October 2020 arXiv papers — page 23

Arun Narayanan, Tara N. Sainath, Ruoming Pang, Jiahui Yu

End-to-end (E2E) automatic speech recognition (ASR) models, by now, have shown competitive performance on several benchmarks. These models are structured to either operate in streaming or non-streaming mode. This work presents cascaded encoders for building a single E2E ASR model that can operate in both these modes simultaneously. The proposed model consist

Meredith L. Rawls, Heidi B. Thiemann, Victor Chemin, Lucianne Walkowicz

Large satellite constellations in low-Earth orbit seek to be the infrastructure for global broadband Internet and other telecommunication needs. We briefly review the impacts of satellite constellations on astronomy and show that the Internet service offered by these satellites will primarily target populations where it is unaffordable, not needed, or both.

Francesco Bronzino, Paul Schmitt, Sara Ayoubi, Hyojoon Kim

Network management often relies on machine learning to make predictions about performance and security from network traffic. Often, the representation of the traffic is as important as the choice of the model. The features that the model relies on, and the representation of those features, ultimately determine model accuracy, as well as where and whether the

Andrea Bertoldi, Philippe Bouyer, Benjamin Canuel

Quantum sensors exploiting matter waves interferometry promise to realize a new generation of Gravitational Wave detectors. The intrinsic stability of specific atomic energy levels makes atom interferometers and clocks ideal candidates to extend the frequency window for the observation of Gravitational Waves in the mid-frequency band, ranging from 10 mHz to

Krishnan Srinivasan, Benjamin Eysenbach, Sehoon Ha, Jie Tan

Safety is an essential component for deploying reinforcement learning (RL) algorithms in real-world scenarios, and is critical during the learning process itself. A natural first approach toward safe RL is to manually specify constraints on the policy's behavior. However, just as learning has enabled progress in large-scale development of AI systems, learnin

Raghavendra Pappagari, Jesús Villalba, Piotr Żelasko, Laureano Moro-Velazquez

Data augmentation is a widely used strategy for training robust machine learning models. It partially alleviates the problem of limited data for tasks like speech emotion recognition (SER), where collecting data is expensive and challenging. This study proposes CopyPaste, a perceptually motivated novel augmentation procedure for SER. Assuming that the presen

Harbir Antil, Andrei Dr{ă}g{ă}nescu, Kiefer Green

In this note we present a multigrid preconditioning method for solving quadratic optimization problems constrained by a fractional diffusion equation. Multigrid methods within the all-at-once approach to solve the first order-order optimality Karush-Kuhn-Tucker (KKT) systems are widely popular, but their development have relied on the underlying systems bein

Nael Alsaleh, Bilal Farooq

In recent years, with the advancements in information and communication technology, different emerging on-demand shared mobility services have been introduced as innovative solutions in the low-density areas, including on-demand transit (ODT), mobility on-demand (MOD) transit, and crowdsourced mobility services. However, due to their infancy, there is a stro

Xi Mo, Usman Sajid, Guanghui Wang

The paper proposes a light-weighted stereo frustums matching module for 3D objection detection. The proposed framework takes advantage of a high-performance 2D detector and a point cloud segmentation network to regress 3D bounding boxes for autonomous driving vehicles. Instead of performing traditional stereo matching to compute disparities, the module direc

Bo Li, David Saad

Infectious diseases that incorporate pre-symptomatic transmission are challenging to monitor, model, predict and contain. We address this scenario by studying a variant of a stochastic susceptible-exposed-infected-recovered model on arbitrary network instances using an analytical framework based on the method of dynamic message-passing. This framework provid

Sidhdharth Sikka, Harshvardhan Sikka

Autonomous spacecraft maneuver planning using an evolutionary algorithmic approach is investigated. Simulated spacecraft were placed into four different initial orbits. Each was allowed a string of thirty delta-v impulse maneuvers in six cartesian directions, the positive and negative x, y and z directions. The goal of the spacecraft maneuver string was to,

Jinwook Huh, Galen Xing, Ziyun Wang, Volkan Isler

Traditional motion planning is computationally burdensome for practical robots, involving extensive collision checking and considerable iterative propagation of cost values. We present a novel neural network architecture which can directly generate the cost-to-go (c2g) function for a given configuration space and a goal configuration. The output of the netwo

Niranda Perera, Vibhatha Abeykoon, Chathura Widanage, Supun Kamburugamuve

In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have broadened the scope of data engineering and opened up new applications in both enterprise and research communities. Aggregations (also termed reduce in functional programming) are

