arXiv papers from April 2020

Mahdi Teimouri

The four-parameter Johnson's SB (JSB) and three-parameter Weibull distributions have received much attention in the field of forestry for characterizing diameters at breast height (DBH). In this work, we suggest the Bayesian method for estimating parameters of the JBS distribution. The maximum likelihood approach uses iterative methods such as Newton-Raphson

Yasumasa Onoe, Greg Durrett

In standard methodology for natural language processing, entities in text are typically embedded in dense vector spaces with pre-trained models. The embeddings produced this way are effective when fed into downstream models, but they require end-task fine-tuning and are fundamentally difficult to interpret. In this paper, we present an approach to creating e

Pauching Yap, Hippolyt Ritter, David Barber

Neural networks are known to suffer from catastrophic forgetting when trained on sequential datasets. While there have been numerous attempts to solve this problem in large-scale supervised classification, little has been done to overcome catastrophic forgetting in few-shot classification problems. We demonstrate that the popular gradient-based model-agnosti

Alessandro Ilic Mezza, Emanuël A. P. Habets, Meinard Müller, Augusto Sarti

The performance of machine learning algorithms is known to be negatively affected by possible mismatches between training (source) and test (target) data distributions. In fact, this problem emerges whenever an acoustic scene classification system which has been trained on data recorded by a given device is applied to samples acquired under different acousti

Dariusz Dereniowski, Aleksander Łukasiewicz, Przemysław Uznański

Consider a generalization of the classical binary search problem in linearly sorted data to the graph-theoretic setting. The goal is to design an adaptive query algorithm, called a strategy, that identifies an initially unknown target vertex in a graph by asking queries. Each query is conducted as follows: the strategy selects a vertex $q$ and receives a rep

Paul Bryan, Mohammad N. Ivaki, Julian Scheuer

We study the long-time existence and behavior for a class of anisotropic non-homogeneous Gauss curvature flows whose stationary solutions, if exist, solve the regular Orlicz-Minkowski problems. As an application, we obtain old and new results for the regular even Orlicz-Minkowski problems; the corresponding $L_p$ version is the even $L_p$-Minkowski problem f

R. Kishor Kumar, A. Gammal, Lauro Tomio

We consider the mass-imbalanced sensibility for the emergence of vortex patterns in the Bose-Einstein condensed binary mixture of rubidium-cesium ($^{85}$Rb-$^{133}$Cs), confined in quasi-two-dimensional harmonic traps, with one species linearly perturbed in one direction. Non-dipolar coupled species are chosen to highlight mass symmetry effects. We first an

Rong An, Li-Sheng Geng, Shi-Sheng Zhang

Charge radii are one of the most fundamental properties of atomic nuclei characterizing their charge distributions. Though the general trend as a function of the mass number is well described by the $A^{1/3}$ rule, some fine structures, such as the evolution along the calcium isotopic chain and the corresponding odd-even staggerings, are notoriously difficul

Naouel Boulkaboul

In this study, we provide an alternatively reformulated interpretation of Gibbons-Hawking radiation as well as inflation. By using a spacetime quantization procedure, proposed recently by L.C. C\'eleri et al., in anti-de Sitter space we show that Gibbons-Hawking radiation is an intrinsic property of the concerned space, that arises due to the existence of a

Emanuele Di Pascale, Hamed Ahmadi, Linda Doyle, Irene Macaluso

Neutral Host Small Cell Providers (SCP) represent a key element of the 5G vision of ultra-dense mobile networks. However, current business models mostly focus on multi-year agreements for large venues, such as stadiums and hotel chains. These business agreements are regulated through binding Service Level Agreements (SLAs), which tend to be too cumbersome an

Karen Meagher, A. S. Razafimahatratra

In this paper we consider the Erd\H{o}s-Ko-Rado property for both $2$-pointwise and $2$-setwise intersecting permutations. Two permutations $\sigma,\tau \in Sym(n)$ are $t$-setwise intersecting if there exists a $t$-subset $S$ of $\{1,2,\dots,n\}$ such that $S^\sigma = S^\tau$. If for each $s\in S$, $s^\sigma = s^\tau$, then we say $\sigma$ and $\tau$ are $t

Chiara Marletto, Vlatko Vedral

We discuss a point, which from time to time has been doubted in the literature: all symmetries, such as those induced by the energy and momentum conservation laws, hold in quantum physics not just "on average", as is sometimes claimed, but exactly in each "branch" of the wavefunction, expressed in the basis where the conserved observable is sharp. We note th

