June 2020 arXiv papers — page 40

Yang Li, Valentino Tosatti

We obtain sharp upper and lower bounds for the diameter of Ricci-flat Kahler metrics on polarized Calabi-Yau degeneration families, as conjectured by Kontsevich-Soibelman.

Huai-Dong Cao, Ernani Ribeiro, Detang Zhou

In this article, we study four-dimensional complete gradient shrinking Ricci solitons. We prove that a four-dimensional complete gradient shrinking Ricci soliton satisfying a pointwise condition involving either the self-dual or anti-self-dual part of the Weyl tensor is either Einstein, or a finite quotient of either the Gaussian shrinking soliton $\Bbb{R}^4

A. Williamson, P. Dickinson, T. Lambrou, J. C. Murray

Recent advancements in Convolutional Neural Networks have yielded super-human levels of performance in image recognition tasks [13, 25]; however, with increasing volumes of parcels crossing UK borders each year, classification of threats becomes integral to the smooth operation of UK borders. In this work we propose the first pipeline to effectively process

Claudio Bonati, Alessio Franchi, Andrea Pelissetto, Ettore Vicari

We investigate the low-temperature behavior of two-dimensional (2D) RP$^{N-1}$ models, characterized by a global O($N$) symmetry and a local ${\mathbb Z}_2$ symmetry. For $N=3$ we perform large-scale simulations of four different 2D lattice models: two standard lattice models and two different constrained models. We also consider a constrained mixed O(3)-RP$

Aleksey Fedoseev, Akerke Tleugazy, Luiza Labazanova, Dzmitry Tsetserukou

This paper proposes a novel concept of a hybrid tactile display with multistimulus feedback, allowing the real-time experience of the position, shape, and texture of the virtual object. The key technology of the TeslaMirror is that we can deliver the sensation of object parameters (pressure, vibration, and electrotactile feedback) without any wearable haptic

Tom Bridgeland, Ian A. B. Strachan

The notion of a Joyce structure was introduced in arXiv:1912.06504 to describe the geometric structure on the space of stability conditions of a CY3 category naturally encoded by the Donaldson-Thomas invariants. In this paper we show that a Joyce structure on a complex manifold defines a complex hyperkähler structure on the total space of its tangent bundle,

Jamil Khalouf-Rivera, Francisco Pérez-Bernal, Miguel Carvajal

We present an extension of the Hamiltonian of the two dimensional limit of the vibron model encompassing all possible interactions up to four-body operators. We apply this Hamiltonian to the modeling of the experimental bending spectrum of fourteen molecules. The bending degrees of freedom of the selected molecular species include all possible situations: li

Omar Rivasplata, Ilja Kuzborskij, Csaba Szepesvari, John Shawe-Taylor

We focus on a stochastic learning model where the learner observes a finite set of training examples and the output of the learning process is a data-dependent distribution over a space of hypotheses. The learned data-dependent distribution is then used to make randomized predictions, and the high-level theme addressed here is guaranteeing the quality of pre

Luca Banszerus, Thomas Fabian, Samuel Möller, Eike Icking

A gate-defined quantum dot in bilayer graphene is utilized as a sensitive electrometer for probing the charge density of its environment. Under the influence of a perpendicular magnetic field, the charge carrier density of the channel region next to the quantum dot oscillates due to the formation of Landau levels. This is experimentally observed as oscillati

Jacob McErlean, John Malik, Yu-Ting Lin, Ronen Talmon

Objective: We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one EDR signal that is of higher quality. Methods: We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal qua

Alexander E. Patkowski

This paper establishes new results concerning asymptotic expansions of $q$-series related to partial theta functions. We first establish a new method to obtain asymptotic expansions using a result of Ono and Lovejoy, and then build on these observations to obtain asymptotic expansions for related multi hypergeometric series.

Hanrui Wang, Xingbo Dong, Zhe Jin, Andrew Beng Jin Teoh

In cancellable biometrics (CB) schemes, template security is achieved by applying, mainly non-linear, transformations to the biometric template. The transformation is designed to preserve the template distance/similarity in the transformed domain. Despite its effectiveness, the security issues attributed to similarity preservation property of CB are underest

Oscar Randal-Williams

We construct a ring homomorphism comparing the tautological ring, fixing a point, of a closed smooth manifold with that of its stabilisation by $S^{2a} \times S^{2b}$.

