October 2020 arXiv papers — page 43

Changfeng Yu, Cheng Zhang, Jie Wang

Accurate extraction of body text from PDF-formatted academic documents is essential in text-mining applications for deeper semantic understandings. The objective is to extract complete sentences in the body text into a txt file with the original sentence flow and paragraph boundaries. Existing tools for extracting text from PDF documents would often mix body

Elizabeth Gasparim, Bruno Suzuki

We show that an instanton with high charge can provoke the creation of extra curvature on the space that holds it. Geometrically, this corresponds to a new surgery operation, which we name grafting. Curvature around a sphere increases by grafting when the charge of an instanton decays.

Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White

Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that are safe for deployment, they assume that the underlying problem is stationary. However, many real-world problems of interest exhibit non-stationarity, and when stakes are high, the

David Lipshutz, Charlie Windolf, Siavash Golkar, Dmitri B. Chklovskii

Learning latent features from time series data is an important problem in both machine learning and brain function. One approach, called Slow Feature Analysis (SFA), leverages the slowness of many salient features relative to the rapidly varying input signals. Furthermore, when trained on naturalistic stimuli, SFA reproduces interesting properties of cells i

Arij Riabi, Thomas Scialom, Rachel Keraron, Benoît Sagot

Coupled with the availability of large scale datasets, deep learning architectures have enabled rapid progress on the Question Answering task. However, most of those datasets are in English, and the performances of state-of-the-art multilingual models are significantly lower when evaluated on non-English data. Due to high data collection costs, it is not rea

Marc Abeille, Louis Faury, Clément Calauzènes

Logistic Bandits have recently attracted substantial attention, by providing an uncluttered yet challenging framework for understanding the impact of non-linearity in parametrized bandits. It was shown by Faury et al. (2020) that the learning-theoretic difficulties of Logistic Bandits can be embodied by a large (sometimes prohibitively) problem-dependent con

Marco Casagrande, Mauro Conti, Eleonora Losiouk

Automated contact tracing is a key solution to control the spread of airborne transmittable diseases: it traces contacts among individuals in order to alert people about their potential risk of being infected. The current SARS-CoV-2 pandemic put a heavy strain on the healthcare system of many countries. Governments chose different approaches to face the spre

ibrahim Yilmaz, Ambareen Siraj

More and more conventional electromechanical meters are being replaced with smart meters because of their substantial benefits such as providing faster bi-directional communication between utility services and end users, enabling direct load control for demand response, energy saving, and so on. However, the fine-grained usage data provided by smart meter br

Shurjo Banerjee, Jesse Thomason, Jason J. Corso

Autonomous robot systems for applications from search and rescue to assistive guidance should be able to engage in natural language dialog with people. To study such cooperative communication, we introduce Robot Simultaneous Localization and Mapping with Natural Language (RobotSlang), a benchmark of 169 natural language dialogs between a human Driver control

Hao Cheng, Xiaodong Liu, Lis Pereira, Yaoliang Yu

We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and noisy inputs. Theoretically, we provide a connection of two recent methods, Jacobian Regularization and Virtual Adversarial Training, under this framework. Additionally, we generalize

Dhivya Chandrasekaran, Vijay Mago

Semantic textual similarity is one of the open research challenges in the field of Natural Language Processing. Extensive research has been carried out in this field and near-perfect results are achieved by recent transformer-based models in existing benchmark datasets like the STS dataset and the SICK dataset. In this paper, we study the sentences in these

Shiying Xiong, Yunjin Tong, Xingzhe He, Shuqi Yang

Predicting the behaviors of Hamiltonian systems has been drawing increasing attention in scientific machine learning. However, the vast majority of the literature was focused on predicting separable Hamiltonian systems with their kinematic and potential energy terms being explicitly decoupled while building data-driven paradigms to predict nonseparable Hamil

Philipp del Hougne, K. Brahima Yeo, Philippe Besnier, Matthieu Davy

Wavefront shaping (WFS) has emerged as powerful tool to control the propagation of diverse wave phenomena (light, sound, microwaves, ...) in disordered matter for applications including imaging, communication, energy transfer, micromanipulation, and scattering anomalies. Nonetheless, in practice the necessary coherent control of multiple input channels remai

John Brennan, Stephen Bonner, Amir Atapour-Abarghouei, Philip T Jackson

With the growing significance of graphs as an effective representation of data in numerous applications, efficient graph analysis using modern machine learning is receiving a growing level of attention. Deep learning approaches often operate over the entire adjacency matrix -- as the input and intermediate network layers are all designed in proportion to the

