June 2020 arXiv papers — page 20

Yunfeng He, Hengtao, He, Chao-Kai Wen

Constant envelope (CE) precoding design is of great interest for massive multiuser multi-input multi-output systems because it can significantly reduce hardware cost and power consumption. However, existing CE precoding algorithms are hindered by excessive computational overhead. In this letter, a novel model-driven deep learning (DL)-based network that comb

Beomdu Lim, Jongsuk Hong, Hyeong-Sik Yun, Narae Hwang

Stellar kinematics provides the key to understanding the formation process and dynamical evolution of stellar systems. Here, we present a kinematic study of the massive star-forming region W4 in the Cassiopeia OB6 association using the Gaia Data Release 2 and high-resolution optical spectra. This star-forming region is composed of a core cluster (IC 1805) an

Jason Ge, Xingguo Li, Haoming Jiang, Han Liu

We describe a new library named picasso, which implements a unified framework of pathwise coordinate optimization for a variety of sparse learning problems (e.g., sparse linear regression, sparse logistic regression, sparse Poisson regression and scaled sparse linear regression) combined with efficient active set selection strategies. Besides, the library al

Byung Hee An, Tomasz Maciazek

We study geometric presentations of braid groups for particles that are constrained to move on a graph, i.e. a network consisting of nodes and edges. Our proposed set of generators consists of exchanges of pairs of particles on junctions of the graph and of certain circular moves where one particle travels around a simple cycle of the graph. We point out tha

Zhijian Yang, Junhao Wen, Christos Davatzikos

Machine learning methods applied to complex biomedical data has enabled the construction of disease signatures of diagnostic/prognostic value. However, less attention has been given to understanding disease heterogeneity. Semi-supervised clustering methods can address this problem by estimating multiple transformations from a (e.g. healthy) control (CN) grou

Aman Khalid

In this paper, we present an implementation of a cuckoo filter for membership testing, optimized for distributed data stores operating in high workloads. In large databases, querying becomes inefficient using traditional search methods. To achieve optimal performance it is necessary to use probabilistic data structures to test the membership of a given key,

Daichi Akiyama, Keisuke Imoto, Noriyuki Tonami, Yuki Okamoto

Many methods of sound event detection (SED) based on machine learning regard a segmented time frame as one data sample to model training. However, the sound durations of sound events vary greatly depending on the sound event class, e.g., the sound event ``fan'' has a long time duration, while the sound event ``mouse clicking'' is instantaneou

D. M. Evans, D. R. Småbråten, T. S. Holstad, P. E. Vullum

Dislocations are one-dimensional (1D) topological line defects where the lattice deviates from the perfect crystal structure. The presence of dislocations transcends condensed matter research and gives rise to a diverse range of emergent phenomena [1-6], ranging from geological effects [7] to light emission from diodes [8]. Despite their ubiquity, to date, t

Ethan P. White, Richard K. Guy, Renate Scheidler

An $(a,b)$-difference necklace of length $n$ is a circular arrangement of the integers $0, 1, 2, \ldots , n-1$ such that any two neighbours have absolute difference $a$ or $b$. We prove that, subject to certain conditions on $a$ and $b$, such arrangements exist, and provide recurrence relations for the number of $(a,b)$-difference necklaces for $( a, b ) = (

Mahdi Ebrahimi

Let $G$ be a finite group and let $\rm{Irr}(G)$ be the set of all irreducible complex characters of $G$. Let $\rm{cd}(G)$ be the set of all character degrees of $G$ and denote by $ρ(G)$ the set of primes which divide some character degrees in $\rm{cd}(G)$. The character graph $Δ(G)$ associated to $G$ is a graph whose vertex set is $ρ(G)$ and there is an edge

Rohini S Giles, Thomas K Greathouse, Richard G Cosentino, Glenn S Orton

Over the last eight years, a rich dataset of mid-infrared CH4 observations from the TEXES instrument at IRTF has been used to characterize the thermal evolution of Jupiter's stratosphere. These data were used to produce vertically-resolved temperature maps between latitudes of 50°S and 50°N, allowing us to track approximately two periods of Jupiter's

Yashar Esfahani Monfared, Mita Dasog

Group 4 transition metal nitride (TMN) nanoparticles (NPs) display strong plasmonic responses in the visible and near-infrared regimes, exhibit high melting points and significant chemical stability and thus are potential earth-abundant alternatives to Au and Ag based plasmonic applications. However, a detailed understanding of the relationship between TMN N

