October 2020 arXiv papers — page 9

Seokhwan Yun, Ki Hoon Lee, Chaebin Kim, Junghwan Park

$Li_{2}RuO_{3}$ with a honeycomb structure undergoes a drastic transition from a regular honeycomb lattice with the $C2/m$ space group to a valence bond solid state of the $P2_{1}/m$ space group with an extremely strong dimerization at 550 K. We synthesized $Li_{2}Ru_{1-x}Mn_{x}O_{3}$ with a full solid solution and investigated doping effects on the valence

Nicolas Swenson, Aditi S. Krishnapriyan, Aydin Buluc, Dmitriy Morozov

Understanding protein structure-function relationships is a key challenge in computational biology, with applications across the biotechnology and pharmaceutical industries. While it is known that protein structure directly impacts protein function, many functional prediction tasks use only protein sequence. In this work, we isolate protein structure to make

Bethlee M. Lindor, Joel D. Hartman, Gáspár Á. Bakos, Waqas Bhatti

We report the discovery by the ground-based HATNet survey of the transiting exoplanet HAT-P-68b, which has a mass of 0.724 $\pm$ 0.043 $M_{Jup}$, and radius of 1.072 $\pm$ 0.012 $R_{Jup}$. The planet is in a circular P = 2.2984-day orbit around a moderately bright V = 13.937 $\pm$ 0.030 magnitude K dwarf star of mass 0.673 $+$ 0.020 $-$0.014 $M_{\odot}$, and

Rushani Wijesuriya, Margarita Moreno-Betancur, John B. Carlin, Anurika P. De Silva

Three-level data structures arising from repeated measures on individuals clustered within larger units are common in health research studies. Missing data are prominent in such studies and are often handled via multiple imputation (MI). Although several MI approaches can be used to account for the three-level structure, including adaptations to single- and

Ryotaro Nakata, Akira Otsuka

A cyber range, a practical and highly educational information security exercise system, is difficult to implement in educational institutions because of the high cost of implementing and maintaining it. Therefore, there is a need for a cyber range that can be adopted and maintained at a low cost. Recently, container type virtualization is gaining attention a

B. Li, P. Antolin, M. -Z. Guo, A. A. Kuznetsov

Characterized by cyclic axisymmetric perturbations to both the magnetic and fluid parameters, magnetohydrodynamic fast sausage modes (FSMs) have proven useful for solar coronal seismology given their strong dispersion. This review starts by summarizing the dispersive properties of the FSMs in the canonical configuration where the equilibrium quantities are t

Taro Fukazawa, Hisazumi Akai, Yosuke Harashima, Takashi Miyake

We theoretically investigate spin-wave dispersion in rare-earth magnet compounds by using first-principles calculations and a method we call the reciprocal-space algorithm (RSA). The value of the calculated exchange stiffness for Nd$_2$Fe$_{14}$B is within the range of reported experimental values. We find that the exchange stiffness is considerably anisotro

Xiang Yue, Xinliang Frederick Zhang, Ziyu Yao, Simon Lin

Clinical question answering (QA) aims to automatically answer questions from medical professionals based on clinical texts. Studies show that neural QA models trained on one corpus may not generalize well to new clinical texts from a different institute or a different patient group, where large-scale QA pairs are not readily available for model retraining. T

Nahuel Zamponi, Emiliano Zamponi, Sergio A Cannas, Dante R Chialvo

A growing body of evidence suggests that the structure of mitochondrial networks is poised near criticality, an intermediate regime lying in between order and disorder. Such description fits well with the idea that biological systems, in general, may benefit from the long-range correlations and large flexibility conferred by a critical regime. Despite the at

Stefan McCabe, Leo Torres, Timothy LaRock, Syed Arefinul Haque

Over the last two decades, alongside the increased availability of large network datasets, we have witnessed the rapid rise of network science. For many systems, however, the data we have access to is not a direct description of the underlying network. More and more, we see the drive to study networks that have been inferred or reconstructed from non-network

Thomas Hines, Kazys Stepanas, Fletcher Talbot, Inkyu Sa

This paper presents an autonomous navigation system for ground robots traversing aggressive unstructured terrain through a cohesive arrangement of mapping, deliberative planning and reactive behaviour modules. All systems are aware of terrain slope, visibility and vehicle orientation, enabling robots to recognize, plan and react around unobserved areas and o

Yukito Onodera, Hiroki Takano, Daisuke Hisano, Yu Nakayama

The world is often stricken by catastrophic disasters. On-demand drone-mounted visible light communication (VLC) networks are suitable for monitoring disaster-stricken areas for leveraging disaster-response operations. The concept of an image sensor-based VLC has also attracted attention in the recent past for establishing stable links using unstably moving

Subhra Debnath, Abhik Kumar Sanyal

We show that as in the case of isotropic models, the `Dirac Algorithm' and `Modified Horowitz' Formalism' lead to identical phase-space structure of the Hamiltonian for the gravitational action with curvature squared terms, in anisotropic space-time, viz, Bianchi-I, Bianchi-III and Kantowski-Sachs models too.