Nick Huggett, Fedele Lizzi, Tushar Menon

Noncommutative geometries generalize standard smooth geometries, parametrizing the noncommutativity of dimensions with a fundamental quantity with the dimensions of area. The question arises then of whether the concept of a region smaller than the scale - and ultimately the concept of a point - makes sense in such a theory. We argue that it does not, in two

Giulia Bertagnolli, Manlio De Domenico

Complex networks are characterized by latent geometries induced by their topology or by the dynamics on the top of them. In the latter case, different network-driven processes induce distinct geometric features that can be captured by adequate metrics. Random walks, a proxy for a broad spectrum of processes, from simple contagion to metastable synchronizatio

Brian Allen, Raquel Perales

Given a compact, connected, and oriented manifold with boundary $M$ and a sequence of smooth Riemannian metrics defined on it, $g_j$, we prove volume preserving intrinsic flat convergence of the sequence to the smooth Riemannian metric $g_0$ provided $g_j$ always measures vectors strictly larger than or equal to $g_0$, the diameter of $g_j$ is uniformly boun

Adam B. Cahaya, Alejandro O. Leon, Mojtaba Rahimi Aliabad, Gerrit E. W. Bauer

Dilute alloys of rare earths have played a vital role in understanding magnetic phenomena. Here, we model the ground state of dilute 4f rare-earth impurities in light metals. When the 4f subshells are open (but not half-filled), the spin-orbit coupling imprints a rotational charge current of conduction electrons around rare-earth atoms. The sign and amplitud

Nicolay Rusnachenko, Natalia Loukachevitch

In the sentiment attitude extraction task, the aim is to identify <<attitudes>> -- sentiment relations between entities mentioned in text. In this paper, we provide a study on attention-based context encoders in the sentiment attitude extraction task. For this task, we adapt attentive context encoders of two types: (1) feature-based; (2) self-based. In our s

Hansjoerg Albrecher, Mogens Bladt, Jorge Yslas

The class of inhomogeneous phase-type distributions (IPH) was recently introduced in Albrecher and Bladt (2019) as an extension of the classical phase-type (PH) distributions. Like PH distributions, the class of IPH is dense in the class of distributions on the positive halfline, but leads to more parsimonious models in the presence of heavy tails. In this p

Stefano Cresci

On the morning of November 9th 2016, the world woke up to the shocking outcome of the US Presidential elections: Donald Trump was the 45th President of the United States of America. An unexpected event that still has tremendous consequences all over the world. Today, we know that a minority of social bots, automated social media accounts mimicking humans, pl

Denis M. Basko, Robert S. Whitney

J. E. Hirsch [EPL 130 (2020) 17006] claimed an inconsistency between thermodynamics and the theory of superconductivity. We argue that he overlooked a crucial term which determines the supercurrent dynamics and ensures energy conservation by providing an internal energy source for the Joule heating. Thermodynamic consistency is restored by restoring energy c

Chris Fox, Paul Wiegert

If a transiting exoplanet has a moon, that moon could be detected directly from the transit it produces itself, or indirectly via the transit timing variations it produces in its parent planet. There is a range of parameter space where the Kepler Space Telescope is sensitive to the TTVs exomoons might produce, though the moons themselves would be too small t

Sebastian M. Cioabă, Gordon Royle, Zhao Kuang Tan

In this paper, we study the flip graph on the perfect matchings of a complete graph of even order. We investigate its combinatorial and spectral properties including connections to the signed reversal graph and we improve a previous upper bound on its chromatic number.

Max Sagebaum, Johannes Blühdorn, Nicolas R. Gauger

The identification of primal variables and adjoint variables is usually done via indices in operator overloading algorithmic differentiation tools. One approach is a linear management scheme, which is easy to implement and supports memory optimization for copy statements. An alternative approach performs a reuse of indices, which requires more implementation

Kevin J. McGown, Frank Thorne, Amanda Tucker

We prove several results concerning genus numbers of quintic fields: we compute the proportion of quintic fields with genus number one; we prove that a positive proportion of quintic fields have arbitrarily large genus number; and we compute the average genus number of quintic fields. All of these results also hold when restricted to $S_5$-quintic fields onl

Kees Wapenaar

With the Marchenko method, Green&#39;s functions in the subsurface can be retrieved from seismic reflection data at the surface. State-of-the-art Marchenko methods work well for propagating waves but break down for evanescent waves. This paper discusses a first step towards extending the Marchenko method for evanescent waves and analyses its possibilities an

