OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Anomalous Faces with 'No Peeking' Autoencoders
Venue: arXiv cs.CV 2018
Year: 2018

Detecting anomalous faces has important applications. For example, a system might tell when a train driver is incapacitated by a medical event, and assist in adopting a safe recovery strategy. These applications are demanding, because they require accurate detection of rare anomalies that may be seen only at runtime. Such a setting causes supervised methods to perform poorly. We describe a method for detecting an anomalous face image that meets these requirements. We construct a feature vector that reliably has large entries for anomalous images, then use various simple unsupervised methods to score the image based on the feature. Obvious constructions (autoencoder codes; autoencoder residuals) are defeated by a 'peeking' behavior in autoencoders. Our feature construction removes rectangular patches from the image, predicts the likely content of the patch conditioned on the rest of the image using a specially trained autoencoder, then compares the result to the image. High scores suggest that the patch was difficult for an autoencoder to predict, and so is likely anomalous. We demonstrate that our method can identify real anomalous face images in pools of typical images, taken from celeb-A, that is much larger than usual in state-of-the-art experiments. A control experiment based on our method with another set of normal celebrity images - a 'typical set', but nonceleb-A are not identified as anomalous; confirms this is not due to special properties of celeb-A.
Improving Power Grid Resilience Through Predictive Outage Estimation
Venue: arXiv cs.SY 2018
Year: 2018

In this paper, in an attempt to improve power grid resilience, a machine learning model is proposed to predictively estimate the component states in response to extreme events. The proposed model is based on a multi-dimensional Support Vector Machine (SVM) considering the associated resilience index, i.e., the infrastructure quality level and the time duration that each component can withstand the event, as well as predicted path and intensity of the upcoming extreme event. The outcome of the proposed model is the classified component state data to two categories of outage and operational, which can be further used to schedule system resources in a predictive manner with the objective of maximizing its resilience. The proposed model is validated using Ä-fold cross-validation and model benchmarking techniques. The performance of the model is tested through numerical simulations and based on a well-defined and commonly-used performance measure.
An Anytime Algorithm for Task and Motion MDPs
Venue: arXiv cs.AI 2018
Year: 2018

Integrated task and motion planning has emerged as a challenging problem in sequential decision making, where a robot needs to compute high-level strategy and low-level motion plans for solving complex tasks. While high-level strategies require decision making over longer time-horizons and scales, their feasibility depends on low-level constraints based upon the geometries and continuous dynamics of the environment. The hybrid nature of this problem makes it difficult to scale; most existing approaches focus on deterministic, fully observable scenarios. We present a new approach where the high-level decision problem occurs in a stochastic setting and can be modeled as a Markov decision process. In contrast to prior efforts, we show that complete MDP policies, or contingent behaviors, can be computed effectively in an anytime fashion. Our algorithm continuously improves the quality of the solution and is guaranteed to be probabilistically complete. We evaluate the performance of our approach on a challenging, realistic test problem: autonomous aircraft inspection. Our results show that we can effectively compute consistent task and motion policies for the most likely execution-time outcomes using only a fraction of the computation required to develop the complete task and motion policy.
New High Performance GPGPU Code Transformation Framework Applied to Large Production Weather Prediction Code
Venue: arXiv cs.DC 2018
Year: 2018

We introduce "Hybrid Fortran", a new approach that allows a high performance GPGPU port for structured grid Fortran codes. This technique only requires minimal changes for a CPU targeted codebase, which is a significant advancement in terms of productivity. It has been successfully applied to both dynamical core and physical processes of ASUCA, a Japanese mesoscale weather prediction model with more than 150k lines of code. By means of a minimal weather application that resembles ASUCA's code structure, Hybrid Fortran is compared to both a performance model as well as today's commonly used method, OpenACC. As a result, the Hybrid Fortran implementation is shown to deliver the same or better performance than OpenACC and its performance agrees with the model both on CPU and GPU. In a full scale production run, using an ASUCA grid with 1581 x 1301 x 58 cells and real world weather data in 2km resolution, 24 NVIDIA Tesla P100 running the Hybrid Fortran based GPU port are shown to replace more than 50 18-core Intel Xeon Broadwell E5-2695 v4 running the reference implementation - an achievement comparable to more invasive GPGPU rewrites of other weather models.
Online Machine Learning in Big Data Streams
Venue: arXiv cs.DC 2018
Year: 2018