Marina Toger, Ian Shuttleworth, John Östh

Mobile phone data -- with file sizes scaling into terabytes -- easily overwhelm the computational capacity available to some researchers. Moreover, for ethical reasons, data access is often granted only to particular subsets, restricting analyses to cover single days, weeks, or geographical areas. Consequently, it is frequently impossible to set a particular

Yu Cheng, Zhe Gan, Yizhe Zhang, Oussama Elachqar

We introduce a new task, Contextual Text Style Transfer - translating a sentence into a desired style with its surrounding context taken into account. This brings two key challenges to existing style transfer approaches: ($i$) how to preserve the semantic meaning of target sentence and its consistency with surrounding context during transfer; ($ii$) how to t

Sai Kanth Dacha, Thomas E. Murphy

Nonlinear propagation of signals in single-mode fiber is well understood, and is typically observed by measuring the temporal profile or optical spectrum of an emerging signal. In multimode fibers, the nonlinearity has both a spatial and a temporal element, and a complete investigation of the interactions between propagating modes requires resolving the outp

Daniel Lokshtanov, Saket Saurabh, Vaishali Surianarayanan

In the Min $k$-Cut problem, input is an edge weighted graph $G$ and an integer $k$, and the task is to partition the vertex set into $k$ non-empty sets, such that the total weight of the edges with endpoints in different parts is minimized. When $k$ is part of the input, the problem is NP-complete and hard to approximate within any factor less than $2$. Rece

Moein Malekakhlagh, Easwar Magesan, David C. McKay

We present a comprehensive theoretical study of the cross-resonance gate operation covering estimates for gate parameters and gate error as well as analyzing spectator qubits and multi-qubit frequency collisions. We start by revisiting the derivation of effective Hamiltonian models following Magesan et al. (arXiv:1804.04073). Transmon qubits are commonly mod

Kyle Pietrzyk, Ilenia Battiato

Acoustic streaming is the net time-averaged flow that results from the nonlinearities in an oscillating flow. Extensive research has sought to identify different physical mechanisms and types of acoustic streaming in systems of various geometries. While streaming in a channel maintains one of the simplest geometries, dimensional analysis of the governing equ

Thanos Tagaris, Andreas Stafylopatis

Lack of transparency has been the Achilles heal of Neural Networks and their wider adoption in industry. Despite significant interest this shortcoming has not been adequately addressed. This study proposes a novel framework called Hide-and-Seek (HnS) for training Interpretable Neural Networks and establishes a theoretical foundation for exploring and compari

Gideon Maillette de Buy Wenniger, Thomas van Dongen, Eleri Aedmaa, Herbert Teun Kruitbosch

Training recurrent neural networks on long texts, in particular scholarly documents, causes problems for learning. While hierarchical attention networks (HANs) are effective in solving these problems, they still lose important information about the structure of the text. To tackle these problems, we propose the use of HANs combined with structure-tags which

Patrick Xia, João Sedoc, Benjamin Van Durme

We investigate modeling coreference resolution under a fixed memory constraint by extending an incremental clustering algorithm to utilize contextualized encoders and neural components. Given a new sentence, our end-to-end algorithm proposes and scores each mention span against explicit entity representations created from the earlier document context (if any

Hans Dermot Doran, Monika Reif, Marco Oehler, Curdin Stoehr

Autonomous robots and drones will work collaboratively and cooperatively in tomorrow's industry and agriculture. Before this becomes a reality, some form of standardised communication between man and machine must be established that specifically facilitates communication between autonomous machines and both trained and untrained human actors in the working e

Christian Noack, Philippe Sosoe

In this paper, we consider four integrable models of directed polymers for which the free energy is known to exhibit KPZ fluctuations. A common framework for the analysis of these models was introduced in our recent work on the O'Connell-Yor polymer. We derive estimates for the central moments of the partition function, of any order, on the near-optimal scal

Brandon Hanson, Oliver Roche-Newton, Misha Rudnev

Let $f$ be a smooth real function with strictly monotone first $k$ derivatives. We show that for a finite set $A$, with $|A+A|\leq K|A|$, $|2^kf(A)-(2^k-1)f(A)|\gg_k |A|^{k+1-o(1)}/K^{O_k(1)}$. We deduce several new sum-product type implications, e.g. that $A+A$ being small implies unbounded growth for a many enough times iterated product set $A \cdots A$.

Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo

Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global information dissemination and load imbalance across uneven sample lengths. State-of-the-art decentralized optimizers mitigate the problem, but require more iterations to achieve the same

Dinesh Raghu, Nikhil Gupta, Mausam

Task-oriented dialog (TOD) systems often need to formulate knowledge base (KB) queries corresponding to the user intent and use the query results to generate system responses. Existing approaches require dialog datasets to explicitly annotate these KB queries -- these annotations can be time consuming, and expensive. In response, we define the novel problems

Guillermo A. Martínez-Mascorro, José R. Abreu-Pederzini, José C. Ortiz-Bayliss, Hugo Terashima-Marín

Crime generates significant losses, both human and economic. Every year, billions of dollars are lost due to attacks, crimes, and scams. Surveillance video camera networks are generating vast amounts of data, and the surveillance staff can not process all the information in real-time. The human sight has its limitations, where the visual focus is among the m

Hans Dermot Doran

In many industrial sectors such as factory automation and process control sensor redundancy is required to ensure reliable and highly-available operation. Measured values from N-redundant sensors are typically subjected to some voting scheme to determine a value which is used in further processing. In this paper we present a voting framework which allows the

Raghunandan M. Rao, Vuk Marojevic, Jeffrey H. Reed

This paper considers an underlay pulsed radar-cellular spectrum sharing scenario, where the cellular system uses pilot-aided demodulation, statistical channel state information (S-CSI) estimation and limited feedback schemes. Under a realistic system model, upper and lower bounds are derived on the probability that at least a specified number of pilot signal

Liliana I. Carvalho, Daniel. M. Silva, R. C. Sofia

Novel Internet of Things (IoT) requirements derived from a broader interconnection of heterogeneous devices have pushed the horizons of Cloud computing and are giving rise to a wider decentralisation of applications and data centers. An answer to the underlying network concerns, such as the need to lower the resulting latency due to heavy computation needs,o

Marc Burger, Alessandra Iozzi, Anne Parreau, Marie Beatrice Pozzetti

We announce results on a compactification of general character varieties that has good topological properties and give various interpretations of its ideal points. We relate this to the Weyl chamber length compactification and apply our results to the theory of maximal and Hitchin representations.

Raviteja Anantha, Srinivas Chappidi, William Dawoodi

Voice Assistants aim to fulfill user requests by choosing the best intent from multiple options generated by its Automated Speech Recognition and Natural Language Understanding sub-systems. However, voice assistants do not always produce the expected results. This can happen because voice assistants choose from ambiguous intents - user-specific or domain-spe

Iwona Chlebicka, Anna Zatorska-Goldstein

We study properties of $\mathcal{A}$-harmonic and $\mathcal{A}$-superharmonic functions involving an operator having generalized Orlicz-growth embracing besides Orlicz case also natural ranges of variable exponent and double-phase cases. In particular, Harnack's Principle and Minimum Principle are provided for $\mathcal{A}$-superharmonic functions and bounda

Zhengtao Gan, Orion L. Kafka, Niranjan Parab, Cang Zhao

We leverage dimensional analysis and genetic programming (a type of machine learning) to discover two strikingly simple but universal scaling laws, which remain accurate for different materials, processing conditions, and machines in metal three-dimensional (3D) printing. The first one is extracted from high-fidelity high-speed synchrotron X-ray imaging, and

Bhuvan Malladihalli Shashidhara, Darshan Mehta, Yash Kale, Dan Morris

Camera Traps are extensively used to observe wildlife in their natural habitat without disturbing the ecosystem. This could help in the early detection of natural or human threats to animals, and help towards ecological conservation. Currently, a massive number of such camera traps have been deployed at various ecological conservation areas around the world,

Sarthak Jain, Sarah Wiegreffe, Yuval Pinter, Byron C. Wallace

In many settings it is important for one to be able to understand why a model made a particular prediction. In NLP this often entails extracting snippets of an input text `responsible for' corresponding model output; when such a snippet comprises tokens that indeed informed the model's prediction, it is a faithful explanation. In some settings, faithfulness

Nicolò Vallarano, Claudio Tessone, Tiziano Squartini

Cryptocurrencies are distributed systems that allow exchanges of native (and non-) tokens among participants. The complete historical bookkeeping and its wide availability opens up an unprecedented possibility, i.e. that of understanding the evolution of their network structure while gaining useful insight on the relationships between user' behaviour and cry

Tomislav Ivek, Domagoj Vlah

We describe our submission to the Extreme Value Analysis 2019 Data Challenge in which teams were asked to predict extremes of sea surface temperature anomaly within spatio-temporal regions of missing data. We present a computational framework which reconstructs missing data using convolutional deep neural networks. Conditioned on incomplete data, we employ a