George Bouzianis, Lane P. Hughston

We consider the problem of optimal hedging in an incomplete market with an established pricing kernel. In such a market, prices are uniquely determined, but perfect hedges are usually not available. We work in the rather general setting of a Lévy-Ito market, where assets are driven jointly by an $n$-dimensional Brownian motion and an independent Poisson rand

Xingwen Zheng, Wei Wang, Liang Li, Guangming Xie

The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviours. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based on ALLS-measured hydrodynamic pressu

David Pfau, Irina Higgins, Aleksandar Botev, Sébastien Racanière

We present a novel nonparametric algorithm for symmetry-based disentangling of data manifolds, the Geometric Manifold Component Estimator (GEOMANCER). GEOMANCER provides a partial answer to the question posed by Higgins et al. (2018): is it possible to learn how to factorize a Lie group solely from observations of the orbit of an object it acts on? We show t

Slawomir Dinew

We study a Monge-Ampère type equation in the class of $p$-plurisubharmonic functions and establish first and second order interior estimates. As an application of these we show that $p$-plurisubharmonic functions with constant operator and quadratic growth must be quadratic polynomials.

Arijit Sehanobish, Neal G. Ravindra, David van Dijk

A molecular and cellular understanding of how SARS-CoV-2 variably infects and causes severe COVID-19 remains a bottleneck in developing interventions to end the pandemic. We sought to use deep learning to study the biology of SARS-CoV-2 infection and COVID-19 severity by identifying transcriptomic patterns and cell types associated with SARS-CoV-2 infection

Marek Nečada, Päivi Törmä

The multiple scattering method T-matrix (MSTMM) can be used to solve the electromagnetic response of systems consisting of many compact scatterers, retaining a good level of accuracy while using relatively few degrees of freedom, largely surpassing other methods in the number of scatterers it can deal with. Here we extend the method to infinite periodic stru

Sakin Demir

Let $(x_n)$ be a sequence and $ρ\geq 1$. For a fixed sequences $n_1<n_2<n_3<\dots$, and $M$ define the oscillation operators $$\mathcal{O}_ρ(x_n)=\left(\sum_{k=1}^\infty\sup_{\substack{n_k\leq m< n_{k+1}\\m\in M}}\left|x_m-x_{n_k}\right|^ρ\right)^{1/ρ}.$$ Let $(X,\mathscr{B} ,μ, τ)$ be a dynamical system with $(X,\mathscr{B} ,μ)$ a probability space and $τ$

Moritz Dittmann, Haowu Wang

Gritsenko, Skoruppa and Zagier associated to a root system $R$ a theta block $\vartheta_R$, which is a Jacobi form of lattice index. We classify the theta blocks $\vartheta_R$ of $q$-order $1$ and show that their Gritsenko lift is a strongly-reflective Borcherds product of singular weight, which is related to Conway&#39;s group $\operatorname{Co}_0$. As a co

Ashish R. Hota, Jaydeep Godbole, Philip E Paré

Motivated by the ongoing pandemic COVID-19, we propose a closed-loop framework that combines inference from testing data, learning the parameters of the dynamics and optimal resource allocation for controlling the spread of the susceptible-infected-recovered (SIR) epidemic on networks. Our framework incorporates several key factors present in testing data, s

Beren Millidge, Alexander Tschantz, Anil K Seth, Christopher L Buckley

Active Inference (AIF) is an emerging framework in the brain sciences which suggests that biological agents act to minimise a variational bound on model evidence. Control-as-Inference (CAI) is a framework within reinforcement learning which casts decision making as a variational inference problem. While these frameworks both consider action selection through

Jianrong Xu, Boyu Diao, Bifeng Cui, Kang Yang

Deep learning has achieved impressive results in many areas, but the deployment of edge intelligent devices is still very slow. To solve this problem, we propose a novel compression and acceleration method based on data distribution characteristics for deep neural networks, namely Pruning Filter via Gaussian Distribution Feature (PFGDF). Compared with previo

Klaus Floettmann

The generation of electron vortex beams and the conversion of these beams into beams without angular momentum by means of astigmatic optical systems, or vice versa, has been pursued in the optical and the electron microscopy community, but also in the accelerator community in the past decades. Despite different conceptual approaches similar results have been

Haralampos Geranios, Alexander Kleshchev, Lucia Morotti

A conjecture going back to the eighties claims that there are no non-trivial self-extensions of irreducible modules over symmetric groups if the characteristic of the ground field is not equal to $2$. We obtain some partial positive results on this conjecture.

