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Metric-Optimized Example Weights
Real-world machine learning applications often have complex test metrics, and may have training and test data that follow different distributions. We propose addressing these issues by using a weighted loss function with a standard convex loss, but with weights on the training examples that are learned to optimize th...
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Interior Eigensolver for Sparse Hermitian Definite Matrices Based on Zolotarev's Functions
This paper proposes an efficient method for computing selected generalized eigenpairs of a sparse Hermitian definite matrix pencil (A, B). Based on Zolotarev's best rational function approximations of the signum function and conformal mapping techniques, we construct the best rational function approximation of a rect...
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A Polya-Vinogradov-type inequality on $\mathbb{Z}[i]$
We establish a Polya-Vinogradov-type bound for finite periodic multipicative characters on the Gaussian integers.
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Distinction of representations via Bruhat-Tits buildings of p-adic groups
Introductory and pedagogical treatmeant of the article : P. Broussous "Distinction of the Steinberg representation", with an appendix by François Courtès, IMRN 2014, no 11, 3140-3157. To appear in Proceedings of Chaire Jean Morlet, Dipendra Prasad, Volker Heiermann Ed. 2017. Contains modified and simplified proofs of...
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Face Super-Resolution Through Wasserstein GANs
Generative adversarial networks (GANs) have received a tremendous amount of attention in the past few years, and have inspired applications addressing a wide range of problems. Despite its great potential, GANs are difficult to train. Recently, a series of papers (Arjovsky & Bottou, 2017a; Arjovsky et al. 2017b; and ...
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Network-based protein structural classification
Experimental determination of protein function is resource-consuming. As an alternative, computational prediction of protein function has received attention. In this context, protein structural classification (PSC) can help, by allowing for determining structural classes of currently unclassified proteins based on th...
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Deep Learning for Environmentally Robust Speech Recognition: An Overview of Recent Developments
Eliminating the negative effect of non-stationary environmental noise is a long-standing research topic for automatic speech recognition that stills remains an important challenge. Data-driven supervised approaches, including ones based on deep neural networks, have recently emerged as potential alternatives to tradi...
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The function field Sathé-Selberg formula in arithmetic progressions and `short intervals'
We use a function field analogue of a method of Selberg to derive an asymptotic formula for the number of (square-free) monic polynomials in $\mathbb{F}_q[X]$ of degree $n$ with precisely $k$ irreducible factors, in the limit as $n$ tends to infinity. We then adapt this method to count such polynomials in arithmetic ...
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Polynomial Relations Between Matrices of Graphs
We derive a correspondence between the eigenvalues of the adjacency matrix $A$ and the signless Laplacian matrix $Q$ of a graph $G$ when $G$ is $(d_1,d_2)$-biregular by using the relation $A^2=(Q-d_1I)(Q-d_2I)$. This motivates asking when it is possible to have $X^r=f(Y)$ for $f$ a polynomial, $r>0$, and $X,\ Y$ matr...
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Random active path model of deep neural networks with diluted binary synapses
Deep learning has become a powerful and popular tool for a variety of machine learning tasks. However, it is challenging to understand the mechanism of deep learning from a theoretical perspective. In this work, we propose a random active path model to study collective properties of deep neural networks with binary s...
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Super Generalized Central Limit Theorem: Limit distributions for sums of non-identical random variables with power-laws
In nature or societies, the power-law is present ubiquitously, and then it is important to investigate the mathematical characteristics of power-laws in the recent era of big data. In this paper we prove the superposition of non-identical stochastic processes with power-laws converges in density to a unique stable di...
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On the Adjacency Spectra of Hypertrees
We extend the results of Zhang et al. to show that $\lambda$ is an eigenvalue of a $k$-uniform hypertree $(k \geq 3)$ if and only if it is a root of a particular matching polynomial for a connected induced subtree. We then use this to provide a spectral characterization for power hypertrees. Notably, the situation is...
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Coherent structures and spectral energy transfer in turbulent plasma: a space-filter approach
Plasma turbulence at scales of the order of the ion inertial length is mediated by several mechanisms, including linear wave damping, magnetic reconnection, formation and dissipation of thin current sheets, stochastic heating. It is now understood that the presence of localized coherent structures enhances the dissip...
