June 2020 arXiv papers — page 23

Jakub K. Sowa, Jan A. Mol. Erik M. Gauger

Thermoelectric energy conversion is perhaps the most promising of the potential applications of molecular electronics. Ultimately, it is desirable for this technology to operate at around room temperature, and it is therefore important to consider the role of dissipative effects in these conditions. Here, we develop a theory of thermoelectricity which accoun

Dan P. Clemens, L. R. Cashman, C. Cerny, A. M. El-Batal

The Galactic Plane Infrared Polarization Survey (GPIPS) seeks to characterize the magnetic field in the dusty Galactic disk using near-infrared stellar polarimetry. All GPIPS observations were completed using the 1.83 m Perkins telescope and Mimir instrument. GPIPS observations surveyed 76 sq-deg of the northern Galactic plane, from Galactic longitudes 18 to

Anya Katsevich, Afonso Bandeira

We derive an asymptotic expansion for the log likelihood of Gaussian mixture models (GMMs) with equal covariance matrices in the low signal-to-noise regime. The expansion reveals an intimate connection between two types of algorithms for parameter estimation: the method of moments and likelihood optimizing algorithms such as Expectation-Maximization (EM). We

Komal Agrawal, Paul Pollack

Fix $a \in \mathbb{Z}$, $a\notin \{0,\pm 1\}$. A simple argument shows that for each $ε> 0$, and almost all (asymptotically 100% of) primes $p$, the multiplicative order of $a$ modulo $p$ exceeds $p^{\frac12-ε}$. It is an open problem to show the same result with $\frac12$ replaced by any larger constant. We show that if $a,b$ are multiplicatively independen

Rasool Fakoor, Pratik Chaudhari, Alexander J. Smola

This paper prescribes a suite of techniques for off-policy Reinforcement Learning (RL) that simplify the training process and reduce the sample complexity. First, we show that simple Deterministic Policy Gradient works remarkably well as long as the overestimation bias is controlled. This is contrast to existing literature which creates sophisticated off-pol

Rogemar A. Riffel, Thaisa Storchi-Bergmann, Nadia L. Zakamska, Rogerio Riffel

The role of feedback from Active Galactic Nuclei (AGN) in the evolution of galaxies is still not not fully understood, mostly due to the lack of observational constraints in the multi-phase gas kinematics on the ten to hundred parsec scales. We have used the Gemini Near-infrared Integral Field Spectrograph (NIFS) to map the molecular and ionized gas kinemati

Djallel Bouneffouf

We consider a novel variant of the contextual bandit problem (i.e., the multi-armed bandit with side-information, or context, available to a decision-maker) where the context used at each decision may be corrupted ("useless context"). This new problem is motivated by certain on-line settings including clinical trial and ad recommendation applications

Ruijie Yang

In this paper, we study the $L^2$-minimal extension problem for polarized variations of Hodge structures over Hermitian symmetric domains. We are able to explicitly find the $L^2$-minimal extensions using a group-theoretic construction. In particular, this gives a construction without using $L^2$-estimates as in the Ohsawa-Takegoshi type extension theorems.

Maximilian J. Grill, Jonathan Kernes, Valentin M. Slepukhin, Wolfgang A. Wall

We consider the propagation of tension along specific filament of a semiflexible filament network in response to the application of a point force using a combination of numerical simulations and analytic theory. We find the distribution of force within the network is highly heterogenous, with a small number of fibers supporting a significant fraction of the

Christopher Snyder, Sriram Vishwanath

The ability of deep learning (DL) to improve the practice of medicine and its clinical outcomes faces a looming obstacle: model interpretation. Without description of how outputs are generated, a collaborating physician can neither resolve when the model's conclusions are in conflict with his or her own, nor learn to anticipate model behavior. Current re

Phillip M Bressie, David N Yetter

We provide the expected constructions of weakly $ω$-categorified models (in the sense of Bressie) of the theory of groups and quandles which arise by replacing the homotopies used to give equivalence relations in the theory of fundamental groups, fundamental quandles, and knot quandles with homotopies of all orders used as arrows of categorical dimensions on

Min Zhang, Weizhang Huang, Jianxian Qiu

A rezoning-type adaptive moving mesh discontinuous Galerkin method is proposed for the numerical solution of the shallow water equations with non-flat bottom topography. The well-balance property is crucial to the simulation of perturbation waves over the lake-at-rest steady state such as waves on a lake or tsunami waves in the deep ocean. To ensure the well

