September 2020 arXiv papers — page 2

Yusuke Nomura

The variational wave functions based on neural networks have recently started to be recognized as a powerful ansatz to represent quantum many-body states accurately. In order to show the usefulness of the method among all available numerical methods, it is imperative to investigate the performance in challenging many-body problems for which the exact solutio

Qi Cai, Yu Wang, Yingwei Pan, Ting Yao

This paper explores useful modifications of the recent development in contrastive learning via novel probabilistic modeling. We derive a particular form of contrastive loss named Joint Contrastive Learning (JCL). JCL implicitly involves the simultaneous learning of an infinite number of query-key pairs, which poses tighter constraints when searching for inva

Neng Wan, Aditya Gahlawat, Naira Hovakimyan, Evangelos A. Theodorou

A distributed stochastic optimal control solution is presented for cooperative multi-agent systems. The network of agents is partitioned into multiple factorial subsystems, each of which consists of a central agent and neighboring agents. Local control actions that rely only on agents' local observations are designed to optimize the joint cost functions

Tommaso d'Orsi, Gleb Novikov, David Steurer

We consider a robust linear regression model $y=Xβ^* + η$, where an adversary oblivious to the design $X\in \mathbb{R}^{n\times d}$ may choose $η$ to corrupt all but an $α$ fraction of the observations $y$ in an arbitrary way. Prior to our work, even for Gaussian $X$, no estimator for $β^*$ was known to be consistent in this model except for quadratic sample

Boris Adamczewski, Michael Drmota, Clemens Müllner

In this paper we develop a method to transfer density results for primitive automatic sequences to logarithmic-density results for general automatic sequences. As an application we show that the logarithmic densities of any automatic sequence along squares $(n^2)_{n\geq 0}$ and primes $(p_n)_{n\geq 1}$ exist and are computable. Furthermore, we give for these

Han Wu, Wenjie Ruan, Jiangtao Wang, Dingchang Zheng

The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not fully understand. However, through model interpretation, black-box models can promptly reveal significant biomarkers that medical practitioners may h

Guido Cossu, Luigi Del Debbio, Andreas Juttner, Ben Kitching-Morley

We present a study of the IR behaviour of a three-dimensional super-renormalisable quantum field theory (QFT) consisting of a scalar field in the adjoint of $SU(N)$ with a $φ^4$ interaction. A bare mass is required for the theory to be massless at the quantum level. In perturbation theory the critical mass is ambiguous due to infrared (IR) divergences and we

Luigi Del Debbio, Elizabeth Dobson, Andreas Jüttner, Ben Kitching-Morley

A nonperturbative determination of the energy-momentum tensor is essential for understanding the physics of strongly coupled systems. The ability of the Wilson flow to eliminate divergent contact terms makes it a practical method for renormalizing the energy-momentum tensor on the lattice. In this paper, we utilize the Wilson flow to define a procedure to re

Eric Chesebro, Jason DeBlois, Neil R Hoffman, Christian Millichap

Neumann and Reid conjecture that there are exactly three knot complements which admit hidden symmetries. This paper establishes several results that provide evidence for the conjecture. Our main technical tools provide obstructions to having infinitely many fillings of a cusped manifold produce knot complements admitting hidden symmetries. Applying these too

Orcan Ogetbil, Narayan Ganesan, Bernhard Hientzsch

We propose Monte Carlo calibration algorithms for three models: local volatility with stochastic interest rates, stochastic local volatility with deterministic interest rates, and finally stochastic local volatility with stochastic interest rates. For each model, we include detailed derivations of the corresponding SDE systems, and list the required input da

Vasily Golyshev, Matt Kerr, Tokio Sasaki

The Apéry numbers of Fano varieties are asymptotic invariants of their quantum differential equations. In this paper, we initiate a program to exhibit these invariants as (mirror to) limiting extension classes of higher cycles on the associated Landau-Ginzburg models -- and thus, in particular, as periods. We also construct an ``Apéry motive'', whose

Kate Pattle, Shih-Ping Lai, Melvyn Wright, Simon Coudé

We present ALMA Band 7 polarisation observations of the OMC-1 region of the Orion molecular cloud. We find that the polarisation pattern observed in the region is likely to have been significantly altered by the radiation field of the $>10^{4}$ L$_{\odot}$ high-mass protostar Orion Source I. In the protostar's optically thick disc, polarisation is likely

Eoin Quinn

Non-canonical degrees of freedom provide one of the most promising routes towards characterising a range of important phenomena in condensed matter physics. Potential candidates include the pseudogap regime of the cuprates, heavy-fermion behaviour, and also indeed magnetically ordered systems. Nevertheless it remains an open question whether non-canonical al

