December 2020 arXiv papers — page 81

Clement Gauchy, Jerome Stenger, Roman Sueur, Bertrand Iooss

Robustness analysis is an emerging field in the domain of uncertainty quantification. It consists of analysing the response of a computer model with uncertain inputs to the perturbation of one or several of its input distributions. Thus, a practical robustness analysis methodology should rely on a coherent definition of a distribution perturbation. This pape

Nicholas Egan, John Bohannon

The prevalence of ambiguous acronyms make scientific documents harder to understand for humans and machines alike, presenting a need for models that can automatically identify acronyms in text and disambiguate their meaning. We introduce new methods for acronym identification and disambiguation: our acronym identification model projects learned token embeddi

Faeze Brahman, Vered Shwartz, Rachel Rudinger, Yejin Choi

The black-box nature of neural models has motivated a line of research that aims to generate natural language rationales to explain why a model made certain predictions. Such rationale generation models, to date, have been trained on dataset-specific crowdsourced rationales, but this approach is costly and is not generalizable to new tasks and domains. In th

Vassilios Vargiamidis, P. Vasilopoulos, M. Tahir, Neophytos Neophytou

A valley-contrasting Berry curvature in bilayer transition metal dichalcogenides with spin-orbit coupling can generate valley magnetization when the inversion symmetry is broken, for example, by an electric field, regardless of time-reversal symmetry. A nontrivial Berry curvature can also lead to anomalous transport responses, such as the anomalous Hall effe

Danish Syed, Naman Gandhi, Arushi Arora, Nilesh Kadam

Annually the gaming industry spends approximately $15 billion in marketing reinvestment. However, this amount is spent without any consideration for the skill and luck of the player. For a casino, an unskilled player could fetch ~4 times more revenue than a skilled player. This paper describes a video recognition system that is based on an extension of the M

Andrew Craig, Brian A. Davey, Miroslav Haviar

Bilattices, which provide an algebraic tool for simultaneously modelling knowledge and truth, were introduced by N.D. Belnap in a 1977 paper entitled 'How a computer should think'. Prioritised default bilattices include not only Belnap's four values, for `true' ($t$), `false'($f$), `contradiction' ($\top$) and `no information' ($\bot$), but also indexed fami

Yae Jee Cho, Samarth Gupta, Gauri Joshi, Osman Yağan

Due to communication constraints and intermittent client availability in federated learning, only a subset of clients can participate in each training round. While most prior works assume uniform and unbiased client selection, recent work on biased client selection has shown that selecting clients with higher local losses can improve error convergence speed.

Cody Covington, Justin Malave, Kalman Varga

Coupled Maxwell and time-dependent orbital-free calculations are implemented and tested to describe the interaction of electromagnetic waves and matter. The currents and induced fields predicted by the orbital-free calculations are compared to time-dependent density functional calculations and very good agreement is found for various systems including jelliu

James G. McHugh, Pavlos Mouratidis, Kenny Jolley

The ripplocation is a crystallographic defect which is unique to layered materials, combining nanoscale delamination with the crystallographic slip of a basal dislocation. Here, we have studied basal dislocations and ripplocations, in single and multiple van der Waals layers, using analytical and computational techniques. Expressions for the energetic and st

Matt Hohertz

In a previous paper, we introduced the Collatz polynomials $P_N(z)$, whose coefficients are the terms of the Collatz sequence of the positive integer $N$. Our work in this paper expands on our previous results, using the Enestr\"om-Kakeya Theorem to tighten our old bounds of the roots of $P_N(z)$ and giving precise conditions under which these new bounds are

Andrea Zanette

Several practical applications of reinforcement learning involve an agent learning from past data without the possibility of further exploration. Often these applications require us to 1) identify a near optimal policy or to 2) estimate the value of a target policy. For both tasks we derive \emph{exponential} information-theoretic lower bounds in discounted

José W. F. Valle

I discuss neutrino mixing ansatze, such as the generalized Tri-bimaximal and bi-large mixing patterns, and their utility in describing the oscillation data. Unitarity tests and probes of the absolute neutrino mass scale are briefly discussed. A short overview of neutrino mass generation is given. I discuss an orbifold approach to the flavor problem and the r

Mihai Christodorescu, Wanyun Catherine Gu, Ranjit Kumaresan, Mohsen Minaei

Digital payments traditionally rely on online communications with several intermediaries such as banks, payment networks, and payment processors in order to authorize and process payment transactions. While these communication networks are designed to be highly available with continuous uptime, there may be times when an end-user experiences little or no acc

