June 2020 arXiv papers — page 31

Chao Liu, Cuiyun Gao, Xin Xia, David Lo

Deep learning (DL) techniques have gained significant popularity among software engineering (SE) researchers in recent years. This is because they can often solve many SE challenges without enormous manual feature engineering effort and complex domain knowledge. Although many DL studies have reported substantial advantages over other state-of-the-art models

Veli Safak

Becker (1973) presents a bilateral matching model in which scalar types describe agents. For this framework, he establishes the conditions under which positive sorting between agents' attributes is the unique market outcome. Becker's celebrated sorting result has been applied to address many economic questions. However, recent empirical studies in th

Tao Wang, Zhiyong Qiu, Fulvio Zonca, Sergio Briguglio

Hybrid MHD-gyrokinetic code simulations are used to investigate the dynamics of frequency sweeping reversed shear Alfvén eigenmode (RSAE) strongly driven by energetic particles (EPs) during plasma current ramp-up in a conventional tokamak configuration. A series of weakly reversed shear equilibria representing time slices of long timescale MHD equilibrium ev

Clément Savaro, Antoine Campagne, Miguel Calpe Linares, Pierre Augier

We investigate experimentally stratified turbulence forced by waves. Stratified turbulence is present in oceans and it is expected to be dominated by nonlinear interaction of internal gravity waves as described by the Garrett & Munk spectrum. In order to reach turbulent regimes dominated by stratification we use the Coriolis facility in Grenoble (France) whi

Goro Akagi, Giulio Schimperna

We consider a model for the evolution of damage in elastic materials originally proposed by Michel Frémond. For the corresponding PDE system we prove existence and uniqueness of a local in time strong solution. The main novelty of our result stands in the fact that, differently from previous contributions, we assume no occurrence of any type of regularizing

David Sturzenegger, Aetienne Sardon, Stefan Deml, Thomas Hardjono

Contact tracing is paramount to fighting the pandemic but it comes with legitimate privacy concerns. This paper proposes a system enabling both, contact tracing and data privacy. We propose the use of the Intel SGX trusted execution environment to build a privacy-preserving contact tracing backend. While the concept of a confidential computing backend propos

Noe Elisa, Longzhi Yang, Honglei Li, Fei Chao

Since its inception as a solution for secure cryptocurrencies sharing in 2008, the blockchain technology has now become one of the core technologies for secure data sharing and storage over trustless and decentralised peer-to-peer systems. E-government is amongst the systems that stores sensitive information about citizens, businesses and other affiliates, a

Antonio Candelieri, Riccardo Perego, Francesco Archetti

Searching for accurate Machine and Deep Learning models is a computationally expensive and awfully energivorous process. A strategy which has been gaining recently importance to drastically reduce computational time and energy consumed is to exploit the availability of different information sources, with different computational costs and different "fidel

Noe Elisa, Longzhi Yang, Fei Chao, Yi Cao

Electronic government (e-government) uses information and communication technologies to deliver public services to individuals and organisations effectively, efficiently and transparently. E-government is one of the most complex systems which needs to be distributed, secured and privacy-preserved, and the failure of these can be very costly both economically

Satoru Fukasawa

An elementary proof of a theorem of Montanucci and Zini on the automorphism group of generalized Aritn-Schreier-Mumford curves is presented, with the argument of Korchmaros and Montanucci for Artin-Schreier-Mumford curves being improved. Although the characteristic of a ground field is assumed to be odd in the article of Montanucci and Zini, the proof in the

Antoine Deza, Lionel Pournin

A point in the $d$-dimensional integer lattice $\mathbb{Z}^d$ is primitive when its coordinates are relatively prime. Two primitive points are multiples of one another when they are opposite, and for this reason, we consider half of the primitive points within the lattice, the ones whose first non-zero coordinate is positive. We solve the packing problem tha

Mathieu Karamitros, Jeremy Brown, Nathanael Lampe, Dousatsu Sakata

The Independent Reaction Time method is a computationally efficient Monte-Carlo based approach to simulate the evolution of initially heterogeneously distributed reaction-diffusion systems that has seen wide-scale implementation in the field of radiation chemistry modeling. The method gains its efficiency by preventing multiple calculations steps before a re

Alex Sokolov, Denis Filimonov

Training a spoken language understanding system, as the one in Alexa, typically requires a large human-annotated corpus of data. Manual annotations are expensive and time consuming. In Alexa Skill Kit (ASK) user experience with the skill greatly depends on the amount of data provided by skill developer. In this work, we present an automatic natural language

