercooled binary atomic liquid mixture consists of common neighbour structures similar to those found in the equilibrium crystal phase, a Laves structure. Despite the large accumulation of crystal-like structure, we establish that the supercooled liquid represents a true metastable liquid and

Danila Cherkashin, Pavel Prozorov

Let $G$ be a simple graph with $n$ vertices and $\pm 1$-weights on edges. Suppose that for every edge $e$ the sum of edges adjacent to $e$ (including $e$ itself) is positive. Then the sum of weights over edges of $G$ is at least $-\frac{n^2}{25}$. Also we provide an example of a weighted graph with described properties and the sum of weights $-(1+o(1))\frac{

Ziyan Wang, Timur Bagautdinov, Stephen Lombardi, Tomas Simon

Photorealistic rendering of dynamic humans is an important ability for telepresence systems, virtual shopping, synthetic data generation, and more. Recently, neural rendering methods, which combine techniques from computer graphics and machine learning, have created high-fidelity models of humans and objects. Some of these methods do not produce results with

Volodymyr Vovchenko, Volker Koch

We revisit the problem of particlization of a QCD fluid into hadrons and resonances at the end of the fluid dynamical stage in relativistic heavy-ion collisions in a context of fluctuation measurements. The existing methods sample an ideal hadron resonance gas, therefore, they do not capture the non-Poissonian nature of the grand-canonical fluctuations, expe

U. Bruzzo, V. N. Rubtsov

We study some spectral sequences associated with a locally free $\mathcal O_X$-module $\mathcal A$ which has a Lie algebroid structure. Here $X$ is either a complex manifold or a regular scheme over an algebraically closed field $k$. One spectral sequence can be associated with $\mathcal A$ by choosing a global section $V$ of $\mathcal A$, and considering a

Ishmam Zabir, Ahmed Maksud, Gaojie Chen, Brian M. Sadler

This paper considers the secrecy performance of several schemes for multi-antenna transmission to single-antenna users with full-duplex (FD) capability against randomly distributed single-antenna eavesdroppers (EDs). These schemes and related scenarios include transmit antenna selection (TAS), transmit antenna beamforming (TAB), artificial noise (AN) from th

Brittany Johnson, Jesse Bartola, Rico Angell, Katherine Keith

Modern software relies heavily on data and machine learning, and affects decisions that shape our world. Unfortunately, recent studies have shown that because of biases in data, software systems frequently inject bias into their decisions, from producing better closed caption transcriptions of men's voices than of women's voices to overcharging people of col

Rajesh Kumar, Can Isik, Vir V Phoha

In this work, we examine the impact of Treadmill Assisted Gait Spoofing (TAGS) on Wearable Sensor-based Gait Authentication (WSGait). We consider more realistic implementation and deployment scenarios than the previous study, which focused only on the accelerometer sensor and a fixed set of features. Specifically, we consider the situations in which the impl

Boltachev A. V., Savin A. Yu

Given a manifold with boundary endowed with an action of a discrete group on it, we consider the algebra of operators generated by elements in the Boutet de Monvel algebra of pseudodifferential boundary value problems and shift operators acting on functions on the manifold and its boundary. Under certain conditions on the group action, we construct a Chern c

Lucas S. Flores, Heitor C. M. Fernandes, Marco A. Amaral, Mendeli H. Vainstein

Finding ways to overcome the temptation to exploit one another is still a challenge in behavioural sciences. In the framework of evolutionary game theory, punishing strategies are frequently used to promote cooperation in competitive environments. Here, we introduce altruistic punishers in the spatial public goods game. This strategy acts as a cooperator in

Antoine Comeau-Lapointe

We fix an elliptic curve $E/\mathbb{F}_q(t)$ and consider the family $\{E\otimes\chi_D\}$ of $E$ twisted by quadratic Dirichlet characters. The one-level density of their $L$-functions is shown to follow orthogonal symmetry for test functions with Fourier transform supported inside $(-1,1)$. As an application, we obtain an upper bound of 3/2 on the average a

