October 2020 arXiv papers — page 95

Damien de Mijolla, Christopher Frye, Markus Kunesch, John Mansir

The importance of explainability in machine learning continues to grow, as both neural-network architectures and the data they model become increasingly complex. Unique challenges arise when a model's input features become high dimensional: on one hand, principled model-agnostic approaches to explainability become too computationally expensive; on the ot

Fernando E. Rosas, Pedro A. M. Mediano, Michael Gastpar

Learning and compression are driven by the common aim of identifying and exploiting statistical regularities in data, which opens the door for fertile collaboration between these areas. A promising group of compression techniques for learning scenarios is normalised maximum likelihood (NML) coding, which provides strong guarantees for compression of small da

A. E. Dorokhov, R. N. Faustov, A. P. Martynenko, F. A. Martynenko

The energy interval $ (3S-1S) $ in muonic hydrogen is calculated on the basis of quasipotential approach in quantum electrodynamics. We take into account different corrections of orders $α^3÷α^6 $, which are determined by relativistic effects, the effects of vacuum polarization, nuclear structure and recoil, as well as combined corrections including the abov

Dariusz Kosz, Mariusz Mirek, Paweł Plewa, Błazej Wróbel

Dependencies of the optimal constants in strong and weak type bounds will be studied between maximal functions corresponding to the Hardy--Littlewood averaging operators over convex symmetric bodies acting on $\mathbb R^d$ and $\mathbb Z^d$. Firstly, we show, in the full range of $p\in[1,\infty]$, that these optimal constants in $L^p(\mathbb R^d)$ are always

Yan Zhang, Yi Zhou, Kaiyi Ji, Michael M. Zavlanos

Zeroth-order optimization (ZO) typically relies on two-point feedback to estimate the unknown gradient of the objective function. Nevertheless, two-point feedback can not be used for online optimization of time-varying objective functions, where only a single query of the function value is possible at each time step. In this work, we propose a new one-point

Nicolas Martinet, Joachim Harnois-Déraps, Eric Jullo, Peter Schneider

We forecast and optimize the cosmological power of various weak-lensing aperture mass ($M_{\rm ap}$) map statistics for future cosmic shear surveys, including peaks, voids, and the full distribution of pixels (1D $M_{\rm ap}$). These alternative methods probe the non-Gaussian regime of the matter distribution, adding complementary cosmological information to

Alexandra DeLucia, Aaron Mueller, Xiang Lisa Li, João Sedoc

Narrative generation is an open-ended NLP task in which a model generates a story given a prompt. The task is similar to neural response generation for chatbots; however, innovations in response generation are often not applied to narrative generation, despite the similarity between these tasks. We aim to bridge this gap by applying and evaluating advances i

Martin W. Hess, Annalisa Quaini, Gianluigi Rozza

This paper focuses on reduced-order models (ROMs) built for the efficient treatment of PDEs having solutions that bifurcate as the values of multiple input parameters change. First, we consider a method called local ROM that uses k-means algorithm to cluster snapshots and construct local POD bases, one for each cluster. We investigate one key ingredient of t

Takao Inoué

In this Part I, we shall prove the consistency of arithmetic without complete induction from a point of view of strong negation, using its embedding to the tableau system $\bf SN$ of constructive arithmetic with strong negation without complete induction, for which two types of cut elimination theorems hold. One is $\bf SN$-cut elimination theorem for the fu

Shijie Li, Jinhui Yi, Yazan Abu Farha, Juergen Gall

With the advances in capturing 2D or 3D skeleton data, skeleton-based action recognition has received an increasing interest over the last years. As skeleton data is commonly represented by graphs, graph convolutional networks have been proposed for this task. While current graph convolutional networks accurately recognize actions, they are too expensive for

Alexander R. Pruss

Classical countably additive real-valued probabilities come at a philosophical cost: in many infinite situations, they assign the same probability value -- namely, zero -- to cases that are impossible as well as to cases that are possible. There are three non-classical approaches to probability that can avoid this drawback: full conditional probabilities, qu

Benjamin Howard, Qirui Li

We compute the intersection multiplicities of special cycles in Lubin-Tate spaces, and formulate a new arithmetic fundamental lemma relating these intersections to derivatives of orbital integrals.

