OpenRaiser/Intern-Atlas huggingface dataset:
Simple and efficient GPU parallelization of existing H-Matrix accelerated BEM code
Venue: arXiv cs.DC 2017
Year: 2017

In this paper, we demonstrate how GPU-accelerated BEM routines can be used in a simple black-box fashion to accelerate fast boundary element formulations based on Hierarchical Matrices (H-Matrices) with ACA (Adaptive Cross Approximation). In particular, we focus on the expensive evaluation of the discrete weak form of boundary operators associated with the Laplace and the Helmholtz equation in three space dimensions. The method is based on offloading the CPU assembly of elements during the ACA assembly onto a GPU device and to use threading strategies across ACA blocks to create sufficient workload for the GPU. The proposed GPU strategy is designed such that it can be implemented in existing code with minimal changes to the surrounding application structure. This is in particular interesting for existing legacy code that is not from the ground-up designed with GPU computing in mind. Our benchmark study gives realistic impressions of the benefits of GPU-accelerated BEM simulations by using state-of-the-art multi-threaded computations on modern high-performance CPUs as a reference, rather than drawing synthetic comparisons with single-threaded codes. Speed-up plots illustrate that performance gains up to a factor of 5.5 could be realized with GPU computing under these conditions. This refers to a boundary element model with about 4 million unknowns, whose H-Matrix weak form associated with a real-valued (Laplace) boundary operator is set up in only 100 minutes harnessing the two GPUs instead of 9 hours when using the 20 CPU cores at disposal only. The benchmark study is followed by a particularly demanding real-life application, where we compute the scattered high-frequency sound field of a submarine to demonstrate the increase in overall application performance from moving to a GPU-based ACA assembly.
A Foundry of Human Activities and Infrastructures
Venue: arXiv cs.AI 2017
Year: 2017

Direct representation knowledgebases can enhance and even provide an alternative to document-centered digital libraries. Here we consider realist semantic modeling of everyday activities and infrastructures in such knowledgebases. Because we want to integrate a wide variety of topics, a collection of ontologies (a foundry) and a range of other knowledge resources are needed. We first consider modeling the routine procedures that support human activities and technologies. Next, we examine the interactions of technologies with aspects of social organization. Then, we consider approaches and issues for developing and validating explanations of the relationships among various entities.
Mandarin tone modeling using recurrent neural networks
Venue: arXiv cs.SD 2017
Year: 2017

We propose an Encoder-Classifier framework to model the Mandarin tones using recurrent neural networks (RNN). In this framework, extracted frames of features for tone classification are fed in to the RNN and casted into a fixed dimensional vector (tone embedding) and then classified into tone types using a softmax layer along with other auxiliary inputs. We investigate various configurations that help to improve the model, including pooling, feature splicing and utilization of syllable-level tone embeddings. Besides, tone embeddings and durations of the contextual syllables are exploited to facilitate tone classification. Experimental results on Mandarin tone classification show the proposed network setups improve tone classification accuracy. The results indicate that the RNN encoder-classifier based tone model flexibly accommodates heterogeneous inputs (sequential and segmental) and hence has the advantages from both the sequential classification tone models and segmental classification tone models.
Reducing Certification Granularity to Increase Adaptability of Avionics Software
Venue: arXiv cs.SE 2017
Year: 2017

A strong certification process is required to insure the safety of airplanes, and more specifically the robustness of avionics applications. To implement this process, the development of avionics software must follow long and costly procedures. Most of these procedures have to be reexecuted each time the software is modified. In this paper, we propose a framework to reduce the cost and time impact of a software modification. With this new approach, the piece of software likely to change is isolated from the rest of the application, so it can be certified independently. This helps the system integrator to adapt an avionics application to the specificities of the target airplane, without the need for a new certification of the application.
ICN-aware Network Slicing Framework for Mobile Data Distribution
Venue: arXiv cs.NI 2017
Year: 2017

Network slicing offers an opportunity to realize ICN as a slice in 5G deployment. We demonstrate this through a generic service orchestration framework operating on commodity compute, storage and bandwidth resource pool to realize multiple ICN service slices. Specifically, we show the dynamic creation of real-time audio/video conferencing slices, over which multi-participant communication is enabled. These slices leverage ICN features of name-based routing, integrated security, inherent support for multicasting and mobility, and in-network caching-and-computing to scale and deliver services efficiently, while dynamically adapting to varying service demands. Proposed framework also enables mobility-on-demand feature as a service over an ICN slice to more effectively support producer mobility over multi-access links, such as LTE, Wifi and Ethernet, as will be demonstrated with our demo.
On the Monetary Loss Due to Passive and Active Attacks on MIMO Smart Grid Communications
Venue: arXiv cs.NI 2017
Year: 2017