Matej Filip

We consider deformations of a pair $(X,\partial X)$, where $X$ is an affine toric Gorenstein variety and $\partial X$ is its boundary. We compute the tangent and obstruction space for the corresponding deformation functor and for an admissible lattice degree $m$ we construct the miniversal deformation of $(X,\partial X)$ in degrees $-km$, for all $k\in \math

Yiran Wang, Eric Chung, Shubin Fu

In this paper, we propose a coupled Discrete Empirical Interpolation Method (DEIM) and Generalized Multiscale Finite element method (GMsFEM) to solve nonlinear parabolic equations with application to the Allen-Cahn equation. The Allen-Cahn equation is a model for nonlinear reaction-diffusion process. It is often used to model interface motion in time, e.g. p

Apostol Vassilev, Munawar Hasan, Honglan Jin

When people try to understand nuanced language they typically process multiple input sensor modalities to complete this cognitive task. It turns out the human brain has even a specialized neuron formation, called sagittal stratum, to help us understand sarcasm. We use this biological formation as the inspiration for designing a neural network architecture th

Kirill Amelin, Johannes Engelmayer, Johan Viirok, Urmas Nagel

Close to the quantum critical point of the transverse-field Ising spin-chain model, an exotic dynamic spectrum was predicted to emerge upon a perturbative longitudinal field. The dynamic spectrum consists of eight particles and is governed by the symmetry of the $E_8$ Lie algebra. Here we report on high-resolution terahertz spectroscopy of quantum spin dynam

Yiran Wang, Eric Chung, Shubin Fu, Michael Presho

In this paper, we consider an online enrichment procedure using the Generalized Multiscale Finite Element Method (GMsFEM) in the context of a two-phase flow model in heterogeneous porous media. The coefficient of the elliptic equation is referred to as the permeability and is the main source of heterogeneity within the model. The elliptic pressure equation i

T. Mendes-Santos, X. Turkeshi, M. Dalmonte, Alex Rodriguez

The identification of universal properties from minimally processed data sets is one goal of machine learning techniques applied to statistical physics. Here, we study how the minimum number of variables needed to accurately describe the important features of a data set - the intrinsic dimension ($I_d$) - behaves in the vicinity of phase transitions. We empl

Vincent Gaebel, Michel Bonne, Tom Reichert, Ajdin Burnic

The deuteron coalescence parameter $B_2$ in proton+proton and nucleus+nucleus collisions in the energy range of $\sqrt{s_{NN}}=$ 900 - 7000 GeV for proton+proton and $\sqrt{s_{NN}}=$ 2 - 2760 GeV for nucleus+nucleus collisions is analyzed with the Ultrarelativistic Quantum Molecular Dynamics (UrQMD) transport model, supplemented by an event-by-event phase sp

Klaus Floettmann, Dmitry Karlovets

Electron vortex beams offer unique opportunities for the study of chiral or magnetic structures in electron microscopes and of fundamental effects of quantum interference in particle physics. Immersing a cathode in a solenoid field presents a highly efficient and flexible method for the generation of vortex electron beams which is utilized at accelerators, b

ATLAS Collaboration

A search for new physics with non-resonant signals in dielectron and dimuon final states in the mass range above 2 TeV is presented. This is the first search for non-resonant signals in dilepton final states at the LHC to use a background estimate from the data. The data, corresponding to an integrated luminosity of 139 fb$^{-1}$, were recorded by the ATLAS

D. Sztenkiel, K. Gas, J. Z. Domagala, D. Hommel

A ferromagnetic coupling between localized Mn spins was predicted in a series of \textit{ab initio} and tight binding calculations and experimentally verified for the dilute magnetic semiconductor Ga$_{1-x}$Mn$_x$N. In the limit of small Mn concentrations, $x \lesssim 0.01$, the paramagnetic properties of this material were successfully described using a sin

Jean-Yves Charbonnel

The commuting variety of a reductive Lie algebra $\mathfrak{g}$ is the underlying variety of a well defined subscheme of $\mathfrak{g}\times\mathfrak{g}$. In this note, it is proved that this scheme is normal and Cohen-Macaulay. In particular, its ideal of definition is a prime ideal. As a matter of fact, this theorem results from a so called Property (P) fo

Rosanna Turrisi, Rémi Flamary, Alain Rakotomamonjy, Massimiliano Pontil

The problem of domain adaptation on an unlabeled target dataset using knowledge from multiple labelled source datasets is becoming increasingly important. A key challenge is to design an approach that overcomes the covariate and target shift both among the sources, and between the source and target domains. In this paper, we address this problem from a new p