Yusen Zhang, Xiangyu Dong, Shuaichen Chang, Tao Yu

Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input. However, in the real scenario, users can input any text that may not be able to be answered by a SQL query. In this work, we propose TriageSQL, the first cross-domain text-to-SQ

Seyyid Emre Sofuoglu, Selin Aviyente

Anomaly detection in spatiotemporal data is a challenging problem encountered in a variety of applications including hyperspectral imaging, video surveillance, and urban traffic monitoring. Existing anomaly detection methods are most suited for point anomalies in sequence data and cannot deal with temporal and spatial dependencies that arise in spatiotempora

David Lipshutz, Cengiz Pehlevan, Dmitri B. Chklovskii

An important problem in neuroscience is to understand how brains extract relevant signals from mixtures of unknown sources, i.e., perform blind source separation. To model how the brain performs this task, we seek a biologically plausible single-layer neural network implementation of a blind source separation algorithm. For biological plausibility, we requir

Cunjian Chen, Arun Ross

Convolutional Neural Networks (CNNs) are being increasingly used to address the problem of iris presentation attack detection. In this work, we propose attention-guided iris presentation attack detection (AG-PAD) to augment CNNs with attention mechanisms. Two types of attention modules are independently appended on top of the last convolutional layer of the

Sholeh Razavian, Matteo G. A. Paris, Marco G. Genoni

The estimation of more than one parameter in quantum mechanics is a fundamental problem with relevant practical applications. In fact, the ultimate limits in the achievable estimation precision are ultimately linked with the non-commutativity of different observables, a peculiar property of quantum mechanics. We here consider several estimation problems for

Scott Aaronson, Shalev Ben-David, Robin Kothari, Shravas Rao

Based on the recent breakthrough of Huang (2019), we show that for any total Boolean function $f$, $\bullet \quad \mathrm{deg}(f) = O(\widetilde{\mathrm{deg}}(f)^2)$: The degree of $f$ is at most quadratic in the approximate degree of $f$. This is optimal as witnessed by the OR function. $\bullet \quad \mathrm{D}(f) = O(\mathrm{Q}(f)^4)$: The deterministic q

Chia-Nan Yeh, Sergei Iskakov, Dominika Zgid, Emanuel Gull

In this work, we use the thermodynamically consistent and conserving self-energy embedding theory (SEET) to study the spectra of the prototypical undistorted cubic perovskites SrVO$_3$ and SrMnO$_3$. In the strongly correlated metallic SrVO$_3$ we find that the usual attribution of the satellite peaks at -1.8eV to Hund or Hubbard physics in the $t_{2g}$ orbi

Tobias Eder, Viktor Hangya, Alexander Fraser

Good quality monolingual word embeddings (MWEs) can be built for languages which have large amounts of unlabeled text. MWEs can be aligned to bilingual spaces using only a few thousand word translation pairs. For low resource languages training MWEs monolingually results in MWEs of poor quality, and thus poor bilingual word embeddings (BWEs) as well. This pa

Laure Thompson, David Mimno

Clustering token-level contextualized word representations produces output that shares many similarities with topic models for English text collections. Unlike clusterings of vocabulary-level word embeddings, the resulting models more naturally capture polysemy and can be used as a way of organizing documents. We evaluate token clusterings trained from sever

Gengyu Xu, George V. Eleftheriades, Sean V. Hum

We present methods for analyzing and designing cylindrical electromagnetic metasurfaces with non-circular cross sections based on conformal transformations. It can be difficult to treat surfaces with non-canonical geometries since they generally do not admit straightforward solutions to the Helmholtz wave equation subject to the appropriate boundary conditio

Y. Eren Suyolcu, Jiaxin Sun, Berit H. Goodge, Jisung Park

We demonstrate a-axis YBa2Cu3O7-x/PrBa2Cu3O7-x/YBa2Cu3O7-x trilayers grown on (100) LaAlO3 substrates with improved interface smoothness. The trilayers are synthesized by ozone-assisted molecular-beam epitaxy. The thickness of the PrBa2Cu3O7-x layer is held constant at 8 nm and the thickness of the YBa2Cu3O7-x layers is varied from 24 nm to 100 nm. X-ray dif

Liangming Pan, Wenhu Chen, Wenhan Xiong, Min-Yen Kan

Obtaining training data for multi-hop question answering (QA) is time-consuming and resource-intensive. We explore the possibility to train a well-performed multi-hop QA model without referencing any human-labeled multi-hop question-answer pairs, i.e., unsupervised multi-hop QA. We propose MQA-QG, an unsupervised framework that can generate human-like multi-

