OpenRaiser/Intern-Atlas huggingface dataset:
Scalable linear solvers for sparse linear systems from large-scale numerical simulations
Venue: arXiv cs.MS 2017
Year: 2017

This paper presents our work on designing scalable linear solvers for large-scale reservoir simulations. The main objective is to support implementation of parallel reservoir simulators on distributed-memory parallel systems, where MPI (Message Passing Interface) is employed for communications among computation nodes. Distributed matrix and vector modules are designed, which are the base of our parallel linear systems. Commonly-used Krylov subspace linear solvers are implemented, including the restarted GMRES method, the LGMRES method, and the BiCGSTAB method. It also has an interface to a parallel algebraic multigrid solver, BoomerAMG from HYPRE. Parallel general-purpose preconditioners and special preconditioners for reservoir simulations are also developed. The numerical experiments show that our linear solvers have excellent scalability using thousands of CPU cores.
Gate-Variants of Gated Recurrent Unit (GRU) Neural Networks
Venue: arXiv cs.NE 2017
Year: 2017

The paper evaluates three variants of the Gated Recurrent Unit (GRU) in recurrent neural networks (RNN) by reducing parameters in the update and reset gates. We evaluate the three variant GRU models on MNIST and IMDB datasets and show that these GRU-RNN variant models perform as well as the original GRU RNN model while reducing the computational expense.
Coherency in One-Shot Gesture Recognition
Venue: arXiv cs.HC 2017
Year: 2017

User's intentions may be expressed through spontaneous gesturing, which have been seen only a few times or never before. Recognizing such gestures involves one shot gesture learning. While most research has focused on the recognition of the gestures itself, recently new approaches were proposed to deal with gesture perception and production as part of the same problem. The framework presented in this work focuses on learning the process that leads to gesture generation, rather than mining the gesture's associated features. This is achieved using kinematic, cognitive and biomechanic characteristics of human interaction. These factors enable the artificial production of realistic gesture samples originated from a single observation. The generated samples are then used as training sets for different state-of-the-art classifiers. Performance is obtained first, by observing the machines' gesture recognition percentages. Then, performance is computed by the human recognition from gestures performed by robots. Based on these two scenarios, a composite new metric of coherency is proposed relating to the amount of agreement between these two conditions. Experimental results provide an average recognition performance of 89.2% for the trained classifiers and 92.5% for the participants. Coherency in recognition was determined at 93.6%. While this new metric is not directly comparable to raw accuracy or other pure performance-based standard metrics, it provides a quantifier for validating how realistic the machine generated samples are and how accurate the resulting mimicry is.
Representations of the Multicast Network Problem
Venue: arXiv cs.IT 2017
Year: 2017

We approach the problem of linear network coding for multicast networks from different perspectives. We introduce the notion of the coding points of a network, which are edges of the network where messages combine and coding occurs. We give an integer linear program that leads to choices of paths through the network that minimize the number of coding points. We introduce the code graph of a network, a simplified directed graph that maintains the information essential to understanding the coding properties of the network. One of the main problems in network coding is to understand when the capacity of a multicast network is achieved with linear network coding over a finite field of size q. We explain how this problem can be interpreted in terms of rational points on certain algebraic varieties.
A GPU-Based Solution to Fast Calculation of Betweenness Centrality on Large Weighted Networks
Venue: arXiv cs.SI 2017
Year: 2017

Recent decades have witnessed the tremendous development of network science, which indeed brings a new and insightful language to model real systems of different domains. Betweenness, a widely employed centrality in network science, is a decent proxy in investigating network loads and rankings. However, the extremely high computational cost greatly prevents its applying on large networks. Though several parallel algorithms have been presented to reduce its calculation cost on unweighted networks, a fast solution for weighted networks, which are in fact more ubiquitous than unweighted ones in reality, is still missing. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of betweenness centrality on very large and weighted networks. Comprehensive and systematic evaluations on both synthetic and real-world networks demonstrate that our solution can arrive the performance of 30x to 150x speedup over the CPU implementation by integrating the work-efficient and warp-centric strategies. Our algorithm is completely open-sourced and free to the community and it is public available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPU on personal computers and servers, our solution will indeed offer unprecedented opportunities for exploring the betweenness related problems in network science.
A Minimum Reconfiguration Probability Routing Algorithm for RWA in All-Optical Networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we present a detailed study of Minimum Reconfiguration Probability Routing (MRPR) algorithm, and its performance evaluation in comparison with Adaptive unconstrained routing (AUR) and Least Loaded routing (LLR) algorithms. We have minimized the effects of failures on link and router failure in the network under changing load conditions, we assess the probability of service and number of light path failures due to link or route failure on Wavelength Interchange(WI) network. The computation complexity is reduced by using Kalman Filter(KF) techniques. The minimum reconfiguration probability routing (MRPR) algorithm selects most reliable routes and assign wavelengths to connections in a manner that utilizes the light path(LP) established efficiently considering all possible requests.
Light traffic behavior under the power-of-two load balancing strategy: The case of heterogeneous servers
Venue: arXiv cs.PF 2017
Year: 2017

