June 2020 arXiv papers — page 10

Pierre-Francois Marteau

In this paper, we propose DiFF-RF, an ensemble approach composed of random partitioning binary trees to detect point-wise and collective (as well as contextual) anomalies. Thanks to a distance-based paradigm used at the leaves of the trees, this semi-supervised approach solves a drawback that has been identified in the isolation forest (IF) algorithm. Moreov

Tejas Shetty

A great number of quantum control papers have used one of the variants of the monotonically convergent variational control algorithm of Krotov (as described in Maday and Turinici (2003), Tannor et al. (1992), Zhu and Rabitz (1998), etc). The paper "Speeding up Thermalisation via Open Quantum System Variational Optimisation" by N, Suri, et al. [EPJST

Gianluca Levi, Aleksei V. Ivanov, Hannes Jónsson

The development of variational density functional theory approaches to excited electronic states is impeded by limitations of the commonly used self-consistent field (SCF) procedure. A method based on a direct optimization approach as well as the maximum overlap method is presented and the performance compared with previously proposed SCF strategies. Excited

Die Zhang, Huilin Zhou, Hao Zhang, Xiaoyi Bao

This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing. We construct a tree to encode salient interactions extracted by the DNN. Six metrics are proposed to analyze properties of interactions between constituents in a sentence. The interaction is defined based on Shapley

Jie Ren, Mingjie Li, Zexu Liu, Quanshi Zhang

This paper aims to define, quantify, and analyze the feature complexity that is learned by a DNN. We propose a generic definition for the feature complexity. Given the feature of a certain layer in the DNN, our method disentangles feature components of different complexity orders from the feature. We further design a set of metrics to evaluate the reliabilit

Pratik Mazumder, Pravendra Singh, Vinay P. Namboodiri

In this paper, we propose an approach to improve few-shot classification performance using a composite rotation based auxiliary task. Few-shot classification methods aim to produce neural networks that perform well for classes with a large number of training samples and classes with less number of training samples. They employ techniques to enable the networ

Arpan Das, Shreyansh S. Dave, Oindrila Ganguly, Ajit M. Srivastava

We propose a new analogue model of gravity - the evolving quark gluon plasma (QGP) produced in relativistic heavy ion collisions. This quark gluon plasma is the "most inviscid" fluid known. Such low kinematic viscosity is believed to reflect strongly correlated nature for QGP in these experiments. Hence, it may provide a good example of a quantum flu

Emanuele Laface, Benjamin Tyler Folsom

The common treatment of time-dependent potentials, such as those used for radio frequency cavities, is to average a potential's time component through the interval that the reference particle spends in the cavity. Such an approach, using the so-called transit-time factor, uses time as the independent variable in the Hamiltonian. In this paper, we instead

Georgi Georgiev

In this paper we will explore the 2D system describing trapped ionic system in the quadrapole field with a superposition of rationally symmetric hexapole and octopole fields for meromorphic integrability. We use the Lyapunov's and Ziglin-Morales-Ruiz-Ramis's classical methods for the proofs. The monodromy group of the normal variational equation (NVE

Renzo Caballero, Ahmed Kebaier, Marco Scavino, Raúl Tempone

We develop a data-driven methodology based on parametric Itô's Stochastic Differential Equations (SDEs) to capture the real asymmetric dynamics of forecast errors. Our SDE framework features time-derivative tracking of the forecast, time-varying mean-reversion parameter, and an improved state-dependent diffusion term. Proofs of the existence, strong uniq

Simon Nowak

We study the higher Hölder regularity of local weak solutions to a class of nonlinear nonlocal elliptic equations with kernels that satisfy a mild continuity assumption. An interesting feature of our main result is that the obtained regularity is better than one might expect when considering corresponding results for local elliptic equations in divergence fo

Hazel Murray, David Malone

The multi-armed bandit is a mathematical interpretation of the problem a gambler faces when confronted with a number of different machines (bandits). The gambler wants to explore different machines to discover which machine offers the best rewards, but simultaneously wants to exploit the most profitable machine. A password guesser is faced with a similar dil

Matthieu Astorg, Fabrizio Bianchi

We continue our investigation of the parameter space of families of polynomial skew products. Assuming that the base polynomial has a Julia set not totally disconnected and is neither a Chebyshev nor a power map, we prove that, near any bifurcation parameter, one can find parameters where $k$ critical points bifurcate \emph{independently}, with $k$ up to the

Tyler J. VanderWeele, Stijn Vansteelandt

Factor analysis is often used to assess whether a single univariate latent variable is sufficient to explain most of the covariance among a set of indicators for some underlying construct. When evidence suggests that a single factor is adequate, research often proceeds by using a univariate summary of the indicators in subsequent research. Implicit in such p

Frederik Ravn Klausen

Using recent couplings we provide counterexamples to monotonicity properties of percolation models related to graphical representations of the Ising model. We further prove a new coupling of the double random current model to the loop-$\mathrm{O}(1)$-model.

