June 2020 arXiv papers — page 35

Yuxin Mao, Lujie Zhou, Naixue Xiong

Online social networks have become incredibly popular in recent years, which prompts an increasing number of companies to promote their brands and products through social media. This paper presents an approach for identifying influential nodes in online social network for brand communication. We first construct a weighted network model for the users and thei

Andrii Sokolov, Dmytro Mishagli, Panagiotis Giounanlis, Imran Bashir

The construction of quantum computer simulators requires advanced software which can capture the most significant characteristics of the quantum behavior and quantum states of qubits in such systems. Additionally, one needs to provide valid models for the description of the interface between classical circuitry and quantum core hardware. In this study, we mo

Rui Wang, Yuesheng Xu

Constructing or learning a function from a finite number of sampled data points (measurements) is a fundamental problem in science and engineering. This is often formulated as a minimum norm interpolation problem, regularized learning problem or, in general, a semi-discrete inverse problem, in certain functional spaces. The choice of an appropriate space is

Cristian Fernando Moukarzel, Gonzalo Peraza-Mues, Osvaldo Carvente

It is shown that vibrated packings of frictional disks self-organize cooperatively onto a rotational-transport state where the long-time angular velocity $\barω_i$ of each disk $i$ is nonzero. Steady rotation is mediated by the spontaneous breaking of local reflection symmetry, arising when the cages in which disks are constrained by their neighbors acquire

Julien Fageot, Matthieu Simeoni

We introduce a general framework for the reconstruction of periodic multivariate functions from finitely many and possibly noisy linear measurements. The reconstruction task is formulated as a penalized convex optimization problem, taking the form of a sum between a convex data fidelity functional and a sparsity-promoting total variation based penalty involv

Cla Duri Tschannen, Georgy Gordeev, Stephanie Reich, Lei Shi

We experimentally quantify the Raman scattering from individual carbyne chains confined in double-walled carbon nanotubes. We find that the resonant differential Raman cross section of confined carbyne is on the order of $10^{-22}$ cm$^{2}\,$sr$^{-1}$ per atom, making it the strongest Raman scatterer ever reported.

Jessica Krick, Jonathan Fraine, Jim Ingalls, Sinan Deger

We present a new method employing machine learning techniques for measuring astrophysical features by correcting systematics in IRAC high precision photometry using Random Forests. The main systematic in IRAC light curve data is position changes due to unavoidable telescope motions coupled with an intrapixel response function. We aim to use the large amount

Wenxiong Chen, Pengyan Wang, Yahui Niu, Yunyun Hu

In this paper, we develop a systematical approach in applying an asymptotic method of moving planes to investigate qualitative properties of positive solutions for fractional parabolic equations. We first obtain a series of needed key ingredients such as narrow region principles, and various asymptotic maximum and strong maximum principles for antisymmetric

Haitao Wang, Yiming Zhao

Let $P$ be a path graph of $n$ vertices embedded in a metric space. We consider the problem of adding a new edge to $P$ so that the radius of the resulting graph is minimized, where any center is constrained to be one of the vertices of $P$. Previously, the "continuous" version of the problem where a center may be a point in the interior of an edge o

Georgios E. Zouraris

We consider an initial and Dirichlet boundary value problem for a semilinear, two dimensional heat equation over a rectangular domain. The problem is discretized in time by a version of the Relaxation Scheme proposed by C. Besse (C. R. Acad. Sci. Paris Sér. I, vol. 326 (1998)) for the nonlinear Schrödinger equation and in space by a standard second order fin

František Dinnbier, Pavel Kroupa

Star clusters form out of the densest parts of infrared dark clouds. The emergence of massive stars expels the residual gas, which has not formed stars yet. Gas expulsion lowers the gravitational potential of the embedded cluster, unbinding many of the cluster stars. These stars then move on their own trajectories in the external gravitational field of the G

Xiaofeng Ma, Michael Kirby, Chris Peterson

The shape and orientation of data clouds reflect variability in observations that can confound pattern recognition systems. Subspace methods, utilizing Grassmann manifolds, have been a great aid in dealing with such variability. However, this usefulness begins to falter when the data cloud contains sufficiently many outliers corresponding to stray elements f