Debarnab Mitra, Lev Tauz, Lara Dolecek

In certain blockchain systems, light nodes are clients that download only a small portion of the block. Light nodes are vulnerable to data availability (DA) attacks where a malicious node hides an invalid portion of the block from the light nodes. Recently, a technique based on erasure codes called Coded Merkle Tree (CMT) was proposed by Yu et al. that enabl

Benjamin Howard

We compute the arithmetic volumes of integral models of unitary Shimura curves. This establishes the base case of an inductive argument to compute the arithmetic volumes of unitary Shimura varieties of higher dimension, to appear in subsequent work of Bruinier and the author.

Hongliang Li, Manish Bhatt, Zhen Qu, Shiming Zhang

It is known that changes in the mechanical properties of tissues are associated with the onset and progression of certain diseases. Ultrasound elastography is a technique to characterize tissue stiffness using ultrasound imaging either by measuring tissue strain using quasi-static elastography or natural organ pulsation elastography, or by tracing a propagat

Kurt Johansson, Mustazee Rahman

This article studies the inhomogeneous geometric polynuclear growth model, the distribution of which is related to Schur functions. We explain a method to derive its distribution functions in both space-like and time-like directions, focusing on the two-time distribution. Asymptotics of the two-time distribution in the KPZ-scaling limit is then considered, e

Pankaj Jyoti Mahanta

We prove an explicit formula to count the partitions of $n$ whose product of the summands is at most $n$. In the process, we also deduce a result to count the multiplicative partitions of $n$.

Markus Nissl, Emanuel Sallinger, Stefan Schulte, Michael Borkowski

In recent years, manifold blockchain protocols have been proposed by researchers and industrial companies alike. This has led to a very heterogeneous blockchain landscape. Accordingly, it would be desirable if blockchains could interact with each other. However, current blockchain technologies offer only limited support for interoperability, thus preventing

Jonas Kornprobst, Alexander Paulus, Thomas F. Eibert

The inaccuracy of the classical magnetic field integral equation (MFIE) is a long-studied problem. We investigate one of the potential approaches to solve the accuracy problem: higher-order discretization schemes. While these are able to offer increased accuracy, we demonstrate that the accuracy problem may still be present. We propose an advanced scheme bas

Vincent Le Guen, Nicolas Thome

Probabilistic forecasting consists in predicting a distribution of possible future outcomes. In this paper, we address this problem for non-stationary time series, which is very challenging yet crucially important. We introduce the STRIPE model for representing structured diversity based on shape and time features, ensuring both probable predictions while be

Vishwali Mhasawade, Rumi Chunara

Algorithmic systems are known to impact marginalized groups severely, and more so, if all sources of bias are not considered. While work in algorithmic fairness to-date has primarily focused on addressing discrimination due to individually linked attributes, social science research elucidates how some properties we link to individuals can be conceptualized a

Guido Baardink, Gino Cassella, Luke Neville, Paul A. Milewski

Chiral edge states can transmit energy along imperfect interfaces in a topologically robust and unidirectional manner when protected by bulk-boundary correspondence. However, in continuum systems, the number of states at an interface can depend on boundary conditions. Here we design interfaces that host a net flux of the number of modes into a region, trappi

Juan V. Guerrero, Alberto Accardi

We revisit the derivation of collinear factorization for Deep Inelastic Scattering at sub-asymptotic values of the four momentum transfer squared, where the masses of the particles participating in the interaction cannot be neglected. By using an inclusive jet function to describe the scattered quark final state, we can restrict the needed parton kinematic a

Alexander McCleary, Amit Patel

We build a functorial pipeline for persistent homology. The input to this pipeline is a filtered simplicial complex indexed by any finite metric lattice and the output is a persistence diagram defined as the M\"obius inversion of its birth-death function. We adapt the Reeb graph edit distance to each of our categories and prove that both functors in our pipe

Siddhartha Karmakar, Sujata Pandey, Subhendu Rakshit

High energy astrophysical neutrinos interacting with ultralight dark matter (DM) can undergo flavour oscillations that induce an energy dependence in the flavour ratios. Such a dependence on the neutrino energy will reflect in the track to shower ratio in neutrino telescopes like IceCube or KM3NeT. This opens up a possibility to study DM density profiles of

Swagat R. Das, Jessy Jose, Manash R Samal, Shaobo Zhang

The processes which regulate the star-formation within molecular clouds are still not well understood. Various star-formation scaling relations have been proposed to explain this issue by formulating a relation between star-formation rate surface density ($\rm Σ_{SFR}$) and the underlying gas surface density ($\rm Σ_{gas}$). In this work, we test various sta