Suhan Son, Young Jae Shin, Kaixuan Zhang, Jeacheol Shin

That one can stack van der Waals materials with atomically sharp interfaces has provided a new material platform of constructing heterostructures. The technical challenge of mechanical stacking is picking up the exfoliated atomically thin materials after mechanical exfoliation without chemical and mechanical degradation. Chemically inert hexagonal boron nitr

Loic Dumortier, Stefano Mossa

We have applied recent machine learning advances, deep convolutional neural network, to three-dimensional (voxels) soft matter data, generated by Molecular Dynamics computer simulation. We have focused on the structural and phase properties of a coarse-grained model of hydrated ionic surfactants. We have trained a classifier able to automatically detect the

Yung-Ting Lee, Chi-Cheng Lee, Masahiro Fukuda, Taisuke Ozaki

A method to analyze optical transitions is developed by combining the Kubo-Greenwood formula with the unfolding method to construct an unfolded electronic band structure with optical transition weights, which allows us to investigate how optical transitions are perturbed by imperfections such as impurity, vacancy, and structural distortions. Based on the Kub

Emīls Ozoliņš, Kārlis Freivalds, Agris Šostaks

Convolutional neural networks have become the main tools for processing two-dimensional data. They work well for images, yet convolutions have a limited receptive field that prevents its applications to more complex 2D tasks. We propose a new neural model, called Matrix Shuffle-Exchange network, that can efficiently exploit long-range dependencies in 2D data

Anibal Pacheco-Sanchez, Nikolaos Mavredakis, Pedro C. Feijoo, Wei Wei

The trap-induced hysteresis on the performance of a graphene field-effect transistor is experimentally diminished here by applying consecutive gate-to-source voltage pulses of opposing polarity. This measurement scheme is a practical and suitable approach to obtain reproducible device characteristics. Trap-affected and trap-free experimental data enable a di

Viktor Könye, Masao Ogata

Using a microscopic theory for the magnetoconductivity at low magnetic fields we show how the Hall and longitudinal conductivity can be calculated in the low scattering rate limit. In the lowest order of the scattering rate, we recover the result of the semiclassical Boltzmann transport theory. At higher order, we get corrections containing the Berry curvatu

Ernesto Ortega, David Machado, Alejandro Lage-Castellanos

We apply the cavity master equation (CME) approach to epidemics models. We explore mostly the susceptible-infectious-susceptible (SIS) model, which can be readily treated with the CME as a two-state. We show that this approach is more accurate than individual based and pair based mean field methods, and a previously published dynamic message passing scheme.

Alexander A. Borisenko

It is proved the generalization of Toponogov theorem about the length of the curve in two-dimensional Riemannian manifolds in the case of two-dimensional Alexandrov spaces.

Xiao Wang, Shaohua Fan, Kun Kuang, Chuan Shi

Most of existing clustering algorithms are proposed without considering the selection bias in data. In many real applications, however, one cannot guarantee the data is unbiased. Selection bias might bring the unexpected correlation between features and ignoring those unexpected correlations will hurt the performance of clustering algorithms. Therefore, how

Tao Xin

Quantum device characterization via state tomography plays an important role in both validating quantum hardware and processing quantum information, but it needs the exponential number of the measurements. For the systems with XX+YY-type couplings and Z readouts, such as superconducting quantum computing (SQC) systems, traditional quantum state tomography (Q

Ren Yang, Luc Van Gool, Radu Timofte

We introduce an open source Tensorflow implementation of the Deep Video Compression (DVC) method in this technical report. DVC is the first end-to-end optimized learned video compression method, achieving better MS-SSIM performance than the Low-Delay P (LDP) very fast setting of x265 and comparable PSNR performance with x265 (LDP very fast). At the time of w

Haruya Mizutani

We study the scattering theory for the Schrödinger and wave equations with rough potentials in a scale of homogeneous Sobolev spaces. The first half of the paper concerns with an inverse-square potential in both of subcritical and critical constant cases, which is a particular model of scaling-critical singular perturbations. In the subcritical case, the exi