Walther Neuper

Software tools of Automated Reasoning are too sophisticated for general use in mathematics education and respective reasoning, while Lucas-Interpretation provides a general concept for integrating such tools into educational software with the purpose to reliably and flexibly check formal input of students. This paper gives the first technically concise descr

Filip Marić, Sana Stojanović-{\Dj}urđević

The International Mathematical Olympiad (IMO) is perhaps the most celebrated mental competition in the world and as such is among the greatest grand challenges for Artificial Intelligence (AI). The IMO Grand Challenge, recently formulated, requires to build an AI that can win a gold medal in the competition. We present some initial steps that could help to t

Asta Halkjær From, Jørgen Villadsen, Patrick Blackburn

Proof assistants are important tools for teaching logic. We support this claim by discussing three formalizations in Isabelle/HOL used in a recent course on automated reasoning. The first is a formalization of System W (a system of classical propositional logic with only two primitive symbols), the second is the Natural Deduction Assistant (NaDeA), and the t

Mauricio Ayala-Rincón, Thaynara Arielly de Lima

This work discusses an approach to teach to mathematicians the importance and effectiveness of the application of Interactive Theorem Proving tools in their specific fields of interest. The approach aims to motivate the use of such tools through short courses. In particular, it is discussed how, using as case-of-study algebraic notions and properties, the us

Maciej Besta, Michal Podstawski, Linus Groner, Edgar Solomonik

We reduce the cost of communication and synchronization in graph processing by analyzing the fastest way to process graphs: pushing the updates to a shared state or pulling the updates to a private state.We investigate the applicability of this push-pull dichotomy to various algorithms and its impact on complexity, performance, and the amount of used locks,

Yeong-Dae Kwon, Jinho Choo, Byoungjip Kim, Iljoo Yoon

In neural combinatorial optimization (CO), reinforcement learning (RL) can turn a deep neural net into a fast, powerful heuristic solver of NP-hard problems. This approach has a great potential in practical applications because it allows near-optimal solutions to be found without expert guides armed with substantial domain knowledge. We introduce Policy Opti

Yangyang Guo, Liqiang Nie, Zhiyong Cheng, Qi Tian

Recent studies have pointed out that many well-developed Visual Question Answering (VQA) models are heavily affected by the language prior problem, which refers to making predictions based on the co-occurrence pattern between textual questions and answers instead of reasoning visual contents. To tackle it, most existing methods focus on enhancing visual feat

Thomas Bernhardt, Catherine Donnelly

The number of people who receive a stable income for life from a closed pooled annuity fund is studied. Income stability is defined as keeping the income within a specified tolerance of the initial income in a fixed proportion of future scenarios. The focus is on quantifying the effect of the number of members, which drives the level of idiosyncratic longevi

Hao Liang

We prove a factorization theorem for Fuchsian groups similar to those proved by Agol and Liu for 3-manifold groups. As an application, we build Makanin-Razborov diagrams, which parametrize the collection of all discrete representations from an arbitrary but fixed finitely generated group $G$ to ${\rm PSL}(2, \mathbb{R})$. We define a new class of groups call

Jifeng Wu, Chenqiang Hua, Bin Liu, Yanwei Cui

Chemical doping of topological materials may provide a possible route for realizing topological superconductivity. However, all such cases known so far are based on chalcogenides. Here we report the discovery of superconductivity induced by Re doping in the topological semimetal Mo$_{5}$Si$_{3}$ with a tetragonal structure. Partial substitution of Re for Mo

M. ~Carver, A. ~Celentano, K. ~Hicks, L. ~Marsicano

The quark structure of the $f_2(1270)$ meson has, for many years, been assumed to be a pure quark-antiquark ($q\bar{q}$) resonance with quantum numbers $J^{PC} = 2^{++}$. Recently, it was proposed that the $f_2(1270)$ is a molecular state made from the attractive interaction of two $\rho$-mesons. Such a state would be expected to decay strongly to final stat

Curtis Holliman, Logan Hyslop

The Cauchy problem for a higher order modification of the nonlinear Shcrodinger equation (MNLS) on the line is shown to be well-posed in Sobolev spaces with exponent $\ge 0$. This result is achieved by demonstrating that the associated integral operator is a contraction on a Bourgain space that has been adapted to the particular linear symbol present in the