Hanbin Son, Taeoh Kim, Hyeongmin Lee, Sangyoun Lee

To enhance image compression performance, recent deep neural network-based research can be divided into three categories: a learnable codec, a postprocessing network, and a compact representation network. The learnable codec has been designed for an end-to-end learning beyond the conventional compression modules. The postprocessing network increases the qual

Yuriko Baba, Francisco Domínguez-Adame, Gloria Platero, Rafael A. Molina

We study the effect of the Rashba spin-orbit coupling on the Fermi arcs of topological Dirac semimetals. The Rashba coupling is induced by breaking the inversion symmetry at the surface. Remarkably, this coupling could be enhanced by the interaction with the substrate and controlled by an external electric field. We study analytically and numerically the rot

W. A. Zúñiga-Galindo

We study a p-adic reaction-diffusion system and the associated Turing patterns. We establish an instability criteria and show that the Turing patterns are not classical patterns consisting of alternating domains. Instead of this, a Turing pattern consists of several domains (clusters), each of them supporting a different pattern but with the same parameter v

Angelica I Aviles-Rivero, Philip Sellars, Carola-Bibiane Schönlieb, Nicolas Papadakis

Can one learn to diagnose COVID-19 under extreme minimal supervision? Since the outbreak of the novel COVID-19 there has been a rush for developing Artificial Intelligence techniques for expert-level disease identification on Chest X-ray data. In particular, the use of deep supervised learning has become the go-to paradigm. However, the performance of such m

Darko Volkov, Yulong Jiang

We show in this paper a Lipschitz stability result for a crack inverse problem in half space. The direct problem is a Laplace equation with zero Neumann condition on the top boundary. The forcing term is a discontinuity across the crack. This formulation can be related to geological faults in elastic media or to irrotational incompressible flows in a half sp

Pierre Gosselin, Aïleen Lotz, Marc Wambst

This paper models the dynamics of a large set of interacting neurons within the framework of statistical field theory. We use a method initially developed in the context of statistical field theory [44] and later adapted to complex systems in interaction [45][46]. Our model keeps track of individual interacting neurons dynamics but also preserves some of the

Jean-Luc Marichal, Naïm Zenaïdi

In 1922, Harald Bohr and Johannes Mollerup established a remarkable characterization of the Euler gamma function using its log-convexity property. A decade later, Emil Artin investigated this result and used it to derive the basic properties of the gamma function using elementary methods of the calculus. Bohr-Mollerup's theorem was then adopted by Nicola

Keyu Tian, Chen Lin, Ming Sun, Luping Zhou

The recent progress on automatically searching augmentation policies has boosted the performance substantially for various tasks. A key component of automatic augmentation search is the evaluation process for a particular augmentation policy, which is utilized to return reward and usually runs thousands of times. A plain evaluation process, which includes fu

George Mamatsashvili, George Chagelishvili, Martin E. Pessah, Frank Stefani

We investigate sustenance and dependence on magnetic Prandtl number (${\rm Pm}$) for magnetorotational instability (MRI)-driven turbulence in astrophysical Keplerian disks with zero net magnetic flux using standard shearing box simulations. We focus on the turbulence dynamics in Fourier space, capturing specific/noncanonical anisotropy of nonlinear processes

Danae Sánchez Villegas, Daniel Preoţiuc-Pietro, Nikolaos Aletras

Physical places help shape how we perceive the experiences we have there. For the first time, we study the relationship between social media text and the type of the place from where it was posted, whether a park, restaurant, or someplace else. To facilitate this, we introduce a novel data set of $\sim$200,000 English tweets published from 2,761 different po

Ruben W. Verweij, Stefania Ketzetzi, Joost de Graaf, Daniela J. Kraft

Geometric confinement strongly influences the behavior of microparticles in liquid environments. However, to date, nonspherical particle behaviors close to confining boundaries, even as simple as planar walls, remain largely unexplored. Here, we measure the height distribution and orientation of colloidal dumbbells above walls by means of digital in-line hol

Limin Ma

In this paper, we present a unified analysis of the superconvergence property for a large class of mixed discontinuous Galerkin methods. This analysis applies to both the Poisson equation and linear elasticity problems with symmetric stress formulations. Based on this result, some locally postprocess schemes are employed to improve the accuracy of displaceme