Tuomas Hytönen, Stefanos Lappas

In a previous paper by one of us, a "compact version" of Rubio de Francia's weighted extrapolation theorem was proved, which allows one to extrapolate the compactness of an operator from just one space to the full range of weighted spaces, where this operator is bounded. In this paper, we obtain generalizations of this extrapolation of compactnes

Aditi Shenvi, Jim Q. Smith

Chain Event Graphs (CEGs) are a recent family of probabilistic graphical models - a generalisation of Bayesian Networks - providing an explicit representation of structural zeros, structural missing values and context-specific conditional independences within their graph topology. A CEG is constructed from an event tree through a sequence of transformations

Sneha Chaubey, Shivani Goel

We study the distribution of the generalized gcd and lcm functions on average. The generalized gcd function, denoted by $(m,n)_b$, is the largest $b$-th power divisor common to $m$ and $n$. Likewise, the generalized lcm function, denoted by $[m,n]_b$, is the smallest $b$-th power multiple common to $m$ and $n$. We derive asymptotic formulas for the average o

Xian-Hu Zha, Chao Zhang, Dengdong Fan, Pengxiang Xu

Variational quantum eigensolver (VQE) is demonstrated to be the promising methodology for quantum chemistry based on near-term quantum devices. However, many problems are yet to be investigated for this methodology, such as the influences of optimization algorithm and basis size on the accuracy and efficiency for quantum computing. To address these issues, f

Michaela Sojkova, Edmund Dobrocka, Peter Hutar, Valeria Taskova

Few-layer PtSe2 films are promising candidates for applications in high-speed electronics, spintronics and photodetectors. Reproducible fabrication of large-area highly crystalline films is, however, still a challenge. Here, we report the fabrication of epitaxially aligned PtSe2 films using one-zone selenization of pre-sputtered platinum layers. We have stud

Stefano Riolo, Andrea Seppi

In 2010, Kerckhoff and Storm discovered a path of hyperbolic 4-polytopes eventually collapsing to an ideal right-angled cuboctahedron. This is expressed by a deformation of the inclusion of a discrete reflection group (a right-angled Coxeter group) in the isometry group of hyperbolic 4-space. More recently, we have shown that the path of polytopes can be ext

Maximilian Buser, Sebastian Greschner, Ulrich Schollwöck, Thierry Giamarchi

In the context of experimental advances in the realization of artificial magnetic fields in quantum gases, we discuss feasible schemes to extend measurements of the Hall polarization to a study of the Hall voltage, allowing for direct comparison with solid state systems. Specifically, for the paradigmatic example of interacting flux ladders, we report on cha

Camille Pouchol, Olivier Verdier

We look at continuum solutions in optimisation problems associated to linear inverse problems $y = Ax$ with non-negativity constraint $x \geq 0$. We focus on the case where the noise model leads to maximum likelihood estimation through general divergences, which covers a wide range of common noise statistics such as Gaussian and Poisson. Considering $x$ as a

Peng Wang, Zirui Zhou, Anthony Man-Cho So

Community detection in graphs that are generated according to stochastic block models (SBMs) has received much attention lately. In this paper, we focus on the binary symmetric SBM -- in which a graph of $n$ vertices is randomly generated by first partitioning the vertices into two equal-sized communities and then connecting each pair of vertices with probab

Kathrin Lehmann, Marjan Shayegan, Gerhard A. Blab, Nancy R. Forde

Multi-step assembly of individual protein building blocks is key to the formation of essential higher-order structures inside and outside of cells. Optical tweezers is a technique well suited to investigate the mechanics and dynamics of these structures at a variety of size scales. In this mini-review, we highlight experiments that have used optical tweezers

Shafqat Riaz, Michal Szanecki, Andrzej Niedzwiecki, Dimitry Ayzenberg

A fraction of the electromagnetic radiation emitted from the surface of a geometrically thin and optically thick accretion disk of a black hole returns to the disk because of the strong light bending in the vicinity of the compact object (returning radiation). While such radiation clearly affects the observed spectrum of the source, it is often neglected in

Peter Bradshaw, Tomáš Masařík, Ladislav Stacho

For a given $\varepsilon > 0$, we say that a graph $G$ is $\varepsilon$-flexibly $k$-choosable if the following holds: for any assignment $L$ of color lists of size $k$ on $V(G)$, if a preferred color from a list is requested at any set $R$ of vertices, then at least $\varepsilon |R|$ of these requests are satisfied by some $L$-coloring. We consider the ques

