OpenRaiser/Intern-Atlas huggingface dataset:
Symbolic Algorithms for Graphs and Markov Decision Processes with Fairness Objectives
Venue: arXiv cs.DS 2018
Year: 2018

Given a model and a specification, the fundamental model-checking problem asks for algorithmic verification of whether the model satisfies the specification. We consider graphs and Markov decision processes (MDPs), which are fundamental models for reactive systems. One of the very basic specifications that arise in verification of reactive systems is the strong fairness (aka Streett) objective. Given different types of requests and corresponding grants, the objective requires that for each type, if the request event happens infinitely often, then the corresponding grant event must also happen infinitely often. All $ω$-regular objectives can be expressed as Streett objectives and hence they are canonical in verification. To handle the state-space explosion, symbolic algorithms are required that operate on a succinct implicit representation of the system rather than explicitly accessing the system. While explicit algorithms for graphs and MDPs with Streett objectives have been widely studied, there has been no improvement of the basic symbolic algorithms. The worst-case numbers of symbolic steps required for the basic symbolic algorithms are as follows: quadratic for graphs and cubic for MDPs. In this work we present the first sub-quadratic symbolic algorithm for graphs with Streett objectives, and our algorithm is sub-quadratic even for MDPs. Based on our algorithmic insights we present an implementation of the new symbolic approach and show that it improves the existing approach on several academic benchmark examples.
A comparative analysis of state-of-the-art SQL-on-Hadoop systems for interactive analytics
Venue: arXiv cs.DB 2018
Year: 2018

Hadoop is emerging as the primary data hub in enterprises, and SQL represents the de facto language for data analysis. This combination has led to the development of a variety of SQL-on-Hadoop systems in use today. While the various SQL-on-Hadoop systems target the same class of analytical workloads, their different architectures, design decisions and implementations impact query performance. In this work, we perform a comparative analysis of four state-of-the-art SQL-on-Hadoop systems (Impala, Drill, Spark SQL and Phoenix) using the Web Data Analytics micro benchmark and the TPC-H benchmark on the Amazon EC2 cloud platform. The TPC-H experiment results show that, although Impala outperforms other systems (4.41x - 6.65x) in the text format, trade-offs exists in the parquet format, with each system performing best on subsets of queries. A comprehensive analysis of execution profiles expands upon the performance results to provide insights into performance variations, performance bottlenecks and query execution characteristics.
A Modified Approximate Dynamic Programming Algorithm for Community-level Food Security Following Disasters
Venue: arXiv cs.CE 2018
Year: 2018

In the aftermath of an extreme natural hazard, community residents must have access to functioning food retailers to maintain food security. Food security is dependent on supporting critical infrastructure systems, including electricity, potable water, and transportation. An understanding of the response of such interdependent networks and the process of post-disaster recovery is the cornerstone of an efficient emergency management plan. In this study, the interconnectedness among different critical facilities, such as electrical power networks, water networks, highway bridges, and food retailers, is modeled. The study considers various sources of uncertainty and complexity in the recovery process of a community to capture the stochastic behavior of the spatially distributed infrastructure systems. The study utilizes an approximate dynamic programming (ADP) framework to allocate resources to restore infrastructure components efficiently. The proposed ADP scheme enables us to identify near-optimal restoration decisions at the community level. Furthermore, we employ a simulated annealing (SA) algorithm to complement the proposed ADP framework and to identify near-optimal actions accurately. In the sequel, we use the City of Gilroy, California, USA to illustrate the applicability of the proposed methodology following a severe earthquake. The approach can be implemented efficiently to identify practical policy interventions to hasten recovery of food systems and to reduce adverse food-insecurity impacts for other hazards and communities.
Adaptive Group Shuffled Decoding for LDPC Codes
Venue: arXiv cs.IT 2018
Year: 2018

We propose new grouping methods for group shuffled (GS) decoding of both regular and irregular low-density parity check cods. These methods are independent of the check-to-variable message formula used. Integer-valued metrics for measuring the reliability of each tentative variable node (VN) decision and the associated likelihood of being corrected are developed. The metrics are used to determine the VN updating priority so the grouping may vary in each iteration. We estimate the computation complexity needed to adaptively regroup VNs. Numerical results show that our GS algorithms improve the performance of some existing GS belief-propagation decoders.
Trajectory Optimization of Robots with Regenerative Drive Systems: Numerical and Experimental Results
Venue: arXiv cs.SY 2018
Year: 2018