Jinpeng Guo, Xiaozhe Wang, Boon-Teck Ooi

In this paper, a wide-area measurement system (WAMS)-based method is proposed to estimate the system state matrix for AC system with integrated voltage source converters (VSCs) and identify the electromechanical modes. The proposed method is purely model-free, requiring no knowledge of accurate network topology and system parameters. Numerical studies in the

Mithun Thudiyangal, Kenichi Kasamatsu, Bishwajyoti Dey, Panayotis G. Kevrekidis

We study two-dimensional quantum turbulence in miscible binary Bose-Einstein condensates in either a harmonic trap or a steep-wall trap through the numerical simulations of the Gross-Pitaevskii equations. The turbulence is generated through a Gaussian stirring potential. When the condensates have unequal intra-component coupling strengths or asymmetric trap

Tadej Starčič

We describe the recursive algorithmic procedure to compute the stabilizers of the group of complex orthogonal matrices with respect to the action of similarity on the set of all symmetric matrices. Futhermore, lower bounds for dimensions of the stabilizers for the action of orthogonal $*$-conjugation on Hermitian matrices are obtained. We also prove a result

Yuchen Pang, Tianyi Hao, Annika Dugad, Yiqing Zhou

Simulation of quantum systems is challenging due to the exponential size of the state space. Tensor networks provide a systematically improvable approximation for quantum states. 2D tensor networks such as Projected Entangled Pair States (PEPS) are well-suited for key classes of physical systems and quantum circuits. However, direct contraction of PEPS netwo

Chris Bourne, Yoshiko Ogata

We introduce an index for symmetry protected topological (SPT) phases of infinite fermionic chains with an on-site symmetry given by a finite group $G$. This index takes values in $\mathbb{Z}_2 \times H^1(G,\mathbb{Z}_2) \times H^2(G, U(1)_{\mathfrak{p}})$ with a generalized Wall group law under stacking. We show that this index is an invariant of the classi

Turner Johnson, Andrew J. Benson, Daniel Grin

For idealized (spherical, smooth) dark matter halos described by single-parameter density profiles (such as the NFW profile) there exists a one-to-one mapping between the energy of the halo and the scale radius of its density profile. The energy therefore uniquely determines the concentration parameter of such halos. We exploit this fact to predict the conce

Peter D. Hislop, Christoph A. Marx

We continue our study of the dependence of the density of states measure and related spectral functions of Schrödinger operators on the potential. Whereas our earlier work focused on random Schrödinger operators, we extend these results to Schrödinger operators on infinite graphs with deterministic potentials and ergodic potentials, and improve our results f

Antonio De Rosa, Aida Khajavirad

We introduce the ratio-cut polytope defined as the convex hull of ratio-cut vectors corresponding to all partitions of $n$ points in $\mathbb R^m$ into at most $K$ clusters. This polytope is closely related to the convex hull of the feasible region of a number of clustering problems such as K-means clustering and spectral clustering. We study the facial stru

Jesse Vig, Ali Madani, Lav R. Varshney, Caiming Xiong

Transformer architectures have proven to learn useful representations for protein classification and generation tasks. However, these representations present challenges in interpretability. In this work, we demonstrate a set of methods for analyzing protein Transformer models through the lens of attention. We show that attention: (1) captures the folding str

Nicolas Garcia Trillos, Javier Morales

In this paper we introduce a theoretical framework for semi-discrete optimization using ideas from optimal transport. Our primary motivation is in the field of deep learning, and specifically in the task of neural architecture search. With this aim in mind, we discuss the geometric and theoretical motivation for new techniques for neural architecture search

Nicolas Garcia Trillos, Felix Morales, Javier Morales

In this paper we introduce two algorithms for neural architecture search (NASGD and NASAGD) following the theoretical work by two of the authors [5] which used the geometric structure of optimal transport to introduce the conceptual basis for new notions of traditional and accelerated gradient descent algorithms for the optimization of a function on a semi-d

Linda M. Carpenter, Taylor Murphy, Matthew J. Smylie

In this work we study the collider phenomenology of color-octet scalars (sgluons) in minimal supersymmetric models endowed with a global continuous $R$ symmetry. We systematically catalog the significant decay channels of scalar and pseudoscalar sgluons and identify novel features that are natural in these models. These include decays in nonstandard diboson

Thomás Jung Spier

In this work, we prove a refinement of the Gallai-Edmonds structure theorem for weighted matching polynomials by Ku and Wong. Our proof uses a connection between matching polynomials and branched continued fractions. We also show how this is related to a modification by Sylvester of the classical Sturm's theorem on the number of zeros of a real polynomia