Eduardo Ochs

I used to believe that my conventions for drawing diagrams for categorical statements could be written down in one page or less, and that the only tricky part was the technique for reconstructing objects "from their names"... but then I found out that this is not so. This is an attempt to explain, with motivations and examples, all the conventions be

Miyu Suzuki

Suppose that $F$ is a non-Archimedean local field and $D$ is a central division algebra over $F$. Let $n$ be a positive integer. We show a classification modulo essentially square-integrable representations of standard modules of $\mathrm{GL}_n(D)$ which have non-zero linear periods. By this classification, the conjecture of Prasad and Takloo-Bighash is redu

Y. Ishii, S. Horio, Y. Noda, M. Hiraishi

Muon spin rotation and resonant soft X-ray scattering experiments on prototype multiferroics RMn2O5 (R = Y, Sm) are used to demonstrate that the local electric displacements are driven by the spin-current (SC) mechanism. Small local electric displacements were evaluated by observing spin polarization at ligand O ions, for which implanted muons served as an e

Jung Hee Kim, Siyeong Lee, Suk-Ju Kang

Recently, high dynamic range (HDR) image reconstruction based on the multiple exposure stack from a given single exposure utilizes a deep learning framework to generate high-quality HDR images. These conventional networks focus on the exposure transfer task to reconstruct the multi-exposure stack. Therefore, they often fail to fuse the multi-exposure stack i

Jinhyuk Lee, Sean S. Yi, Minbyul Jeong, Mujeen Sung

The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it. One reason why the fight is difficult is due to the lack of information and knowledge. In this work, we outline our effort to contribute to shrinking this knowledge vacuum by creating covidAsk, a question answering (QA) system

Wance Wang, Mohan Shen, Chang-Ling Zou, Wei Fu

We numerically model key building blocks of a phononic integrated circuit that enable phonon routing in high-acoustic-index waveguides. Our particular focus is on Gallium Nitride-on-sapphire phononic platform which has recently demonstrated high acoustic confinement in its top layer without the use of suspended structures. We start with systematic simulation

P. Cheema, M. M. Alamdari, G. A. Vio

In the field of structural engineering analysis, a common requirement is to calculate the modal frequencies of a structure that has undergone an update, either naturally (such as from material degradation), or due to man-made influences (by placing point masses along a structure). In addition to this requirement, it is common to only have access to truncated

M. Novello, A. E. S. Hartmann

In a recent paper we have analyzed the Spinor Theory of Gravity (STG) which is based on the intimate relation between Fermi (weak) interaction and gravity. We presented the hypothesis that the effect of matter upon the metric that represents gravitational interaction in General Relativity is an effective one. This lead us to consider gravitation to be the re

M. Novello, A. E. S. Hartmann

We follow an old suggestion made by Stueckelberg that there exists an intimate connection between weak interaction and gravity, symbolized by the relationship between the Fermi and Newton\rq s constants. We analyze the hypothesis that the effect of matter upon the metric that represents gravitational interaction in General Relativity is an effective one. Thi

Wei-Jiang Gong, Yu-Hang Xue, Xiao-Qi Wang, Lian-Lian Zhang

We theoretically study the transport properties in the T-shaped double-quantum-dot structure, by introducing the Majorana bound state (MBS) to couple to the dot in the main channel. It is found that the side-coupled dot governs the effect of the MBS on the transport behavior. When its level is consistent with the energy zero point, the MBS contributes little

Corby Rosset, Chenyan Xiong, Minh Phan, Xia Song

How much knowledge do pretrained language models hold? Recent research observed that pretrained transformers are adept at modeling semantics but it is unclear to what degree they grasp human knowledge, or how to ensure they do so. In this paper we incorporate knowledge-awareness in language model pretraining without changing the transformer architecture, ins

P. Ko, Yong Tang

The recently reported result from XENON1T experiment indicates that there is an excess with $3.5σ$ significance in the electronic recoil events. Interpreted as new physics, new sources are needed to account for the electronic scattering. We show that a dark fermion $ψ$ with mass about $ \mathcal{O}\left(10\right)$ MeV from semi-annihilation of $Z_3$ dark mat

