November 2020 arXiv papers — page 48

Godwin Enemali, Rui Zhang, Hugh McCann, Chang Liu

Chemical Species Tomography (CST) has been widely applied for imaging of critical gas-phase parameters in industrial processes. To acquire high-fidelity images, CST is typically implemented by line-of-sight Wavelength Modulation Spectroscopy (WMS) measurements from multiple laser beams. The modulated transmission signal on each laser beam needs to be a) digi

Alireza Zareian, Kevin Dela Rosa, Derek Hao Hu, Shih-Fu Chang

Despite the remarkable accuracy of deep neural networks in object detection, they are costly to train and scale due to supervision requirements. Particularly, learning more object categories typically requires proportionally more bounding box annotations. Weakly supervised and zero-shot learning techniques have been explored to scale object detectors to more

David Haley, David Doty

The thermodynamic binding networks (TBN) model is a tool for studying engineered molecular systems. The TBN model allows one to reason about their behavior through a simplified abstraction that ignores details about molecular composition, focusing on two key determinants of a system's energetics common to any chemical substrate: how many molecular bonds are

J. C. Ndogmo

A new method for the Lie group classification of differential equations is proposed. It is based of the determination of all possible cases of linear dependence of certain indeterminate appearing in the determining equations of symmetries of the equation. The method is simple and systematic and applied to a family of hyperbolic equations. Moreover, as the sa

Cristina Vasconcelos, Hugo Larochelle, Vincent Dumoulin, Nicolas Le Roux

Image pre-processing in the frequency domain has traditionally played a vital role in computer vision and was even part of the standard pipeline in the early days of deep learning. However, with the advent of large datasets, many practitioners concluded that this was unnecessary due to the belief that these priors can be learned from the data itself. Frequen

Anton Xue, Nikolai Matni

We establish data-driven versions of the System Level Synthesis (SLS) parameterization of achievable closed-loop system responses for a linear-time-invariant system over a finite-horizon. Inspired by recent work in data-driven control that leverages tools from behavioral theory, we show that optimization problems over system-responses can be posed using only

Diego Dominici

We study the Dickson polynomials of the (k+1)-th kind over the field of complex numbers. We show that they are a family of co-recursive orthogonal polynomials with respect to a quasi-definite moment functional L_{k}. We find an integral representation for L_{k} and compute explicit expressions for all of its moments.

Johannes Schneider, Rene Abraham, Christian Meske, Jan vom Brocke

Artificial Intelligence (AI) governance regulates the exercise of authority and control over the management of AI. It aims at leveraging AI through effective use of data and minimization of AI-related cost and risk. While topics such as AI governance and AI ethics are thoroughly discussed on a theoretical, philosophical, societal and regulatory level, there

Di Feng, Ali Harakeh, Steven Waslander, Klaus Dietmayer

Capturing uncertainty in object detection is indispensable for safe autonomous driving. In recent years, deep learning has become the de-facto approach for object detection, and many probabilistic object detectors have been proposed. However, there is no summary on uncertainty estimation in deep object detection, and existing methods are not only built with

Junwei Liang

With the advancement in computer vision deep learning, systems now are able to analyze an unprecedented amount of rich visual information from videos to enable applications such as autonomous driving, socially-aware robot assistant and public safety monitoring. Deciphering human behaviors to predict their future paths/trajectories and what they would do from

Amir Atapour-Abarghouei, Andrew Stephen McGough, David Stanley Wall

As cybercriminals scale up their operations to increase their profits or inflict greater harm, we argue that there is an equal need to respond to their threats by scaling up cybersecurity. To achieve this goal, we have to develop a co-productive approach towards data collection and sharing by overcoming the cybersecurity data sharing paradox. This is where w

James Z. Hare, Cesar A. Uribe, Lance Kaplan, Ali Jadbabaie

This paper studies the problem of distributed classification with a network of heterogeneous agents. The agents seek to jointly identify the underlying target class that best describes a sequence of observations. The problem is first abstracted to a hypothesis-testing framework, where we assume that the agents seek to agree on the hypothesis (target class) t

Hotae Lee, Monimoy Bujarbaruah, Francesco Borrelli

"Bubble Ball" is a game built on a 2D physics engine, where a finite set of objects can modify the motion of a bubble-like ball. The objective is to choose the set and the initial configuration of the objects, in order to get the ball to reach a target flag. The presence of obstacles, friction, contact forces and combinatorial object choices make the game ha