Prateek Gupta, Maxime Gasse, Elias B. Khalil, M. Pawan Kumar

A recent Graph Neural Network (GNN) approach for learning to branch has been shown to successfully reduce the running time of branch-and-bound algorithms for Mixed Integer Linear Programming (MILP). While the GNN relies on a GPU for inference, MILP solvers are purely CPU-based. This severely limits its application as many practitioners may not have access to

MINOS+ Collaboration, :, P. Adamson, I. Anghel

We report the final measurement of the neutrino oscillation parameters $Δm^2_{32}$ and $\sin^2θ_{23}$ using all data from the MINOS and MINOS+ experiments. These data were collected using a total exposure of $23.76 \times 10^{20}$ protons on target producing $ν_{mu}$ and $\overline{ν_μ}$ beams and 60.75 kt$\cdot$yr exposure to atmospheric neutrinos. The meas

Jiefeng Chen, Yixuan Li, Xi Wu, Yingyu Liang

Detecting out-of-distribution (OOD) inputs is critical for safely deploying deep learning models in an open-world setting. However, existing OOD detection solutions can be brittle in the open world, facing various types of adversarial OOD inputs. While methods leveraging auxiliary OOD data have emerged, our analysis on illuminative examples reveals a key ins

Richárd Balka, Márton Elekes, Viktor Kiss, Donát Nagy

We answer a question of Banakh, Jabłońska and Jabłoński by showing that for $d\ge 2$ there exists a compact set $K \subseteq \mathbb{R}^d$ such that the projection of $K$ onto each hyperplane is of non-empty interior, but $K+K$ is nowhere dense. The proof relies on a random construction. A natural approach in the proofs is to construct such a $K$ in the unit

Praneet Bhoj, Roman Gerasimov, Brian Keating

Among the greatest mysteries in cosmology are the flatness problem, concerning the lack of curvature of the universe, and the homogeneity problem, questioning why the universe is almost isotropic despite having regions that are causally disconnected. These problems served as motivation for the theory of inflation, which suggests a period of exponential expan

Ariel Lellouch, Ryan Schultz, Nathaniel J. Lindsey, Biondo Biondi

We show the capabilities of a downhole Distributed Acoustic Sensing (DAS) array in detecting, locating and characterizing low-magnitude earthquakes occurring in the vicinity of the Frontier Observatory for Research in Geothermal Energy (FORGE) site in Utah. 10.5 days of continuous data were acquired in a monitoring well at the FORGE geothermal site during th

L. Coraggio, G. De Gregorio, A. Gargano, N. Itaco

We report in this paper a study in terms of the nuclear shell model about the location of the calcium isotopes drip line. The starting point is considering the realistic two-body potential derived by Entem and Machleidt within chiral perturbation theory at next-to-next-to-next-to-leading order (N3LO), as well as a chiral three-body force at next-to-next-to-l

Samuel P. Fraiberger, Pablo Astudillo, Lorenzo Candeago, Alex Chunet

Using smartphone location data from Colombia, Mexico, and Indonesia, we investigate how non-pharmaceutical policy interventions intended to mitigate the spread of the COVID-19 pandemic impact human mobility. In all three countries, we find that following the implementation of mobility restriction measures, human movement decreased substantially. Importantly,

Ioana-Claudia Lazăr

We give a sufficient condition for the collapsibility of finite square 2-complexes. We show that any finite, CAT(0) square 2-complex retracts to a point through CAT(0) subspaces.

Chris Mingard, Guillermo Valle-Pérez, Joar Skalse, Ard A. Louis

Overparameterised deep neural networks (DNNs) are highly expressive and so can, in principle, generate almost any function that fits a training dataset with zero error. The vast majority of these functions will perform poorly on unseen data, and yet in practice DNNs often generalise remarkably well. This success suggests that a trained DNN must have a strong

Ruslan Rakhimov, Emil Bogomolov, Alexandr Notchenko, Fung Mao

DensePose estimation task is a significant step forward for enhancing user experience computer vision applications ranging from augmented reality to cloth fitting. Existing neural network models capable of solving this task are heavily parameterized and a long way from being transferred to an embedded or mobile device. To enable Dense Pose inference on the e

Linnea M. Lemma, Michael M. Norton, Alexandra M. Tayar, Stephen J. DeCamp

In microtubule-based active nematics, motor-driven extensile motion of microtubule bundles powers chaotic large-scale dynamics. We quantify the interfilament sliding motion both in isolated bundles and in a dense active nematic. The extension speed of an isolated microtubule pair is comparable to the molecular motor stepping speed. In contrast, the net exten

Francis X. Diebold

Entering and exiting the Pandemic Recession, I study the high-frequency real-activity signals provided by a leading nowcast, the ADS Index of Business Conditions produced and released in real time by the Federal Reserve Bank of Philadelphia. I track the evolution of real-time vintage beliefs and compare them to a later-vintage chronology. Real-time ADS plung