Roland M. Crocker, Mark R. Krumholz, Todd A. Thompson

Cosmic rays (CRs) are a plausible mechanism for launching winds of cool material from the discs of star-forming galaxies. However, there is no consensus on what types of galaxies likely host CR-driven winds, or what role these winds might play in regulating galaxies' star formation rates. Using a detailed treatment of the transport and losses of hadronic

Roland M. Crocker, Mark R. Krumholz, Todd A. Thompson

In the Milky Way, cosmic rays (CRs) are dynamically important in the interstellar medium, contribute to hydrostatic balance, and may help regulate star formation. However, we know far less about the importance of CRs in galaxies whose gas content or star formation rate differ significantly from those of the Milky Way. Here we construct self-consistent models

Len Bos, Michael A. Slawinski, Theodore Stanoev

The velocità ascensionale media (VAM) is measurement that quantifies a cyclist's climbing ability. It depends on both the ground speed of a bicycle-cyclist system and the slope of an incline. To maximize the ascent speed, the solution to the brachistochrone problem determines that the optimal curve for the incline is a straight line, which is a hill with

Zeke Xie, Xinrui Wang, Huishuai Zhang, Issei Sato

Adaptive Moment Estimation (Adam), which combines Adaptive Learning Rate and Momentum, would be the most popular stochastic optimizer for accelerating the training of deep neural networks. However, it is empirically known that Adam often generalizes worse than Stochastic Gradient Descent (SGD). The purpose of this paper is to unveil the mystery of this behav

Li Chen, Kun Yang

In this paper, a series of $ν=2/5$ fractional quantum Hall wave functions are constructed from conformal field theory(CFT). They share the same topological properties with states constructed by Jain's composite fermion approach. Upon exact lowest Landau level(LLL) projection, some of Jain composite fermion states would not survive if constraints on Landa

Adrian David, Yasha Neiman

We consider the holographic duality between 4d type-A higher-spin gravity and a 3d free vector model. It is known that the Feynman diagrams for boundary correlators can be encapsulated in an HS-algebraic twistorial expression. This expression can be evaluated not just on separate boundary insertions, but on entire finite source distributions. We do so for th

Surbhi Goel, Aravind Gollakota, Adam Klivans

We give the first statistical-query lower bounds for agnostically learning any non-polynomial activation with respect to Gaussian marginals (e.g., ReLU, sigmoid, sign). For the specific problem of ReLU regression (equivalently, agnostically learning a ReLU), we show that any statistical-query algorithm with tolerance $n^{-(1/ε)^b}$ must use at least $2^{n^c}

Sheng Zhang, Zongyuan Fu, Bingbing Zhu, Guangyu Fan

Generating intense ultrashort pulses with high-quality spatial modes is crucial for ultrafast and strong-field science. This can be accomplished by controlling propagation of femtosecond pulses under the influence of Kerr nonlinearity and achieving stable propagation with high intensity. In this work, we propose that the generation of spatial solitons in per

Ferran Garcia, Martin Seilmayer, André Giesecke, Frank Stefani

The long therm behavior of chaotic flows is investigated by means of time dependent frequency analysis. The system under test consists of an electrically conducting fluid, confined between two differentially rotating spheres. The spherical setup is exposed to an axial magnetic field. The classical Fourier Transform method provides a first estimation of the t

Zahi M. Kakish, Karthik Elamvazhuthi, Spring Berman

In this paper, we present a reinforcement learning approach to designing a control policy for a "leader" agent that herds a swarm of "follower" agents, via repulsive interactions, as quickly as possible to a target probability distribution over a strongly connected graph. The leader control policy is a function of the swarm distribution, whic

Gursharn Kaur, Adrian Röllin

Our main results are quantitative bounds in the multivariate normal approximation of centred subgraph counts in random graphs generated by a general graphon and independent vertex labels. We are interested in these statistics because they are key to understanding fluctuations of regular subgraph counts -- a cornerstone of dense graph limit theory. We also id

Mir Hameeda, Behnam Pourhassan, Syed Masood, Mir Faizal

In this paper, we analyze the effects of expansion on large scale structure formation in our Universe. We do that by incorporating a cosmological constant term in the gravitational partition function. This gravitational partition function with a cosmological constant is used for analyzing the thermodynamics of this system. We analyze the virial expansion for

Samuel M. Howell, William C. Stone, Kate Craft, Christopher German

This is a community white paper submitted to the Decadal Survey in Planetary Science and Astrobiology, reflecting the views of the NASA Astrobiology Program's Research Coordination Network for Ocean Worlds (NOW). We recommend the establishment of a dedicated Ocean Worlds Exploration Program within NASA to provide sustained funding support for the science