Maxwell A. T. Marple, Thomas A. Wynn, Diyi Cheng, Ryosuke Shimizu

Lithium phosphorus oxynitride (LiPON) is an amorphous solid-state lithium ion conductor displaying exemplary cyclability against lithium metal anodes. There is no definitive explanation for this stability due to the limited understanding of the structure of LiPON. We provide a structural model of RF-sputtered LiPON via experimental and computational spectros

Tianjian Lu, Thibault Marin, Yue Zhuo, Yi-Fan Chen

The advanced magnetic resonance (MR) image reconstructions such as the compressed sensing and subspace-based imaging are considered as large-scale, iterative, optimization problems. Given the large number of reconstructions required by the practical clinical usage, the computation time of these advanced reconstruction methods is often unacceptable. In this w

Lucas Pagliosa, Rodrigo Mello

Concept Drift (CD) detection intends to continuously identify changes in data stream behaviors, supporting researchers in the study and modeling of real-world phenomena. Motivated by the lack of learning guarantees in current CD algorithms, we decided to take advantage of the Statistical Learning Theory (SLT) to formalize the necessary requirements to ensure

Vikash Sehwag, Rajvardhan Oak, Mung Chiang, Prateek Mittal

With increasing expressive power, deep neural networks have significantly improved the state-of-the-art on image classification datasets, such as ImageNet. In this paper, we investigate to what extent the increasing performance of deep neural networks is impacted by background features? In particular, we focus on background invariance, i.e., accuracy unaffec

Abdul Ahad Abro, Ufaque Shaikh

Virtualization is an emerging and optimistic prospect in the IT industry. Its impact has a footprint widely in digital infrastructure. Many innovativeness sectors utilized the concept of virtualization to reduce the cost of frameworks. In this paper, we have designed and developed storage virtualization for physical functional solutions. It is an auspicious

Andrei G. Lebed

We review our recent theoretical results about inequivalence between passive gravitational mass and energy for a composite quantum body at a macroscopic level. In particular, we consider macroscopic ensembles of the simplest composite quantum bodies - hydrogen atoms. Our results are as follows. For the most ensembles, the Einstein's Equivalence Principle

Daniela Curin, Ignacio F. Ranea-Sandoval, Milva G. Orsaria, Gustavo A. Contrera

The aim of this work is the study of hybrid stars interiors from the equations of state describing the matter composing them. We used modern hadronic equations of state to describe the matter in the outer core of these stars. The quark phase is modeled by an extended version of the Field Correlator Method, in which vector interactions among quarks and color

Guo-Niu Han

We introduce a large family of combinatorial objects, called standard puzzles, defined by very simple rules. We focus on the standard puzzles for which the enumeration problems can be solved by explicit formulas or by classical numbers, such as binomial coefficients, Fibonacci numbers, tangent numbers, Catalan numbers, $\ldots$

Martin Schnell, Monika Goikoetxea, Iban Amenabar, P. Scott Carney

Scattering-type scanning near-field optical microscopy (s-SNOM), and its derivate, Fourier transform infrared nanospectroscopy (nano-FTIR) are emerging techniques for infrared (IR) nanoimaging and spectroscopy with applications in diverse fields ranging from nanophotonics, chemical imaging and materials science. However, spectroscopic nanoimaging is still ch

Stefano Di Sabatino, J. A. Berger, Pina Romaniello

The optical spectra of two-dimensional (2D) periodic systems provide a challenge for time-dependent density-functional theory (TDDFT) because of the large excitonic effects in these materials. In this work we explore how accurately these spectra can be described within a pure Kohn-Sham time-dependent density-functional framework, i.e., a framework in which n

G. -N. Han, S. -M. Ma

In this paper we first present summation formulas for $k$-order Eulerian polynomials and $1/k$-Eulerian polynomials. We then present combinatorial expansions of $(c(x)D)^n$ in terms of inversion sequences as well as $k$-Young tableaux, where $c(x)$ is a differentiable function in the indeterminate $x$ and $D$ is the derivative with respect to $x$. We define

Dennis Jack, Klaus-Peter Schröder, Philippe Eenens, Uwe Wolter

With our robotic 1.2 m TIGRE telescope, we were able to obtain eight optical spectra with intermediate resolution (R = 20,000) of the Nova V659 Sct during different phases of its outburst. We present a list of the lines found in the Nova spectra. The most common features are H I, O I, Na I, Fe II and Ca II. Studying the spectral evolution of the strong featu