We investigate energy-optimal control of robots with ultracapacitor based regenerative drive systems. Based on a previously introduced framework, a fairly generic model is considered for the robot and the drive system. An optimal control problem is formulated to find point-to point trajectories maximizing the amount of energy regenerated and stored in the capacitor. The optimization problem, its numerical solution and an experimental evaluation are demonstrated using a PUMA 560 manipulator. A comprehensive experimental setup was prepared to evaluate power flows and energy regeneration. Tracking of optimal trajectories was enforced on the robot using a standard robust passivity based control approach. Experimental results show that when following optimal trajectories, a reduction of about 13\% in energy consumption can be achieved for the conditions of the study.
TipsC: Tips and Corrections for programming MOOCs
Venue: arXiv cs.AI 2018
Year: 2018

With the widespread adoption of MOOCs in academic institutions, it has become imperative to come up with better techniques to solve the tutoring and grading problems posed by programming courses. Programming being the new 'writing', it becomes a challenge to ensure that a large section of the society is exposed to programming. Due to the gradient in learning abilities of students, the course instructor must ensure that everyone can cope up with the material, and receive adequate help in completing assignments while learning along the way. We introduce TipsC for this task. By analyzing a large number of correct submissions, TipsC can search for correct codes resembling a given incorrect solution. Without revealing the actual code, TipsC then suggests changes in the incorrect code to help the student fix logical runtime errors. In addition, this also serves as a cluster visualization tool for the instructor, revealing different patterns in user submissions. We evaluated the effectiveness of TipsC's clustering algorithm on data collected from previous offerings of an introductory programming course conducted at IIT Kanpur where the grades were given by human TAs. The results show the weighted average variance of marks for clusters when similar submissions are grouped together is 47% less compared to the case when all programs are grouped together.
Generative Spatiotemporal Modeling Of Neutrophil Behavior
Venue: arXiv cs.CV 2018
Year: 2018

Cell motion and appearance have a strong correlation with cell cycle and disease progression. Many contemporary efforts in machine learning utilize spatio-temporal models to predict a cell's physical state and, consequently, the advancement of disease. Alternatively, generative models learn the underlying distribution of the data, creating holistic representations that can be used in learning. In this work, we propose an aggregate model that combine Generative Adversarial Networks (GANs) and Autoregressive (AR) models to predict cell motion and appearance in human neutrophils imaged by differential interference contrast (DIC) microscopy. We bifurcate the task of learning cell statistics by leveraging GANs for the spatial component and AR models for the temporal component. The aggregate model learned results offer a promising computational environment for studying changes in organellar shape, quantity, and spatial distribution over large sequences.
A Note on Kaldi's PLDA Implementation
Venue: arXiv cs.LG 2018
Year: 2018

Some explanations to Kaldi's PLDA implementation to make formula derivation easier to catch.
Subspace-Orbit Randomized Decomposition for Low-rank Matrix Approximation
Venue: arXiv cs.NA 2018
Year: 2018

An efficient, accurate and reliable approximation of a matrix by one of lower rank is a fundamental task in numerical linear algebra and signal processing applications. In this paper, we introduce a new matrix decomposition approach termed Subspace-Orbit Randomized singular value decomposition (SOR-SVD), which makes use of random sampling techniques to give an approximation to a low-rank matrix. Given a large and dense data matrix of size $m\times n$ with numerical rank $k$, where $k \ll \text{min} \{m,n\}$, the algorithm requires a few passes through data, and can be computed in $O(mnk)$ floating-point operations. Moreover, the SOR-SVD algorithm can utilize advanced computer architectures, and, as a result, it can be optimized for maximum efficiency. The SOR-SVD algorithm is simple, accurate, and provably correct, and outperforms previously reported techniques in terms of accuracy and efficiency. Our numerical experiments support these claims.
Lisp, Jazz, Aikido -- Three Expressions of a Single Essence
Venue: arXiv cs.PL 2018
Year: 2018