Louis Leblond, Melissa Hicks

Scientific laboratories are among the most challenging course components to integrate into online instruction. Available technology restricts the design and nature of experiments and it can be hard to replicate the collaborative lab environment where frequent and immediate instructor feedback is the norm. Here we report on technological and pedagogical aspec

Stefan Hoffmann

We investigate the state complexity of the permutation operation, or the commutative closure, on Alphabetical Pattern Constraints (APC). This class corresponds to level $3/2$ of the Straubing-Th{é}rien Hierarchy and includes the finite, the piecewise-testable, or $\mathcal J$-trivial, and the $\mathcal R$-trivial and $\mathcal L$-trivial languages. We give a

Shreyansh S. Dave, Saumia P. S., Ajit M. Srivastava

Flow has emerged as a crucial probe for the properties of the thermalized medium produced in relativistic heavy-ion collisions. The evolution of initial state fluctuations leaves imprints on the power spectrum of flow coefficients. Therefore flow coefficients are a crucial probe of initial state fluctuations arising from the parton distributions of the colli

Kai Wang, Luis Herranz, Anjan Dutta, Joost van de Weijer

We propose bookworm continual learning(BCL), a flexible setting where unseen classes can be inferred via a semantic model, and the visual model can be updated continually. Thus BCL generalizes both continual learning (CL) and zero-shot learning (ZSL). We also propose the bidirectional imagination (BImag) framework to address BCL where features of both past a

Sam Dillavou, Yohai Bar Sinai, Michael P Brenner, Shmuel M Rubinstein

Classically, the quantity of contact area $A_R$ between two bodies is considered a proxy for the force of friction. However, bond density across the interface - quality of contact - is also relevant, and contemporary debate often centers around the relative importance of these two factors. In this work, we demonstrate that a third factor, often overlooked, p

Fabio Arnez, Huascar Espinoza, Ansgar Radermacher, François Terrier

A key factor for ensuring safety in Autonomous Vehicles (AVs) is to avoid any abnormal behaviors under undesirable and unpredicted circumstances. As AVs increasingly rely on Deep Neural Networks (DNNs) to perform safety-critical tasks, different methods for uncertainty quantification have recently been proposed to measure the inevitable source of errors in d

Stanley F. Dermott, Dan Li, Apostolos A. Christou, Thomas J. J. Kehoe

A determination of the dynamical evolution of the asteroid belt is difficult because the asteroid belt has evolved since the time of asteroid formation through mechanisms that include: (1) catastrophic collisions, (2) rotational disruption, (3) chaotic orbital evolution and (4) orbital evolution driven by Yarkovsky radiation forces. The timescales of these l

L. A. López, J. C. Olvera

We analyzed the frequency shifts of photons emitted from massive particles that move in stable geodesics around black holes with non-linear electrodynamics. The motion of the photon is modified by an effective metric when the Einstein gravity is coupled with non-linear electrodynamics. The kinematic shifts for photons are modified by the magnetic and electri

Span Spanbauer, Cameron Freer, Vikash Mansinghka

We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC. An involutive generative model represents a probability kernel $G(ϕ\mapsto ϕ')$ as an involutive (i.e., self-inverting) deterministic function $f(ϕ, π)$ on an enlarged state sp

Abishek Sankararaman, Soumya Basu, Karthik Abinav Sankararaman

Online learning in a two-sided matching market, with demand side agents continuously competing to be matched with supply side (arms), abstracts the complex interactions under partial information on matching platforms (e.g. UpWork, TaskRabbit). We study the decentralized serial dictatorship setting, a two-sided matching market where the demand side agents hav

Syed A. Uddin, Christopher R. Burns, M. M. Phillips, Nicholas B. Suntzeff

We present optical and near-infrared ($ugriYJH$) photometry of host galaxies of Type Ia supernovae (SN~Ia) observed by the \textit{Carnegie Supernova Project-I}. We determine host galaxy stellar masses and, for the first time, study their correlation with SN~Ia standardized luminosity across optical and near-infrared ($uBgVriYJH$) bands. In the individual ba

Jesús E. Castellanos-Águila, Lucas Lodeiro, Eduardo Menéndez-Proupin, Ana L. Montero-Alejo

Cuprous oxide has been conceived as a potential alternative to traditional organic hole transport layers in hybrid halide perovskite-based solar cells. Device simulations predict record efficiencies using this semiconductor, but experimental results do not yet show this trend. More detailed knowledge about the Cu$_2$O/perovskite interface is mandatory to imp