Van Duong Dinh, Luigi Forcella

We establish blow-up results for systems of NLS equations with quadratic interaction in anisotropic spaces. We precisely show finite time blow-up or grow-up for cylindrical symmetric solutions. With our construction, we moreover prove some polynomial lower bounds on the kinetic energy of global solutions in the mass-critical case, which in turn implies grow-

Nagi Khalil

A model for continuous-opinion dynamics is proposed and studied by taking advantage of its similarities with a mono-dimensional granular gas. Agents interact as in the Deffuant model, with a parameter $\alpha$ controlling the persuasibility of the individuals. The interaction coincides with the collision rule of two grains moving on a line, provided opinions

Robert Pluta, Bernard Russo

T*-categories are introduced as a ternary generalization of C*-categories. Their linking C*-categories are constructed and the Gelfand-Naimark representation theorems of Zettl for C*-ternary rings and for W*-ternary rings, are generalized to T*-categories. Biduals of C*-categories and of T*-categories are considered.

Jiaxuan Wang, Jenna Wiens, Scott Lundberg

Many existing approaches for estimating feature importance are problematic because they ignore or hide dependencies among features. A causal graph, which encodes the relationships among input variables, can aid in assigning feature importance. However, current approaches that assign credit to nodes in the causal graph fail to explain the entire graph. In lig

Christian Sarmiento-Cano, Mauricio Suárez-Durán, Rolando Calderón-Ardila, Adriana Vásquez-Ramírez

ARTI is a complete framework designed to simulate the signals produced by the secondary particles emerging from the interaction of single, multiple and even, the complete flux of primary cosmic rays with the atmosphere. These signals are simulated for any particle detector located at any place (latitude, longitude and altitude), including the real-time atmos

Warren Siegel, Yu-Ping Wang

We propose 4-point S-matrices for three-dimensional F-theory. We will use the twistor formalism to facilitate constructing the amplitude. We write the amplitude in a way such that the F-symmetry (U-duality symmetry) is manifest. The amplitude can be schematically written as $A_{4} = w^{4}/stu$, where $w$ is an analog of the linearized Weyl tensor in F-theory

Chun-Hao Yang, Baba C. Vemuri

In the recent past, nested structures in Riemannian manifolds has been studied in the context of dimensionality reduction as an alternative to the popular principal geodesic analysis (PGA) technique, for example, the principal nested spheres. In this paper, we propose a novel framework for constructing a nested sequence of homogeneous Riemannian manifolds. C

María J. Cáceres, Alejandro Ramos-Lora

The Nonlinear Noisy Leaky Integrate and Fire neuronal models are mathematical models that describe the activity of neural networks. These models have been studied at a microscopic level, using Stochastic Differential Equations, and at a mesoscopic/macroscopic level, through the mean field limits using Fokker-Planck type equations. The aim of this paper is to

Robert Leaman, Zhiyong Lu

The number of unique terms in the scientific literature used to refer to either SARS-CoV-2 or COVID-19 is remarkably large and has continued to increase rapidly despite well-established standardized terms. This high degree of term variation makes high recall identification of these important entities difficult. In this manuscript we present an extensive dict

Jean-Baptiste Lamare, Tobi Olatunji, Li Yao

Ample evidence suggests that better machine learning models may be steadily obtained by training on increasingly larger datasets on natural language processing (NLP) problems from non-medical domains. Whether the same holds true for medical NLP has by far not been thoroughly investigated. This work shows that this is indeed not always the case. We reveal the

Grzegorz Jagiella

We study definable topological dynamics of some algebraic group actions over an arbitrary NIP field $K$. We show that the Ellis group of the universal definable flow of $\mathrm{SL}_2(K)$ is non-trivial if the multiplicative group of $K$ is not type-definably connected, providing a way to find multiple counterexamples to the Ellis group conjecture, particula

Luana Ruiz, Fernando Gama, Alejandro Ribeiro, Elvin Isufi

Graph convolutional neural networks (GCNNs) learn compositional representations from network data by nesting linear graph convolutions into nonlinearities. In this work, we approach GCNNs from a state-space perspective revealing that the graph convolutional module is a minimalistic linear state-space model, in which the state update matrix is the graph shift

Aleksandra Edwards, David Rogers, Jose Camacho-Collados, Hélène de Ribaupierre

The task of text and sentence classification is associated with the need for large amounts of labelled training data. The acquisition of high volumes of labelled datasets can be expensive or unfeasible, especially for highly-specialised domains for which documents are hard to obtain. Research on the application of supervised classification based on small amo