Prateek Gupta, Tegan Maharaj, Martin Weiss, Nasim Rahaman

The rapid global spread of COVID-19 has led to an unprecedented demand for effective methods to mitigate the spread of the disease, and various digital contact tracing (DCT) methods have emerged as a component of the solution. In order to make informed public health choices, there is a need for tools which allow evaluation and comparison of DCT methods. We i

Seo Woo Han, Doug Young Suh

Inpainting has been continuously studied in the field of computer vision. As artificial intelligence technology developed, deep learning technology was introduced in inpainting research, helping to improve performance. Currently, the input target of an inpainting algorithm using deep learning has been studied from a single image to a video. However, deep lea

Jie Du, Yanan Lin, Zhongguo Zhou

By using certain quantum differential operators, we construct a super representation for the quantum queer supergroup U_v(q_n). The underlying space of this representation is a deformed polynomial superalgebra in 2n^2 variables whose homogeneous components can be used as the underlying spaces of queer q-Schur superalgebras. We then extend the representation

Sarah Dean, Andrew J. Taylor, Ryan K. Cosner, Benjamin Recht

Modern nonlinear control theory seeks to develop feedback controllers that endow systems with properties such as safety and stability. The guarantees ensured by these controllers often rely on accurate estimates of the system state for determining control actions. In practice, measurement model uncertainty can lead to error in state estimates that degrades t

A. Collevecchio, K. Hamza, M. Shi, R. J. Williams

Given the increments of a simple symmetric random walk $(X_n)_{n\ge0}$, we characterize all possible ways of recycling these increments into a simple symmetric random walk $(Y_n)_{n\ge0}$ adapted to the filtration of $(X_n)_{n\ge0}$. We study the long term behavior of a suitably normalized two-dimensional process $((X_n,Y_n))_{n\ge0}$. In particular, we prov

Gideon Kowadlo, Abdelrahman Ahmed, David Rawlinson

Established experimental procedures for one-shot machine learning do not test the ability to learn or remember specific instances of classes, a key feature of animal intelligence. Distinguishing specific instances is necessary for many real-world tasks, such as remembering which cup belongs to you. Generalisation within classes conflicts with the ability to

Thomas Paireder, Christian Motz, Mario Huemer

Radio frequency transceivers operating in in-band full-duplex or frequency-division duplex mode experience strong transmitter leakage. Combined with receiver nonlinearities, this causes intermodulation products in the baseband, possibly with higher power than the desired receive signal. In order to restore the receiver signal-to-noise ratio in such scenarios

Stephanie Clark, Rob J Hyndman, Dan Pagendam, Louise M Ryan

This paper discusses several modern approaches to regression analysis involving time series data where some of the predictor variables are also indexed by time. We discuss classical statistical approaches as well as methods that have been proposed recently in the machine learning literature. The approaches are compared and contrasted, and it will be seen tha

Nikolaos Nikolaou, Ingo P. Waldmann, Angelos Tsiaras, Mario Morvan

The last decade has witnessed a rapid growth of the field of exoplanet discovery and characterisation. However, several big challenges remain, many of which could be addressed using machine learning methodology. For instance, the most prolific method for detecting exoplanets and inferring several of their characteristics, transit photometry, is very sensitiv

Yu Wang, Maxim Bykov, Elena Bykova, Xiao Zhang1

Polynitrogen molecules represent the ultimate high energy-density materials as they have a huge potential chemical energy originating from their high enthalpy. However, synthesis and storage of such compounds remain a big challenge because of difficulties to find energy efficient synthetic routes and stabilization mechanisms. Compounds of metals with nitroge

Yaroslav Tserkovnyak, Eran Maniv, James Analytis

Current-induced spin torques in layered magnetic heterostructures have many commonalities across broad classes of magnetic materials. These include not only collinear ferromagnets, ferrimagnets, and antiferromagnets, but also more complex noncollinear spin systems. We develop a general Lagrangian-Rayleigh approach for studying the role of dissipative torques

Bruno El-Bennich, G. E. R. Zambrano, Eduardo Rojas

Based on the Generalized Quantum Electrodynamics expression for the Podolsky propagator, which preserves gauge invariance for massive photons, we propose a model for the massive gluon propagator that reproduces well-known features of established strong-interaction models in the framework of the Dyson-Schwinger equation. By adjusting the Podolsky mass and the