F. Fillion-Gourdeau, E. Lorin, S. MacLean

The dynamics of low energy electrons in general static strained graphene surface is modelled mathematically by the Dirac equation in curved space-time. In Cartesian coordinates, a parametrization of the surface can be straightforwardly obtained, but the resulting Dirac equation is intricate for general surface deformations. Two different strategies are intro

Rachel Guo, Lily Xu, Drew Cronin, Francis Okeke

Illegal wildlife poaching is driving the loss of biodiversity. To combat poaching, rangers patrol expansive protected areas for illegal poaching activity. However, rangers often cannot comprehensively search such large parks. Thus, the Protection Assistant for Wildlife Security (PAWS) was introduced as a machine learning approach to help identify the areas w

Shuocheng Guo, Xinwu Qian, Jun Liu

This study presents an innovative solution for powering electric vehicles, named Charging-as-a-Service (CaaS), that concerns the potential large-scale adoption of light-duty electric vehicles (LDEV) in the Mobility-as-a-Service (MaaS) industry. Analogous to the MaaS, the core idea of the CaaS is to dispatch service vehicles (SVs) that carry modular battery u

Ibrahim Yusuf, George Igwegbe, Oluwafemi Azeez

The simplicity and expressiveness of a histogram render it a useful feature in different contexts including deep learning. Although the process of computing a histogram is non-differentiable, researchers have proposed differentiable approximations, which have some limitations. A differentiable histogram that directly approximates the hard-binning operation i

Dhanunjaya Mitta, Soumick Chatterjee, Oliver Speck, Andreas Nürnberger

Segmentation of biomedical images can assist radiologists to make a better diagnosis and take decisions faster by helping in the detection of abnormalities, such as tumors. Manual or semi-automated segmentation, however, can be a time-consuming task. Most deep learning based automated segmentation methods are supervised and rely on manually segmented ground-

Abe Leite, Saúl A. Blanco

The use of automatic grading tools has become nearly ubiquitous in large undergraduate programming courses, and recent work has focused on improving the quality of automatically generated feedback. However, there is a relative lack of data directly comparing student outcomes when receiving computer-generated feedback and human-written feedback. This paper ad

Aparna Khare, Srinivas Parthasarathy, Shiva Sundaram

Emotion recognition is a challenging task due to limited availability of in-the-wild labeled datasets. Self-supervised learning has shown improvements on tasks with limited labeled datasets in domains like speech and natural language. Models such as BERT learn to incorporate context in word embeddings, which translates to improved performance in downstream t

Marcin Bownik, Li-An Daniel Wang

We obtain a differential characterization for the anisotropic Hardy space $H_A^p$ by identifying it with a parabolic Hardy space associated with a general continuous group. This allows $H_A^p$ to be defined using a parabolic differential equation of Calderon and Torchinsky. We also provide a classification of dilations corresponding to equivalent anisotropic

Rewon Child

We present a hierarchical VAE that, for the first time, generates samples quickly while outperforming the PixelCNN in log-likelihood on all natural image benchmarks. We begin by observing that, in theory, VAEs can actually represent autoregressive models, as well as faster, better models if they exist, when made sufficiently deep. Despite this, autoregressiv

R. Bufalo, M. Ghasemkhani, A. Soto

In this paper, we examine the problem of anomalies of the fermionic currents in the context of the very special relativity (VSR). We consider the VSR contributions to the triangle amplitude $\left\langle J_{5}^{\lambda} J^{\mu} J^{\nu} \right\rangle $, which allows the evaluation of the vector and axial Ward identities. Actually, we observe that the VSR nonl

Youngkyu Kim, Karen May Wang, Youngsoo Choi

A classical reduced order model (ROM) for dynamical problems typically involves only the spatial reduction of a given problem. Recently, a novel space-time ROM for linear dynamical problems has been developed, which further reduces the problem size by introducing a temporal reduction in addition to a spatial reduction without much loss in accuracy. The autho

Krunal Shah, Nitish Gupta, Dan Roth

The recent success of machine learning systems on various QA datasets could be interpreted as a significant improvement in models' language understanding abilities. However, using various perturbations, multiple recent works have shown that good performance on a dataset might not indicate performance that correlates well with human's expectations from models