Arunava Chakraborty, Rahul Ragesh, Mahir Shah, Nipun Kwatra

Generative adversarial networks (GANs) have been remarkably successful in learning complex high dimensional real word distributions and generating realistic samples. However, they provide limited control over the generation process. Conditional GANs (cGANs) provide a mechanism to control the generation process by conditioning the output on a user defined inp

David Bieber, Charles Sutton, Hugo Larochelle, Daniel Tarlow

Graph neural networks (GNNs) have emerged as a powerful tool for learning software engineering tasks including code completion, bug finding, and program repair. They benefit from leveraging program structure like control flow graphs, but they are not well-suited to tasks like program execution that require far more sequential reasoning steps than number of G

Alexander B. Walter, Neelay Fruitwala, Sarah Steiger, John I. Bailey

We present the MKID Exoplanet Camera (MEC), a z through J band (800 - 1400 nm) integral field spectrograph located behind The Subaru Coronagraphic Extreme Adaptive Optics (SCExAO) at the Subaru Telescope on Maunakea that utilizes Microwave Kinetic Inductance Detectors (MKIDs) as the enabling technology for high contrast imaging. MEC is the first permanently

Alexander P. Rader, Ionela G. Mocanu, Vaishak Belle, Brendan Juba

Robust learning in expressive languages with real-world data continues to be a challenging task. Numerous conventional methods appeal to heuristics without any assurances of robustness. While probably approximately correct (PAC) Semantics offers strong guarantees, learning explicit representations is not tractable, even in propositional logic. However, recen

Serge Assaad, Shuxi Zeng, Chenyang Tao, Shounak Datta

A key to causal inference with observational data is achieving balance in predictive features associated with each treatment type. Recent literature has explored representation learning to achieve this goal. In this work, we discuss the pitfalls of these strategies - such as a steep trade-off between achieving balance and predictive power - and present a rem

S. Sara Mahdavi, Michael D. Peacock, William J. Morris, Ingrid T. Spadinger

Purpose: Automatic planning for focal low-dose-rate prostate brachytherapy (LDR-PB) boost. Methods: A simulated annealing approach was utilized for creating automatic focal LDR-PB boost plans using both geometric (i.e. adherence of isodose contours to structure contours) and dosimetric constraints. In 46 patients, four unilateral focal lesion scenarios were

Komal Krishna Mogilipalepu, Sumanth Kumar Modukuri, Amarlingam Madapu, Sundeep Prabhakar Chepuri

This paper proposes a federated learning technique for deep algorithm unfolding with applications to sparse signal recovery and compressed sensing. We refer to this architecture as Fed-CS. Specifically, we unfold and learn the iterative shrinkage thresholding algorithm for sparse signal recovery without transporting to a central location, the training data d

Hamid Rahkooy, Cristian Vargas Montero

We study binomiality of the steady state ideals of chemical reaction networks. Considering rate constants as indeterminates, the concept of unconditional binomiality has been introduced and an algorithm based on linear algebra has been proposed in a recent work for reversible chemical reaction networks, which has a polynomial time complexity upper bound on t

Fatemeh Kazemi, Sascha Kurz, Emina Soljanin, Alex Sprintson

A major concern in cloud/edge storage systems is serving a large number of users simultaneously. The service rate region is introduced recently as an important performance metric for coded distributed systems, which is defined as the set of all data access requests that can be simultaneously handled by the system. This paper studies the problem of designing

Julia Siekiera, Marius Köppel, Edwin Simpson, Kevin Stowe

The ability to rank creative natural language provides an important general tool for downstream language understanding and generation. However, current deep ranking models require substantial amounts of labeled data that are difficult and expensive to obtain for different domains, languages and creative characteristics. A recent neural approach, the DirectRa

Maximiliano Isi, Simone Mastrogiovanni, Matthew Pitkin, Ornella Juliana Piccinni

We present a method for assigning a statistical significance to detection candidates in targeted searches for continuous gravitational waves from known pulsars, without assuming the detector noise is Gaussian and stationary. We take advantage of the expected Doppler phase modulation of the signal induced by Earth's orbital motion, as well as the amplitude mo

Ashkan Bashardoust, Hannah C. Beilinson, Sorelle A. Friedler, Jiajie Ma

Social network position confers power and social capital. In the setting of online social networks that have massive reach, creating mathematical representations of social capital is an important step towards understanding how network position can differentially confer advantage to different groups and how network position can itself be a source of advantage