Deok-Hoon Jeong, Anezka Kvasnickova, Jean-Baptiste Boutin, David Cebron

The withdrawal of a liquid or the translation of a liquid slug in a capillary tube leads to the deposition of a thin film on the inner wall. When particles or contaminants are present in the liquid, they deposit and contaminate the tube if the liquid film is sufficiently thick. In this article, we experimentally investigate the condition under which particle

Kevin D. Osborn, Waltraut Wustmann

Reversible logic gates were previously implemented in superconducting circuits as adiabatic-reversible gates, which are powered with a sufficiently slow clock. In contrast, we are studying ballistic-reversible gates, where fluxons serve to both encode the information and power the gates. No power is applied to the gate apart from the energy of the input flux

Xiaohui Gao, Bonggu Shim

Ionization is a fundamental process in intense laser-matter interactions, and is known to cause plasma defocusing and intensity clamping. Here, we investigate theoretically the propagation dynamics of an intense laser pulse in a helium gas jet in the ionization saturation regime, and we find that the pulse undergoes self-focusing and self-compression through

Jason Taylor

We introduce a novel convolutional network architecture with an interpretable latent space for modeling guitar amplifiers. Leveraging domain knowledge of popular amplifiers spanning a range of styles, the proposed system intuitively combines or subtracts characteristics of different amplifiers, allowing musicians to design entirely new guitar timbres.

Magdaléna Tinková, Ezra Waxman, Mikuláš Zindulka

We say that a prime number $p$ is an $\textit{Artin prime}$ for $g$ if $g$ mod $p$ generates the group $(\mathbb{Z}/p\mathbb{Z})^{\times}$. For appropriately chosen integers $d$ and $g$, we present a conjecture for the asymptotic number $\pi_{d,g}(x)$ of primes $p \leq x$ such that both $p$ and $p+d$ are Artin primes for $g$. In particular, we identify a cla

K. Aditya Mohan, Alan D. Kaplan

We present a novel neural network architecture called AutoAtlas for fully unsupervised partitioning and representation learning of 3D brain Magnetic Resonance Imaging (MRI) volumes. AutoAtlas consists of two neural network components: one neural network to perform multi-label partitioning based on local texture in the volume, and a second neural network to c

Tassia Ferreira, Tianqing Zhang, Nianyi Chen, Scott Dodelson

Covariance matrices are among the most difficult pieces of end-to-end cosmological analyses. In principle, for two-point functions, each component involves a four-point function, and the resulting covariance often has hundreds of thousands of elements. We investigate various compression mechanisms capable of vastly reducing the size of the covariance matrix

Kunjal Panchal

Honey Encryption is an approach to encrypt the messages using low min-entropy keys, such as weak passwords, OTPs, PINs, credit card numbers. The ciphertext is produces, when decrypted with any number of incorrect keys, produces plausible-looking but bogus plaintext called "honey messages". But the current techniques used in producing the decoy plaintexts do

I. Danhoni, F. S. Navarra

It is believed that in non-central relativistic heavy ion collisions a very strong magnetic field is formed. There are several studies of the effects of this field, where $\vec{B}$ is calculated with the expressions of classical electrodynamics. A quantum field may be approximated by a classical one when the number of field quanta in each field mode is suffi

P. Micke, T. Leopold, S. A. King, E. Benkler

Precision spectroscopy of atomic systems is an invaluable tool for the advancement of our understanding of fundamental interactions and symmetries. Recently, highly charged ions (HCI) have been proposed for sensitive tests of physics beyond the Standard Model and as candidates for high-accuracy atomic clocks. However, the implementation of these ideas has be

Mohammad Akbari, Bahman Gharesifard, Tamas Linder

We provide a set of counterexamples for the monotonicity of the Newton-Hewer method for solving the discrete-time algebraic Riccati equation in dynamic settings, drawing a contrast with the Riccati difference equation.

Yin Zhang, Derek Zhiyuan Cheng, Tiansheng Yao, Xinyang Yi

Highly skewed long-tail item distribution is very common in recommendation systems. It significantly hurts model performance on tail items. To improve tail-item recommendation, we conduct research to transfer knowledge from head items to tail items, leveraging the rich user feedback in head items and the semantic connections between head and tail items. Spec

Yongjun He, Jiacheng Lu, Tianzheng Wang

Data stalls are a major overhead in main-memory database engines due to the use of pointer-rich data structures. Lightweight coroutines ease the implementation of software prefetching to hide data stalls by overlapping computation and asynchronous data prefetching. Prior solutions, however, mainly focused on (1) individual components and operations and (2) i