Subhradeep Kayal, Shuai Chen, Marleen de Bruijne

Self-supervised learning has proven to be invaluable in making best use of all of the available data in biomedical image segmentation. One particularly simple and effective mechanism to achieve self-supervision is inpainting, the task of predicting arbitrary missing areas based on the rest of an image. In this work, we focus on image inpainting as the self-s

Saheli Mitra, Giuseppe Foffi, Anshul D. S. Parmar, Premkumar Leishangthem

We present a numerical investigation of the density fluctuations in a model glass under cyclic shear deformation. At low amplitude of shear, below yielding, the system reaches a steady absorbing state in which density fluctuations are suppressed revealing a clear fingerprint of hyperuniformity up to a finite length scale. The opposite scenario is observed ab

Ehsan Siavashi, Mahshid Rahnamay-Naeini

Components connected over a network influence each other and interact in various ways. Examples of such systems are networks of computing nodes, which the nodes interact by exchanging workload, for instance, for load balancing purposes. In this paper, we first study the Influence Model, a networked Markov chain framework, for modeling network interactions an

Gernot Akemann, Friedrich Götze, Thorsten Neuschel

We compute the average characteristic polynomial of the hermitised product of $M$ real or complex Wigner matrices of size $N\times N$ and the average of the characteristic polynomial of a product of $M$ such Wigner matrices times the characteristic polynomial of the conjugate matrix. Surprisingly, the results agree with that of the product of $M$ real or com

Nils Clausen

Based on observations it seems that a considerable proportion of university students in Namibia need to enhance their academic performance in regard to be fully competitive in a globalised working environment. This specifically includes thorough understanding of mathematics, as it forms an integral part of modern knowledge disciplines such as information tec

Sainath G, Vignesh S, Siddarth S, G Suganya

With the onset of Electric vehicles, and them becoming more and more popular, autonomous cars are the future in the travel/driving experience. The barrier to reaching level 5 autonomy is the difficulty in the collection of data that incorporates good driving habits and the lack thereof. The problem with current implementations of self-driving cars is the nee

Noah Olander

We extend Orlov's result that certain functors between derived categories of smooth projective varieties are Fourier--Mukai transforms to the case when those varieties are smooth and proper.

V. B. Gildenburg, I. A. Pavlichenko

A computer simulation of dynamics of an optical discharge produced in the volume of a transparent dielectric (fused silica) by a focused femtosecond laser pulse was carried out taking into account the possibility of developing small-scale ionization-field instability. The presence of small foreign inclusions in the fused silica was taken into account with th

Mayee F. Chen, Daniel Y. Fu, Frederic Sala, Sen Wu

Our goal is to enable machine learning systems to be trained interactively. This requires models that perform well and train quickly, without large amounts of hand-labeled data. We take a step forward in this direction by borrowing from weak supervision (WS), wherein models can be trained with noisy sources of signal instead of hand-labeled data. But WS reli

Mayank Jain, Shilpa Manandhar, Yee Hui Lee, Stefan Winkler

Long-Short-Term-Memory (LSTM) networks have been used extensively for time series forecasting in recent years due to their ability of learning patterns over different periods of time. In this paper, this ability is applied to learning the pattern of Global Positioning System (GPS)-based Precipitable Water Vapor (PWV) measurements over a period of 4 hours. Th

Hector A. Barriga-Acosta, Paul M. Gartside

Roitman's combinatorial principle $Δ$ is equivalent to monotone normality of the nabla product, $\nabla (ω+1)^ω$. If $\{ X_n : n\in ω\}$ is a family of metrizable spaces and $\nabla_n X_n$ is monotonically normal, then $\nabla_n X_n$ is hereditarily paracompact. Hence, if $Δ$ holds then the box product $\square (ω+1)^ω$ is paracompact. Large fragments of

Vikram Singh, Vikram Singh

Originating in Wuhan, the novel coronavirus, severe acute respiratory syndrome 2 (SARS-CoV-2), has astonished health-care systems across globe due to its rapid and simultaneous spread to the neighboring and distantly located countries. To gain the systems level understanding of the role of global transmission routes in the COVID-19 spread, in this study, we

Michael Winer, Shao-Kai Jian, Brian Swingle

A long period of linear growth in the spectral form factor provides a universal diagnostic of quantum chaos at intermediate times. By contrast, the behavior of the spectral form factor in disordered integrable many-body models is not well understood. Here we study the two-body Sachdev-Ye-Kitaev model and show that the spectral form factor features an exponen