Maxim Bichuch, Zachary Feinstein

In this work we present an equilibrium formulation for price impacts. This is motivated by the Buhlmann equilibrium in which assets are sold into a system of market participants, e.g. a fire sale in systemic risk, and can be viewed as a generalization of the Esscher premium. Existence and uniqueness of clearing prices for the liquidation of a portfolio are s

Philip Welch

We consider how changes in transfinite machine architecture can sometimes alter substantially their capabilities. We approach the subject by answering three open problems touching on: firstly differing halting time considerations for machines with multiple as opposed to single heads, secondly space requirements, and lastly limit rules. We: 1) use admissibili

Sharan Srinivas, Surya Ramachandiran

To understand the important dimensions of service quality from the passenger's perspective and tailor service offerings for competitive advantage, airlines can capitalize on the abundantly available online customer reviews (OCR). The objective of this paper is to discover company- and competitor-specific intelligence from OCR using an unsupervised text analy

ShahRukh Athar, Albert Pumarola, Francesc Moreno-Noguer, Dimitris Samaras

Facial Expressions induce a variety of high-level details on the 3D face geometry. For example, a smile causes the wrinkling of cheeks or the formation of dimples, while being angry often causes wrinkling of the forehead. Morphable Models (3DMMs) of the human face fail to capture such fine details in their PCA-based representations and consequently cannot ge

Yuhao Kang, Azriel Z. Genack

Understanding vanishing transmission in Fano resonances in quantum systems and metamaterials, and ultralow transmission in disordered media is key to elucidating optical interactions. Using analytic theory and numerical simulations, we discover the topological structure and motion under deformation of transmission zeros in the complex energy plane. We demons

Dmytro Khomenko, David R. Reichman, Francesco Zamponi

Tunnelling Two-Level Systems (TLS) dominate the physics of glasses at low temperatures. Yet TLS are extremely rare and it is extremely difficult to directly observe them $\it{in \, silico}$. It is thus crucial to develop simple structural predictors that can provide markers for determining if a TLS is present in a given glass region. It has been speculated t

MicroBooNE collaboration, P. Abratenko, M. Alrashed, R. An

We present the performance of a semantic segmentation network, SparseSSNet, that provides pixel-level classification of MicroBooNE data. The MicroBooNE experiment employs a liquid argon time projection chamber for the study of neutrino properties and interactions. SparseSSNet is a submanifold sparse convolutional neural network, which provides the initial ma

Jonas H. Joergensen, Henrik Bruus

We present an effective thermoviscous theory of acoustofluidics including pressure acoustics, thermoviscous boundary layers, and streaming for fluids embedded in elastic cavities. By including thermal fields, we thus extend the effective viscous theory by Bach and Bruus, J. Acoust. Soc. Am. 144, 766 (2018). The acoustic temperature field and the thermoviscou

Christopher M. Colose, Jacob Haqq-Misra, Eric T. Wolf, Anthony D. Del Genio

Much attention has been given to the climate dynamics and habitable boundaries of synchronously rotating planets around low mass stars. However, other rotational states are possible, particularly when higher eccentricity orbits can be maintained in a system, including spin-orbit resonant configurations. Additionally, the oscillating strain as a planet moves

Seongjun Choi, Meng-Che "Turbo" Ho, Mark Pengitore

A group is said to hae a rational growth with respect to the generating set if the growth series is a rational polynomial. It was shown by Parry that a subset of torus bundle groups exhibits rational growth. We generalize this result to other torus bundle groups.

Mohammadreza Ebrahimi, Ning Zhang, James Hu, Muhammad Taqi Raza

Anti-malware engines are the first line of defense against malicious software. While widely used, feature engineering-based anti-malware engines are vulnerable to unseen (zero-day) attacks. Recently, deep learning-based static anti-malware detectors have achieved success in identifying unseen attacks without requiring feature engineering and dynamic analysis

H. P. Martins, S. A. Khan, G. Conti, A. A. Greer

Hybrid multiferroics such as BiFeO$_3$ (BFO) and La$_{0.7}$Sr$_{0.3}$MnO$_3$ (LSMO) heterostructures are highly interesting functional systems due to their complex electronic and magnetic properties. One of the key parameters influencing the emergent properties is the quality of interfaces, where varying interdiffusion lengths can give rise to different chem

