OpenRaiser/Intern-Atlas huggingface dataset:
NetKernel: Making Network Stack Part of the Virtualized Infrastructure
Venue: arXiv cs.NI 2019
Year: 2019

This paper presents a system called NetKernel that decouples the network stack from the guest virtual machine and offers it as an independent module. NetKernel represents a new paradigm where network stack can be managed as part of the virtualized infrastructure. It provides important efficiency benefits: By gaining control and visibility of the network stack, operator can perform network management more directly and flexibly, such as multiplexing VMs running different applications to the same network stack module to save CPU cores, and enforcing fair bandwidth sharing with distributed congestion control. Users also benefit from the simplified stack deployment and better performance. For example mTCP can be deployed without API change to support nginx natively, and shared memory networking can be readily enabled to improve performance of colocated VMs. Testbed evaluation using 100G NICs shows that NetKernel preserves the performance and scalability of both kernel and userspace network stacks, and provides the same isolation as the current architecture.
Training recurrent neural networks robust to incomplete data: application to Alzheimer's disease progression modeling
Venue: arXiv cs.CV 2019
Year: 2019

Disease progression modeling (DPM) using longitudinal data is a challenging machine learning task. Existing DPM algorithms neglect temporal dependencies among measurements, make parametric assumptions about biomarker trajectories, do not model multiple biomarkers jointly, and need an alignment of subjects' trajectories. In this paper, recurrent neural networks (RNNs) are utilized to address these issues. However, in many cases, longitudinal cohorts contain incomplete data, which hinders the application of standard RNNs and requires a pre-processing step such as imputation of the missing values. Instead, we propose a generalized training rule for the most widely used RNN architecture, long short-term memory (LSTM) networks, that can handle both missing predictor and target values. The proposed LSTM algorithm is applied to model the progression of Alzheimer's disease (AD) using six volumetric magnetic resonance imaging (MRI) biomarkers, i.e., volumes of ventricles, hippocampus, whole brain, fusiform, middle temporal gyrus, and entorhinal cortex, and it is compared to standard LSTM networks with data imputation and a parametric, regression-based DPM method. The results show that the proposed algorithm achieves a significantly lower mean absolute error (MAE) than the alternatives with p < 0.05 using Wilcoxon signed rank test in predicting values of almost all of the MRI biomarkers. Moreover, a linear discriminant analysis (LDA) classifier applied to the predicted biomarker values produces a significantly larger AUC of 0.90 vs. at most 0.84 with p < 0.001 using McNemar's test for clinical diagnosis of AD. Inspection of MAE curves as a function of the amount of missing data reveals that the proposed LSTM algorithm achieves the best performance up until more than 74% missing values. Finally, it is illustrated how the method can successfully be applied to data with varying time intervals.
Weighted Mean Curvature
Venue: arXiv cs.CV 2019
Year: 2019

In image processing tasks, spatial priors are essential for robust computations, regularization, algorithmic design and Bayesian inference. In this paper, we introduce weighted mean curvature (WMC) as a novel image prior and present an efficient computation scheme for its discretization in practical image processing applications. We first demonstrate the favorable properties of WMC, such as sampling invariance, scale invariance, and contrast invariance with Gaussian noise model; and we show the relation of WMC to area regularization. We further propose an efficient computation scheme for discretized WMC, which is demonstrated herein to process over 33.2 giga-pixels/second on GPU. This scheme yields itself to a convolutional neural network representation. Finally, WMC is evaluated on synthetic and real images, showing its superiority quantitatively to total-variation and mean curvature.
Learning-Based Animation of Clothing for Virtual Try-On
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a learning-based clothing animation method for highly efficient virtual try-on simulation. Given a garment, we preprocess a rich database of physically-based dressed character simulations, for multiple body shapes and animations. Then, using this database, we train a learning-based model of cloth drape and wrinkles, as a function of body shape and dynamics. We propose a model that separates global garment fit, due to body shape, from local garment wrinkles, due to both pose dynamics and body shape. We use a recurrent neural network to regress garment wrinkles, and we achieve highly plausible nonlinear effects, in contrast to the blending artifacts suffered by previous methods. At runtime, dynamic virtual try-on animations are produced in just a few milliseconds for garments with thousands of triangles. We show qualitative and quantitative analysis of results
Specification and Inference of Trace Refinement Relations
Venue: arXiv cs.PL 2019
Year: 2019