We consider a multi-server queueing system under the power-of-two policy with Poisson job arrivals, heterogeneous servers and a general job requirement distribution; each server operates under the first-come first-serve policy and there are no buffer constraints. We analyze the performance of this system in light traffic by evaluating the first two light traffic derivatives of the average job response time. These expressions point to several interesting structural features associated with server heterogeneity in light traffic: For unequal capacities, the average job response time is seen to decrease for small values of the arrival rate, and the more diverse the server speeds, the greater the gain in performance. These theoretical findings are assessed through limited simulations.
Opportunities for Analog Coding in Emerging Memory Systems
Venue: arXiv cs.IT 2017
Year: 2017

The exponential growth in data generation and large-scale data analysis creates an unprecedented need for inexpensive, low-latency, and high-density information storage. This need has motivated significant research into multi-level memory systems that can store multiple bits of information per device. Although both the memory state of these devices and much of the data they store are intrinsically analog-valued, both are quantized for use with digital systems and discrete error correcting codes. Using phase change memory as a prototypical multi-level storage technology, we herein demonstrate that analog-valued devices can achieve higher capacities when paired with analog codes. Further, we find that storing analog signals directly through joint-coding can achieve low distortion with reduced coding complexity. By jointly optimizing for signal statistics, device statistics, and a distortion metric, finite-length analog encodings can perform comparable to digital systems with asymptotically infinite large encodings. These results show that end-to-end analog memory systems have not only the potential to reach higher storage capacities than discrete systems, but also to significantly lower coding complexity, leading to faster and more energy efficient storage.
Improving grasp performance using in-hand proximity and contact sensing
Venue: arXiv cs.RO 2017
Year: 2017

We describe the grasping and manipulation strategy that we employed at the autonomous track of the Robotic Grasping and Manipulation Competition at IROS 2016. A salient feature of our architecture is the tight coupling between visual (Asus Xtion) and tactile perception (Robotic Materials), to reduce the uncertainty in sensing and actuation. We demonstrate the importance of tactile sensing and reactive control during the final stages of grasping using a Kinova Robotic arm. The set of tools and algorithms for object grasping presented here have been integrated into the open-source Robot Operating System (ROS).
DeadNet: Identifying Phototoxicity from Label-free Microscopy Images of Cells using Deep ConvNets
Venue: arXiv cs.CV 2017
Year: 2017

Exposure to intense illumination light is an unavoidable consequence of fluorescence microscopy, and poses a risk to the health of the sample in every live-cell fluorescence microscopy experiment. Furthermore, the possible side-effects of phototoxicity on the scientific conclusions that are drawn from an imaging experiment are often unaccounted for. Previously, controlling for phototoxicity in imaging experiments required additional labels and experiments, limiting its widespread application. Here we provide a proof-of-principle demonstration that the phototoxic effects of an imaging experiment can be identified directly from a single phase-contrast image using deep convolutional neural networks (ConvNets). This lays the groundwork for an automated tool for assessing cell health in a wide range of imaging experiments. Interpretability of such a method is crucial for its adoption. We take steps towards interpreting the classification mechanism of the trained ConvNet by visualizing salient features of images that contribute to accurate classification.
Multi-Erasure Locally Recoverable Codes Over Small Fields For Flash Memory Array
Venue: arXiv cs.IT 2017
Year: 2017

Erasure codes play an important role in storage systems to prevent data loss. In this work, we study a class of erasure codes called Multi-Erasure Locally Recoverable Codes (ME-LRCs) for flash memory array. Compared to previous related works, we focus on the construction of ME-LRCs over small fields. We first develop upper and lower bounds on the minimum distance of ME-LRCs. These bounds explicitly take the field size into account. Our main contribution is to propose a general construction of ME-LRCs based on generalized tensor product codes, and study their erasure-correcting property. A decoding algorithm tailored for erasure recovery is given. We then prove that our construction yields optimal ME-LRCs with a wide range of code parameters. Finally, we present several families of ME-LRCs over different fields.
Multimodal Fusion via a Series of Transfers for Noise Removal
Venue: arXiv cs.CV 2017
Year: 2017