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Bayesian Sparsification of Recurrent Neural Networks
Recurrent neural networks show state-of-the-art results in many text analysis tasks but often require a lot of memory to store their weights. Recently proposed Sparse Variational Dropout eliminates the majority of the weights in a feed-forward neural network without significant loss of quality. We apply this techniqu...
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Malaria Likelihood Prediction By Effectively Surveying Households Using Deep Reinforcement Learning
We build a deep reinforcement learning (RL) agent that can predict the likelihood of an individual testing positive for malaria by asking questions about their household. The RL agent learns to determine which survey question to ask next and when to stop to make a prediction about their likelihood of malaria based on...
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Curious Minds Wonder Alike: Studying Multimodal Behavioral Dynamics to Design Social Scaffolding of Curiosity
Curiosity is the strong desire to learn or know more about something or someone. Since learning is often a social endeavor, social dynamics in collaborative learning may inevitably influence curiosity. There is a scarcity of research, however, focusing on how curiosity can be evoked in group learning contexts. Inspir...
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Communication Complexity of Discrete Fair Division
We initiate the study of the communication complexity of fair division with indivisible goods. We focus on some of the most well-studied fairness notions (envy-freeness, proportionality, and approximations thereof) and valuation classes (submodular, subadditive and unrestricted). Within these parameters, our results ...
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A proof of the Muir-Suffridge conjecture for convex maps of the unit ball in $\mathbb C^n$
We prove (and improve) the Muir-Suffridge conjecture for holomorphic convex maps. Namely, let $F:\mathbb B^n\to \mathbb C^n$ be a univalent map from the unit ball whose image $D$ is convex. Let $\mathcal S\subset \partial \mathbb B^n$ be the set of points $\xi$ such that $\lim_{z\to \xi}\|F(z)\|=\infty$. Then we prov...
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Continuous Functional Calculus for Quaternionic Bounded Normal Operators
In this article we give an approach to define continuous functional calculus for bounded quaternionic normal operators defined on a right quaternionic Hilbert space.
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Evolution of structure, magnetism and electronic transport in doped pyrochlore iridate Y$_2$Ir$_{2-x}$Ru$_{x}$O$_7$
The interplay between spin-orbit coupling (SOC) and electron correlation ($U$) is considered for many exotic phenomena in iridium oxides. We have investigated the evolution of structural, magnetic and electronic properties in pyrochlore iridate Y$_2$Ir$_{2-x}$Ru$_{x}$O$_7$ where the substitution of Ru has been aimed ...
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Cieliebak's Invariance Theorem and contact structures via connected sums
We present a strong version of Abouzaid's No-Escape Lemma, which allows varying contact forms on the boundary and which can be used instead of the Maximum Principle. Moreover, we give a clarified proof of Cieliebak's Invariance Theorem for Symplectic homology under subcritical handle attachment. Finally, we introduce...
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Learning Graph Weighted Models on Pictures
Graph Weighted Models (GWMs) have recently been proposed as a natural generalization of weighted automata over strings and trees to arbitrary families of labeled graphs (and hypergraphs). A GWM generically associates a labeled graph with a tensor network and computes a value by successive contractions directed by its...
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Solutions for biharmonic equations with steep potential wells
In this paper, we are concerned with the existence of least energy solutions for the following biharmonic equations: $$\Delta^2 u+(\lambda V(x)-\delta)u=|u|^{p-2}u \quad in\quad \mathbb{R}^N$$ where $N\geq 5, 2<p\leq\frac{2N}{N-4}, \lambda>0$ is a parameter, $V(x)$ is a nonnegative potential function with nonempty ze...
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General three and four person two color Hat Game
N distinguishable players are randomly fitted with a white or black hat, where the probabilities of getting a white or black hat may be different for each player, but known to all the players. All players guess simultaneously the color of their own hat observing only the hat colors of the other N-1 players. It is als...