Divya Ojha, Sandhya Dwarkadas

Timing side channels have been used to extract cryptographic keys and sensitive documents, even from trusted enclaves. In this paper, we focus on cache side channels created by access to shared code or data in the memory hierarchy. This vulnerability is exploited by several known attacks, e.g, evict+reload for recovering an RSA key and Spectre variants for d

G. X. Wang

By means of variable moment kernel polynomial method, we analyze the localization properties of $β$-graphyne sheet subjected to the Anderson disorder. To detect the localization transition we focus on the scaling behavior of the normalized typical density of states. We find that there takes place a metal-insulator transition and the critical disorder strengt

Byron Tasseff, Carleton Coffrin, Russell Bent, Kaarthik Sundar

As the use of renewable generation has increased, electric power systems have become increasingly reliant on natural gas-fired power plants as fast ramping sources for meeting fluctuating bulk power demands. This dependence has introduced new vulnerabilities to the power grid, including disruptions to gas transmission networks from natural and man-made disas

Kiet Van Nguyen, Duc-Vu Nguyen, Anh Gia-Tuan Nguyen, Ngan Luu-Thuy Nguyen

Over 97 million people speak Vietnamese as their native language in the world. However, there are few research studies on machine reading comprehension (MRC) for Vietnamese, the task of understanding a text and answering questions related to it. Due to the lack of benchmark datasets for Vietnamese, we present the Vietnamese Question Answering Dataset (UIT-Vi

Marco Pegoraro, Merih Seran Uysal, Wil M. P. van der Aalst

Process mining is a discipline which concerns the analysis of execution data of operational processes, the extraction of models from event data, the measurement of the conformance between event data and normative models, and the enhancement of all aspects of processes. Most approaches assume that event data is accurately capture behavior. However, this is no

Mohamed Abbas Hedjazi, Yakup Genc

Recent GAN-based (Generative adversarial networks) inpainting methods show remarkable improvements and generate plausible images using multi-stage networks or Contextual Attention Modules (CAM). However, these techniques increase the model complexity limiting their application in low-resource environments. Furthermore, they fail in generating high-resolution

Huanrui Yang, Jingyang Zhang, Hongliang Dong, Nathan Inkawhich

Recent research finds CNN models for image classification demonstrate overlapped adversarial vulnerabilities: adversarial attacks can mislead CNN models with small perturbations, which can effectively transfer between different models trained on the same dataset. Adversarial training, as a general robustness improvement technique, eliminates the vulnerabilit

Marc van Kreveld, Tillmann Miltzow, Tim Ophelders, Willem Sonke

Given two shapes $A$ and $B$ in the plane with Hausdorff distance $1$, is there a shape $S$ with Hausdorff distance $1/2$ to and from $A$ and $B$? The answer is always yes, and depending on convexity of $A$ and/or $B$, $S$ may be convex, connected, or disconnected. We show that our result can be generalised to give an interpolated shape between $A$ and $B$ f

Theodore R. Sumers, Mark K. Ho, Robert D. Hawkins, Karthik Narasimhan

We explore unconstrained natural language feedback as a learning signal for artificial agents. Humans use rich and varied language to teach, yet most prior work on interactive learning from language assumes a particular form of input (e.g., commands). We propose a general framework which does not make this assumption, using aspect-based sentiment analysis to

Mathis Hoffmann, Claudia Buerhop-Lutz, Luca Reeb, Tobias Pickel

Automated inspection plays an important role in monitoring large-scale photovoltaic power plants. Commonly, electroluminescense measurements are used to identify various types of defects on solar modules but have not been used to determine the power of a module. However, knowledge of the power at maximum power point is important as well, since drops in the p

Mel Vecerik, Jean-Baptiste Regli, Oleg Sushkov, David Barker

A robot's ability to act is fundamentally constrained by what it can perceive. Many existing approaches to visual representation learning utilize general-purpose training criteria, e.g. image reconstruction, smoothness in latent space, or usefulness for control, or else make use of large datasets annotated with specific features (bounding boxes, segmenta

Sarah M. Harrison, Natalie M. Paquette, Daniel Persson, Roberto Volpato

We study some special features of $F_{24}$, the holomorphic $c=12$ superconformal field theory (SCFT) given by 24 chiral free fermions. We construct eight different Lie superalgebras of "physical" states of a chiral superstring compactified on $F_{24}$, and we prove that they all have the structure of Borcherds-Kac-Moody superalgebras. This produces