I. V. Anikin

The effective potential is known to be given by the vacuum diagrams. In this paper we show that the massive loop integrations corresponding to the vacuum diagrams can be expressed through the massless loop integrations with the corresponding massive prefactor provided the special treatment of $δ(0)$-singularity. In its turn, the massless loop integrations pr

Wanlin Li, Daniel Litt, Nick Salter, Padmavathi Srinivasan

We formulate a tropical analogue of Grothendieck's section conjecture: that for every stable graph G of genus g>2, and every field k, the generic curve with reduction type G over k satisfies the section conjecture. We prove many cases of this conjecture. In so doing we produce many examples of curves satisfying the section conjecture over fields of geometric

Delilah E. A. Gates, Shahar Hadar, Alexandru Lupsasca

We consider monochromatic and isotropic photon emission from circular equatorial Kerr orbiters. We derive analytic expressions for the photon escape probability and the redshift-dependent total flux collected on the celestial sphere as a function of emission radius and black hole parameters. These calculations crucially involve the critical curve delineating

Mona Minakshee Manjaree Bhukta, Braj Bhusan Singh, Sougata Mallick, Stanislas Rohart

Topological magnetic textures, characterized by integer topological charge $S$, are potential candidates in future magnetic logic and memory devices, due to their smaller size and expected low threshold current density for their motion. An essential requirement to stabilize them is the Dzyaloshinskii-Moriya interaction (DMI) which promotes a particular chira

Davide Gaiotto, Ji Hoon Lee, Benoit Vicedo, Jingxiang Wu

We describe the relation between integrable Kondo problems in products of chiral $SU(2)$ WZW models and affine $SU(2)$ Gaudin models. We propose a full ODE/IM solution of the spectral problem for these models.

Vladyslav Kozii, Alexander Avdoshkin, Shudan Zhong, Joel E. Moore

We study how the intrinsic anomalous Hall conductivity is modified in two-dimensional crystals with broken time-reversal symmetry due to weak inhomogeneity of the applied electric field. Focusing on a clean noninteracting two-band system without band crossings, we derive the general expression for the Hall conductivity at small finite wave vector $q$ to orde

Pascal Anastasopoulos, Massimo Bianchi, Dario Consoli, Elias Kiritsis

The mixing of graviphotons and dark brane photons to the Standard Model hypercharge is analyzed in full generality, in weakly-coupled string theory. Both the direct mixing as well as effective terms that provide mixing after inclusion of SM corrections are estimated to lowest order. The results are compared with Effective Field Theory (EFT) couplings, origin

Alexandra Spaulding, Philip Chang

Stars that pass too close to a supermassive black hole are disrupted by the black hole's tidal gravity and leads to some debris being ejected while the remainder accretes into the black hole. To better study the physics of this debris, we use the moving mesh code MANGA to follow the evolution of the star from its initial encounter to its complete destruc

Jose Luis Blázquez-Salcedo, Christian Knoll, Eugen Radu

We construct a specific example of a class of traversable wormholes in Einstein-Dirac-Maxwell theory in four spacetime dimensions, without needing any form of exotic matter. Restricting to a model with two massive fermions in a singlet spinor state, we show the existence of spherically symmetric asymptotically flat configurations which are free of singularit

A. Bouttefeux, M. Laine

Many lattice studies of heavy quark diffusion originate from a colour-electric correlator, obtained as a leading term after an expansion in the inverse of the heavy-quark mass. In view of the fact that the charm quark is not particularly heavy, we consider subleading terms in the expansion. Working out correlators up to $O(1/M^2)$, we argue that the leading

Thales A. Gutcke, Rüdiger Pakmor, Thorsten Naab, Volker Springel

We introduce the LYRA project, a new high resolution galaxy formation model built within the framework of the cosmological hydro-dynamical moving mesh code AREPO. The model resolves the multi-phase interstellar medium down to 10 K. It forms individual stars sampled from the initial mass function (IMF), and tracks their lifetimes and death pathways individual

Tim B. Miller, Pieter van Dokkum, Shany Danieli, Jiaxuan Li

Stellar mass estimates of massive galaxies are susceptible to systematic errors in their photometry, due to their extended light profiles. In this study we use data from the Dragonfly Wide Field Survey (DWFS) to accurately measure the total luminosities and colors of nearby massive galaxies. The low surface brightness limits of the survey ($μ_g \approx $ 31

