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Atomic and electronic structures of stable linear carbon chains on Ag-nanoparticles
In this work, we report X-ray photoelectron (XPS) and valence band (VB) spectroscopy measurements of surfactant-free silver nanoparticles and silver/linear carbon chains (Ag@LCC) structures prepared by pulse laser ablation (PLA) in water. Our measurements demonstrate significant oxidation only on the surfaces of the ...
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The unsaturated flow in porous media with dynamic capillary pressure
In this paper we consider a degenerate pseudoparabolic equation for the wetting saturation of an unsaturated two-phase flow in porous media with dynamic capillary pressure-saturation relationship where the relaxation parameter depends on the saturation. Following the approach given in [12] the existence of a weak sol...
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Dissociation of one-dimensional matter-wave breathers due to quantum many-body effects
We use the ab initio Bethe Ansatz dynamics to predict the dissociation of one-dimensional cold-atom breathers that are created by a quench from a fundamental soliton. We find that the dissociation is a robust quantum many-body effect, while in the mean-field (MF) limit the dissociation is forbidden by the integrabili...
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Human Eye Visual Hyperacuity: A New Paradigm for Sensing?
The human eye appears to be using a low number of sensors for image capturing. Furthermore, regarding the physical dimensions of cones-photoreceptors responsible for the sharp central vision-, we may realize that these sensors are of a relatively small size and area. Nonetheless, the eye is capable to obtain high res...
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Structured Matrix Estimation and Completion
We study the problem of matrix estimation and matrix completion under a general framework. This framework includes several important models as special cases such as the gaussian mixture model, mixed membership model, bi-clustering model and dictionary learning. We consider the optimal convergence rates in a minimax s...
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Performance evaluation of PSD for silicon ECAL
We are developing position sensitive silicon detectors (PSD) which have an electrode at each of four corners so that the incident position of a charged particle can be obtained using signals from the electrodes. It is expected that the position resolution the electromagnetic calorimeter (ECAL) of the ILD detector wil...
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Online Improper Learning with an Approximation Oracle
We revisit the question of reducing online learning to approximate optimization of the offline problem. In this setting, we give two algorithms with near-optimal performance in the full information setting: they guarantee optimal regret and require only poly-logarithmically many calls to the approximation oracle per ...
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TC^0 circuits for algorithmic problems in nilpotent groups
Recently, Macdonald et. al. showed that many algorithmic problems for finitely generated nilpotent groups including computation of normal forms, the subgroup membership problem, the conjugacy problem, and computation of subgroup presentations can be done in Logspace. Here we follow their approach and show that all th...
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Alperin-McKay natural correspondences in solvable and symmetric groups for the prime $p=2$
Let $G$ be a finite solvable or symmetric group and let $B$ be a $2$-block of $G$. We construct a canonical correspondence between the irreducible characters of height zero in $B$ and those in its Brauer first main correspondent. For symmetric groups our bijection is compatible with restriction of characters.
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Reversible temperature exchange upon thermal contact
According to a well-known principle of thermodynamics, the transfer of heat between two bodies is reversible when their temperatures are infinitesimally close. As we demonstrate, a little-known alternative exists: two bodies with temperatures different by an arbitrary amount can completely exchange their temperatures...
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On consequences of measurements of turbulent Lewis number from observations
Almost all parameterizations of turbulence in NWP models and GCM make the assumption of equality of exchange coefficients for heat $K_h$ and water $K_w$. However, large uncertainties exists in old papers published in the 1950s, 1960s and 1970s, where the turbulent Lewis number Le_t $= K_h / K_w$ have been evaluated f...
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Complex Networks Analysis for Software Architecture: an Hibernate Call Graph Study
Recent advancements in complex network analysis are encouraging and may provide useful insights when applied in software engineering domain, revealing properties and structures that cannot be captured by traditional metrics. In this paper, we analyzed the topological properties of Hibernate library, a well-known Java...