Vinicius M. A. Souza, Denis M. dos Reis, Andre G. Maletzke, Gustavo E. A. P. A. Batista

Streaming data are increasingly present in real-world applications such as sensor measurements, satellite data feed, stock market, and financial data. The main characteristics of these applications are the online arrival of data observations at high speed and the susceptibility to changes in the data distributions due to the dynamic nature of real environmen

Chaoqi Yang, Ruijie Wang, Fangwei Gao, Dachun Sun

Recent re-opening policies in the US, following a period of social distancing measures, introduced a significant increase in daily COVID-19 infections, calling for a roll-back or substantial revisiting of these policies in many states. The situation is suggestive of difficulties modeling the impact of partial distancing/re-opening policies on future epidemic

Brian D Goodwin, Corey Jaskolski, Can Zhong, Herick Asmani

X-ray and computed tomography (CT) scanning technologies for COVID-19 screening have gained significant traction in AI research since the start of the coronavirus pandemic. Despite these continuous advancements for COVID-19 screening, many concerns remain about model reliability when used in a clinical setting. Much has been published, but with limited trans

M. Burrows, R. B. Baker, Ch. Elster, S. P. Weppner

Background: Calculating microscopic effective interactions (optical potentials) for elastic nucleon-nucleus scattering has already in the past led to a large body of work. For first-order calculations a nucleon-nucleon (\textit{NN}) interaction and a one-body density of the nucleus were taken as input to rigorous calculations of microscopic full-folding calc

Shriram Krishnamurthi, Emmanuel Schanzer, Joe Gibbs Politz, Benjamin S. Lerner

The Bootstrap Project's Data Science curriculum has trained about 100 teachers who are using it around the country. It is specifically designed to aid adoption at a wide range of institutions. It emphasizes valuable curricular goals by drawing on both the education literature and on prior experience with other computing outreach projects. It embraces "three

Nur Geffen Lan, Emmanuel Chemla, Shane Steinert-Threlkeld

We propose a general framework to study language emergence through signaling games with neural agents. Using a continuous latent space, we are able to (i) train using backpropagation, (ii) show that discrete messages nonetheless naturally emerge. We explore whether categorical perception effects follow and show that the messages are not compositional.

Philip D. Mannheim, Peter Lowdon, Stanley J. Brodsky

We compare light-front quantization and instant-time quantization both at the level of operators and at the level of their Feynman diagram matrix elements. At the level of operators light-front quantization and instant-time quantization lead to equal light-front time commutation (or anticommutation) relations that appear to be quite different from equal inst

Fu Li, Tian Li, Girish S. Agarwal

Electron-multiplying charge-coupled-device cameras (EMCCDs) have been used to observe quantum noise reductions in beams of light in the transverse spatial degree of freedom. For the quantum noise reduction in the temporal domain, "bucket detectors," usually composed of photodiodes with operational amplifiers, are used to register the intensity fluctuations i

A. Panaitescu

We derive basic analytical results for the timing and decay of the GRB-counterpart and delayed-afterglow light-curves for a brief emission episode from a relativistic surface endowed with angular structure, consisting of a uniform Core of size theta_c (Lorentz factor Gamma_c and surface emissivity i_nu are angle-independent) and an axially-symmetric power-la

Xiantao Li

We present embedding procedures for the non-Markovian stochastic Schr\"{o}dinger equations, arising from studies of quantum systems coupled with bath environments. By introducing auxiliary wave functions, it is demonstrated that the non-Markovian dynamics can be embedded in extended, but Markovian, stochastic models. Two embedding procedures are presented. T

Zhen Wang, Liwei Liu, Yiqiang Q. Zhao

In this paper, equilibrium strategies and optimal balking strategies of customers in a constant retrial queue with multiple vacations and the $N$-policy under two information levels, respectively, are investigated. We assume that there is no waiting area in front of the server and an arriving customer is served immediately if the server is idle; otherwise (t

Yu Zeng, Sanaa Hamid Mohamed, T. E. H. El-Gorashi, Jaafar M. H. Elmirghani

In this paper, we investigate optical wireless repeaters as relay terminals between a transmitter and a user in an Infrared Optical Wireless Communication (IROWC) system. A delay adaptation method is introduced to solve the problem of irregular signal arrival time from different relay terminals. Three different relay terminal deployment scenarios were invest