Jad C. Halimeh, Daniele Trapin, Maarten Van Damme, Markus Heyl

In recent years, dynamical quantum phase transitions (DQPTs) have emerged as a useful theoretical concept to characterize nonequilibrium states of quantum matter. DQPTs are marked by singular behavior in an \textit{effective free energy} $λ(t)$, which, however, is a global measure, making its experimental or theoretical detection challenging in general. We i

Kostas Tzanavaris, Pau Amaro Seoane

In the context of mathematical cosmology, the study of necessary and sufficient conditions for a semi-Riemannian manifold to be a (generalised) Robertson-Walker space-time is important. In particular, it is a requirement for the development of initial data to reproduce or approximate the standard cosmological model. Usually these conditions involve the Einst

Jordy de Vries, Herbert K. Dreiner, Julian Y. Günther, Zeren Simon Wang

We study the prospects of a displaced-vertex search of sterile neutrinos at the Large Hadron Collider (LHC) in the framework of the neutrino-extended Standard Model Effective Field Theory ($ν$SMEFT). The production and decay of sterile neutrinos can proceed via the standard active-sterile neutrino mixing in the weak current, as well as through higher-dimensi

Stéphane Ouvry, Alexios P. Polychronakos

We introduce a shifted version of the binomial theorem, and use it to study some remarkable trigonometric integrals and their explicit rewriting in terms of binomial multiple sums. Motivated by the expressions of area generating functions arising in the counting of closed walks on various lattices, we propose similar sums involving fractional values of the a

Brent J. Shapiro-Albert, Jeffrey S. Hazboun, Maura A. McLaughlin, Michael T. Lam

In this paper we introduce a new Python package, the Pulsar Signal Simulator, or PsrSigSim, which is designed to simulate a pulsar signal from emission at the pulsar, through the interstellar medium, to observation by a radio telescope, and digitization in a standard data format. We use the PsrSigSim to simulate observations of three millisecond pulsars, PSR

Horng Sheng Chia

We show that rotating black holes do not experience any tidal deformation when they are perturbed by a weak and adiabatic gravitational field. The tidal deformability of an object is quantified by the so-called "Love numbers", which describe the object's linear response to its external tidal field. In this work, we compute the Love numbers of Ker

Shamit Kachru, Richard Nally, Wenzhe Yang

In recent work, we conjectured that Calabi-Yau threefolds defined over $\mathbb{Q}$ and admitting a supersymmetric flux compactification are modular, and associated to (the Tate twists of) weight-two cuspidal Hecke eigenforms. In this work, we will address two natural follow-up questions, of both a physical and mathematical nature, that are surprisingly clos

Logan J. Cross, Xiangxiong Zhang

The monotonicity of discrete Laplacian, i.e., inverse positivity of stiffness matrix, implies discrete maximum principle, which is in general not true for high order accurate schemes on unstructured meshes. On the other hand, it is possible to construct high order accurate monotone schemes on structured meshes. All previously known high order accurate invers

Amitay Dror, Michal Feldman, Erel Segal-Halevi

We study fair allocation of indivisible goods among additive agents with feasibility constraints. In these settings, every agent is restricted to get a bundle among a specified set of feasible bundles. Such scenarios have been of great interest to the AI community due to their applicability to real-world problems. Following some impossibility results, we res

Khyathi Raghavi Chandu, Alan W Black

Neural text generation metamorphosed into several critical natural language applications ranging from text completion to free form narrative generation. In order to progress research in text generation, it is critical to absorb the existing research works and position ourselves in this massively growing field. Specifically, this paper surveys the fundamental

Wenbin Zhang, Liming Zhang, Dieter Pfoser, Liang Zhao

Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of proteins (i.e., graph of amino acids). Existing work typically focuses on static rather than dynamic graphs, which are actually very important in the applications such as protein foldi

Chi Tian, Stefano Anselmi, Matthew F. Carney, John T. Giblin

The curvature of a spacetime, either in a topological sense, or averaged over super-horizon-sized patches, is often equated with the global curvature term that appears in Friedmann's equation. In general, however, the Universe is inhomogeneous, and gravity is a nonlinear theory, thus any curvature perturbations violate the assumptions of the FLRW model;

Margaret Lazzarini, Benjamin F. Williams, Meredith Durbin, Julianne Dalcanton

We present our analysis of high quality high mass X-ray binary (HMXB) candidates in M31 selected from point-source optical-counterpart candidates from the Chandra-PHAT survey catalog. We fit the spectral energy distributions (SEDs) of optical counterpart candidates using the Bayesian Extinction and Stellar Tool (BEAST). We used the best-fit luminosity, effec