V. A. Dougalis, A. Duran, L. Saridaki

In this paper a three-parameter family of Boussinesq systems is studied. The systems have been proposed as models of the propagation of long internal waves along the interface of a two-layer system of fluids with rigid-lid condition for the upper layer and under a Boussinesq regime for the flow in both layers. We first present some theoretical properties of

Christina Hedges, Ruth Angus, Geert Barentsen, Nicholas Saunders

NASA's TESS mission \citep{tess} has produced high precision photometry of millions of stars to the community. The majority of TESS observations have a duration of $\approx$27 days, corresponding to a single observation during a TESS sector. A small subset of TESS targets are observed for multiple sectors, with approximately 1-2\% of targets falling in the C

Yi-Hsien Du, Umang Mehta, Dam Thanh Son

We consider the problem of calculating the excitation spectrum of a gas of nonrelativistic anyons. When the anyons have statistics close to fermionic and the statistical angle has the form $\theta=\pi (1-\frac{1}{k})$ where $k$ is a large integer, the problem can be solved by employing the method of bosonization, which maps the problem to that of an infinite

Ajay Brahmakshatriya, Yunming Zhang, Changwan Hong, Shoaib Kamil

The performance of graph programs depends highly on the algorithm, the size and structure of the input graphs, as well as the features of the underlying hardware. No single set of optimizations or one hardware platform works well across all settings. To achieve high performance, the programmer must carefully select which set of optimizations and hardware pla

Shadrack Awah Buo

Deepfake technology (DT) has taken a new level of sophistication. Cybercriminals now can manipulate sounds, images, and videos to defraud and misinform individuals and businesses. This represents a growing threat to international institutions and individuals which needs to be addressed. This paper provides an overview of deepfakes, their benefits to society,

Viacheslav Dubeyko

Knowledge is the most precious asset of humankind. People extract the experience from the data that provide for us the reality through the feelings. Generally speaking, it is possible to see the analogy of knowledge elaboration between humankind's way and the artificial system's way. Digital data are the "feelings" of an artificial system, and it needs to in

Xu Han, Xiaohui Chen, Li-Ping Liu

When formulated as an unsupervised learning problem, anomaly detection often requires a model to learn the distribution of normal data. Previous works apply Generative Adversarial Networks (GANs) to anomaly detection tasks and show good performances from these models. Motivated by the observation that GAN ensembles often outperform single GANs in generation

Mohammad Reza Rahmati

The Hodge numerical invariants of a variation of Hodge structure over a smooth quas--projective variety are a measure of complexity for the global twisting of the limit mixed Hodge structure when it degenerates. These invariants appear in inequalities which they may have correction terms, called Arakelov inequalities. One may investigate the correction term

Lorenzo Luciano, Imre Kiss, Peter William Beardshear, Esther Kadosh

The performance levels of a computing machine running a given workload configuration are crucial for both users and providers of computing resources. Knowing how well a computing machine is running with a given workload configuration is critical to making proper computing resource allocation decisions. In this paper, we introduce a novel framework for derivi

Anastasios Kyrillidis, Moshe Y. Vardi, Zhiwei Zhang

We explore the potential of continuous local search (CLS) in SAT solving by proposing a novel approach for finding a solution of a hybrid system of Boolean constraints. The algorithm is based on CLS combined with belief propagation on binary decision diagrams (BDDs). Our framework accepts all Boolean constraints that admit compact BDDs, including symmetric B

Adedolapo Okanlawon, Huichen Yang, Avishek Bose, William Hsu

We present a machine learning framework and a new test bed for data mining from the Slurm Workload Manager for high-performance computing (HPC) clusters. The focus was to find a method for selecting features to support decisions: helping users decide whether to resubmit failed jobs with boosted CPU and memory allocations or migrate them to a computing cloud.

Mohammad Reza Rahmati

We generalize the main result of Demailly \cite{D2} for the bundles $E_{k,m}^{GG}(V^*)$ of jet differentials of order $k$ and weighted degree $m$ to the bundles $E_{k,m}(V^*)$ of the invariant jet differentials of order $k$ and weighted degree $m$. Namely, Theorem 0.5 from \cite{D2} and Theorem 9.3 from \cite{D1} provide a lower bound $\frac{c^k}{k}m^{n+kr-1

Roie Dann, Ronnie Kosloff

Quantum dynamics of driven open systems should be compatible with both quantum mechanic and thermodynamic principles. By formulating the thermodynamic principles in terms of a set of postulates we obtain a thermodynamically consistent master equation. Following an axiomatic approach, we base the analysis on an autonomous description, incorporating the drive