Joseph Gal

Precise fitting of the experimental data analyzed separately for each identified crystallographic phase alpha, omega, beta, beta prime and beta double prime yield different bulk moduli Bo and Bo prime and different zero pressure volumes (Vo) than those claimed in the literature. Special attention is given to the bcc phases indicating cascading transitions be

Yuri Manin, Matilde Marcolli

In this paper we develop a novel mathematical formalism for the modeling of neural information networks endowed with additional structure in the form of assignments of resources, either computational or metabolic or informational. The starting point for this construction is the notion of summing functors and of Segal&#39;s Gamma-spaces in homotopy theory. Th

Zhengbing He

As a newly-emerging travel mode in the era of mobile internet, ride-hailing that connects passengers with private-car drivers via an online platform has been very popular all over the world. Although it attracts much attention in both practice and theory, the understanding of ride-hailing is still very limited largely because of the lack of related data. For

P. Diego-Palazuelos, P. Vielva, E. Martínez-González, R. B. Barreiro

Most of the CMB experiments proposed for the next generation aim to detect the Primordial Gravitational Wave Background (PGWB). The fulfillment of this objective depends on our capacity to separate Galactic foreground emissions and to \emph{delens} the secondary B-mode component induced by weak gravitational lensing. Focusing on the latter of these efforts,

Jonas Knoerr

We construct valuations on the space of finite-valued convex functions using integration of differential forms over the differential cycle associated to a convex function. We describe the kernel of this procedure and show that the intersection of this space of smooth valuations with the space of all continuous dually epi-translation invariant valuations on c

Peng Suo, Huiyun Zhang, Shengnan Yan, Wenjie Zhang

As a newly emergent type-II Dirac semimetal, Platinum Telluride (PtTe2) stands out from other 2D noble-transition-metal dichalcogenides for the unique structure and novel physical properties, such as high carrier mobility, strong electron-phonon coupling and tunable bandgap, which make the PtTe2 a good candidate for applications in optoelectronics, valleytro

Simon Eberle, Xavier Ros-Oton, Georg S. Weiss

In this paper we give a full classification of global solutions of the obstacle problem for the fractional Laplacian (including the thin obstacle problem) with compact coincidence set and at most polynomial growth in dimension $N \geq 3$. We do this in terms of a bijection onto a set of polynomials describing the asymptotics of the solution. Furthermore we p

Radu D. Stochiţoiu, Marian Petrica, Traian Rebedea, Ionel Popescu

Analysing and understanding the transmission and evolution of the COVID-19 pandemic is mandatory to be able to design the best social and medical policies, foresee their outcomes and deal with all the subsequent socio-economic effects. We address this important problem from a computational and machine learning perspective. More specifically, we want to stati

Augustin Mouze

For a holomorphic function $f$ in the open unit disc $\mathbb{D}$ and $ζ\in\mathbb{D}$, $S_n(f,ζ)$ denotes the $n$-th partial sum of the Taylor development of $f$ at $ζ$. Given an increasing sequence of positive integers $μ=(μ_n)$, we consider the classes $\mathcal{U}(\mathbb{D},ζ)$ (resp. $\mathcal{U}^{(μ)}(\mathbb{D},ζ)$) of such functions $f$ such that th

Jerzy Białkowski, Karin Erdmann, Adam Hajduk, Andrzej Skowroński

We describe the structure and properties of the finite-dimensional symmetric algebras over an algebraically closed field $K$ which are socle equivalent to the general weighted surface algebras of triangulated surfaces, investigated in Erdmann and Skowroński (J. Algebra 544:170-227, 2020). In particular, we prove that all these algebras are tame periodic alge

Luisa Paoluzzi

For any integer n > 2, the n-fold cyclic branched cover M of an alternating prime knot K in the 3-sphere determines K, meaning that if K is a knot in the 3-sphere that is not equivalent to K then its n-fold cyclic branched cover cannot be homeomorphic to M.