Yi Lai

We find a family of 3d steady gradient Ricci solitons that are flying wings. This verifies a conjecture by Hamilton. For a 3d flying wing, we show that the scalar curvature does not vanish at infinity. The 3d flying wings are collapsed. For dimension $n\ge 4$, we find a family of $\mathbb{Z}_2\times O(n-1)$-symmetric but non-rotationally symmetric n-dimensio

Deepan Muthirayan, Jianjun Yuan, Pramod P. Khargonekar

We address the problem of simultaneously learning and control in an online receding horizon control setting. We consider the control of an unknown linear dynamical system with general cost functions and affine constraints on the control input. Our goal is to develop an online learning algorithm that minimizes the dynamic regret, which is defined as the diffe

Qiang Miao, Thomas Barthel

With increasing subsystem size and energy, bipartite entanglement entropies of energy eigenstates cross over from the groundstate scaling to a volume law. In previous work, we pointed out that, when strong or weak eigenstate thermalization (ETH) applies, the entanglement entropies of all or, respectively, almost all eigenstates follow a single crossover func

Leendert Hayen

We present an $\mathcal{O}(α)$ Standard Model calculation of the inner radiative corrections to Gamow-Teller $β$ decays. We find that \textit{a priori} contributions arise from the photonic vertex correction and $γW$ box diagram. Upon evaluation most elastic contributions vanish due to crossing symmetry or cancellation between isoscalar and isovector photoni

Makesh Narsimhan Sreedhar, Kun Ni, Siva Reddy

The ubiquitous nature of chatbots and their interaction with users generate an enormous amount of data. Can we improve chatbots using this data? A self-feeding chatbot improves itself by asking natural language feedback when a user is dissatisfied with its response and uses this feedback as an additional training sample. However, user feedback in most cases

Ali Varamesh, Ali Diba, Tinne Tuytelaars, Luc Van Gool

We present a new framework for self-supervised representation learning by formulating it as a ranking problem in an image retrieval context on a large number of random views (augmentations) obtained from images. Our work is based on two intuitions: first, a good representation of images must yield a high-quality image ranking in a retrieval task; second, we

A. Ayyer, S. Goldstein, J. L. Lebowitz, E. R. Speer

We describe the translation invariant stationary states (TIS) of the one-dimensional facilitated asymmetric exclusion process in continuous time, in which a particle at site $i\in\mathbb{Z}$ jumps to site $i+1$ (respectively $i-1$) with rate $p$ (resp. $1-p$), provided that site $i-1$ (resp. $i+1$) is occupied and site $i+1$ (resp. $i-1$) is empty. All TIS s

Gustavo Alves Prudencio de Morais, Lucas Barbosa Marcos, Filipe Marques Barbosa, Bruno Henrique Groenner Barbosa

The ability to deal with systems parametric uncertainties is an essential issue for heavy self-driving vehicles in unconfined environments. In this sense, robust controllers prove to be efficient for autonomous navigation. However, uncertainty matrices for this class of systems are usually defined by algebraic methods which demand prior knowledge of the syst

Fatemeh Mohammadi, Leonid Monin, Matteo Parisi

Any totally positive $(k+m)\times n$ matrix induces a map $π_+$ from the positive Grassmannian ${\rm Gr}_+(k,n)$ to the Grassmannian ${\rm Gr}(k,k+m)$, whose image is the amplituhedron $\mathcal{A}_{n,k,m}$ and is endowed with a top-degree form called the canonical form ${\bfΩ}(\mathcal{A}_{n,k,m})$. This construction was introduced by Arkani-Hamed and Trnka

Murphy Berzish, Mitja Kulczynski, Federico Mora, Florin Manea

We present a novel length-aware solving algorithm for the quantifier-free first-order theory over regex membership predicate and linear arithmetic over string length. We implement and evaluate this algorithm and related heuristics in the Z3 theorem prover. A crucial insight that underpins our algorithm is that real-world instances contain a wealth of informa

Samuel N. Cohen, Tanut Treetanthiploet

We consider a general multi-armed bandit problem with correlated (and simple contextual and restless) elements, as a relaxed control problem. By introducing an entropy regularisation, we obtain a smooth asymptotic approximation to the value function. This yields a novel semi-index approximation of the optimal decision process. This semi-index can be interpre