Martina Lihter, Michael Graf, Damir Iveković, Tzu-Hsien Shen

The surface modification of 2D semiconducting materials, such as transition metal dichalcogenides (TMDCs), is becoming important for a diverse range of applications, such as biosensing, catalysis, energy generation and energy storage. Due to the chemical inertness of their basal plane, the surface modification of 2D TMDCs is mainly limited to their defective

Danka Lučić, Tapio Rajala, Jyrki Takanen

We prove an asymptotically sharp dimension upper-bound for the boundary of bounded simply-connected planar Sobolev $W^{1,p}$-extension domains via the weak mean porosity of the boundary. The sharpness of our estimate is shown by examples.

Bhaskar Mukhoty, Govind Gopakumar, Prateek Jain, Purushottam Kar

We provide the first global model recovery results for the IRLS (iteratively reweighted least squares) heuristic for robust regression problems. IRLS is known to offer excellent performance, despite bad initializations and data corruption, for several parameter estimation problems. Existing analyses of IRLS frequently require careful initialization, thus off

Marina Sedinkina, Nikolas Breitkopf, Hinrich Schütze

In this paper, we automatically create sentiment dictionaries for predicting financial outcomes. We compare three approaches: (I) manual adaptation of the domain-general dictionary H4N, (ii) automatic adaptation of H4N and (iii) a combination consisting of first manual, then automatic adaptation. In our experiments, we demonstrate that the automatically adap

Jaeyoung Kwak, Michael H Lees, Wentong Cai, Marcus EH Ong

People often help others who are in trouble, especially in emergency evacuation situations. For instance, during the 2005 London bombings, it was reported that evacuees helped injured persons to escape the place of danger. In terms of game theory, it can be understood that such helping behavior provides a collective good while it is a costly behavior because

Tao Feng, Koji Momihara, Morgan Rodgers, Qing Xiang

Cameron-Liebler line classes were introduced in \cite{CL}, and motivated by a question about orbits of collineation groups of $\PG(3,q)$. These line classes have appeared in different contexts under disguised names such as Boolean degree one functions, regular codes of covering radius one, and tight sets. In this paper we construct an infinite family of Came

Sajjad Marri

Three-body calculations of $I=\frac{1}{2}$, $J^π=\frac{1}{2}^{+}$ state of $K\bar{K}N$ system were performed. Using separable potentials for two-body interactions in the Faddeev equation, different reaction process for $K\bar{K}N$ three-body system were studied. Within this method, the $πΣ{K}$ and $πη{N}$ mass spectra were extracted. Different types of $\bar

Chelsea Miller, Peter J. Smith, Pawel A. Dmochowski, Harsh Tataria

We examine the favorable propagation (FP) behavior of a massive multi-user multiple-input-multiple-output (MU-MIMO) system equipped with a uniform linear array (ULA), horizontal uniform rectangular array (HURA) or uniform circular array (UCA) using a ray-based channel model with user cluster sharing. We demonstrate FP for these systems and provide analytical

Kazumi Kasaura

By works of Kedlaya and Shiho, it is known that, for a smooth variety $\overline{X}$ over a field of positive characteristic and its simple normal crossing divisor $Z$, an overconvergent isocrystal on the compliment of $Z$ satisfying a certain monodromy condition can be extended to a convergent log isocrystal on $\left(\overline{X}, \mathcal{M}_Z\right)$, wh

Fuyu Dong, Yu Yu, Jun Zhang, Xiaohu Yang

The integrated Sachs-Wolfe (ISW) effect is caused by the decay of cosmological gravitational potential, and is therefore a unique probe of dark energy. However, its robust detection is still problematic. Various tensions between different data sets, different large scale structure (LSS) tracers, and between data and the $Λ$CDM theory prediction, exist. We pr

Guchong Li, Giorgio Battistelli, Luigi Chisci, Wei Yi

This paper addresses distributed multi-target tracking (DMTT) over a network of sensors having different fields-of-view (FoVs). Specifically, a cardinality probability hypothesis density (CPHD) filter is run at each sensor node. Due to the fact that each sensor node has a limited FoV, the commonly adopted fusion methods become unreliable. In fact, the monito

Alan Fang, Anisha G. Singh, Joshua A. W. Straquadine, Ian R. Fisher

Pd-intercalated ErTe$_3$ is studied as a model system to explore the effect of "intertwined" superconducting and charge density wave (CDW) orders. Despite the common wisdom that superconductivity emerges only when CDW is suppressed, we present data from STM and AC susceptibility measurements that show no direct competition between CDW order and super

