OpenRaiser/Intern-Atlas huggingface dataset:
Fault diagnosability of data center networks
Venue: arXiv cs.DC 2017
Year: 2017

The data center networks $D_{n,k}$, proposed in 2008, has many desirable features such as high network capacity. A kind of generalization of diagnosability for network $G$ is $g$-good-neighbor diagnosability which is denoted by $t_g(G)$. Let $κ^g(G)$ be the $R^g$-connectivity. Lin et. al. in [IEEE Trans. on Reliability, 65 (3) (2016) 1248--1262] and Xu et. al in [Theor. Comput. Sci. 659 (2017) 53--63] gave the same problem independently that: the relationship between the $R^g$-connectivity $κ^g(G)$ and $t_g(G)$ of a general graph $G$ need to be studied in the future. In this paper, this open problem is solved for general regular graphs. We firstly establish the relationship of $κ^g(G)$ and $t_g(G)$, and obtain that $t_g(G)=κ^g(G)+g$ under some conditions. Secondly, we obtain the $g$-good-neighbor diagnosability of $D_{k,n}$ which are $t_g(D_{k,n})=(g+1)(k-1)+n+g$ for $1\leq g\leq n-1$ under the PMC model and the MM model, respectively. Further more, we show that $D_{k,n}$ is tightly super $(n+k-1)$-connected for $n\geq 2$ and $k\geq 2$ and we also prove that the largest connected component of the survival graph contains almost all of the remaining vertices in $D_{k,n}$ when $2k+n-2$ vertices removed.
New Beam Tracking Technique for Millimeter Wave-band Communications
Venue: arXiv cs.IT 2017
Year: 2017

In this paper, we propose an efficient beam tracking method for mobility scenario in mmWave-band communications. When the position of the mobile changes in mobility scenario, the base-station needs to perform beam training frequently to track the time-varying channel, thereby spending significant resources for training beams. In order to reduce the training overhead, we propose a new beam training approach called "beam tracking" which exploits the continuous nature of time varying angle of departure (AoD) for beam selection. We show that transmission of only two training beams is enough to track the time-varying AoD at good accuracy. We derive the optimal selection of beam pair which minimizes Cramer-Rao Lower Bound (CRLB) for AoD estimation averaged over statistical distribution of the AoD. Our numerical results demonstrate that the proposed beam tracking scheme produces better AoD estimation than the conventional beam training protocol with less training overhead.
Dynamic load balancing for large-scale adaptive finite element computation
Venue: arXiv cs.DC 2017
Year: 2017

For the parallel computation of partial differential equations, one key is the grid partitioning. It requires that each process owns the same amount of computations, and also, the partitioning quality should be proper to reduce the communications among processes. When calculating the partial differential equations using adaptive finite element methods, the grid and the basis functions adjust in each iteration, which introduce load balancing issues. The grid should be redistributed dynamically. This paper studies dynamic load balancing algorithms and the implementation on the adaptive finite element platform PHG. The numerical experiments show that algorithms studied in this paper have good partitioning quality, and they are efficient.
OLA-RAW: Scalable Exploration over Raw Data
Venue: arXiv cs.DB 2017
Year: 2017