Alexander Gutkin, Tatiana Merkulova, Martin Jansche

The use of linguistic typological resources in natural language processing has been steadily gaining more popularity. It has been observed that the use of typological information, often combined with distributed language representations, leads to significantly more powerful models. While linguistic typology representations from various resources have mostly

Sunrose Shrestha

A square-tiled surface (STS) is a branched cover of the standard square torus with branching over exactly one point. In this paper we consider a randomizing model for STSs and generalizations to branched covers of other simple translation surfaces which we call polygon-tiled surfaces. We obtain a local central limit theorem for the genus and subsequently obt

Ishan Agarwal, Oded Regev, Yi Tang

We show that for any $n$-dimensional lattice $\mathcal{L} \subseteq \mathbb{R}^n$, the torus $\mathbb{R}^n/\mathcal{L}$ can be embedded into Hilbert space with $O(\sqrt{n\log n})$ distortion. This improves the previously best known upper bound of $O(n\sqrt{\log n})$ shown by Haviv and Regev (APPROX 2010) and approaches the lower bound of $\Omega(\sqrt{n})$ d

Adolfo O. Fumega, Jan Phillips, Victor Pardo

Transition metal dichalcogenides are promising candidates to show long-range ferromagnetic order in the single-layer limit. Based on ab initio calculations, we report the emergence of a charge density wave (CDW) phase in monolayer 1T-CrTe$_2$. We demonstrate that this phase is the ground state in the single-layer limit at any strain value. We obtain an optic

Jing Han, Kun Qian, Meishu Song, Zijiang Yang

The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we foc

Austin Clyde, Tom Brettin, Alexander Partin, Maulik Shaulik

By combining various cancer cell line (CCL) drug screening panels, the size of the data has grown significantly to begin understanding how advances in deep learning can advance drug response predictions. In this paper we train >35,000 neural network models, sweeping over common featurization techniques. We found the RNA-seq to be highly redundant and informa

Ivan Gonzalez, Rustum Choksi, Jean-Christophe Nave

Finding optimal (or low energy) centroidal Voronoi tessellations (CVTs) on a 2D domain is a challenging problem. One must navigate an energy landscape whose desirable critical points have sufficiently small basins of attractions that they are inaccessible with Monte-Carlo initialized gradient descent methods. We present a simple deterministic method for effi

Stanisław Saganowski, Przemysław Kazienko, Maciej Dzieżyc, Patrycja Jakimów

Wearables equipped with pervasive sensors enable us to monitor physiological and behavioral signals in our everyday life. We propose the WellAff system able to recognize affective states for wellbeing support. It also includes health care scenarios, in particular patients with chronic kidney disease (CKD) suffering from bipolar disorders. For the need of a l

Andrea Muolo, Markus Reiher

A new explicitly correlated functional form for expanding the wave function of an N-particle system with arbitrary angular momentum and parity is presented. We develop the projection-based approach, numerically exploited in our previous work [J. Chem. Phys. 149, 184105 (2018)], to explicitly correlated Gausssians with one-axis shifted centers and derive the

C. B. da Porciuncula

A natural consequence of the fractional calculus is its extension to a matrix order of differentiation and integration. A matrix-order derivative definition and a matrix-order integration arise from the generalization of the gamma function applied to the fractional differintegration definition. This work focuses on some results applied to the Riemann-Liouvil

A. Deller, S. D Hogan

High Rydberg states of nitric oxide (NO) with principal quantum numbers between 40 and 100 and lifetimes in excess of 10 $\mu$s have been prepared by resonance enhanced two-color two-photon laser excitation from the X $^2\Pi_{1/2}$ ground state through the A $^2\Sigma^+$ intermediate state. Molecules in these long-lived Rydberg states were detected and chara

Peter Nelson, Kazuhiro Nomoto

A simple binary matroid is called $I_4$-free if none of its rank-4 flats are independent sets. These objects can be equivalently defined as the sets $E$ of points in $PG(n-1,2)$ for which $|E \cap F|$ is not a basis of $F$ for any four-dimensional flat $F$. We prove a decomposition theorem that exactly determines the structure of all $I_4$-free and triangle-

David-Alexandre Beaupre, Guillaume-Alexandre Bilodeau

Multispectral disparity estimation is a difficult task for many reasons: it has all the same challenges as traditional visible-visible disparity estimation (occlusions, repetitive patterns, textureless surfaces), in addition of having very few common visual information between images (e.g. color information vs. thermal information). In this paper, we propose