Ismail El Baggari, David J. Baek, Michael J. Zachman, Di Lu

The self-organization of strongly interacting electrons into superlattice structures underlies the properties of many quantum materials. How these electrons arrange within the superlattice dictates what symmetries are broken and what ground states are stabilized. Here we show that cryogenic scanning transmission electron microscopy enables direct mapping of

Hanlin Zhang, Shuai Lin, Weiyang Liu, Pan Zhou

Recently, there has been increasing interest in the challenge of how to discriminatively vectorize graphs. To address this, we propose a method called Iterative Graph Self-Distillation (IGSD) which learns graph-level representation in an unsupervised manner through instance discrimination using a self-supervised contrastive learning approach. IGSD involves a

Alexander Quandt, Andrii Kyrylchuk, Gotthard Seifert, David Tománek

We present a realistic phenomenological description of liquid transport through defective, layered membranes. We derive general expressions based on conventional models of laminar flow and extend the formalism to accommodate slip flow. We consider different types of defects including in-layer vacancies that provide an activation-free tortuous path through th

G. Laskaris, W. Ji, X. Yan, J. Zhou

The first measurement of the $\vec{^3He}(\vec{\gamma},p)d$ process was performed at the High Intensity $\gamma$-ray Source (HI$\gamma$S) facility at Triangle Universities Nuclear Laboratory (TUNL) using a circularly polarized, monoenergetic $\gamma$-ray beam and a longitudinally polarized $^3$He target. The spin-dependent asymmetry and the contribution from

Raúl Nozal, Jose Luis Bosque, Ramon Beivide

Heterogeneous systems are present from powerful supercomputers, to mobile devices, including desktop computers, thanks to their excellent performance and energy consumption. The ubiquity of these architectures in both desktop systems and medium-sized service servers allow enough variability to exploit a wide range of problems, such as multimedia workloads, v

Judy Borowski, Roland S. Zimmermann, Judith Schepers, Robert Geirhos

Feature visualizations such as synthetic maximally activating images are a widely used explanation method to better understand the information processing of convolutional neural networks (CNNs). At the same time, there are concerns that these visualizations might not accurately represent CNNs' inner workings. Here, we measure how much extremely activating im

Alban Farchi, Patrick Laloyaux, Massimo Bonavita, Marc Bocquet

The idea of using machine learning (ML) methods to reconstruct the dynamics of a system is the topic of recent studies in the geosciences, in which the key output is a surrogate model meant to emulate the dynamical model. In order to treat sparse and noisy observations in a rigorous way, ML can be combined to data assimilation (DA). This yields a class of it

Eduardo Pavez, Benjamin Girault, Antonio Ortega, Philip A. Chou

In the past decade, several multi-resolution representation theories for graph signals have been proposed. Bipartite filter-banks stand out as the most natural extension of time domain filter-banks, in part because perfect reconstruction, orthogonality and bi-orthogonality conditions in the graph spectral domain resemble those for traditional filter-banks. T

Ryan McKenna, Daniel Sheldon

We consider the problem of differentially private selection. Given a finite set of candidate items and a quality score for each item, our goal is to design a differentially private mechanism that returns an item with a score that is as high as possible. The most commonly used mechanism for this task is the exponential mechanism. In this work, we propose a ne

Artur Czerwinski

The article establishes a framework for dynamic generation of informationally complete POVMs in quantum state tomography. Assuming that the evolution of a quantum system is given by a dynamical map in the Kraus representation, one can switch to the Heisenberg picture and define the measurements in the time domain. Consequently, starting with an incomplete se

Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven A. Hicks

Gastrointestinal (GI) pathologies are periodically screened, biopsied, and resected using surgical tools. Usually the procedures and the treated or resected areas are not specifically tracked or analysed during or after colonoscopies. Information regarding disease borders, development and amount and size of the resected area get lost. This can lead to poor f

Rajsekhar Mohapatra, Christoph Federrath, Prateek Sharma

Turbulent gas motions are observed in the intracluster medium (ICM). The ICM is density-stratified, with the gas density being highest at the centre of the cluster and decreasing radially outwards. As a result of this, Kolmogorov (homogeneous, isotropic) turbulence theory does not apply to the ICM. The gas motions are instead explained by anisotropic stratif

Edwin W. Huang

In the hole-doped cuprates, the pseudogap refers to a suppression of the density of states at low energies, in the absence of superconducting long-range order. Numerous calculations of the Hubbard model show a pseudogap in the single-particle spectra, with striking similarities to photoemission and tunneling experiments on cuprates. However, no clear mechani

William Lemaire, Maher Benhouria, Konin Koua, Wei Tong

Clinical trials previously demonstrated the notable capacity to elicit visual percepts in blind patients affected with retinal diseases by electrically stimulating the remaining neurons on the retina. However, these implants restored very limited visual acuity and required transcutaneous cables traversing the eyeball, leading to reduced reliability and compl

S. I. Dimitrov

In this paper, we establish a theorem of Bombieri -- Vinogradov type for exponential sums over Piatetski-Shapiro primes $p= [n^{1/\gamma}]$ with $\frac{865}{886}<\gamma < 1$.