Mario Alloza, Jesus Gonzalo, Carlos Sanz

We provide evidence that many narrative shocks used by prominent literature are persistent. We show that the two leading methods to estimate impulse responses to an independently identified shock (local projections and distributed lag models) treat persistence differently, hence identifying different objects. We propose corrections to re-establish the equiva

S. A. Walker, M. S. Mirakhor, J. ZuHone, J. S. Sanders

We present new XMM-Newton observations extending the mosaic of the Perseus cluster out to the virial radius to the west. Previous studies with ROSAT have reported a large excess in surface brightness to the west, possibly the result of large scale gas sloshing, but lacked the spatial resolution and depth to determine if this excess lay behind a cold front. I

Ali Zahabi

The BPS bound states of D4-D2-D0 branes on the non-compact divisors of Calabi-Yau threefolds and the instantons in the dual quiver gauge theories are previously studied using two-dimensional crystal melting model and dimer model. Using the tropical geometry associated with the toric quiver, we study the asymptotic of the quiver gauge theory to compute some o

Althea V. Moorhead

Burns et al. (1979) use the parameter beta to describe the ratio of radiation pressure to gravity experienced by a small particle in the solar system. The central potential that these particles experience is effectively reduced by a factor of (1 - beta), lowering the escape velocity. Burns et al. (1979) also provide a simple expression for the value of beta

Mario Martone

The constraining mathematical structure of the Coulomb branch of four dimensional $\mathcal{N}=2$ supersymmetric theories is discussed. The presentation follows a somewhat different route from other excellent reviews on the subject and it is geared towards using this tool to classify four dimensional $\mathcal{N}=2$ superconformal field theories. This is the

Lintao Ye, Sandip Roy, Shreyas Sundaram

Given a linear dynamical system affected by noise, we study the problem of optimally placing sensors (at design-time) subject to a sensor placement budget constraint in order to minimize the trace of the steady-state error covariance of the corresponding Kalman filter. While this problem is NP-hard in general, we consider the underlying graph associated with

Pavel Osinenko, Lukas Beckenbach, Thomas Göhrt, Stefan Streif

Reinforcement learning (RL) in the context of control systems offers wide possibilities of controller adaptation. Given an infinite-horizon cost function, the so-called critic of RL approximates it with a neural net and sends this information to the controller (called "actor"). However, the issue of closed-loop stability under an RL-method is still n

Ricardo Augusto Borsoi, Cédric Richard, André Ferrari, Jie Chen

The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that

Y. A. Azzam, M. I. Nouh, A. A. Shaker

Innovation in the ground and space-based instruments has taken us into a new age of spectroscopy, in which a large amount of stellar content is becoming available. So, automatic classification of stellar spectra became subjective in recent years due to the availability of large observed spectral database as well as the theoretical spectra. In the present pap

Yun Zhang, Patrick Michel

During close approaches to planets or stars, the morphological and dynamical properties of rubble-pile small bodies can be modified, and some may catastrophically break up. This phenomenon is of particular interest for understanding the evolution and population of small bodies, and for making predictions for future encounters. Previous numerical explorations

Luís M. S. Russo, Alexandre P. Francisco

We consider the problem of identifying tandem scattered subsequences within a string. Our algorithm identifies a longest subsequence which occurs twice without overlap in a string. This algorithm is based on the Hunt-Szymanski algorithm, therefore its performance improves if the string is not self similar. This occurs naturally on strings over large alphabet

B. Davier, P. Dollfus, S. Volz, J. Shiomi

A semi-analytical model for studying thermal transport at the nanoscale, able to accurately describe both the effect of out of equilibrium transport and the thermal transfer at interfaces, is presented. Our approach is based on the definition of pseudo local temperatures distinguishing the phonon populations according to the direction of their velocity. This

Joseph P. Byrne, Boulis M. Ibrahim, Xiaoyu Zong

An asset pricing model using long-run capital share growth risk has recently been found to successfully explain U.S. stock returns. Our paper adopts a recursive preference utility framework to derive an heterogeneous asset pricing model with capital share risks.While modeling capital share risks, we account for the elevated consumption volatility of high inc

Tevfik Can Yuce, Fatih Dinc, Agata M. Brańczyk

Symmetry-protected subradiance is known to guarantee high qubit storage times in free space. We show that in one-dimensional waveguides, this is also true, but that even longer qubit storage times can be identified by considering the eigenspectrum of the qubit-qubit coupling matrix. In the process, we introduce three theorems about $N$ qubits coupled to a on