Jamie Scott

We define and develop a homotopy invariant notion for the topological complexity of a map $f:X \to Y$, denoted TC($f$), that interacts with TC($X$) and TC($Y$) in the same way cat($f$) interacts with cat($X$) and cat($Y$). Furthermore, TC($f$) and cat($f$) satisfy the same inequalities as TC($X$) and cat($X$). We compare it to other invariants defined in the

Yoji Yamato

In recent years, utilization of heterogeneous hardware other than small core CPU such as GPU, FPGA or many core CPU is increasing. However, when using heterogeneous hardware, barriers of technical skills such as OpenMP, CUDA and OpenCL are high. Based on that, I have proposed environment-adaptive software that enables automatic conversion, configuration, and

Benaoumeur Bakhti, Gerhard Müller

We present a new method for studying equilibrium properties of interacting fluids in an arbitrary external field. The fluid is composed of monodisperse spherical particles with hard-core repulsion and additional interactions of arbitrary shape and limited range. Our method of analysis is exact in one dimension and provides demonstrably good approximations in

Abolfazl Hashemi, Anish Acharya, Rudrajit Das, Haris Vikalo

In decentralized optimization, it is common algorithmic practice to have nodes interleave (local) gradient descent iterations with gossip (i.e. averaging over the network) steps. Motivated by the training of large-scale machine learning models, it is also increasingly common to require that messages be {\em lossy compressed} versions of the local parameters.

Daniel Beauchamp, Keith M. Chugg

This paper proposes a novel foreground linearization scheme for a high-speed CS-DAC. The technique leverages neural networks (NNs) to derive a LUT that maps the inverse of the DAC transfer characteristic onto the input codes. The algorithm is shown to improve conventional methods by at least 6dB in terms of intermodulation (IM) performance for frequencies up

Maimoonah Ahmed, Ben Cameron

Various models to quantify the reliability of a network have been studied where certain components of the graph may fail at random and the probability that the remaining graph is connected is the proxy for reliability. In this work we introduce a strengthening of one of these models by considering the probability that the remaining graph is not just connecte

Michael Voskoglou

A new fuzzy method is developed using triangular/trapezoidal fuzzy numbers for evaluating a group's mean performance, when qualitative grades instead of numerical scores are used for assessing its members' individual performance. Also, a new technique is developed for solving Linear Programming problems with fuzzy coefficients and everyday life applications

Paulo A. Brandão, João P. Mendonça, S. B. Cavalcanti

We derive simple formulae for the transmittance $T$ and reflectance $R$ of Gaussian-Schell beams incident upon any stratified dielectric structure by using second-order classical coherence theory in the space-frequency picture. The formalism is applied to a particular structure consisting of a double-layer, with balanced gain and loss, satisfying the parity-

Marek Balcerzak, Paolo Leonetti

Let $\mathscr{X}$ be the set of positive real sequences $x=(x_n)$ such that the series $\sum_n x_n$ is divergent. For each $x \in \mathscr{X}$, let $\mathcal{I}_x$ be the collection of all $A\subseteq \mathbf{N}$ such that the subseries $\sum_{n \in A}x_n$ is convergent. Moreover, let $\mathscr{A}$ be the set of sequences $x \in \mathscr{X}$ such that $\lim_

Dominik Hahn, Juan-Diego Urbina, Klaus Richter, Remy Dubertrand

The study of non-linear oscillator chains in classical many-body dynamics has a storied history going back to the seminal work of Fermi, Pasta, Ulam and Tsingou (FPUT). We introduce a new family of such systems which consist of chains of $N$ harmonically coupled particles with the non-linearity introduced by confining the motion of each individual particle t

Ying Fan, Yifei Ming

Balancing exploration and exploitation is crucial in reinforcement learning (RL). In this paper, we study model-based posterior sampling for reinforcement learning (PSRL) in continuous state-action spaces theoretically and empirically. First, we show the first regret bound of PSRL in continuous spaces which is polynomial in the episode length to the best of

Erwin Hernández, Felipe Lepe, Jesus Vellojin

In this work we propose and analyze an abstract parameter dependent model written as a mixed variational formulation based on Volterra integrals of second kind. For the analysis, we consider a suitable adaptation to the classic mixed theory in the Volterra equations setting, and prove the well posedness of the resulting mixed viscoelastic formulation. Error

Hashim A. Hashim, Abdelrahman E. E. Eltoukhy

Navigation solutions suitable for cases when both autonomous robot's pose (\textit{i.e}., attitude and position) and its environment are unknown are in great demand. Simultaneous Localization and Mapping (SLAM) fulfills this need by concurrently mapping the environment and observing robot's pose with respect to the map. This work proposes a nonlinear observe

