OpenRaiser/Intern-Atlas huggingface dataset:
A Simple Yet Efficient Rank One Update for Covariance Matrix Adaptation
Venue: arXiv cs.NE 2017
Year: 2017

In this paper, we propose an efficient approximated rank one update for covariance matrix adaptation evolution strategy (CMA-ES). It makes use of two evolution paths as simple as that of CMA-ES, while avoiding the computational matrix decomposition. We analyze the algorithms' properties and behaviors. We experimentally study the proposed algorithm's performances. It generally outperforms or performs competitively to the Cholesky CMA-ES.
Uncertainty Averse Pushing with Model Predictive Path Integral Control
Venue: arXiv cs.RO 2017
Year: 2017

Planning robust robot manipulation requires good forward models that enable robust plans to be found. This work shows how to achieve this using a forward model learned from robot data to plan push manipulations. We explore learning methods (Gaussian Process Regression, and an Ensemble of Mixture Density Networks) that give estimates of the uncertainty in their predictions. These learned models are utilised by a model predictive path integral (MPPI) controller to plan how to push the box to a goal location. The planner avoids regions of high predictive uncertainty in the forward model. This includes both inherent uncertainty in dynamics, and meta uncertainty due to limited data. Thus, pushing tasks are completed in a robust fashion with respect to estimated uncertainty in the forward model and without the need of differentiable cost functions. We demonstrate the method on a real robot, and show that learning can outperform physics simulation. Using simulation, we also show the ability to plan uncertainty averse paths.
Deep Semantic Abstractions of Everyday Human Activities: On Commonsense Representations of Human Interactions
Venue: arXiv cs.RO 2017
Year: 2017

We propose a deep semantic characterization of space and motion categorically from the viewpoint of grounding embodied human-object interactions. Our key focus is on an ontological model that would be adept to formalisation from the viewpoint of commonsense knowledge representation, relational learning, and qualitative reasoning about space and motion in cognitive robotics settings. We demonstrate key aspects of the space & motion ontology and its formalization as a representational framework in the backdrop of select examples from a dataset of everyday activities. Furthermore, focussing on human-object interaction data obtained from RGBD sensors, we also illustrate how declarative (spatio-temporal) reasoning in the (constraint) logic programming family may be performed with the developed deep semantic abstractions.
Validation of hardware events for successful performance pattern identification in High Performance Computing
Venue: arXiv cs.DC 2017
Year: 2017

Hardware performance monitoring (HPM) is a crucial ingredient of performance analysis tools. While there are interfaces like LIKWID, PAPI or the kernel interface perf\_event which provide HPM access with some additional features, many higher level tools combine event counts with results retrieved from other sources like function call traces to derive (semi-)automatic performance advice. However, although HPM is available for x86 systems since the early 90s, only a small subset of the HPM features is used in practice. Performance patterns provide a more comprehensive approach, enabling the identification of various performance-limiting effects. Patterns address issues like bandwidth saturation, load imbalance, non-local data access in ccNUMA systems, or false sharing of cache lines. This work defines HPM event sets that are best suited to identify a selection of performance patterns on the Intel Haswell processor. We validate the chosen event sets for accuracy in order to arrive at a reliable pattern detection mechanism and point out shortcomings that cannot be easily circumvented due to bugs or limitations in the hardware.
The Provable Virtue of Laziness in Motion Planning
Venue: arXiv cs.RO 2017
Year: 2017

The Lazy Shortest Path (LazySP) class consists of motion-planning algorithms that only evaluate edges along shortest paths between the source and target. These algorithms were designed to minimize the number of edge evaluations in settings where edge evaluation dominates the running time of the algorithm; but how close to optimal are LazySP algorithms in terms of this objective? Our main result is an analytical upper bound, in a probabilistic model, on the number of edge evaluations required by LazySP algorithms; a matching lower bound shows that these algorithms are asymptotically optimal in the worst case.
Learning Entrepreneurship with Serious Games - A Classroom Approach
Venue: arXiv cs.CY 2017
Year: 2017

The use of educational games for pedagogical practice can provide new conceptions of teaching-learning in an interactive environment stimulating the acquisition of new knowledge. The so-called serious games are focused on the goal of transmitting educational content or training to the user. In the context of entrepreneurship, serious games appear to have greater importance due to the multidisciplinary of the knowledge needed. Therefore, we propose the adoption of the Entrexplorer game in the context of a university classroom. The game is a cloud-based serious game about the theme of entrepreneurship where users can access learning contents that will assist them in the acquisition of entrepreneurial skills. The organization of the game in eight levels with six additional floors let students learn the different dimensions of an entrepreneurship project while progressing during the gameplay.
Dispenser Concept for Unmanned Aerial Vehicles (UAV, Drone, UAS)
Venue: arXiv cs.CY 2017
Year: 2017