Isom H. Herron, Ronald E. Mickens

We construct a class of exponential type solutions for the linear, delayed heat equation. These representations may be used to provide a priori ansatzes for certain boundary and/or initial-value problems arising in heat transfer. Several of the important mathematical properties of the representations are examined, including a discussion of the dependence on

Xinyu Hua, Lei Li, Lifeng Hua, Lu Wang

Automatic identification of mentioned entities in social media posts facilitates quick digestion of trending topics and popular opinions. Nonetheless, this remains a challenging task due to limited context and diverse name variations. In this paper, we study the problem of entity linking for Chinese news comments given mentions' spans. We hypothesize tha

Cyrus Rashtchian, David P. Woodruff, Hanlin Zhu

We consider the general problem of learning about a matrix through vector-matrix-vector queries. These queries provide the value of $\boldsymbol{u}^{\mathrm{T}}\boldsymbol{M}\boldsymbol{v}$ over a fixed field $\mathbb{F}$ for a specified pair of vectors $\boldsymbol{u},\boldsymbol{v} \in \mathbb{F}^n$. To motivate these queries, we observe that they generali

Kailash C. Misra, Ernie Stitzinger, Xingjian Yu

Leibniz algebras are certain generalizations of Lie algebras. Motivated by the concept of subinvariance in group theory, Schenkman studied properties of subinvariant subalgebras of a Lie algebra. In this paper we define subinvariant subalgebras of Leibniz algebras and study their properties. It is shown that the signature results on subinvariance in Lie alge

Daniel Vallières

We study how the $\ell$-adic valuation of the number of spanning trees varies in regular abelian $\ell$-towers of multigraphs. We show that for an infinite family of regular abelian $\ell$-towers of bouquets, the behavior of the $\ell$-adic valuation of the number of spanning trees behave similarly to the $\ell$-adic valuation of the class numbers in $\mathb

Thiago Rateke, Aldo von Wangenheim

One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, and it is also important that this system can extract essential information that may influence the vehicle's behavior, whether it will be generating an aler

Kevin Stehle, Günther Schindler, Holger Fröning

Systolic arrays are a promising computing concept which is in particular inline with CMOS technology trends and linear algebra operations found in the processing of artificial neural networks. The recent success of such deep learning methods in a wide set of applications has led to a variety of models, which albeit conceptual similar as based on convolutions

Yushi Hu, Shane Settle, Karen Livescu

Acoustic word embeddings (AWEs) are vector representations of spoken word segments. AWEs can be learned jointly with embeddings of character sequences, to generate phonetically meaningful embeddings of written words, or acoustically grounded word embeddings (AGWEs). Such embeddings have been used to improve speech retrieval, recognition, and spoken term disc

Dmytro Vovchuk, Sergei Kosulnikov, Roman E. Noskov, Pavel Ginzburg

Interference phenomena render tailoring propagation of electromagnetic waves by controlling phases of several scattering channels. Huygens element, being a representative example of this approach, allows enhancement of the scattering from an object in a forward direction, while the reflection is suppressed. However, a typical resonant realization of Huygens

Ashesh Ghosh, Kenneth S. Schweizer

We theoretically study the effect of external deformation on activated structural relaxation and elementary aspects of the nonlinear mechanical response of glassy hard sphere fluids in the context of the nonequilibrium version of the Elastically Collective Nonlinear Langevin Equation (ECNLE) theory. ECNLE theory describes activated relaxation as a coupled lo

Sebastian Rosengren

Many random growth models have the property that the set of discovered sites, scaled properly, converges to some deterministic set as time grows. Such results are known as shape theorems. Typically, not much is known about the shapes. For first passage percolation on $\mathbb{Z}^d$ we only know that the shape is convex, compact, and inherits all the symmetri

V. S. Gurin

A series of the cadmium selenide magic size clusters with the cores of Cd$_8$Se$_{13}$ terminated with H- and N-containing ligands are calculated at the DFT level with different functionals and ECP basis sets. The optimum functional was selected on the basis of calculation for diatomic CdSe and other smallest clusters with available experimental data and the

Avetik Karagulyan, Arnak S. Dalalyan

We study the problem of sampling from a probability distribution on $\mathbb R^p$ defined via a convex and smooth potential function. We consider a continuous-time diffusion-type process, termed Penalized Langevin dynamics (PLD), the drift of which is the negative gradient of the potential plus a linear penalty that vanishes when time goes to infinity. An up