Manyun Huang, Junbo Zhao, Zhinong Wei, Marco Pau

Hybrid AC/DC distribution systems are becoming a popular means to accommodate the increasing penetration of distributed energy resources and flexible loads. This paper proposes a distributed and robust state estimation (DRSE) method for hybrid AC/DC distribution systems using multiple sources of data. In the proposed distributed implementation framework, a u

Ann-Katrin U. Michel, Felix Donat, Aurelia Siegfried, Olesya Yarema

Germanium telluride (GeTe), a phase-change material, is known to exhibit four different structural phases: three at room temperature (one amorphous and two crystalline, $\alpha$ and $\gamma$) and one at high temperature (crystalline $\beta$). Because transitions between the amorphous and crystalline phases lead to significant changes in material properties (

N. Bart, C. Dangel, P. Zajac, N. Spitzer

Precise control of the properties of semiconductor quantum dots (QDs) is vital for creating novel devices for quantum photonics and advanced opto-electronics. Suitable low QD-density for single QD devices and experiments are challenging to control during epitaxy and are typically found only in limited regions of the wafer. Here, we demonstrate how convention

Ahmed A. Elkamshishy, Chris H. Greene

Resonances in particle transmission through a 1D finite lattice are studied in the presence of a finite number of impurities. Although this is a one-dimensional system that is classically integrable and has no chaos, studying the statistical properties of the spectrum such as the level spacing distribution and the spectral rigidity shows quantum chaos signat

Marc Sabate-Vidales, David Šiška, Lukasz Szpruch

Using a combination of recurrent neural networks and signature methods from the rough paths theory we design efficient algorithms for solving parametric families of path dependent partial differential equations (PPDEs) that arise in pricing and hedging of path-dependent derivatives or from use of non-Markovian model, such as rough volatility models in Jacqui

Quinn E. Minor, Manoj Kaplinghat, Tony H. Chan, Emily Simon

We demonstrate that the perturbations of strongly lensed images by low-mass dark matter subhalos are significantly impacted by the concentration of the perturbing subhalo. For subhalo concentrations expected in $\Lambda$CDM, significant constraints on the concentration can be obtained at HST resolution for subhalos with masses larger than about $10^{10}M_\od

Alessandro Carotenuto, Fedele Lizzi, Mattia Manfredonia, Flavio Mercati

We present a quantization of the functions of spacetime, i.e.\ a map, analog to Weyl map, which reproduces the $\kappa$-Minkowski commutation relations, and it has the desirable properties of mapping square integrable funcions into Hilbert-Schmidt operators, as well as real functions into self-adjoint operators. The map is based on Mellin transform on radial

Quinn E. Minor, Sophia Gad-Nasr, Manoj Kaplinghat, Simona Vegetti

The presence of an invisible substructure has previously been detected in the gravitational lens galaxy SDSSJ0946+1006 through its perturbation of the lensed images. Using flexible models for the main halo and the subhalo perturbation to fit the lensed images, we demonstrate that the subhalo has an extraordinarily high central density and steep density slope

Joshua D. Lothringer, Zafar Rustamkulov, David K. Sing, Neale P. Gibson

A primary goal of exoplanet characterization is to use a planet's current composition to understand how that planet formed. For example, the C/O ratio has long been recognized as carrying important information on the chemistry of volatile species. Refractory elements, like Fe, Mg, and Si, are usually not considered in this conversation because they condense

Zhentian Qian, Kartik Patath, Jie Fu, Jing Xiao

It is often desirable to capture and map semantic information of an environment during simultaneous localization and mapping (SLAM). Such semantic information can enable a robot to better distinguish places with similar low-level geometric and visual features and perform high-level tasks that use semantic information about objects to be manipulated and envir

E. Ponizovskaya Devine, Wayesh Qarony, Ahasan Ahamed, Ahmed S Mayet

Silicon photodiode based CMOS sensors with backside-illumination for 300 to 1000 nm wavelength range were studied. We showed that a single hole in the photodiode increases the optical efficiency of the pixel. In near-infrared wavelengths, the enhancement allows 70% absorption in a 3 microns thick Si. It is 4x better than for the flat pixel. We compared diffe

Glenn Hurlbert, Essak Seddiq

Graph pebbling is a network optimization model for satisfying vertex demands with vertex supplies (called pebbles), with partial loss of pebbles in transit. The pebbling number of a demand in a graph is the smallest number for which every placement of that many supply pebbles satisfies the demand. The Target Conjecture (Herscovici-Hester-Hurlbert, 2009) posi

Sophie Kriz

In this paper, for a finite group, we discuss a method for calculating equivariant homology with constant coefficients. We apply it to completely calculate the geometric fixed points of the equivariant spectrum representing equivariant (co)homology with constant coefficients. We also treat a more complicated example of inverting the standard representation i

Antonio Ricardo Martines

In this work it is presented an exact power series formula for the Kallen-Sabry vacuum polarization potential.