Y. He, T. G. Blackburn, T. Toncian, A. V. Arefiev

Creation of electrons and positrons from light alone is a basic prediction of quantum electrodynamics, but yet to be observed. Here we show that it is possible to create ${>}10^8$ positrons by dual laser irradiation of a structured plasma target, at intensities of $2 \times 10^{22} \mathrm{W}\mathrm{cm}^{-2}$. In contrast to previous work, the pair creation

Elena Bashtova, Elena Lenena

We consider a Jackson network with regenerative input flows in which every server is subject to a random environment influence generating breakdowns and repairs. They occur in accordance with two independent sequences of i.i.d. random variables. We establish a theorem on the strong approximation of the vector of queue lengths by a reflected Brownian motion i

Maxim Yu. Khlopov

The observational evidence for the inflationary cosmology with baryosynthesis and dark matter/energy can be viewed as the messenger for new physics, which governed the Universe origin, evolution and structure. To specify the physics beyond the Standard model (BSM), underlying the modern cosmological paradigm additional model dependent messengers are proposed

Joao Ramos, Yanran Ding, Young-woo Sim, Kevin Murphy

This letter introduces HOPPY, an open-source, low-cost, robust, and modular kit for robotics education. The robot dynamically hops around a rotating gantry with a fixed base. The kit lowers the entry barrier for studying dynamic robots and legged locomotion in real systems. The kit bridges the theoretical content of fundamental robotic courses and real dynam

Alexandros K. Spiliotis, Michalis Xygkis, Konstantinos Tazes, George E. Katsoprinakis

Spin polarized atomic ensembles can be used for the precise measurement of magnetic field. Conventional atomic magnetometers have demonstrated high sensitivities, albeit at low detection bandwidth, fundamentally limited by the Larmor precession frequency of the atoms. Here, we introduce a new type of atomic magnetometer which can realize sensitive detection

Luis Felipe Longo Micchi, Niayesh Afshordi, Cecilia Chirenti

The first direct observations of gravitational waves (GWs) by the LIGO collaboration have motivated different tests of General Relativity (GR), including the search for extra pulses following the GR waveform for the coalescence of compact objects. The motivation for these searches comes from the alternative proposal that the final compact object could differ

Andy Goldschmidt, Eurika Kaiser, Jonathan L. Dubois, Steven L. Brunton

Data-driven methods for establishing quantum optimal control (QOC) using time-dependent control pulses tailored to specific quantum dynamical systems and desired control objectives are critical for many emerging quantum technologies. We develop a data-driven regression procedure, bilinear dynamic mode decomposition (biDMD), that leverages time-series measure

Jeniya Tabassum, Sydney Lee, Wei Xu, Alan Ritter

This paper presents the results of the wet lab information extraction task at WNUT 2020. This task consisted of two sub tasks: (1) a Named Entity Recognition (NER) task with 13 participants and (2) a Relation Extraction (RE) task with 2 participants. We outline the task, data annotation process, corpus statistics, and provide a high-level overview of the par

Bin Xu, Jun Hou, Junzhe Shi, Huayi Li

Reinforcement Learning (RL) is widely utilized in the field of robotics, and as such, it is gradually being implemented in the Hybrid Electric Vehicle (HEV) supervisory control. Even though RL exhibits excellent performance in terms of fuel consumption minimization in simulation, the large learning iteration number needs a long learning time, making it hardl

Gal Porat

Let $K$ be a finite extension of $\mathbb{Q}_{p}$ and let $\Gamma$ be the Galois group of the cyclotomic extension of $K$. Fontaine's theory gives a classification of $p$-adic representations of $\mathrm{Gal}\left(\overline{K}/K\right)$ in terms of $(\varphi,\Gamma)$-modules. A useful aspect of this classification is Berger's dictionary which expresses invar

Abbas Javan Jafari, Diego Elias Costa, Rabe Abdalkareem, Emad Shihab

Dependency management in modern software development poses many challenges for developers who wish to stay up to date with the latest features and fixes whilst ensuring backwards compatibility. Project maintainers have opted for varied, and sometimes conflicting, approaches for maintaining their dependencies. Opting for unsuitable approaches can introduce bu

Swaroop Gowdra Shanthakumar, Anand Seetharam, Arti Ramesh

In this paper, we collect and study Twitter communications to understand the societal impact of COVID-19 in the United States during the early days of the pandemic. With infections soaring rapidly, users took to Twitter asking people to self isolate and quarantine themselves. Users also demanded closure of schools, bars, and restaurants as well as lockdown o

Javier Pliego

We show that almost every positive integer can be expressed as a sum of four squares of integers represented as the sums of three positive cubes.