Taylor Shin, Yasaman Razeghi, Robert L. Logan, Eric Wallace

The remarkable success of pretrained language models has motivated the study of what kinds of knowledge these models learn during pretraining. Reformulating tasks as fill-in-the-blanks problems (e.g., cloze tests) is a natural approach for gauging such knowledge, however, its usage is limited by the manual effort and guesswork required to write suitable prom

Eric Lybrand, Rayan Saab

We propose a new computationally efficient method for quantizing the weights of pre- trained neural networks that is general enough to handle both multi-layer perceptrons and convolutional neural networks. Our method deterministically quantizes layers in an iterative fashion with no complicated re-training required. Specifically, we quantize each neuron, or

Kazi Zakia Sultana, Zadia Codabux, Byron Williams

Context: Security is vital to software developed for commercial or personal use. Although more organizations are realizing the importance of applying secure coding practices, in many of them, security concerns are not known or addressed until a security failure occurs. The root cause of security failures is vulnerable code. While metrics have been used to pr

Huai-Ke Guo, Kuver Sinha, Chen Sun, Joshua Swaim

We study the properties of Bose-Einstein Condensate (BEC) systems consisting of two scalars, focusing on both the case where the BEC is stellar scale as well as the case when it is galactic scale. After studying the stability of such systems and making contact with existing single scalar limits, we undertake a numerical study of the two interacting scalars u

Angelo Zinzi, Carlotta Pittori, Rosa Tagliamonte, Elisa Nichelli

Since 2017 the Italian Space Agency (ASI) participates to so-called "Alternanza Scuola-Lavoro" (i.e., "school-work synergy") outreach projects promoted by the Italian government, and the ASI Space Science Data Center (SSDC) actively contributes to them, with the primary aim of bringing students closer to space-related activities before choosing their univers

Lukas Holik, Petr Janku, Anthony W. Lin, Philipp Rümmer

String analysis is the problem of reasoning about how strings are manipulated by a program. It has numerous applications including automatic detection of cross-site scripting (XSS). A popular string analysis technique includes symbolic executions, which at their core use string (constraint) solvers. Such solvers typically reason about constraints expressed i

Roger Granda, Tinne Tuytelaars, Jose Oramas

We present a method for adversarial attack detection based on the inspection of a sparse set of neurons. We follow the hypothesis that adversarial attacks introduce imperceptible perturbations in the input and that these perturbations change the state of neurons relevant for the concepts modelled by the attacked model. Therefore, monitoring the status of the

Alexandra Bates, Joseph Bates

We present evidence of the feasibility of using billion core approximate computers to run simple U(1) sigma models, and discuss how the approach might be extended to Lattice Quantum Chromodynamics (LQCD) models. This work is motivated by the extreme time, power, and cost needed to run LQCD on current computing hardware. We show that, using massively parallel

Karthik Srinivasan, Amit Kumar, Parameshwaran Iyer, Abhinav Joshi

Response Surface Methodology (RSM) introduced in the paper (Box & Wilson, 1951) explores the relationships between explanatory and response variables in complex settings and provides a framework to identify correct settings for the explanatory variables to yield the desired response. RSM involves setting up sequential experimental designs followed by applica

Qiushi Zhang, Ruiyang Li, Eungkyu Lee, Tengfei Luo

Photothermal surface bubbles play important roles in a wide range of applications like catalysis, microfluidics and biosensing, but their formation on a transparent substrate immersed in a plasmonic nanoparticle (NP) suspension has an unknown origin. Here, we show that NPs deposited on the substrate by dispersive optical forces are responsible for the nuclea

Clifford Cheung, James Mangan

We explore the scattering amplitudes of fluid quanta described by the Navier-Stokes equation and its non-Abelian generalization. These amplitudes exhibit universal infrared structures analogous to the Weinberg soft theorem and the Adler zero. Furthermore, they satisfy on-shell recursion relations which together with the three-point scattering amplitude furni

Mao Ye, Lemeng Wu, Qiang Liu

Despite the great success of deep learning, recent works show that large deep neural networks are often highly redundant and can be significantly reduced in size. However, the theoretical question of how much we can prune a neural network given a specified tolerance of accuracy drop is still open. This paper provides one answer to this question by proposing

Carlos Ortiz Marrero, Mária Kieferová, Nathan Wiebe

We argue that an excess in entanglement between the visible and hidden units in a Quantum Neural Network can hinder learning. In particular, we show that quantum neural networks that satisfy a volume-law in the entanglement entropy will give rise to models not suitable for learning with high probability. Using arguments from quantum thermodynamics, we then s