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Pencilled regular parallelisms
Over any field $\mathbb K$, there is a bijection between regular spreads of the projective space ${\rm PG}(3,{\mathbb K})$ and $0$-secant lines of the Klein quadric in ${\rm PG}(5,{\mathbb K})$. Under this bijection, regular parallelisms of ${\rm PG}(3,{\mathbb K})$ correspond to hyperflock determining line sets (hfd...
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The loss surface of deep and wide neural networks
While the optimization problem behind deep neural networks is highly non-convex, it is frequently observed in practice that training deep networks seems possible without getting stuck in suboptimal points. It has been argued that this is the case as all local minima are close to being globally optimal. We show that t...
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Gap Acceptance During Lane Changes by Large-Truck Drivers-An Image-Based Analysis
This paper presents an analysis of rearward gap acceptance characteristics of drivers of large trucks in highway lane change scenarios. The range between the vehicles was inferred from camera images using the estimated lane width obtained from the lane tracking camera as the reference. Six-hundred lane change events ...
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Diversity-Sensitive Conditional Generative Adversarial Networks
We propose a simple yet highly effective method that addresses the mode-collapse problem in the Conditional Generative Adversarial Network (cGAN). Although conditional distributions are multi-modal (i.e., having many modes) in practice, most cGAN approaches tend to learn an overly simplified distribution where an inp...
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Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis
Global Style Tokens (GSTs) are a recently-proposed method to learn latent disentangled representations of high-dimensional data. GSTs can be used within Tacotron, a state-of-the-art end-to-end text-to-speech synthesis system, to uncover expressive factors of variation in speaking style. In this work, we introduce the...
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Independence of Sources in Social Networks
Online social networks are more and more studied. The links between users of a social network are important and have to be well qualified in order to detect communities and find influencers for example. In this paper, we present an approach based on the theory of belief functions to estimate the degrees of cognitive ...
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Classical and quantum systems: transport due to rare events
We review possible mechanisms for energy transfer based on 'rare' or 'non-perturbative' effects, in physical systems that present a many-body localized phenomenology. The main focus is on classical systems, with or without quenched disorder. For non-quantum systems, the breakdown of localization is usually not regard...
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An a Priori Exponential Tail Bound for k-Folds Cross-Validation
We consider a priori generalization bounds developed in terms of cross-validation estimates and the stability of learners. In particular, we first derive an exponential Efron-Stein type tail inequality for the concentration of a general function of n independent random variables. Next, under some reasonable notion of...
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Relaxed Wasserstein with Applications to GANs
We propose a novel class of statistical divergences called \textit{Relaxed Wasserstein} (RW) divergence. RW divergence generalizes Wasserstein divergence and is parametrized by a class of strictly convex and differentiable functions. We establish for RW divergence several probabilistic properties, which are critical ...
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Extremal functions for the Moser--Trudinger inequality of Adimurthi--Druet type in $W^{1,N}(\mathbb R^N)$
We study the existence and nonexistence of maximizers for variational problem concerning to the Moser--Trudinger inequality of Adimurthi--Druet type in $W^{1,N}(\mathbb R^N)$ \[ MT(N,\beta, \alpha) =\sup_{u\in W^{1,N}(\mathbb R^N), \|\nabla u\|_N^N + \|u\|_N^N\leq 1} \int_{\mathbb R^N} \Phi_N(\beta(1+\alpha \|u\|_N^N...
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Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning
This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using...
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The first order partial differential equations resolved with any derivatives
In this paper we discuss the first order partial differential equations resolved with any derivatives. At first, we transform the first order partial differential equation resolved with respect to a time derivative into a system of linear equations. Secondly, we convert it into a system of the first order linear part...
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The Genus-One Global Mirror Theorem for the Quintic Threefold
We prove the genus-one restriction of the all-genus Landau-Ginzburg/Calabi-Yau conjecture of Chiodo and Ruan, stated in terms of the geometric quantization of an explicit symplectomorphism determined by genus-zero invariants. This provides the first evidence supporting the higher-genus Landau-Ginzburg/Calabi-Yau corr...