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The Impact of Local Geometry and Batch Size on Stochastic Gradient Descent for Nonconvex Problems
In several experimental reports on nonconvex optimization problems in machine learning, stochastic gradient descent (SGD) was observed to prefer minimizers with flat basins in comparison to more deterministic methods, yet there is very little rigorous understanding of this phenomenon. In fact, the lack of such work h...
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Cosmology and the Origin of the Universe: Historical and Conceptual Perspectives
From a modern perspective cosmology is a historical science in so far that it deals with the development of the universe since its origin some 14 billion years ago. The origin itself may not be subject to scientific analysis and explanation. Nonetheless, there are theories that claim to explain the ultimate origin or...
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Semisimple and separable algebras in multi-fusion categories
We give a classification of semisimple and separable algebras in a multi-fusion category over an arbitrary field in analogy to Wedderben-Artin theorem in classical algebras. It turns out that, if the multi-fusion category admits a semisimple Drinfeld center, the only obstruction to the separability of a semisimple al...
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Ensemble dependence of fluctuations and the canonical/micro-canonical equivalence of ensembles
We study the equivalence of microcanonical and canonical ensembles in continuous systems, in the sense of the convergence of the corresponding Gibbs measures. This is obtained by proving a local central limit theorem and a local large deviations principle. As an application we prove a formula due to Lebowitz-Percus-V...
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Algorithms for Positive Semidefinite Factorization
This paper considers the problem of positive semidefinite factorization (PSD factorization), a generalization of exact nonnegative matrix factorization. Given an $m$-by-$n$ nonnegative matrix $X$ and an integer $k$, the PSD factorization problem consists in finding, if possible, symmetric $k$-by-$k$ positive semidefi...
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Remarks about Synthetic Upper Ricci Bounds for Metric Measure Spaces
We discuss various characterizations of synthetic upper Ricci bounds for metric measure spaces in terms of heat flow, entropy and optimal transport. In particular, we present a characterization in terms of semiconcavity of the entropy along certain Wasserstein geodesics which is stable under convergence of mm-spaces....
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Structured Deep Hashing with Convolutional Neural Networks for Fast Person Re-identification
Given a pedestrian image as a query, the purpose of person re-identification is to identify the correct match from a large collection of gallery images depicting the same person captured by disjoint camera views. The critical challenge is how to construct a robust yet discriminative feature representation to capture ...
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Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
Recently, methods have been proposed that perform texture synthesis and style transfer by using convolutional neural networks (e.g. Gatys et al. [2015,2016]). These methods are exciting because they can in some cases create results with state-of-the-art quality. However, in this paper, we show these methods also have...
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Dynamical and Topological Aspects of Consensus Formation in Complex Networks
The present work analyses a particular scenario of consensus formation, where the individuals navigate across an underlying network defining the topology of the walks. The consensus, associated to a given opinion coded as a simple messages, is generated by interactions during the agent's walk and manifest itself in t...
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Measurement of authorship by publications: a normative approach
Administrators in all academic organizations across the world have to deal with the unenviable task of comparing researchers on the basis of their academic contributions. This job is further complicated by the need for comparing single author publication with joint author publications. Unfortunately, however, there i...
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Ordering Garside groups
We introduce a condition on Garside groups that we call Dehornoy structure. An iteration of such a structure leads to a left order on the group. We show conditions for a Garside group to admit a Dehornoy structure, and we apply these criteria to prove that the Artin groups of type A and I 2 (m), m $\ge$ 4, have Dehor...
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Self-Taught Support Vector Machine
In this paper, a new approach for classification of target task using limited labeled target data as well as enormous unlabeled source data is proposed which is called self-taught learning. The target and source data can be drawn from different distributions. In the previous approaches, covariate shift assumption is ...
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Continuum of classical-field ensembles from canonical to grand canonical and the onset of their equivalence
The canonical and grand-canonical ensembles are two usual marginal cases for ultracold Bose gases, but real collections of experimental runs commonly have intermediate properties. Here we study the continuum of intermediate cases, and look into the appearance of ensemble equivalence as interaction rises for mesoscopi...
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Extending Partial Representations of Unit Circular-arc Graphs
The partial representation extension problem, introduced by Klavík et al. (2011), generalizes the recognition problem. In this short note we show that this problem is NP-complete for unit circular-arc graphs.