Jakob Ameres

This article presents a novel and practically useful link between geometric integration, low-discrepancy sampling and code coupling for Lagrangian and Eulerian Vlasov-Poisson solvers. Low-discrepancy sequences, also called quasi-random sequences (Quasi Monte Carlo), provide convergence rates close to $\mathcal{O}( N^{-1})$ which are far superior to (pseudo)

Jose Bernabeu

Symmetries in the Physical Laws of Nature lead to observable effects. Beyond regularities and conserved magnitudes, the last decades in Particle Physics have seen the identification of symmetries, and their well defined breaking, as the guiding principle for the elementary constituents of matter and their interactions. Flavour SU(3) symmetry of hadrons led t

Kevin P. Knudson

We define the notion of an approximate triangulation for a manifold $M$ embedded in euclidean space. The basic idea is to build a nested family of simplicial complexes whose vertices lie in $M$ and use persistent homology to find a complex in the family whose homology agrees with that of $M$. Our key examples are various Grassmann manifolds $G_k({\mathbb R}^

Di Cao, Junbo Zhao, Weihao Hu, Fei Ding

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage controls, but this is difficult to obtain in practice. This paper develops a model-free approach based on the surrogate model and deep reinforcement learning (DRL). We have also extended it to deal with unbalanced three-phase scenarios. The key idea is t

Jasser Jasser, Ivan Garibay, Steve Scheinert, Alexander V. Mantzaris

Online users discuss and converse about all sorts of topics on social networks. Facebook, Twitter, Reddit are among many other networks where users can have this freedom of information sharing. The abundance of information shared over these networks makes them an attractive area for investigating all aspects of human behavior on information dissemination. Am

Filipo Sharevski, Peter Jachim

This paper presents an automated adversarial mechanism called WikipediaBot. WikipediaBot allows an adversary to create and control a bot infrastructure for the purpose of adversarial edits of Wikipedia articles. The WikipediaBot is a self-contained mechanism with modules for generating credentials for Wikipedia editors, bypassing login protections, and a pro

Kristen Gardner, Jazeem Abdul Jaleel, Alexander Wickeham, Sherwin Doroudi

Heterogeneity is becoming increasingly ubiquitous in modern large-scale computer systems. Developing good load balancing policies for systems whose resources have varying speeds is crucial in achieving low response times. Indeed, how best to dispatch jobs to servers is a classical and well-studied problem in the queueing literature. Yet the bulk of existing

Than Htut Soe, Oda Elise Nordberg, Frode Guribye, Marija Slavkovik

To ensure that users of online services understand what data are collected and how they are used in algorithmic decision-making, the European Union's General Data Protection Regulation (GDPR) specifies informed consent as a minimal requirement. For online news outlets consent is commonly elicited through interface design elements in the form of a pop-up.

Allan Freitas, Rodrigo Ávalos

The aim of this paper is to present a version of the generalized Pohozaev-Schoen identity in the context of asymptotically euclidean manifolds. Since these kind of geometric identities have proven to be a very powerful tool when analysing different geometric problems for compact manifolds, we will present a variety of applications within this new context. Am

Mahmoud Said Elsayed, Nhien-An Le-Khac, Soumyabrata Dev, Anca Delia Jurcut

Software-Defined Networking (SDN) is an emerging paradigm, which evolved in recent years to address the weaknesses in traditional networks. The significant feature of the SDN, which is achieved by disassociating the control plane from the data plane, facilitates network management and allows the network to be efficiently programmable. However, the new archit

Rafael Redondo, Jaume Gibert

Existing face datasets often lack sufficient representation of occluding objects, which can hinder recognition, but also supply meaningful information to understand the visual context. In this work, we introduce Extended Labeled Faces in-the-Wild (ELFW), a dataset supplementing with additional face-related categories -- and also additional faces -- the origi

J. Zhuleku, J. Warnecke, H. Peter

Observations show that the coronal X-ray emission of the Sun and other stars depends on the surface magnetic field. Using power-law scaling relations between different physical parameters, we build an analytical model to connect the observed X-ray emission to the magnetic flux. The basis for our model are the scaling laws of Rosner, Tucker \& Vaiana (RTV) th