Isaac Caswell, Theresa Breiner, Daan van Esch, Ankur Bapna

Large text corpora are increasingly important for a wide variety of Natural Language Processing (NLP) tasks, and automatic language identification (LangID) is a core technology needed to collect such datasets in a multilingual context. LangID is largely treated as solved in the literature, with models reported that achieve over 90% average F1 on as many as 1

Shubhangi Tandon, Saratchandra Indrakanti, Amit Jaiswal, Svetlana Strunjas

Large scale eCommerce platforms such as eBay carry a wide variety of inventory and provide several buying choices to online shoppers. It is critical for eCommerce search engines to showcase in the top results the variety and selection of inventory available, specifically in the context of the various buying intents that may be associated with a search query.

Loyal Durand, Phuoc Ha

We show that the remarks of V.\ Petrov in the preceding Comment are misdirected in the region of very small momentum transfers relevant to the study of Coulomb-nuclear interference in proton-proton scattering, and clarify the connection between our results and those of Cahn and of Kundr\'{a}t and Lokaji\v{c}ek which he cites.

Kaiyu Yang, Jia Deng

Parsing sentences into syntax trees can benefit downstream applications in NLP. Transition-based parsers build trees by executing actions in a state transition system. They are computationally efficient, and can leverage machine learning to predict actions based on partial trees. However, existing transition-based parsers are predominantly based on the shift

Javier Pliego

We investigate the existence of representations of every large positive integer as a sum of $k$-th powers of integers represented as certain diagonal forms. In particular, we consider a family of diagonal forms and discuss the problem of giving a uniform upper bound over the family for the number of variables needed to have such representations.

Alex McAvoy, John Wakeley

In order to accommodate the empirical fact that population structures are rarely simple, modern studies of evolutionary dynamics allow for complicated and highly-heterogeneous spatial structures. As a result, one of the most difficult obstacles lies in making analytical deductions, either qualitative or quantitative, about the long-term outcomes of evolution

Li-Chia Yang, Alexander Lerch

We propose a visually conditioned music remixing system by incorporating deep visual and audio models. The method is based on a state of the art audio-visual source separation model which performs music instrument source separation with video information. We modified the model to work with user-selected images instead of videos as visual input during inferen

Waixiang Cao, Chunmei Wang, Junping Wang

This article introduces a new primal-dual weak Galerkin (PDWG) finite element method for second order elliptic interface problems with ultra-low regularity assumptions on the exact solution and the interface and boundary data. It is proved that the PDWG method is stable and accurate with optimal order of error estimates in discrete and Sobolev norms. In part

Aadirupa Saha, Tomer Koren, Yishay Mansour

We introduce the problem of regret minimization in Adversarial Dueling Bandits. As in classic Dueling Bandits, the learner has to repeatedly choose a pair of items and observe only a relative binary `win-loss' feedback for this pair, but here this feedback is generated from an arbitrary preference matrix, possibly chosen adversarially. Our main result is an

Vyacheslav Futorny, João Schwarz

We study holonomic modules for the rings of invariant differential operators on affine commutative domains with finite Krull dimension with respect to arbitrary actions of finite groups. We prove the Bernstein inequality for these rings. Our main tool is the filter dimension introduced by Bavula. We extend the results for the invariants of the Weyl algebra w

Zahra Mousavi Kouzehkanan, Reshad Hosseini, Babak Nadjar Araabi

Many edge and contour detection algorithms give a soft-value as an output and the final binary map is commonly obtained by applying an optimal threshold. In this paper, we propose a novel method to detect image contours from the extracted edge segments of other algorithms. Our method is based on an undirected graphical model with the edge segments set as the

Moses Charikar, Paul Liu

In the W-streaming model, an algorithm is given $O(n \mathrm{polylog} n)$ space and must process a large graph of up to $O(n^2)$ edges. In this short note we give two algorithms for edge colouring under the W-streaming model. For edge colouring in W-streaming, a colour for every edge must be determined by the time all the edges are streamed. Our first algori

Javier Pliego

We give an upper bound for the minimum $s$ with the property that every sufficiently large integer can be represented as the sum of $s$ positive $k$-th powers of integers represented as the sum of three positive cubes for the cases $2\leq k\leq 4.$