Vivienne F. Baldassare, Claire Dickey, Marla Geha, Amy E. Reines

We present high resolution spectroscopy taken with the Keck Echellete Spectrograph and Imager to measure stellar velocity dispersions for eight active dwarf galaxies ($M_{\ast}<3\times10^{9}~M_{\odot}$) with virial black hole masses. We double the number of systems in this stellar mass regime with measurements of both black hole mass ($M_{\rm BH}$) and stell

Bert Vandenbroucke, Peter Camps

(Context) Monte Carlo radiative transfer (MCRT) is a widely used technique to model the interaction between radiation and a medium, and plays an important role in astrophysical modelling and when comparing those models with observations. (Aims) In this work, we present a novel approach to MCRT that addresses the challenging memory access patterns of traditio

Ambra Nanni, Denis Burgarella, Patrice Theulé, Benoit Côté

The chemical enrichment in the interstellar medium (ISM) of galaxies is regulated by several physical processes: stellar evolution, grain formation and destruction, galactic inflows and outflows. Understanding such processes is essential to follow the chemical enrichment of galaxies through the cosmic epochs, and to interpret the observations. Despite the im

D. N. Hosking, A. A. Schekochihin, S. A. Balbus

The fundamental difference between incompressible ideal magnetohydrodynamics and the dynamics of a non-conducting fluid is that magnetic fields exert a tension force that opposes their bending; magnetic fields behave like elastic strings threading the fluid. It is natural, therefore, to expect that a magnetic field tangled at small length scales should resis

David McKeen, Maxim Pospelov, Nirmal Raj

We show that in a special class of dark sector models, the hydrogen atom can serve as a portal to new physics, through its decay occurring in abundant populations in the Sun and on Earth. The large fluxes of hydrogen decay daughter states can be detected via their decay or scattering. By constructing two models for either detection channel, we show that the

Johannes Hoja, Leonardo Medrano Sandonas, Brian G. Ernst, Alvaro Vazquez-Mayagoitia

We introduce QM7-X, a comprehensive dataset of 42 physicochemical properties for $\approx$ 4.2 M equilibrium and non-equilibrium structures of small organic molecules with up to seven non-hydrogen (C, N, O, S, Cl) atoms. To span this fundamentally important region of chemical compound space (CCS), QM7-X includes an exhaustive sampling of (meta-)stable equili

Éric Brunet, Anh Dung Le, Alfred H. Mueller, Stéphane Munier

In a branching process, the number of particles increases exponentially with time, which makes numerical simulations for large times difficult. In many applications, however, only the region close to the extremal particles is relevant (the &#34;tip&#34;). We present a simple algorithm which allows to simulate a branching random walk in one dimension, keeping

Ivan Bacher, Hossein Javidnia, Soumyabrata Dev, Rahul Agrahari

Over the past decade, the evolution of video-sharing platforms has attracted a significant amount of investments on contextual advertising. The common contextual advertising platforms utilize the information provided by users to integrate 2D visual ads into videos. The existing platforms face many technical challenges such as ad integration with respect to o

Nirvana Caballero, Elisabeth Agoritsas, Vivien Lecomte, Thierry Giamarchi

Controlling interfaces is highly relevant from a technological point of view. However, their rich and complex behavior makes them very difficult to describe theoretically, and hence to predict. In this work, we establish a procedure to connect two levels of descriptions of interfaces: for a bulk description, we consider a two-dimensional Ginzburg-Landau mode

Laszlo Palla

An N=2 SUSY extension of the Schrödinger symmetry is shown to exist in the solution space of the free planar Lévy-Leblond equation, an N=1 part of which survives for the gauged version of the equation and also when it is coupled to Chern-Simons theory.

John S. Antrobus, Yusuke Shono, Wolfgang M. Pauli, Bala Sundaram

Recognition of every word is accomplished by close collaboration of bottom-up sub-word and word recognition neural networks with top-down cognitive word context expectations. The utility of this context appropriate collaboration is substantial savings in recognition time, accuracy and cortical neural processing resources. Repetition priming, the simplest for

Amy M. Gottlieb, Stephen S. Eikenberry, Kendall Ackley, Curtis DeWitt

We analyzed multiwavelength observations of the previously identified Galactic center X-ray binary CXO 174528.79-290942.8 (XID 6592) and determine that the near-infrared counterpart is a red supergiant based on its spectrum and luminosity. Scutum X-1 is the only previously known X-ray binary with a red supergiant donor star and closely resembles XID 6592 in

Weibin Mo, Zhengling Qi, Yufeng Liu

Recent development in the data-driven decision science has seen great advances in individualized decision making. Given data with individual covariates, treatment assignments and outcomes, policy makers best individualized treatment rule (ITR) that maximizes the expected outcome, known as the value function. Many existing methods assume that the training and