Kiho Yoon

This paper examines necessary and sufficient conditions for the uniqueness of dynamic Groves mechanisms when the domain of valuations is restricted. Our approach is to appropriately define the total valuation function, which is the expected discounted sum of each period's valuation function from the allocation and thus a dynamic counterpart of the static

Stephen Devlin, David Uminsky

We address the question of how to quantify the contributions of groups of players to team success. Our approach is based on spectral analysis, a technique from algebraic signal processing, which has several appealing features. First, our analysis decomposes the team success signal into components that are naturally understood as the contributions of player g

Mostafa Motamedifar

The dramatic growth of research areas within the province of quantum state transmission (QST) is rapidly accelerating. An important insight to understand the process of QST can be fulfilled by considering the dynamical behavior of its entanglement content. One well-established approach to continuously transfer quantum states is utilizing spin structures. Her

Yifan Mao, Soubhik Deb, Shaileshh Bojja Venkatakrishnan, Sreeram Kannan

A key performance metric in blockchains is the latency between when a transaction is broadcast and when it is confirmed (the so-called, confirmation latency). While improvements in consensus techniques can lead to lower confirmation latency, a fundamental lower bound on confirmation latency is the propagation latency of messages through the underlying peer-t

Mingyu Guo, Hideaki Hata, Ali Babar

Optimizing within the affine maximizer auctions (AMA) is an effective approach for revenue maximizing mechanism design. The AMA mechanisms are strategy-proof and individually rational (if the agents' valuations for the outcomes are nonnegative). Every AMA mechanism is characterized by a list of parameters. By focusing on the AMA mechanisms, we turn mecha

Mingyu Guo, Hideaki Hata, Ali Babar

Markets for zero-day exploits (software vulnerabilities unknown to the vendor) have a long history and a growing popularity. We study these markets from a revenue-maximizing mechanism design perspective. We first propose a theoretical model for zero-day exploits markets. In our model, one exploit is being sold to multiple buyers. There are two kinds of buyer

Hui Li

We describe the Jacobson topology of the primitive ideal space of self-similar k-graph C*-algebras under certain conditions.

Pinki, Krishnendra Shekhawat

Let G = (V, E) be a planar triangulated graph (PTG) having every face triangular. A rectilinear dual or an orthogonal floor plan (OFP) of G is obtained by partitioning a rectangle into \mid V \mid rectilinear regions (modules) where two modules are adjacent if and only if there is an edge between the corresponding vertices in G. In this paper, a linear-time

Shota Kanasugi, Dushko Kuzmanovski, Alexander V. Balatsky, Youichi Yanase

We demonstrate that SrTiO$_3$ can be a platform for observing the bulk odd-frequency superconducting state owing to the multiorbital/multiband nature. We consider a three-orbital tight-binding model for SrTiO$_3$ in the vicinity of a ferroelectric critical point. Assuming an intraorbital spin-singlet $s$-wave superconducting order parameter, it is shown that

N. Jahan, M. N. I. Khan, F. -U. -Z. Chowdhury, A. K. M. Akhter Hossain

Polycrystalline Yb substituted NiZn nanoferrites with the compositions of Ni0.5Zn0.5YbxFe2-xO4 (x= 0.00, 0.04, 0.08, 0.12, 0.16 and 0.20) have been synthesized using sol gel auto combustion technique. Single phase cubic spinel structure has been confirmed by the X ray diffraction (XRD) patterns. Larger lattice constants of the compositions are found with inc

Jonathan Kadmon, Jonathan Timcheck, Surya Ganguli

Biological neural networks face a formidable task: performing reliable computations in the face of intrinsic stochasticity in individual neurons, imprecisely specified synaptic connectivity, and nonnegligible delays in synaptic transmission. A common approach to combatting such biological heterogeneity involves averaging over large redundant networks of $N$

Mingyu Guo, Yong Yang, Muhammad Ali Babar

We study a cost sharing problem derived from bug bounty programs, where agents gain utility by the amount of time they get to enjoy the cost shared information. Once the information is provided to an agent, it cannot be retracted. The goal, instead of maximizing revenue, is to pick a time as early as possible, so that enough agents are willing to cost share

Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao

Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server who has access to user information is ill-suited in many applications. To tackle this problem, we develop a new deep learning framework under an untrusted server setting, which inclu