M. Adnane, L. Djoudi, M. Merabet, M. Boucharef

In this paper, the structural, electronic and magnetic properties of the GdSn$_3$, CmSn$_3$ and Gd$_x$Cm$_{1-x}$Sn$_3$ compounds ($x = 0.25$, $0.5$ and $0.75$) were studied using the full-potential linearized augmented plane wave method, within the generalized gradient approximation$+U$. The ground-state properties are determined for the bulk materials GdSn$

Elisa Affili, Serena Dipierro, Luca Rossi, Enrico Valdinoci

In this monograph, we introduce a new model in population dynamics that describes two species sharing the same environmental resources in a situation of open hostility. The interactions among these populations are described not in terms of random encounters, but via the strategic decisions of one population that can attack the other according to different le

Yang Gao, Hongping Gan, Haiwei CHen, Chunyi Liu

Block compressive sensing is a well-known signal acquisition and reconstruction paradigm with widespread application prospects in science, engineering and cybernetic systems. However, state-of-the-art block-based image compressive sensing (BCS) methods generally suffer from two issues. The sparsifying domain and the sensing matrices widely used for image acq

Tomoyuki Tanaka, Kyouhei Wakasa

We consider the wave equation with a cubic convolution $\partial_t^2 u-Δu=(|x|^{-γ}*u^2)u$ in three space dimensions. Here, $0<γ<3$ and $*$ stands for the convolution in the space variables. It is well known that if initial data are smooth, small and compactly supported, then $γ\ge2$ assures unique global existence of solutions. On the other hand, it is also

Vincent Gripon, Matthias Löwe, Franck Vermet

Recently authors have introduced the idea of training discrete weights neural networks using a mix between classical simulated annealing and a replica ansatz known from the statistical physics literature. Among other points, they claim their method is able to find robust configurations. In this paper, we analyze this so-called &#34;replicated simulated annea

Edward D. Lee, Christopher P. Kempes, Geoffrey B. West

Population-level scaling in ecological systems arises from individual growth and death with competitive constraints. We build on a minimal dynamical model of metabolic growth where the tension between individual growth and mortality determines population size distribution. We include resource competition based on shared capture area separately. By varying re

José Coutinho, Vitor J. B. Torres, Ivana Capan, Tomislav Brodar

In the last two decades we have assisted to a rush towards finding a He3-replacing technology capable of detecting neutrons emitted from fissile isotopes. The demand stems from applications like nuclear war-head screening or preventing illicit traffic of radiological materials. Semiconductor detectors stand among the stronger contenders, particularly those b

Carlos Perales-González

Ensemble approaches introduced in the Extreme Learning Machine (ELM) literature mainly come from methods that relies on data sampling procedures, under the assumption that the training data are heterogeneously enough to set up diverse base learners. To overcome this assumption, it was proposed an ELM ensemble method based on the Negative Correlation Learning

S. I. Kalmykov, D. Karp, A. Kuznetsov

We prove a duality relation for generalized basic hypergeometric functions. It forms a $q$-extension of a recent result of the second and the third named authors and generalizes both a $q$-hypergeometric identity due to the third named author (jointly with Feng and Yang) and a recent identity for the Heine&#39;s ${}_2ϕ_{1}$ function due to Suzuki. We further

Ermis Mitsou, Giuseppe Fanizza, Nastassia Grimm, Jaiyul Yoo

Analytical computations in relativistic cosmology can be split into two sets: time evolution relating the initial conditions to the observer&#39;s light-cone and light propagation to obtain observables. Cosmological perturbation theory in the FLRW coordinates constitutes an efficient tool for the former task, but the latter is dramatically simpler in light-c

Ricardo Sanchez-Matilla, Andrea Cavallaro

Out-of-home audience measurement aims to count and characterize the people exposed to advertising content in the physical world. While audience measurement solutions based on computer vision are of increasing interest, no commonly accepted benchmark exists to evaluate and compare their performance. In this paper, we propose the first benchmark for digital ou

Júlia Borràs, Guillem Alenyà, Carme Torras

Cloth manipulation is very relevant for domestic robotic tasks, but it presents many challenges due to the complexity of representing, recognizing and predicting the behaviour of cloth under manipulation. In this work, we propose a generic, compact and simplified representation of the states of cloth manipulation that allows for representing tasks as sequenc

Raik Suttner, Sergey Dashkovskiy

We analyze stability and robustness properties of an extremum seeking scheme that employs oscillatory dither signals with sufficiently large amplitudes and frequencies. Our study takes both input and output disturbances into account. We consider general $L_\infty$-disturbances, which may resonate with the oscillatory dither signals. A suitable change of coor