Geetanjali Bihani, Julia Taylor Rayz

Static word embeddings encode word associations, extensively utilized in downstream NLP tasks. Although prior studies have discussed the nature of such word associations in terms of biases and lexical regularities captured, the variation in word associations based on the embedding training procedure remains in obscurity. This work aims to address this gap by

Didong Li, Andrew Jones, Barbara Engelhardt

Dimension reduction is useful for exploratory data analysis. In many applications, it is of interest to discover variation that is enriched in a "foreground" dataset relative to a "background" dataset. Recently, contrastive principal component analysis (CPCA) was proposed for this setting. However, the lack of a formal probabilistic model makes it difficult

Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy

Understanding generalization in deep learning is arguably one of the most important questions in deep learning. Deep learning has been successfully adopted to a large number of problems ranging from pattern recognition to complex decision making, but many recent researchers have raised many concerns about deep learning, among which the most important is gene

Albert Zhan, Ruihan Zhao, Lerrel Pinto, Pieter Abbeel

Recent advances in unsupervised representation learning significantly improved the sample efficiency of training Reinforcement Learning policies in simulated environments. However, similar gains have not yet been seen for real-robot reinforcement learning. In this work, we focus on enabling data-efficient real-robot learning from pixels. We present Contrasti

Chanwoo Kim, Dhananjaya Gowda, Dongsoo Lee, Jiyeon Kim

In this paper, we review various end-to-end automatic speech recognition algorithms and their optimization techniques for on-device applications. Conventional speech recognition systems comprise a large number of discrete components such as an acoustic model, a language model, a pronunciation model, a text-normalizer, an inverse-text normalizer, a decoder ba

Joseph Schindler, Evan Frangipane, Anthony Aguirre

We consider the black hole information problem in an explicitly defined spacetime modelling black hole evaporation. Using this context we review basic aspects of the problem, with a particular effort to be unambiguous about subtle topics, for instance precisely what is meant by entropy in various circumstances. We then focus on questions of unitarity, and ar

Rémi Reboulet

Given a polarized projective variety (X,L) over any non-Archimedean field, assuming continuity of envelopes, we show that the space of finite-energy metrics on L is a geodesic metric space, where geodesics are given as maximal psh segments. Given two continuous psh metrics, we show that the maximal segment joining them is furthermore continuous.

Nick E. Mavromatos, Joan Sola

In previous works we have derived a Running Vacuum Model (RVM) for a string Universe, which provides an effective description of the evolution of 4-dimensional string-inspired cosmologies from inflation till the present epoch. In the context of this "stringy RVM" version, it is assumed that the early Universe is characterised by purely gravitational degrees

Atri Bhattacharya, Jean-René Cudell, Rami Oueslati, Arno Vanthieghem

We study the consequences of high-energy collider data on the best fits to total, elastic, and inelastic cross sections for $pp$ and $p\bar{p}$ scattering using two very distinct unitarisation schemes: the eikonal and the $U$-matrix. Despite their analytic differences, we find that the two schemes lead to almost identical predictions up to EeV energies, with

Mihaela Rosca, Theophane Weber, Arthur Gretton, Shakir Mohamed

How sensitive should machine learning models be to input changes? We tackle the question of model smoothness and show that it is a useful inductive bias which aids generalization, adversarial robustness, generative modeling and reinforcement learning. We explore current methods of imposing smoothness constraints and observe they lack the flexibility to adapt

Jun Jet Tai, Mauro S. Innocente, Owais Mehmood

In this work, a novel high-speed railway fastener detector is introduced. This fully convolutional network, dubbed FasteNet, foregoes the notion of bounding boxes and performs detection directly on a predicted saliency map. Fastenet uses transposed convolutions and skip connections, the effective receptive field of the network is 1.5$\times$ larger than the

V. A. Khoze, A. D. Martin, M. G. Ryskin

We describe a QCD based model which incorporates the main properties of the inclusive particle distributions expected for diffractive processes, including the diffractive dissociation at high energies. We study, in turn, the total cross section, $\sigma_{tot}$, the differential elastic, $d\sigma_{el}/dt$, cross section, the dependence of the single proton di

Salman Mohammadi, Anders Kirk Uhrenholt, Bjørn Sand Jensen

The effective application of representation learning to real-world problems requires both techniques for learning useful representations, and also robust ways to evaluate properties of representations. Recent work in disentangled representation learning has shown that unsupervised representation learning approaches rely on fully supervised disentanglement me

C. Circosta, V. Mainieri, I. Lamperti, P. Padovani

Feedback from AGN is thought to be key in shaping the life cycle of their host galaxies by regulating star-formation activity. Therefore, to understand the impact of AGN on star formation, it is essential to trace the molecular gas out of which stars form. In this paper we present the first systematic study of the CO properties of AGN hosts at z~2 for a samp

Ernest Ma

In the context of a left-right extension of the standard model of quarks and leptons with the addition of a gauged $U(1)_D$ dark symmetry, it is shown how the electron may obtain a radiative mass in one loop and two Dirac neutrinos obtain masses in three loops.