Thaddeus D. Komacek, Daniel R. Chavas, Dorian S. Abbot

Hurricanes are one of the most extreme storm systems that occur on Earth, characterized by strong rainfall and fast winds. The terrestrial exoplanets that will be characterized with future infrared space telescopes orbit M dwarf stars. As a result, the best observable terrestrial exoplanets have vastly different climates than Earth, with a large dayside-to-n

Iván Lombardero, Mario Ochoa, Naoya Miyashita, Yoshitaka Okada

Solar cells manufactured on top of Ge substrates suffer from inherent drawbacks that hinder or limit their potential. The most deleterious ones are heavy weight, high bulk recombination, lack of photon confinement and an increase of the heat absorption. The use of thinned Ge substrates is herein proposed as a possible solution to the aforementioned challenge

Kasper Van Gasse, Zaijun Chen, Edoardo Vicentini, Jeonghyun Huh

Frequency combs, spectra of evenly-spaced narrow phase-coherent laser lines, have revolutionized precision measurements. On-chip frequency comb generators hold much promise for fully-integrated instruments of time and frequency metrology. While outstanding developments are being reported with Kerr, quantum cascade and microring electro-optic combs, the field

Danijela Marković, Julie Grollier

Quantum neuromorphic computing physically implements neural networks in brain-inspired quantum hardware to speed up their computation. In this perspective article, we show that this emerging paradigm could make the best use of the existing and near future intermediate size quantum computers. Some approaches are based on parametrized quantum circuits, and use

Daniel Graves, Nhat M. Nguyen, Kimia Hassanzadeh, Jun Jin

Reinforcement learning using a novel predictive representation is applied to autonomous driving to accomplish the task of driving between lane markings where substantial benefits in performance and generalization are observed on unseen test roads in both simulation and on a real Jackal robot. The novel predictive representation is learned by general value fu

Sandesh Bharadwaj, Kunal Chanda

Gait recognition is a biometric technology that identifies individuals in a video sequence by analysing their style of walking or limb movement. However, this identification is generally sensitive to appearance changes and conventional feature descriptors such as Gait Energy Image (GEI) lose some of the dynamic information in the gait sequence. Active Energy

Avery Meiksin, Piero Madau

Allowing for enhanced Ly$α$ photon line emission from Population III dominated stellar systems in the first forming galaxies, we show the 21-cm cosmic dawn signal at $10<z<30$ may substantially differ from standard scenarios. Energy transfer by Ly$α$ photons emerging from galaxies may heat intergalactic gas if HII regions within galaxies are recombination bo

Nandan Kumar Jha, Shreyas Ravishankar, Sparsh Mittal, Arvind Kaushik

The number of processing elements (PEs) in a fixed-sized systolic accelerator is well matched for large and compute-bound DNNs; whereas, memory-bound DNNs suffer from PE underutilization and fail to achieve peak performance and energy efficiency. To mitigate this, specialized dataflow and/or micro-architectural techniques have been proposed. However, due to

Rajat Saini, Nandan Kumar Jha, Bedanta Das, Sparsh Mittal

The capability of the self-attention mechanism to model the long-range dependencies has catapulted its deployment in vision models. Unlike convolution operators, self-attention offers infinite receptive field and enables compute-efficient modeling of global dependencies. However, the existing state-of-the-art attention mechanisms incur high compute and/or pa

Nandan Kumar Jha, Rajat Saini, Subhrajit Nag, Sparsh Mittal

The number of groups ($g$) in group convolution (GConv) is selected to boost the predictive performance of deep neural networks (DNNs) in a compute and parameter efficient manner. However, we show that naive selection of $g$ in GConv creates an imbalance between the computational complexity and degree of data reuse, which leads to suboptimal energy efficienc

Jakob Jordan, João Sacramento, Mihai A. Petrovici, Walter Senn

Cue integration, the combination of different sources of information to reduce uncertainty, is a fundamental computational principle of brain function. Starting from a normative model we show that the dynamics of multi-compartment neurons with conductance-based dendrites naturally implement the required probabilistic computations. The associated error-driven

Nandan Kumar Jha, Sparsh Mittal, Govardhan Mattela

The recent trend in deep neural networks (DNNs) research is to make the networks more compact. The motivation behind designing compact DNNs is to improve energy efficiency since by virtue of having lower memory footprint, compact DNNs have lower number of off-chip accesses which improves energy efficiency. However, we show that making DNNs compact has indire