Angelo Vera Rivera, Ahmed Refaey, Ekram Hossain

In the context of Multi-access Edge Computing (MEC), the task sharing mechanism among edge servers is an activity of vital importance for speeding up the computing process and thereby improve user experience. The distributed resources in the form of edge servers are expected to collaborate with each other in order to boost overall performance of a MEC system

Raphael J. L. Townshend, Brent Townshend, Stephan Eismann, Ron O. Dror

Many quantities we are interested in predicting are geometric tensors; we refer to this class of problems as geometric prediction. Attempts to perform geometric prediction in real-world scenarios have been limited to approximating them through scalar predictions, leading to losses in data efficiency. In this work, we demonstrate that equivariant networks hav

Sarika Jalan, Ajay Deep Kachhvah, Hawoong Jeong

The phenomenon of explosive synchronization, which originates from hypersensitivity to small perturbation caused by some form of frustration prevailed in various physical and biological systems, has been shown to lead events of cascading failure of the power grid to chronic pain or epileptic seizure in the brain. Furthermore, networks provide a powerful mode

Lu Liu, Wenan Guo, Anders W. Sandvik

We use QMC simulations to study effects of disorder on the $S=1/2$ Heisenberg model with exchange constant $J$ on the square lattice supplemented by multispin interactions $Q$. It was found recently [L. Lu et al., Phys. Rev. X 8, 041040 (2018)] that the ground state of this $J$-$Q$ model with random couplings undergoes a quantum phase transition from the Née

Jing Shi, Xuankai Chang, Pengcheng Guo, Shinji Watanabe

Neural sequence-to-sequence models are well established for applications which can be cast as mapping a single input sequence into a single output sequence. In this work, we focus on one-to-many sequence transduction problems, such as extracting multiple sequential sources from a mixture sequence. We extend the standard sequence-to-sequence model to a condit

Jing Shi, Jiaming Xu, Yusuke Fujita, Shinji Watanabe

Speech separation has been extensively explored to tackle the cocktail party problem. However, these studies are still far from having enough generalization capabilities for real scenarios. In this work, we raise a common strategy named Speaker-Conditional Chain Model to process complex speech recordings. In the proposed method, our model first infers the id

Michael Famiano, A. Baha Balantekin, Toshitaka Kajino, Motohiko Kusakabe

Coulomb screening and weak interactions in a hot, magnetized plasma are investigated. Coulomb screening is evaluated in a relativistic thermal plasma in which electrons and positrons are in equilibrium. In addition to temperature effects, effects on weak screening from a strong external magnetic field are evaluated. In high fields, the electron transverse mo

Eric G. Brown, Oktay Goktas, W. K. Tham

Quantum Amplitude Estimation (QAE) -- a technique by which the amplitude of a given quantum state can be estimated with quadratically fewer queries than by standard sampling -- is a key sub-routine in several important quantum algorithms, including Grover search and Quantum Monte-Carlo methods. An obstacle to implementing QAE in near-term noisy intermediate-

Daniel Jarrett, Mihaela van der Schaar

Evidence-based decision-making entails collecting (costly) observations about an underlying phenomenon of interest, and subsequently committing to an (informed) decision on the basis of accumulated evidence. In this setting, active sensing is the goal-oriented problem of efficiently selecting which acquisitions to make, and when and what decision to settle o

Eduardo G. Altmann

Analyses of urban scaling laws assume that observations in different cities are independent of the existence of nearby cities. Here we introduce generative models and data-analysis methods that overcome this limitation by modelling explicitly the effect of interactions between individuals at different locations. Parameters that describe the scaling law and t

Gábor Czédli, Lillian Oluoch

Let $n>3$ be a natural number. By a 1975 result of H. Strietz, the lattice Part$(n)$ of all partitions of an $n$-element set has a four-element generating set. In 1983, L. Zádori gave a new proof of this fact with a particularly elegant construction. Based on his construction from 1983, the present paper gives a lower bound on the number $ν(n)$ of four-eleme

Yujie Hu, Changzhen Wang, Ruiyang Li, Fahui Wang

Estimating a massive drive time matrix between locations is a practical but challenging task. The challenges include availability of reliable road network (including traffic) data, programming expertise, and access to high-performance computing resources. This research proposes a method for estimating a nationwide drive time matrix between ZIP code areas in

Yi-Jie Zhu, Yujie Hu, Jennifer M. Collins

Understanding the spatiotemporal road network accessibility during a hurricane evacuation, the level of ease of residents in an area in reaching evacuation destination sites through the road network, is a critical component of emergency management. While many studies have attempted to measure road accessibility (either in the scope of evacuation or beyond),