Yujiao Hao, Boyu Wang, Rong Zheng

Wearable sensor-based human activity recognition (HAR) has been a research focus in the field of ubiquitous and mobile computing for years. In recent years, many deep models have been applied to HAR problems. However, deep learning methods typically require a large amount of data for models to generalize well. Significant variances caused by different partic

Michalis Lazarou, Tania Stathaki, Yannis Avrithis

Few-shot learning amounts to learning representations and acquiring knowledge such that novel tasks may be solved with both supervision and data being limited. Improved performance is possible by transductive inference, where the entire test set is available concurrently, and semi-supervised learning, where more unlabeled data is available. Focusing on these

Miquel Royo, Massimiliano Stengel

We develop a fundamental theory of the long-range electrostatic interactions in two-dimensional crystals by performing a rigorous study of the nonanalyticities of the Coulomb kernel. We find that the dielectric functions are best represented by $2\times 2$ matrices, with nonuniform macroscopic potentials that are two-component hyperbolic functions of the out

Masami Ouchi, Yoshiaki Ono, Takatoshi Shibuya

Hydrogen Lyman-$\alpha$ (Ly$\alpha$) emission has been one of the major observational probes for the high redshift universe, since the first discoveries of high-$z$ Ly$\alpha$ emitting galaxies in the late 1990s. Due to the strong Ly$\alpha$ emission originated by resonant scattering and recombination of the most-abundant element, Ly$\alpha$ observations wit

Peihan Tu, Li-Yi Wei, Koji Yatani, Takeo Igarashi

Repetitive patterns are ubiquitous in natural and human-made objects, and can be created with a variety of tools and methods. Manual authoring provides unmatched degree of freedom and control, but can require significant artistic expertise and manual labor. Computational methods can automate parts of the manual creation process, but are mainly tailored for d

Jamie Tayar, Zachary R. Claytor, Daniel Huber, Jennifer van Saders

Our understanding of the properties and demographics of exoplanets critically relies on our ability to determine fundamental properties of their host stars. The advent of Gaia and large spectroscopic surveys has now made it in principle possible to infer properties of individual stars, including most exoplanet hosts, to very high precision. However, we show

Jian Song

The $J$-equation proposed by Donaldson is a complex Hessian quotient equation on K\"ahler manifolds. The solvability of the $J$-equation is proved by Song-Weinkove to be equivalent to the existence of a subsolution. It is also conjectured by Lejmi-Szekelyhidi to be equivalent to a stability condition in terms of holomorphic intersection numbers as an analogu

Mohsin Zafar, Qiuyun Xu, Rayyan Manwar

Photoacoustic imaging (PAI) is a powerful imaging modality that relies on the PA effect. PAI works on the principle of electromagnetic energy absorption by the exogenous contrast agents and/or endogenous molecules present in the biological tissue, consequently generating ultrasound waves. PAI combines a high optical contrast with a high acoustic spatiotempor

Carsten Wiuf, Chuang Xu

Stochastic reaction networks (SRNs) provide models of many real-world networks. Examples include networks in epidemiology, pharmacology, genetics, ecology, chemistry, and social sciences. Here, we model stochastic reaction networks by continuous time Markov chains (CTMCs) and pay special attention to one-dimensional mass-action SRNs (1-d stoichiometric subsp

Zahra VaraminyBahnemiry, Jessie Galasso, Houari Sahraoui

This paper addresses the issue of correcting type errors in model transformations in realistic scenarios where neither predefined patches nor behavior-safe guards such as test suites are available. Instead of using predefined patches targeting isolated errors of specific categories, we propose to explore the space of possible patches by combining basic edit