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Lipschitz continuity of quasiconformal mappings and of the solutions to second order elliptic PDE with respect to the distance ratio metric
The main aim of this paper is to study the Lipschitz continuity of certain $(K, K')$-quasiconformal mappings with respect to the distance ratio metric, and the Lipschitz continuity of the solution of a quasilinear differential equation with respect to the distance ratio metric.
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On Estimation of Conditional Modes Using Multiple Quantile Regressions
We propose an estimation method for the conditional mode when the conditioning variable is high-dimensional. In the proposed method, we first estimate the conditional density by solving quantile regressions multiple times. We then estimate the conditional mode by finding the maximum of the estimated conditional densi...
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Kondo destruction in a quantum paramagnet with magnetic frustration
We report results of isothermal magnetotransport and susceptibility measurements at elevated magnetic fields B down to very low temperatures T on high-quality single crystals of the frustrated Kondo-lattice system CePdAl. They reveal a B*(T) line within the paramagnetic part of the phase diagram. This line denotes a ...
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Deep supervised learning using local errors
Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requires waiting for the propagation of error signals from higher layers. Learning using delayed and non-local errors makes it hard to reconci...
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Theoretical and Computational Guarantees of Mean Field Variational Inference for Community Detection
The mean field variational Bayes method is becoming increasingly popular in statistics and machine learning. Its iterative Coordinate Ascent Variational Inference algorithm has been widely applied to large scale Bayesian inference. See Blei et al. (2017) for a recent comprehensive review. Despite the popularity of th...
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The Chandra Deep Field South as a test case for Global Multi Conjugate Adaptive Optics
The era of the next generation of giant telescopes requires not only the advent of new technologies but also the development of novel methods, in order to exploit fully the extraordinary potential they are built for. Global Multi Conjugate Adaptive Optics (GMCAO) pursues this approach, with the goal of achieving good...
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Probabilistic Causal Analysis of Social Influence
Mastering the dynamics of social influence requires separating, in a database of information propagation traces, the genuine causal processes from temporal correlation, i.e., homophily and other spurious causes. However, most studies to characterize social influence, and, in general, most data-science analyses focus ...
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Advanced reduced-order models for moisture diffusion in porous media
It is of great concern to produce numerically efficient methods for moisture diffusion through porous media, capable of accurately calculate moisture distribution with a reduced computational effort. In this way, model reduction methods are promising approaches to bring a solution to this issue since they do not degr...
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Gapless quantum spin chains: multiple dynamics and conformal wavefunctions
We study gapless quantum spin chains with spin 1/2 and 1: the Fredkin and Motzkin models. Their entangled groundstates are known exactly but not their excitation spectra. We first express the groundstates in the continuum which allows for the calculation of spin and entanglement properties in a unified fashion. Doing...
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Real-time public transport service-level monitoring using passive WiFi: a spectral clustering approach for train timetable estimation
A new area in which passive WiFi analytics have promise for delivering value is the real-time monitoring of public transport systems. One example is determining the true (as opposed to the published) timetable of a public transport system in real-time. In most cases, there are no other publicly-available sources for ...
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Finding Network Motifs in Large Graphs using Compression as a Measure of Relevance
We introduce a new method for finding network motifs: interesting or informative subgraph patterns in a network. Current methods for finding motifs rely on the frequency of the motif: specifically, subgraphs are motifs when their frequency in the data is high compared to the expected frequency under a null model. To ...
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A Bag-of-Words Equivalent Recurrent Neural Network for Action Recognition
The traditional bag-of-words approach has found a wide range of applications in computer vision. The standard pipeline consists of a generation of a visual vocabulary, a quantization of the features into histograms of visual words, and a classification step for which usually a support vector machine in combination wi...
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Limit on graviton mass from galaxy cluster Abell 1689
To date, the only limit on graviton mass using galaxy clusters was obtained by Goldhaber and Nieto in 1974, using the fact that the orbits of galaxy clusters are bound and closed, and extend up to 580 kpc. From positing that only a Newtonian potential gives rise to such stable bound orbits, a limit on the graviton ma...