T. L. Bubon, S. M. Perepelytsya

The dynamics of the structured water molecules in the hydration shell of the DNA double helix is of paramount importance for the understanding of many biological mechanisms. In particular, the vibrational dynamics of a water spine that is formed in the DNA minor groove is the aim of the present study. Within the framework of the developed phenomenological mo

Brian Quistorff, Gentry Johnson

Restricting randomization in the design of experiments (e.g., using blocking/stratification, pair-wise matching, or rerandomization) can improve the treatment-control balance on important covariates and therefore improve the estimation of the treatment effect, particularly for small- and medium-sized experiments. Existing guidance on how to identify these va

Dhruv Guliani, Francoise Beaufays, Giovanni Motta

We propose using federated learning, a decentralized on-device learning paradigm, to train speech recognition models. By performing epochs of training on a per-user basis, federated learning must incur the cost of dealing with non-IID data distributions, which are expected to negatively affect the quality of the trained model. We propose a framework by which

Shahriar Shahabuddin, Mahmoud A. Albreem, Mohammad Shahanewaz Shahabuddin, Zaheer Khan

Approximate matrix inversion based methods is widely used for linear massive multiple-input multiple-output (MIMO) received symbol vector detection. Such detectors typically utilize the diagonally dominant channel matrix of a massive MIMO system. Instead of diagonal matrix, a stair matrix can be utilized to improve the error-rate performance of a massive MIM

Hengrui Cai, Chengchun Shi, Rui Song, Wenbin Lu

We consider off-policy evaluation (OPE) in continuous treatment settings, such as personalized dose-finding. In OPE, one aims to estimate the mean outcome under a new treatment decision rule using historical data generated by a different decision rule. Most existing works on OPE focus on discrete treatment settings. To handle continuous treatments, we develo

F. Crasto de Lima, R. H. Miwa, A. Fazzio

Jacutingate, a recently discovered Brazilian naturally occurring mineral, has shown to be the first experimental realization of the Kane-Mele topological model. In this letter we have unveiled a class of materials $M_2NX_3$ ($M$=Ni, Pt, Pd; $N$=Zn, Cd, Hg; and $X$=S, Se, Te), sharing jacutingaite's key features, i.e., high stability, and topological phase. B

Aziz Kharoof

We describe two ways to define higher order Toda brackets in a pointed simplicial model category $\mathcal{D}$: one is a recursive definition using model categorical constructions, and the second uses the associated simplicial enrichment. We show that these two definitions agree, by providing a third, diagrammatic, description of the Toda bracket, and explai

Shanjukta Nath

The Deferred Acceptance algorithm is a popular school allocation mechanism thanks to its strategy proofness. However, with application costs, strategy proofness fails, leading to an identification problem. In this paper, I address this identification problem by developing a new Threshold Rank setting that models the entire rank order list as a one-step utili

Austin R. Benson, Anil Damle, Alex Townsend

We draw connections between simple neural networks and under-determined linear systems to comprehensively explore several interesting theoretical questions in the study of neural networks. First, we emphatically show that it is unsurprising such networks can achieve zero training loss. More specifically, we provide lower bounds on the width of a single hidde

N. Pellatz, S. Roy, J-W. Lee, J. L. Schad

Time resolved measurements provide a new way to disentangle complex interactions in quantum materials due to their different timescales. We used pump-probe Raman scattering to investigate the apical oxygen vibration in YBa$_2$Cu$_3$O$_{6+x}$ under nonequilibrium conditions. Time-dependence of the phonon population demonstrated strong electron-phonon coupling

Alexander Rodriguez, Aleksey Generozov, Ann-Marie Madigan

The nucleus of our nearest, large galactic neighbor, M31, contains an eccentric nuclear disk--a disk of stars on eccentric, apsidally-aligned orbits around a supermassive black hole (SMBH). Previous studies of eccentric nuclear disks considered only an isolated disk, and did not study their dynamics under galaxy mergers (particularly a perturbing SMBH). Here

Luzia Anna Niggli, Patric Eichelberger, Christian Bangerter, Heiner Baur

Background: Skin marker-based analysis of functional spinal movement is a promising method for quantifying longitudinal effects of treatment interventions in patients with spinal pathologies. However, observed day-to-day changes might not necessarily be due to a treatment intervention, but can result from errors related to soft tissue artifacts, marker place

Martin von Kurnatowski, Jochen Schmid, Patrick Link, Rebekka Zache

Optimization in engineering requires appropriate models. In this article, a regression method for enhancing the predictive power of a model by exploiting expert knowledge in the form of shape constraints, or more specifically, monotonicity constraints, is presented. Incorporating such information is particularly useful when the available data sets are small