Parham Hamidi, Jishnu Ray

Let $p$ be an odd prime and let $E$ be an elliptic curve defined over a number field $F$ with good reduction at primes above $p$. In this survey article, we give an overview of some of the important results proven for the fine Selmer group and the signed Selmer groups over cyclotomic towers as well as the signed Selmer groups over $\mathbb{Z}_p^2$-extensions

Ryuichi Ohta, Loïc Herpin, Victor M. Bastidas, Takehiko Tawara

Strain-mediated interaction between phonons and telecom photons is demonstrated using excited states of erbium ions embedded in a mechanical resonator. Owing to the extremely long-lived nature of rare-earth ions, the dissipation rate of the optical resonance falls below that of the mechanical one. Thus, a reversed dissipation regime is achieved in the optica

Parvathi Kooloth, David Sondak, Leslie M. Smith

For two-dimensional Rayleigh-Bénard convection, classes of unstable, steady solutions were previously computed using numerical continuation (Waleffe, 2015; Sondak, 2015). The `primary' steady solution bifurcates from the conduction state at $Ra \approx 1708$, and has a characteristic aspect ratio (length/height) of approximately $2$. The primary solution

Han Lin Shang, Steven Haberman

An essential input of annuity pricing is the future retiree mortality. From observed age-specific mortality data, modeling and forecasting can be taken place in two routes. On the one hand, we can first truncate the available data to retiree ages and then produce mortality forecasts based on a partial age-range model. On the other hand, with all available da

Firoza Kabir, M. Mofazzel Hosen, Xiaxin Ding, Christopher Lane

Three-dimensional topological insulators (TIs) have emerged as a unique state of quantum matter and generated enormous interests in condensed matter physics. The surfaces of a three dimensional (3D) TI are composed of a massless Dirac cone, which is characterized by the Z2 topological invariant. Introduction of magnetism on the surface of TI is essential to

Austin M. Ferrenti, Nathalie P. de Leon, Jeff D. Thompson, R. J. Cava

Atom-like defects in solid-state hosts are promising candidates for the development of quantum information systems, but despite their importance, the host substrate/defect combinations currently under study have almost exclusively been found serendipitously. Here we systematically evaluate the suitability of host materials by applying a combined four-stage d

Noriyuki Sogabe

In this thesis, we study the novel dynamic critical phenomena induced by superfluidity and the chiral magnetic effect in Quantum Chromodynamics (QCD), respectively. In particular, we find the new dynamic universality class of the high-density QCD critical point between the nuclear and quark superfluid phases beyond the conventional Hohnenberg and Halperin&#3

David T. Frazier

In many instances, the application of approximate Bayesian methods is hampered by two practical features: 1) the requirement to project the data down to low-dimensional summary, including the choice of this projection, which ultimately yields inefficient inference; 2) a possible lack of robustness to deviations from the underlying model structure. Motivated

Alexander Zhiliakov, Yifei Wang, Annalisa Quaini, Maxim Olshanskii

Membrane phase-separation is a mechanism that biological membranes often use to locally concentrate specific lipid species in order to organize diverse membrane processes. Phase separation has also been explored as a tool for the design of liposomes with heterogeneous and spatially organized surfaces. These "patchy" liposomes are promising platforms

Dmitri Bykov

We show that flag manifold $σ$-models (including $\mathbb{CP}^{n-1}$, Grassmannian models as special cases) and their deformed versions may be cast in the form of gauged bosonic Thirring/Gross-Neveu-type systems. Quantum mechanically the gauging is violated by chiral anomalies, which may be cancelled by adding fermions. We conjecture that such models are int

Ryan Vogt, Maximilian Puelma Touzel, Eli Shlizerman, Guillaume Lajoie

Recurrent neural networks (RNNs) have been successfully applied to a variety of problems involving sequential data, but their optimization is sensitive to parameter initialization, architecture, and optimizer hyperparameters. Considering RNNs as dynamical systems, a natural way to capture stability, i.e., the growth and decay over long iterates, are the Lyap

M. J. Graham, K. E. S. Ford, B. McKernan, N. P. Ross

We report the first plausible optical electromagnetic (EM) counterpart to a (candidate) binary black hole (BBH) merger. Detected by the Zwicky Transient Facility (ZTF), the EM flare is consistent with expectations for a kicked BBH merger in the accretion disk of an active galactic nucleus (AGN), and is unlikely ($<O(0.01\%$)) due to intrinsic variability of