In-situ processing has been proposed as a novel data exploration solution in many domains generating massive amounts of raw data, e.g., astronomy, since it provides immediate SQL querying over raw files. The performance of in-situ processing across a query workload is, however, limited by the speed of full scan, tokenizing, and parsing of the entire data. Online aggregation (OLA) has been introduced as an efficient method for data exploration that identifies uninteresting patterns faster by continuously estimating the result of a computation during the actual processing---the computation can be stopped as early as the estimate is accurate enough to be deemed uninteresting. However, existing OLA solutions have a high upfront cost of randomly shuffling and/or sampling the data. In this paper, we present OLA-RAW, a bi-level sampling scheme for parallel online aggregation over raw data. Sampling in OLA-RAW is query-driven and performed exclusively in-situ during the runtime query execution, without data reorganization. This is realized by a novel resource-aware bi-level sampling algorithm that processes data in random chunks concurrently and determines adaptively the number of sampled tuples inside a chunk. In order to avoid the cost of repetitive conversion from raw data, OLA-RAW builds and maintains a memory-resident bi-level sample synopsis incrementally. We implement OLA-RAW inside a modern in-situ data processing system and evaluate its performance across several real and synthetic datasets and file formats. Our results show that OLA-RAW chooses the sampling plan that minimizes the execution time and guarantees the required accuracy for each query in a given workload. The end result is a focused data exploration process that avoids unnecessary work and discards uninteresting data.
EasyInterface: A toolkit for rapid development of GUIs for research prototype tools
Venue: arXiv cs.SE 2017
Year: 2017

In this paper we describe EasyInterface, an open-source toolkit for rapid development of web-based graphical user interfaces (GUIs). This toolkit addresses the need of researchers to make their research prototype tools available to the community, and integrating them in a common environment, rapidly and without being familiar with web programming or GUI libraries in general. If a tool can be executed from a command-line and its output goes to the standard output, then in few minutes one can make it accessible via a web-interface or within Eclipse. Moreover, the toolkit defines a text-based language that can be used to get more sophisticated GUIs, e.g., syntax highlighting, dialog boxes, user interactions, etc. EasyInterface was originally developed for building a common frontend for tools developed in the Envisage project.
Visual Saliency Prediction Using a Mixture of Deep Neural Networks
Venue: arXiv cs.CV 2017
Year: 2017

Visual saliency models have recently begun to incorporate deep learning to achieve predictive capacity much greater than previous unsupervised methods. However, most existing models predict saliency using local mechanisms limited to the receptive field of the network. We propose a model that incorporates global scene semantic information in addition to local information gathered by a convolutional neural network. Our model is formulated as a mixture of experts. Each expert network is trained to predict saliency for a set of closely related images. The final saliency map is computed as a weighted mixture of the expert networks' output, with weights determined by a separate gating network. This gating network is guided by global scene information to predict weights. The expert networks and the gating network are trained simultaneously in an end-to-end manner. We show that our mixture formulation leads to improvement in performance over an otherwise identical non-mixture model that does not incorporate global scene information.
ArchiveWeb: collaboratively extending and exploring web archive collections - How would you like to work with your collections?
Venue: arXiv cs.DL 2017
Year: 2017

Curated web archive collections contain focused digital content which is collected by archiving organizations, groups, and individuals to provide a representative sample covering specific topics and events to preserve them for future exploration and analysis. In this paper, we discuss how to best support collaborative construction and exploration of these collections through the ArchiveWeb system. ArchiveWeb has been developed using an iterative evaluation-driven design-based research approach, with considerable user feedback at all stages. The first part of this paper describes the important insights we gained from our initial requirements engineering phase during the first year of the project and the main functionalities of the current ArchiveWeb system for searching, constructing, exploring, and discussing web archive collections. The second part summarizes the feedback we received on this version from archiving organizations and libraries, as well as our corresponding plans for improving and extending the system for the next release.
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we investigate an emerging application, 3D scene understanding, likely to be significant in the mobile space in the near future. The goal of this exploration is to reduce execution time while meeting our quality of result objectives. In previous work we showed for the first time that it is possible to map this application to power constrained embedded systems, highlighting that decision choices made at the algorithmic design-level have the most impact. As the algorithmic design space is too large to be exhaustively evaluated, we use a previously introduced multi-objective Random Forest Active Learning prediction framework dubbed HyperMapper, to find good algorithmic designs. We show that HyperMapper generalizes on a recent cutting edge 3D scene understanding algorithm and on a modern GPU-based computer architecture. HyperMapper is able to beat an expert human hand-tuning the algorithmic parameters of the class of Computer Vision applications taken under consideration in this paper automatically. In addition, we use crowd-sourcing using a 3D scene understanding Android app to show that the Pareto front obtained on an embedded system can be used to accelerate the same application on all the 83 smart-phones and tablets crowd-sourced with speedups ranging from 2 to over 12.
Smart Inverter Impacts on California Distribution Feeders with Increasing PV Penetration: A Case Study
Venue: arXiv cs.SY 2017
Year: 2017