R. D. B. Fontana, P. A. González, Eleftherios Papantonopoulos, Yerko Vásquez

The anomalous decay rate of the quasinormal modes occurs when the longest-lived modes are the ones with higher angular number. Such behaviour has been recently studied in different static spacetimes, for scalar and fermionic perturbations, being observed in both cases. In this work, we extend the existent studies to the charged spacetimes, namely, the Reissn

Dimitris Boskos, Dimos V. Dimarogonas

The goal of this paper is to obtain online abstractions for coupled multi-agent systems in a decentralized manner. A discrete model which captures the motion capabilities of each agent is derived over a bounded time-horizon, by discretizing a corresponding overapproximation of the agent's reachable states. The individual abstractions' composition provides a

Meven Lennon-Bertrand, Kenji Maillard, Nicolas Tabareau, Éric Tanter

We investigate gradual variations on the Calculus of Inductive Construction (CIC) for swifter prototyping with imprecise types and terms. We observe, with a no-go theorem, a crucial tradeoff between graduality and the key properties of normalization and closure of universes under dependent product that CIC enjoys. Beyond this Fire Triangle of Graduality, we

Parthapratim Biswas, Dil Limbu

This paper presents an $ab$ $initio$ study of hydrogen dynamics inside nanometer-size voids in $a$-Si within the framework of the density-functional theory for a varying hydrogen load of 10 to 30 H atoms/void at the low and high temperature of 400 K and 700 K, respectively. Using the local density approximation and its generalized-gradient counterpart, the d

Rui Wang, Danielle Maddix, Christos Faloutsos, Yuyang Wang

How can we learn a dynamical system to make forecasts, when some variables are unobserved? For instance, in COVID-19, we want to forecast the number of infected and death cases but we do not know the count of susceptible and exposed people. While mechanics compartment models are widely used in epidemic modeling, data-driven models are emerging for disease fo

Tom Grimes, Eric Church, William Pitts, Lynn Wood

We present a novel variant of Domain Adversarial Networks with impactful improvements to the loss functions, training paradigm, and hyperparameter optimization. New loss functions are defined for both forks of the DANN network, the label predictor and domain classifier, in order to facilitate more rapid gradient descent, provide more seamless integration int

Tianchen Zhao, James Stokes, Oliver Knitter, Brian Chen

An identification is found between meta-learning and the problem of determining the ground state of a randomly generated Hamiltonian drawn from a known ensemble. A model-agnostic meta-learning approach is proposed to solve the associated learning problem and a preliminary experimental study of random Max-Cut problems indicates that the resulting Meta Variati

Arshia Anjum, Sriman Srisa Saran Mishra

Gravity plays an important part in the experiments and discoveries of the modern world. But how was it discovered? Surely Newton and Einstein were not the only people to observe it and account for it. It had been a long path before the full theory for Gravitation could be formulated with open ends for more add-ons and modifications. All the contributions fro

Martin Scharlemann

In 1980 J. Powell \cite{Po} proposed that five specific elements sufficed to generate the Goeritz group for any genus Heegaard splitting of the 3-sphere. Here we prove that a natural expansion of Powell's proposed generators, to include all eyeglass twists and all topological conjugates of Powell's generators does suffice.