Zura Kakushadze

After a market downturn, especially in an uncertain economic environment such as the current state, there can be a relatively long period with a sideways market, where indexes, stocks, etc., move in channels with support and resistance levels. We discuss option pricing in such scenarios, in both cases of unattainable as well as attainable boundaries, and obt

Yalong Yang, Tim Dwyer, Kim Marriott, Bernhard Jenny

We introduce Tilt Map, a novel interaction technique for intuitively transitioning between 2D and 3D map visualisations in immersive environments. Our focus is visualising data associated with areal features on maps, for example, population density by state. Tilt Map transitions from 2D choropleth maps to 3D prism maps to 2D bar charts to overcome the limita

Jonathan Bodine, Dorit S. Hochbaum

Decision trees are a widely used method for classification, both by themselves and as the building blocks of multiple different ensemble learning methods. The Max-Cut decision tree involves novel modifications to a standard, baseline model of classification decision tree construction, precisely CART Gini. One modification involves an alternative splitting me

Shuai Zhang, Meng Wang, Sijia Liu, Pin-Yu Chen

Although graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice, their theoretical guarantee on generalizability remains elusive in the literature. In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification

Fenil Doshi, Jimit Gandhi, Deep Gosalia, Sudhir Bagul

Social media networks and chatting platforms often use an informal version of natural text. Adversarial spelling attacks also tend to alter the input text by modifying the characters in the text. Normalizing these texts is an essential step for various applications like language translation and text to speech synthesis where the models are trained over clean

James Nakamura, Shuang Liang, Geoffrey C. Gardner, Michael J. Manfra

Utilizing an electronic Fabry-Perot interferometer in which Coulomb charging effects are suppressed, we report experimental observation of anyonic braiding statistics for the $ν=1/3$ fractional quantum Hall state. Strong Aharonov-Bohm interference of the $ν=1/3$ edge mode is punctuated by discrete phase slips consistent with an anyonic phase of $θ_{anyon}=\f

Isabel Haasler, Rahul Singh, Qinsheng Zhang, Johan Karlsson

We study multi-marginal optimal transport problems from a probabilistic graphical model perspective. We point out an elegant connection between the two when the underlying cost for optimal transport allows a graph structure. In particular, an entropy regularized multi-marginal optimal transport is equivalent to a Bayesian marginal inference problem for proba

Eric Sergio Boria, Mohamed Badhrudeen, Guillemette Fonteix, Sybil Derrible

While many municipalities and organizations see value in converting infrastructure data from Computer-Aided Design (CAD) to Geographic Information System (GIS) format, the process can be complex, expensive, and time-consuming. Given that municipal employees often prefer to continue performing work in both CAD and GIS, depending on the type of work required,

Thomas Hasenzagl, Filippo Pellegrino, Lucrezia Reichlin, Giovanni Ricco

We develop a medium-size semi-structural time series model of inflation dynamics that is consistent with the view - often expressed by central banks - that three components are important: a trend anchored by long-run expectations, a Phillips curve and temporary fluctuations in energy prices. We find that a stable long-term inflation trend and a well identifi

Marcelo M. Morato, Igor M. L. Pataro, Marcus V. Americano da Costa, Julio E. Normey-Rico

The COVID-19 pandemic is the profoundest health crisis of the 21rst century. The SARS-CoV-2 virus arrived in Brazil around March, 2020 and its social and economical backlashes are catastrophic. In this paper, it is investigated how Model Predictive Control (MPC) could be used to plan appropriate social distancing policies to mitigate the pandemic effects in

Ethan Steinberg, Nikolaos Ignatiadis, Steve Yadlowsky, Yizhe Xu

Observational studies are valuable for estimating the effects of various medical interventions, but are notoriously difficult to evaluate because the methods used in observational studies require many untestable assumptions. This lack of verifiability makes it difficult both to compare different observational study methods and to trust the results of any par

Sinead A. Williamson, Jette Henderson

Understanding how two datasets differ can help us determine whether one dataset under-represents certain sub-populations, and provides insights into how well models will generalize across datasets. Representative points selected by a maximum mean discrepency (MMD) coreset can provide interpretable summaries of a single dataset, but are not easily compared ac

Vinicius Coelho, Luciana Salgado

We provide a Kingman-like Theorem for arbitrary finite measures and a version of Birkhoff&#39;s Theorem for bounded observable. As an application, we show that Birkhoff&#39;s limit exists for some continuous observable, in an example of Bowen.