Reuben R. W. Wang, Andrew G. Sykes, John L. Bohn

We study the nonequilibrium dynamics of an ultracold, non-degenerate dipolar gas of $^{164}$Dy atoms in a cylindrically symmetric harmonic trap. To do so, we investigate the normal modes and linear response of the gas when driven by means of periodic modulations to the trap axial-frequency. We find that the resonant response of the gas depends strongly on th

Andreas Blommaert

We calculate bulk and boundary correlators in JT gravity by summing over geometries. The answers are reproduced by computing suitable ensemble averages of correlators of chaotic quantum systems. We then consider bulk correlators at large spatial separations and find that semiclassical decay eventually makes way for erratic oscillations around some nonzero an

I. Martin, A. Alcaniz, A. Jimenez, G. Lopez

Similar to other high quality crystalline absorbers, an accurate knowledge of surface passivation of crystalline Germanium (c-Ge) substrates is crucial for a straightforward improvement of photovoltaic device performance. For crystalline silicon devices, this information is typically obtained by quasisteady state photoconductance (QSS-PC) technique using Sin

W. F. Schlotter, M. Beye, S. Zohar, G. Coslovich

X-ray absorption spectroscopy (XAS) is a powerful and well established technique with sensitivity to elemental and chemical composition. Despite these advantages, its implementation has not kept pace with the development of ultrafast pulsed x-ray sources where XAS can capture femtosecond chemical processes. X-ray Free Electron Lasers (XFELs) deliver femtosec

Toby Dylan Hocking, Anuraag Srivastava

In data sequences measured over space or time, an important problem is accurate detection of abrupt changes. In partially labeled data, it is important to correctly predict presence/absence of changes in positive/negative labeled regions, in both the train and test sets. One existing dynamic programming algorithm is designed for prediction in unlabeled test

Gor Oganesyan

The follow-up of $γ$-ray bursts (GRBs) by the X-ray telescope (XRT, 0.3-10 keV) on board the Neil Gehrels Swift Observatory led to the discovery of a shallow decay phase (the so-called plateau) of the X-ray emission in a good fraction of GRBs. This unexpected temporal behaviour does not fit the standard GRB afterglow expectation. Thus, in the last years, man

Gor Oganesyan

The origin of prompt emission in $γ$-ray bursts (GRBs) is highly debated topic. The observed spectra are supposed to play a crucial role in constraining the location of the emitting region, the strength of the magnetic field and the distribution of the accelerated particles. The apparent inconsistency of the prompt emission spectra with the synchrotron radia

Jean-François Fortin, Wen-Jie Ma, Witold Skiba

Seven-point functions have two inequivalent topologies or channels. The comb channel has been computed previously and here we compute scalar conformal blocks in the extended snowflake channel in $d$ dimensions. Our computation relies on the known action of the differential operator that sets up the operator product expansion in embedding space. The scalar co

Cheng Peng, Stefan Stanojevic

We study various properties of the soft modes in the $\mathcal{N}=2$ supersymmetric SYK model.

Nemanja Kaloper, Morgane König, Albion Lawrence, James H. C. Scargill

We revisit two-field hybrid inflation as an effective field theory for low-scale inflation with sub-Planckian scalar field ranges. We focus on a prototype model by Stewart because it allows for a red spectral tilt, which still fits the current data. We describe the constraints on this model imposed by current CMB measurements. We then explore the stability o

Peter McGill, Andrew Everall, Douglas Boubert, Leigh Smith

Precision astrometry from the second Gaia data release has allowed astronomers to predict 5,787 microlensing events, with 528 of these having maximums within the extended Gaia mission (J2014.5 - J2026.5). Future analysis of the Gaia time-series astrometry of these events will, in some cases, lead to precise gravitational mass measurements of the lens. We fin

Karan Dsilva, Tomer Shenar, Hugues Sana, Pablo Marchant

It is now well established that the majority of massive stars reside in multiple systems. However, the effect of multiplicity is not sufficiently understood, resulting in a plethora of uncertainties about the end stages of massive-star evolution. In order to investigate these uncertainties, it is useful to study massive stars just before their demise. Classi

S. Lombardo, F. Prada, E. Hugot, S. Basa

We present here the Calar Alto Schmidt-Lemaitre Telescope (CASTLE) concept, a technology demonstrator for curved detectors, that will be installed at the Calar Alto Observatory (Spain). This telescope has a wide field of view (2.36x1.56 deg^2) and a design, optimised to generate a Point Spread Function with very low level wings and reduced ghost features, wh