Istvan Berkes, Bence Borda

Let $\alpha$ be an irrational number, let $X_1, X_2, \ldots$ be independent, identically distributed, integer-valued random variables, and put $S_k=\sum_{j=1}^k X_j$. Assuming that $X_1$ has finite variance or heavy tails $P (|X_1|>t)\sim ct^{-\beta}$, $0<\beta<2$, in Part I of this paper we proved that, up to logarithmic factors, the order of magnitude of t

Elliot Creager, Jörn-Henrik Jacobsen, Richard Zemel

Learning models that gracefully handle distribution shifts is central to research on domain generalization, robust optimization, and fairness. A promising formulation is domain-invariant learning, which identifies the key issue of learning which features are domain-specific versus domain-invariant. An important assumption in this area is that the training ex

Sualeh Asif, Francesc Fité, Dylan Pentland

We study the sequence of zeta functions $Z(C_p,T)$ of a generic Picard curve $C:y^3=f(x)$ defined over $\mathbb{Q}$ at primes $p$ of good reduction for $C$. We define a degree 9 polynomial $ψ_f\in \mathbb{Q}[x]$ such that the splitting field of $ψ_f(x^3/2)$ is the $2$-torsion field of the Jacobian of $C$. We prove that, for all but a density zero subset of p

Xing Shi Cai, Guillem Perarnau

We consider the stationary distribution of the simple random walk on the directed configuration model with bounded degrees. Provided that the minimum out-degree is at least $2$, with high probability (whp) there is a unique stationary distribution. We show that the minimum positive stationary value is whp $n^{-(1+C+o(1))}$ for some constant $C \ge 0$ determi

Duncan A. Brown, Karan Vahi, Michela Taufer, Von Welch

In 2016, LIGO and Virgo announced the first observation of gravitational waves from a binary black hole merger, known as GW150914. To establish the confidence of this detection, large-scale scientific workflows were used to measure the event&#39;s statistical significance. They used code written by the LIGO/Virgo and were executed on the LIGO Data Grid. The

Sebastian Pokutta, Christoph Spiegel, Max Zimmer

This paper studies the empirical efficacy and benefits of using projection-free first-order methods in the form of Conditional Gradients, a.k.a. Frank-Wolfe methods, for training Neural Networks with constrained parameters. We draw comparisons both to current state-of-the-art stochastic Gradient Descent methods as well as across different variants of stochas

David B Dunson, Hau-Tieng Wu, Nan Wu

In nonparametric regression, it is common for the inputs to fall in a restricted subset of Euclidean space. Typical kernel-based methods that do not take into account the intrinsic geometry of the domain across which observations are collected may produce sub-optimal results. In this article, we focus on solving this problem in the context of Gaussian proces

Jacob Nibauer, Eric J. Baxter, Bhuvnesh Jain, Jennifer L. van Saders

The Sun has been found to be depleted in refractory (rock-forming) elements relative to nearby solar analogs, suggesting a potential indicator of planet formation. Given the small amplitude of the depletion, previous analyses have primarily relied on high signal-to-noise stellar spectra and a strictly differential approach to determine elemental abundances.

Amit Bhoonah, Joseph Bramante, Sarah Schon, Ningqiang Song

Cold interstellar gas clouds provide an exciting new method to discover dark matter. Their immense size makes them uniquely sensitive to interactions from the heaviest, most rarefied dark matter models. Using gas cloud observations, we derive constraints on heavy composite dark matter coupled to the Standard Model through a light dark photon for dark matter

Pietro Baldi

We introduce a modified version of the Whitney extension operators for collections of functions from a closed subset of $\mathbb{R}^n$ into scales of Banach spaces with smoothing operators. We prove an extension theorem for collections whose elements take values in different spaces of the scale. A motivation for considering this kind of collections comes fro

Drew Jamieson, Marilena Loverde

We introduce the position-dependent probability distribution function (PDF) of the smoothed matter field as a cosmological observable. In comparison to the PDF itself, the spatial variation of the position-dependent PDF is simpler to model and has distinct dependence on cosmological parameters. We demonstrate that the position-dependent PDF is characterized

Yuan Miao, Enej Ilievski, Oleksandr Gamayun

Using the algebro-geometric approach, we study the structure of semi-classical eigenstates in a weakly-anisotropic quantum Heisenberg spin chain. We outline how classical nonlinear spin waves governed by the anisotropic Landau-Lifshitz equation arise as coherent macroscopic low-energy fluctuations of the ferromagnetic ground state. Special emphasis is devote

