arXiv papers from June 2021

Asad Ali Khan, Omar A. Beg, Sara Ahmed

The distributed cooperative controllers for inverter-based systems rely on communication networks that make them vulnerable to cyber anomalies. In addition, the distortion effects of such anomalies may also propagate throughout inverter-based cyber-physical systems due to the cooperative cyber layer. In this paper, an intelligent anomaly mitigation technique

Inhee Lee, Chi Zhang, Simranjeet Singh, Brendan McCullian

We investigate the physical origin of nonlinear damping due to mode coupling between several auto-oscillatory modes driven by spin-orbit torque in constricted Py/Pt heterostructures by examining the dependence of auto-oscillation on temperature and applied field orientation. We observe a transition in the nonlinear damping of the auto-oscillation modes extra

B. M. Henson, J. A. Ross, K. F. Thomas, C. N. Kuhn

Despite quantum electrodynamics (QED) being one of the most stringently tested theories underpinning modern physics, recent precision atomic spectroscopy measurements have uncovered several small discrepancies between experiment and theory. One particularly powerful experimental observable that tests QED independently of traditional energy level measurements

I. E. Ochs, N. J. Fisch

In the classic Landau damping initial value problem, where a planar electrostatic wave transfers energy and momentum to resonant electrons, a recoil reaction occurs in the nonresonant particles to ensure momentum conservation. To explain how net current can be driven in spite of this conservation, the literature often appeals to mechanisms that transfer this

L. C. G. Govia, G. J. Ribeill, G. E. Rowlands, T. A. Ohki

The nascent computational paradigm of quantum reservoir computing presents an attractive use of near-term, noisy-intermediate-scale quantum processors. To understand the potential power and use cases of quantum reservoir computing, it is necessary to define a conceptual framework to separate its constituent components and determine their impacts on performan

Monitirtha Dey

Familywise error rate (FWER) has been a cornerstone in simultaneous inference for decades, and the classical Bonferroni method has been one of the most prominent frequentist approaches for controlling FWER. The present article studies the behavior of the FWER for Bonferroni procedure in a multiple testing problem. We establish upper bounds on FWER for Bonfer

Marcin Bienkowski, Martin Böhm, Martin Koutecký, Thomas Rothvoß

In the online balanced graph repartitioning problem, one has to maintain a clustering of $n$ nodes into $\ell$ clusters, each having $k = n / \ell$ nodes. During runtime, an online algorithm is given a stream of communication requests between pairs of nodes: an inter-cluster communication costs one unit, while the intra-cluster communication is free. An algo

Martin Braquet, Efstathios Bakolas

We propose a decentralized auction-based algorithm for the solution of dynamic task allocation problems for spatially distributed multi-agent systems. In our approach, each member of the multi-agent team is assigned to at most one task from a set of spatially distributed tasks, while several agents can be allocated to the same task. The task assignment is dy

Takato Yasuno, Junichiro Fujii, Sakura Fukami

For steel product manufacturing in indoor factories, steel defect detection is important for quality control. For example, a steel sheet is extremely delicate, and must be accurately inspected. However, to maintain the painted steel parts of the infrastructure around a severe outdoor environment, corrosion detection is critical for predictive maintenance. In

Oleksandr I. Malyi, Xin-Gang Zhao, Annette Bussmann-Holder, Alex Zunger

We consider theoretically the paramagnetic phases of EuTiO3 that represent configurations created by two sets of microscopic degrees of freedom (m-DOF): positional symmetry breaking due to octahedral rotations and magnetic symmetry breaking due to spin disorder. The effect of these sets of m-DOFs on the electronic structure and properties of the para phases

Michael P. Ross, Timesh Mistry, Laurence Datrier, Jeff Kissel

The precise calibration of the strain readout of the LIGO gravitational wave observatories is paramount to the accurate interpretation of gravitational wave events. This calibration is traditionally done by imparting a known force on the test masses of the observatory via radiation pressure. Here we describe the implementation of an alternative calibration s

Beren Millidge

In this PhD thesis, we explore and apply methods inspired by the free energy principle to two important areas in machine learning and neuroscience. The free energy principle is a general mathematical theory of the necessary information-theoretic behaviours of systems that maintain a separation from their environment. A core postulate of the theory is that co

A. Hannasch, M. LaBerge, R. Zgadzaj, J. P. Couperus Cabadağ

We generate inverse Compton scattered X-rays in both linear and nonlinear regimes with a 250 MeV laser wakefield electron accelerator and plasma mirror by retro-reflecting the unused drive laser light to scatter from the accelerated electrons. We characterize the X-rays using a CsI(Tl) voxelated scintillator that measures their total energy and divergence as