Kwei-Herng Lai, Daochen Zha, Kaixiong Zhou, Xia Hu

Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information from neighbors with stackable network modules. Motivated by the observation that different nodes often requir

V. Paziy, L. M. Fraile, H. Mach, B. Olaizola

The $β^{-}$ decay of $^{81}$Zn to the neutron magic $N=50$ nucleus $^{81}$Ga, with only three valence protons with respect to $^{78}$Ni, was investigated. The study was performed at the ISOLDE facility at CERN by means of $γ$ spectroscopy. The $^{81}$Zn half-life was determined to be $T_{1/2}=290(4)$ ms while the $β$-delayed neutron emission probability was

Mimi Dai

We derive dyadic models for the magnetohydrodynamics with Hall effect by including the intermittency dimension as a parameter. For such dyadic models, existence of global weak solutions is established. In addition, local strong solution is obtained; while global strong solution is obtained in the case of high intermittency dimension. Moreover, we show that p

Bhuvanesh Sundar

We present a protocol to experimentally measure the infinite-temperature out-of-time-ordered correlation (OTOC) -- which is a probe of quantum information scrambling in a system -- for systems with a Hamiltonian which has either a chiral symmetry or a particle-hole symmetry. We show that the OTOC can be obtained by preparing two entangled systems, evolving t

Parthapratim Pradhan

We compute area (or entropy) product formula for Newman-Unti-Tamburino (NUT) class of black holes. Specifically, we derive the area product of outer horizon and inner horizon (${ \mathcal{H}}^{\pm }$) for Taub-NUT, Euclidean Taub-NUT black hole, Reissner-Nordström--Taub-NUT black hole, Kerr-Taub-NUT black hole and Kerr-Newman-Taub-NUT black hole under the fo

Esther M. Arkin, Rathish Das, Jie Gao, Mayank Goswami

Motivated by indoor localization by tripwire lasers, we study the problem of cutting a polygon into small-size pieces, using the chords of the polygon. Several versions are considered, depending on the definition of the &#34;size&#34; of a piece. In particular, we consider the area, the diameter, and the radius of the largest inscribed circle as a measure of

Mingyuan Jiu, Hichem Sahbi

Deep kernel map networks have shown excellent performances in various classification problems including image annotation. Their general recipe consists in aggregating several layers of singular value decompositions (SVDs) -- that map data from input spaces into high dimensional spaces -- while preserving the similarity of the underlying kernels. However, the

Khimya Khetarpal, Zafarali Ahmed, Gheorghe Comanici, David Abel

Reinforcement learning algorithms usually assume that all actions are always available to an agent. However, both people and animals understand the general link between the features of their environment and the actions that are feasible. Gibson (1977) coined the term &#34;affordances&#34; to describe the fact that certain states enable an agent to do certain

Daniele Spinoso, Alvaro Orsi, Carlos López-Sanjuan, Silvia Bonoli

We present the photometric determination of the bright-end (L_Lya>10^43.5 erg/s) of the Lya luminosity function (LF) within four redshifts windows in the interval 2.2<z<3.3. Our work is based on the Javalambre Photometric Local Universe Survey (J-PLUS) first data-release, which provides multiple narrow-band measurements over ~1000 deg^2, with limiting magnit

Ryan Goh, Björn de Rijk

We consider pattern-forming fronts in the complex Ginzburg-Landau equation with a traveling spatial heterogeneity which destabilizes, or quenches, the trivial ground state while progressing through the domain. We consider the regime where the heterogeneity propagates with speed $c$ just below the linear invasion speed of the pattern-forming front in the asso

Hideyuki Mizuno, Hua Tong, Atsushi Ikeda, Stefano Mossa

It is a persistent problem in condensed matter physics that glasses exhibit vibrational and thermal properties that are markedly different from those of crystals. While recent works have advanced our understanding of vibrational excitations in glasses at the harmonic approximation limit, efforts in understanding finite-temperature anharmonic processes have b

Samuel L. Smith, Erich Elsen, Soham De

It has long been argued that minibatch stochastic gradient descent can generalize better than large batch gradient descent in deep neural networks. However recent papers have questioned this claim, arguing that this effect is simply a consequence of suboptimal hyperparameter tuning or insufficient compute budgets when the batch size is large. In this paper,

Jonathan J. Carter, Sam J. Cooper, Edward Thrift, Joseph Briggs

Reducing the impact of seismic activity on the motion of suspended optics is essential for the operation of ground-based gravitational wave detectors. During periods of increased seismic activity, low-frequency ground translation and tilt cause the Advanced LIGO observatories to lose `lock&#39;, reducing their duty cycles. This paper applies modern global-op