Josef Rukavicka

We say that a language $L$ is \emph{constantly growing} if there is a constant $c$ such that for every word $u\in L$ there is a word $v\in L$ with $\vert u\vert<\vert v\vert\leq c+\vert u\vert$. We say that a language $L$ is \emph{geometrically growing} if there is a constant $c$ such that for every word $u\in L$ there is a word $v\in L$ with $\vert u\vert<\

Robert Poole-McKenzie, Andreea S. Font, Billy Boxer, Ian G. McCarthy

Dark matter (DM) direct detection experiments aim to place constraints on the DM--nucleon scattering cross-section and the DM particle mass. These constraints depend sensitively on the assumed local DM density and velocity distribution function. While astrophysical observations can inform the former (in a model-dependent way), the latter is not directly acce

Tomoyuki Ichiba, Nicole Tianjiao Yang

The relative arbitrage portfolio outperforms a benchmark portfolio over a given time-horizon with probability one. With market price of risk processes depending on the market portfolio and investors, this paper analyzes the multi-agent optimization of relative arbitrage opportunities in the coupled system of market and wealth dynamics. We construct a well-po

Manimala Chakraborti, Sven Heinemeyer, Ipsita Saha

The electroweak (EW) sector of the Minimal Supersymmetric Standard Model (MSSM) can account for a variety of experimental data. The lighest supersymmetric particle (LSP), which we take as the lightest neutralino, $\tilde χ_1^0$, can account for the observed Dark Matter (DM) content of the universe via coannihilation with the next-to-LSP (NLSP), while being i

Christopher C. Lovell, James E. Geach, Romeel Davé, Desika Narayanan

Matching the number counts of high-$z$ sub-millimetre-selected galaxies (SMGs) has been a long standing problem for galaxy formation models. In this paper, we use 3D dust radiative transfer to model the sub-mm emission from galaxies in the SIMBA cosmological hydrodynamic simulations, and compare predictions to the latest single-dish observational constraints

Emily Moravec, Anthony Gonzalez, Simon Dicker, Stacey Alberts

We present a multi-wavelength investigation of the radio galaxy population in the galaxy cluster MOO J1506+5137 at $z$=1.09$\pm$0.03, which in previous work we identified as having multiple complex radio sources. The combined dataset used in this work includes data from the Low-Frequency Array Two-metre Sky Survey (LoTSS), NSF&#39;s Karl G. Jansky Very Large

Stefano Lanza, Fernando Marchesano, Luca Martucci, Irene Valenzuela

Swampland criteria like the Weak Gravity Conjecture should not only apply to particles, but also to other lower-codimension charged objects in 4d EFTs like strings and membranes. However, the description of the latter is in general subtle due to their large backreaction effects. In the context of 4d $\mathcal{N}=1$ EFTs, we consider $\frac12$BPS strings and

Lena Murchikova, Eric J. Murphy, Dariusz C. Lis, Lee Armus

The extreme ultraviolet (EUV) spectra of distant star-forming regions cannot be probed directly using either ground- or space-based telescopes due to the high cross-section for interaction of EUV photons with the interstellar medium. This makes EUV spectra poorly constrained. The mm/submm recombination lines of H and He, which can be observed from the ground

Allen Scheie

We introduce PyCrystalField, a Python software package for calculating single-ion crystal electric field (CEF) Hamiltonians. This software can calculate a CEF Hamiltonian \textit{ab initio} from a point charge model for any transition or rare earth ion in either the $J$ basis or the $LS$ basis, perform symmetry analysis to identify nonzero CEF parameters, ca

Pablo A. Cano, Ángel Murcia

We describe a non-minimal higher-derivative extension of Einstein-Maxwell theory in which electrically-charged black holes and point charges have globally regular gravitational and electromagnetic fields. We provide an exact static spherically symmetric solution of this theory that reduces to the Reissner-Nordström one at weak coupling, but in which the sing

Andrey Katz, Ennio Salvioni, Bibhushan Shakya

We discuss a minimal realization of the strongly interacting massive particle (SIMP) framework. The model includes a dark copy of QCD with three colors and three light flavors. A massive dark photon, kinetically mixed with the Standard Model hypercharge, maintains kinetic equilibrium between the dark and visible sectors. One of the dark mesons is necessarily

Kota Yanase, Noritaka Shimizu

The theoretical uncertainty in the nuclear Schiff moment is an obstacle to set constraints on $ CP $ violation beyond the standard model from experimental upper bounds on atomic electric dipole moments. We perform large-scale shell-model calculations of the $^{129}$Xe and $^{199}$Hg nuclei with realistic effective interactions. To estimate the Schiff moments