Meenakshi Khosla, Gia H. Ngo, Keith Jamison, Amy Kuceyeski

The increasing popularity of naturalistic paradigms in fMRI (such as movie watching) demands novel strategies for multi-subject data analysis, such as use of neural encoding models. In the present study, we propose a shared convolutional neural encoding method that accounts for individual-level differences. Our method leverages multi-subject data to improve

Son N. T. Tu

We study the asymptotic behavior, as $λ\rightarrow 0^+$, of the state-constraint Hamilton--Jacobi equation $ϕ(λ) u_λ(x) + H(x,Du_λ(x)) = 0$ in $(1+r(λ))Ω$ and the corresponding additive eigenvalues, or ergodic constant $H(x,Dv(x)) = c(λ)$ in $(1+r(λ))Ω$ with state-constraint. Here, $Ω$ is a bounded domain of $ \mathbb{R}^n$, $ϕ(λ), r(λ):(0,\infty)\rightarrow

Ali Mirzaeian, Sai Manoj, Ashkan Vakil, Houman Homayoun

Deep convolutional neural networks have shown high efficiency in computer visions and other applications. However, with the increase in the depth of the networks, the computational complexity is growing exponentially. In this paper, we propose a novel solution to reduce the computational complexity of convolutional neural network models used for many class i

Elliot J. Carr

Multispecies contaminant transport in the Earth's subsurface is commonly modelled using advection-dispersion equations coupled via first-order reactions. Analytical and semi-analytical solutions for such problems are highly sought after but currently limited to either one species, homogeneous media, certain reaction networks, specific boundary conditions

T. C. Bartolo, J. S. Smith, B. Muralidharan, C. Müller

Majorana fermions act as their own antiparticle, and they have long been thought to be confined to the realm of pure theory. However, interest in them has recently resurfaced, as it was realized through the work of Kitaev that some experimentally accessible condensed matter systems can host these exotic excitations as bound states on the boundaries of 1D cha

Yi Xing, Zhongxiang Wang

The microquasar V404 Cygni (also known as GS 2023+338) was previously reported to have weak GeV gamma-ray emission in sub-day time periods during its 2015 outburst. In order to provide more detailed information at the high energy range for this black hole binary system, we conduct analysis to the data obtained with the Large Area Telescope (LAT) onboard the

Tanya S. Roussy, Daniel A. Palken, William B. Cairncross, Benjamin M. Brubaker

We use our recent electric dipole moment (EDM) measurement data to constrain the possibility that the HfF$^+$ EDM oscillates in time due to interactions with candidate dark matter axion-like particles (ALPs). We employ a Bayesian analysis method which accounts for both the look-elsewhere effect and the uncertainties associated with stochastic density fluctua

Steve Hanneke, Samory Kpotufe

Multitask learning and related areas such as multi-source domain adaptation address modern settings where datasets from $N$ related distributions $\{P_t\}$ are to be combined towards improving performance on any single such distribution ${\cal D}$. A perplexing fact remains in the evolving theory on the subject: while we would hope for performance bounds tha

Michael Fuchs, Bernhard Gittenberger, Marefatollah Mansouri

In previous work, we gave asymptotic counting results for the number of tree-child and normal networks with $k$ reticulation vertices and explicit exponential generating functions of the counting sequences for $k=1,2,3$. The purpose of this note is two-fold. First, we make some corrections to our previous approach which overcounted the above numbers and thus

Brantly Callaway, Sonia Karami

This paper considers identifying and estimating the Average Treatment Effect on the Treated (ATT) when untreated potential outcomes are generated by an interactive fixed effects model. That is, in addition to time-period and individual fixed effects, we consider the case where there is an unobserved time invariant variable whose effect on untreated potential

Chris Gustin, Lukas Hanschke, Katarina Boos, Jonathan R. A. Müller

We present spectroscopic experiments and theory of a quantum dot driven bichromatically by two strong coherent lasers. In particular, we explore the regime where the drive strengths are substantial enough to merit a general non-perturbative analysis, resulting in a rich higher-order Floquet dressed-state energy structure. We show high resolution spectroscopy