Panagiotis Iosif, Nikolaos Stergioulas

We present equilibrium sequences of rotating relativistic stars, constructed with a new rotation law that was proposed by Uryu et al. (2017). We choose rotational parameters motivated by simulations of binary neutron star merger remnants, but otherwise adopt a cold, relativistic N=1 polytropic EOS, in order to perform a detailed comparison to published equil

Mark Robert Baker, Natalia Kiriushcheva, Sergei Kuzmin

Multiple methods for deriving the energy-momentum tensor for a physical theory exist in the literature. The most common methods are to use Noether's first theorem with the 4-parameter Poincar\'{e} translation, or to write the action in a curved spacetime and perform variation with respect to the metric tensor, then return to a Minkowski spacetime. These are

Adam J. Thorpe, Kendric R. Ortiz, Meeko M. K. Oishi

We present algorithms for performing data-driven stochastic reachability as an addition to SReachTools, an open-source stochastic reachability toolbox. Our method leverages a class of machine learning techniques known as kernel embeddings of distributions to approximate the safety probabilities for a wide variety of stochastic reachability problems. By repre

Doğa Gürgünoğlu, Berkan Dulek, Sinan Gezici

The optimal power adaptation problem is investigated for vector parameter estimation according to various Fisher information based optimality criteria. By considering an observation model that involves a linear transformation of the parameter vector and an additive noise component with an arbitrary probability distribution, six different optimal power alloca

Ulrich Finkler, Michele Merler, Rameswar Panda, Mayoore S. Jaiswal

Neural Architecture Search (NAS) is a powerful tool to automatically design deep neural networks for many tasks, including image classification. Due to the significant computational burden of the search phase, most NAS methods have focused so far on small, balanced datasets. All attempts at conducting NAS at large scale have employed small proxy sets, and th

Christopher L. Dean, Stephen J. Lee, Jason Pacheco, John W. Fisher

We present an approach to data fusion that combines the interpretability of structured probabilistic graphical models with the flexibility of neural networks. The proposed method, lightweight data fusion (LDF), emphasizes posterior analysis over latent variables using two types of information: primary data, which are well-characterized but with limited avail

Zhu Yi, Qing Gao, Yungui Gong, Zong-hong Zhu

With the enhancement mechanism provided by a noncanonical kinetic term with a peak, the amplitude of primordial curvature perturbations can be enhanced by seven orders of magnitude at small scales while keeping to be consistent with observations at large scales. The peak function and inflationary potential are not restricted in this mechanism. We use the Hig

Junhyeok Ahn, Luis Sentis

Model-based reinforcement learning (MBRL) algorithms can attain significant sample efficiency but require an appropriate network structure to represent system dynamics. Current approaches include white-box modeling using analytic parameterizations and black-box modeling using deep neural networks. However, both can suffer from a bias-variance trade-off in th

Zheng Gong, Yinren Shou, Yuhui Tang, Xueqing Yan

We propose a scheme to overcome the great challenge of polarization loss in spin-polarized ion acceleration. When a petawatt laser pulse penetrates through a compound plasma target consisting of a double layer slab and prepolarized hydrogen halide gas, a strong forward moving quasistatic longitudinal electric field is constructed by the self-generated laser-

T. Kallman, M. Bautista, J. Deprince, J. A. Garcia

Relativistically broadened and redshifted 6.4 -- 6.9 keV iron K lines are observed from many accretion powered objects, including X-ray binaries and active galactic nuclei (AGN). Existence of gas close to the central engine implies large radiation intensities and correspondingly large gas densities if the gas is to remain partially ionized. Simple estimates

Thembelihle Dlamini, Sifiso Vilakati

The massive deployment of small cell Base Stations (SBSs) empowered with computing capabilities presents one of the most ingenious solutions adopted for 5G cellular networks towards meeting the foreseen data explosion and the ultra-low latency demanded by mobile applications. This empowerment of SBSs with Multi-access Edge Computing (MEC) has emerged as a te

Sharareh Sayyad, Jinlong Yu, Adolfo G. Grushin, Lukas M. Sieberer

The development of non-Hermitian topological band theory has led to observations of novel topological phenomena in effectively classical, driven and dissipative systems. However, for open quantum many-body systems, the absence of a ground state presents a challenge to define robust signatures of non-Hermitian topology. We show that such a signature is provid

Yasser Dahou, Marouane Tliba, Kevin McGuinness, Noel O'Connor

The spherical domain representation of 360 video/image presents many challenges related to the storage, processing, transmission and rendering of omnidirectional videos (ODV). Models of human visual attention can be used so that only a single viewport is rendered at a time, which is important when developing systems that allow users to explore ODV with head

Zhaozhong Shi

Beauty quarks are considered as one of the best probes of the strongly interacting medium created in relativistic heavy-ion collisions because they are predominantly produced via initial hard scatterings. Measurements of B meson production provide information about the diffusion of beauty quarks and the flavor dependence of in-medium energy loss. In these st

Antonio Lerario, Léo Mathis

We prove that in a globally subanalytic family of convex bodies the set of zonoids is log-analytic, and in particular it is definable in the o-minimal structure generated by globally subanalytic sets and the graph of the exponential function.