Hirosi Ooguri, Tadashi Takayanagi

McNamara and Vafa conjectured that any pair of consistent quantum gravity theories can be connected by a domain wall. We test the conjecture in the context of the AdS/CFT correspondence. There are topological constraints on existence of an interface between the corresponding conformal field theories. We discuss how to construct domain walls in AdS predicted

Assaf Horesh, Itai Sfaradi, Mattias Ergon, Cristina Barbarino

We report the discovery and panchromatic followup observations of the young Type Ic supernova, SN2020oi, in M100, a grand design spiral galaxy at a mere distance of $14$ Mpc. We followed up with observations at radio, X-ray and optical wavelengths from only a few days to several months after explosion. The optical behaviour of the supernova is similar to tho

B. Grémaud, G. G. Batrouni

We use unbiased computational methods to elucidate the onset and properties of pair superfluidity in two-species fermionic and bosonic systems with onsite interspecies attraction loaded in one-dimensional optical lattice. We compare results from quantum Monte Carlo (QMC) and density matrix renormalization group (DMRG), emphasizing the one-to-one corresponden

J. Trevor Mendel, Alessandra Beifiori, Roberto Saglia, Ralf Bender

We study the dynamical properties of massive quiescent galaxies at $1.4 < z < 2.1$ using deep Hubble Space Telescope WFC3/F160W imaging and a combination of literature stellar velocity dispersion measurements and new near-infrared spectra obtained using KMOS on the ESO VLT. We use these data to show that the typical dynamical-to-stellar mass ratio has increa

Dalya Baron, Brice Ménard

Scientists aim to extract simplicity from observations of the complex world. An important component of this process is the exploration of data in search of trends. In practice, however, this tends to be more of an art than a science. Among all trends existing in the natural world, one-dimensional trends, often called sequences, are of particular interest as

Alejandro Ungría Hirte, Moritz Platscher, Thomas Joyce, Jeremy J. Heit

We show that high quality, diverse and realistic-looking diffusion-weighted magnetic resonance images can be synthesized using deep generative models. Based on professional neuroradiologists&#39; evaluations and diverse metrics with respect to quality and diversity of the generated synthetic brain images, we present two networks, the Introspective Variationa

G. B. Lima Neto, F. Durret, T. F. Laganá, R. E. G. Machado

Groups are the most common association of galaxies in the Universe, found in different configuration states such as loose, compact and fossil groups. We have studied the galaxy group MKW 4s, dominated by the giant early-type galaxy NGC 4104 at z=0.0282. Our aim was to understand the evolutionary stage of this group and to place it within the framework of the

Sarah Leslie, Eva Schinnerer, Daizhong Liu, Benjamin Magnelli

We provide a coherent, uniform measurement of the evolution of the logarithmic star formation rate (SFR) - stellar mass ($M_*$) relation, called the main sequence of star-forming galaxies (MS), for galaxies out to $z\sim5$. We measure the MS using mean stacks of 3 GHz radio continuum images to derive average SFRs for $\sim$200,000 mass-selected galaxies at $

T. Heuser, M. Pflüger, I. Fischer, J. A. Lott

Brain-inspired computing concepts like artificial neural networks have become promising alternatives to classical von Neumann computer architectures. Photonic neural networks target the realizations of neurons, network connections and potentially learning in photonic substrates. Here, we report the development of a nanophotonic hardware platform of fast and

Anna Fino, Alberto Raffero

We review recent results concerning closed G$_2$-structures on seven-dimensional manifolds. In particular, we discuss the construction of examples and some related problems.

João Paulo dos Santos, Ruy Tojeiro

In this article we classify the conformally flat Euclidean hypersurfaces of dimension three with three distinct principal curvatures of $\mathbb{R}^4$, $\mathbb{S}^3\times \mathbb{R}$ and $\mathbb{H}^3\times \mathbb{R}$ with the property that the tangent component of the vector field $\partial/\partial t$ is a principal direction at any point. Here $\partial

E. J. Kolmes, V. I. Geyko, N. J. Fisch

When a gas in an externally imposed potential field is compressed, temperature gradients appear. This has been called the piezothermal effect. It is possible to analytically calculate the time-dependent behavior of the piezothermal effect using a linearized fluid model. Quantitative differences between the fluid-model results and previous numerical calculati