Antonio Fazzi, Nicola Guglielmi, Christian Lubich

We propose and study an algorithm for computing a nearest passive system to a given non-passive linear time-invariant system (with much freedom in the choice of the metric defining `nearest', which may be restricted to structured perturbations), and also a closely related algorithm for computing the structured distance of a given passive system to non-passiv

Hassan Allami, Jacob J. Krich

Highly mismatched alloys (HMAs) have band structures strongly modified due to the introduction of the alloying element. We consider HMAs where the isolated state of the alloying element is near the host conduction band, which causes the conduction band to split into two bands. We determine the bulk plasma frequency when the lower-energy band is partially occ

Robert Williams, Catherine Manea, Bruce Margon, Nidia Morrell

Identifications of a large fraction of previously unidentified lines in the complex spectrum of the low mass, late-type LMC [WC11] star J060819.93-715737.4 have been made utilizing electronic databases. There are an exceptionally large number of C II emission lines originating from autoionizing (ai) levels. Resonance fluorescence between the C II ground stat

Zhenwei Dai, Aditya Desai, Reinhard Heckel, Anshumali Shrivastava

Estimating and storing the covariance (or correlation) matrix of high-dimensional data is computationally challenging because both memory and computational requirements scale quadratically with the dimension. Fortunately, high-dimensional covariance matrices as observed in text, click-through, meta-genomics datasets, etc are often sparse. In this paper, we c

Jochem Oorschot, Chen Zhou

The block maxima (BM) approach in extreme value analysis fits a sample of block maxima to the Generalized Extreme Value (GEV) distribution. We consider all potential blocks from a sample, which leads to the All Block Maxima (ABM) estimator. Different from existing estimators based on the BM approach, the ABM estimator is permutation invariant. We show the as

Imtiaz Ahmed, Travis Galoppo, Xia Hu, Yu Ding

Dimensionality reduction is a crucial first step for many unsupervised learning tasks including anomaly detection and clustering. Autoencoder is a popular mechanism to accomplish dimensionality reduction. In order to make dimensionality reduction effective for high-dimensional data embedding nonlinear low-dimensional manifold, it is understood that some sort

Tai Hoang, Ngo Anh Vien

We introduce a Bayesian (deep) model-based reinforcement learning method (RoMBRL) that can capture model uncertainty to achieve sample-efficient policy optimisation. We propose to formulate the model-based policy optimisation problem as a Bayes-adaptive Markov decision process (BAMDP). RoMBRL maintains model uncertainty via belief distributions through a dee

Shubhang Bhatnagar, Sachin Goyal, Darshan Tank, Amit Sethi

The goal of pool-based active learning is to judiciously select a fixed-sized subset of unlabeled samples from a pool to query an oracle for their labels, in order to maximize the accuracy of a supervised learner. However, the unsaid requirement that the oracle should always assign correct labels is unreasonable for most situations. We propose an active lear

T. Libbrecht, J. P. Bjørgen, J. Leenaarts, J. de la Cruz Rodríguez

Aims. We aim to explain line formation of He I D3 and He I 10830 {\AA} in small-scale reconnection events. Methods. We make use of a simulated Ellerman bomb (EB), present in a Bifrost-generated radiative Magnetohydrodynamics (rMHD) snapshot. The resulting He I D3 and He I 10830 \AA\ line intensities are synthesized in 3D using the non-LTE Multi3D code. We co

Vanessa Polito, Bart De Pontieu, Paola Testa, David H. Brooks

Active region (AR) outflows have been studied in detail since the launch of \textit{Hinode}/EIS and are believed to provide a possible source of mass and energy to the slow solar wind. In this work, we investigate the lower atmospheric counterpart of AR outflows using observations from the \textit{Interface Region Imaging Spectrograph} (\textit{IRIS}). We fi

Anuj Kumar More, Mohua Banerjee

The article is a study of two algebraic structures, the `contrapositionally complemented pseudo-Boolean algebra' (ccpBa) and `contrapositionally $\vee$ complemented pseudo-Boolean algebra' (c$\vee$cpBa). The algebras have recently been obtained from a topos-theoretic study of categories of rough sets. The salient feature of these algebras is that there are t

Guido De Luca, Carlos Luna

Android is a platform for mobile devices that captures more than 85% of the total market-share. Currently, mobile devices allow people to develop multiple tasks in different areas. Regrettably, the benefits of using mobile devices are counteracted by increasing security risks. The important and critical role of these systems makes them a prime target for for