Antonio Figueroa, Malte Goettsche

Several applications such as nuclear forensics, nuclear fuel cycle simulations and sensitivity analysis require methods to quickly compute spent fuel nuclide compositions for various irradiation histories. Traditionally, this has been done by interpolating between one-group cross-sections that have been pre-computed from nuclear reactor simulations for a gri

Peggy Cénac, Antoine Godichon-Baggioni, Bruno Portier

Non linear regression models are a standard tool for modeling real phenomena, with several applications in machine learning, ecology, econometry... Estimating the parameters of the model has garnered a lot of attention during many years. We focus here on a recursive method for estimating parameters of non linear regressions. Indeed, these kinds of methods, w

Léonard Hussenot, Robert Dadashi, Matthieu Geist, Olivier Pietquin

The study of exploration in the domain of decision making has a long history but remains actively debated. From the vast literature that addressed this topic for decades under various points of view (e.g., developmental psychology, experimental design, artificial intelligence), intrinsic motivation emerged as a concept that can practically be transferred to

Emilio Elizalde, Martiros Khurshudyan

Bayesian (Probabilistic) Machine Learning is used to probe the opacity of the Universe. It relies on a generative process where the model is the key object to generate the data involving the unknown parameters of the model, our prior beliefs, and allows us to get the posterior results. The constraints on the cosmic opacity are determined for two flat models,

Domenic P. J. Germano, James M. Osborne

Multicellular tissues are the building blocks of many biological systems and organs. These tissues are not static, but dynamically change over time. Even if the overall structure remains the same there is a turnover of cells within the tissue. This dynamic homeostasis is maintained by numerous governing mechanisms which are finely tuned in such a way that th

Stuart Eiffert, Kunming Li, Mao Shan, Stewart Worrall

Understanding and predicting the intention of pedestrians is essential to enable autonomous vehicles and mobile robots to navigate crowds. This problem becomes increasingly complex when we consider the uncertainty and multimodality of pedestrian motion, as well as the implicit interactions between members of a crowd, including any response to a vehicle. Our

Abdulqader Mahmoud, Florin Ciubotaru, Frederic Vanderveken, Andrii V. Chumak

This paper provides a tutorial overview over recent vigorous efforts to develop computing systems based on spin waves instead of charges and voltages. Spin-wave computing can be considered as a subfield of spintronics, which uses magnetic excitations for computation and memory applications. The tutorial combines backgrounds in spin-wave and device physics as

Veli Safak

In this paper, I solve a 60-year old question posed by Cohen&#39;s seminal paper (1960) and offer an agreement measure centered around the chance-expected agreement while isolating marginally forced agreement and disagreement. To achieve this, I formulate the minimum feasible agreement given row and column marginals by devising a new algorithm that minimizes

Andreas Hinterreiter, Marc Streit, Bernhard Kainz

High-dimensional latent representations learned by neural network classifiers are notoriously hard to interpret. Especially in medical applications, model developers and domain experts desire a better understanding of how these latent representations relate to the resulting classification performance. We present Projective Latent Interventions (PLIs), a tech

Luca Battaglia, Maria Medina, Angela Pistoia

We consider the problem of prescribing Gaussian and geodesic curvatures for a conformal metric on the unit disk. This is equivalent to solving the following P.D.E. \begin{equation*}\begin{cases}-Δu=2K(z)e^u&\hbox{in}\;\mathbb{D}^2,\\ \partial_νu+2=2h(z)e^\frac u2&\hbox{on}\;\partial\mathbb{D}^2,\end{cases} \end{equation*} where $K,h$ are the prescribed curva

K. Kovalenko, I. Sendiña-Nadal, N. Khalil, A. Dainiak

The past two decades have seen significant successes in our understanding of complex networked systems, from the mapping of real-world social, biological and technological networks to the establishment of generative models recovering their observed macroscopic patterns. These advances, however, are restricted to pairwise interactions, captured by dyadic link

Andreas Emil Feldmann, David Saulpic

We study clustering problems such as k-Median, k-Means, and Facility Location in graphs of low highway dimension, which is a graph parameter modeling transportation networks. It was previously shown that approximation schemes for these problems exist, which either run in quasi-polynomial time (assuming constant highway dimension) [Feldmann et al. SICOMP 2018

Chaojie Mo, Dmitry A. Fedosov

Synchronization of two actuated sheets serves as a simple model for the interaction between flagellated microswimmers. Various factors, including inertia, sheet elasticity, and fluid viscoelasticity, have been suggested to facilitate the synchronization of two sheets; however, the importance of different contributions to this process still remains unclear. W

Patrick Delorme

We show that the Hilbert subspace of $L^2(G(F)\backslash G(\A))$ generated by wave packets of Eisenstein series built from discrete series is the whole space. Together with the work of Lapid \cite{L1}, it achieves a proof of the spectral theorem of Langlands based on the work of Bernstein and Lapid \cite{BL} on the meromorphic continuation of Eisenstein seri