The relation between Science (what we can explain) and Art (what we can't) has long been acknowledged and while every science contains an artistic part, every art form also needs a bit of science. Among all scientific disciplines, programming holds a special place for two reasons. First, the artistic part is not only undeniable but also essential. Second, and much like in a purely artistic discipline, the act of programming is driven partly by the notion of aesthetics: the pleasure we have in creating beautiful things. Even though the importance of aesthetics in the act of programming is now unquestioned, more could still be written on the subject. The field called "psychology of programming" focuses on the cognitive aspects of the activity, with the goal of improving the productivity of programmers. While many scientists have emphasized their concern for aesthetics and the impact it has on their activity, few computer scientists have actually written about their thought process while programming. What makes us like or dislike such and such language or paradigm? Why do we shape our programs the way we do? By answering these questions from the angle of aesthetics, we may be able to shed some new light on the art of programming. Starting from the assumption that aesthetics is an inherently transversal dimension, it should be possible for every programmer to find the same aesthetic driving force in every creative activity they undertake, not just programming, and in doing so, get deeper insight on why and how they do things the way they do. On the other hand, because our aesthetic sensitivities are so personal, all we can really do is relate our own experiences and share it with others, in the hope that it will inspire them to do the same. My personal life has been revolving around three major creative activities, of equal importance: programming in Lisp, playing Jazz music, and practicing Aikido. But why so many of them, why so different ones, and why these specifically? By introspecting my personal aesthetic sensitivities, I eventually realized that my tastes in the scientific, artistic, and physical domains are all motivated by the same driving forces, hence unifying Lisp, Jazz, and Aikido as three expressions of a single essence, not so different after all. Lisp, Jazz, and Aikido are governed by a limited set of rules which remain simple and unobtrusive. Conforming to them is a pleasure. Because Lisp, Jazz, and Aikido are inherently introspective disciplines, they also invite you to transgress the rules in order to find your own. Breaking the rules is fun. Finally, if Lisp, Jazz, and Aikido unify so many paradigms, styles, or techniques, it is not by mere accumulation but because they live at the meta-level and let you reinvent them. Working at the meta-level is an enlightening experience. Understand your aesthetic sensitivities and you may gain considerable insight on your own psychology of programming. Mine is perhaps common to most lispers. Perhaps also common to other programming communities, but that, is for the reader to decide...
Regional Priority Based Anomaly Detection using Autoencoders
Venue: arXiv cs.CV 2018
Year: 2018

In the recent times, autoencoders, besides being used for compression, have been proven quite useful even for regenerating similar images or help in image denoising. They have also been explored for anomaly detection in a few cases. However, due to location invariance property of convolutional neural network, autoencoders tend to learn from or search for learned features in the complete image. This creates issues when all the items in the image are not equally important and their location matters. For such cases, a semi supervised solution - regional priority based autoencoder (RPAE) has been proposed. In this model, similar to object detection models, a region proposal network identifies the relevant areas in the images as belonging to one of the predefined categories and then those bounding boxes are fed into appropriate decoder based on the category they belong to. Finally, the error scores from all the decoders are combined based on their importance to provide total reconstruction error.
MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification
Venue: arXiv cs.CV 2018
Year: 2018

Traffic sign recognition is a very important computer vision task for a number of real-world applications such as intelligent transportation surveillance and analysis. While deep neural networks have been demonstrated in recent years to provide state-of-the-art performance traffic sign recognition, a key challenge for enabling the widespread deployment of deep neural networks for embedded traffic sign recognition is the high computational and memory requirements of such networks. As a consequence, there are significant benefits in investigating compact deep neural network architectures for traffic sign recognition that are better suited for embedded devices. In this paper, we introduce MicronNet, a highly compact deep convolutional neural network for real-time embedded traffic sign recognition designed based on macroarchitecture design principles (e.g., spectral macroarchitecture augmentation, parameter precision optimization, etc.) as well as numerical microarchitecture optimization strategies. The resulting overall architecture of MicronNet is thus designed with as few parameters and computations as possible while maintaining recognition performance, leading to optimized information density of the proposed network. The resulting MicronNet possesses a model size of just ~1MB and ~510,000 parameters (~27x fewer parameters than state-of-the-art) while still achieving a human performance level top-1 accuracy of 98.9% on the German traffic sign recognition benchmark. Furthermore, MicronNet requires just ~10 million multiply-accumulate operations to perform inference, and has a time-to-compute of just 32.19 ms on a Cortex-A53 high efficiency processor. These experimental results show that highly compact, optimized deep neural network architectures can be designed for real-time traffic sign recognition that are well-suited for embedded scenarios.
Land use mapping in the Three Gorges Reservoir Area based on semantic segmentation deep learning method
Venue: arXiv cs.CV 2018
Year: 2018

The Three Gorges Dam, a massive cross-century project spans the Yangtze River by the town of Sandouping, located in Yichang, Hubei province, China, was built to provide great power, improve the River shipping, control floods in the upper reaches of the Yangtze River, and increase the dry season flow in the middle and lower reaches of the Yangtze River. Benefits are enormous and comprehensive. However, the social and environmental impacts are also immense and far-reaching to its surrounding areas. Mapping land use /land cover changed (LUCC) is critical for tracking the impacts. Remote sensing has been proved to be an effective way to map and monitor land use change in real time and in large areas such as the Three Gorges Reservoir Area(TGRA) by using pixel based or oriented based classifier in different resolution. In this paper, we first test the state of the art semantic segmentation deep learning classifiers for LUCC mapping with 7 categories in the TGRA area with rapideye 5m resolution data. The topographic information was also added for better accuracy in mountain area. By compared with the pixel-based classifier, the semantic segmentation deep learning method has better accuracy and robustness at 5m resolution level.
Efficient Clear Air Turbulence Avoidance Algorithms using IoT for Commercial Aviation
Venue: arXiv cs.NI 2018
Year: 2018