Dmitriy F. Kuznetsov

The article is devoted to the formulation and proof of the theorem on convergence with probability 1 of expansion of iterated Ito stochastic integrals of arbitrary multiplicity based on generalized multiple Fourier series converging in the sense of norm in Hilbert space. The cases of multiple Fourier-Legendre series and multiple trigonomertic Fourier series

Firas Fredj, Yasser Al-Eryani, Setareh Maghsudi, Mohamed Akrout

The emergence of new wireless technologies together with the requirement of massive connectivity results in several technical issues such as excessive interference, high computational demand for signal processing, and lengthy processing delays. In this work, we propose several beamforming techniques for an uplink cell-free network with centralized, semi-dist

Adam R Kosiorek, Hyunjik Kim, Danilo J Rezende

A set is an unordered collection of unique elements--and yet many machine learning models that generate sets impose an implicit or explicit ordering. Since model performance can depend on the choice of order, any particular ordering can lead to sub-optimal results. An alternative solution is to use a permutation-equivariant set generator, which does not spec

Ziyu Wang, Alexander Novikov, Konrad Zolna, Jost Tobias Springenberg

Offline reinforcement learning (RL), also known as batch RL, offers the prospect of policy optimization from large pre-recorded datasets without online environment interaction. It addresses challenges with regard to the cost of data collection and safety, both of which are particularly pertinent to real-world applications of RL. Unfortunately, most off-polic

M. A. López-Castaño, J. F. González-Saavedra, A. Rodríguez-Rivas, E. Abad

We study in this work the dynamics of a collection of identical hollow spheres (ping-pong balls) that rest on a horizontal metallic grid. Fluidization is achieved by means of a turbulent air current coming from below. The upflow is adjusted so that the particles do not levitate over the grid, resulting in quasi-two-dimensional dynamics. We show that the beha

Ali Mirzaeian, Jana Kosecka, Houman Homayoun, Tinoosh Mohsenin

This paper proposes an ensemble learning model that is resistant to adversarial attacks. To build resilience, we introduced a training process where each member learns a radically distinct latent space. Member models are added one at a time to the ensemble. Simultaneously, the loss function is regulated by a reverse knowledge distillation, forcing the new me

Alex Kovner, Eugene Levin, Ming Li, Michael Lublinsky

Further developing ideas set forth in \cite{KLL}, we discuss QCD Reggeon Field Theory (RFT) and formulate restrictions imposed on its Hamiltonian by the unitarity of underlying QCD. We identify explicitly the QCD RFT Hilbert space, provide algebra of the basic degrees of freedom (Wilson lines and their duals) and the algorithm for calculating the scattering

Aydin Deger, Fredrik Brange, Christian Flindt

We investigate the Ising model in one, two, and three dimensions using a cumulant method that allows us to determine the Lee-Yang zeros from the magnetization fluctuations in small lattices. By doing so with increasing system size, we are able to determine the convergence point of the Lee-Yang zeros in the thermodynamic limit and thereby predict the occurren

Alessandro Bravetti, Manuel de León, Juan Carlos Marrero, Edith Padrón

We prove that, under some natural conditions, Hamiltonian systems on a contact manifold $C$ can be split into a Reeb dynamics on an open subset of $C$ and a Liouville dynamics on a submanifold of $C$ of codimension 1. For the Reeb dynamics we find an invariant measure. Moreover, we show that, under certain completeness conditions, the existence of an invaria

Daniel G. Figueroa, Adrien Florio, Francisco Torrenti, Wessel Valkenburg

We present a comprehensive discussion on lattice techniques for the simulation of scalar and gauge field dynamics in an expanding universe. After reviewing the continuum formulation of scalar and gauge field interactions in Minkowski and FLRW backgrounds, we introduce basic tools for the discretization of field theories, including lattice gauge invariant tec

Thomas A. Ivey, Spiro Karigiannis

A twisted-austere $k$-fold $(M, μ)$ in $\mathbb R^n$ consists of a $k$-dimensional submanifold $M$ of $\mathbb R^n$ together with a closed $1$-form $μ$ on $M$ such that the `twisted conormal bundle&#39; $N^* M + μ$ is a special Lagrangian submanifold of $\mathbb C^n$. The 1-form $μ$ and the second fundamental form of $M$ must satisfy a particular system of c

James B. Dent, Bhaskar Dutta, Jayden L. Newstead, Adrian Thompson

We show that XENON1T and future liquid xenon (LXe) direct detection experiments are sensitive to axions through the standard $g_{aγ}aF\tilde{F}$ operators due to inverse-Primakoff scattering. This previously neglected channel significantly improves the sensitivity to the axion-photon coupling, with a reach extending to $g_{aγ} \sim 10^{-10}$ GeV$^{-1}$ for a