Necati A. Ayan, Nilson L. Damasceno, Sushil Chaskar, Peron R. de Sousa

In this paper, our goal is to analyze and compare cellular network usage data from pre-lockdown, during lockdown, and post-lockdown phases surrounding the COVID-19 pandemic to understand and model human mobility patterns during the pandemic, and evaluate the effect of lockdowns on mobility. To this end, we collaborate with one of the main cellular network pr

Xiao Li, Guanyi Chen, Chenghua Lin, Ruizhe Li

We propose DGST, a novel and simple Dual-Generator network architecture for text Style Transfer. Our model employs two generators only, and does not rely on any discriminators or parallel corpus for training. Both quantitative and qualitative experiments on the Yelp and IMDb datasets show that our model gives competitive performance compared to several stron

Jeremy Budd, Yves van Gennip, Jonas Latz

This paper introduces a semi-discrete implicit Euler (SDIE) scheme for the Allen-Cahn equation (ACE) with fidelity forcing on graphs. Bertozzi and Flenner (2012) pioneered the use of this differential equation as a method for graph classification problems, such as semi-supervised learning and image segmentation. In Merkurjev, Kosti\'c, and Bertozzi (2013), a

Anqi Zhao, Peng Ding

Fisher's randomization test (FRT) delivers exact $p$-values under the strong null hypothesis of no treatment effect on any units whatsoever and allows for flexible covariate adjustment to improve the power. Of interest is whether the procedure could also be valid for testing the weak null hypothesis of zero average treatment effect. Towards this end, we eval

Benjamin Nachman

Modern machine learning tools offer exciting possibilities to qualitatively change the paradigm for new particle searches. In particular, new methods can broaden the search program by gaining sensitivity to unforeseen scenarios by learning directly from data. There has been a significant growth in new ideas and they are just starting to be applied to experim

Antonio Lerario, Michele Stecconi

Let $D$ be a disk in $\mathbb{R}^n$ and $f\in C^{r+2}(D, \mathbb{R}^k)$. We deal with the problem of the algebraic approximation of the set $j^{r}f^{-1}(W)$ consisting of the set of points in the disk $D$ where the $r$-th jet extension of $f$ meets a given semialgebraic set $W\subset J^{r}(D, \mathbb{R}^k).$ Examples of sets arising in this way are the zero

Saptarshi Roy, Shiladitya Mal, Aditi Sen De

Prior information about the input state can be utilized to enhance the efficiency of quantum teleportation which we quantify using the first two moments of fidelity. The input knowledge is introduced by relaxing the uniformity assumption in the distribution of the input state and considering non-uniform distributions, namely the polar cap and von Mises-Fishe

Iro Laina, Ruth C. Fong, Andrea Vedaldi

The increasing impact of black box models, and particularly of unsupervised ones, comes with an increasing interest in tools to understand and interpret them. In this paper, we consider in particular how to characterise visual groupings discovered automatically by deep neural networks, starting with state-of-the-art clustering methods. In some cases, cluster

The LIGO Scientific Collaboration, the Virgo Collaboration, R. Abbott, T. D. Abbott

We search for gravitational-wave transients associated with gamma-ray bursts detected by the Fermi and Swift satellites during the first part of the third observing run of Advanced LIGO and Advanced Virgo (1 April 2019 15:00 UTC - 1 October 2019 15:00 UTC). 105 gamma-ray bursts were analyzed using a search for generic gravitational-wave transients; 32 gamma-

Gia Wulandari, Detlef Plump

We consider Hoare-style verification for the graph programming language GP 2. In previous work, graph properties were specified by so-called E-conditions which extend nested graph conditions. However, this type of assertions is not easy to comprehend by programmers that are used to formal specifications in standard first-order logic. In this paper, we presen

Irina Basieva, Andrei Khrennikov, Masanao Ozawa

We present the novel approach to mathematical modeling of information processes in biosystems. It explores the mathematical formalism and methodology of quantum theory, especially quantum measurement theory. This approach is known as {\it quantum-like} and it should be distinguished from study of genuine quantum physical processes in biosystems (quantum biop

Kevin Batz, Benjamin Lucien Kaminski, Joost-Pieter Katoen, Christoph Matheja

We study a syntax for specifying quantitative "assertions" - functions mapping program states to numbers - for probabilistic program verification. We prove that our syntax is expressive in the following sense: Given any probabilistic program $C$, if a function $f$ is expressible in our syntax, then the function mapping each initial state $\sigma$ to the expe