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Enhancing the Regularization Effect of Weight Pruning in Artificial Neural Networks
Artificial neural networks (ANNs) may not be worth their computational/memory costs when used in mobile phones or embedded devices. Parameter-pruning algorithms combat these costs, with some algorithms capable of removing over 90% of an ANN's weights without harming the ANN's performance. Removing weights from an ANN...
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The Augustin Center and The Sphere Packing Bound For Memoryless Channels
For any channel with a convex constraint set and finite Augustin capacity, existence of a unique Augustin center and associated Erven-Harremoes bound are established. Augustin-Legendre capacity, center, and radius are introduced and proved to be equal to the corresponding Renyi-Gallager entities. Sphere packing bound...
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SuperMinHash - A New Minwise Hashing Algorithm for Jaccard Similarity Estimation
This paper presents a new algorithm for calculating hash signatures of sets which can be directly used for Jaccard similarity estimation. The new approach is an improvement over the MinHash algorithm, because it has a better runtime behavior and the resulting signatures allow a more precise estimation of the Jaccard ...
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On catastrophic forgetting and mode collapse in Generative Adversarial Networks
Generative Adversarial Networks (GAN) are one of the most prominent tools for learning complicated distributions. However, problems such as mode collapse and catastrophic forgetting, prevent GAN from learning the target distribution. These problems are usually studied independently from each other. In this paper, we ...
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Metamodel Construction for Sensitivity Analysis
We propose to estimate a metamodel and the sensitivity indices of a complex model m in the Gaussian regression framework. Our approach combines methods for sensitivity analysis of complex models and statistical tools for sparse non-parametric estimation in multivariate Gaussian regression model. It rests on the const...
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Dynamics of the brain extracellular matrix governed by interactions with neural cells
Neuronal and glial cells release diverse proteoglycans and glycoproteins, which aggregate in the extracellular space and form the extracellular matrix (ECM) that may in turn regulate major cellular functions. Brain cells also release extracellular proteases that may degrade the ECM, and both synthesis and degradation...
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AutoPerf: A Generalized Zero-Positive Learning System to Detect Software Performance Anomalies
We present AutoPerf, a generalized software performance regression diagnosis system. AutoPerf uses autoencoders, an unsupervised learning technique, and hardware performance counters to learn the performance signatures of a program. It then uses this knowledge to identify when newer versions of the program suffer fro...
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Building a bridge between Classical and Quantum Mechanics
The way Quantum Mechanics (QM) is introduced to people used to Classical Mechanics (CM) is by a complete change of the general methodology) despite QM historically stemming from CM as a means to explain experimental results. Therefore, it is desirable to build a bridge from CM to QM. This paper presents a generalizat...
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Size-Independent Sample Complexity of Neural Networks
We study the sample complexity of learning neural networks, by providing new bounds on their Rademacher complexity assuming norm constraints on the parameter matrix of each layer. Compared to previous work, these complexity bounds have improved dependence on the network depth, and under some additional assumptions, a...
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Copula Variational Bayes inference via information geometry
Variational Bayes (VB), also known as independent mean-field approximation, has become a popular method for Bayesian network inference in recent years. Its application is vast, e.g. in neural network, compressed sensing, clustering, etc. to name just a few. In this paper, the independence constraint in VB will be rel...
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Effect of increasing disorder on domains of the two-dimensional Coulomb glass
We have studied a two dimensional lattice model of Coulomb glass for a wide range of disorders at $T\sim 0$. The system was first annealed using Monte Carlo simulation. Further minimization of the total energy of the system was done using Baranovskii et al algorithm followed by cluster flipping to obtain the pseudo g...
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A dynamic network model with persistent links and node-specific latent variables, with an application to the interbank market
We propose a dynamic network model where two mechanisms control the probability of a link between two nodes: (i) the existence or absence of this link in the past, and (ii) node-specific latent variables (dynamic fitnesses) describing the propensity of each node to create links. Assuming a Markov dynamics for both me...