Modern software is constantly changing. Researchers and practitioners are increasingly aware that verification tools can be impactful if they embrace change through analyses that are compositional and span program versions. Reasoning about similarities and differences between programs goes back to Benton, who introduced state-based refinement relations, which were extended by Yang and others. However, to our knowledge, refinement relations have not been explored for traces. We present a novel theory that allows one to perform compositional reasoning about the similarities/differences between how fragments of two different programs behave over time through the use of what we call trace-refinement relations. We take a reactive view of programs and found Kleene Algebra with Tests (KAT) [Kozen] to be a natural choice to describe traces since it permits algebraic reasoning and has built-in composition. Our theory involves a two-step semantic abstraction from programs to KAT, and then our trace refinement relations correlate behaviors by (i) categorizing program behaviors into trace classes through KAT intersection and (ii) correlating atomic events/conditions across programs with KAT hypotheses. We next describe a synthesis algorithm that iteratively constructs trace-refinement relations between two programs by exploring sub-partitions of their traces, iteratively abstracting them as KAT expressions, discovering relationships through a custom edit-distance algorithm, and applying strategies (i) and (ii) above. We have implemented this algorithm as {\sc knotical}, the first tool capable of synthesizing trace-refinement relations. It built from the ground up in Ocaml, using InterProc and SymKAT. We have demonstrated that useful relations can be efficiently generated across a suite of 37 benchmarks that include changing fragments of array programs, systems code, and web servers.
Counterpoint by Convolution
Venue: arXiv cs.LG 2019
Year: 2019

Machine learning models of music typically break up the task of composition into a chronological process, composing a piece of music in a single pass from beginning to end. On the contrary, human composers write music in a nonlinear fashion, scribbling motifs here and there, often revisiting choices previously made. In order to better approximate this process, we train a convolutional neural network to complete partial musical scores, and explore the use of blocked Gibbs sampling as an analogue to rewriting. Neither the model nor the generative procedure are tied to a particular causal direction of composition. Our model is an instance of orderless NADE (Uria et al., 2014), which allows more direct ancestral sampling. However, we find that Gibbs sampling greatly improves sample quality, which we demonstrate to be due to some conditional distributions being poorly modeled. Moreover, we show that even the cheap approximate blocked Gibbs procedure from Yao et al. (2014) yields better samples than ancestral sampling, based on both log-likelihood and human evaluation.
A Nonlinear Observer for Free-Floating Target Motion using only Pose Measurements
Venue: arXiv cs.RO 2019
Year: 2019

In this paper, we design a nonlinear observer to estimate the inertial pose and the velocity of a free-floating non-cooperative satellite (Target) using only relative pose measurements. In the context of control design for orbital robotic capture of such a non-cooperative Target, due to lack of navigational aids, only a relative pose estimate may be obtained from slow-sampled and noisy exteroceptive sensors. The velocity, however, cannot be measured directly. To address this problem, we develop a model-based observer which acts as an internal model for Target kinematics/dynamics and therefore, may act as a predictor during periods of no measurement. To this end, firstly, we formalize the estimation problem on the SE(3) Lie group with different state and measurement spaces. Secondly, we develop the kinematics and dynamics observer such that the overall observer error dynamics possesses a stability property. Finally, the proposed observer is validated through robust Monte-Carlo simulations and experiments on a robotic facility.
Prototype-based classifiers in the presence of concept drift: A modelling framework
Venue: arXiv cs.LG 2019
Year: 2019

We present a modelling framework for the investigation of prototype-based classifiers in non-stationary environments. Specifically, we study Learning Vector Quantization (LVQ) systems trained from a stream of high-dimensional, clustered data.We consider standard winner-takes-all updates known as LVQ1. Statistical properties of the input data change on the time scale defined by the training process. We apply analytical methods borrowed from statistical physics which have been used earlier for the exact description of learning in stationary environments. The suggested framework facilitates the computation of learning curves in the presence of virtual and real concept drift. Here we focus on timedependent class bias in the training data. First results demonstrate that, while basic LVQ algorithms are suitable for the training in non-stationary environments, weight decay as an explicit mechanism of forgetting does not improve the performance under the considered drift processes.
POI Semantic Model with a Deep Convolutional Structure
Venue: arXiv cs.IR 2019
Year: 2019

When using the electronic map, POI retrieval is the initial and important step, whose quality directly affects the user experience. Similarity between user query and POI information is the most critical feature in POI retrieval. An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry. In this paper, we propose a POI latent semantic model based on deep networks, which can effectively extract query features and POI information features for the similarity calculation. Our model describes the semantic information of complex texts at multiple layers, and achieves multi-field matches by modeling POI's name and detailed address respectively. Our model is evaluated by the POI retrieval ranking datasets, including the labeled data of relevance and real-world user click data in POI retrieval. Results show that our model significantly outperforms our competitors in POI retrieval ranking tasks. The proposed algorithm has become a critical component of an online system serving millions of people everyday.
Performance of Cell-Free Massive MIMO with Rician Fading and Phase Shifts
Venue: arXiv cs.IT 2019
Year: 2019