Amit Adhikary, Nabarun Chakrabarty, Indrani Chakraborty, Jayita Lahiri

An $H^\pm W^\mp Z$ interaction at the tree level is common feature of new physics models that feature scalar triplets. In this study, we aim to probe the strength of the aforementioned interaction in a model-agnostic fashion at the futuristic 27 TeV proton-proton collider. We assume that the $H^\pm$ couples dominantly to ($W^\pm,Z$) and ($t,b$). We specifica

Alexei Oblomkov, Lev Rozansky

We establish an isomorphism between the Khovanov-Rozansky triply graded link homology and the geometric triply graded homology due to the authors. Hence we provide an interpretation of the Khovanov-Rozansky homology of the closure of a braid $\beta$ as the space of derived sections of a $\mathbb{C}^*\times \mathbb{C}^*$- equivariant sheaf $Tr(\beta)$ on the

Anna M. Suliga, Irene Tamborra

The exciting possibility of detecting supernova, solar, and atmospheric neutrinos with coherent neutrino-nucleus scattering detectors is within reach, opening up new avenues to probe New Physics. We explore the possibility of constraining non-standard coherent neutrino-nucleus scattering through astrophysical neutrinos. Sensitivity bounds on the mass and cou

Steven A. Frank, Frank J. Bruggeman

A recent article in Nature Physics unified key results from thermodynamics, statistics, and information theory. The unification arose from a general equation for the rate of change in the information content of a system. The general equation describes the change in the moments of an observable quantity over a probability distribution. One term in the equatio

Shangda Li, Devendra Singh Chaplot, Yao-Hung Hubert Tsai, Yue Wu

Advances in visual navigation methods have led to intelligent embodied navigation agents capable of learning meaningful representations from raw RGB images and perform a wide variety of tasks involving structural and semantic reasoning. However, most learning-based navigation policies are trained and tested in simulation environments. In order for these poli

W. Peter Maksym, Giuseppina Fabbiano, Martin Elvis, Luis C. Ho

The biconical radiation pattern extending from an active galactic nucleus (AGN) may strongly photoionize the circumnuclear interstellar medium (ISM) and stimulate emission from the narrow line region (NLR). Observations of the NLR may provide clues to the structure of dense material that preferentially obscures the bicone at certain angles, and may reveal th

Octavio Arriaga, Matias Valdenegro-Toro, Mohandass Muthuraja, Sushma Devaramani

In this paper we introduce the Perception for Autonomous Systems (PAZ) software library. PAZ is a hierarchical perception library that allow users to manipulate multiple levels of abstraction in accordance to their requirements or skill level. More specifically, PAZ is divided into three hierarchical levels which we refer to as pipelines, processors, and bac

Shameer Abdeen, Daniel Kennefick, Julia Kennefick, Rafael Eufrasio

Stationary Density Wave Theory predicts the existence of an age gradient across the spiral arms with a phase crossing at the co-rotation radius. Using star formation history (SFH) maps of 12 nearby spiral galaxies derived from \textsc{LIGHTNING} \citep{Eufracio:2017}, a spectral energy distribution (SED) fitting procedure, and by using \textsc{Spirality} \ci

Andreas Blommaert

We investigate out of time ordered correlators in the bulk of dS JT gravity, using Schwarzian perturbation theory, and propose that these out of time ordered correlators are encoded on the second sheet of the gravitational path integral, different sheets corresponding to different gravitational operator orderings. Implementing this in practice, we establish

Hiroki Takeda, Soichiro Morisaki, Atsushi Nishizawa

The physical degrees of freedom of a gravitational wave (GW) are imprints of the nature of gravity. We can test a gravity theory by searching for polarization modes beyond general relativity. The LIGO-Virgo collaboration analyzed several GW events in the O1 and O2 observing runs in the pure polarization framework, where they perform the Bayesian model select

Lindsay Sanneman, Christopher Fourie, Julie A. Shah

Robotics and related technologies are central to the ongoing digitization and advancement of manufacturing. In recent years, a variety of strategic initiatives around the world including "Industry 4.0", introduced in Germany in 2011 have aimed to improve and connect manufacturing technologies in order to optimize production processes. In this work, we study

Javier Pliego

We investigate the asymptotic formula for the number of representations of a large positive integer as a sum of $k$-th powers of integers represented as the sums of three positive cubes, counted with multiplicities. We also obtain a lower bound for the number of representations when the sums of three cubes are counted without multiplicities.