Saul Jacka, Ma. Elena Hérnandez-Hérnandez

{Consider a càdlàg local martingale $M$ with square brackets $[M]$. In this paper, we provide upper and lower bounds for expectations of the type ${\mathbb E} [M]^{q/2}_τ$, for any stopping time $τ$ and $q\ge 2$, in terms of predictable processes. This result can be thought of as a Burkholder-Davis-Gundy type inequality in the sense that it can be used to re

Joe G Donaldson, Peter Schall, Laura Rossi

Manipulating the way in which colloidal particles self-organise is a central challenge in the design of functional soft materials. Meeting this challenge requires the use of building blocks that interact with one another in a highly specific manner. Their fabrication, however, is limited by the complexity of the available synthesis procedures. Here, we demon

Erich Poppitz, F. David Wandler

We explicitly calculate the topological terms that arise in IR effective field theories for $SU(N)$ gauge theories on $\mathbb{R}^3 \times S^1$ by integrating out all but the lightest modes. We then show how these terms match all global-symmetry &#39;t Hooft anomalies of the UV description. We limit our discussion to theories with abelian 0-form symmetries,

Simone Bianchi

The first idea of establishing a public astronomical observatory in Florence, Capital of the Grand Duchy of Tuscany, dates to the mid of the 18th century. Initially, the use of a low building on a high ground was proposed, and the hill of Arcetri suggested as a proper location. At the end of the century, the Florence Observatory - or Specola - was built inst

Hao-Hao Li, Gen Ye, Yun-Song Piao

As suggested by the swampland conjectures, de Sitter (dS) space might be highly unstable if it exists at all. During inflation, the short-lived dS states will decay through a cascade of the first-order phase transition (PT). We find that the gravitational waves (GWs) yielded by such a PT will be &#34;reddened&#34; by subsequent dS expansion, which may result

Tong Yu, Nicolas Padoy

This paper tackles a new problem in computer vision: mid-stream video-to-video retrieval. This task, which consists in searching a database for content similar to a video right as it is playing, e.g. from a live stream, exhibits challenging characteristics. Only the beginning part of the video is available as query and new frames are constantly added as the

Okan Köpüklü, Jiapeng Zheng, Hang Xu, Gerhard Rigoll

Distracted drivers are more likely to fail to anticipate hazards, which result in car accidents. Therefore, detecting anomalies in drivers&#39; actions (i.e., any action deviating from normal driving) contains the utmost importance to reduce driver-related accidents. However, there are unbounded many anomalous actions that a driver can do while driving, whic

Claudia Bucur, Serena Dipierro, Luca Lombardini, José M. Mazón

We investigate the convergence as $p\searrow1$ of the minimizers of the $W^{s,p}$-energy for $s\in(0,1)$ and $p\in(1,\infty)$ to those of the $W^{s,1}$-energy, both in the pointwise sense and by means of $Γ$-convergence. We also address the convergence of the corresponding Euler-Lagrange equations, and the equivalence between minimizers and weak solutions. A

Hongfei Xu, Qiuhui Liu, Josef van Genabith, Deyi Xiong

The Transformer translation model is based on the multi-head attention mechanism, which can be parallelized easily. The multi-head attention network performs the scaled dot-product attention function in parallel, empowering the model by jointly attending to information from different representation subspaces at different positions. In this paper, we present

Xi-An Li, Zhi-Qin John Xu, Lei Zhang

Algorithms based on deep neural networks (DNNs) have attracted increasing attention from the scientific computing community. DNN based algorithms are easy to implement, natural for nonlinear problems, and have shown great potential to overcome the curse of dimensionality. In this work, we utilize the multi-scale DNN-based algorithm (MscaleDNN) proposed by Li

Renan Souza, Leonardo G. Azevedo, Vítor Lourenço, Elton Soares

Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In these domains, users need to perform comprehensive data analyses combining scientific data and ML models to provide for cri

Jiaoyan Chen, Pan Hu, Ernesto Jimenez-Ruiz, Ole Magnus Holter

Semantic embedding of knowledge graphs has been widely studied and used for prediction and statistical analysis tasks across various domains such as Natural Language Processing and the Semantic Web. However, less attention has been paid to developing robust methods for embedding OWL (Web Ontology Language) ontologies which can express a much wider range of s

Youri Xu, E Haihong, Meina Song, Wenyu Song

Static knowledge graph (SKG) embedding (SKGE) has been studied intensively in the past years. Recently, temporal knowledge graph (TKG) embedding (TKGE) has emerged. In this paper, we propose a Recursive Temporal Fact Embedding (RTFE) framework to transplant SKGE models to TKGs and to enhance the performance of existing TKGE models for TKG completion. Differe

Andreas Mono

This is a short note generalizing the construction from arXiv:1906.07410, arXiv:2009.04955 to multi-indices. We recommend to consider both references first. We obtain polar harmonic Maaß forms of non-positive integral weight if the dimension is even and greater than $2$. We provide explicit examples in dimension $4$, $6$, $8$, and $10$.