Kuantay Boshkayev, Talgar Konysbayev, Ergali Kurmanov, Orlando Luongo

We consider the possibility that the Milky Way's dark matter halo possesses a non vanishing equation of state. Consequently, we evaluate the contribution due to the speed of sound, assuming that the dark matter content of the galaxy behaves like a fluid with pressure. In particular, we model the dark matter distribution via an exponential sphere profile in t

Ronald Orozco López

In this paper a Ward-Fonten\'e differential universal algebra is constructed. In this algebra it is possible to obtain a product $\psi$-rule and a general $\psi$-rule of Leibniz for any calculus on sequences. In particular, the simplicial polytopic calculus and the calculus on Bell numbers are introduced.

Xiangchong Li, Hironao Miyatake, Wentao Luo, Surhud More

We present the galaxy shear catalog that will be used for the three-year cosmological weak gravitational lensing analyses using data from the Wide layer of the Hyper Suprime-Cam (HSC) Subaru Strategic Program (SSP) Survey. The galaxy shapes are measured from the $i$-band imaging data acquired from 2014 to 2019 and calibrated with image simulations that resem

Arsha Nagrani, Shan Yang, Anurag Arnab, Aren Jansen

Humans perceive the world by concurrently processing and fusing high-dimensional inputs from multiple modalities such as vision and audio. Machine perception models, in stark contrast, are typically modality-specific and optimised for unimodal benchmarks, and hence late-stage fusion of final representations or predictions from each modality (`late-fusion') i

Tobias Boege, Thomas Kahle, Andreas Kretschmer, Frank Röttger

Gaussian double Markovian models consist of covariance matrices constrained by a pair of graphs specifying zeros simultaneously in the covariance matrix and its inverse. We study the semi-algebraic geometry of these models, in particular their dimension, smoothness and connectedness as well as algebraic and combinatorial properties.

Alexander E. Beasley, Phil Ayres, Martin Tegelaar, Michail-Antisthenis Tsompanas

Mycelium networks are promising substrates for designing unconventional computing devices providing rich topologies and geometries where signals propagate and interact. Fulfilling our long-term objectives of prototyping electrical analog computers from living mycelium networks, including networks hybridised with nanoparticles, we explore the possibility of i

A. Hannasch, L. Lisi, J. Brooks, X. Cheng

We reconstruct spectra of secondary x-rays generated from a 500 MeV - 2 GeV laser plasma electron accelerator. A compact (7.5 $\times$ 7.5 $\times$ 15 cm), modular x-ray calorimeter made of alternating layers of absorbing materials and imaging plates records the single-shot x-ray depth-energy distribution. X-rays range from few-MeV inverse Compton scattered

S. Ota, G. Christian, W. N. Catford, G. Lotay

We studied $\alpha$ cluster states in $^{26}$Mg via the $^{22}$Ne($^{6}$Li,$d\gamma$)$^{26}$Mg reaction in inverse kinematics at an energy of $7$ MeV/nucleon. States between $E_x$ = 4 - 12 MeV in $^{26}$Mg were populated and relative $\alpha$ spectroscopic factors were determined. Some of these states correspond to resonances in the Gamow window of the $^{22

Tanya Berry, Jennifer R. Morey, Kathryn E. Arpino, Jin-Hu Dou

The kagom\'e lattice is a fruitful source of novel physical states of matter, including the quantum spin liquid and Dirac fermions. Here we report a structural, thermodynamic, and transport study of the two-dimensional kagom\'e metal-organic frameworks Ni_3(HIB)v2 and Cu_3(HIB)_2 (HIB = hexaiminobenzene). Magnetization measurements yield Curie constants of 1

Ling Sun, Evan Goetz, Jeffrey S. Kissel, Joseph Betzwieser

We present the probability distribution of the systematic errors in the most accurate, high-latency version of the reconstructed dimensionless strain $h$, at the Hanford and Livingston LIGO detectors, used for gravitational-wave astrophysical analysis, including parameter estimation, in the last five months of the third observing run (O3B). This work extends

V. Pavlenko, D. Kim, H. L. Andrews, D. V. Gorelov

In this paper, we present the results of experimental observation of strong-field photoemission from a diamond field-emitter array (DFEA) illuminated by a focused laser beam with 1035 nm wavelength. Having the advantage of high emission current and low beam emittance, DFEAs can emit ultra-short high charge electron bunches required for multiple accelerator a