The area of online machine learning in big data streams covers algorithms that are (1) distributed and (2) work from data streams with only a limited possibility to store past data. The first requirement mostly concerns software architectures and efficient algorithms. The second one also imposes nontrivial theoretical restrictions on the modeling methods: In the data stream model, older data is no longer available to revise earlier suboptimal modeling decisions as the fresh data arrives. In this article, we provide an overview of distributed software architectures and libraries as well as machine learning models for online learning. We highlight the most important ideas for classification, regression, recommendation, and unsupervised modeling from streaming data, and we show how they are implemented in various distributed data stream processing systems. This article is a reference material and not a survey. We do not attempt to be comprehensive in describing all existing methods and solutions; rather, we give pointers to the most important resources in the field. All related sub-fields, online algorithms, online learning, and distributed data processing are hugely dominant in current research and development with conceptually new research results and software components emerging at the time of writing. In this article, we refer to several survey results, both for distributed data processing and for online machine learning. Compared to past surveys, our article is different because we discuss recommender systems in extended detail.
A complete hand-drawn sketch vectorization framework
Venue: arXiv cs.CV 2018
Year: 2018

Vectorizing hand-drawn sketches is a challenging task, which is of paramount importance for creating CAD vectorized versions for the fashion and creative workflows. This paper proposes a complete framework that automatically transforms noisy and complex hand-drawn sketches with different stroke types in a precise, reliable and highly-simplified vectorized model. The proposed framework includes a novel line extraction algorithm based on a multi-resolution application of Pearson's cross correlation and a new unbiased thinning algorithm that can get rid of scribbles and variable-width strokes to obtain clean 1-pixel lines. Other contributions include variants of pruning, merging and edge linking procedures to post-process the obtained paths. Finally, a modification of the original Schneider's vectorization algorithm is designed to obtain fewer control points in the resulting Bezier splines. All the proposed steps of the framework have been extensively tested and compared with state-of-the-art algorithms, showing (both qualitatively and quantitatively) its outperformance.
Accumulation of Knowledge in Para-Scientific Areas. The Case of Analytic Philosophy
Venue: arXiv cs.DL 2018
Year: 2018

This study analyzes how the accumulation of knowledge takes place in para-scientific areas, focusing on the case of Analytic Philosophy. The theoretical framework chosen for the analysis is Kuhn's theory of normal science. The methodology employed is qualitative citation context analysis. A sample of 60 papers published in leading Analytic Philosophy journals between 1950 and 2009 is analyzed, and a specific classsificatory scheme is developed to classify citations according to their epistemological function. Compared to previous studies of citation context, this is the first paper that includes the temporal dimension into the analysis of citation context, in order to gain insights into the process of knowledge accumulation. Interestingly, the results show that Analytic Philosophy started accumulating after Second World War, but in a peculiar way. The accumulation was not matched by a corresponding rising consensus. This can be explained by the hypothesis that AP underwent a process of fragmentation in sub-fields during the second half of the century.
Cloud No Longer a Silver Bullet, Edge to the Rescue
Venue: arXiv cs.NI 2018
Year: 2018

This paper takes the position that, while cognitive computing today relies heavily on the cloud, we will soon see a paradigm shift where cognitive computing primarily happens on network edges. The shift toward edge devices is fundamentally propelled both by technological constraints in data centers and wireless network infrastructures, as well as practical considerations such as privacy and safety. The remainder of this paper lays out our view of how these constraints will impact future cognitive computing. Bringing cognitive computing to edge devices opens up several new opportunities and challenges, some of which demand new solutions and some of which require us to revisit entrenched techniques in light of new technologies. We close the paper with a call to action for future research.
Study of Knowledge-Aided Iterative Detection and Decoding for Multiuser MIMO Systems
Venue: arXiv cs.IT 2018
Year: 2018

In this work, we consider the problem of reduced latency of low-density parity-check (LDPC) codes with iterative detection and decoding (IDD) receiver in multiuser multiple-antenna systems. The proposed knowledge-aided IDD (KA-IDD) system employs a minimum mean-square error detector with refined iterative processing and a reweighted belief propagation (BP) decoding algorithm. We present reweighted BP decoding algorithms, which exploit the knowledge of short cycles in the graph structure and reweighting factors derived from the expansion of hypergraphs. Simulation results show that the proposed KA-IDD scheme and algorithms outperform prior art and require a reduced number of decoding iterations.
Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps
Venue: arXiv cs.LG 2018
Year: 2018

Recent developments in the field of robot grasping have shown great improvements in the grasp success rates when dealing with unknown objects. In this work we improve on one of the most promising approaches, the Grasp Quality Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We propose a new architecture for the GQ-CNN and describe practical improvements that increase the model validation accuracy from 92.2% to 95.8% and from 85.9% to 88.0% on respectively image-wise and object-wise training and validation splits.
Authorship Attribution Using the Chaos Game Representation
Venue: arXiv cs.CL 2018
Year: 2018