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Self-Calibration of Mobile Manipulator Kinematic and Sensor Extrinsic Parameters Through Contact-Based Interaction
We present a novel approach for mobile manipulator self-calibration using contact information. Our method, based on point cloud registration, is applied to estimate the extrinsic transform between a fixed vision sensor mounted on a mobile base and an end effector. Beyond sensor calibration, we demonstrate that the me...
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Towards Open Data for the Citation Content Analysis
The paper presents first results of the CitEcCyr project funded by RANEPA. The project aims to create a source of open citation data for research papers written in Russian. Compared to existing sources of citation data, CitEcCyr is working to provide the following added values: a) a transparent and distributed archit...
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Real Time Collision Detection and Identification for Robotic Manipulators
The majority of everyday tasks involve interacting with unstructured environments. This implies that, in order for robots to be truly useful they must be able to handle contacts. This paper explores how a particle filter can be used to localize a contact point and estimate the external force. We demonstrate the capab...
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Time-dynamic inference for non-Markov transition probabilities under independent right-censoring
In this article, weak convergence of the general non-Markov state transition probability estimator by Titman (2015) is established which, up to now, has not been verified yet for other general non-Markov estimators. A similar theorem is shown for the bootstrap, yielding resampling-based inference methods for statisti...
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Multitask Learning with CTC and Segmental CRF for Speech Recognition
Segmental conditional random fields (SCRFs) and connectionist temporal classification (CTC) are two sequence labeling methods used for end-to-end training of speech recognition models. Both models define a transcription probability by marginalizing decisions about latent segmentation alternatives to derive a sequence...
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Electronic structure and non-linear optical properties of organic photovoltaic systems with potential applications on solar cell devices: A DFT approach
The use of eco-friendly materials for the environment has been addressed as a critical issue in the development of systems for renewable energy applications. In this regard, the investigation of organic photovoltaic (OPV) molecules for the implementation in solar cells, has become a subject of intense research in the...
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Connectivity Learning in Multi-Branch Networks
While much of the work in the design of convolutional networks over the last five years has revolved around the empirical investigation of the importance of depth, filter sizes, and number of feature channels, recent studies have shown that branching, i.e., splitting the computation along parallel but distinct thread...
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A multiple timescales approach to bridging spiking- and population-level dynamics
A rigorous bridge between spiking-level and macroscopic quantities is an on-going and well-developed story for asynchronously firing neurons, but focus has shifted to include neural populations exhibiting varying synchronous dynamics. Recent literature has used the Ott--Antonsen ansatz (2008) to great effect, allowin...
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Two-Stream RNN/CNN for Action Recognition in 3D Videos
The recognition of actions from video sequences has many applications in health monitoring, assisted living, surveillance, and smart homes. Despite advances in sensing, in particular related to 3D video, the methodologies to process the data are still subject to research. We demonstrate superior results by a system w...
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DNA translocation through alpha-haemolysin nano-pores with potential application to macromolecular data storage
Digital information can be encoded in the building-block sequence of macromolecules, such as RNA and single-stranded DNA. Methods of "writing" and "reading" macromolecular strands are currently available, but they are slow and expensive. In an ideal molecular data storage system, routine operations such as write, rea...
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Contextuality from missing and versioned data
Traditionally categorical data analysis (e.g. generalized linear models) works with simple, flat datasets akin to a single table in a database with no notion of missing data or conflicting versions. In contrast, modern data analysis must deal with distributed databases with many partial local tables that need not alw...
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Reveal the Mantle and K-40 Components of Geoneutrinos with Liquid Scintillator Cherenkov Neutrino Detectors
In this article we present an idea of using liquid scintillator Cherenkov neutrino detectors to detect the mantle and K-40 components of geoneutrinos. Liquid scintillator Cherenkov detectors feature both energy and direction measurement for charge particles. Geoneutrinos can be detected with the elastic scattering pr...
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Improving Stock Movement Prediction with Adversarial Training
This paper contributes a new machine learning solution for stock movement prediction, which aims to predict whether the price of a stock will be up or down in the near future. The key novelty is that we propose to employ adversarial training to improve the generalization of a recurrent neural network model. The ratio...