Near-infrared imaging has been considered as a solution to provide high quality photographs in dim lighting conditions. This imaging system captures two types of multimodal images: one is near-infrared gray image (NGI) and the other is the visible color image (VCI). NGI is noise-free but it is grayscale, whereas the VCI has colors but it contains noise. Moreover, there exist serious edge and brightness discrepancies between NGI and VCI. To deal with this problem, a new transfer-based fusion method is proposed for noise removal. Different from conventional fusion approaches, the proposed method conducts a series of transfers: contrast, detail, and color transfers. First, the proposed contrast and detail transfers aim at solving the serious discrepancy problem, thereby creating a new noise-free and detail-preserving NGI. Second, the proposed color transfer models the unknown colors from the denoised VCI via a linear transform, and then transfers natural-looking colors into the newly generated NGI. Experimental results show that the proposed transfer-based fusion method is highly successful in solving the discrepancy problem, thereby describing edges and textures clearly as well as removing noise completely on the fused images. Most of all, the proposed method is superior to conventional fusion methods and guided filtering, and even the state-of-the-art fusion methods based on scale map and layer decomposition.
Optimization on Product Submanifolds of Convolution Kernels
Venue: arXiv cs.CV 2017
Year: 2017

Recent advances in optimization methods used for training convolutional neural networks (CNNs) with kernels, which are normalized according to particular constraints, have shown remarkable success. This work introduces an approach for training CNNs using ensembles of joint spaces of kernels constructed using different constraints. For this purpose, we address a problem of optimization on ensembles of products of submanifolds (PEMs) of convolution kernels. To this end, we first propose three strategies to construct ensembles of PEMs in CNNs. Next, we expound their geometric properties (metric and curvature properties) in CNNs. We make use of our theoretical results by developing a geometry-aware SGD algorithm (G-SGD) for optimization on ensembles of PEMs to train CNNs. Moreover, we analyze convergence properties of G-SGD considering geometric properties of PEMs. In the experimental analyses, we employ G-SGD to train CNNs on Cifar-10, Cifar-100 and Imagenet datasets. The results show that geometric adaptive step size computation methods of G-SGD can improve training loss and convergence properties of CNNs. Moreover, we observe that classification performance of baseline CNNs can be boosted using G-SGD on ensembles of PEMs identified by multiple constraints.
Retractability, games and orchestrators for session contracts
Venue: arXiv cs.LO 2017
Year: 2017

Session contracts is a formalism enabling to investigate client/server interaction protocols and to interpret session types. We extend session contracts in order to represent outputs whose actual sending in an interaction depends on a third party or on a mutual agreement between the partners. Such contracts are hence adaptable, or as we say "affectible". In client/server systems, in general, compliance stands for the satisfaction of all client's requests by the server. We define an abstract notion of "affectible compliance" and show it to have a precise three-party game-theoretic interpretation. This in turn is shown to be equivalent to a compliance based on interactions that can undergo a sequence of failures and rollbacks, as well as to a compliance based on interactions which can be mediated by an orchestrator. Besides, there is a one-to-one effective correspondence between winning strategies and orchestrators. The relation of subcontract for affectible contracts is also investigated.
The Influence of Teamwork Quality on Software Team Performance
Venue: arXiv cs.SE 2017
Year: 2017

Traditionally, software quality is thought to depend on sound software engineering and development methodologies such as structured programming and agile development. However, high quality software depends just as much on high quality collaboration within the team. Since the success rate of software development projects is low (Wateridge, 1995; The Standish Group, 2009), it is important to understand which characteristics of interactions within software development teams significantly influence performance. Hoegl and Gemuenden (2001) reported empirical evidence for the relation between teamwork quality and software quality, using a six-factor teamwork quality (TWQ) model. This article extends the work of Hoegl and Gemuenden (2001) with the aim of finding additional factors that may influence software team performance. We introduce three new TWQ factors: trust, value sharing, and coordination of expertise. The relationship between TWQ and team performance and the improvement of the model are tested using data from 252 team members and stakeholders. Results show that teamwork quality is significantly related to team performance, as rated by both team members and stakeholders: TWQ explains 81% of the variance of team performance as rated by team members and 61% as rated by stakeholders. This study shows that trust, shared values, and coordination of expertise are important factors for team leaders to consider in order to achieve high quality software team work.
Pseudospectral methods for density functional theory in bounded and unbounded domains
Venue: arXiv cs.CE 2017
Year: 2017