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Predicting stock market movements using network science: An information theoretic approach
A stock market is considered as one of the highly complex systems, which consists of many components whose prices move up and down without having a clear pattern. The complex nature of a stock market challenges us on making a reliable prediction of its future movements. In this paper, we aim at building a new method ...
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Bi-monotonic independence for pairs of algebras
In this article, the notion of bi-monotonic independence is introduced as an extension of monotonic independence to the two-faced framework for a family of pairs of algebras in a non-commutative space. The associated cumulants are defined and a moment-cumulant formula is derived in the bi-monotonic setting. In genera...
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Evidence of Significant Energy Input in the Late Phase of a Solar Flare from NuSTAR X-Ray Observations
We present observations of the occulted active region AR12222 during the third {\em NuSTAR} solar campaign on 2014 December 11, with concurrent {\em SDO/}AIA and {\em FOXSI-2} sounding rocket observations. The active region produced a medium size solar flare one day before the observations, at $\sim18$UT on 2014 Dece...
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Gender Bias in Sharenting: Both Men and Women Mention Sons More Often Than Daughters on Social Media
Gender inequality starts before birth. Parents tend to prefer boys over girls, which is manifested in reproductive behavior, marital life, and parents' pastimes and investments in their children. While social media and sharing information about children (so-called "sharenting") have become an integral part of parenth...
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Spatial disease mapping using Directed Acyclic Graph Auto-Regressive (DAGAR) models
Hierarchical models for regionally aggregated disease incidence data commonly involve region specific latent random effects that are modelled jointly as having a multivariate Gaussian distribution. The covariance or precision matrix incorporates the spatial dependence between the regions. Common choices for the preci...
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Bag-of-Words Method Applied to Accelerometer Measurements for the Purpose of Classification and Energy Estimation
Accelerometer measurements are the prime type of sensor information most think of when seeking to measure physical activity. On the market, there are many fitness measuring devices which aim to track calories burned and steps counted through the use of accelerometers. These measurements, though good enough for the av...
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Investigation of the commensurate magnetic structure in heavy fermion CePt2In7 using magnetic resonant X-ray diffraction
We investigated the magnetic structure of the heavy fermion compound CePt$_2$In$_7$ below $T_N~=5.34(2)$ K using magnetic resonant X-ray diffraction at ambient pressure. The magnetic order is characterized by a commensurate propagation vector ${k}_{1/2}~=~\left( \frac{1}{2} , \frac{1}{2}, \frac{1}{2}\right)$ with spi...
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Constant-Time Predictive Distributions for Gaussian Processes
One of the most compelling features of Gaussian process (GP) regression is its ability to provide well-calibrated posterior distributions. Recent advances in inducing point methods have sped up GP marginal likelihood and posterior mean computations, leaving posterior covariance estimation and sampling as the remainin...
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Analysis of Thompson Sampling for Gaussian Process Optimization in the Bandit Setting
We consider the global optimization of a function over a continuous domain. At every evaluation attempt, we can observe the function at a chosen point in the domain and we reap the reward of the value observed. We assume that drawing these observations are expensive and noisy. We frame it as a continuum-armed bandit ...
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Dependency Graph Approach for Multiprocessor Real-Time Synchronization
Over the years, many multiprocessor locking protocols have been designed and analyzed. However, the performance of these protocols highly depends on how the tasks are partitioned and prioritized and how the resources are shared locally and globally. This paper answers a few fundamental questions when real-time tasks ...
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On topological cyclic homology
Topological cyclic homology is a refinement of Connes--Tsygan's cyclic homology which was introduced by Bökstedt--Hsiang--Madsen in 1993 as an approximation to algebraic $K$-theory. There is a trace map from algebraic $K$-theory to topological cyclic homology, and a theorem of Dundas--Goodwillie--McCarthy asserts tha...
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Local Partition in Rich Graphs
Local graph partitioning is a key graph mining tool that allows researchers to identify small groups of interrelated nodes (e.g. people) and their connective edges (e.g. interactions). Because local graph partitioning is primarily focused on the network structure of the graph (vertices and edges), it often fails to c...