Daniel Pellegrino, Anselmo Raposo

The Kahane--Salem--Zygmund inequality for multilinear forms in $\ell_{\infty}$ spaces claims that, for all positive integers $m,n_{1},...,n_{m}$, there exists an $m$-linear form $A\colon\ell_{\infty}^{n_{1}}\times\cdots\times \ell_{\infty}^{n_{m}}\longrightarrow\mathbb{K}$ ($\mathbb{K}=\mathbb{R}$ or $\mathbb{C}$) of the type \[ A(z^{(1)},...,z^{(m)})=\sum_{

Fabian L. M. Bernal, Bruno Gonano, Fredrik Lundvall, David S. Wragg

We report a novel synthesis method for, and structural and magnetic characterization of the fluoroperovskite $\mathrm{NaFeF_3}$. We have developed a wet-chemical method that allows preparation of large volumes of air-sensitive fluoroperovskites with high purity. $\mathrm{NaFeF_3}$ has a Néel temperature ($T_N$) of 90 K and a Weiss constant ($θ$) of -124 K, c

Hyeonsoo Lee, Won-Ki Jeong

Segmentation is a fundamental process in microscopic cell image analysis. With the advent of recent advances in deep learning, more accurate and high-throughput cell segmentation has become feasible. However, most existing deep learning-based cell segmentation algorithms require fully annotated ground-truth cell labels, which are time-consuming and labor-int

Marianna Chatzakou

The purpose of this note is to compare the properties of the symbolic pseudo-differential calculus on the Heisenberg and on the Engel groups; nilpotent Lie groups of 2-step and 3-step, respectively. Here we provide a preliminary analysis of the structure and of the symbolic calculus with symbols parametrized by $(λ,μ)$ on the Engel group, while for the case

Amir N. Khan

We perform a constrained spectral fit analysis of the excess observed in the electron recoil energy spectrum by XENON1T with neutrino magnetic moment, charge radius, neutrino millicharge and new light vector and scalar mediators. Within the limits allowed by other laboratory experiments we find that the excess can be explained in the range$\ (2-4)\times 10^{

Hao Fu, Matteo Lucca, Silvia Galli, Elia S. Battistelli

Measurements of the cosmic microwave background (CMB) spectral distortions (SDs) will open a new window on the very early universe, providing new information complementary to that gathered from CMB temperature and polarization anisotropies. In this paper, we study their synergy as a function of the characteristics of the considered experiments. In particular

Maria Archidiacono, Stefano Gariazzo, Carlo Giunti, Steen Hannestad

Sterile neutrinos with a mass in the eV range have been invoked as a possible explanation of a variety of short baseline (SBL) neutrino oscillation anomalies. However, if one considers neutrino oscillations between active and sterile neutrinos, such neutrinos would have been fully thermalised in the early universe, and would be therefore in strong conflict w

Pietro Benedusi, Michael Minion, Rolf Krause

We consider two parallel-in-time approaches applied to a (reaction) diffusion problem, possibly non-linear. In particular, we consider PFASST (Parallel Full Approximation Scheme in Space and Time) and space-time multilevel strategies. For both approaches, we start from an integral formulation of the continuous time-dependent problem. Then, a collocation form

C. D. Dashwood, L. S. I. Veiga, Q. Faure, J. G. Vale

We show how complex modulated order can spontaneously emerge when magnetic interactions compete in a metal with polar lattice distortions. Combining neutron and resonant x-ray scattering with symmetry analysis, we reveal that the spin reorientation in Ca$_3$Ru$_2$O$_7$ is mediated by a magnetic cycloid whose eccentricity evolves smoothly but rapidly with tem

Julien Launay, Iacopo Poli, François Boniface, Florent Krzakala

Despite being the workhorse of deep learning, the backpropagation algorithm is no panacea. It enforces sequential layer updates, thus preventing efficient parallelization of the training process. Furthermore, its biological plausibility is being challenged. Alternative schemes have been devised; yet, under the constraint of synaptic asymmetry, none have scal

R. Siebenmorgen, J. Krełowski, J. Smoker, G. Galazutdinov

The precise characteristics of clouds and the nature of dust in the diffuse interstellar medium can only be extracted by inspecting the rare cases of single-cloud sightlines. In our nomenclature such objects are identified by interstellar lines, such as K I, that show at a resolving power of $λ/Δλ\sim 75,000$ one dominating Doppler component that accounts fo