Huawei Fan, Ling-Wei Kong, Xingang Wang, Alan Hastings

Transients are fundamental to ecological systems with significant implications to management, conservation, and biological control. We uncover a type of transient synchronization behavior in spatial ecological networks whose local dynamics are of the chaotic, predator-prey type. In the parameter regime where there is phase synchronization among all the patch

Armin Lederer, Alexandre Capone, Thomas Beckers, Jonas Umlauft

Despite the existence of formal guarantees for learning-based control approaches, the relationship between data and control performance is still poorly understood. In this paper, we propose a Lyapunov-based measure for quantifying the impact of data on the certifiable control performance. By modeling unknown system dynamics through Gaussian processes, we can

Sara Sottile, Ozan Kahramanoğulları, Mattia Sensi

With the premise that social interactions are described by power-law distributions, we study a SIR stochastic dynamic on a static scale-free random network generated via configuration model. We verify our model with respect to deterministic considerations and provide a theoretical result on the probability of the extinction of the disease. Based on this cali

Aoife Boyle, Fabian Schmidt

We demonstrate the impact on forecasted neutrino mass constraints of extending galaxy clustering and CMB lensing predictions from linear to next-to-leading-order power spectra. The redshift-space 1-loop power spectrum model we adopt requires an additional four free bias parameters, a velocity bias parameter and two new stochastic parameters. These additional

Ahmad Lalti, Yuri Khotyaintsev, Daniel B. Graham, Andris Vaivads

Whistler waves are thought to play an essential role in the dynamics of collisionless shocks. We use the magnetospheric multiscale (MMS) spacecraft to study whistler waves around the lower hybrid frequency, upstream of 11 quasi-perpendicular super-critical shocks. We apply the 4-spacecraft timing method to unambiguously determine the wave vector $\mathbf{k}$

Avishek Das, David T. Limmer

Using large deviation theory and principles of stochastic optimal control, we show that rare molecular dynamics trajectories conditioned on assembling a specific target structure encode a set of interactions and external forces that lead to enhanced stability of that structure. Such a relationship can be formulated into a variational principle, for which we

Lukas Knips

Quantum entanglement is one of the core features of quantum theory. While it is typically revealed by measurements along carefully chosen directions, here we review different methods based on so-called random or randomized measurements. Although this approach might seem inefficient at first, sampling correlations in various random directions is a powerful to

A. Pastorello, G. Valerin, M. Fraser, N. Elias-Rosa

We present the results of our monitoring campaigns of the luminous red novae (LRNe) AT 2020hat in NGC 5068 and AT 2020kog in NGC 6106. The two objects were imaged (and detected) before their discovery by routine survey operations. They show a general trend of slow luminosity rise, lasting at least a few months. The subsequent major LRN outbursts were extensi

Tony Pourmohamad

The CompModels package for R provides a suite of computer model test functions that can be used for computer model prediction/emulation, uncertainty quantification, and calibration, but in particular, the sequential optimization of computer models. The package is a mix of real-world physics problems, known mathematical functions, and black-box functions that

A. Pastorello, M. Fraser, G. Valerin, A. Reguitti

We present the follow-up campaign of the luminous red nova (LRN) AT~2019zhd, the third event of this class observed in M 31. The object was followed by several sky surveys for about five months before the outburst, during which it showed a slow luminosity rise. In this phase, the absolute magnitude ranged from M_r=-2.8+-0.2 mag to M_r=-5.6+-0.1 mag. Then, ov

Dilovan B. Serindag, Stevanus K. Nugroho, Paul Mollière, Ernst J. W. de Mooij

[abridged] Efficient absorption of stellar UV and visible radiation by TiO and VO is predicted to drive temperature inversions in the upper atmospheres of hot Jupiters. However, few inversions or detections of TiO or VO have been reported, and results are often contradictory. Using the improved ExoMol Toto line list, we searched for TiO emission in the daysi

Marco Frasca, Anish Ghoshal

We investigate the non-perturbative degrees of freedom in the class of non-local Higgs theories that have been proposed as an ultraviolet completion 4-D Quantum Field Theory (QFT) generalizing the kinetic energy operators to an infinite series of higher derivatives inspired by string field theory. At the perturbative level, the degrees of freedom of non-loca