A. C. Burgess, P. Danziger, T. Traetta

Let $F$ be a $2$-regular graph of order $v$. The Oberwolfach problem $OP(F)$, posed in 1967 and still open, asks for a decomposition of $K_v$ into copies of $F$. In this paper we show that $OP(F)$ has a solution whenever $F$ has a sufficiently large cycle which meets a given lower bound and, in addition, has a single-flip automorphism, which is an involutory

Jiazhu Dai, Siwei Xiong

Capsule network is a type of neural network that uses the spatial relationship between features to classify images. By capturing the poses and relative positions between features, its ability to recognize affine transformation is improved, and it surpasses traditional convolutional neural networks (CNNs) when handling translation, rotation and scaling. The S

Arthur J. Krener

The two contributions of this paper are as follows. The first is the solution of an infinite dimensional, boundary controlled Linear Quadratic Regulator by the simple and constructive method of completing the square. The second contribution is the extension of Al&#39;brekht&#39;s method to the optimal stabilization of a boundary controlled, nonlinear Reactio

Alisha Sharma, Ryan Nett, Jonathan Ventura

We introduce a convolutional neural network model for unsupervised learning of depth and ego-motion from cylindrical panoramic video. Panoramic depth estimation is an important technology for applications such as virtual reality, 3D modeling, and autonomous robotic navigation. In contrast to previous approaches for applying convolutional neural networks to p

Aravind Sankaran, Paolo Bientinesi

In scientific computing, it is common that a mathematical expression can be computed by many different algorithms (sometimes over hundreds), each identifying a specific sequence of library calls. Although mathematically equivalent, those algorithms might exhibit significant differences in terms of performance. However in practice, due to fluctuations, there

Quentin Baghi, James Ira Thorpe, Jacob Slutsky, John Baker

The future space-based gravitational wave observatory LISA will consist of a constellation of three spacecraft in a triangular constellation, connected by laser interferometers with 2.5 million-kilometer arms. Among other challenges, the success of the mission strongly depends on the quality of the cancellation of laser frequency noise, whose power lies eigh

Nikhil Churamani, Pablo Barros, Hatice Gunes, Stefan Wermter

Collaborative interactions require social robots to adapt to the dynamics of human affective behaviour. Yet, current approaches for affective behaviour generation in robots focus on instantaneous perception to generate a one-to-one mapping between observed human expressions and static robot actions. In this paper, we propose a novel framework for personality

Nicole Bäuerle, Alexander Glauner

In this paper, we consider risk-sensitive Markov Decision Processes (MDPs) with Borel state and action spaces and unbounded cost under both finite and infinite planning horizons. Our optimality criterion is based on the recursive application of static risk measures. This is motivated by recursive utilities in the economic literature, has been studied before

Vinicius M. de Pinho, Marcello L. R. de Campos, Luis Uzeda Garcia, Dalia Popescu

5G is designed to be an essential enabler and a leading infrastructure provider in the communication technology industry by supporting the demand for the growing data traffic and a variety of services with distinct requirements. The use of deep learning and computer vision tools has the means to increase the environmental awareness of the network with inform

Prashant K. Jha, Prathamesh S. Desai, Debdeep Bhattacharya, Robert Lipton

Usage, manipulation, transport, delivery, and mixing of granular or particulate media, comprised of spherical or polyhedral particles, is commonly encountered in industrial sectors of construction (cement and rock fragments), pharmaceutics (tablets), and transportation (ballast). Elucidating particulate media&#39;s behavior in concert with particle attrition

Xinyu Yang, Majid Mirmehdi, Tilo Burghardt

In this paper we show that learning video feature spaces in which temporal cycles are maximally predictable benefits action classification. In particular, we propose a novel learning approach termed Cycle Encoding Prediction (CEP) that is able to effectively represent high-level spatio-temporal structure of unlabelled video content. CEP builds a latent space

Dinghao Yang, Wei Gao

In this paper, we propose a point cloud classification method based on graph neural network and manifold learning. Different from the conventional point cloud analysis methods, this paper uses manifold learning algorithms to embed point cloud features for better considering the geometric continuity on the surface. Then, the nature of point cloud can be acqui

Shazia Afzal, Rajmohan C, Manish Kesarwani, Sameep Mehta

Data exploration and quality analysis is an important yet tedious process in the AI pipeline. Current practices of data cleaning and data readiness assessment for machine learning tasks are mostly conducted in an arbitrary manner which limits their reuse and results in loss of productivity. We introduce the concept of a Data Readiness Report as an accompanyi