System, design and methodology to load and dispense different articles from an autonomous aircraft are disclosed. In one embodiment, the design of a unique detachable dispenser for delivery of articles is described along with an intelligent methodology of loading and delivering the articles to and from the dispenser. Design of the dispenser, interaction of the dispenser with the flight control unit and ground control or base-station, and interaction of the base station with the sender or recipient of the article, are also described.
Quality and Innovation with Blockchain Technology
Venue: arXiv cs.CY 2017
Year: 2017

In recent years, hype surrounding the proliferation of blockchain-based technology has been significant. Apart from the creation of bitcoin and other cryptocurrencies, it has been difficult to determine what practical utility might lie in the adoption of blockchain, mainly because there are so few in existence at present. Even so, interest in the technology has increased tremendously. This paper is a primer for software quality professionals. It briefly describes the history of blockchain technology, attempts to define and disambiguate terminology, fosters a general understanding of how blockchain works, and discusses how and why software quality professionals might want to invest time and energy in learning about, implementing, or using blockchain-based technologies in their own organizations -- or alternatively, improving the quality of blockchain technology itself.
Driving Behavior Analysis through CAN Bus Data in an Uncontrolled Environment
Venue: arXiv cs.LG 2017
Year: 2017

Cars can nowadays record several thousands of signals through the CAN bus technology and potentially provide real-time information on the car, the driver and the surrounding environment. This paper proposes a new method for the analysis and classification of driver behavior using a selected subset of CAN bus signals, specifically gas pedal position, brake pedal pressure, steering wheel angle, steering wheel momentum, velocity, RPM, frontal and lateral acceleration. Data has been collected in a completely uncontrolled experiment, where 64 people drove 10 cars for or a total of over 2000 driving trips without any type of pre-determined driving instruction on a wide variety of road scenarios. We propose an unsupervised learning technique that clusters drivers in different groups, and offers a validation method to test the robustness of clustering in a wide range of experimental settings. The minimal amount of data needed to preserve robust driver clustering is also computed. The presented study provides a new methodology for near-real-time classification of driver behavior in uncontrolled environments.
Bollywood Movie Corpus for Text, Images and Videos
Venue: arXiv cs.CY 2017
Year: 2017

In past few years, several data-sets have been released for text and images. We present an approach to create the data-set for use in detecting and removing gender bias from text. We also include a set of challenges we have faced while creating this corpora. In this work, we have worked with movie data from Wikipedia plots and movie trailers from YouTube. Our Bollywood Movie corpus contains 4000 movies extracted from Wikipedia and 880 trailers extracted from YouTube which were released from 1970-2017. The corpus contains csv files with the following data about each movie - Wikipedia title of movie, cast, plot text, co-referenced plot text, soundtrack information, link to movie poster, caption of movie poster, number of males in poster, number of females in poster. In addition to that, corresponding to each cast member the following data is available - cast name, cast gender, cast verbs, cast adjectives, cast relations, cast centrality, cast mentions. We present some preliminary results on the task of bias removal which suggest that the data-set is quite useful for performing such tasks.
Measurement Context Extraction from Text: Discovering Opportunities and Gaps in Earth Science
Venue: arXiv cs.IR 2017
Year: 2017

We propose Marve, a system for extracting measurement values, units, and related words from natural language text. Marve uses conditional random fields (CRF) to identify measurement values and units, followed by a rule-based system to find related entities, descriptors and modifiers within a sentence. Sentence tokens are represented by an undirected graphical model, and rules are based on part-of-speech and word dependency patterns connecting values and units to contextual words. Marve is unique in its focus on measurement context and early experimentation demonstrates Marve's ability to generate high-precision extractions with strong recall. We also discuss Marve's role in refining measurement requirements for NASA's proposed HyspIRI mission, a hyperspectral infrared imaging satellite that will study the world's ecosystems. In general, our work with HyspIRI demonstrates the value of semantic measurement extractions in characterizing quantitative discussion contained in large corpuses of natural language text. These extractions accelerate broad, cross-cutting research and expose scientists new algorithmic approaches and experimental nuances. They also facilitate identification of scientific opportunities enabled by HyspIRI leading to more efficient scientific investment and research.
Improved Coresets for Kernel Density Estimates
Venue: arXiv cs.LG 2017
Year: 2017