We consider multiple source nodes (consumers) communicating wirelessly their energy demands to the meter data-management system (MDMS) over the subarea gateway(s). We quantify the impacts of passive and active security attacks on the wireless communications system's reliability and security as well as the energy-demand estimation-error cost in dollars paid by the utility. We adopt a multiple-input multiple-output multi-antenna-eavesdropper (MIMOME) wiretap channel model. To secure the MIMO wireless communication system, the legitimate nodes generate artificial noise (AN) vectors to mitigate the effect of the passive eavesdropping attacks. In addition, we propose a redundant design where multiple gateways are assumed to coexist in each subarea to forward the consumers' energy-demand messages. We quantify the redundant designs impact on the communication reliability between the consumers and the MDMS and on the energy-demand estimation-error cost.
SegICP-DSR: Dense Semantic Scene Reconstruction and Registration
Venue: arXiv cs.RO 2017
Year: 2017

To enable autonomous robotic manipulation in unstructured environments, we present SegICP-DSR, a real- time, dense, semantic scene reconstruction and pose estimation algorithm that achieves mm-level pose accuracy and standard deviation (7.9 mm, σ=7.6 mm and 1.7 deg, σ=0.7 deg) and suc- cessfully identified the object pose in 97% of test cases. This represents a 29% increase in accuracy, and a 14% increase in success rate compared to SegICP in cluttered, unstruc- tured environments. The performance increase of SegICP-DSR arises from (1) improved deep semantic segmentation under adversarial training, (2) precise automated calibration of the camera intrinsic and extrinsic parameters, (3) viewpoint specific ray-casting of the model geometry, and (4) dense semantic ElasticFusion point clouds for registration. We benchmark the performance of SegICP-DSR on thousands of pose-annotated video frames and demonstrate its accuracy and efficacy on two tight tolerance grasping and insertion tasks using a KUKA LBR iiwa robotic arm.
A Theory of Slicing for Probabilistic Control-Flow Graphs
Venue: arXiv cs.PL 2017
Year: 2017

We present a theory for slicing probabilistic imperative programs -- containing random assignments, and ``observe'' statements (for conditioning) -- represented as probabilistic control-flow graphs (pCFGs) whose nodes modify probability distributions. We show that such a representation allows direct adaptation of standard machinery such as data and control dependence, postdominators, relevant variables, etc to the probabilistic setting. We separate the specification of slicing from its implementation: first we develop syntactic conditions that a slice must satisfy; next we prove that any such slice is semantically correct; finally we give an algorithm to compute the least slice. To generate smaller slices, we may in addition take advantage of knowledge that certain loops will terminate (almost) always. A key feature of our syntactic conditions is that they involve two disjoint slices such that the variables of one slice are probabilistically independent of the variables of the other. This leads directly to a proof of correctness of probabilistic slicing. In a companion article we show adequacy of the semantics of pCFGs with respect to the standard semantics of structured probabilistic programs.
Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Hand gesture recognition has long been a hot topic in human computer interaction. Traditional camera-based hand gesture recognition systems cannot work properly under dark circumstances. In this paper, a Doppler Radar based hand gesture recognition system using convolutional neural networks is proposed. A cost-effective Doppler radar sensor with dual receiving channels at 5.8GHz is used to acquire a big database of four standard gestures. The received hand gesture signals are then processed with time-frequency analysis. Convolutional neural networks are used to classify different gestures. Experimental results verify the effectiveness of the system with an accuracy of 98%. Besides, related factors such as recognition distance and gesture scale are investigated.
A Semantics for Probabilistic Control-Flow Graphs
Venue: arXiv cs.PL 2017
Year: 2017

This article develops a novel operational semantics for probabilistic control-flow graphs (pCFGs) of probabilistic imperative programs with random assignment and "observe" (or conditioning) statements. The semantics transforms probability distributions (on stores) as control moves from one node to another in pCFGs. We relate this semantics to a standard, expectation-transforming, denotational semantics of structured probabilistic imperative programs, by translating structured programs into (unstructured) pCFGs, and proving adequacy of the translation. This shows that the operational semantics can be used without loss of information, and is faithful to the "intended" semantics and hence can be used to reason about, for example, the correctness of transformations (as we do in a companion article).
Intelligent Collision Management in Dynamic Environments for Human-Centered Robots
Venue: arXiv cs.RO 2017
Year: 2017