Rhea Sanjay Sukthanker, Zhiwu Huang, Suryansh Kumar, Erik Goron Endsjo

In this paper, we propose a new neural architecture search (NAS) problem of Symmetric Positive Definite (SPD) manifold networks, aiming to automate the design of SPD neural architectures. To address this problem, we first introduce a geometrically rich and diverse SPD neural architecture search space for an efficient SPD cell design. Further, we model our ne

Marion Bartl, Malvina Nissim, Albert Gatt

Contextualized word embeddings have been replacing standard embeddings as the representational knowledge source of choice in NLP systems. Since a variety of biases have previously been found in standard word embeddings, it is crucial to assess biases encoded in their replacements as well. Focusing on BERT (Devlin et al., 2018), we measure gender bias by stud

The LIGO Scientific Collaboration, the Virgo Collaboration, R. Abbott, T. D. Abbott

We report on the population of the 47 compact binary mergers detected with a false-alarm rate 1/yr in the second LIGO--Virgo Gravitational-Wave Transient Catalog, GWTC-2. We observe several characteristics of the merging binary black hole (BBH) population not discernible until now. First, we find that the primary mass spectrum contains structure beyond a pow

J. Becerra González, J. A. Acosta-Pulido, W. Boschin, R. Clavero

The study of gamma-ray blazars is usually hindered due to the lack of information on their redshifts and on their low energy photon fields. This information is key to understand the effect on the gamma-ray absorption due to either extragalactic background light and/or intrinsic absorption and emission processes. All this information has also an impact on the

Tim Draws, Nava Tintarev, Ujwal Gadiraju, Alessandro Bozzon

The way pages are ranked in search results influences whether the users of search engines are exposed to more homogeneous, or rather to more diverse viewpoints. However, this viewpoint diversity is not trivial to assess. In this paper we use existing and novel ranking fairness metrics to evaluate viewpoint diversity in search result rankings. We conduct a co

Mukul Bhattacharya, Pawan Kumar, Eric V. Linder

Fast radio burst (FRB) discoveries are occurring rapidly, with thousands expected from upcoming surveys. The dispersion measures (DM) observed for FRB include important information on cosmological distances and the ionization state of the universe from the redshift of emission until today. Rather than considering the DM--redshift relation, we investigate the

The LIGO Scientific Collaboration, the Virgo Collaboration, R. Abbott, T. D. Abbott

Gravitational waves enable tests of general relativity in the highly dynamical and strong-field regime. Using events detected by LIGO-Virgo up to 1 October 2019, we evaluate the consistency of the data with predictions from the theory. We first establish that residuals from the best-fit waveform are consistent with detector noise, and that the low- and high-

Alejandro Jiménez-Cano, Yuri N. Obukhov

We derive the exact gravitational wave solutions in a general class of quadratic metric-affine gauge gravity models. The Lagrangian includes all possible linear and quadratic invariants constructed from the torsion, nonmetricity and the curvature. The ansatz for the gravitational wave configuration and the properties of the wave solutions are patterned follo

R. Abbott, T. D. Abbott, S. Abraham, F. Acernese

We report on gravitational wave discoveries from compact binary coalescences detected by Advanced LIGO and Advanced Virgo in the first half of the third observing run (O3a) between 1 April 2019 15:00 UTC and 1 October 2019 15:00. By imposing a false-alarm-rate threshold of two per year in each of the four search pipelines that constitute our search, we prese

Mariano Celada, Ricardo Escobedo, Merced Montesinos

In this paper we perform in a manifestly $SO(n-1,1)$ [or, alternatively $SO(n)$] covariant fashion, the canonical analysis of general relativity in $n$ dimensions written as a constrained $BF$ theory. Since the Lagrangian action of the theory can be written in two classically equivalent ways, we analyze each case separately. We show that for either action th

Giuseppe De Laurentis, Daniel Maître

We present all two-loop five-parton leading-colour finite remainders in the spinor-helicity formalism by analysing numerical evaluations of their known expressions in terms of Mandelstam invariants. Recasting them in terms of spinor-helicity variables allows us to obtain expressions which are more compact, faster to evaluate, numerically more stable and mani

Andreas Orthey, Marc Toussaint

Sampling-based planning methods often become inefficient due to narrow passages. Narrow passages induce a higher runtime, because the chance to sample them becomes vanishingly small. In recent work, we showed that narrow passages can be approached by relaxing the problem using admissible lower-dimensional projections of the state space. Those relaxations oft