Yao Zhang, William Zame, Mihaela van der Schaar

Successful application of machine learning models to real-world prediction problems, e.g. financial forecasting and personalized medicine, has proved to be challenging, because such settings require limiting and quantifying the uncertainty in the model predictions, i.e. providing valid and accurate prediction intervals. Conformal Prediction is a distribution

Erdem Bıyık, Dylan P. Losey, Malayandi Palan, Nicholas C. Landolfi

Reward functions are a common way to specify the objective of a robot. As designing reward functions can be extremely challenging, a more promising approach is to directly learn reward functions from human teachers. Importantly, data from human teachers can be collected either passively or actively in a variety of forms: passive data sources include demonstr

Ming Lin, Hesen Chen, Xiuyu Sun, Qi Qian

Many mission-critical systems are based on GPU for inference. It requires not only high recognition accuracy but also low latency in responding time. Although many studies are devoted to optimizing the structure of deep models for efficient inference, most of them do not leverage the architecture of \textbf{modern GPU} for fast inference, leading to suboptim

Joseph Bramante, Ningqiang Song

The identity of dark matter is being sought with increasingly sensitive and voluminous underground detectors. Recently the XENON1T collaboration reported excess electronic recoil events, with most of these having recoil energies around $1-30$ keV. We show that a straightforward model of inelastic dark matter produced via early universe thermal freeze-out ann

Yutian Lei, Xin Xu

In this paper, we are concerned with a Liouville-type result of the nonlinear integral equation \begin{equation*} u(x)=\overrightarrow{l}+C_*\int_{\mathbb{R}^{n}}\frac{u(1-|u|^{2})}{|x-y|^{n-α}}dy. \end{equation*} Here $u: \mathbb{R}^{n} \to \mathbb{R}^{k}$ is a bounded, uniformly continuous and differentiable function with $k \geq 1$ and $1<α<n$, $\overrigh

Melissa A. Huggan, Richard J. Nowakowski

Combinatorial games are two-player games of pure strategy where the players, usually called Left and Right, move alternately. In this paper, we introduce Cheating Robot games. These arise from simultaneous-play combinatorial games where one player has insider information (&#39;cheats&#39;). Play occurs in rounds. At the beginning of a round, both players kno

Shiwei Liu, Tim Van der Lee, Anil Yaman, Zahra Atashgahi

Sparse neural networks are effective approaches to reduce the resource requirements for the deployment of deep neural networks. Recently, the concept of adaptive sparse connectivity, has emerged to allow training sparse neural networks from scratch by optimizing the sparse structure during training. However, comparing different sparse topologies and determin

Shashanka Ubaru, Sanjeeb Dash, Arya Mazumdar, Oktay Gunluk

In modern multilabel classification problems, each data instance belongs to a small number of classes from a large set of classes. In other words, these problems involve learning very sparse binary label vectors. Moreover, in large-scale problems, the labels typically have certain (unknown) hierarchy. In this paper we exploit the sparsity of label vectors an

Othmane El moize, Zouhair Mouayn

We combine continuous $q^{-1}$-Hermite Askey polynomials with new $2D$ orthogonal polynomials introduced by Ismail and Zhang as $q$-analogs for complex Hermite polynomials to construct a new set of coherent states depending on a nonnegative integer parameter $m$. In the analytic case corresponding to $m=0$, we recover a known result on the Ar\&#34;ık-Coon os

Neda Rezaei, Fardin Saedpanah

Standard discontinuous Galerkin methods, based on piecewise polynomials of degree $ \qq=0,1$, are considered for temporal semi-discretization for second order hyperbolic equations. The main goal of this paper is to present a simple and straightforward a priori error analysis of optimal order with minimal regularity requirement on the solution. Uniform norm i

Edgar A. Bernal, Jonathan D. Hauenstein, Dhagash Mehta, Margaret H. Regan

Parameterized systems of polynomial equations arise in many applications in science and engineering with the real solutions describing, for example, equilibria of a dynamical system, linkages satisfying design constraints, and scene reconstruction in computer vision. Since different parameter values can have a different number of real solutions, the paramete

Christian Tjandraatmadja, Ross Anderson, Joey Huchette, Will Ma

We improve the effectiveness of propagation- and linear-optimization-based neural network verification algorithms with a new tightened convex relaxation for ReLU neurons. Unlike previous single-neuron relaxations which focus only on the univariate input space of the ReLU, our method considers the multivariate input space of the affine pre-activation function