Classical Density Functional Theory (DFT) is a statistical-mechanical framework to analyze fluids, which accounts for nanoscale fluid inhomogeneities and non-local intermolecular interactions. DFT can be applied to a wide range of interfacial phenomena, as well as problems in adsorption, colloidal science and phase transitions in fluids. Typical DFT equations are highly non-linear, stiff and contain several convolution terms. We propose a novel, efficient pseudo-spectral collocation scheme for computing the non-local terms in real space with the help of a specialized Gauss quadrature. Due to the exponential accuracy of the quadrature and a convenient choice of collocation points near interfaces, we can use grids with a significantly lower number of nodes than most other reported methods. We demonstrate the capabilities of our numerical methodology by studying equilibrium and dynamic two-dimensional test cases with single- and multispecies hard-sphere and hard-disc particles modelled with fundamental measure theory, with and without van der Waals attractive forces, in bounded and unbounded physical domains. We show that our results satisfy statistical mechanical sum rules.
A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction
Venue: arXiv cs.CV 2017
Year: 2017

The inception network has been shown to provide good performance on image classification problems, but there are not much evidences that it is also effective for the image restoration or pixel-wise labeling problems. For image restoration problems, the pooling is generally not used because the decimated features are not helpful for the reconstruction of an image as the output. Moreover, most deep learning architectures for the restoration problems do not use dense prediction that need lots of training parameters. From these observations, for enjoying the performance of inception-like structure on the image based problems we propose a new convolutional network-in-network structure. The proposed network can be considered a modification of inception structure where pool projection and pooling layer are removed for maintaining the entire feature map size, and a larger kernel filter is added instead. Proposed network greatly reduces the number of parameters on account of removed dense prediction and pooling, which is an advantage, but may also reduce the receptive field in each layer. Hence, we add a larger kernel than the original inception structure for not increasing the depth of layers. The proposed structure is applied to typical image-to-image learning problems, i.e., the problems where the size of input and output are same such as skin detection, semantic segmentation, and compression artifacts reduction. Extensive experiments show that the proposed network brings comparable or better results than the state-of-the-art convolutional neural networks for these problems.
Counterexample-guided Abstraction Refinement for POMDPs
Venue: arXiv cs.LO 2017
Year: 2017

Partially Observable Markov Decision Process (POMDP) is widely used to model probabilistic behavior for complex systems. Compared with MDPs, POMDP models a system more accurate but solving a POMDP generally takes exponential time in the size of its state space. This makes the formal verification and synthesis problems much more challenging for POMDPs, especially when multiple system components are involved. As a promising technique to reduce the verification complexity, the abstraction method tries to find an abstract system with a smaller state space but preserves enough properties for the verification purpose. While abstraction based verification has been explored extensively for MDPs, in this paper, we present the first result of POMDP abstraction and its refinement techniques. The main idea follows the counterexample-guided abstraction refinement (CEGAR) framework. Starting with a coarse guess for the POMDP abstraction, we iteratively use counterexamples from formal verification to refine the abstraction until the abstract system can be used to infer the verification result for the original POMDP. Our main contributions have two folds: 1) we propose a novel abstract system model for POMDP and a new simulation relation to capture the partial observability then prove the preservation on a fragment of Probabilistic Computation Tree Logic (PCTL); 2) to find a proper abstract system that can prove or disprove the satisfaction relation on the concrete POMDP, we develop a novel refinement algorithm. Our work leads to a sound and complete CEGAR framework for POMDP.
A Tutorial on Modeling and Analysis of Dynamic Social Networks. Part I
Venue: arXiv cs.SY 2017
Year: 2017

In recent years, we have observed a significant trend towards filling the gap between social network analysis and control. This trend was enabled by the introduction of new mathematical models describing dynamics of social groups, the advancement in complex networks theory and multi-agent systems, and the development of modern computational tools for big data analysis. The aim of this tutorial is to highlight a novel chapter of control theory, dealing with applications to social systems, to the attention of the broad research community. This paper is the first part of the tutorial, and it is focused on the most classical models of social dynamics and on their relations to the recent achievements in multi-agent systems.