In this paper, we study the uplink (UL) and downlink (DL) spectral efficiency (SE) of a cell-free massive multiple-input-multiple-output (MIMO) system with Rician fading channels. The phase of the line-of-sight (LoS) path is modeled as a uniformly distributed random variable to take the phase-shifts due to mobility and phase noise into account. Considering the availability of prior information at the access points (APs), the phase-aware minimum mean square error (MMSE), non-aware linear MMSE (LMMSE), and least-square (LS) estimators are derived. The MMSE estimator requires perfectly estimated phase knowledge whereas the LMMSE and LS are derived without it. In the UL, a two-layer decoding method is investigated in order to mitigate both coherent and non-coherent interference. Closed-form UL SE expressions with phase-aware MMSE, LMMSE, and LS estimators are derived for maximum-ratio (MR) combining in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. In the DL, two different transmission modes are studied: coherent and non-coherent. Closed-form DL SE expressions for both transmission modes with MR precoding are derived for the three estimators. Numerical results show that the LSFD improves the UL SE performance and coherent transmission mode performs much better than non-coherent transmission in the DL. Besides, the performance loss due to the lack of phase information depends on the pilot length and it is small when the pilot contamination is low.
Secure distributed filtering for unstable dynamics under compromised observations
Venue: arXiv cs.SY 2019
Year: 2019

In this paper, we consider a secure distributed filtering problem for linear time-invariant systems with bounded noises and unstable dynamics under compromised observations. A malicious attacker is able to compromise a subset of the agents and manipulate the observations arbitrarily. We first propose a recursive distributed filter consisting of two parts at each time. The first part employs a saturation-like scheme, which gives a small gain if the innovation is too large. The second part is a consensus operation of state estimates among neighboring agents. A sufficient condition is then established for the boundedness of estimation error, which is with respect to network topology, system structure, and the maximal compromised agent subset. We further provide an equivalent statement, which connects to 2s-sparse observability in the centralized framework in certain scenarios, such that the sufficient condition is feasible. Numerical simulations are finally provided to illustrate the developed results.
On-line learning dynamics of ReLU neural networks using statistical physics techniques
Venue: arXiv cs.LG 2019
Year: 2019

We introduce exact macroscopic on-line learning dynamics of two-layer neural networks with ReLU units in the form of a system of differential equations, using techniques borrowed from statistical physics. For the first experiments, numerical solutions reveal similar behavior compared to sigmoidal activation researched in earlier work. In these experiments the theoretical results show good correspondence with simulations. In ove-rrealizable and unrealizable learning scenarios, the learning behavior of ReLU networks shows distinctive characteristics compared to sigmoidal networks.
Scene Segmentation-Based Luminance Adjustment for Multi-Exposure Image Fusion
Venue: arXiv cs.MM 2019
Year: 2019

We propose a novel method for adjusting luminance for multi-exposure image fusion. For the adjustment, two novel scene segmentation approaches based on luminance distribution are also proposed. Multi-exposure image fusion is a method for producing images that are expected to be more informative and perceptually appealing than any of the input ones, by directly fusing photos taken with different exposures. However, existing fusion methods often produce unclear fused images when input images do not have a sufficient number of different exposure levels. In this paper, we point out that adjusting the luminance of input images makes it possible to improve the quality of the final fused images. This insight is the basis of the proposed method. The proposed method enables us to produce high-quality images, even when undesirable inputs are given. Visual comparison results show that the proposed method can produce images that clearly represent a whole scene. In addition, multi-exposure image fusion with the proposed method outperforms state-of-the-art fusion methods in terms of MEF-SSIM, discrete entropy, tone mapped image quality index, and statistical naturalness.
An Explicit Construction of Optimal Streaming Codes for Channels with Burst and Arbitrary Erasures
Venue: arXiv cs.IT 2019
Year: 2019

This paper presents a new construction of error correcting codes which achieves optimal recovery of a streaming source over a packet erasure channel. The channel model considered is the sliding window erasure model, with burst and arbitrary losses, introduced by Badr et al. . Recently, two independents works by Fong et al. and Krishnan and Kumar have identified optimal streaming codes within this framework. In this paper, we introduce streaming code when the rate of the code is at least 1/2. Our proposed construction is explicit and systematic, uses off-the-shelf maximum distance separable (MDS) codes and maximum rank distance (MRD) Gabidulin block codes as constituent codes and achieves the optimal error correction. It presents a natural generalization to the construction of Martinian and Sundberg to tolerate an arbitrary number of sparse erasures. The field size requirement which depends on the constituent MDS and MRD codes is also analyzed.
LYRICS: a General Interface Layer to Integrate Logic Inference and Deep Learning
Venue: arXiv cs.LG 2019
Year: 2019