Zhu-Fang Cui, Minghui Ding, Fei Gao, Khepani Raya

Strangeness was discovered roughly seventy years ago, lodged in a particle now known as the kaon, $K$. Kindred to the pion, $π$; both states are massless in the absence of Higgs-boson couplings. Kaons and pions are Nature&#39;s most fundamental Nambu-Goldstone modes. Their properties are largely determined by the mechanisms responsible for emergent mass in t

Pedro P. Ferreira, Antonio L. R. Manesco, Thiago T. Dorini, Lucas E. Correa

In the present work, we investigated the electronic and elastic properties in equilibrium and under strain of the type-II Dirac semimetal NiTe$_2$ using density functional theory (DFT). Our results demonstrate the tunability of Dirac nodes&#39; energy and momentum with strain and that it is possible to bring them closer to the Fermi level, while other metall

Ramon Ferrer-i-Cancho, Carlos Gómez-Rodríguez, Juan Luis Esteban

A fundamental problem in network science is the normalization of the topological or physical distance between vertices, that requires understanding the range of variation of the unnormalized distances. Here we investigate the limits of the variation of the physical distance in linear arrangements of the vertices of trees. In particular, we investigate variou

John F. Nagle, Alec Ramsay

Assuming that partisan fairness and responsiveness are important aspects of redistricting, it is important to measure them. Many measures of partisan bias are satisfactory for states that are balanced with roughly equal proportions of voters for the two major parties. It has been less clear which metrics measure fairness robustly when the proportion of the v

Sergey Fomin, Eugenii Shustin

We initiate the study of a class of real plane algebraic curves which we call expressive. These are the curves whose defining polynomial has the smallest number of critical points allowed by the topology of the set of real points of a curve. This concept can be viewed as a global version of the notion of a real morsification of an isolated plane curve singul

Alessandro Nuara, Francesco Trovò, Nicola Gatti

The SARS-CoV-2 outbreak changed the everyday life of almost all the people over the world.Currently, we are facing with the problem of containing the spread of the virus both using the more effective forced lockdown, which has the drawback of slowing down the economy of the involved countries, and by identifying and isolating the positive individuals, which,

Eric Bunch, Daniel Dickinson, Jeffery Kline, Glenn Fung

Metric space magnitude, an active subject of research in algebraic topology, originally arose in the context of biology, where it was used to represent the effective number of distinct species in an environment. In a more general setting, the magnitude of a metric space is a real number that aims to quantify the effective number of distinct points in the spa

Emmanuel Abbe, Jianqing Fan, Kaizheng Wang

Principal Component Analysis (PCA) is a powerful tool in statistics and machine learning. While existing study of PCA focuses on the recovery of principal components and their associated eigenvalues, there are few precise characterizations of individual principal component scores that yield low-dimensional embedding of samples. That hinders the analysis of v

Andi Nika, Sepehr Elahi, Çağın Ararat, Cem Tekin

We consider the problem of optimizing a vector-valued objective function $\boldsymbol{f}$ sampled from a Gaussian Process (GP) whose index set is a well-behaved, compact metric space $({\cal X},d)$ of designs. We assume that $\boldsymbol{f}$ is not known beforehand and that evaluating $\boldsymbol{f}$ at design $x$ results in a noisy observation of $\boldsym

C. Reuter, J. D. Vieira, J. S. Spilker, A. Weiss

The South Pole Telescope (SPT) has systematically identified 81 high-redshift, strongly gravitationally lensed, dusty star-forming galaxies (DSFGs) in a 2500 square degree cosmological mm-wave survey. We present the final spectroscopic redshift survey of this flux-limited ($S_{870\, \mathrm{μm}} > 25\, \mathrm{mJy}$) sample, initially selected at $1.4$ mm. T

Man-Kwun Chiu, Matias Korman, Martin Suderland, Takeshi Tokuyama

We consider the problem of digitalizing Euclidean segments. Specifically, we look for a constructive method to connect any two points in $\mathbb{Z}^d$. The construction must be {\em consistent} (that is, satisfy the natural extension of the Euclidean axioms) while resembling them as much as possible. Previous work has shown asymptotically tight results in t

A S M Rizvi, Leandro Bertholdo, Joao Ceron, John Heidemann

IP anycast is used for services such as DNS and Content Delivery Networks (CDN) to provide the capacity to handle Distributed Denial-of-Service (DDoS) attacks. During a DDoS attack service operators redistribute traffic between anycast sites to take advantage of sites with unused or greater capacity. Depending on site traffic and attack size, operators may i