Luis Alberto Rosero, Iago Pacheco Gomes, Júnior Anderson Rodrigues da Silva, Tiago Cesar dos Santos

The objective of the first CARLA autonomous driving challenge was to deploy autonomous driving systems to lead with complex traffic scenarios where all participants faced the same challenging traffic situations. According to the organizers, this competition emerges as a way to democratize and to accelerate the research and development of autonomous vehicles

Andreas Andreakis, Ioannis Papapanagiotou

It is a commonly observed pattern for applications to utilize multiple heterogeneous databases where each is used to serve a specific need such as storing the canonical form of data or providing advanced search capabilities. For applications it is hence desired to keep multiple databases in sync. We have observed a series of distinct patterns that have tried

David Haritone Shikumo, Oluoch Oluoch, Joshua Matanda Wepukhulu

A significant number of the non-financial firms listed at Nairobi Securities Exchange (NSE) have been experiencing declining financial performance which deter investors from investing in such firms. The lenders are also not willing to lend to such firms. As such, the firms struggle to raise funds for their operations. Prudent financing decisions can lead to

F. C. Cerqueira-Campos, A. Rodríguez-Ardila, R. Riffell, M. Marinello

Coronal-Line Forest Active Galactic Nuclei (CLiF AGN) are characterized by strong high-ionization lines, which contrast to what is found in most AGNs. Here, we carry out a multiwavelength analysis aimed at understanding the physical processes in the Narrow Line Region (NLR) of these objects and unveiling if they are indeed a special class of AGN. By comparin

Abhishek Anand, Jainendra K Jain, G J Sreejith

States of strongly interacting particles are of fundamental interest in physics, and can produce exotic emergent phenomena and topological structures. We consider here two-dimensional electrons in a magnetic field, and, departing from the standard practice of restricting to the lowest LL, introduce a model short-range interaction that is infinitely strong co

Alejandro Ayala, Eleazar Cuautle, Isabel Domínguez, M. Rodríguez-Cahuantzi

An important observable to understand the properties of the matter produced in heavy-ion collisions is its strangeness content. Recent experimental results show that in semi-central collisions, the $\Lambda$ and $\bar{\Lambda}$ global polarization show differences that increase at low energies. This behaviour has been described using a model where these part

Akash Goel, Luca V. Iliesiu, Jorrit Kruthoff, Zhenbin Yang

We classify the possible boundary conditions in JT gravity and discuss their exact quantization. Each boundary condition that we study will reveal new features in JT gravity related to its matrix integral interpretation, its factorization properties and ensemble averaging interpretation, the definition of the theory at finite cutoff, its relation to the phys

Justin S. Spilker, Manuel Aravena, Kedar A. Phadke, Matthieu Bethermin

Galactic outflows of molecular gas are a common occurrence in galaxies and may represent a mechanism by which galaxies self-regulate their growth, redistributing gas that could otherwise have formed stars. We previously presented the first survey of molecular outflows at z > 4 towards a sample of massive, dusty galaxies. Here we characterize the physical pro

Justin S. Spilker, Kedar A. Phadke, Manuel Aravena, Matthieu Bethermin

Massive galaxy-scale outflows of gas are one of the most commonly-invoked mechanisms to regulate the growth and evolution of galaxies throughout the universe. While the gas in outflows spans a large range of temperatures and densities, the cold molecular phase is of particular interest because molecular outflows may be capable of suppressing star formation i

Ruari Mackenzie, Gabriele Pezzulli, Sebastiano Cantalupo, Raffaella A. Marino

We present the results from a MUSE survey of twelve $z\simeq3.15$ quasars, which were selected to be much fainter (20<i<23) than in previous studies of Giant Ly$\alpha$ Nebulae around the brightest quasars (16.6<i<18.7). We detect HI Ly$\alpha$ nebulae around 100% of our target quasars, with emission extending to scales of at least 60 physical kpc, and up to