Diego Pacheco, Leonardo de Lima, Carla Silva Oliveira

Let $G=(V,E)$ be a simple undirected and connected graph on $n$ vertices. The Graovac--Ghorbani index of a graph $G$ is defined as $$ABC_{GG}(G)= \sum_{uv \in E(G)} \sqrt{\frac{n_{u}+n_{v}-2} {n_{u} n_{v}}},$$ where $n_u$ is the number of vertices closer to vertex $u$ than vertex $v$ of the edge $uv \in E(G)$ and $n_{v}$ is defined analogously. It is well-kn

Thy Thy Tran, Phong Le, Sophia Ananiadou

Unsupervised relation extraction (URE) extracts relations between named entities from raw text without manually-labelled data and existing knowledge bases (KBs). URE methods can be categorised into generative and discriminative approaches, which rely either on hand-crafted features or surface form. However, we demonstrate that by using only named entities to

Mario I. Molina

We examine the existence of nonlinear modes and their temporal dynamics, in arrays of split-ring resonators, using a fractional extension of the Laplacian in the evolution equation. We find a closed-form expression for the dispersion relation as a function of the fractional exponent as well as an exact expression for the critical coupling between rings, beyo

Anoop Kunchukuttan, Divyanshu Kakwani, Satish Golla, Gokul N. C.

We present the IndicNLP corpus, a large-scale, general-domain corpus containing 2.7 billion words for 10 Indian languages from two language families. We share pre-trained word embeddings trained on these corpora. We create news article category classification datasets for 9 languages to evaluate the embeddings. We show that the IndicNLP embeddings significan

Benjamin Schiller, Johannes Daxenberger, Iryna Gurevych

We rely on arguments in our daily lives to deliver our opinions and base them on evidence, making them more convincing in turn. However, finding and formulating arguments can be challenging. In this work, we train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, st

Kevin Costello, Tudor Dimofte, Davide Gaiotto

We study the holomorphic twist of 3d ${\cal N}=2$ gauge theories in the presence of boundaries, and the algebraic structure of bulk and boundary local operators. In the holomorphic twist, both bulk and boundary local operators form chiral algebras (\emph{a.k.a.} vertex operator algebras). The bulk algebra is commutative, endowed with a shifted Poisson bracke

Raghav Chhetri, Stephan Preibisch, Nico Stuurman

Microscopes have morphed from purely optical instruments into motorized, robotic machines that form images on digital sensors rather than eyeballs. This continuing trend towards automation and digitization enables many new approaches to microscopy that would have been impossible or impractical without computer interfaces. Accordingly, todays development of n

Amir Neshastegaran, Ali Norouzifar, Iman Izadi

Industrial plants are prone to faults. To notify the operator of a fault occurrence, alarms are utilized as a basic part of modern computer-controlled plants. However, due to the interconnections of different parts of a plant, a single fault often propagates through the plant and triggers a (sometimes large) number of alarms. A graphical plant topology can h

Guimu Guo, Da Yan, M. Tamer Özsu, Zhe Jiang

Given a user-specified minimum degree threshold $\gamma$, a $\gamma$-quasi-clique is a subgraph $g=(V_g,E_g)$ where each vertex $v\in V_g$ connects to at least $\gamma$ fraction of the other vertices (i.e., $\lceil \gamma\cdot(|V_g|-1)\rceil$ vertices) in $g$. Quasi-clique is one of the most natural definitions for dense structures useful in finding communit

H. Audi, Y. Viero, N. Alwhaibi, Z. Chen

We demonstrate that the conductance switching of benzo-bis(imidazole) molecules upon protonation depends on the lateral functional groups. The protonated H-substituted molecule shows a higher conductance than the neutral one (Gpro>Gneu), while the opposite (Gneu>Gpro) is observed for a molecule laterally functionalized by amino-phenyl groups. These results a

Sinan Özgür Özgün, Anne-Marie Rickmann, Abhijit Guha Roy, Christian Wachinger

The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications. However, current neural networks tend to forget previously learned tasks when trained on new ones, i.e., they suffer from Catastrophic Forgetting (CF). The objective of Continual Learning (CL) is to alleviate this prob

Andrea Caputo, Alexander J. Millar, Edoardo Vitagliano

In the presence of an external magnetic field the axion and the photon mix. In particular, the dispersion relation of a longitudinal plasmon always crosses the dispersion relation of the axion (for small axion masses), thus leading to a resonant conversion. Using thermal field theory we concisely derive the axion emission rate, applying it to astrophysical a