Jingyun Jia, Philip K. Chan

Open set recognition (OSR) is the problem of classifying the known classes, meanwhile identifying the unknown classes when the collected samples cannot exhaust all the classes. There are many applications for the OSR problem. For instance, the frequently emerged new malware classes require a system that can classify the known classes and identify the unknown

Rossella Bartolo, Erasmo Caponio, Alessio Pomponio

We consider a Dirichlet problem for the mean curvature operator in the Minkowski spacetime, obtaining a necessary and sufficient condition for the existence of a spacelike solution, with prescribed mean curvature, which is the graph of a function defined on a domain equal to the complement in $\mathbb R^n$ of the union of a finite number of bounded Lipschitz

M. Agostini, K. Altenmüller, S. Appel, V. Atroshchenko

For most of their existence stars are fueled by the fusion of hydrogen into helium proceeding via two theoretically well understood processes, namely the $pp$ chain and the CNO cycle. Neutrinos emitted along such fusion processes in the solar core are the only direct probe of the deep interior of the star. A complete spectroscopy of neutrinos from the {\it p

William DeRocco, Peter W. Graham, Surjeet Rajendran

Stellar cooling arguments place strict restrictions on a wide variety of models of new physics. In this paper, we argue that mechanisms to evade these constraints are restricted by thermodynamic arguments, then present a minimal model extension that allows new particles to evade all stellar constraints. In doing this, we demonstrate that interesting paramete

Clement Vignac, Andreas Loukas, Pascal Frossard

Message-passing has proved to be an effective way to design graph neural networks, as it is able to leverage both permutation equivariance and an inductive bias towards learning local structures in order to achieve good generalization. However, current message-passing architectures have a limited representation power and fail to learn basic topological prope

Ningchuan Zhang

In this paper, we give a new explanation of congruences of Eisenstein series of level $Γ_1(N)$ and character $χ$. Our approach is based on Katz&#39;s algebro-geometric explanation of $p$-adic congruences of normalized Eisenstein series $E_{2k}$ of level $1$. One crucial step in our argument is to reformulate a Riemann-Hilbert correspondence in Katz&#39;s exp

Enrique Vasco, María J. Ramírez-Peral, Enrique G. Michel, Celia Polop

Mullins&#39; theory predicts the buildup of adatoms during surface diffusion at the edges of grooves where grain boundaries emerge to the surface of a polycrystalline film. However, the mesoscopic nature of this theory prevents the identification of the atomic scale physical mechanisms involved in this phenomenon. Here, we interpret the buildup of adatoms in

M. K. Joshi, A. Fabre, C. Maier, T. Brydges

We present experiments on polarization gradient cooling of Ca$^+$ multi-ion Coulomb crystals in a linear Paul trap. Polarization gradient cooling of the collective modes of motion whose eigenvectors have overlap with the symmetry axis of the trap is achieved by two counter-propagating laser beams with mutually orthogonal linear polarizations that are blue-de

Ville Tengvall

We introduce a certain integrability condition for the reciprocal of the Jacobian determinant which guarantees the local homeomorphism property of quasiregular mappings with a small inner dilatation. This condition turns out to be sharp in the planar case. We also show that every branch point of a quasiregular mapping with a small inner dilatation is a Lebes

Sylvain Prolhac

The Bethe ansatz solution of periodic TASEP is formulated in terms of a ramified covering from a Riemann surface to the sphere. The joint probability distribution of height fluctuations at $n$ distinct times has in particular a relatively simple expression as a function of $n$ variables on the Riemann surface built from exponentials of Abelian integrals, tra

Mohammadmehdi Ataei, Markus Bussmann, Vahid Shaayegan, Franco Costa

Volume of fluid (VOF) methods are extensively used to track fluid interfaces in numerical simulations, and many VOF algorithms require that the interface be reconstructed geometrically. For this purpose, the Piecewise Linear Interface Construction (PLIC) technique is most frequently used, which for reasons of geometric complexity can be slow and difficult to

DUNE Collaboration, B. Abi, R. Acciarri, M. A. Acero

The sensitivity of the Deep Underground Neutrino Experiment (DUNE) to neutrino oscillation is determined, based on a full simulation, reconstruction, and event selection of the far detector and a full simulation and parameterized analysis of the near detector. Detailed uncertainties due to the flux prediction, neutrino interaction model, and detector effects