Chao Wang, Yoni Schattner, Steven A. Kivelson

Systems with strong electron-phonon couplings typically exhibit various forms of charge order, while strong electron-electron interactions lead to magnetism. We use determinant quantum Monte Carlo (DQMC) calculations to solve a model on a square lattice with a caricature of these interactions. In the limit where electron-electron interactions dominate it has

Murray Brightman, Charlotte Ward, Daniel Stern, Kunal Mooley

We present the discovery of a luminous X-ray transient, serendipitously detected by Swift's X-ray Telescope (XRT) on 2020 February 5, located in the nucleus of the galaxy SDSS J143359.16+400636.0 at z=0.099 (luminosity distance $D_{\rm L}=456$ Mpc). The transient was observed to reach a peak luminosity of $\sim10^{44}$ erg s$^{-1}$ in the 0.3--10 keV X-ray b

Anna de Graaff, Rachel Bezanson, Marijn Franx, Arjen van der Wel

We use the Fundamental Plane (FP) to measure the redshift evolution of the dynamical mass-to-light ratio ($M_{\mathrm{dyn}}/L$) and the dynamical-to-stellar mass ratio ($M_{\mathrm{dyn}}/M_*$). Although conventionally used to study the properties of early-type galaxies, we here obtain stellar kinematic measurements from the Large Early Galaxy Astrophysics Ce

Tommaso Tufarelli, Daniel Friedrich, Heiko Groß, Joachim Hamm

Coupling $N$ identical emitters to the same field mode is well-established method to enhance light matter interaction. However, the resulting $\sqrt{N}$ boost of the coupling strength comes at the cost of a "linearized" (effectively semi-classical) dynamics. Here, we instead demonstrate a new approach for enhancing the coupling constant of a \textit{single}

Cornelius Rampf, Oliver Hahn

Perturbation theory is an indispensable tool for studying the cosmic large-scale structure, and establishing its limits is therefore of utmost importance. One crucial limitation of perturbation theory is shell-crossing, which is the instance when cold-dark-matter trajectories intersect for the first time. We investigate Lagrangian perturbation theory (LPT) a

James M. Cline, Guillermo Gambini, Samuel D. McDermott, Matteo Puel

The core-cusp problem persists as an unresolved tension between the predictions of $\Lambda$CDM cosmology and observations of dark matter (DM) profiles in dwarf spheroidal and other galaxies. We present a novel scenario for converting cusps into cores through reactivation of DM annihilation in galaxies at late times. This can happen in asymmetric DM models w

S. Marino, A. Zurlo, V. Faramaz, J. Milli

Radial substructure in the form of rings and gaps has been shown to be ubiquitous among protoplanetary discs. This could be the case in exoKuiper belts as well, and evidence for this is emerging. In this paper we present ALMA observations of the debris/planetesimal disc surrounding HD 206893, a system that also hosts two massive companions at 2 and 11 au. Ou

Arnav Tripathy, Max Zimet

We extend our recent study of K3 metrics near the $T^4/Z_2$ orbifold locus to the other torus orbifold loci. In particular, we provide several new constructions of K3 surfaces as hyper-K\"ahler quotients, which yield new formulae for K3 metrics. We then relate these to the construction of arXiv:1810.10540. As a corollary, we derive infinitely many constraint

Ting Liu, Jennifer J. Sun, Long Zhao, Jiaping Zhao

Recognition of human poses and actions is crucial for autonomous systems to interact smoothly with people. However, cameras generally capture human poses in 2D as images and videos, which can have significant appearance variations across viewpoints that make the recognition tasks challenging. To address this, we explore recognizing similarity in 3D human bod

Sohini Upadhyay, Mikhail Yurochkin, Mayank Agarwal, Yasaman Khazaeni

We formulate a new problem at the intersectionof semi-supervised learning and contextual bandits,motivated by several applications including clini-cal trials and ad recommendations. We demonstratehow Graph Convolutional Network (GCN), a semi-supervised learning approach, can be adjusted tothe new problem formulation. We also propose avariant of the linear co

Qichuan Geng, Hong Zhang, Na Jiang, Xiaojuan Qi

We present an Object-aware Feature Aggregation (OFA) module for video object detection (VID). Our approach is motivated by the intriguing property that video-level object-aware knowledge can be employed as a powerful semantic prior to help object recognition. As a consequence, augmenting features with such prior knowledge can effectively improve the classifi