Xu He, Min Lin

Connectionist models such as neural networks suffer from catastrophic forgetting. In this work, we study this problem from the perspective of information theory and define forgetting as the increase of description lengths of previous data when they are compressed with a sequentially learned model. In addition, we show that continual learning approaches based

Jonathan von Schroeder

This paper discusses an approach, based on the subsampling boostrap and FDR control, to improve the stability of feature selection. It furthermore presents the finite sample distribution of the correlation coefficient recently proposed by Chatterjee (2020) under the setting relevant for this paper. Finally an application to matrix-assisted laser desorption/i

Michele Pizzochero, Kristiāns Čerņevičs, Gabriela Borin Barin, Shiyong Wang

On-surface synthesis has recently emerged as an effective route towards the atomically precise fabrication of graphene nanoribbons of controlled topologies and widths. However, whether and to which degree structural disorder occurs in the resulting samples is a crucial issue for prospective applications that remains to be explored. Here, we experimentally id

Afsah Anwar, Ahmed Abusnaina, Songqing Chen, Frank Li

Vulnerability databases are vital sources of information on emergent software security concerns. Security professionals, from system administrators to developers to researchers, heavily depend on these databases to track vulnerabilities and analyze security trends. How reliable and accurate are these databases though? In this paper, we explore this question

Vesselin Drensky

Let $A_1,\ldots,A_s$ be unitary commutative rings which do not have non-trivial idempotents and let $A=A_1\oplus\cdots\oplus A_s$ be their direct sum. We describe all idempotents in the $2\times 2$ matrix ring $M_2(A[[X]])$ over the ring $A[[X]]$ of formal power series with coefficients in $A$ and in arbitrary set of variables $X$. We apply this result to th

Uchenna Chukwu, Raouf Dridi, Jesse Berwald, Michael Booth

Graph partitioning is one of an important set of well-known compute-intense (NP-hard) graph problems that devolve to discrete constrained optimization. We sampled solutions to the problem via two different quantum-ready methods to understand the pros and cons of each method. First we formulated and sampled the problem as a quadratic unconstrained binary opti

A. Fuente, S. P. Treviño-Morales, R. Le Gal, P. Riviere-Marichalar

A large effort has been made to detect warm gas in the planet formation zone of circumstellar discs using space and ground-based near infrared facilities. GV Tau N, the most obscured component of the GV Tau system, is an outstanding source, being one of the first targets detected in HCN and the only one detected in CH$_4$ so far. Although near infrared obser

Elias B. Frantz, Nicholas J. Harmon Stephen R. McMillan, Stephen J. Moxim, Michael E. Flatte

We report on a method by which we can systematically extract spectroscopic information such as isotropic electron-nuclear hyperfine coupling constants from near-zero field magnetoresistance spectra. The method utilizes a least squares fitting of models developed from the stochastic quantum Liouville equation. We applied our fitting algorithm to two distinct

C. Acha, M. Barella, G. A. Sanca, F. Gomez Marlasca

An YBCO-based test structure corresponding to the family of ReRAM devices associated with the valence change mechanism is presented. We have characterized its electrical response previous to its lift-off to a Low Earth Orbit (LEO) using standard electronics and also with the dedicated LabOSat-01 controller. Similar results were obtained in both cases. After

Natarajan S, Chandrmohan R, Swaminathan R

From the Wheeler-Dewitt solutions, the scale factor of the initial universe is discussed. In this study, scale factors from Wheeler-Dewitt solutions, loop quantum gravity, and phantom energy dominated stages are compared. Certain modifications have been attempted in scale factor and quantum potentials driven by canonical quantum gravity approaches. Their res

Jos Stam

This paper proposes a new equation from continuous adjoint theory to compute the gradient of quantities governed by the Transport Theory of light. Unlike discrete gradients ala autograd, which work at the code level, we first formulate the continuous theory and then discretize it. The key insight of this paper is that computing gradients in Transport Theory

Nafis I Karim, Alexandru Maries, Chandralekha Singh

Instruction is unlikely to be effective if instructors do not know the common alternate conceptions of introductory physics students and explicitly take into account common student difficulties in their instructional design. Here, we discuss research involving the Conceptual Survey of Electricity and Magnetism (CSEM) to evaluate one aspect of the pedagogical

Michael C. Fu, Bingqing Li, Rongwen Wu, Tianqi Zhang

We consider option pricing using a discrete-time Markov switching stochastic volatility with co-jump model, which can model volatility clustering and varying mean-reversion speeds of volatility. For pricing European options, we develop a computationally efficient method for obtaining the probability distribution of average integrated variance (AIV), which is