In this context, a major focus of this thesis is on unintentional collisions, where a straight goal is to eliminate injury from users and passerby's via realtime sensing and control systems. A less obvious focus is to combine collision response with tools from motion planning in order to produce intelligent safety behaviors that ensure the safety of multiple people or objects. Yet, an even more challenging problem is to anticipate future collisions between objects external to the robot and have the robot intervene to prevent imminent accidents. In this dissertation, we study all of these sophisticated flavors of collision reaction and intervention. We investigate in-depth multiple key and interesting topics related to collisions and safety of mobile robots and robotic manipulators operating in human environments. Overall we deeply investigate collisions from many perspectives and develop techniques that borrow and contribute to the areas of mechatronic design, sensor processing, feedback controls, motion planning, and probabilistic reasoning methods. The result of this study is a set of key experiments and guidelines to deal with collisions in mobile robots and robotic manipulators. This study aims at influencing future studies on field operations of robots and accelerate the employment of advanced robots in our daily environments without compromising our safety.
Multi-mode Tracking of a Group of Mobile Agents
Venue: arXiv cs.IT 2017
Year: 2017

We consider the problem of tracking a group of mobile nodes with limited available computational and energy resources given noisy RSSI measurements and position estimates from group members. The multilateration solutions are known for energy efficiency. However, these solutions are not directly applicable to dynamic grouping scenarios where neighbourhoods and resource availability may frequently change. Existing algorithms such as cluster-based GPS duty-cycling, individual-based tracking, and multilateration-based tracking can only partially deal with the challenges of dynamic grouping scenarios. To cope with these challenges in an effective manner, we propose a new group-based multi-mode tracking algorithm. The proposed algorithm takes the topological structure of the group as well as the availability of the resources into consideration and decides the best solution at any particular time instance. We consider a clustering approach where a cluster head coordinates the usage of resources among the cluster members. We evaluate the energy-accuracy trade-off of the proposed algorithm for various fixed sampling intervals. The evaluation is based on the 2D position tracks of 40 nodes generated using Reynolds' flocking model. For a given energy budget, the proposed algorithm reduces the mean tracking error by up to $20\%$ in comparison to the existing energy-efficient cooperative algorithms. Moreover, the proposed algorithm is as accurate as the individual-based tracking while using almost half the energy.
A Tutorial on Canonical Correlation Methods
Venue: arXiv cs.LG 2017
Year: 2017

Canonical correlation analysis is a family of multivariate statistical methods for the analysis of paired sets of variables. Since its proposition, canonical correlation analysis has for instance been extended to extract relations between two sets of variables when the sample size is insufficient in relation to the data dimensionality, when the relations have been considered to be non-linear, and when the dimensionality is too large for human interpretation. This tutorial explains the theory of canonical correlation analysis including its regularised, kernel, and sparse variants. Additionally, the deep and Bayesian CCA extensions are briefly reviewed. Together with the numerical examples, this overview provides a coherent compendium on the applicability of the variants of canonical correlation analysis. By bringing together techniques for solving the optimisation problems, evaluating the statistical significance and generalisability of the canonical correlation model, and interpreting the relations, we hope that this article can serve as a hands-on tool for applying canonical correlation methods in data analysis.
Beetle Antennae Search without Parameter Tuning (BAS-WPT) for Multi-objective Optimization
Venue: arXiv cs.NE 2017
Year: 2017

Beetle antennae search (BAS) is an efficient meta-heuristic algorithm inspired by foraging behaviors of beetles. This algorithm includes several parameters for tuning and the existing results are limited to solve single objective optimization. This work pushes forward the research on BAS by providing one variant that releases the tuning parameters and is able to handle multi-objective optimization. This new approach applies normalization to simplify the original algorithm and uses a penalty function to exploit infeasible solutions with low constraint violation to solve the constraint optimization problem. Extensive experimental studies are carried out and the results reveal efficacy of the proposed approach to constraint handling.
Evaluation of Handover Exchange Schemes Between Two Cognitive Radio Base Stations with and without Buffers
Venue: arXiv cs.NI 2017
Year: 2017

This article investigates and evaluate a handover exchange scheme between two secondary users (SUs) moving in different directions across the handover region of neighbouring cell in a cognitive radio network. More specifically, this investigation compares the performance of SUs in a cellular cognitive radio network with and without channel exchange systems. The investigation shows reduced handover failure, blocking, forced and access probabilities respectively, for handover exchange scheme with buffer as compared to the other scenario.
Structure Regularized Bidirectional Recurrent Convolutional Neural Network for Relation Classification
Venue: arXiv cs.CL 2017
Year: 2017

Relation classification is an important semantic processing task in the field of natural language processing (NLP). In this paper, we present a novel model, Structure Regularized Bidirectional Recurrent Convolutional Neural Network(SR-BRCNN), to classify the relation of two entities in a sentence, and the new dataset of Chinese Sanwen for named entity recognition and relation classification. Some state-of-the-art systems concentrate on modeling the shortest dependency path (SDP) between two entities leveraging convolutional or recurrent neural networks. We further explore how to make full use of the dependency relations information in the SDP and how to improve the model by the method of structure regularization. We propose a structure regularized model to learn relation representations along the SDP extracted from the forest formed by the structure regularized dependency tree, which benefits reducing the complexity of the whole model and helps improve the $F_{1}$ score by 10.3. Experimental results show that our method outperforms the state-of-the-art approaches on the Chinese Sanwen task and performs as well on the SemEval-2010 Task 8 dataset\footnote{The Chinese Sanwen corpus this paper developed and used will be released in the further.
Deep Learning and Model Predictive Control for Self-Tuning Mode-Locked Lasers
Venue: arXiv cs.LG 2017
Year: 2017