J. Motoa-Manzano, J. Bayron Orjuela-Quintana, Thiago S. Pereira, César A. Valenzuela-Toledo

In this paper, we study a triad of inhomogeneous scalar fields, known as "solid", as a source of homogeneous but anisotropic dark energy. By using a dynamical system approach, we find that anisotropic accelerated solutions can be realized as attractor points for suitable choices of the parameters of the model. We complement the dynamical analysis with a nume

Giovanni Ometto, Giovanni Montesano, Usha Chakravarthy, Frank Kee

The area of the foveal avascular zone (FAZ) from en face images of optical coherence tomography angiography (OCTA) is one of the most common measurement based on this technology. However, its use in clinic is limited by the high variation of the FAZ area across normal subjects, while the calculation of the volumetric measurement of the FAZ is limited by the

Manuel Meyer, Maria Petropoulou, Ian Christie

The exact mechanism for the production of fast $\gamma$-ray variability in blazars remains debated. Magnetic reconnection, in which plasmoids filled with relativistic particles and magnetic fields are formed, is a viable candidate to explain the broadband electromagnetic spectrum and variability of these objects. Using state-of-the-art magnetic reconnection

Anthony S. Tai, Chunfeng Huang

We propose a simple, data-driven approach to help guide hyperparameter selection for neural network initialization. We leverage the relationship between neural network and Gaussian process models having corresponding activation and covariance functions to infer the hyperparameter values desirable for model initialization. Our experiment shows that marginal l

Rob Arthan, Paulo Oliva

In this paper we present a quantitative analysis of the first and second Borel-Cantelli Lemmas and of two of their generalisations: the Erd\H{o}s-R\'enyi Theorem, and the Kochen-Stone Theorem. We will see that the first three results have direct quantitative formulations, giving an explicit relationship between quantitative formulations of the assumptions an

Willie Béatrix-Drouhet

To gain more insights about what could be the source of the Alcubierre warp drive, a technique designed to find an orthogonal basis from the metric expression is used. It is deduced that exotic fluids can produce the same stress-energy tensor as the Alcubierre warp drive. For different configurations of warp acceleration and velocity, constraints on density,

Kayla Bollinger, Hayden Schaeffer

This paper presents a nonlinear model reduction method for systems of equations using a structured neural network. The neural network takes the form of a "three-layer" network with the first layer constrained to lie on the Grassmann manifold and the first activation function set to identity, while the remaining network is a standard two-layer ReLU neural net

Artur Czerwinski, Karolina Sedziak-Kacprowicz, Piotr Kolenderski

We present a comprehensive framework for quantum state tomography (QST) of time-bin qudits sent through a fiber. Starting from basic assumptions, we define a positive-operator valued measure (POVM) which is then applied to the quantum state reconstruction problem. A realistic scenario is considered where the time uncertainty of the detector is treated as a s

Pratyay Banerjee, Chitta Baral, Man Luo, Arindam Mitra

A recent work has shown that transformers are able to "reason" with facts and rules in a limited setting where the rules are natural language expressions of conjunctions of conditions implying a conclusion. Since this suggests that transformers may be used for reasoning with knowledge given in natural language, we do a rigorous evaluation of this with respec

Wenzhao Li, C. J. O. Reichhardt, B. Jankó, C. Reichhardt

We examine pinning and dynamics of Abrikosov vortices interacting with pinning centers placed in a moir\'e pattern for varied moir\'e lattice angles. We find a series of magic angles at which the critical current shows a pronounced dip corresponding to lattices in which the vortices can flow along quasi-one-dimensional channels. At these magic angles, the vo

Stavroula Skylaki, Ali Oskooei, Omar Bari, Nadja Herger

Named Entity Recognition (NER) is the task of identifying and classifying named entities in unstructured text. In the legal domain, named entities of interest may include the case parties, judges, names of courts, case numbers, references to laws etc. We study the problem of legal NER with noisy text extracted from PDF files of filed court cases from US cour