Alexandru Maries, Chandralekha Singh

Drawing appropriate diagrams is a useful problem solving heuristic that can transform a problem into a representation that is easier to exploit for solving it. One major focus while helping introductory physics students learn effective problem solving is to help them understand that drawing diagrams can facilitate problem solution. We conducted an investigat

Masahiro Yamamoto

We prove the uniqueness in determining both orders of fractional time derivatives and spatial derivatives in diffusion equations by pointwise data. The proof relies on the eigenfunction expansion and the asymptotics of the Mittag-Leffler function.

R. Ridden-Harper, B. E. Tucker, M. Gully-Santiago, G. Barentsen

The K2 mission of the Kepler Space Telescope offers a unique possibility to examine sources of both Galactic and Extra-galactic origin with high cadence photometry. Alongside the multitude of supernovae and quasars detected within targeted galaxies, it is likely that Kepler has serendipitously observed many transients throughout K2. Such events will likely h

Guillaume Ausset, Stephan Clémençon, François Portier

Motivated by a wide variety of applications, ranging from stochastic optimization to dimension reduction through variable selection, the problem of estimating gradients accurately is of crucial importance in statistics and learning theory. We consider here the classic regression setup, where a real valued square integrable r.v. $Y$ is to be predicted upon ob

M. Scourfield, S. Viti, S. Garcia-Burillo, A. Saintonge

We present results from Atacama Large Millimeter/submillimeter Array (ALMA) observations of CS from the nearby galaxy NGC 1068 ($\sim14$ Mpc). This Seyfert 2 barred galaxy possesses a circumnuclear disc (CND, $r\sim200$ pc) and a starburst ring (SB ring, $r\sim1.3$ kpc). These high-resolution maps ($\sim0.5$&#34;, $\sim35$ pc) allow us to analyse specific su

Joby P. Kochappan, Aparajita Sen, Tuhin Ghosh, Pravabati Chingangbam

We test the statistical isotropy (SI) of the $E$-mode polarization of the cosmic microwave background (CMB) radiation observed by the Planck satellite using two statistics, namely, the contour Minkowski Tensor (CMT) and the Directional statistic ($\mathcal{D}$ statistic). The parameter $α$ obtained from the CMT provides information of the alignment of struct

Rahul Singh, Isabel Haasler, Qinsheng Zhang, Johan Karlsson

We consider incremental inference problems from aggregate data for collective dynamics. In particular, we address the problem of estimating the aggregate marginals of a Markov chain from noisy aggregate observations in an incremental (online) fashion. We propose a sliding window Sinkhorn belief propagation (SW-SBP) algorithm that utilizes a sliding window fi

Hasan J. Khan, Ayaz Mehmani, Maša Prodanović, David DiCarlo

Carbonates are highly heterogeneous formations with large variations in pore size distribution and pore space topology, which results in complex multiphase flow behavior. Here we investigate the spontaneous imbibition behavior of fluids in vuggy carbonates. Glass beads of 1.0 mm diameter, with dissolvable inclusions, are sintered to form multiple configurati

Alberto Enciso, Daniel Peralta-Salas, Álvaro Romaniega

In this paper we show that, with probability 1, a random Beltrami field exhibits chaotic regions that coexist with invariant tori of complicated topologies. The motivation to consider this question, which arises in the study of stationary Euler flows in dimension 3, is V.I. Arnold&#39;s 1965 conjecture that a typical Beltrami field exhibits the same complexi

Stephan J. Garbin, Marek Kowalski, Matthew Johnson, Jamie Shotton

Generating photorealistic images of human faces at scale remains a prohibitively difficult task using computer graphics approaches. This is because these require the simulation of light to be photorealistic, which in turn requires physically accurate modelling of geometry, materials, and light sources, for both the head and the surrounding scene. Non-photore

Hemani Chhabra, Garima Mishra, Yijing Cao, Domen Prešern

To study the elastic properties of rod-like DNA nanostructures, we perform long simulations of these structure using the oxDNA coarse-grained model. By analysing the fluctuations in these trajectories we obtain estimates of the bend and twist persistence lengths, and the underlying bend and twist elastic moduli and couplings between them. Only on length scal