Liane Bernstein, Alexander Sludds, Ryan Hamerly, Vivienne Sze

As deep neural network (DNN) models grow ever-larger, they can achieve higher accuracy and solve more complex problems. This trend has been enabled by an increase in available compute power; however, efforts to continue to scale electronic processors are impeded by the costs of communication, thermal management, power delivery and clocking. To improve scalab

J Soria, Y Chen, A Stathopoulos

Shared mobility-on-demand services are expanding rapidly in cities around the world. As a prominent example, app-based ridesourcing is becoming an integral part of many urban transportation ecosystems. Despite the centrality, limited public availability of detailed temporal and spatial data on ridesourcing trips has limited research on how new services inter

Hsun Lee, Hsu-Chun Hsiao

Existing verifiable e-sortition systems are impractical due to computationally expensive verification (linear to the duration of the registration phase, T) or the ease of being denial of service. Based on the advance in verifiable delay functions, we propose a verifiable e-sortition scheme whose result can be efficiently verified in constant time with respec

Aayush Bansal

We present a case-study to improve the task-specific representation by leveraging a million unlabelled images without any extra knowledge. We propose an exceedingly simple method of conditioning an existing representation on a diverse data distribution and observe that a model trained on diverse examples acts as a better initialization. We extensively study

Nikolaos K. Kollas, Dimitris Moustos, Kostas Blekos

We study the effects of motion for an Unruh-DeWitt detector, modeled as a two-level system, on the amount of coherence extracted, when it interacts with a massless scalar coherent field in 1+1 Minkowski spacetime. We observe that compared to a detector at rest, for certain values of the initial energy of the field and the interaction duration, the amount is

C. Freiheit, D. M. Anand, H. R. Ossareh

This paper presents a novel reference governor scheme for overshoot mitigation in tracking control systems. Our proposed scheme, referred to as the Reference Governor with Dynamic Constraint (RG-DC), recasts the overshoot mitigation problem as a constraint management problem. The outcome of this reformulation is a dynamic Maximal Admissible Set (MAS), which

A. Naveena Kumara, C. L. Ahmed Rizwan, Kartheek Hegde, Md Sabir Ali

In this article, we analytically investigate the microstructure of a five-dimensional neutral Gauss-Bonnet black hole, in the background of anti-de Sitter spacetime, using scalar curvature of the Ruppeiner geometry constructed via adiabatic compressibility. The microstructure details associated with the small-large black hole phase transition are probed in t

Shuaihang Yuan, Xiang Li, Anthony Tzes, Yi Fang

In this paper, we deal with the problem to predict the future 3D motions of 3D object scans from previous two consecutive frames. Previous methods mostly focus on sparse motion prediction in the form of skeletons. While in this paper we focus on predicting dense 3D motions in the from of 3D point clouds. To approach this problem, we propose a self-supervised

Michele Delli Veneri, Louis Desdoigts, Morgan A. Schmitz, Alberto Krone-Martins

Astrometric detection involves a precise measurement of stellar positions, and is widely regarded as the leading concept presently ready to find earth-mass planets in temperate orbits around nearby sun-like stars. The TOLIMAN space telescope[39] is a low-cost, agile mission concept dedicated to narrow-angle astrometric monitoring of bright binary stars. In p

Gabriel M. Lando

Using phase-space complexification, an Initial Value Representation (IVR) for the semiclassical propagator in position space is obtained as a composition of inverse Segal-Bargmann (S-B) transforms of the semiclassical coherent state propagator. The result is shown to be free of caustic singularities and identical to the Herman-Kluk (H-K) propagator, found ub

Benjamin C. Bromley, Scott J. Kenyon

We consider a scenario where the small satellites of Pluto and Charon grew within a disk of debris from an impact between Charon and a trans-Neptunian Object (TNO). After Charon&#39;s orbital motion boosts the debris into a disk-like structure, rapid orbital damping of meter-size or smaller objects is essential to prevent the subsequent re-accretion or dynam

Yefim Shulman, Thao Ngo, Joachim Meyer

Focusing on personal information disclosure, we apply control theory and the notion of the Order of Control to study people&#39;s understanding of the implications of information disclosure and their tendency to consent to disclosure. We analyzed the relevant literature and conducted a preliminary online study (N = 220) to explore the relationship between th