Timothy J. H. Hele, Bartomeu Monserrat, Antonios M. Alvertis

The energies of molecular excited states arise as solutions to the electronic Schr\"{o}dinger equation and are often compared to experiment. At the same time, nuclear quantum motion is known to be important and to induce a red-shift of excited state energies. However, it is thus far unclear whether incorporating nuclear quantum motion in molecular excited st

P. Santini, M. Castellano, E. Merlin, A. Fontana

The emergence of passive galaxies in the early Universe results from the interplay among the processes responsible for their rapid assembly and for the abrupt shut-down of their SF. Investigating the individual properties and demographics of early passive galaxies will improve our understanding of these mechanisms. In this work we present a follow-up analysi

Federica Maria Surace, Alessio Lerose

Simulating real-time evolution in theories of fundamental interactions represents one of the central challenges in contemporary theoretical physics. Cold-atom platforms stand as promising candidates to realize quantum simulations of non-perturbative phenomena in gauge theories, such as vacuum decay and hadron collisions, in prohibitive conditions for direct

Robert A. Simcoe, Masafusa Onoue, Anna-Christina Eilers, Eduardo Banados

We analyze relative abundances and ionization conditions in a strong absorption system at z=6.84, seen in the spectrum of the z=7.54 background quasar ULAS J134208.10+092838.61. Singly ionized C, Si, Fe, Mg, and Al measurements are consistent with a warm neutral medium that is metal-poor but not chemically pristine. Firm non-detections of C IV and Si IV impl

Michael S. Petersen, Jorge Peñarrubia

The Large Magellanic Cloud is the most massive satellite galaxy of the Milky Way, with an estimated mass exceeding a tenth of the mass of the Milky Way. Just past its closest approach of about 50 kpc, and flying by the Milky Way at an astonishing speed of 327 km/s, the Large Magellanic Cloud can affect our Galaxy in a number of ways, including dislodging the

Rosie Y. Talbot, Martin A. Bourne, Debora Sijacki

Jets launched by active galactic nuclei (AGN) are believed to play a significant role in shaping the properties of galaxies and provide an energetically viable mechanism through which galaxies can become quenched. Here we present a novel AGN feedback model, which we have incorporated into the AREPO code, that evolves the black hole mass and spin as the accre

Pietro Ferrero, Jerome P. Gauntlett, Juan Manuel Pérez Ipiña, Dario Martelli

We construct supersymmetric $AdS_3\times \Sigma$ solutions of minimal gauged supergravity in $D=5$, where $\Sigma$ is a two-dimensional orbifold known as a spindle. Remarkably, these uplift on $S^5$, or more generally on any regular Sasaki-Einstein manifold, to smooth solutions of type IIB supergravity. The solutions are dual to $d=2$, $\mathcal{N}=(0,2)$ SC

Douglas Boubert, Andrew Everall, Jack Fraser, Amery Gration

The completeness of the Gaia catalogues heavily depends on the status of that space telescope through time. Stars are only published with each of the astrometric, photometric and spectroscopic data products if they are detected a minimum number of times. If there is a gap in scientific operations, a drop in the detection efficiency or Gaia deviates from the

Luisa Lucie-Smith, Hiranya V. Peiris, Andrew Pontzen, Brian Nord

The evolution of linear initial conditions present in the early universe into extended halos of dark matter at late times can be computed using cosmological simulations. However, a theoretical understanding of this complex process remains elusive; in particular, the role of anisotropic information in the initial conditions in establishing the final mass of d

Xinlei Chen, Kaiming He

Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain conditions for avoiding collapsing solutions. In this paper, we report surprising empirical results that simple Siamese networks can learn mea

Michele Redi, Andrea Tesi, Hannah Tillim

Dark sectors with purely gravitational couplings to the Standard Model are unavoidably populated from the SM plasma by graviton exchange, and naturally provide DM candidates. We examine the production in the relativistic regime where the dark sector is approximately scale invariant, providing general analytical formulas that depend solely on the central char

Dawei Ding, Hsiang-Sheng Ku, Yaoyun Shi, Hui-Hai Zhao

Superconducting quantum circuits is one of the leading candidates for a universal quantum computer. Designing novel qubit and multiqubit superconducting circuits requires the ability to simulate and analyze the properties of a general circuit. In particular, going outside the transmon approach, we cannot make assumptions on anharmonicity, thus precluding bla