Debajyoti Datta, Shashwat Kumar, Laura Barnes, Tom Fletcher

A growing body of recent evidence has highlighted the limitations of natural language processing (NLP) datasets and classifiers. These include the presence of annotation artifacts in datasets, classifiers relying on shallow features like a single word (e.g., if a movie review has the word &#34;romantic&#34;, the review tends to be positive), or unnecessary w

Paul Brown, Trevor Fenner

In this paper, we introduce a new representation for ordered trees, the weight sequence representation. We then use this to construct new representations for both rooted trees and free trees, namely the canonical weight sequence representation. We construct algorithms for generating the weight sequence representations for all rooted and free trees of order n

Romain Tessera

In this note we prove that if a finitely generated amenable group admits a regular map to a direct product of a hyperbolic space and a euclidean space, then it must be virtually nilpotent. We deduce that an amenable group regularly embeds into a hyperbolic group if and only if it is virtually nilpotent, answering a question of Hume and Sisto. We describe an

Soutrik Mandal, Jing Qin, Ruth M. Pfeiffer

Typically, case-control studies to estimate odds-ratios associating risk factors with disease incidence from logistic regression only include cases with newly diagnosed disease. Recently proposed methods allow incorporating information on prevalent cases, individuals who survived from disease diagnosis to sampling, into cross-sectionally sampled case-control

Cashous Bortner, Nicolette Meshkat

We introduce a class of linear compartmental models called identifiable path/cycle models which have the property that all of the monomial functions of parameters associated to the directed cycles and paths from input compartments to output compartments are identifiable and give sufficient conditions to obtain one. Removing leaks, we then show how one can ob

Che-Yu Chen, Yu-Hsien Kung, Pisin Chen

The rapid advancement of gravitational wave astronomy in recent years has paved the way for the burgeoning development of black hole spectroscopy, which enhances the possibility of testing black holes by their quasinormal modes (QNMs). In this paper, the axial gravitational perturbations and the QNM frequencies of black holes in the hybrid metric-Palatini gr

David F. Anderson, Tung D. Nguyen

Deficiency zero is an important network structure and has been the focus of many celebrated results within reaction network theory. In our previous paper \textit{Prevalence of deficiency zero reaction networks in an Erd\H os-Rényi framework}, we provided a framework to quantify the prevalence of deficiency zero among randomly generated reaction networks. Spe

Zhengyu Hu

Coastal vegetation serves as an ecological defense and plays an important role when facing marine disaster such as storm surge. In this study, we investigated experimentally the wave attenuation, including wave height and wave force decay, and flow field within rigid vegetation by means of particle imaging velocimetry (PIV). Drag coefficient of vegetation is

A. N. Petrov

We construct conserved quantities in pure Lovelock gravity for both static and dynamic Vaydia-type black holes with AdS, dS and flat asymptotics, applying field-theoretical formalism developed earlier. Global energy (where applicable), quasi-local energy together with fluxes of these quantities are presented for both types of black holes, considering asympto

Xiaocong Xu, Yipei Chen, Chang Liu, Zhihui Li

In this paper, the unified gas-kinetic wave-particle (UGKWP) method is further developed for diatomic gas with the energy exchange between translational and rotational modes for flow study in all regimes. The multiscale transport mechanism in UGKWP is coming from the direct modeling in a discretized space, where the cell&#39;s Knudsen number, defined by the

Pascal Zimmer, Roland Weinreich, Christian T. Zenger, Aydin Sezgin

In this paper, we investigate physical-layer security (PLS) methods for proximity-based group-key establishment and proof of location. Fields of application include secure car-to-car communication, privacy-preserving and secure distance evidence for healthcare or location-based feature activation. Existing technologies do not solve the problem satisfactorily

Massimo Boninsegni

First principle computer simulations of a thin parahydrogen film adsorbed on a silica substrate at low temperature (below 6 K) yield no evidence that the top layer is liquid and/or in the proximity of a superfluid transition, as claimed in recent experimental work [T. Makiuchi et al., Phys. Rev. Lett. 123, 245301 (2019)]. Computed values of first and second

Hechuan Jiang, Xiaojue Zhu, Dongpu Wang, Sander G. Huisman

High-Rayleigh number convective turbulence is ubiquitous in many natural phenomena and in industries, such as atmospheric circulations, oceanic flows, flows in the fluid core of planets, and energy generations. In this work, we present a novel approach to boost the Rayleigh number in thermal convection by exploiting centrifugal acceleration and rapidly rotat