The Chaos Game Representation, a method for creating images from nucleotide sequences, is modified to make images from chunks of text documents. Machine learning methods are then applied to train classifiers based on authorship. Experiments are conducted on several benchmark data sets in English, including the widely used Federalist Papers, and one in Portuguese. Validation results for the trained classifiers are competitive with the best methods in prior literature. The methodology is also successfully applied for text categorization with encouraging results. One classifier method is moreover seen to hold promise for the task of digital fingerprinting.
A hybridizable discontinuous Galerkin method for two-phase flow in heterogeneous porous media
Venue: arXiv cs.CE 2018
Year: 2018

We present a new method for simulating incompressible immiscible two-phase flow in porous media. The semi-implicit method decouples the wetting phase pressure and saturation equations. The equations are discretized using a hybridizable discontinuous Galerkin (HDG) method. The proposed method is of high order, conserves global/local mass balance, and the number of globally coupled degrees of freedom is significantly reduced compared to standard interior penalty discontinuous Galerkin methods. Several numerical examples illustrate the accuracy and robustness of the method. These examples include verification of convergence rates by manufactured solutions, common 1D benchmarks and realistic discontinuous permeability fields.
Artificial intelligence and pediatrics: A synthetic mini review
Venue: arXiv cs.AI 2018
Year: 2018

The use of artificial intelligence intelligencein medicine can be traced back to 1968 when Paycha published his paper Le diagnostic a l'aide d'intelligences artificielle, presentation de la premiere machine diagnostri. Few years later Shortliffe et al. presented an expert system named Mycin which was able to identify bacteria causing severe blood infections and to recommend antibiotics. Despite the fact that Mycin outperformed members of the Stanford medical school in the reliability of diagnosis it was never used in practice due to a legal issue who do you sue if it gives a wrong diagnosis?. However only in 2016 when the artificial intelligence software built into the IBM Watson AI platform correctly diagnosed and proposed an effective treatment for a 60-year-old womans rare form of leukemia the AI use in medicine become really popular.On of first papers presenting the use of AI in paediatrics was published in 1984. The paper introduced a computer-assisted medical decision making system called SHELP.
Counting Homomorphisms to Trees Modulo a Prime
Venue: arXiv cs.CC 2018
Year: 2018

Many important graph theoretic notions can be encoded as counting graph homomorphism problems, such as partition functions in statistical physics, in particular, independent sets and colourings. In this article we study the complexity of $\#_p\mathsf{H}$OMS$\mathsf{T}$O$H$, the problem of counting graph homomorphisms from an input graph to a graph $H$ modulo a prime number $p$. Dyer and Greenhill proved a dichotomy stating that the tractability of non-modular counting graph homomorphisms depends on the structure of the input graph. Many intractable cases in non-modular counting become tractable in modular counting due to the common phenomenon of cancellation. However, in subsequent studies on counting modulo $2$ the influence, the structure of $H$ has on the tractability, was shown to persist, yielding similar dichotomies. Our main result shows that for every tree $H$ and every prime $p$ the problem $\#_p\mathsf{H}$OMS$\mathsf{T}$O$H$ is either polynomial time computable or $\#_p\mathsf{P}$-complete. This addresses the conjecture of Faben and Jerrum stating this dichotomy for every graph $H$ when counting modulo 2. In order to prove this result, we study the structural properties of a homomorphism. As an important interim, this study yields a dichotomy for the problem of weighted counting independent sets in a bipartite graph modulo some prime $p$. Our results are the first suggesting that such dichotomies hold not only for the one-bit functions of the modulo~2 case but for the modular counting functions of all primes $p$.
The Mean-Field Approximation: Information Inequalities, Algorithms, and Complexity
Venue: arXiv cs.LG 2018
Year: 2018

The mean field approximation to the Ising model is a canonical variational tool that is used for analysis and inference in Ising models. We provide a simple and optimal bound for the KL error of the mean field approximation for Ising models on general graphs, and extend it to higher order Markov random fields. Our bound improves on previous bounds obtained in work in the graph limit literature by Borgs, Chayes, Lovász, Sós, and Vesztergombi and another recent work by Basak and Mukherjee. Our bound is tight up to lower order terms. Building on the methods used to prove the bound, along with techniques from combinatorics and optimization, we study the algorithmic problem of estimating the (variational) free energy for Ising models and general Markov random fields. For a graph $G$ on $n$ vertices and interaction matrix $J$ with Frobenius norm $\| J \|_F$, we provide algorithms that approximate the free energy within an additive error of $εn \|J\|_F$ in time $\exp(poly(1/ε))$. We also show that approximation within $(n \|J\|_F)^{1-δ}$ is NP-hard for every $δ> 0$. Finally, we provide more efficient approximation algorithms, which find the optimal mean field approximation, for ferromagnetic Ising models and for Ising models satisfying Dobrushin's condition.
Multi-Pass Streaming Algorithms for Monotone Submodular Function Maximization
Venue: arXiv cs.DS 2018
Year: 2018