Azadeh Moradinezhad Dizgah, Matteo Biagetti, Emiliano Sefusatti, Vincent Desjacques

Upcoming galaxy redshift surveys promise to significantly improve current limits on primordial non-Gaussianity (PNG) through measurements of 2- and 3-point correlation functions in Fourier space. However, realizing the full potential of this dataset is contingent upon having both accurate theoretical models and optimized analysis methods. Focusing on the loc

Elias Bernreuther, Saniya Heeba, Felix Kahlhoefer

We study the phenomenology and detection prospects of a sub-GeV Dirac dark matter candidate with resonantly enhanced annihilations via a dark photon mediator. The model evades cosmological constraints on light thermal particles in the early universe while simultaneously being in reach of current and upcoming terrestrial experiments. We conduct a global analy

Mark Booth, Michael Schulz, Alexander V. Krivov, Sebastián Marino

HD 38206 is an A0V star in the Columba association, hosting a debris disc first discovered by IRAS. Further observations by Spitzer and Herschel showed that the disc has two components, likely analogous to the asteroid and Kuiper belts of the Solar System. The young age of this star makes it a prime target for direct imaging planet searches. Possible planets

Mark Van Raamsdonk, Christopher Waddell

${\cal N} = 4$ Supersymmetric Yang-Mills (SYM) theory can be defined on a half-space with a variety of boundary conditions preserving scale invariance and half of the original supersymmetry; more general theories with the same symmetry can be obtained by coupling to a 3D SCFT at the boundary. Each of these theories is characterized by a quantity called "boun

Rodolfo Gambini, Jorge Pullin

We review the Montevideo Interpretation of quantum mechanics, which is based on the use of real clocks to describe physics, using the framework recently introduced by Hoehn, Smith and Lock to treat the problem of time in generally covariant systems. The use of the new formalism makes the whole construction more accessible to readers without familiarity with

Francois Foucart, Alexander Chernoglazov, Michael Boyle, Tanja Hinderer

The availability of accurate numerical waveforms is an important requirement for the creation and calibration of reliable waveform models for gravitational wave astrophysics. For black hole-neutron star binaries, very few accurate waveforms are however publicly available. Most recent models are calibrated to a large number of older simulations with good para

Matthew P. G. Robbins, Laura J. Henderson, Robert B. Mann

The quantum vacuum has long been known to be characterized by field correlations between spacetime points. These correlations can be swapped with a pair of particle detectors, modelled as simple two-level quantum systems (Unruh-DeWitt detectors) via a process known as entanglement harvesting. We study this phenomenon in the presence of a rotating BTZ black h

Li-Wei Yu, Dong-Ling Deng

Non-Hermitian topological phases bear a number of exotic properties, such as the non-Hermitian skin effect and the breakdown of conventional bulk-boundary correspondence. In this paper, we introduce an unsupervised machine learning approach to classify non-Hermitian topological phases based on diffusion maps, which are widely used in manifold learning. We fi

Changrim Ahn, Matthias Staudacher

We refine the recently introduced notion of eclectic spin chains by including a maximal number of deformation parameters. These models are integrable, nearest-neighbor n-state spin chains with exceedingly simple non-hermitian Hamiltonians. They turn out to be non-diagonalizable in the multiparticle sector (n>2), where their "spectrum" consists of an intricat

Stewart Morawetz, Isaac J. S. De Vlugt, Juan Carrasquilla, Roger G. Melko

Generative models are a promising technology for the enhancement of quantum simulators. These machine learning methods are capable of reconstructing a quantum state from experimental measurements, and can aid in the calculation of physical observables. In this paper, we employ a recurrent neural network (RNN) to reconstruct the ground state of the spin-1/2 X

Clara C. Wanjura, Matteo Brunelli, Andreas Nunnenkamp

In order for non-Hermitian (NH) topological effects to be relevant for practical applications, it is necessary to study disordered systems. In the absence of disorder, certain driven-dissipative cavity arrays with engineered non-local dissipation display directional amplification when associated with a non-trivial winding number of the NH dynamic matrix. In

J. Neilsen, J. Homan, J. F. Steiner, G. Marcel

After 26 years in outburst, the black hole X-ray binary GRS 1915+105 dimmed considerably in early 2018; its flux dropped sharply in mid-2019, and it has remained faint ever since. This faint period, the "obscured state," is punctuated by occasional X-ray flares, many of which have been observed by NICER as part of our regular monitoring program. Here we pres