In spite of the amazing results obtained by deep learning in many applications, a real intelligent behavior of an agent acting in a complex environment is likely to require some kind of higher-level symbolic inference. Therefore, there is a clear need for the definition of a general and tight integration between low-level tasks, processing sensorial data that can be effectively elaborated using deep learning techniques, and the logic reasoning that allows humans to take decisions in complex environments. This paper presents LYRICS, a generic interface layer for AI, which is implemented in TersorFlow (TF). LYRICS provides an input language that allows to define arbitrary First Order Logic (FOL) background knowledge. The predicates and functions of the FOL knowledge can be bound to any TF computational graph, and the formulas are converted into a set of real-valued constraints, which participate to the overall optimization problem. This allows to learn the weights of the learners, under the constraints imposed by the prior knowledge. The framework is extremely general as it imposes no restrictions in terms of which models or knowledge can be integrated. In this paper, we show the generality of the approach showing some use cases of the presented language, including model checking, supervised learning and collective classification.
Short Datathon for the Interdisciplinary Development of Data Analysis and Visualization Skills
Venue: arXiv cs.SE 2019
Year: 2019

Understanding the major fraud problems in the world and interpreting the data available for analysis is a current challenge that requires interdisciplinary knowledge to complement the knowledge of computer professionals. Collaborative events (called Hackathons, Datathons, Codefests, Hack Days, etc.) have become relevant in several fields. Examples of fields which are explored in these events include startup development, open civic innovation, corporate innovation, and social issues. These events have features that favor knowledge exchange to solve challenges. In this paper, we present an event format called Short Datathon, a Hackathon for the development of exploratory data analysis and visualization skills. Our goal is to evaluate if participating in a Short Datathon can help participants learn basic data analysis and visualization concepts. We evaluated the Short Datathon in two case studies, with a total of 20 participants, carried out at the Federal University of Technology - Paraná. In both case studies we addressed the issue of tax evasion using real world data. We describe, as a result of this work, the qualitative aspects of the case studies and the perception of the participants obtained through questionnaires. Participants stated that the event helped them understand more about data analysis and visualization and that the experience with people from other areas during the event made data analysis more efficient. Further studies are necessary to evolve the format of the event and to evaluate its effectiveness.
Reduced and Aggregated Distribution Grid Representations Approximated by Polyhedral Sets
Venue: arXiv cs.SY 2019
Year: 2019

In this paper we present a novel tractable method to compute reduced and aggregated distribution grid representations that provide an interface in the form of active and reactive power (PQ) capability areas for improving transmission service operator - distribution service operator (TSO-DSO) interactions. Based on a lossless linear power flow approximation we derive polyhedral sets to determine a reduced PQ operating region capturing all voltage magnitude and branch power flow constraints of the entire distribution grid. To demonstrate the usefulness of our method, we compare the capability area obtained from the polyhedral approximation with an area generated by multiple optimal power flow (OPF) solutions for different distribution grids. While the approximation errors are reasonable, especially for low voltage (LV) grids, the computational complexity to compute the PQ capability area can be significantly reduced with our proposed method.
Morphing Contact Representations of Graphs
Venue: arXiv cs.CG 2019
Year: 2019

We consider the problem of morphing between contact representations of a plane graph. In an $\mathcal F$-contact representation of a plane graph $G$, vertices are realized by internally disjoint elements from a family $\mathcal F$ of connected geometric objects. Two such elements touch if and only if their corresponding vertices are adjacent. These touchings also induce the same embedding as in $G$. In a morph between two $\mathcal F$-contact representations we insist that at each time step (continuously throughout the morph) we have an $\mathcal F$-contact representation. We focus on the case when $\mathcal{F}$ is the family of triangles in $\mathbb{R}^2$ that are the lower-right half of axis-parallel rectangles. Such RT-representations exist for every plane graph and right triangles are one of the simplest families of shapes supporting this property. Thus, they provide a natural case to study regarding morphs of contact representations of plane graphs. We study piecewise linear morphs, where each step is a linear morph moving the endpoints of each triangle at constant speed along straight-line trajectories. We provide a polynomial-time algorithm that decides whether there is a piecewise linear morph between two RT-representations of an $n$-vertex plane triangulation, and, if so, computes a morph with $\mathcal O(n^2)$ linear morphs. As a direct consequence, we obtain that for $4$-connected plane triangulations there is a morph between every pair of RT-representations where the ``top-most'' triangle in both representations corresponds to the same vertex. This shows that the realization space of such RT-representations of any $4$-connected plane triangulation forms a connected set.