The impacts of high PV penetration on distribution feeders have been well documented within the last decade. To mitigate these impacts, interconnection standards have been amended to allow PV inverters to regulate voltage locally. However, there is a deficiency of literature discussing how these inverters will behave on real feeders under increasing PV penetration. In this paper, we simulate several deployment scenarios of these inverters on a real California distribution feeder. We show that minimum and maximum voltage, tap operations, and voltage variability are improved due to the inverters. Line losses were shown to increase at high PV penetrations as a side effect. Furthermore, we find inverter sizing was shown to be important as PV penetration increased. Finally we show that increasing the number of inverters and removing the deadband from the Volt/VAr control curve improves the effectiveness.
Multi-Hop Extensions of Energy-Efficient Wireless Sensor Network Time Synchronization
Venue: arXiv cs.NI 2017
Year: 2017

We present the multi-hop extensions of the recently proposed energy-efficient time synchronization scheme for wireless sensor networks, which is based on the asynchronous source clock frequency recovery and reversed two-way message exchanges. We consider two hierarchical extensions based on packet relaying and time-translating gateways, respectively, and analyze their performance with respect to the number of layers and the delay variations through simulations. The simulation results demonstrate that the time synchronization performance of the packet relaying, which has lower complexity, is close to that of time-translating gateways.
Competitive division of a mixed manna
Venue: arXiv cs.GT 2017
Year: 2017

A mixed manna contains goods (that everyone likes), bads (that everyone dislikes), as well as items that are goods to some agents, but bads or satiated to others. If all items are goods and utility functions are homothetic, concave (and monotone), the Competitive Equilibrium with Equal Incomes maximizes the Nash product of utilities: hence it is welfarist (determined utility-wise by the feasible set of profiles), single-valued and easy to compute. We generalize the Gale-Eisenberg Theorem to a mixed manna. The Competitive division is still welfarist and related to the product of utilities or disutilities. If the zero utility profile (before any manna) is Pareto dominated, the competitive profile is unique and still maximizes the product of utilities. If the zero profile is unfeasible, the competitive profiles are the critical points of the product of disutilities on the efficiency frontier, and multiplicity is pervasive. In particular the task of dividing a mixed manna is either good news for everyone, or bad news for everyone. We refine our results in the practically important case of linear preferences, where the axiomatic comparison between the division of goods and that of bads is especially sharp. When we divide goods and the manna improves, everyone weakly benefits under the competitive rule; but no reasonable rule to divide bads can be similarly Resource Monotonic. Also, the much larger set of Non Envious and Efficient divisions of bads can be disconnected so that it will admit no continuous selection.
Semantic URL Analytics to Support Efficient Annotation of Large Scale Web Archives
Venue: arXiv cs.IR 2017
Year: 2017

Long-term Web archives comprise Web documents gathered over longer time periods and can easily reach hundreds of terabytes in size. Semantic annotations such as named entities can facilitate intelligent access to the Web archive data. However, the annotation of the entire archive content on this scale is often infeasible. The most efficient way to access the documents within Web archives is provided through their URLs, which are typically stored in dedicated index files.The URLs of the archived Web documents can contain semantic information and can offer an efficient way to obtain initial semantic annotations for the archived documents. In this paper, we analyse the applicability of semantic analysis techniques such as named entity extraction to the URLs in a Web archive. We evaluate the precision of the named entity extraction from the URLs in the Popular German Web dataset and analyse the proportion of the archived URLs from 1,444 popular domains in the time interval from 2000 to 2012 to which these techniques are applicable. Our results demonstrate that named entity recognition can be successfully applied to a large number of URLs in our Web archive and provide a good starting point to efficiently annotate large scale collections of Web documents.
gearshifft - The FFT Benchmark Suite for Heterogeneous Platforms
Venue: arXiv cs.PF 2017
Year: 2017