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Weighted estimates for positive operators and Doob maximal operators on filtered measure spaces
We characterize strong type and weak type inequalities with two weights for positive operators on filtered measure spaces. These estimates are probabilistic analogues of two-weight inequalities for positive operators associated to the dyadic cubes in $\mathbb R^n$ due to Lacey, Sawyer and Uriarte-Tuero \cite{LaSaUr}....
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Sentiment Identification in Code-Mixed Social Media Text
Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying the presence of sentiment in the text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as posi...
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SVSGAN: Singing Voice Separation via Generative Adversarial Network
Separating two sources from an audio mixture is an important task with many applications. It is a challenging problem since only one signal channel is available for analysis. In this paper, we propose a novel framework for singing voice separation using the generative adversarial network (GAN) with a time-frequency m...
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Microscopic theory of refractive index applied to metamaterials: Effective current response tensor corresponding to standard relation $n^2 = \varepsilon_{\mathrm{eff}} μ_{\mathrm{eff}}$
In this article, we first derive the wavevector- and frequency-dependent, microscopic current response tensor which corresponds to the "macroscopic" ansatz $\vec D = \varepsilon_0 \varepsilon_{\mathrm{eff}} \vec E$ and $\vec B = \mu_0 \mu_{\mathrm{eff}} \vec H$ with wavevector- and frequency-independent, "effective" ...
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Semidefinite Relaxation-Based Optimization of Multiple-Input Wireless Power Transfer Systems
An optimization procedure for multi-transmitter (MISO) wireless power transfer (WPT) systems based on tight semidefinite relaxation (SDR) is presented. This method ensures physical realizability of MISO WPT systems designed via convex optimization -- a robust, semi-analytical and intuitive route to optimizing such sy...
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Counterfactual Reasoning with Disjunctive Knowledge in a Linear Structural Equation Model
We consider the problem of estimating counterfactual quantities when prior knowledge is available in the form of disjunctive statements. These include disjunction of conditions (e.g., "the patient is more than 60 years of age") as well as disjuction of antecedants (e.g., "had the patient taken either drug A or drug B...
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Post-edit Analysis of Collective Biography Generation
Text generation is increasingly common but often requires manual post-editing where high precision is critical to end users. However, manual editing is expensive so we want to ensure this effort is focused on high-value tasks. And we want to maintain stylistic consistency, a particular challenge in crowd settings. We...
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The SysML/KAOS Domain Modeling Approach
A means of building safe critical systems consists of formally modeling the requirements formulated by stakeholders and ensuring their consistency with respect to application domain properties. This paper proposes a metamodel for an ontology modeling formalism based on OWL and PLIB. This modeling formalism is part of...
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Neighborhood selection with application to social networks
The topic of this paper is modeling and analyzing dependence in stochastic social networks. Using a latent variable block model allows the analysis of dependence between blocks via the analysis of a latent graphical model. Our approach to the analysis of the graphical model then is based on the idea underlying the ne...
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Spinless hourglass nodal-line semimetals
Nodal-line semimetals, one of the topological semimetals, have degeneracy along nodal lines where the band gap is closed. In many cases, the nodal lines appear accidentally, and in such cases it is impossible to determine whether the nodal lines appear or not, only from the crystal symmetry and the electron filling. ...
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Average-radius list-recovery of random linear codes: it really ties the room together
We analyze the list-decodability, and related notions, of random linear codes. This has been studied extensively before: there are many different parameter regimes and many different variants. Previous works have used complementary styles of arguments---which each work in their own parameter regimes but not in others...
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Insights on representational similarity in neural networks with canonical correlation
Comparing different neural network representations and determining how representations evolve over time remain challenging open questions in our understanding of the function of neural networks. Comparing representations in neural networks is fundamentally difficult as the structure of representations varies greatly,...