With the growth of commercial aviation over the last few decades there have been many applications designed to improve the efficiency of flight operations as well as safety and security. A number of these applications are based on the gathered data from flights; the data is usually acquired from the various sensors available on the aircraft. There are numerous sensors among the electrical and electronics devices on an aircraft, most of which are essential for the proper functioning of the same. With the sensors being operational throughout the time of movement of the aircraft, a large amount of data is collected during each flight. Normally, most of the gathered data are stored on a storage device on the aircraft, and are analyzed and studied later off-site for research purposes focusing on improving airline operation and efficiently maintaining the same. In certain cases, when there is data transfer during the flight, it is between the aircraft and an air-traffic-control (ATC) tower, which serves as the base station. The aircraft equipped with all these sensors, which can gather and exchange data, form a framework of Internet of things (IoT). Detecting and avoiding any form of turbulence for an aircraft is vital; it adds to the safety of both passengers and aircraft while reducing the operating cost of the airline. Therefore, in this paper, we study techniques to detect and avoid Clear Air Turbulence (CAT), which is a specific type of turbulence, based on the IoT framework of aircraft. We propose algorithms that consider both direct and indirect communication between aircraft within a specific region. Using simulation results, we show that our proposed techniques of direct communication using the IoT framework is faster than conventional techniques involving radio communication via both single ATC tower and multiple ATC towers.
Performance Optimization in Mobile-Edge Computing via Deep Reinforcement Learning
Venue: arXiv cs.NI 2018
Year: 2018

To improve the quality of computation experience for mobile devices, mobile-edge computing (MEC) is emerging as a promising paradigm by providing computing capabilities within radio access networks in close proximity. Nevertheless, the design of computation offloading policies for a MEC system remains challenging. Specifically, whether to execute an arriving computation task at local mobile device or to offload a task for cloud execution should adapt to the environmental dynamics in a smarter manner. In this paper, we consider MEC for a representative mobile user in an ultra dense network, where one of multiple base stations (BSs) can be selected for computation offloading. The problem of solving an optimal computation offloading policy is modelled as a Markov decision process, where our objective is to minimize the long-term cost and an offloading decision is made based on the channel qualities between the mobile user and the BSs, the energy queue state as well as the task queue state. To break the curse of high dimensionality in state space, we propose a deep $Q$-network-based strategic computation offloading algorithm to learn the optimal policy without having a priori knowledge of the dynamic statistics. Numerical experiments provided in this paper show that our proposed algorithm achieves a significant improvement in average cost compared with baseline policies.
CompNet: Complementary Segmentation Network for Brain MRI Extraction
Venue: arXiv cs.CV 2018
Year: 2018

Brain extraction is a fundamental step for most brain imaging studies. In this paper, we investigate the problem of skull stripping and propose complementary segmentation networks (CompNets) to accurately extract the brain from T1-weighted MRI scans, for both normal and pathological brain images. The proposed networks are designed in the framework of encoder-decoder networks and have two pathways to learn features from both the brain tissue and its complementary part located outside of the brain. The complementary pathway extracts the features in the non-brain region and leads to a robust solution to brain extraction from MRIs with pathologies, which do not exist in our training dataset. We demonstrate the effectiveness of our networks by evaluating them on the OASIS dataset, resulting in the state of the art performance under the two-fold cross-validation setting. Moreover, the robustness of our networks is verified by testing on images with introduced pathologies and by showing its invariance to unseen brain pathologies. In addition, our complementary network design is general and can be extended to address other image segmentation problems with better generalization.
A Neuronal Planar Modeling for Handwriting Signature based on Automatic Segmentation
Venue: arXiv cs.CV 2018
Year: 2018

This paper deals with offline handwriting signature verification.We propose a planar neuronal model of signature image. Planarmodelsare generally based on delimiting homogenous zones ofimages; we propose in this paper an automatic segmentationapproach into bands of signature images. Signature image ismodeled by a planar neuronal model with horizontal secondarymodels and a verticalprincipal model. The proposed methodhas been tested on two databases. The first is the one we havecollected; it includes 6000 signaturescorresponding to 60writers. The second is the public GPDS-300 database including16200 signature corresponding to 300 persons. The achievedresults are promising.
Multimodal Biometric Authentication Using Choquet Integral and Genetic Algorithm
Venue: arXiv cs.CV 2018
Year: 2018

The Choquet integral is a tool for the information fusion that is very effective in the case where fuzzy measures associated with it are well chosen. In this paper,we propose a new approach for calculating fuzzy measures associated with the Choquet integral in a context of data fusion in multimodal biometrics. The proposed approach is based on genetic algorithms. It has been validated in two databases: the first base is relative to synthetic scores and the second one is biometrically relating to the face, fingerprintand palmprint. The results achieved attest the robustness of the proposed approach.