Yi-Hsuan Hsieh, Pei-Chi Huang, Aloysius K Mok

Robot programming typically makes use of a set of mechanical skills that is acquired by machine learning. Because there is in general no guarantee that machine learning produces robot programs that are free of surprising behavior, the safe execution of a robot program must utilize monitoring modules that take sensor data as inputs in real time to ensure the

Maphuthego Etu Maditsi, Thulani Phakathi, Francis Lugayizi, Michael Esiefarienrhe

Global System for Mobile Communications (GSM) is a cellular network that is popular and has been growing in recent years. It was developed to solve fragmentation issues of the first cellular system, and it addresses digital modulation methods, level of the network structure, and services. It is fundamental for organizations to become learning organizations t

N. Majernik, G. Andonian, R. Roussel, S. Doran

Emittance exchange beamlines employ transverse masks to create drive and witness beams of variable longitudinal profile and bunch spacing. Recently, this approach has been used to create advanced driver profiles and demonstrate record-breaking plasma wakefield transformer ratios [Roussel, R., et al., Phys. Rev. Lett. 124, 044802 (2020)], a crucial advancemen

Ashwinkumar Ganesan, Francis Ferraro, Tim Oates

We propose a Bi-Directional Manifold Alignment (BDMA) that learns a non-linear mapping between two manifolds by explicitly training it to be bijective. We demonstrate BDMA by training a model for a pair of languages rather than individual, directed source and target combinations, reducing the number of models by 50%. We show that models trained with BDMA in

Ali Ayub, Huiqing Hu, Guangwei Zhou, Carter Fendley

Robots may soon play a role in higher education by augmenting learning environments and managing interactions between instructors and learners. Little, however, is known about how the presence of robots in the learning environment will influence academic integrity. This study therefore investigates if and how college students cheat while engaged in a collabo

Rachael C. Aikens, Michael Baiocchi

An important step for any causal inference study design is understanding the distribution of the treated and control subjects in terms of measured baseline covariates. However, not all baseline variation is equally important. In the observational context, balancing on baseline variation summarized in a propensity score can help reduce bias due to self-select

A. Stephens, J. M. Cutshall, T. McPhee, M. Beck

We describe a technique for self consistently characterizing both the quantum state of a single qubit system, and the positive-operator-valued measure (POVM) that describes measurements on the system. The method works with only ten measurements. We assume that a series of unitary transformations performed on the quantum state are fully known, while making mi

V. Vadakkumbatt, M. Hirschel, J. Manley, T. J. Clark

We study a cross-shaped cavity filled with superfluid $^4$He as a prototype resonant-mass gravitational wave detector. Using a membrane and a re-entrant microwave cavity as a sensitive optomechanical transducer, we were able to observe the thermally excited high-$Q$ acoustic modes of the helium at 20 mK temperature and achieved a strain sensitivity of $8 \ti

J. Yan, A. Farrell, L. D. Amorim, N. Vafaei-Najafabadi

Beam-induced ionization injection (B-III) is currently being explored as a method for injecting an electron beam with a controlled density profile into a plasma wakefield accelerator (PWFA). This process is initiated by the fields of an unmatched drive beam where the slice envelope reaches its minimum value, the 'pinch'. To control the injected beam's qualit

Qiang Sun

In this paper, we propose self-tuned robust estimators for estimating the mean of heavy-tailed distributions, which refer to distributions with only finite variances. Our approach introduces a new loss function that considers both the mean parameter and a robustification parameter. By jointly optimizing the empirical loss function with respect to both parame

Armand Gissler, Tim Hoheisel

We study the question as to when the closed convex hull of a K-convex map equals its K-epigraph. In particular, we shed light onto the smallest cone K such that a given map has convex and closed K-epigraph, respectively. We apply our findings to several examples in matrix space as well as to convex composite functions.