Alejandro Schuler, David Walsh, Diana Hall, Jon Walsh

Estimating causal effects from randomized experiments is central to clinical research. Reducing the statistical uncertainty in these analyses is an important objective for statisticians. Registries, prior trials, and health records constitute a growing compendium of historical data on patients under standard-of-care that may be exploitable to this end. Howev

Wanxing Liu

We consider constant scalar curvature K\"{a}hler metrics on a smooth minimal model of general type in a neighborhood of the canonical class, which is the perturbation of the canonical class by a fixed K\"{a}hler metric. We show that sequences of such metrics converge smoothly on compact subsets away from a subvariety to the singular K\"{a}hler Einstein metri

Bradley Isom, Dionyssios Mantzavinos, Atanas Stefanov

A polynomial-in-time growth bound is established for global Sobolev $H^s(\mathbb T)$ solutions to the derivative nonlinear Schr\"odinger equation on the circle with $s>1$. These bounds are derived as a consequence of a nonlinear smoothing effect for an appropriate gauge-transformed version of the periodic Cauchy problem, according to which a solution with it

Edward Raff

There has been increasing concern within the machine learning community that we are in a reproducibility crisis. As many have begun to work on this problem, all work we are aware of treat the issue of reproducibility as an intrinsic binary property: a paper is or is not reproducible. Instead, we consider modeling the reproducibility of a paper as a survival

Oscar Boyadjian, Étienne Boulais, Thomas Gervais

Cloaking effects have now been identified in almost every field of physics. In all cases, substrate-carved metamaterials make the reshaping of the concealed volume impossible. In fluids, recognizing that d'Alembert's paradox describes in itself a hydrodynamic cloaking mechanism, we propose and characterize experimentally a framework to cloak both flow and tr

Linli Shi, Ying Jiang, Fernando R. Fernandez, Lu Lan

As an emerging technology, transcranial focused ultrasound has been demonstrated to successfully evoke motor responses in mice, rabbits, and sensory/motor responses in humans. Yet, the spatial resolution of ultrasound does not allow for high-precision stimulation. Here, we developed a tapered fiber optoacoustic emitter (TFOE) for optoacoustic stimulation of

Giovanni Acquaviva, Alfredo Iorio, Luca Smaldone

We discuss the representations of the algebra of quantization, the canonical commutation relations, in a scalar quantum field theory with spontaneously broken U(1) internal symmetry, when a topological defect of the vortex type is formed via the condensation of Nambu-Goldstone particles. We find that the usual thermodynamic limit is not necessary in order to

Yi Zeng, Shu-Yan Zhang, Hong-Tao Zhou, Yan-Feng Wang

Seismic metamaterials (SMs) are expected to assist or replace traditional isolation systems owing to their strong attenuation of seismic waves. In this paper, a one-dimensional inverted T-shaped SM (1D ITSM) with an ultra-wide first bandgap (FBG) is proposed. The complex band structures are calculated to analyze the wave characteristics of the surface waves

Ariel Pacetti, Angel Villanueva

A superelliptic curve over a DVR ${\mathcal O}$ of residual characteristic $p$ is a curve given by an equation $C:y^n=f(x)$. The purpose of the present article is to describe the Galois representation attached to such a curve under the hypothesis that $f(x)$ has all its roots in the fraction field of ${\mathcal O}$ and that $p \nmid n$. Our results are inspi

Mike Behrisch, Alain Chavarri Villarello, Edith Vargas-García

We investigate the standard context, denoted by $\mathbb{K}\left(\mathcal{L}_{n}\right)$, of the lattice $\mathcal{L}_{n}$ of partitions of a positive integer $n$ under the dominance order. Motivated by the discrete dynamical model to study integer partitions by Latapy and Duong Phan and by the characterization of the supremum and (infimum) irreducible parti

V. Temlyakov, T. Ullrich

Results on asymptotic characteristics of classes of functions with mixed smoothness are obtained in the paper. Our main interest is in estimating the Kolmogorov widths of classes with small mixed smoothness. We prove the corresponding bounds for the unit balls of the trigonometric polynomials with frequencies from a hyperbolic cross. We demonstrate how our r