Matthieu C. Dartiailh, Joseph J. Cuozzo, William Mayer, Joseph Yuan

Josephson junctions hosting Majorana fermions have been predicted to exhibit a 4$\pi$ periodic current phase relation. The experimental consequence of this periodicity is the disappearance of odd steps in Shapiro steps experiments. Experimentally, missing odd Shapiro steps have been observed in a number of materials systems with strong spin-orbit coupling an

M. D. Stritzinger, F. Taddia, M. Fraser, T. M. Tauris

We present optical and near-infrared broadband photometry and optical spectra of AT 2014ej from the the Carnegie Supernova Project-II. These observations are complemented with data from the CHilean Automatic Supernova sEarch, the Public ESO Spectroscopic Survey of Transient Objects, and from the Backyard Observatory Supernova Search. Observational signatures

L. T. Giorgini, U. D. Jentschura, E. M. Malatesta, G. Parisi

For a long time, the predictive limits of perturbative quantum field theory have been limited by our inability to carry out loop calculations to arbitrarily high order, which become increasingly complex as the order of perturbation theory is increased. This problem is exacerbated by the fact that perturbation series derived from loop diagram (Feynman diagram

Luciano Abadias, Glenier Bello, Dmitry Yakubovich

We study the generalization of $m$-isometries and $m$-contractions (for positive integers $m$) to what we call $a$-isometries and $a$-contractions for positive real numbers $a$. We show that any Hilbert space operator, satisfying an inequality of certain class (in hereditary form), is similar to $a$-contractions. This result is based on some Banach algebras

Sarah Kushner, Risa Ulinski, Karan Singh, David I. W. Levin

We propose a novel algorithm to efficiently generate hidden structures to support arrangements of floating rigid objects. Our optimization finds a small set of rods and wires between objects and each other or a supporting surface (e.g., wall or ceiling) that hold all objects in force and torque equilibrium. Our objective function includes a sparsity inducing

Omar Rodríguez-Tzompantzi

We constructed a symplectic realization of the dynamic structure of two interacting spin-two fields in three dimensions. A significant simplification refers to the treatment of constraints: instead of performing a Hamiltonian analysis $\grave{a}\, la$ Dirac, we worked out a method that only uses properties of the pre-symplectic two-form matrix and its corres

Manar A. Elmeiligy, Ali I. El Desouky, Sally M. Elghamrawy

The ongoing outbreak of coronavirus disease (COVID-19) had burst out in Wuhan China, specifically in December 2019. COVID-19 has caused by a new virus that had not been identified in human previously. This was followed by a widespread and rapid spread of this epidemic throughout the world. Daily, the number of the confirmed cases are increasing rapidly, numb

Anish Agarwal, Abdullah Alomar, Arnab Sarker, Devavrat Shah

As we reach the apex of the COVID-19 pandemic, the most pressing question facing us is: can we even partially reopen the economy without risking a second wave? We first need to understand if shutting down the economy helped. And if it did, is it possible to achieve similar gains in the war against the pandemic while partially opening up the economy? To do so

Carolina A. Marques, Luke C. Rhodes, Rosalba Fittipaldi, Veronica Granata

In strongly correlated electron materials, the electronic, spin, and charge degrees of freedom are closely intertwined. This often leads to the stabilization of emergent orders that are highly sensitive to external physical stimuli promising opportunities for technological applications. In perovskite ruthenates, this sensitivity manifests in dramatic changes

Rupert A. C. Croft

We investigate the possibility that a statistical detection of the galaxy parallax shift due to the Earth's motion with respect to the CMB frame (cosmic secular parallax) could be made by the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) or by the Nancy Grace Roman Space Telescope (NGRST), and used to measure the Hubble constant. We make m

Ronan Riochet, Josef Sivic, Ivan Laptev, Emmanuel Dupoux

To reach human performance on complex tasks, a key ability for artificial systems is to understand physical interactions between objects, and predict future outcomes of a situation. This ability, often referred to as intuitive physics, has recently received attention and several methods were proposed to learn these physical rules from video sequences. Yet, m

Pulkit Nahata, Giancarlo Ferrari-Trecate

In this work, we present new secondary regulators for current sharing and voltage balancing in DC microgrids, composed of distributed generation units, dynamic RLC lines, and nonlinear ZIP (constant impedance, constant current, and constant power) loads. The proposed controllers sit atop a primary voltage control layer, and exchange information over a commun