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A Transient Queueing Analysis under Time-varying Arrival and Service Rates for Enabling Low-Latency Services
Understanding the detailed queueing behavior of a networking session is critical in enabling low-latency services over the Internet. Especially when the packet arrival and service rates at the queue of a link vary over time and moreover when the session is short-lived, analyzing the corresponding queue behavior as a ...
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SalientDSO: Bringing Attention to Direct Sparse Odometry
Although cluttered indoor scenes have a lot of useful high-level semantic information which can be used for mapping and localization, most Visual Odometry (VO) algorithms rely on the usage of geometric features such as points, lines and planes. Lately, driven by this idea, the joint optimization of semantic labels an...
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Asynchronous Decentralized Parallel Stochastic Gradient Descent
Most commonly used distributed machine learning systems are either synchronous or centralized asynchronous. Synchronous algorithms like AllReduce-SGD perform poorly in a heterogeneous environment, while asynchronous algorithms using a parameter server suffer from 1) communication bottleneck at parameter servers when ...
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Seamless Resources Sharing in Wearable Networks by Application Function Virtualization
The prevalence of smart wearable devices is increasing exponentially and we are witnessing a wide variety of fascinating new services that leverage the capabilities of these wearables. Wearables are truly changing the way mobile computing is deployed and mobile applications are being developed. It is possible to leve...
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Combining Model-Free Q-Ensembles and Model-Based Approaches for Informed Exploration
Q-Ensembles are a model-free approach where input images are fed into different Q-networks and exploration is driven by the assumption that uncertainty is proportional to the variance of the output Q-values obtained. They have been shown to perform relatively well compared to other exploration strategies. Further, mo...
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Creating a Cybersecurity Concept Inventory: A Status Report on the CATS Project
We report on the status of our Cybersecurity Assessment Tools (CATS) project that is creating and validating a concept inventory for cybersecurity, which assesses the quality of instruction of any first course in cybersecurity. In fall 2014, we carried out a Delphi process that identified core concepts of cybersecuri...
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Critical Learning Periods in Deep Neural Networks
Critical periods are phases in the early development of humans and animals during which experience can irreversibly affect the architecture of neuronal networks. In this work, we study the effects of visual stimulus deficits on the training of artificial neural networks (ANNs). Introducing well-characterized visual d...
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Whole-Body Nonlinear Model Predictive Control Through Contacts for Quadrupeds
In this work we present a whole-body Nonlinear Model Predictive Control approach for Rigid Body Systems subject to contacts. We use a full dynamic system model which also includes explicit contact dynamics. Therefore, contact locations, sequences and timings are not prespecified but optimized by the solver. Yet, thor...
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An informative path planning framework for UAV-based terrain monitoring
Unmanned aerial vehicles (UAVs) represent a new frontier in a wide range of monitoring and research applications. To fully leverage their potential, a key challenge is planning missions for efficient data acquisition in complex environments. To address this issue, this article introduces a general informative path pl...
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Machine Learning on Sequential Data Using a Recurrent Weighted Average
Recurrent Neural Networks (RNN) are a type of statistical model designed to handle sequential data. The model reads a sequence one symbol at a time. Each symbol is processed based on information collected from the previous symbols. With existing RNN architectures, each symbol is processed using only information from ...
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Feynman-Kac equation for anomalous processes with space- and time-dependent forces
Functionals of a stochastic process Y(t) model many physical time-extensive observables, e.g. particle positions, local and occupation times or accumulated mechanical work. When Y(t) is a normal diffusive process, their statistics are obtained as the solution of the Feynman-Kac equation. This equation provides the cr...
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An Online Ride-Sharing Path Planning Strategy for Public Vehicle Systems
As efficient traffic-management platforms, public vehicle (PV) systems are envisioned to be a promising approach to solving traffic congestions and pollutions for future smart cities. PV systems provide online/dynamic peer-to-peer ride-sharing services with the goal of serving sufficient number of customers with mini...