Hélène Seiler, Daniela Zahn, Marios Zacharias, Patrick Hildebrandt

We combine femtosecond electron diffuse scattering experiments and first-principles calculations of the coupled electron-phonon dynamics to provide a detailed momentum-resolved picture of the ultrafast lattice thermalization in a thin film of black phosphorus. The measurements reveal the emergence of highly anisotropic non-thermal phonon populations which pe

Niels Bruun Ipsen, Pierre-Alexandre Mattei, Jes Frellsen

When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables

Jennifer D&#39;Souza, Sören Auer

We describe an annotation initiative to capture the scholarly contributions in natural language processing (NLP) articles, particularly, for the articles that discuss machine learning (ML) approaches for various information extraction tasks. We develop the annotation task based on a pilot annotation exercise on 50 NLP-ML scholarly articles presenting contrib

Patrick Ingram

We study the dynamics of the map endomorphism of N-dimensional projective space defined by f(X)=AX^d, where A is a matrix and d is at least 2. When d>N^2+N+1, we show that the critical height of such a morphism is comparable to its height in moduli space, confirming a case of a natural generalization of a conjecture of Silverman.

Ambroise van Roekeghem, Jesús Carrete, Natalio Mingo

The Quantum Self-Consistent Ab-Initio Lattice Dynamics package (QSCAILD) is a python library that computes temperature-dependent effective 2nd and 3rd order interatomic force constants in crystals, including anharmonic effects. QSCAILD&#39;s approach is based on the quantum statistics of a harmonic model. The program requires the forces acting on displaced a

Injo Hur, Yeansu Kim

We construct irreducible balanced non-transitive sets of $n$-sided dice for any positive integer $n$, which was raised in \cite[Question 5.2]{SS17}. One main tool of the construction is to study so-called fair sets of dice. Furthermore, we also study the distribution of the probabilities of balanced non-transitive sets of dice. For a lower bound, we show tha

Roberta Raileanu, Max Goldstein, Denis Yarats, Ilya Kostrikov

Deep reinforcement learning (RL) agents often fail to generalize to unseen scenarios, even when they are trained on many instances of semantically similar environments. Data augmentation has recently been shown to improve the sample efficiency and generalization of RL agents. However, different tasks tend to benefit from different kinds of data augmentation.

Congqiao Li, Ying An, Claude Charlot, Roberto Covarelli

Loop-induced $ZZ$ production can be enhanced by the large gluon flux at the LHC, and thus should be taken into account in relevant experimental analyses. We present for the first time the results of a fully exclusive simulation based on the matrix elements for loop-induced $ZZ + 0,1,2$-parton processes at leading order, matched to parton showers. The new des

Daniel Hümmer, Oriol Romero-Isart, Arno Rauschenbeutel, Philipp Schneeweiss

Quantum control of atoms at ultrashort distances from surfaces would open a new paradigm in quantum optics and offer a novel tool for the investigation of near-surface physics. Here, we investigate the motional states of atoms that are bound weakly to the surface of a hot optical nanofiber. We theoretically demonstrate that with optimized mechanical properti

L. Bandiera, A. Sytov, D. De Salvador, A. Mazzolari

We report on the measurements of the spectra of gamma radiation generated by 855 MeV electrons in bent silicon and germanium crystals at MAMI (MAinzer MIkrotron). The crystals were 15 μm thick along the beam direction to ensure high deflection efficiency. Their (111) crystalline planes were bent by means of a piezo-actuated mechanical holder, which allowed t

Zhao Yuan, Antonio Zecchino, Rachid Cherkaoui, Mario Paolone

Frequency response and voltage support are vital ancillary services for power grids. In this paper, we design and experimentally validate a real-time control framework for battery energy storage systems (BESSs) to provide ancillary services to power grids. The objective of the control system is to utilize the full capability of the BESSs to provide ancillary

Gabriele De Chiara, Mauro Antezza

We consider thermal machines powered by locally equilibrium reservoirs that share classical or quantum correlations. The reservoirs are modelled by the so-called collisional model or repeated interactions model. In our framework, two reservoir particles, initially prepared in a thermal state, are correlated through a unitary transformation and afterwards int

Alexandre Defossez, Gabriel Synnaeve, Yossi Adi

We present a causal speech enhancement model working on the raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with skip-connections. It is optimized on both time and frequency domains, using multiple loss functions. Empirical evidence shows that it is capable of removing various kinds of backg