Fast Fourier Transforms (FFTs) are exploited in a wide variety of fields ranging from computer science to natural sciences and engineering. With the rising data production bandwidths of modern FFT applications, judging best which algorithmic tool to apply, can be vital to any scientific endeavor. As tailored FFT implementations exist for an ever increasing variety of high performance computer hardware, choosing the best performing FFT implementation has strong implications for future hardware purchase decisions, for resources FFTs consume and for possibly decisive financial and time savings ahead of the competition. This paper therefor presents gearshifft, which is an open-source and vendor agnostic benchmark suite to process a wide variety of problem sizes and types with state-of-the-art FFT implementations (fftw, clfft and cufft). gearshifft provides a reproducible, unbiased and fair comparison on a wide variety of hardware to explore which FFT variant is best for a given problem size.
Integrating Soft Robotics with ROS - A hybrid pick and place arm
Venue: arXiv cs.RO 2017
Year: 2017

Soft robotic systems present a variety of new opportunities for solving complex problems. The use of soft robotic grippers, for example, can simplify the complexity in tasks such as the of grasping irregular and delicate objects. Adoption of soft robotics by academia and industry, however, has been slow and this is, in-part, due to the amount of hardware and software that must be developed from scratch for each use of soft system components. In this paper we detail the design, fabrication and validation of an open-source framework that we designed to lower the barrier to entry for integrating soft robotic subsystems. This framework is built on ROS (the Robot Operating System) and we use it to demonstrate a modular, soft-hard hybrid system which is capable of completing pick and place tasks. By lowering this barrier to entry we hope that system designers and researchers will find it easy to integrate soft components into their existing ROS-enabled robotic systems.
Learning a time-dependent master saliency map from eye-tracking data in videos
Venue: arXiv cs.CV 2017
Year: 2017

To predict the most salient regions of complex natural scenes, saliency models commonly compute several feature maps (contrast, orientation, motion...) and linearly combine them into a master saliency map. Since feature maps have different spatial distribution and amplitude dynamic ranges, determining their contributions to overall saliency remains an open problem. Most state-of-the-art models do not take time into account and give feature maps constant weights across the stimulus duration. However, visual exploration is a highly dynamic process shaped by many time-dependent factors. For instance, some systematic viewing patterns such as the center bias are known to dramatically vary across the time course of the exploration. In this paper, we use maximum likelihood and shrinkage methods to dynamically and jointly learn feature map and systematic viewing pattern weights directly from eye-tracking data recorded on videos. We show that these weights systematically vary as a function of time, and heavily depend upon the semantic visual category of the videos being processed. Our fusion method allows taking these variations into account, and outperforms other state-of-the-art fusion schemes using constant weights over time. The code, videos and eye-tracking data we used for this study are available online: http://antoinecoutrot.magix.net/public/research.html
On the Input-Degradedness and Input-Equivalence Between Channels
Venue: arXiv cs.IT 2017
Year: 2017

A channel $W$ is said to be input-degraded from another channel $W'$ if $W$ can be simulated from $W'$ by randomization at the input. We provide a necessary and sufficient condition for a channel to be input-degraded from another one. We show that any decoder that is good for $W'$ is also good for $W$. We provide two characterizations for input-degradedness, one of which is similar to the Blackwell-Sherman-Stein theorem. We say that two channels are input-equivalent if they are input-degraded from each other. We study the topologies that can be constructed on the space of input-equivalent channels, and we investigate their properties. Moreover, we study the continuity of several channel parameters and operations under these topologies.
Maritime situational awareness using adaptive multi-sensor management under hazy conditions
Venue: arXiv cs.CV 2017
Year: 2017