We study the construction of coresets for kernel density estimates. That is we show how to approximate the kernel density estimate described by a large point set with another kernel density estimate with a much smaller point set. For characteristic kernels (including Gaussian and Laplace kernels), our approximation preserves the $L_\infty$ error between kernel density estimates within error $ε$, with coreset size $2/ε^2$, but no other aspects of the data, including the dimension, the diameter of the point set, or the bandwidth of the kernel common to other approximations. When the dimension is unrestricted, we show this bound is tight for these kernels as well as a much broader set. This work provides a careful analysis of the iterative Frank-Wolfe algorithm adapted to this context, an algorithm called \emph{kernel herding}. This analysis unites a broad line of work that spans statistics, machine learning, and geometry. When the dimension $d$ is constant, we demonstrate much tighter bounds on the size of the coreset specifically for Gaussian kernels, showing that it is bounded by the size of the coreset for axis-aligned rectangles. Currently the best known constructive bound is $O(\frac{1}ε \log^d \frac{1}ε)$, and non-constructively, this can be improved by $\sqrt{\log \frac{1}ε}$. This improves the best constant dimension bounds polynomially for $d \geq 3$.
Modular decomposition of transitive graphs and transitively orienting their complements
Venue: arXiv cs.DM 2017
Year: 2017

The modular decomposition of a graph is a canonical representation of its modules. Algorithms for computing the modular decomposition of directed and undirected graphs differ significantly, with the undirected case being simpler, and algorithms for directed graphs often work by reducing the problem to decomposing undirected graphs. In this paper we show that transitive acyclic digraphs have the same strong modules as their undirected versions. This simplifies reduction for transitive digraphs, requiring only the computation of strongly connected components. Furthermore, we are interested in permutation graphs, where both the graph and its complement are transitively orientable. Such graphs may be represented indirectly, as the transitive closure of a given graph. For non-transitive graphs we present a linear-time algorithm which allows us to identify prime-free modules w.r.t their transitive closure, which speeds up both modular decomposition and transitive orientation for sparse graphs. Finally, we show that any transitive orientation of a digraph's complement also transitively orients the complement of the digraph's transitive closure, allowing us to find such orientations in (near-)linear time.
Securing UAV Communications Via Trajectory Optimization
Venue: arXiv cs.IT 2017
Year: 2017

Unmanned aerial vehicle (UAV) communications has drawn significant interest recently due to many advantages such as low cost, high mobility, and on-demand deployment. This paper addresses the issue of physical-layer security in a UAV communication system, where a UAV sends confidential information to a legitimate receiver in the presence of a potential eavesdropper which are both on the ground. We aim to maximize the secrecy rate of the system by jointly optimizing the UAV's trajectory and transmit power over a finite horizon. In contrast to the existing literature on wireless security with static nodes, we exploit the mobility of the UAV in this paper to enhance the secrecy rate via a new trajectory design. Although the formulated problem is non-convex and challenging to solve, we propose an iterative algorithm to solve the problem efficiently, based on the block coordinate descent and successive convex optimization methods. Specifically, the UAV's transmit power and trajectory are each optimized with the other fixed in an alternating manner until convergence. Numerical results show that the proposed algorithm significantly improves the secrecy rate of the UAV communication system, as compared to benchmark schemes without transmit power control or trajectory optimization.
STDP Based Pruning of Connections and Weight Quantization in Spiking Neural Networks for Energy Efficient Recognition
Venue: arXiv cs.NE 2017
Year: 2017

Spiking Neural Networks (SNNs) with a large number of weights and varied weight distribution can be difficult to implement in emerging in-memory computing hardware due to the limitations on crossbar size (implementing dot product), the constrained number of conductance levels in non-CMOS devices and the power budget. We present a sparse SNN topology where non-critical connections are pruned to reduce the network size and the remaining critical synapses are weight quantized to accommodate for limited conductance levels. Pruning is based on the power law weight-dependent Spike Timing Dependent Plasticity (STDP) model; synapses between pre- and post-neuron with high spike correlation are retained, whereas synapses with low correlation or uncorrelated spiking activity are pruned. The weights of the retained connections are quantized to the available number of conductance levels. The process of pruning non-critical connections and quantizing the weights of critical synapses is performed at regular intervals during training. We evaluated our sparse and quantized network on MNIST dataset and on a subset of images from Caltech-101 dataset. The compressed topology achieved a classification accuracy of 90.1% (91.6%) on the MNIST (Caltech-101) dataset with 3.1x (2.2x) and 4x (2.6x) improvement in energy and area, respectively. The compressed topology is energy and area efficient while maintaining the same classification accuracy of a 2-layer fully connected SNN topology.
Generalized Gaussian Multiterminal Source Coding: The Symmetric Case
Venue: arXiv cs.IT 2017
Year: 2017