A. Parra-Rodriguez, I. L. Egusquiza

We develop a systematic procedure to quantise canonically Hamiltonians of light-matter models of transmission lines coupled through lumped linear lossless ideal nonreciprocal elements, that break time-reversal symmetry, in a circuit QED set-up. This is achieved through a description of the distributed subsystems in terms of both flux and charge fields. We pr

Keith Burghardt, Tad Hogg, Raissa M. D'Souza, Kristina Lerman

Crowdsourcing systems aggregate decisions of many people to help users quickly identify high-quality options, such as the best answers to questions or interesting news stories. A long-standing issue in crowdsourcing is how option quality and human judgement heuristics interact to affect collective outcomes, such as the perceived popularity of options. We add

Efe Bozkir, Shahram Eivazi, Mete Akgün, Enkelejda Kasneci

Eye tracking data collection in the virtual reality context is typically carried out in laboratory settings, which usually limits the number of participants or consumes at least several months of research time. In addition, under laboratory settings, subjects may not behave naturally due to being recorded in an uncomfortable environment. In this work, we pro

King'ori S. Ngumo, Kioko W. Collins, Shikumo H. David

Microfinance provides strength to boost the economic activities of low-income earners and thus contributes to eradication of poverty. However, microfinance institutions face stringent competition from commercial banks; the growth of microloan activities of commercial banks may confront microfinance institutions with increased competition for borrowers. In Ke

Devin S. Johnson, Brian M. Brost, Mevin B. Hooten

We propose a multistage method for making inference at all levels of a Bayesian hierarchical model (BHM) using natural data partitions to increase efficiency by allowing computations to take place in parallel form using software that is most appropriate for each data partition. The full hierarchical model is then approximated by the product of independent no

Steven L. Liebling, Carlos Palenzuela, Luis Lehner

Various theoretical arguments motivate an expectation of a phase transition in matter at extreme densities above nuclear density, accompanied by hopes that gravitational wave observations may reveal the properties of such a transition. Instead of adopting a particular theory, we consider here a generic form of first order phase transition using a piecewise p

Aditi Chaudhary, Karthik Raman, Krishna Srinivasan, Jiecao Chen

Pre-trained multilingual language models such as mBERT have shown immense gains for several natural language processing (NLP) tasks, especially in the zero-shot cross-lingual setting. Most, if not all, of these pre-trained models rely on the masked-language modeling (MLM) objective as the key language learning objective. The principle behind these approaches

Abram Akal

The central question in this article is how information does leak out from black holes. Relying on algebraic arguments and the concept of superselection sectors, we propose the existence of certain operators whose correlations extend across the black hole atmosphere and range into the interior. Contained in the full algebra, these black hole intertwiners wil

Zhan Jiang

Extended plus (epf) closure and rank 1 (r1f) closure are two closure operations introduced by Raymond C. Heitmann for rings of mixed characteristic. Recently, he and Linquan Ma proved that epf closure satisfies the usual colon-capturing property under mild conditions. In this paper, we extend their result and prove that epf closure satisfies what we call the

Eric Wallace, Tony Z. Zhao, Shi Feng, Sameer Singh

Adversarial attacks alter NLP model predictions by perturbing test-time inputs. However, it is much less understood whether, and how, predictions can be manipulated with small, concealed changes to the training data. In this work, we develop a new data poisoning attack that allows an adversary to control model predictions whenever a desired trigger phrase is

Xiaotao Gu, Liyuan Liu, Hongkun Yu, Jing Li

Due to the excessive cost of large-scale language model pre-training, considerable efforts have been made to train BERT progressively -- start from an inferior but low-cost model and gradually grow the model to increase the computational complexity. Our objective is to advance the understanding of Transformer growth and discover principles that guide progres

Farzan Farnia, Asuman Ozdaglar

The success of minimax learning problems of generative adversarial networks (GANs) has been observed to depend on the minimax optimization algorithm used for their training. This dependence is commonly attributed to the convergence speed and robustness properties of the underlying optimization algorithm. In this paper, we show that the optimization algorithm

L. P. Chitta, H. Peter, E. R. Priest, S. K. Solanki

Coronal plasma in the cores of solar active regions is impulsively heated to more than 5 MK. The nature and location of the magnetic energy source responsible for such impulsive heating is poorly understood. Using observations of seven active regions from the Solar Dynamics Observatory, we found that a majority of coronal loops hosting hot plasma have at lea

Ali Ramadhan, John Marshall, Andre Souza, Xin Kai Lee

We explore how neural differential equations (NDEs) may be trained on highly resolved fluid-dynamical models of unresolved scales providing an ideal framework for data-driven parameterizations in climate models. NDEs overcome some of the limitations of traditional neural networks (NNs) in fluid dynamical applications in that they can readily incorporate cons

Sarah Cannon, Ari Goldbloom-Helzner, Varun Gupta, JN Matthews

Finding outlying elements in probability distributions can be a hard problem. Taking a real example from Voting Rights Act enforcement, we consider the problem of maximizing the number of simultaneous majority-minority districts in a political districting plan. An unbiased random walk on districting plans is unlikely to find plans that approach this maximum.