Youn Kil Jung, Andrew Gould, Andrzej Udalski, Takahiro Sumi

We report the discovery of a planet in the microlensing event OGLE-2018-BLG-1269, with planet-host mass ratio $q \sim 6\times10^{-4}$, i.e., $0.6$ times smaller than the Jupiter/Sun mass ratio. Combined with the $Gaia$ parallax and proper motion, a strong one-dimensional constraint on the microlens parallax vector allows us to significantly reduce the uncert

Himan Abdollahpouri, Masoud Mansoury

Academic research in recommender systems has been greatly focusing on the accuracy-related measures of recommendations. Even when non-accuracy measures such as popularity bias, diversity, and novelty are studied, it is often solely from the users' perspective. However, many real-world recommenders are often multi-stakeholder environments in which the nee

Kazuki Sumida, Yuya Sakuraba, Keisuke Masuda, Takashi Kono

Weyl semimetals are characterized by the presence of massless band dispersion in momentum space. When a Weyl semimetal meets magnetism, large anomalous transport properties emerge as a consequence of its topological nature. Here, using $in-situ$ spin- and angle-resolved photoelectron spectroscopy combined with $ab\ initio$ calculations, we visualize the spin

Kaidi Cao, Yining Chen, Junwei Lu, Nikos Arechiga

Real-world large-scale datasets are heteroskedastic and imbalanced -- labels have varying levels of uncertainty and label distributions are long-tailed. Heteroskedasticity and imbalance challenge deep learning algorithms due to the difficulty of distinguishing among mislabeled, ambiguous, and rare examples. Addressing heteroskedasticity and imbalance simulta

Romina M. Arroyo, Mark D. Gould, Artem Pulemotov

We investigate the prescribed Ricci curvature problem in the class of left-invariant naturally reductive Riemannian metrics on a non-compact simple Lie group. We obtain a number of conditions for the solvability of the underlying equations and discuss several examples.

Gabriele Bozzola, Vasileios Paschalidis

We perform general-relativistic simulations of charged black holes targeting GW150914. We show that the inspiral is most efficient for detecting black hole charge through gravitational waves and that GW150914 is compatible with having charge-to-mass ratio as high as 0.3. Our work applies to electric and magnetic charge, and to theories with black holes endow

Daisuke Kishimoto, Masahiro Takeda

Let $G$ be the classical group, and let Hom$(\mathbb{Z}^m,G)$ denote the space of commuting $m$-tuples in $G$. First, we refine the formula for the Poincaré series of Hom$(\mathbb{Z}^m,G)$ due to Ramras and Stafa by assigning (signed) integer partitions to (signed) permutations. Using the refined formula, we determine the top term of the Poincaré series, and

Yusuke Nambu, Mechthild Enderle, Tobias Weber, Kazuhisa Kakurai

Polarised neutron scattering is the method of choice to study magnetism in condensed matter. Polarised neutrons are typically very low in flux, and complex experimental configurations further reduce the count rate. Neutron polarisation corrections would therefore be needed. Here we analytically derive formulae of the corrected partial differential scattering

Bo Zhou, Walid Saad

In this paper, a dense Internet of Things (IoT) monitoring system is considered in which a large number of IoT devices contend for channel access so as to transmit timely status updates to the corresponding receivers using a carrier sense multiple access (CSMA) scheme. Under two packet management schemes with and without preemption in service, the closed-for

Meng Zhang, Ahmed Arafa, Ermin Wei, Randall A. Berry

The proliferation of real-time applications has spurred much interest in data freshness, captured by the {\it age-of-information} (AoI) metric. When strategic data sources have private market information, a fundamental economic challenge is how to incentivize them to acquire fresh data and optimize the age-related performance. In this work, we consider an in

André Palóczy

The routes mechanical energy takes from injection to dissipation in the ocean are currently an open problem. The goals of this project are to: 1) Study the energy exchanges between near-inertial waves and the balanced flow using idealized simulations and 2) To derive a new asymptotic model to help develop an understanding of such interactions.