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Complex Hadamard matrices with noncommutative entries
We axiomatize and study the matrices of type $H\in M_N(A)$, having unitary entries, $H_{ij}\in U(A)$, and whose rows and columns are subject to orthogonality type conditions. Here $A$ can be any $C^*$-algebra, for instance $A=\mathbb C$, where we obtain the usual complex Hadamard matrices, or $A=C(X)$, where we obtai...
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Subregular Complexity and Deep Learning
This paper argues that the judicial use of formal language theory and grammatical inference are invaluable tools in understanding how deep neural networks can and cannot represent and learn long-term dependencies in temporal sequences. Learning experiments were conducted with two types of Recurrent Neural Networks (R...
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Observation of oscillatory relaxation in the Sn-terminated surface of epitaxial rock-salt SnSe $\{111\}$ topological crystalline insulator
Topological crystalline insulators have been recently predicted and observed in rock-salt structure SnSe $\{111\}$ thin films. Previous studies have suggested that the Se-terminated surface of this thin film with hydrogen passivation, has a reduced surface energy and is thus a preferred configuration. In this paper, ...
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Chaotic Dynamics of Inner Ear Hair Cells
Experimental records of active bundle motility are used to demonstrate the presence of a low-dimensional chaotic attractor in hair cell dynamics. Dimensionality tests from dynamic systems theory are applied to estimate the number of independent variables sufficient for modeling the hair cell response. Poincare maps a...
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From Dirac semimetals to topological phases in three dimensions: a coupled wire construction
Weyl and Dirac (semi)metals in three dimensions have robust gapless electronic band structures. Their massless single-body energy spectra are protected by symmetries such as lattice translation, (screw) rotation and time reversal. In this manuscript, we discuss many-body interactions in these systems. We focus on str...
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CHIME FRB: An application of FFT beamforming for a radio telescope
We have developed FFT beamforming techniques for the CHIME radio telescope, to search for and localize the astrophysical signals from Fast Radio Bursts (FRBs) over a large instantaneous field-of-view (FOV) while maintaining the full angular resolution of CHIME. We implement a hybrid beamforming pipeline in a GPU corr...
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MRI-PET Registration with Automated Algorithm in Pre-clinical Studies
Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) automatic 3-D registration is implemented and validated for small animal image volumes so that the high-resolution anatomical MRI information can be fused with the low spatial resolution of functional PET information for the localization of lesio...
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Generalization of Special Functions and its Applications to Multiplicative and Ordinary Fractional Derivatives
The goal of this paper is to extend the classical and multiplicative fractional derivatives. For this purpose, it is introduced the new extended modified Bessel function and also given an important relation between this new function I(v,q;x) and the confluent hypergeometric function. Besides, it is used to generalize...
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Incident Light Frequency-based Image Defogging Algorithm
Considering the problem of color distortion caused by the defogging algorithm based on dark channel prior, an improved algorithm was proposed to calculate the transmittance of all channels respectively. First, incident light frequency's effect on the transmittance of various color channels was analyzed according to t...
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Tangent points of d-lower content regular sets and $β$ numbers
We present a generalisation of C. Bishop and P. Jones' result in [BJ1], where they give a characterisation of the tangent points of a Jordan curve in terms of $\beta$ numbers. Instead of the $L^\infty$ Jones' $\beta$ numbers, we use an averaged version of them, firstly introduced by J. Azzam and R. Schul in [AS1]. A ...
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Quasi-Oracle Estimation of Heterogeneous Treatment Effects
Flexible estimation of heterogeneous treatment effects lies at the heart of many statistical challenges, such as personalized medicine and optimal resource allocation. In this paper, we develop a general class of two-step algorithms for heterogeneous treatment effect estimation in observational studies. We first esti...
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Adaptive clustering procedure for continuous gravitational wave searches
In hierarchical searches for continuous gravitational waves, clustering of candidates is an important postprocessing step because it reduces the number of noise candidates that are followed-up at successive stages [1][7][12]. Previous clustering procedures bundled together nearby candidates ascribing them to the same...
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The heat trace for the drifting Laplacian and Schrödinger operators on manifolds
We study the heat trace for both the drifting Laplacian as well as Schrödinger operators on compact Riemannian manifolds. In the case of a finite regularity potential or weight function, we prove the existence of a partial (six term) asymptotic expansion of the heat trace for small times as well as a suitable remaind...