Chiara Marletto, Vlatko Vedral, Laura Knoll, Fabrizio Piacentini

The issue of irreversibility in a universe with time-reversal-symmetric laws is a central problem in physics. % , and, in particular, to statistical mechanics, information theory and quantum thermodynamics. In this letter, we discuss for the first time how irreversibility can emerge within the recently proposed constructor theory framework. Here irreversibil

Huijia Dai, Junchen Hou, Xiao Zhang, Ying Liang

Motivated by the recently experimental reported signatures of the tunable Mott insulating state and superconductivity in an ABC graphene trilayer superlattice, we investigate the charge compressibility, the spin correlation, and the superconducting instability within the Hubbard model on a three-layer honeycomb lattice. It is found that an antiferromagnetica

Yingjie Zhu, Judit Szente, Enrico Landi

The nonthermal broadening of spectral lines formed in the solar corona is often used to seek the evidence of Alfvén waves propagating in the corona. To have a better understanding of the variation of line widths at different altitudes, we measured the line widths of the strong Fe \textsc{xii} 192.4, 193.5, and 195.1 \mboxÅ and Fe \textsc{xiii} 202.0 \mboxÅ i

Okan Köpüklü, Stefan Hörmann, Fabian Herzog, Hakan Cevikalp

Convolutional Neural Networks with 3D kernels (3D-CNNs) currently achieve state-of-the-art results in video recognition tasks due to their supremacy in extracting spatiotemporal features within video frames. There have been many successful 3D-CNN architectures surpassing the state-of-the-art results successively. However, nearly all of them are designed to o

Neeraj, Jimson Mathew, Ranjan Kumar Behera, Zenin Easa Panthakkalakath

The outbreak of COVID-19 i.e. a variation of coronavirus, also known as novel corona virus causing respiratory disease is a big concern worldwide since the end of December 2019. As of September 12, 2020, it has turned into an epidemic outbreak with more than 29 million confirmed cases and around 1 million reported deaths worldwide. It has created an urgent n

Jonas Lähnemann, Vladimir M. Kaganer, Karl K. Sabelfeld, Anastasya E. Kireeva

We investigate the impact of threading dislocations with an edge component (a or a+c-type) on carrier recombination and diffusion in GaN(0001) layers close to the surface as well as in the bulk. To this end, we utilize cathodoluminescence imaging of the top surface of a GaN(0001) layer with a deeply buried (In,Ga)N quantum well. Varying the acceleration volt

Leonid Prigozhin, Vladimir Sokolovsky

A model of high temperature superconducting dynamo, a promising type of flux pumps capable of wireless injection of a large DC current into a superconducting circuit, has recently been chosen as an applied superconductivity benchmark problem and solved using ten different numerical methods (Ainslie et al 2020 Supercond. Sci. Technol. 33 105009). Using expans

Haoyang Cheng, Wenquan Cui, Xu Jianjun

Due to the demand for tackling the problem of streaming data with high dimensional covariates, we propose an online sparse sliced inverse regression (OSSIR) method for online sufficient dimension reduction. The existing online sufficient dimension reduction methods focus on the case when the dimension $p$ is small. In this article, we show that our method ca

Vasileios Skliris, Michael R. K. Norman, Patrick J. Sutton

Convolutional Neural Networks (CNNs) have demonstrated potential for the real-time analysis of data from gravitational-wave detector networks for the specific case of signals from coalescing compact-object binaries such as black-hole binaries. Unfortunately, training these CNNs requires a precise model of the target signal; they are therefore not applicable

Rémy Garnier

Competition between times series often arises in sales prediction, when similar products are on sale on a marketplace. This article provides a model of the presence of cannibalization between times series. This model creates a &#34;competitiveness&#34; function that depends on external features such as price and margin. It also provides a theoretical guarant

Vicenţiu Paşol, Wadim Zudilin

A (folklore?) conjecture states that no holomorphic modular form $F(τ)=\sum_{n=1}^\infty a_nq^n\in q\mathbb Z[[q]]$ exists, where $q=e^{2πiτ}$, such that its anti-derivative $\sum_{n=1}^\infty a_nq^n/n$ has integral coefficients in the $q$-expansion. A recent observation of Broadhurst and Zudilin, rigorously accomplished by Li and Neururer, led to examples o