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Ask the Right Questions: Active Question Reformulation with Reinforcement Learning
We frame Question Answering (QA) as a Reinforcement Learning task, an approach that we call Active Question Answering. We propose an agent that sits between the user and a black box QA system and learns to reformulate questions to elicit the best possible answers. The agent probes the system with, potentially many, n...
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Time consistency for scalar multivariate risk measures
In this paper we present results on dynamic multivariate scalar risk measures, which arise in markets with transaction costs and systemic risk. Dual representations of such risk measures are presented. These are then used to obtain the main results of this paper on time consistency; namely, an equivalent recursive fo...
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A Continuous Beam Steering Slotted Waveguide Antenna Using Rotating Dielectric Slabs
The design, simulation and measurement of a beam steerable slotted waveguide antenna operating in X band are presented. The proposed beam steerable antenna consists of a standard rectangular waveguide (RWG) section with longitudinal slots in the broad wall. The beam steering in this configuration is achieved by rotat...
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Developing an edge computing platform for real-time descriptive analytics
The Internet of Mobile Things encompasses stream data being generated by sensors, network communications that pull and push these data streams, as well as running processing and analytics that can effectively leverage actionable information for transportation planning, management, and business advantage. Edge computi...
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Space Telescope and Optical Reverberation Mapping Project. VII. Understanding the UV anomaly in NGC 5548 with X-Ray Spectroscopy
During the Space Telescope and Optical Reverberation Mapping Project (STORM) observations of NGC 5548, the continuum and emission-line variability became de-correlated during the second half of the 6-month long observing campaign. Here we present Swift and Chandra X-ray spectra of NGC 5548 obtained as a part of the c...
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Design of a Multi-Modal End-Effector and Grasping System: How Integrated Design helped win the Amazon Robotics Challenge
We present the grasping system and design approach behind Cartman, the winning entrant in the 2017 Amazon Robotics Challenge. We investigate the design processes leading up to the final iteration of the system and describe the emergent solution by comparing it with key robotics design aspects. Following our experienc...
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Coping with Construals in Broad-Coverage Semantic Annotation of Adpositions
We consider the semantics of prepositions, revisiting a broad-coverage annotation scheme used for annotating all 4,250 preposition tokens in a 55,000 word corpus of English. Attempts to apply the scheme to adpositions and case markers in other languages, as well as some problematic cases in English, have led us to re...
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A Framework for Generalizing Graph-based Representation Learning Methods
Random walks are at the heart of many existing deep learning algorithms for graph data. However, such algorithms have many limitations that arise from the use of random walks, e.g., the features resulting from these methods are unable to transfer to new nodes and graphs as they are tied to node identity. In this work...
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A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data
Gated Recurrent Unit (GRU) is a recently-developed variation of the long short-term memory (LSTM) unit, both of which are types of recurrent neural network (RNN). Through empirical evidence, both models have been proven to be effective in a wide variety of machine learning tasks such as natural language processing (W...
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Spelling Correction as a Foreign Language
In this paper, we reformulated the spell correction problem as a machine translation task under the encoder-decoder framework. This reformulation enabled us to use a single model for solving the problem that is traditionally formulated as learning a language model and an error model. This model employs multi-layer re...
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Intrinsic p-type W-based transition metal dichalcogenide by substitutional Ta-doping
Two-dimensional (2D) transition metal dichalcogenides (TMDs) have recently emerged as promising candidates for future electronics and optoelectronics. While most of TMDs are intrinsic n-type semiconductors due to electron donating which originates from chalcogen vacancies, obtaining intrinsic high-quality p-type semi...
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Synchronisation of Partial Multi-Matchings via Non-negative Factorisations
In this work we study permutation synchronisation for the challenging case of partial permutations, which plays an important role for the problem of matching multiple objects (e.g. images or shapes). The term synchronisation refers to the property that the set of pairwise matchings is cycle-consistent, i.e. in the fu...
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Associated varieties and Higgs branches (a survey)
Associated varieties of vertex algebras are analogue of the associated varieties of primitive ideals of the universal enveloping algebras of semisimple Lie algebras. They not only capture some of the important properties of vertex algebras but also have interesting relationship with the Higgs branches of four-dimensi...