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Generative Adversarial Networks for Black-Box API Attacks with Limited Training Data
As online systems based on machine learning are offered to public or paid subscribers via application programming interfaces (APIs), they become vulnerable to frequent exploits and attacks. This paper studies adversarial machine learning in the practical case when there are rate limitations on API calls. The adversar...
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Coloring ($P_6$, diamond, $K_4$)-free graphs
We show that every ($P_6$, diamond, $K_4$)-free graph is $6$-colorable. Moreover, we give an example of a ($P_6$, diamond, $K_4$)-free graph $G$ with $\chi(G) = 6$. This generalizes some known results in the literature.
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Habitable Climate Scenarios for Proxima Centauri b With a Dynamic Ocean
The nearby exoplanet Proxima Centauri b will be a prime future target for characterization, despite questions about its retention of water. Climate models with static oceans suggest that an Earth-like Proxima b could harbor a small dayside region of surface liquid water at fairly warm temperatures despite its weak in...
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Level bounds for exceptional quantum subgroups in rank two
There is a long-standing belief that the modular tensor categories $\mathcal{C}(\mathfrak{g},k)$, for $k\in\mathbb{Z}_{\geq1}$ and finite-dimensional simple complex Lie algebras $\mathfrak{g}$, contain exceptional connected étale algebras at only finitely many levels $k$. This premise has known implications for the s...
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Convolution Forgetting Curve Model for Repeated Learning
Most of mathematic forgetting curve models fit well with the forgetting data under the learning condition of one time rather than repeated. In the paper, a convolution model of forgetting curve is proposed to simulate the memory process during learning. In this model, the memory ability (i.e. the central procedure in...
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Efficient computation of multidimensional theta functions
An important step in the efficient computation of multi-dimensional theta functions is the construction of appropriate symplectic transformations for a given Riemann matrix assuring a rapid convergence of the theta series. An algorithm is presented to approximately map the Riemann matrix to the Siegel fundamental dom...
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The careless use of language in quantum information
An imperative aspect of modern science is that scientific institutions act for the benefit of a common scientific enterprise, rather than for the personal gain of individuals within them. This implies that science should not perpetuate existing or historical unequal social orders. Some scientific terminology, though,...
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Origin of meteoritic stardust unveiled by a revised proton-capture rate of $^{17}$O
Stardust grains recovered from meteorites provide high-precision snapshots of the isotopic composition of the stellar environment in which they formed. Attributing their origin to specific types of stars, however, often proves difficult. Intermediate-mass stars of 4-8 solar masses are expected to contribute a large f...
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Some Connections Between Cycles and Permutations that Fix a Set and Touchard Polynomials and Covers of Multisets
We present a new proof of a fundamental result concerning cycles of random permutations which gives some intuition for the connection between Touchard polynomials and the Poisson distribution. We also introduce a rather novel permutation statistic and study its distribution. This quantity, indexed by $m$, is the numb...
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Generalizing the first-difference correlated random walk for marine animal movement data
Animal telemetry data are often analysed with discrete time movement models assuming rotation in the movement. These models are defined with equidistant distant time steps. However, telemetry data from marine animals are observed irregularly. To account for irregular data, a time-irregularised first-difference correl...
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Low spin wave damping in the insulating chiral magnet Cu$_{2}$OSeO$_{3}$
Chiral magnets with topologically nontrivial spin order such as Skyrmions have generated enormous interest in both fundamental and applied sciences. We report broadband microwave spectroscopy performed on the insulating chiral ferrimagnet Cu$_{2}$OSeO$_{3}$. For the damping of magnetization dynamics we find a remarka...
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Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation
For human pose estimation in monocular images, joint occlusions and overlapping upon human bodies often result in deviated pose predictions. Under these circumstances, biologically implausible pose predictions may be produced. In contrast, human vision is able to predict poses by exploiting geometric constraints of j...