Jacob Muller, Jonathan Rohleder

The Krein-von Neumann extension is studied for Schrödinger operators on metric graphs. Among other things, its vertex conditions are expressed explicitly, and its relation to other self-adjoint vertex conditions (e.g. continuity-Kirchhoff) is explored. A variational characterisation for its positive eigenvalues is obtained. Based on this, the behaviour of it

Luigi Gresele, Giancarlo Fissore, Adrián Javaloy, Bernhard Schölkopf

Learning expressive probabilistic models correctly describing the data is a ubiquitous problem in machine learning. A popular approach for solving it is mapping the observations into a representation space with a simple joint distribution, which can typically be written as a product of its marginals -- thus drawing a connection with the field of nonlinear in

Weiguo Yin

One-dimensional systems---ranging from travelling light to circuit cables and from DNA to superstrings---are ubiquitous and critically important to the human knowledge of the universe. However, our engagement with one-dimensional systems in the research and education of spontaneous phase transitions, the phenomena wherein materials can change rapidly between

Jason Saied

Sahi, Stokman, and Venkateswaran have constructed, for each positive integer $n$, a family of Laurent polynomials depending on parameters $q$ and $k$ (in addition to $\lfloor n/2\rfloor$ &#34;metaplectic parameters&#34;), such that the $n=1$ case recovers the nonsymmetric Macdonald polynomials and the $q\rightarrow\infty$ limit yields metaplectic Iwahori-Whi

Vishal Sharma, Curtis Dyreson

Coronavirus Disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 virus (SARS-CoV-2). The virus transmits rapidly; it has a basic reproductive number R of 2.2-2.7. In March 2020, the World Health Organization declared the COVID-19 outbreak a pandemic. COVID-19 is currently affecting more than 200 countries with 6M active cases.

Brian C. Thomas, Cody L. Ratterman

Ozone in Earth&#39;s atmosphere is known to have a radiative forcing effect on climate. Motivated by geochemical evidence for one or more nearby supernovae about 2.6 million years ago, we have investigated the question of whether a supernova at about 50 pc could cause a change in Earth&#39;s climate through its impact on atmospheric ozone concentrations. We

Asmaa Abbas, Mohammed M. Abdelsamea, Mohamed Gaber

Due to the high availability of large-scale annotated image datasets, knowledge transfer from pre-trained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domai

Stefania Patrizi, Tharathep Sangsawang

We consider a semi-linear integro-differential equation in dimension one associated to the half Laplacian %This model describes the evolution of phase transitions associated to dislocations. whose solution represents the atom dislocation in a crystal. The equation comprises the evolutive version of the classical Peierls-Nabarro model. We show that for a larg

Katsuhiro Miguchi

In this paper, we introduce a new variation of the Teichmüller space, namely the deformation space of hyperbolic structures on a surface with both enhancement and decoration. We construct the parameterization of this deformation space, which is a common generalization of the shear coordinates and the $λ$-length coordinates. Furthermore, we introduce the lami

Pavel Exner

We consider a soft quantum waveguide described by a two-dimensional Schrödinger operators with an attractive potential in the form of a channel of a fixed profile built along an infinite smooth curve which is not straight but it is asymptotically straight in a suitable sense. Using Birman-Schwinger principle we show that the discrete spectrum of such an oper

Tobias Lenz

We prove that the homotopy theory of parsummable categories (as defined by Schwede) with respect to the underlying equivalences of categories is equivalent to the usual homotopy theory of symmetric monoidal categories. In particular, this yields a model of symmetric monoidal categories in terms of categories equipped with a strictly commutative, associative,

Daniel Duffy, John S. Biggins

Flat sheets encoded with patterns of contraction/elongation morph into curved surfaces. If the surfaces bear Gauss curvature, the resulting actuation can be strong and powerful. We deploy the Gauss-Bonnet theorem to deduce the Gauss curvature encoded in a pattern of uniform-magnitude contraction/elongation with spatially varying direction, as is commonly imp

Mihalis Maliakas, Dimitra-Dionysia Stergiopoulou

Let $k$ be an infinite field of positive characteristic. We determine all homomorphisms between Weyl modules for $GLn(k)$, where one of the partitions is a hook. As a consequence we obtain a nonvanishing result concerning homomorphisms between Weyl modules for algebraic groups of type B, C and D when one of the partitions is a hook.

Xingrui Yu, Yueming Lyu, Ivor W. Tsang

Imitation learning in a high-dimensional environment is challenging. Most inverse reinforcement learning (IRL) methods fail to outperform the demonstrator in such a high-dimensional environment, e.g., Atari domain. To address this challenge, we propose a novel reward learning module to generate intrinsic reward signals via a generative model. Our generative