Vasil Penchev

The CMI Millennium "P vs NP Problem" can be resolved e.g. if one shows at least one counterexample to the conjecture "P is equal to NP". A certain class of problems being such counterexamples is formulated. This implies the rejection of the hypothesis "P is equal to NP" for any conditions satisfying the formulation of the problem. Thus, the solution "P is di

Marcello Malagutti

Starting from the results of Charles Fefferman and Janos Koll\`ar in Continuous Solutions of Linear Equations [1], we adopt a new approach based on Fefferman's techniques of Glaeser refinement to show a more general result than the one proved by Koll\`ar by using techniques from algebraic geometry. Considering a system of linear equations with semialgebraic

Noirrit Kiran Chandra, Sourabh Bhattacharya

In this article, we investigate the asymptotic properties of Bayesian multiple testing procedures under general dependent setup, when the sample size and the number of hypotheses both tend to infinity. Specifically, we investigate strong consistency of the procedures and asymptotic properties of different versions of false discovery and false non-discovery r

Divya Saxena, Jiannong Cao

Generative Adversarial Networks (GANs) is a novel class of deep generative models which has recently gained significant attention. GANs learns complex and high-dimensional distributions implicitly over images, audio, and data. However, there exists major challenges in training of GANs, i.e., mode collapse, non-convergence and instability, due to inappropriat

Jiaxi Nie, Jacques Verstraete

In this paper, we consider a randomized greedy algorithm for independent sets in $r$-uniform $d$-regular hypergraphs $G$ on $n$ vertices with girth $g$. By analyzing the expected size of the independent sets generated by this algorithm, we show that $\alpha(G)\geq (f(d,r)-\epsilon(g,d,r))n$, where $\epsilon(g,d,r)$ converges to $0$ as $g\rightarrow\infty$ fo

Alex Savatovsky

We prove that for an o-minimal expansion of the real additive group $\cal R$ and a set $P\subseteq \mathbb{R}$ of dimension $0$ such that $\langle\mathcal{R},P\rangle$ is sparse, has definable choice and every definable set has interior or is nowhere dense then, for every definable set $X$, there is a family $\{X_t:\; t\in A\}$ definable in \Cal R and a set

Yiding Hao

LSTM language models have been shown to capture syntax-sensitive grammatical dependencies such as subject-verb agreement with a high degree of accuracy (Linzen et al., 2016, inter alia). However, questions remain regarding whether they do so using spurious correlations, or whether they are truly able to match verbs with their subjects. This paper argues for

Su Jiang, Louis J. Durlofsky

Data-space inversion (DSI) and related procedures represent a family of methods applicable for data assimilation in subsurface flow settings. These methods differ from model-based techniques in that they provide only posterior predictions for quantities (time series) of interest, not posterior models with calibrated parameters. DSI methods require a large nu

Pu Zhao, Pin-Yu Chen, Payel Das, Karthikeyan Natesan Ramamurthy

Mode connectivity provides novel geometric insights on analyzing loss landscapes and enables building high-accuracy pathways between well-trained neural networks. In this work, we propose to employ mode connectivity in loss landscapes to study the adversarial robustness of deep neural networks, and provide novel methods for improving this robustness. Our exp

Christopher W. Murphy

We present a complete basis of dimension-8 operators in the Standard Model Effective Field Theory. Attention is paid to operators that vanish in the absence of flavor structure. There are dimension-8 SMEFT 44,807 operators. We also briefly discuss a few aspects of phenomenology involving dimension-8 operators, including light-by-light scattering and electrow

Sidrah Javed, Osama Amin, Basem Shihada, Mohamed-Slim Alouini

The deviation of continuous and discrete complex random variables from the traditional proper and symmetric assumption to a generalized improper and asymmetric characterization (accounting correlation between a random entity and its complex conjugate), respectively, introduces new design freedom and various potential merits. As such, the theory of impropriet

Li'an Zhuo, Baochang Zhang, Hanlin Chen, Linlin Yang

Neural architecture search (NAS) proves to be among the best approaches for many tasks by generating an application-adaptive neural architecture, which is still challenged by high computational cost and memory consumption. At the same time, 1-bit convolutional neural networks (CNNs) with binarized weights and activations show their potential for resource-lim

Ole Peters, Alexander Adamou, Mark Kirstein, Yonatan Berman

Behavioural economics provides labels for patterns in human economic behaviour. Probability weighting is one such label. It expresses a mismatch between probabilities used in a formal model of a decision (i.e. model parameters) and probabilities inferred from real people's decisions (the same parameters estimated empirically). The inferred probabilities are