Consider a generalized multiterminal source coding system, where $\ell\choose m$ encoders, each observing a distinct size-$m$ subset of $\ell$ ($\ell\geq 2$) zero-mean unit-variance symmetrically correlated Gaussian sources with correlation coefficient $ρ$, compress their observations in such a way that a joint decoder can reconstruct the sources within a prescribed mean squared error distortion based on the compressed data. The optimal rate-distortion performance of this system was previously known only for the two extreme cases $m=\ell$ (the centralized case) and $m=1$ (the distributed case), and except when $ρ=0$, the centralized system can achieve strictly lower compression rates than the distributed system under all non-trivial distortion constraints. Somewhat surprisingly, it is established in the present paper that the optimal rate-distortion performance of the afore-described generalized multiterminal source coding system with $m\geq 2$ coincides with that of the centralized system for all distortions when $ρ\leq 0$ and for distortions below an explicit positive threshold (depending on $m$) when $ρ>0$. Moreover, when $ρ>0$, the minimum achievable rate of generalized multiterminal source coding subject to an arbitrary positive distortion constraint $d$ is shown to be within a finite gap (depending on $m$ and $d$) from its centralized counterpart in the large $\ell$ limit except for possibly the critical distortion $d=1-ρ$.
Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. Therefore it is commonly used in the diagnosis of retinal diseases associated with edema in and under the retinal layers. In this paper, a new framework is proposed for the task of fluid segmentation and detection in retinal OCT images. Based on the raw images and layers segmented by a graph-cut algorithm, a fully convolutional neural network was trained to recognize and label the fluid pixels. Random forest classification was performed on the segmented fluid regions to detect and reject the falsely labeled fluid regions. The leave-one-out cross validation experiments on the RETOUCH database show that our method performs well in both segmentation (mean Dice: 0.7317) and detection (mean AUC: 0.985) tasks.
Recent Advances in Zero-shot Recognition
Venue: arXiv cs.CV 2017
Year: 2017

With the recent renaissance of deep convolution neural networks, encouraging breakthroughs have been achieved on the supervised recognition tasks, where each class has sufficient training data and fully annotated training data. However, to scale the recognition to a large number of classes with few or now training samples for each class remains an unsolved problem. One approach to scaling up the recognition is to develop models capable of recognizing unseen categories without any training instances, or zero-shot recognition/ learning. This article provides a comprehensive review of existing zero-shot recognition techniques covering various aspects ranging from representations of models, and from datasets and evaluation settings. We also overview related recognition tasks including one-shot and open set recognition which can be used as natural extensions of zero-shot recognition when limited number of class samples become available or when zero-shot recognition is implemented in a real-world setting. Importantly, we highlight the limitations of existing approaches and point out future research directions in this existing new research area.
Reduced Order Modelling for the Simulation of Quenches in Superconducting Magnets
Venue: arXiv cs.CE 2017
Year: 2017

This contributions discusses the simulation of magnetothermal effects in superconducting magnets as used in particle accelerators. An iterative coupling scheme using reduced order models between a magnetothermal partial differential model and an electrical lumped-element circuit is demonstrated. The multiphysics, multirate and multiscale problem requires a consistent formulation and framework to tackle the challenging transient effects occurring at both system and device level.
Transfer of Tool Affordance and Manipulation Cues with 3D Vision Data
Venue: arXiv cs.RO 2017
Year: 2017

Future service robots working in human environments, such as kitchens, will face situations where they need to improvise. The usual tool for a given task might not be available and the robot will have to use some substitute tool. The robot needs to select an appropriate alternative tool from the candidates available, and also needs to know where to grasp it, how to orient it and what part to use as the end-effector. We present a system which takes as input a candidate tool's point cloud and weight, and outputs a score for how effective that tool is for a task, and how to use it. Our key novelty is in taking a task-driven approach, where the task exerts a top-down influence on how low level vision data is interpreted. This facilitates the type of 'everyday creativity' where an object such as a wine bottle could be used as a rolling pin, because the interpretation of the object is not fixed in advance, but rather results from the interaction between the bottom-up and top-down pressures at run-time. The top-down influence is implemented by transfer: prior knowledge of geometric features that make a tool good for a task is used to seek similar features in a candidate tool. The prior knowledge is learned by simulating Web models performing the tasks. We evaluate on a set of fifty household objects and five tasks. We compare our system with the closest one in the literature and show that we achieve significantly better results
Matrix-Product Constructions for Hermitian Self-Orthogonal Codes
Venue: arXiv cs.IT 2017
Year: 2017

Self-orthogonal codes have been of interest due to there rich algebraic structures and wide applications. Euclidean self-orthogonal codes have been quite well studied in literature. Here, we have focused on Hermitian self-orthogonal codes. Constructions of such codes have been given based on the well-known matrix-product construction for linear codes. Criterion for the underlying matrix and the input codes required in such constructions have been determined. In many cases, the Hermitian self-orthogonality of the input codes and the assumption that the underlying matrix is unitary can be relaxed. Some special matrices used in the constructions and illustrative examples of good Hermitian self-orthogonal codes have been provided as well.