We consider maximizing a monotone submodular function under a cardinality constraint or a knapsack constraint in the streaming setting. In particular, the elements arrive sequentially and at any point of time, the algorithm has access to only a small fraction of the data stored in primary memory. We propose the following streaming algorithms taking $O(\varepsilon^{-1})$ passes: ----a $(1-e^{-1}-\varepsilon)$-approximation algorithm for the cardinality-constrained problem ---- a $(0.5-\varepsilon)$-approximation algorithm for the knapsack-constrained problem. Both of our algorithms run in $O^\ast(n)$ time, using $O^\ast(K)$ space, where $n$ is the size of the ground set and $K$ is the size of the knapsack. Here the term $O^\ast$ hides a polynomial of $\log K$ and $\varepsilon^{-1}$. Our streaming algorithms can also be used as fast approximation algorithms. In particular, for the cardinality-constrained problem, our algorithm takes $O(n\varepsilon^{-1} \log (\varepsilon^{-1}\log K) )$ time, improving on the algorithm of Badanidiyuru and Vondrák that takes $O(n \varepsilon^{-1} \log (\varepsilon^{-1} K) )$ time.
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
Venue: arXiv cs.CV 2018
Year: 2018

There are at least two categories of errors in radiology screening that can lead to suboptimal diagnostic decisions and interventions:(i)human fallibility and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are developed to help radiologists to compensate for some of these errors. However, despite their significant improvements over conventional screening strategies, most CAD systems do not go beyond their use as second opinion tools due to producing a high number of false positives, which human interpreters need to correct. In parallel with efforts in computerized analysis of radiology scans, several researchers have examined behaviors of radiologists while screening medical images to better understand how and why they miss tumors, how they interact with the information in an image, and how they search for unknown pathology in the images. Eye-tracking tools have been instrumental in exploring answers to these fundamental questions. In this paper, we aim to develop a paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both of the above mentioned research lines: CAD and eye-tracking. We design an eye-tracking interface providing radiologists with a real radiology reading room experience. Then, we propose a novel algorithm that unifies eye-tracking data and a CAD system. Specifically, we present a new graph based clustering and sparsification algorithm to transform eye-tracking data (gaze) into a signal model to interpret gaze patterns quantitatively and qualitatively. The proposed C-CAD collaborates with radiologists via eye-tracking technology and helps them to improve diagnostic decisions. The C-CAD learns radiologists' search efficiency by processing their gaze patterns. To do this, the C-CAD uses a deep learning algorithm in a newly designed multi-task learning platform to segment and diagnose cancers simultaneously.
MAVIS: Managing Datacenters using Smartphones
Venue: arXiv cs.DC 2018
Year: 2018

Distributed monitoring plays a crucial role in managing the activities of cloud-based datacenters. System administrators have long relied on monitoring systems such as Nagios and Ganglia to obtain status alerts on their desktop-class machines. However, the popularity of mobile devices is pushing the community to develop datacenter monitoring solutions for smartphone-class devices. Here we lay out desirable characteristics of such smartphone-based monitoring and identify quantitatively the shortcomings from directly applying existing solutions to this domain. Then we introduce a possible design that addresses some of these shortcomings and provide results from an early prototype, called MAVIS, using one month of monitoring data from approximately 3,000 machines hosted by Purdue's central IT organization.
Implementation of Neural Network and feature extraction to classify ECG signals
Venue: arXiv cs.NE 2018
Year: 2018

This paper presents a suitable and efficient implementation of a feature extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG) signals, for detection and classification of four cardiac diseases: Sleep Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial Fibrillation (AF) and differentiating them from the normal heart beat by using pan Tompkins RR detection followed by feature extraction for classification purpose .The paper also presents a new approach towards signal classification using the existing neural networks classifiers.
TabVec: Table Vectors for Classification of Web Tables
Venue: arXiv cs.IR 2018
Year: 2018

There are hundreds of millions of tables in Web pages that contain useful information for many applications. Leveraging data within these tables is difficult because of the wide variety of structures, formats and data encoded in these tables. TabVec is an unsupervised method to embed tables into a vector space to support classification of tables into categories (entity, relational, matrix, list, and non-data) with minimal user intervention. TabVec deploys syntax and semantics of table cells, and embeds the structure of tables in a table vector space. This enables superior classification of tables even in the absence of domain annotations. Our evaluations in four real world domains show that TabVec improves classification accuracy by more than 20% compared to three state of the art systems, and that those systems require significant in domain training to achieve good results.