Farzan Memarian, Abolfazl Hashemi, Scott Niekum, Ufuk Topcu

We explore methodologies to improve the robustness of generative adversarial imitation learning (GAIL) algorithms to observation noise. Towards this objective, we study the effect of local Lipschitzness of the discriminator and the generator on the robustness of policies learned by GAIL. In many robotics applications, the learned policies by GAIL typically s

Vasudevan Lakshminarayanan, Hoda Kherdfallah, Arya Sarkar, J. Jothi Balaji

Diabetic Retinopathy (DR) is a leading cause of vision loss in the world,. In the past few Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, Artificial Intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss, Both fundu

Luis Lopez, Alvaro Gonzalez-Castellanos, David Pozo, Mardavij Roozbehani

Most of the new technological changes in power systems are expected to take place in distribution grids. The enormous potential for distribution flexibility could meet the transmission system's needs, changing the paradigm of generator-centric energy and ancillary services provided to a demand-centric one, by placing more importance on smaller resources, suc

Alejandro F. Ramírez, Rodrigo Ribeiro

We consider random walks in i.i.d. elliptic random environments which are not uniformly elliptic. We introduce a computable condition in dimension $d=2$ and a general condition valid for dimensions $d\ge 2$ expressed in terms of the exit time from a box, which ensure that local trapping would not inhibit a ballistic behavior of the random walk. An important

Stefan Kesselheim, Andreas Herten, Kai Krajsek, Jan Ebert

In this article, we present JUWELS Booster, a recently commissioned high-performance computing system at the J\"ulich Supercomputing Center. With its system architecture, most importantly its large number of powerful Graphics Processing Units (GPUs) and its fast interconnect via InfiniBand, it is an ideal machine for large-scale Artificial Intelligence (AI)

John Mendonça, Rubén Solera-Ureña, Alberto Abad, Isabel Trancoso

The ComParE 2021 COVID-19 Speech Sub-challenge provides a test-bed for the evaluation of automatic detectors of COVID-19 from speech. Such models can be of value by providing test triaging capabilities to health authorities, working alongside traditional testing methods. Herein, we leverage the usage of pre-trained, problem agnostic, speech representations a

Gopalan Nadathur, Mary Southern

We present a logic named L_{LF} whose intended use is to formalize properties of specifications developed in the dependently typed lambda calculus LF. The logic is parameterized by the LF signature that constitutes the specification. Atomic formulas correspond to typing derivations relative to this signature. The logic includes a collection of propositional

Masataro Asai, Hiroshi Kajino, Alex Fukunaga, Christian Muise

Current domain-independent, classical planners require symbolic models of the problem domain and instance as input, resulting in a knowledge acquisition bottleneck. Meanwhile, although deep learning has achieved significant success in many fields, the knowledge is encoded in a subsymbolic representation which is incompatible with symbolic systems such as pla

Qiang Sun, Rui Mao, Wen-Xin Zhou

This paper proposes the capped least squares regression with an adaptive resistance parameter, hence the name, adaptive capped least squares regression. The key observation is, by taking the resistant parameter to be data dependent, the proposed estimator achieves full asymptotic efficiency without losing the resistance property: it achieves the maximum brea

Murat Cubuktepe, Nils Jansen, Sebastian Junges, Joost-Pieter Katoen

Probabilistic model checking aims to prove whether a Markov decision process (MDP) satisfies a temporal logic specification. The underlying methods rely on an often unrealistic assumption that the MDP is precisely known. Consequently, parametric MDPs (pMDPs) extend MDPs with transition probabilities that are functions over unspecified parameters. The paramet

Braden M. Weight, Arkajit Mandal, Pengfei Huo

We perform on-the-fly non-adiabatic molecular dynamics simulations using the symmetrical quasi-classical (SQC) approach with the recently suggested molecular Tully models: ethylene and fulvene. We attempt to provide benchmarks of the SQC methods using both the square and the triangle windowing schemes as well as the recently proposed electronic zero-point-en

Kaibalyapati Mishra

Recent trends in academics show an increase in enrollment levels in higher education Predominantly in Doctoral programmes where individual scholars institutes and supervisors play the key roles The human factor at receiving end of academic excellence is the scholar having a supervisor at the facilitating end In this paper I try to establish the role of diffe

Ruixiao Sun, Rongze Gui, Himanshu Neema, Yuche Chen

Public-transit systems face a number of operational challenges: (a) changing ridership patterns requiring optimization of fixed line services, (b) optimizing vehicle-to-trip assignments to reduce maintenance and operation codes, and (c) ensuring equitable and fair coverage to areas with low ridership. Optimizing these objectives presents a hard computational

Alex Friedman, Hani Nejadriahi, Rajat Sharma, Yeshaiahu Fainman

We demonstrate the DC-Kerr effect in PECVD Silicon-rich Nitride (SRN) and use it to demonstrate a third order nonlinear susceptibility, \c{hi}^((3)), as high as (6 +/- 0.58)x10-19 m2/v2. We employ spectral shift versus applied voltage measurements in a racetrack ring resonator as a tool by which to characterize the nonlinear susceptibilities of these films.