Nisha Katyal, Gianluigi Ortenzi, John Lee Grenfell, Lena Noack

The magma ocean period was a critical phase determining how Earth atmosphere developed into habitability. However there are major uncertainties in the role of key processes such as outgassing from the planetary interior and escape of species to space that play a major role in determining the atmosphere of early Earth. We investigate the influence of outgassi

Sarbani Roy, Sourav Mukhopadhyay

Quantum secret sharing (QSS) is an important branch of secure multiparty quantum computation. Several schemes for $(n, n)$ threshold QSS based on quantum Fourier transformation (QFT) have been proposed. Inspired by the flexibility of $(t, n)$ threshold schemes, Song {\it et al.} (Scientific Reports, 2017) have proposed a $(t, n)$ threshold QSS utilizing $QFT

Yoshikazu Suzumura, Masao Ogata

The electric conductivity of Dirac electrons in the organic conductor $α$-(BEDT-TTF)$_2$I$_3$ [BEDT-TTF = bis(ethylenedithio)tetrathiafulvalene] under pressure has been examined using a two-dimensional tight-binding (TB) model with both impurity and electron--phonon (e--p) scatterings. We study an anomalous temperature dependence of the conductivity, which s

Simone Diverio, Filippo Fagioli

Let $X$ be a complex manifold, $(E,h)\to X$ be a rank $r$ holomorphic hermitian vector bundle, and $ρ$ be a sequence of dimensions $0 = ρ_0 < ρ_1 < \cdots < ρ_m = r$. Let $Q_{ρ,j}$, $j=1,\dots,m$, be the tautological line bundles over the (possibly incomplete) flag bundle $\mathbb{F}_ρ(E) \to X$ associated to $ρ$, endowed with the natural metrics induced by

Michal Krelina, Jan Nemchik

The $D$-wave admixture in quarkonium wave functions is acquired from the photonlike structure of $V\to Q\bar Q$ transition in the light-front frame widely used in the literature. Such a $D$-wave ballast is not justified by any nonrelativistic model for $Q-\bar Q$ interaction potential and leads to falsified predictions for the cross sections in heavy quarkon

Jamie Bok, Rosalind E. Skelton, Michelle E. Cluver, Thomas H. Jarrett

Using mid-infrared star formation rate and stellar mass indicators in $\textit{WISE}$, we construct and contrast the relation between star formation rate and stellar mass for isolated and paired galaxies. Our samples comprise a selection of AMIGA (isolated galaxies) and pairs of ALFALFA galaxies with HI detections such that we can examine the relationship be

Shinya Sato

The magnetotelluric (MT) responses of the Earth are biased by spatially heterogeneous source fields. Recently, such biases have been reported even in mid-latitude areas, where localized source currents rarely flow. This study focuses on shifts in the MT responses arising from variations in the focus latitude of the solar quiet (Sq) current. The MT responses

Robin Piedeleu, Fabio Zanasi

We develop a fully diagrammatic approach to the theory of finite-state automata, based on reinterpreting their usual state-transition graphical representation as a two-dimensional syntax of string diagrams. Moreover, we provide an equational theory that completely axiomatises language equivalence in this new setting. This theory has two notable features. Fir

Yassine Laguel, Jérôme Malick, Zaid Harchaoui

Classical supervised learning via empirical risk (or negative log-likelihood) minimization hinges upon the assumption that the testing distribution coincides with the training distribution. This assumption can be challenged in modern applications of machine learning in which learning machines may operate at prediction time with testing data whose distributio

Takato Yasuno, Akira Ishii, Masazumi Amakata

Recently, flood damage has become a social problem owing to unexperienced weather conditions arising from climate change. An immediate response to heavy rain is important for the mitigation of economic losses and also for rapid recovery. Spatiotemporal precipitation forecasts may enhance the accuracy of dam inflow prediction, more than 6 hours forward for fl

Delilah Donick, Sandro Claudio Lera

Conventionally, random forests are built from &#34;greedy&#34; decision trees which each consider only one split at a time during their construction. The sub-optimality of greedy implementation has been well-known, yet mainstream adoption of more sophisticated tree building algorithms has been lacking. We examine under what circumstances an implementation of

Kotaro Shimizu, Shun Okumura, Yasuyuki Kato, Yukitoshi Motome

A superposition of spin helices can yield topological spin textures, such as skyrmion and hedgehog lattices. Based on the analogy with the moiré in optics, we study the magnetic and topological properties of such superpositions in a comprehensive way by modulating the interference pattern continuously. We find that the control of the angles between the super