Alexander H. Nitz, Collin D. Capano

GW190521 is the first confident observation of a binary black hole merger with total mass $M > 100\,\mathrm{M}_{\odot}$. Given the lack of observational constraints at these masses, we analyze GW190521 considering two different priors for the binary's masses: uniform in mass ratio and source-frame total mass, and uniform in source-frame component masses. For

Agnese Bissi, Giulia Fardelli, Alessandro Georgoudis

We computed a set of structures which appear in the four-point function of protected operators of dimension two in $\mathcal{N}=4$ Super Yang Mills with $SU(N)$ gauge group, at any order in a large $N$ expansion. They are determined only by leading order CFT data. By focusing on a specific limit, we made connection with the dual supergravity amplitude in fla

Robin H. D. Corbet, Joel B. Coley, Hans A. Krimm, Katja Pottschmidt

Hard X-ray observations with the Neil Gehrels Swift Observatory Burst Alert Telescope (BAT) reveal superorbital modulation in the wind-accreting supergiant high-mass X-ray binary (HMXB) 4U 1538-52 at a period of 14.9130 +/- 0.0026 days that is consistent with four times the 3.73 day orbital period. These periods agree with a previously suggested correlation

Masahiro Nakahara, Sam Streeter

We study weak approximation and the Hilbert property for Campana points, both of importance in recent work on a Manin-type conjecture by Pieropan, Smeets, Tanimoto and Varilly-Alvarado. We show that weak weak approximation implies the Hilbert property for Campana points, and we exploit this to exhibit Campana orbifolds whose sets of Campana points are not th

G. Nikoghosyan, A. Balabekyan, E. A. Kolganova, R. V. Jolos

The eigensolutions of the collective Hamiltonian with different potentials suggested for description of the isovector pair correlations are obtained, analyzed and compared with the experimental energies. It is shown that the isovector pair correlations in nuclei around $^{56}$Ni can be described as anharmonic pairing vibrations. The results obtained indicate

Gary K. Nave,, Nathaniel Hall, Katrina Somers, Brock Davis

Maple trees (genus $\textit{Acer}$) accomplish the task of distributing objects to a wide area by producing seeds which are carried by the wind as they slowly descend to the ground, known as samaras. With the goal of supporting engineering applications, such as gathering environmental data over a broad area, we developed 3D-printed artificial samaras. Here,

Hamid Eftekhari, Moulinath Banerjee, Ya'acov Ritov

We study random designs that minimize the asymptotic variance of a de-biased lasso estimator when a large pool of unlabeled data is available but measuring the corresponding responses is costly. The optimal sampling distribution arises as the solution of a semidefinite program. The improvements in efficiency that result from these optimal designs are demonst

Saeed Khaki, Hieu Pham, Ye Han, Wade Kent

The future landscape of modern farming and plant breeding is rapidly changing due to the complex needs of our society. The explosion of collectable data has started a revolution in agriculture to the point where innovation must occur. To a commercial organization, the accurate and efficient collection of information is necessary to ensure that optimal decisi

Mohammed Mouçouf, Said Zriaa

In this work, new closed-form formulas for the matrix exponential are provided. Our method is direct and elementary, it gives tractable and manageable formulas not current in the extensive literature on this essential subject. Moreover, others are recuperated and generalized. As a consequence, we easily obtain the Chevalley{Jordan decomposition and the spect

David Haritone Shikumo, Mwangi Mirie

Small and Medium Enterprises (SMEs) access to external finance is an issue of significant research interest to academicians. Commercial banks consider many SMEs not to be credit worthy because of their inability to meet some banking requirements. Hence, the objective of this study was to investigate what determines lending to SMEs by commercial banks in Keny

Graham W. Pulford

Although the expectation maximisation (EM) algorithm was introduced in 1970, it remains somewhat inaccessible to machine learning practitioners due to its obscure notation, terse proofs and lack of concrete links to modern machine learning techniques like autoencoded variational Bayes. This has resulted in gaps in the AI literature concerning the meaning of