Tomás E. Müller-Bravo, Claudia P. Gutiérrez, Mark Sullivan, Anders Jerkstrand

Low-luminosity type II supernovae (LL SNe~II) make up the low explosion energy end of core-collapse SNe, but their study and physical understanding remain limited. We present SN\,2016aqf, a LL SN~II with extensive spectral and photometric coverage. We measure a $V$-band peak magnitude of $-14.58$\,mag, a plateau duration of $\sim$100\,days, and an inferred $

Sonia Peggiani, Anna Facibeni, Alberto Milani, Chiara Castiglioni

Polyynes are finite chains formed by sp-hybridized carbon atoms with alternating single and triple bonds and displaying intriguing electronic and optical properties. Pulsed laser ablation in liquid (PLAL) is a well assessed technique for the physical synthesis of hydrogen-capped polyynes in solution, however, their limited stability prevents further exploita

Massimo Cairo, Shahbaz Khan, Romeo Rizzi, Sebastian Schmidt

Given a directed graph $G$ and a pair of nodes $s$ and $t$, an $s$-$t$ bridge of $G$ is an edge whose removal breaks all $s$-$t$ paths of $G$. Similarly, an $s$-$t$ articulation point of $G$ is a node whose removal breaks all $s$-$t$ paths of $G$. Computing the sequence of all $s$-$t$ bridges of $G$ (as well as the $s$-$t$ articulation points) is a basic gra

Mike Lewis, Marjan Ghazvininejad, Gargi Ghosh, Armen Aghajanyan

We introduce MARGE, a pre-trained sequence-to-sequence model learned with an unsupervised multi-lingual multi-document paraphrasing objective. MARGE provides an alternative to the dominant masked language modeling paradigm, where we self-supervise the reconstruction of target text by retrieving a set of related texts (in many languages) and conditioning on t

Mitchell E. Young, Luca Fossati, Tommi T. Koskinen, Michael Salz

Context - Exoplanetary upper atmospheres are low density environments where radiative processes can compete with collisional ones and introduce non-local thermodynamic equilibrium (NLTE) effects into transmission spectra. Aims - We develop a NLTE radiative transfer framework capable of modelling exoplanetary transmission spectra over a wide range of planetar

Lincan Fang, Esko Makkonen, Milica Todorovic, Patrick Rinke

Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cyst

Zhan Gao, Mark Eisen, Alejandro Ribeiro

This paper investigates the optimal resource allocation in free space optical (FSO) fronthaul networks. The optimal allocation maximizes an average weighted sum-capacity subject to power limitation and data congestion constraints. Both adaptive power assignment and node selection are considered based on the instantaneous channel state information (CSI) of th

Nadia Milazzo, Daniel Braun, Olivier Giraud

We consider the problem of separability of quantum channels via the Choi matrix representation given by the Choi-Jamiołkowski isomorphism. We explore three classes of separability across different cuts between systems and ancillae and we provide a solution based on the mapping of the coordinates of the Choi state (in a fixed basis) to a truncated moment sequ

Mohammad Amirabbasi, Mojtaba Alaei

There are two discrepant experimental results on the magnetic ground state of Y$_{2}$Mn$_{2}$O$_{7}$, one study proposes a spin glass state, while another introduces the material as a ferromagnet. In this study, we attempt to resolve this issue by employing density functional theory and Monte Carlo simulations. We derive different spin models by varying the

Moein Borghei, Mona Ghassemi

The aviation industry aims to reduce CO2 emission by reducing energy consumption and benefitting from more electrical systems than those based on fossil fuels. The more electric aircraft (MEA) can take advantage of the drives based on wide bandgap (WBG) based power modules that are lighter and can bear higher voltages and currents. However, the fast-rise and

Francesco Belardinelli, Catalin Dima, Vadim Malvone, Ferucio Tiplea

We show that a history-based variant of alternating bisimulation with imperfect information allows it to be related to a variant of Alternating-time Temporal Logic (ATL) with imperfect information by a full Hennessy-Milner theorem. The variant of ATL we consider has a common knowledge semantics, which requires that the uniform strategy available for a coalit

Kai Brügge, Asja Fischer, Christian Igel

The Metropolis algorithm is arguably the most fundamental Markov chain Monte Carlo (MCMC) method. But the algorithm is not guaranteed to converge to the desired distribution in the case of multivariate binary distributions (e.g., Ising models or stochastic neural networks such as Boltzmann machines) if the variables (sites or neurons) are updated in a fixed

Qingliang Fan, Yaqian Wu

Instrumental variables (IV) regression is a popular method for the estimation of the endogenous treatment effects. Conventional IV methods require all the instruments are relevant and valid. However, this is impractical especially in high-dimensional models when we consider a large set of candidate IVs. In this paper, we propose an IV estimator robust to the