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Deep Learning from Shallow Dives: Sonar Image Generation and Training for Underwater Object Detection
Among underwater perceptual sensors, imaging sonar has been highlighted for its perceptual robustness underwater. The major challenge of imaging sonar, however, arises from the difficulty in defining visual features despite limited resolution and high noise levels. Recent developments in deep learning provide a power...
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Large-scale Feature Selection of Risk Genetic Factors for Alzheimer's Disease via Distributed Group Lasso Regression
Genome-wide association studies (GWAS) have achieved great success in the genetic study of Alzheimer's disease (AD). Collaborative imaging genetics studies across different research institutions show the effectiveness of detecting genetic risk factors. However, the high dimensionality of GWAS data poses significant c...
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Strong Functional Representation Lemma and Applications to Coding Theorems
This paper shows that for any random variables $X$ and $Y$, it is possible to represent $Y$ as a function of $(X,Z)$ such that $Z$ is independent of $X$ and $I(X;Z|Y)\le\log(I(X;Y)+1)+4$ bits. We use this strong functional representation lemma (SFRL) to establish a bound on the rate needed for one-shot exact channel ...
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Emergence of Structured Behaviors from Curiosity-Based Intrinsic Motivation
Infants are experts at playing, with an amazing ability to generate novel structured behaviors in unstructured environments that lack clear extrinsic reward signals. We seek to replicate some of these abilities with a neural network that implements curiosity-driven intrinsic motivation. Using a simple but ecologicall...
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Predicting shim gaps in aircraft assembly with machine learning and sparse sensing
A modern aircraft may require on the order of thousands of custom shims to fill gaps between structural components in the airframe that arise due to manufacturing tolerances adding up across large structures. These shims are necessary to eliminate gaps, maintain structural performance, and minimize pull-down forces r...
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Multivariate central limit theorems for Rademacher functionals with applications
Quantitative multivariate central limit theorems for general functionals of possibly non-symmetric and non-homogeneous infinite Rademacher sequences are proved by combining discrete Malliavin calculus with the smart path method for normal approximation. In particular, a discrete multivariate second-order Poincaré ine...
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Modification of Social Dominance in Social Networks by Selective Adjustment of Interpersonal Weights
According to the DeGroot-Friedkin model of a social network, an individual's social power evolves as the network discusses individual opinions over a sequence of issues. Under mild assumptions on the connectivity of the network, the social power of every individual converges to a constant strictly positive value as t...
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On Abrikosov Lattice Solutions of the Ginzburg-Landau Equations
We prove existence of Abrikosov vortex lattice solutions of the Ginzburg-Landau equations of superconductivity, with multiple magnetic flux quanta per a fundamental cell. We also revisit the existence proof for the Abrikosov vortex lattices, streamlining some arguments and providing some essential details missing in ...
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$0.7-2.5~μ$m spectra of Hilda asteroids
The Hilda asteroids are primitive bodies in resonance with Jupiter whose origin and physical properties are not well understood. Current models posit that these asteroids formed in the outer Solar System and were scattered along with the Jupiter Trojans into their present-day positions during a chaotic episode of dyn...
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Ground-state properties of unitary bosons: from clusters to matter
The properties of cold Bose gases at unitarity have been extensively investigated in the last few years both theoretically and experimentally. In this paper we use a family of interactions tuned to two-body unitarity and very weak three-body binding to demonstrate the universal properties of both clusters and matter....
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Lower bounds on the Noether number
The best known method to give a lower bound for the Noether number of a given finite group is to use the fact that it is greater than or equal to the Noether number of any of the subgroups or factor groups. The results of the present paper show in particular that these inequalities are strict for proper subgroups or ...
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A Survey on the Adoption of Cloud Computing in Education Sector
Education is a key factor in ensuring economic growth, especially for countries with growing economies. Today, students have become more technologically savvy as teaching and learning uses more advance technology day in, day out. Due to virtualize resources through the Internet, as well as dynamic scalability, cloud ...
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Deep Neural Networks as Gaussian Processes
It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width. This correspondence enables exact Bayesian inference for infinite width neural networks on regression tasks by means of e...
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