Jan Schnabel, Lan Cheng, Andreas Köhn

We reveal limitations of several standard coupled-cluster (CC) methods with perturbation-theorybased noniterative or approximate iterative treatments of triple excitations when applied to thedetermination of highly accurate potential energy curves (PECs) of ionic dimers, such as the X $^2\Sigma^+_g$ electronic ground state of Rb$_2^+$. Such computations are

I. R. Lavor, D. R. da Costa, L. Covaci, M. V. Milošević

The moir\'e pattern observed in stacked non-commensurate crystal lattices, such as hetero-bilayers of transition metal dichalcogenides, produces a periodic modulation of their bandgap. Excitons subjected to this potential landscape exhibit a band structure that gives rise to a quasi-particle dubbed moir\'e exciton. In the case of MoS$_2$/WSe$_2$ hetero-bilay

Amber Afshan, Kshitiz Kumar, Jian Wu

Scores from traditional confidence classifiers (CCs) in automatic speech recognition (ASR) systems lack universal interpretation and vary with updates to the underlying confidence or acoustic models (AMs). In this work, we build interpretable confidence scores with an objective to closely align with ASR accuracy. We propose a new sequence-level CC with a ric

Craig M. Tenney, Zachary F. Croft, Jeffrey M. McMahon

Metallic hydrogen is expected to exhibit remarkable physics. Of particular interest in this work is the possibility of high-temperature superconductivity. Comparing calculations of the superconducting critical temperatures of the solid phase to melting temperatures over a range of pressures leads to an interesting question: Will the solid, in a superconducti

Pascal Notin, José Miguel Hernández-Lobato, Yarin Gal

Optimization in the latent space of variational autoencoders is a promising approach to generate high-dimensional discrete objects that maximize an expensive black-box property (e.g., drug-likeness in molecular generation, function approximation with arithmetic expressions). However, existing methods lack robustness as they may decide to explore areas of the

Marco A. Gomez, Christopher D. Cruz-Ancona, Leonid Fridman

We present a new continuous Lyapunov Redesign (LR) methodology for the robust stabilization of a class of uncertain time-delay systems that is based on the so-called Super Twisting Algorithm. The main feature of the proposed approach is that allows one to simultaneously adjust the chattering effect and achieve asymptotic stabilization of the uncertain system

Mark Bugden, Ondrej Hulik, Fridrich Valach, Daniel Waldram

In this note we study exceptional algebroids, focusing on their relation to type IIB superstring theory. We show that a IIB-exact exceptional algebroid (corresponding to the group $E_{n(n)}\times \mathbb{R}^+$, for $n\le 6$) locally has a standard form given by the exceptional tangent bundle. We derive possible twists, given by a flat $\mathfrak{gl}(2,\mathb

S. E. Pastukhova

In the whole space $R^d$, $d\ge 2$, we study homogenization of a divergence form elliptic operator $A_\varepsilon$ of order $2m\ge 4$ with measurable $\varepsilon$-periodic coefficients, where $\varepsilon$ is a small parameter. For the resolvent $(A_\varepsilon+1)^{-1}$, we construct an approximation with the remainder term of order $\varepsilon^2$ in the o

Sean Hooten, Raymond G. Beausoleil, Thomas Van Vaerenbergh

We present a proof-of-concept technique for the inverse design of electromagnetic devices motivated by the policy gradient method in reinforcement learning, named PHORCED (PHotonic Optimization using REINFORCE Criteria for Enhanced Design). This technique uses a probabilistic generative neural network interfaced with an electromagnetic solver to assist in th

Mariana Graña, Alvaro Herráez

The swampland is the set of seemingly consistent low-energy effective field theories that cannot be consistently coupled to quantum gravity. In this review we cover some of the conjectural properties that effective theories should possess in order not to fall in the swampland, and we give an overview of their main applications to particle physics. The latter

Ning Li, Yi Liu, Shanpeng Li, Robert M. Elashoff

In biomedical studies it is common to collect data on multiple biomarkers during study follow-up for dynamic prediction of a time-to-event clinical outcome. The biomarkers are typically intermittently measured, missing at some event times, and may be subject to high biological variations, which cannot be readily used as time-dependent covariates in a standar