Ahmed M. Alaa, Mihaela van der Schaar

Deep learning models achieve high predictive accuracy across a broad spectrum of tasks, but rigorously quantifying their predictive uncertainty remains challenging. Usable estimates of predictive uncertainty should (1) cover the true prediction targets with high probability, and (2) discriminate between high- and low-confidence prediction instances. Existing

Ugur Korkut Pata

The purpose of this study is to investigate the effects of the COVID-19 pandemic on economic policy uncertainty in the US and the UK. The impact of the increase in COVID-19 cases and deaths in the country, and the increase in the number of cases and deaths outside the country may vary. To examine this, the study employs bootstrap ARDL cointegration approach

Sana Ben Hamida, Wafa Abdelmalek, Fathi Abid

Volatility is a key variable in option pricing, trading and hedging strategies. The purpose of this paper is to improve the accuracy of forecasting implied volatility using an extension of genetic programming (GP) by means of dynamic training-subset selection methods. These methods manipulate the training data in order to improve the out of sample patterns f

Han Xiangmin, Wang Jun, Zhou Weijun, Chang Cai

Elastography ultrasound (EUS) provides additional bio-mechanical in-formation about lesion for B-mode ultrasound (BUS) in the diagnosis of breast cancers. However, joint utilization of both BUS and EUS is not popular due to the lack of EUS devices in rural hospitals, which arouses a novel modality im-balance problem in computer-aided diagnosis (CAD) for brea

M. L. Bertotti, S. O. Dovgyi, D. Koroliouk

We study the scenarios of the dynamics of ternary statistical experiments, modeled employing difference equations. The important features are a balance condition and the existence of a steady-state (equilibrium). We give a classification of scenarios of the model evolution which are significantly different between them, depending on the domain of the values

Qingquan Song, Dehua Cheng, Hanning Zhou, Jiyan Yang

Click-Through Rate (CTR) prediction is one of the most important machine learning tasks in recommender systems, driving personalized experience for billions of consumers. Neural architecture search (NAS), as an emerging field, has demonstrated its capabilities in discovering powerful neural network architectures, which motivates us to explore its potential f

Yuze Wang, Kenichi Soga, Jason T. DeJong, Alexandre J. Kabla

Microbial-Induced Carbonate Precipitation (MICP) has been explored for more than a decade as a promising soil improvement technique. However, it is still challenging to predict and control the growth rate and characteristics of CaCO3 precipitates, which directly affect the engineering performance of MICP-treated soils. In this study, we employ a microfluidic

Lars S. Madsen, Muhammad Waleed, Catxere A. Casacio, Alexander B. Stilgoe

Viscosity is an important property of out-of-equilibrium systems such as active biological materials and driven non-Newtonian fluids, and for fields ranging from biomaterials to geology, energy technologies and medicine. However, noninvasive viscosity measurements typically require integration times of seconds. Here we demonstrate a four orders-of-magnitude

Jian Wang, Weike Yuan, Jianjun Bian, Gangfeng Wang

The friction of a nanosized sphere in commensurate contact with a flat substrate is investigated by performing molecular dynamics simulations. Particular focus is on the distribution of shear stress within the contact region. It is noticed that within the slip zone, the local friction coefficient defined by the ratio of shear stress to normal pressure declin

Elad Shamriz, Zhaopin Chen, Boris A. Malomed, Hidetsugu Sakaguchi

This article provides a focused review of recent findings which demonstrate, in some cases quite counter-intuitively, the existence of bound states with a singularity of the density pattern at the center, while the states are physically meaningful because their total norm converges. One model of this type is based on the 2D Gross-Pitaevskii equation (GPE) wh

Tal Arbel, Ismail Ben Ayed, Marleen de Bruijne, Maxime Descoteaux

This compendium gathers all the accepted extended abstracts from the Third International Conference on Medical Imaging with Deep Learning (MIDL 2020), held in Montreal, Canada, 6-9 July 2020. Note that only accepted extended abstracts are listed here, the Proceedings of the MIDL 2020 Full Paper Track are published in the Proceedings of Machine Learning Resea

Youssef Dawoud, Julia Hornauer, Gustavo Carneiro, Vasileios Belagiannis

Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not a sustainable solution for every new microscopy database and cell type. Instead, we assume that we can access a plethora of annotated image data sets from different domains (sourc

Zhen Long, Yipeng Liu, Sixing Zeng, Jiani Liu

Hyperspectral image (HSI) has some advantages over natural image for various applications due to the extra spectral information. During the acquisition, it is often contaminated by severe noises including Gaussian noise, impulse noise, deadlines, and stripes. The image quality degeneration would badly effect some applications. In this paper, we present a HSI

Zhen Long, Ce Zhu, Jiani Liu, Yipeng Liu

Low rank tensor ring model is powerful for image completion which recovers missing entries in data acquisition and transformation. The recently proposed tensor ring (TR) based completion algorithms generally solve the low rank optimization problem by alternating least squares method with predefined ranks, which may easily lead to overfitting when the unknown