Derong Xu, Yue Hao, Yun Luo, Ji Qiang

Crab crossing scheme is an essential collision scheme to achieve high luminosity for the future colliders with large crossing angles. However, when bunch length of one or both colliding beams is comparable with the wavelength of the crab cavity voltage, the nonlinear dependence of the crabbing kick may present a challenge to the beam dynamics of the collidin

Andrew J. Blumberg, Michael A. Mandell

Let $p\in \mathbb Z$ be an odd prime. We show that the fiber sequence for the cyclotomic trace of the sphere spectrum $\mathbb S$ admits an "eigensplitting" that generalizes known splittings on $K$-theory and $TC$. We identify the summands in the fiber as the covers of $\mathbb Z_{p}$-Anderson duals of summands in the $K(1)$-localized algebraic $K$-theory of

Reda Abdellah Kamraoui, Vinh-Thong Ta, Thomas Tourdias, Boris Mansencal

Recently, segmentation methods based on Convolutional Neural Networks (CNNs) showed promising performance in automatic Multiple Sclerosis (MS) lesions segmentation. These techniques have even outperformed human experts in controlled evaluation conditions such as Longitudinal MS Lesion Segmentation Challenge (ISBI Challenge). However state-of-the-art approach

Fabian Ritz, Thomy Phan, Robert Müller, Thomas Gabor

A characteristic of reinforcement learning is the ability to develop unforeseen strategies when solving problems. While such strategies sometimes yield superior performance, they may also result in undesired or even dangerous behavior. In industrial scenarios, a system's behavior also needs to be predictable and lie within defined ranges. To enable the agent

Darren Creutz

A compactly generated group is noncompact if and only if it admits a nonconstant harmonic function (for some, equivalently for every, reasonable measure). This generalizes the known fact that a finitely generated group is infinite if and only if it admits a nonconstant harmonic function (for some, equivalently every, reasonable measure).

Fakai Wang, Kang Zheng, Le Lu, Jing Xiao

Accurate vertebra localization and identification are required in many clinical applications of spine disorder diagnosis and surgery planning. However, significant challenges are posed in this task by highly varying pathologies (such as vertebral compression fracture, scoliosis, and vertebral fixation) and imaging conditions (such as limited field of view an

Kouichi Taira

In this paper, we shall show that the limiting absorption principle for the wave operator on the asymptotically Minkowski spacetime. This problem was previously considered by [A. Vasy, J. Spect. Theory, 10,439-461 , (2020)]. Here, we employ a more transparent tool, the Mourre theory and removes an additional condition which is imposed in his paper. Moreover,

A. V. Olinto, J. Krizmanic, J. H. Adams, R. Aloisio

The Probe Of Extreme Multi-Messenger Astrophysics (POEMMA) is designed to accurately observe ultra-high-energy cosmic rays (UHECRs) and cosmic neutrinos from space with sensitivity over the full celestial sky. POEMMA will observe the extensive air showers (EASs) from UHECRs and UHE neutrinos above 20 EeV via air fluorescence. Additionally, POEMMA will observ

Rahel A. Fainchtein, Adam J. Aviv, Micah Sherr, Stephen Ribaudo

Smart DNS (SDNS) services advertise access to "geofenced" content (typically, video streaming sites such as Netflix or Hulu) that is normally inaccessible unless the client is within a prescribed geographic region. SDNS is simple to use and involves no software installation. Instead, it requires only that users modify their DNS settings to point to an SDNS r

Stephanie M. Bohaichuk, Suhas Kumar, Miguel Muñoz Rojo, R. Stanley Williams

Traditional electronic devices are well-known to improve in speed and energy-efficiency as their dimensions are reduced to the nanoscale. However, this scaling behavior remains unclear for nonlinear dynamical circuit elements, such as Mott neuron-like spiking oscillators, which are of interest for bio-inspired computing. Here we show that shrinking micromete

Max Langer, Yuhe Zhang, Diogo Figueirinhas, Jean-Baptiste Forien

X-ray propagation-based imaging techniques are well-established at synchrotron radiation and laboratory sources. However, most reconstruction algorithms for such image modalities, also known as phase retrieval algorithms, have been developed specifically for one instrument by and for experts, making the development and spreading of the use of such techniques

Yi Zuo, Thomas G. Stewart, Jeffrey D. Blume

Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here we report on a novel variable selection approach called Penalized regression with Second-Generation P-Values (ProSGPV). It captures the true model at the best rate achieved by current standards, is easy to implemen

Weichen Gao, Marius Junge, Weichen Gao

In this paper we study conditions under which, for an inclusion of finite von Neumann algebras $N \subseteq M$, we have the reduced amalgamated free product $\ast_N M$ is embeddable into $(R \bar{\otimes} N_1)^\omega$ for some other finite von Neumann algebra $N_1$, where $R$ is the hyperfinite $\textrm{II}_1$ factor.