Ankit Singh

Unsupervised Domain Adaptation (UDA) aims to align the labeled source distribution with the unlabeled target distribution to obtain domain invariant predictive models. However, the application of well-known UDA approaches does not generalize well in Semi-Supervised Domain Adaptation (SSDA) scenarios where few labeled samples from the target domain are availa

Dean Alvis

By Theorem~1.12 of the paper "A Class of Representations of Hecke Algebras", if $W$ is a Coxeter group whose proper parabolic subgroups are finite, and if the module of a finite $W$-digraph $\Gamma$ is isomorphic to the module of a $W$-graph, then $\Gamma$ must be acyclic. Here we extend this result to Coxeter groups with finite dihedral parabolic subgroups

S. Komossa, D. Grupe, A. Kraus, L. C. Gallo

Our project MOMO (Multiwavelength observations and modelling of OJ 287) consists of dedicated, dense, long-term flux and spectroscopic monitoring and deep follow-up observations of the blazar OJ 287 at >13 frequencies from the radio to the X-ray band since late 2015. In particular, we are using Swift to obtain optical-UV-X-ray spectral energy distributions (

Nuno Oliveira, Norberto Sousa, Isabel Praça

Cybersecurity is a very challenging topic of research nowadays, as digitalization increases the interaction of people, software and services on the Internet by means of technology devices and networks connected to it. The field is broad and has a lot of unexplored ground under numerous disciplines such as management, psychology, and data science. Its large d

Camilla Brizzi, Luigi De Pascale

We discover a new minimality property of the absolute minimisers of supremal functionals (also known as $L^\infty$ Calculus of Variations problems).

Benjamin J. Radford

Text data are an important source of detailed information about social and political events. Automated systems parse large volumes of text data to infer or extract structured information that describes actors, actions, dates, times, and locations. One of these sub-tasks is geocoding: predicting the geographic coordinates associated with events or locations d

Nikhil Muralidhar, Sathappah Muthiah, Patrick Butler, Manish Jain

We describe lessons learned from developing and deploying machine learning models at scale across the enterprise in a range of financial analytics applications. These lessons are presented in the form of antipatterns. Just as design patterns codify best software engineering practices, antipatterns provide a vocabulary to describe defective practices and meth

David Rosser, Dario Gerace, Lucio C. Andreani, Arka Majumdar

The light-matter interaction associated with a two-dimensional (2D) excitonic transition coupled to a zero-dimensional (0D) photonic cavity is fundamentally different from coupling localized excitations in quantum dots or color centers, which have negligible spatial extent compared to the cavity-confined mode profile. By calculating the radiation-matter coup

William P. McCarthy, Robert D. Hawkins, Haoliang Wang, Cameron Holdaway

Many real-world tasks require agents to coordinate their behavior to achieve shared goals. Successful collaboration requires not only adopting the same communicative conventions, but also grounding these conventions in the same task-appropriate conceptual abstractions. We investigate how humans use natural language to collaboratively solve physical assembly

Sohrab Mokhtari, Kang K. Yen, Jin Liu

This paper tries to address the problem of stock market prediction leveraging artificial intelligence (AI) strategies. The stock market prediction can be modeled based on two principal analyses called technical and fundamental. In the technical analysis approach, the regression machine learning (ML) algorithms are employed to predict the stock price trend at

A. E. Brouwer, F. Ihringer, W. M. Kantor

We survey the area of strongly regular graphs satisfying the 4-vertex condition and find several new families. We describe a switching operation on collinearity graphs of polar spaces that produces cospectral graphs. The obtained graphs satisfy the 4-vertex condition if the original graph belongs to a symplectic polar space.

Ian Bogle, Erik G Boman, Karen D Devine, Sivasankaran Rajamanickam

Graph coloring is often used in parallelizing scientific computations that run in distributed and multi-GPU environments; it identifies sets of independent data that can be updated in parallel. Many algorithms exist for graph coloring on a single GPU or in distributed memory, but to the best of our knowledge, hybrid MPI+GPU algorithms have been unexplored un

Daniel Gervini

This paper presents a kriging method for spatial prediction of temporal intensity functions, for situations where a temporal point process is observed at different spatial locations. Assuming that several replications of the processes are available at the spatial sites, this method avoids assumptions like isotropy, which are not valid in many applications. A

Eman Abdullah AlOmar, Ben Christians, Mihal Busho, Ahmed Hamad AlKhalid

Self-Admitted Technical Debt (SATD) is a metaphorical concept to describe the self-documented addition of technical debt to a software project in the form of source code comments. SATD can linger in projects and degrade source-code quality, but it can also be more visible than unintentionally added or undocumented technical debt. Understanding the implicatio