This paper presents a multi-sensor architecture with an adaptive multi-sensor management system suitable for control and navigation of autonomous maritime vessels in hazy and poor-visibility conditions. This architecture resides in the autonomous maritime vessels. It augments the data from on-board imaging sensors and weather sensors with the AIS data and weather data from sensors on other vessels and the on-shore vessel traffic surveillance system. The combined data is analyzed using computational intelligence and data analytics to determine suitable course of action while utilizing historically learnt knowledge and performing live learning from the current situation. Such framework is expected to be useful in diverse weather conditions and shall be a useful architecture to provide autonomy to maritime vessels.
Measuring Gender Inequalities of German Professions on Wikipedia
Venue: arXiv cs.CY 2017
Year: 2017

Wikipedia is a community-created online encyclopedia; arguably, it is the most popular and largest knowledge resource on the Internet. Thus, reliability and neutrality are of high importance for Wikipedia. Previous research [3] reveals gender bias in Google search results for many professions and occupations. Also, Wikipedia was criticized for existing gender bias in biographies [4] and gender gap in the editor community [5, 6]. Thus, one could expect that gender bias related to professions and occupations may be present in Wikipedia. The term gender bias is used here in the sense of conscious or unconscious favoritism towards one gender over another [47] with respect to professions and occupations. The objective of this work is to identify and assess gender bias. To this end, the German Wikipedia articles about professions and occupations were analyzed on three dimensions: redirections, images, and people mentioned in the articles. This work provides evidence for systematic overrepresentation of men in all three dimensions; female bias is only present for a few professions.
A Multi-Gbps Unrolled Hardware List Decoder for a Systematic Polar Code
Venue: arXiv cs.AR 2017
Year: 2017

Polar codes are a new class of block codes with an explicit construction that provably achieve the capacity of various communications channels, even with the low-complexity successive-cancellation (SC) decoding algorithm. Yet, the more complex successive-cancellation list (SCL) decoding algorithm is gathering more attention lately as it significantly improves the error-correction performance of short- to moderate-length polar codes, especially when they are concatenated with a cyclic redundancy check code. However, as SCL decoding explores several decoding paths, existing hardware implementations tend to be significantly slower than SC-based decoders. In this paper, we show how the unrolling technique, which has already been used in the context of SC decoding, can be adapted to SCL decoding yielding a multi-Gbps SCL-based polar decoder with an error-correction performance that is competitive when compared to an LDPC code of similar length and rate. Post-place-and-route ASIC results for 28 nm CMOS are provided showing that this decoder can sustain a throughput greater than 10 Gbps at 468 MHz with an energy efficiency of 7.25 pJ/bit.
Relay Selection in Cooperative Power Line Communication: A Multi-Armed Bandit Approach
Venue: arXiv cs.IT 2017
Year: 2017

Power line communication (PLC) exploits the existence of installed infrastructure of power delivery system, in order to transmit data over power lines. In PLC networks, different nodes of the network are interconnected via power delivery transmission lines, and the data signal is flowing between them. However, the attenuation and the harsh environment of the power line communication channels, makes it difficult to establish a reliable communication between two nodes of the network which are separated by a long distance. Relaying and cooperative communication has been used to overcome this problem. In this paper a two-hop cooperative PLC has been studied, where the data is communicated between a transmitter and a receiver node, through a single array node which has to be selected from a set of available arrays. The relay selection problem can be solved by having channel state information (CSI) at transmitter and selecting the relay which results in the best performance. However, acquiring the channel state information at transmitter increases the complexity of the communication system and introduces undesired overhead to the system. We propose a class of machine learning schemes, namely multi-armed bandit (MAB), to solve the relay selection problem without the knowledge of the channel at the transmitter. Furthermore, we develop a new MAB algorithm which exploits the periodicity of the synchronous impulsive noise of the PLC channel, in order to improve the relay selection algorithm.