Hadrien Cambazard, Nicolas Catusse

A convex partition of a point set P in the plane is a planar partition of the convex hull of P with empty convex polygons or internal faces whose extreme points belong to P. In a convex partition, the union of the internal faces give the convex hull of P and the interiors of the polygons are pairwise disjoint. Moreover, no polygon is allowed to contain a poi

Oliver Hennigh, Susheela Narasimhan, Mohammad Amin Nabian, Akshay Subramaniam

We present SimNet, an AI-driven multi-physics simulation framework, to accelerate simulations across a wide range of disciplines in science and engineering. Compared to traditional numerical solvers, SimNet addresses a wide range of use cases - coupled forward simulations without any training data, inverse and data assimilation problems. SimNet offers fast t

Ery Arias-Castro, Lin Zheng

We consider the problem of matching a template to a noisy signal. Motivated by some recent proposals in the signal processing literature, we suggest a rank-based method and study its asymptotic properties using some well-established techniques in empirical process theory combined with H\'ajek's projection method. The resulting estimator of the shift is shown

Lan N. Nguyen, My T. Thai

In this paper, we study a novel problem, Minimum Robust Multi-Submodular Cover for Fairness (MinRF), as follows: given a ground set $V$; $m$ monotone submodular functions $f_1,...,f_m$; $m$ thresholds $T_1,...,T_m$ and a non-negative integer $r$, MinRF asks for the smallest set $S$ such that for all $i \in [m]$, $\min_{|X| \leq r} f_i(S \setminus X) \geq T_i

Aranyak Mehta, Uri Nadav, Alexandros Psomas, Aviad Rubinstein

We consider the fundamental problem of selecting $k$ out of $n$ random variables in a way that the expected highest or second-highest value is maximized. This question captures several applications where we have uncertainty about the quality of candidates (e.g. auction bids, search results) and have the capacity to explore only a small subset due to an exoge

Joris Raeymaekers

We propose a non-unitary example of holography for the family of two-dimensional logarithmic conformal field theories with negative central charge $c= c_{p,1} = - 6p +13 - 6 p^{-1}$. We argue that at large $p$, these models have a semiclassical gravity-like description which contains, besides the global AdS$_3$ spacetime, a tower of solitonic solutions descr

Jiashuo Jiang, Jiawei Zhang

We consider an online resource allocation problem where multiple resources, each with an individual initial capacity, are available to serve random requests arriving sequentially over multiple discrete time periods. At each time period, one request arrives and its associated reward and size are drawn independently from a known distribution that could be reso

Sean N. Raymond, David Nesvorny

The asteroid belt was dynamically shaped during and after planet formation. Despite representing a broad ring of stable orbits, the belt contains less than one one-thousandth of an Earth mass. The asteroid orbits are dynamically excited with a wide range in eccentricity and inclination and their compositions are diverse, with a general trend toward dry objec

Trevor Seets, Atul Ingle, Martin Laurenzis, Andreas Velten

Single-photon avalanche diodes (SPADs) are a rapidly developing image sensing technology with extreme low-light sensitivity and picosecond timing resolution. These unique capabilities have enabled SPADs to be used in applications like LiDAR, non-line-of-sight imaging and fluorescence microscopy that require imaging in photon-starved scenarios. In this work w

James B. Dent, Bhaskar Dutta, Jayden L. Newstead, Alejandro Rodriguez

Dark Matter (DM) may be comprised of axion-like particles (ALPs) with couplings to photons and the standard model fermions. In this paper we study photon signals arising from cosmic ray (CR) electron scattering on background ALPs. For a range of masses we find that these bounds can place competitive new constraints on the ALP-electron coupling, although in m

Qiming Sun

The expensive cost of computing exact exchange in periodic systems limits the application range of density functional theory with hybrid functionals. To reduce the computational cost of exact change, we present a range-separated algorithm to compute electron repulsion integrals for Gaussian-type crystal basis. The algorithm splits the full-range Coulomb inte

MicroBooNE collaboration, P. Abratenko, M. Alrashed, R. An

Large Liquid Argon Time Projection Chambers (LArTPCs) are being increasingly adopted in neutrino oscillation experiments because of their superb imaging capabilities through the combination of both tracking and calorimetry in a fully active volume. Active LArTPC neutrino detectors at or near the Earth's surface, such as the MicroBooNE experiment, present a u