David Schaller, Marc Hellmuth, Peter F. Stadler

Background. The supertree problem, i.e., the task of finding a common refinement of a set of rooted trees is an important topic in mathematical phylogenetics. The special case of a common leaf set $L$ is known to be solvable in linear time. Existing approaches refine one input tree using information of the others and then test whether the results are isomorp

Margaret I. Doig

We study the Randic index for cactus graphs. It is conjectured to be bounded below by radius (for other than an even path), and it is known to obey several bounds based on diameter. We study radius and diameter for cacti then verify the radius bound and strengthen two diameter bounds for cacti. Along the way, we produce several other bounds for the Randic in

Yang Li, Shihao Ji

Training deep neural networks with an $L_0$ regularization is one of the prominent approaches for network pruning or sparsification. The method prunes the network during training by encouraging weights to become exactly zero. However, recent work of Gale et al. reveals that although this method yields high compression rates on smaller datasets, it performs i

Christopher D. Cruz-Ancona, Leonid Fridman, Hussein Obeid, Salah Laghrouche

In adaptive sliding mode control methods, an updating gain strategy associated with finite-time convergence to the sliding set is essential to deal with matched bounded perturbations with unknown upper-bound. However, the estimation of the finite time of any adaptive design is a complicated task since it depends not only on the upper-bound of unknown perturb

Zixiu Wang, Yiwen Guo, Hu Ding

In many machine learning tasks, a common approach for dealing with large-scale data is to build a small summary, {\em e.g.,} coreset, that can efficiently represent the original input. However, real-world datasets usually contain outliers and most existing coreset construction methods are not resilient against outliers (in particular, an outlier can be locat

Pratik Mazumder, Pravendra Singh, Vinay P. Namboodiri

Deep learning models generally learn the biases present in the training data. Researchers have proposed several approaches to mitigate such biases and make the model fair. Bias mitigation techniques assume that a sufficiently large number of training examples are present. However, we observe that if the training data is limited, then the effectiveness of bia

Paul Bilokon, Antoine Jacquier, Conor McIndoe

We provide a data-driven algorithm to classify market regimes for time series. We utilise the path signature, encoding time series into easy-to-describe objects, and provide a metric structure which establishes a connection between separation of regimes and clustering of points.

Connor Scully-Allison, Katherine E. Isaacs

Gantt charts are frequently used to explore execution traces of large-scale parallel programs found in high-performance computing (HPC). In these visualizations, each parallel processor is assigned a row showing the computation state of a processor at a particular time. Lines are drawn between rows to show communication between these processors. When drawn t

B. M. Villegas-Martínez, H. M. Moya-Cessa, F. Soto-Eguibar

We present an exact analytical solution for a one-dimensional zigzag waveguide array with first and second neighbor interactions. It is found that the waveguide system possess a classical analog to the displaced squeezed number states. The exact solution was compared directly with the numerical solution showing a perfect agreement between both results. The i

N. A. Carella

Let $ x\geq 1 $ be a large number, let $ [x]=x-\{x\} $ be the largest integer function, and let $ \sigma(n)$ be the sum of divisors function. This note presents the first proof of the asymptotic formula for the average order $ \sum_{p\leq x}\sigma([x/p])=c_0x\log \log x+O(x) $ over the primes, where $c_0>0$ is a constant. More generally, $ \sum_{p\leq x}\sig

Elizabeth Clark, Tal August, Sofia Serrano, Nikita Haduong

Human evaluations are typically considered the gold standard in natural language generation, but as models' fluency improves, how well can evaluators detect and judge machine-generated text? We run a study assessing non-experts' ability to distinguish between human- and machine-authored text (GPT2 and GPT3) in three domains (stories, news articles, and recip

Hamid M. Q. Rasheda, Norshahida Mohd Shah, Abdu Saif, Qazwan Abdullah

Ultra-wideband is increasingly advancing as a high data rate wireless technology after the Federal Communication Commission announced the bandwidth of 7.5 GHz (from 3.1 GHz to 10.6 GHz) for ultra-wideband applications. Furthermore, designing a UWB antenna faces more difficulties than designing a narrow band antenna. A suitable UWB antenna should be able to w

Renato Bellotti, Romana Boiger, Andreas Adelmann

Particle accelerators are enabling tools for scientific exploration and discovery in various disciplines. Finding optimized operation points for these complex machines is a challenging task, however, due to the large number of parameters involved and the underlying non-linear dynamics. Here, we introduce two families of data-driven surrogate models, based on