Brian McNair, Eric Margolin, Michael Law, Ya'acov Ritov

This paper aims to revisit and expand upon previous work on the "hot hand" phenomenon in basketball, specifically in the NBA. Using larger, modern data sets, we test streakiness of shooting patterns and the presence of hot hand behavior in free throw shooting, while going further by examining league-wide hot hand trends and the changes in individual player b

Sihang Guo, Ruohan Zhang, Bo Liu, Yifeng Zhu

Deep reinforcement learning (RL) algorithms are powerful tools for solving visuomotor decision tasks. However, the trained models are often difficult to interpret, because they are represented as end-to-end deep neural networks. In this paper, we shed light on the inner workings of such trained models by analyzing the pixels that they attend to during task e

Chiara Sorgentone, Petia Vlahovska

We study the effect of surfactant on the pairwise interactions of drops in an applied uniform DC electric field using a combination of numerical simulations based on a boundary integral formulation and an analytical theory assuming small drop deformations. The surfactant is assumed to be insoluble in the bulk-phase fluids. We show that the surfactant weakens

Robert L. Benedetto, Su-Ion Ih

Let $k$ be a number field with algebraic closure $\bar{k}$, and let $S$ be a finite set of places of $k$ containing all the archimedean ones. Fix $d\geq 2$ and $\alpha \in \bar{k}$ such that the map $z\mapsto z^d+\alpha$ is not postcritically finite. Assuming a technical hypothesis on $\alpha$, we prove that there are only finitely many parameters $c\in\bar{

Murat Babek Salman, Gokhan Muzaffer Guvensen

In this paper, we propose a novel receiver structure for single-carrier transmission with frequency domain equalization (FDE) that is exposed to power amplifier (PA) nonlinearities. A two-stage approach is adopted, in which linear communication channel is equalized at the first stage, and it is followed by a post-distortion where nonlinear distortion is redu

Vitaly Ivanin, Ekaterina Artemova, Tatiana Batura, Vladimir Ivanov

We show-case an application of information extraction methods, such as named entity recognition (NER) and relation extraction (RE) to a novel corpus, consisting of documents, issued by a state agency. The main challenges of this corpus are: 1) the annotation scheme differs greatly from the one used for the general domain corpora, and 2) the documents are wri

Ibrahim Sabuncu, Mehmet Ali Balci, Omer Akguller

In studies on election result prediction based on Twitter data, estimates were made using one of the factors such as the number of positive, negative, and neutral tweets posted about parties, the effect size of these tweets (the number of re-tweets), or the number of people who posted these tweets. However, no study was found that used all of these factors t

Daniel C. Ohrenstein, Patrick Brandao, Daniel Toth, Laurence Lovat

Endoscopic examinations are used to inspect the throat, stomach and bowel for polyps which could develop into cancer. Machine learning systems can be trained to process colonoscopy images and detect polyps. However, these systems tend to perform poorly on objects which appear visually small in the images. It is shown here that combining the single-shot detec

Miguel Martin, Abraham Rueda Zoca

We show that all the symmetric projective tensor products of a Banach space $X$ have the Daugavet property provided $X$ has the Daugavet property and either $X$ is an $L_1$-predual (i.e.\ $X^*$ is isometric to an $L_1$-space) or $X$ is a vector-valued $L_1$-space. In the process of proving it, we get a number of results of independent interest. For instance,

Fouad Elmouhib, Mohamed Talbi, Abdelmalek Azizi

Let $\Gamma \,=\, \mathbb{Q}(\sqrt[5]{n})$ be a pure quintic field, where $n$ is a positive integer $5^{th}$ power-free, $k_0\,=\,\mathbb{Q}(\zeta_5)$ be the cyclotomic field containing a primitive $5^{th}$ root of unity $\zeta_5$, and $k\,=\,\mathbb{Q}(\sqrt[5]{n},\zeta_5)$ the normal closure of $\Gamma$. Let $k_5^{(1)}$ be the Hilbert $5$-class field of $k

Santosh Kumar Radha, Amol Ratnaparkhe, Walter R. L. Lambrecht

Quasi-particle self-consistent $GW$ calculations are presented for the band structures of LiGaO2 and NaGaO2 in the orthorhombic $Pna2_1$ tetrahedrally coordinated crystal structures. Symmetry labeling of the bands near the gap is carried out and effective mass tensors are extracted for the conduction band minimum and crystal field split valence band maxima a

Vikas Aragam, Sonia Paban, Robert Rosati

There are well-known criteria on the potential and field-space geometry for determining if slow-roll, slow-turn, multi-field inflation is possible. However, even though it has been a topic of much recent interest, slow-roll, rapid-turn inflation only has such criteria in the restriction to two fields. In this work, we generalize the two-field, rapid-turn inf