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Optimization Design of Decentralized Control for Complex Decentralized Systems
A new method is developed to deal with the problem that a complex decentralized control system needs to keep centralized control performance. The systematic procedure emphasizes quickly finding the decentralized subcontrollers that matching the closed-loop performance and robustness characteristics of the centralized...
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SAM: Semantic Attribute Modulation for Language Modeling and Style Variation
This paper presents a Semantic Attribute Modulation (SAM) for language modeling and style variation. The semantic attribute modulation includes various document attributes, such as titles, authors, and document categories. We consider two types of attributes, (title attributes and category attributes), and a flexible...
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A Flexible Procedure for Mixture Proportion Estimation in Positive--Unlabeled Learning
Positive--unlabeled (PU) learning considers two samples, a positive set P with observations from only one class and an unlabeled set U with observations from two classes. The goal is to classify observations in U. Class mixture proportion estimation (MPE) in U is a key step in PU learning. Blanchard et al. [2010] sho...
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The Application of SNiPER to the JUNO Simulation
JUNO is a multipurpose neutrino experiment which is designed to determine neutrino mass hierarchy and precisely measure oscillation parameters. As one of the important systems, the JUNO offline software is being developed using the SNiPER software. In this proceeding, we focus on the requirements of JUNO simulation a...
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A Modified Sigma-Pi-Sigma Neural Network with Adaptive Choice of Multinomials
Sigma-Pi-Sigma neural networks (SPSNNs) as a kind of high-order neural networks can provide more powerful mapping capability than the traditional feedforward neural networks (Sigma-Sigma neural networks). In the existing literature, in order to reduce the number of the Pi nodes in the Pi layer, a special multinomial ...
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Proceedings of the 2017 AdKDD & TargetAd Workshop
Proceedings of the 2017 AdKDD and TargetAd Workshop held in conjunction with the 23rd ACM SIGKDD Conference on Knowledge Discovery and Data Mining Halifax, Nova Scotia, Canada.
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On the R-superlinear convergence of the KKT residues generated by the augmented Lagrangian method for convex composite conic programming
Due to the possible lack of primal-dual-type error bounds, the superlinear convergence for the Karush-Kuhn-Tucker (KKT) residues of the sequence generated by augmented Lagrangian method (ALM) for solving convex composite conic programming (CCCP) has long been an outstanding open question. In this paper, we aim to res...
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It's Like Python But: Towards Supporting Transfer of Programming Language Knowledge
Expertise in programming traditionally assumes a binary novice-expert divide. Learning resources typically target programmers who are learning programming for the first time, or expert programmers for that language. An underrepresented, yet important group of programmers are those that are experienced in one programm...
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How do Mixture Density RNNs Predict the Future?
Gaining a better understanding of how and what machine learning systems learn is important to increase confidence in their decisions and catalyze further research. In this paper, we analyze the predictions made by a specific type of recurrent neural network, mixture density RNNs (MD-RNNs). These networks learn to mod...
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Robust Recovery of Missing Data in Electricity Distribution Systems
The advanced operation of future electricity distribution systems is likely to require significant observability of the different parameters of interest (e.g., demand, voltages, currents, etc.). Ensuring completeness of data is, therefore, paramount. In this context, an algorithm for recovering missing state variable...
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Numerical analysis of a nonlinear free-energy diminishing Discrete Duality Finite Volume scheme for convection diffusion equations
We propose a nonlinear Discrete Duality Finite Volume scheme to approximate the solutions of drift diffusion equations. The scheme is built to preserve at the discrete level even on severely distorted meshes the energy / energy dissipation relation. This relation is of paramount importance to capture the long-time be...
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A momentum conserving $N$-body scheme with individual timesteps
$N$-body simulations study the dynamics of $N$ particles under the influence of mutual long-distant forces such as gravity. In practice, $N$-body codes will violate Newton's third law if they use either an approximate Poisson solver or individual timesteps. In this study, we construct a novel $N$-body scheme by combi...
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