Hossein Taheri, Andrey B. Matsko, Lute Maleki, Krzysztof Sacha

Time crystals are periodic states exhibiting spontaneous symmetry breaking in either time-independent or periodically forced quantum many-body systems. Spontaneous modification of discrete time translation symmetry in a periodically driven physical system can create a discrete time crystal (DTC). DTCs constitute a state of matter with properties such as temp

Jake D. Turner, Philippe Zarka, Jean-Mathias Grießmeier, Joseph Lazio

Observing planetary auroral radio emission is the most promising method to detect exoplanetary magnetic fields, the knowledge of which will provide valuable insights into the planet's interior structure, atmospheric escape, and habitability. We present LOFAR-LBA circularly polarized beamformed observations of the exoplanetary systems 55 Cancri, $\upsilon$ An

Monojit Ghosh, Srubabati Goswami, Ananya Mukherjee

In this work we have re-investigated two different kinds of texture zero ansatz of the low energy neutrino mass matrix in view of the Dark-Large-Mixing-Angle (DLMA) solution of the solar neutrino problem which can arise in the presence of non-standard interactions. In particular we revisit the cases of (i) one zero mass matrices when the lowest neutrino mass

Wenzhong Zhang, Wei Cai

In this paper, we propose forward and backward stochastic differential equations (FBSDEs) based deep neural network (DNN) learning algorithms for the solution of high dimensional quasilinear parabolic partial differential equations (PDEs), which are related to the FBSDEs by the Pardoux-Peng theory. The algorithms rely on a learning process by minimizing the

Ranganath Krishnan, Omesh Tickoo

Obtaining reliable and accurate quantification of uncertainty estimates from deep neural networks is important in safety-critical applications. A well-calibrated model should be accurate when it is certain about its prediction and indicate high uncertainty when it is likely to be inaccurate. Uncertainty calibration is a challenging problem as there is no gro

Lennart Gaus, Lothar Bischoff, Michael Bussmann, Eric Cunningham

Extreme states of matter exist throughout the universe e.g. inside planetary cores, stars or astrophysical jets. Such conditions are generated in the laboratory in the interaction of powerful lasers with solids, and their evolution can be probed with femtosecond precision using ultra-short X-ray pulses to study laboratory astrophysics, laser-fusion research

Johannes Breidenbach, Janis Ivanovs, Annika Kangas, Thomas Nord-Larsen

Policy measures and management decisions aiming at enhancing the role of forests in mitigating climate-change require reliable estimates of C-stock dynamics in greenhouse gas inventories (GHGIs). Aim of this study was to assemble design-based estimators to provide estimates relevant for GHGIs using national forest inventory (NFI) data. We improve basic expan

J. E. Ramírez, Bogar Díaz, C. Pajares

We study the radial distribution function of the color sources (strings) formed in hadronic collisions and the requirements to obtain a liquid. As a repulsive interaction is needed, we incorporate a concentric core in the strings as well as the probability that a string allows core-core overlaps. We find systems where the difference between the gas-liquid an

Görkem Giray

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of software development, where algorithms are hard-coded by humans, to ML systems materialized through learning from data. Therefore, we need to revisit our ways of developing software systems and consider the particularities required by these new types of systems. Objec

Ben Heuer

For a smooth rigid space $X$ over a perfectoid field extension $K$ of $\mathbb Q_p$, we investigate how the $v$-Picard group of the associated diamond $X^\diamondsuit$ differs from the analytic Picard group of $X$. To this end, we construct a left-exact "Hodge--Tate logarithm" sequence \[0\to \mathrm{Pic}_{\mathrm{an}}(X)\to \mathrm{Pic}_v(X^\diamondsuit)\to

Daniel W. Song, Konstantinos Mamouras, Ang Chen, Nathan Dautenhahn

Despite significant research and engineering efforts, many of today's important computer systems suffer from bugs. To increase the reliability of software systems, recent work has applied formal verification to certify the correctness of such systems, with recent successes including certified file systems and certified cryptographic protocols, albeit using q

Kavya Dayananda, Nima Karimian

As the Covid-19 pandemic grips the world, healthcare systems are being reshaped, where the e-health concepts become more likely to be accepted. Wearable devices often carry sensitive information from users which are exposed to security and privacy risks. Moreover, users have always had the concern of being counterfeited between the fabrication process and ve