Ram Band, Graham Cox, Sebastian Egger

A Laplacian eigenfunction on a two-dimensional Riemannian manifold provides a natural partition into Neumann domains (a.k.a. a Morse--Smale complex). This partition is generated by gradient flow lines of the eigenfunction, which bound the so-called Neumann domains. We prove that the Neumann Laplacian defined on a Neumann domain is self-adjoint and has a pure

Peter Frankl, Andreas Holmsen, Andrey Kupavskii

Given a family of sets on the plane, we say that the family is intersecting if for any two sets from the family their interiors intersect. In this paper, we study intersecting families of triangles with vertices in a given set of points. In particular, we show that if a set $P$ of $n$ points is in convex position, then the largest intersecting family of tria

Jörn Sass, Dorothee Westphal

We study a utility maximization problem in a financial market with a stochastic drift process, combining a worst-case approach with filtering techniques. Drift processes are difficult to estimate from asset prices, and at the same time optimal strategies in portfolio optimization problems depend crucially on the drift. We approach this problem by setting up

Tim Wallis, Philip T. Barton, Nikolaos Nikiforakis

This article presents a multi-physics methodology for the numerical simulation of physical systems that involve the non-linear interaction of multi-phase reactive fluids and elastoplastic solids, inducing high strain-rates and high deformations. Each state of matter is governed by a single system of non-linear, inhomogeneous partial differential equations, w

Amey Gupta, Daksh Lohiya

We investigate the possibility of accounting for the observed anomalous velocities of stars in galaxies to be a result of a dynamic prescription for having a galaxy generate and prescribe its own inertial frame. It is demonstrated that within observational uncertainties, an overall rotation of an inertial frame dragged by a galaxy is a comfortable fit to the

Lei He, Aouf Nabil, Bifeng Song

Autonomous navigation in unknown complex environment is still a hard problem, especially for small Unmanned Aerial Vehicles (UAVs) with limited computation resources. In this paper, a neural network-based reactive controller is proposed for a quadrotor to fly autonomously in unknown outdoor environment. The navigation controller makes use of only current sen

Carla Boix-Constant, Samuel Mañas-Valero, Rosa Córdoba, José J. Baldoví

Due to their anisotropy, layered materials are excellent candidates for studying the interplay between the in-plane and out-of-plane entanglement in strongly correlated systems. A relevant example is provided by 1T-TaS2, which exhibits a multifaceted electronic and magnetic scenario due to the existence of several charge density wave (CDW) configurations. It

Nikolai Huckle, Noa Garcia, Yuta Nakashima

Computational art analysis has, through its reliance on classification tasks, prioritised historical datasets in which the artworks are already well sorted with the necessary annotations. Art produced today, on the other hand, is numerous and easily accessible, through the internet and social networks that are used by professional and amateur artists alike t

H. Schmickler

Depending on the application people use time-domain or frequency-domain signals in order to measure or describe processes. First we will look at the definition of these terms, produce some mathematical background and then apply the tools to measurements made in the accelerator domain. We will first look at signals produced by a single bunch passing once thro

C. Quesne

We show that the method developed by Gangopadhyaya, Mallow, and their coworkers to deal with (translationally) shape invariant potentials in supersymmetric quantum mechanics and consisting in replacing the shape invariance condition, which is a difference-differential equation, by an infinite set of partial differential equations can be generalized to deform

Utkarsh Bhardwaj, Andrea E. Sand, Manoj Warrier

The morphology of defects formed in collision cascades is an essential aspect of the subsequent evolution of the microstructure. The morphological composition of a defect decides its stability, interaction, and migration properties. We compare the defect morphologies in the primary radiation damage caused by high energy collision cascades simulated using thr

Marco Valentino, Mokanarangan Thayaparan, André Freitas

Most of the contemporary approaches for multi-hop Natural Language Inference (NLI) construct explanations considering each test case in isolation. However, this paradigm is known to suffer from semantic drift, a phenomenon that causes the construction of spurious explanations leading to wrong conclusions. In contrast, this paper proposes an abductive framewo

ATLAS Collaboration

The observation of forward proton scattering in association with lepton pairs ($e^+e^-+p$ or $μ^+μ^-+p$) produced via photon fusion is presented. The scattered proton is detected by the ATLAS Forward Proton spectrometer while the leptons are reconstructed by the central ATLAS detector. Proton-proton collision data recorded in 2017 at a center-of-mass energy