Self-tuning optical systems are of growing importance in technological applications such as mode-locked fiber lasers. Such self-tuning paradigms require {\em intelligent} algorithms capable of inferring approximate models of the underlying physics and discovering appropriate control laws in order to maintain robust performance for a given objective. In this work, we demonstrate the first integration of a {\em deep learning} (DL) architecture with {\em model predictive control} (MPC) in order to self-tune a mode-locked fiber laser. Not only can our DL-MPC algorithmic architecture approximate the unknown fiber birefringence, it also builds a dynamical model of the laser and appropriate control law for maintaining robust, high-energy pulses despite a stochastically drifting birefringence. We demonstrate the effectiveness of this method on a fiber laser which is mode-locked by nonlinear polarization rotation. The method advocated can be broadly applied to a variety of optical systems that require robust controllers.
Curve-Structure Segmentation from Depth Maps: A CNN-based Approach and Its Application to Exploring Cultural Heritage Objects
Venue: arXiv cs.CV 2017
Year: 2017

Motivated by the important archaeological application of exploring cultural heritage objects, in this paper we study the challenging problem of automatically segmenting curve structures that are very weakly stamped or carved on an object surface in the form of a highly noisy depth map. Different from most classical low-level image segmentation methods that are known to be very sensitive to the noise and occlusions, we propose a new supervised learning algorithm based on Convolutional Neural Network (CNN) to implicitly learn and utilize more curve geometry and pattern information for addressing this challenging problem. More specifically, we first propose a Fully Convolutional Network (FCN) to estimate the skeleton of curve structures and at each skeleton pixel, a scale value is estimated to reflect the local curve width. Then we propose a dense prediction network to refine the estimated curve skeletons. Based on the estimated scale values, we finally develop an adaptive thresholding algorithm to achieve the final segmentation of curve structures. In the experiment, we validate the performance of the proposed method on a dataset of depth images scanned from unearthed pottery sherds dating to the Woodland period of Southeastern North America.
Bandwidth Adaptive & Error Resilient MBR Exact Repair Regenerating Codes
Venue: arXiv cs.IT 2017
Year: 2017

Regenerating codes are efficient methods for distributed storage in storage networks, where node failures are common. They guarantee low cost data reconstruction and repair through accessing only a predefined number of arbitrarily chosen storage nodes in the network. In this work we consider two simultaneous extensions to the original regenerating codes framework introduced in [1]; i) both data reconstruction and repair are resilient to the presence of a certain number of erroneous nodes in the network and ii) the number of helper nodes in every repair is not fixed, but is a flexible parameter that can be selected during the runtime. We study the fundamental limits of required total repair bandwidth and provide an upper bound for the storage capacity of these codes under these assumptions. We then focus on the minimum repair bandwidth (MBR) case and derive the exact storage capacity by presenting explicit coding schemes with exact repair, which achieve the upper bound of the storage capacity in the considered setup. To this end, we first provide a more natural extension of the well-known Product Matrix (PM) MBR codes [2], modified to provide flexibility in the choice of number of helpers in each repair, and simultaneously be robust to erroneous nodes in the network. This is achieved by proving the non-singularity of family of matrices in large enough finite fields. We next provide another extension of the PM codes, based on novel repair schemes which enable flexibility in the number of helpers and robustness against erroneous nodes without any extra cost in field size compared to the original PM codes.
Metric Learning-based Generative Adversarial Network
Venue: arXiv cs.LG 2017
Year: 2017

Generative Adversarial Networks (GANs), as a framework for estimating generative models via an adversarial process, have attracted huge attention and have proven to be powerful in a variety of tasks. However, training GANs is well known for being delicate and unstable, partially caused by its sig- moid cross entropy loss function for the discriminator. To overcome such a problem, many researchers directed their attention on various ways to measure how close the model distribution and real distribution are and have applied dif- ferent metrics as their objective functions. In this paper, we propose a novel framework to train GANs based on distance metric learning and we call it Metric Learning-based Gener- ative Adversarial Network (MLGAN). The discriminator of MLGANs can dynamically learn an appropriate metric, rather than a static one, to measure the distance between generated samples and real samples. Afterwards, MLGANs update the generator under the newly learned metric. We evaluate our ap- proach on several representative datasets and the experimen- tal results demonstrate that MLGANs can achieve superior performance compared with several existing state-of-the-art approaches. We also empirically show that MLGANs could increase the stability of training GANs.