Mohammad Javad Shayegan, Kiarash Shamsi

The financial industry is a pioneer in Blockchain technology. One of the most popular platforms in Token-based banking is the flexible Stellar platform. This platform is open-source, and today, its wide range of features makes it possible for many countries and companies to use it in cryptocurrency and Token-based modern banking. This network charges a fee f

H. R. Andreasyan, T. Yu. Magakian, T. A. Movsessian, A. V. Moiseev

Based on new observations during 2015-2020 and published data, the unusual eruptive variables PV Cep and V350 Cep are examined. It is shown that PV Cep underwent a regular outburst followed by a drop in brightness that lasted overall from 2011 to 2019 and is still in a deep minimum. The outburst was accompanied by substantial changes in the intensity and pro

Xianzhi Du, Barret Zoph, Wei-Chih Hung, Tsung-Yi Lin

The speed-accuracy Pareto curve of object detection systems have advanced through a combination of better model architectures, training and inference methods. In this paper, we methodically evaluate a variety of these techniques to understand where most of the improvements in modern detection systems come from. We benchmark these improvements on the vanilla

D. P. Nadlinger, P. Drmota, D. Main, B. C. Nichol

Precise control of charged particles in radio-frequency (Paul) traps requires minimising excess micromotion induced by stray electric fields. We present a method to detect and compensate such fields through amplitude modulation of the radio-frequency trapping field. Modulation at frequencies close to the motional modes of the trapped particle excites coheren

Roy Dong, Heling Zhang, Lillian J. Ratliff

As data-driven methods are deployed in real-world settings, the processes that generate the observed data will often react to the decisions of the learner. For example, a data source may have some incentive for the algorithm to provide a particular label (e.g. approve a bank loan), and manipulate their features accordingly. Work in strategic classification a

C. Hansel, M. Yadav, P. Manwani, W. An

A future plasma based linear collider has the potential to reach unprecedented energies and transform our understanding of high energy physics. The extremely dense beams in such a device would cause the plasma ions to fall toward the axis. For more mild ion motion, this introduces a nonlinear perturbation to the focusing fields inside of the bubble. However,

Tomas Codina, Olaf Hohm, Diego Marques

We compute the cosmological reduction of general string theories, including bosonic, heterotic and type II string theory to order $\alpha'^{3}$, i.e., with up to eight derivatives. To this end we refine recently introduced methods that allow one to bring the reduced theory in one dimension to a canonical form with only first-order time derivatives. The resul

Nicolas Loizou, Hugo Berard, Gauthier Gidel, Ioannis Mitliagkas

Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) [Mescheder et al., 2017]. SGDA is known to converge to a stationary point for specific classes of games, but current convergence analyses require a bounded

Dezhong Yao, Wanning Pan, Yutong Dai, Yao Wan

Federated learning enables multiple clients to collaboratively learn a global model by periodically aggregating the clients' models without transferring the local data. However, due to the heterogeneity of the system and data, many approaches suffer from the "client-drift" issue that could significantly slow down the convergence of the global model training.

Manoranjan Singha, Sima Roy

One of the main obstacle to study compactness in topological spaces via ideals was the definition of ideal convergence of subsequences as in the existing literature according to which subsequence of an ideal convergent sequence may fail to be ideal convergent with respect to same ideal. This obstacle has been get removed in this article and notions of I comp

Mohsen Khodadi, Gaetano Lambiase, David F. Mota

Thanks to the release of the extraordinary EHT image of shadow attributed to the M87* supermassive black hole (SMBH), we have a novel window to assess the validity of fundamental physics in the strong-field regime. Motivated by this, we consider Johannsen \& Psaltis metric parameterized by mass, spin, and an additional dimensionless hair parameter $\epsilon$

D. Jaffino Stargen, Kinjalk Lochan

One of the primary reasons behind the difficulty in observing the Unruh effect is that for achievable acceleration scales the finite temperature effects are significant only for the low frequency modes of the field. Since the density of field modes falls for small frequencies in free space, the field modes which are relevant for the thermal effects would be

Sankalp Gilda, Stark C. Draper, Sebastien Fabbro, William Mahoney

We leverage state-of-the-art machine learning methods and a decade's worth of archival data from CFHT to predict observatory image quality (IQ) from environmental conditions and observatory operating parameters. Specifically, we develop accurate and interpretable models of the complex dependence between data features and observed IQ for CFHT's wide-field cam