query: Semi-automated map creation for fast deployment of AGV fleets in modern logistics

positive: Graph SLAM based mapping for AGV localization in large-scale warehouses The operation of industrial Automated Guided Vehicles (AGV) today requires designated infrastructure and readily available maps for their localization. In logistics, high effort and investment is necessary to enable the introduction of AGVs. Within the SICK AG coordinated EU-funded research project PAN-Robots we aim to reduce the installation time and costs dramatically by semi-automated plant exploration and localization based on natural landmarks. In this paper, we present our current mapping and localization results based on measurement data acquired at the site of our project partner Coca-Cola Iberian Partners in Bilbao, Spain. We evaluate our solution in terms of accuracy of the map, i.e. comparing landmark position estimates with a ground truth map of millimeter accuracy. The localization results are shown based on artificial landmarks as well as natural landmarks (gridmaps) based on the same graph based optimization solution.

positive: V-Clip: Fast and Robust Polyhedral Collision Detection This article presents the Voronoi-clip, or V-Clip, collision detection alogrithm for polyhedral objects specified by a boundary representation. V-Clip tracks the closest pair of features between convex polyhedra, using an approach reminiscent of the Lin-Canny closest features algorithm. V-Clip is an improvement over the latter in several respects. Coding complexity is reduced, and robustness is significantly improved; the implementation has no numerical tolerances and does not exhibit cycling problems. The algorithm also handles penetrating polyhedra, and can therefore be used to detect collisions between nonvconvex polyhedra described as hierarchies of convex pieces. The article presents the theoretical principles of V-Clip, and gives a pseudocode description of the algorithm. It also documents various test that compare V-Clip, Lin-Canny, and the Enhanced GJK algorithm, a simplex-based algorithm that is widely used for the same application. The results show V-Clip to be a strong contender in this field, comparing favorably with the other algorithms in most of the tests, in term of both performance and robustness.

positive: Vision for Mobile Robot Navigation: A Survey This paper surveys the developments of the last 20 years in the area of vision for mobile robot navigation. Two major components of the paper deal with indoor navigation and outdoor navigation. For each component, we have further subdivided our treatment of the subject on the basis of structured and unstructured environments. For indoor robots in structured environments, we have dealt separately with the cases of geometrical and topological models of space. For unstructured environments, we have discussed the cases of navigation using optical flows, using methods from the appearance-based paradigm, and by recognition of specific objects in the environment.

positive: SHORTEST PATHS FOR THE REEDS-SHEPP CAR : A WORKED OUT EXAMPLE OF THE USE OF GEOMETRIC TECHNIQUES IN NONLINEAR OPTIMAL CONTROL We illustrate the use of the techniques of modern geometric optimal control theory by studying the shortest paths for a model of a car that can move forwards and backwards. This problem was discussed in recent work by Reeds and Shepp who showed, by special methods, (a) that shortest path motion could always be achieved by means of trajectories of a special kind, namely, concatenations of at most five pieces, each of which is either a straight line or a circle, and (b) that these concatenations can be classified into 48 three-parameter families. We show how these results fit in a much more general framework, and can be discovered and proved by applying in a systematic way the techniques of Optimal Control Theory. It turns out that the “classical” optimal control tools developed in the 1960’s, such as the Pontryagin Maximum Principle and theorems on the existence of optimal trajectories, are helpful to go part of the way and get some information on the shortest paths, but do not suffice to get the full result. On the other hand, when these classical techniques are combined with the use of a more recently developed body of theory, namely, geometric methods based on the Lie algebraic analysis of trajectories, then one can recover the full power of the Reeds-Shepp results, and in fact slightly improve upon them by lowering their 48 to a 46.

positive: Autonomous robotic exploration using occupancy grid maps and graph SLAM based on Shannon and Rényi Entropy In this paper we examine the problem of autonomously exploring and mapping an environment using a mobile robot. The robot uses a graph-based SLAM system to perform mapping and represents the map as an occupancy grid. In this setting, the robot must trade-off between exploring new area to complete the task and exploiting the existing information to maintain good localization. Selecting actions that decrease the map uncertainty while not significantly increasing the robot's localization uncertainty is challenging. We present a novel information-theoretic utility function that uses both Shannon's and Rényi's definitions of entropy to jointly consider the uncertainty of the robot and the map. This allows us to fuse both uncertainties without the use of manual tuning. We present simulations and experiments comparing the proposed utility function to state-of-the-art utility functions, which only use Shannon's entropy. We show that by using the proposed utility function, the robot and map uncertainties are smaller than using other existing methods.

negative: Advances in Variational Inference Many modern unsupervised or semi-supervised machine learning algorithms rely on Bayesian probabilistic models. These models are usually intractable and thus require approximate inference. Variational inference (VI) lets us approximate a high-dimensional Bayesian posterior with a simpler variational distribution by solving an optimization problem. This approach has been successfully used in various models and large-scale applications. In this review, we give an overview of recent trends in variational inference. We first introduce standard mean field variational inference, then review recent advances focusing on the following aspects: (a) scalable VI, which includes stochastic approximations, (b) generic VI, which extends the applicability of VI to a large class of otherwise intractable models, such as non-conjugate models, (c) accurate VI, which includes variational models beyond the mean field approximation or with atypical divergences, and (d) amortized VI, which implements the inference over local latent variables with inference networks. Finally, we provide a summary of promising future research directions.

negative: Stacking-based deep neural network: Deep analytic network on convolutional spectral histogram features Stacking-based deep neural network (S-DNN), in general, denotes a deep neural network (DNN) resemblance in terms of its very deep, feedforward network architecture. The typical S-DNN aggregates a variable number of individually learnable modules in series to assemble a DNN-alike alternative to the targeted object recognition tasks. This work likewise devises an S-DNN instantiation, dubbed deep analytic network (DAN), on top of the spectral histogram (SH) features. The DAN learning principle relies on ridge regression, and some key DNN constituents, specifically, rectified linear unit, fine-tuning, and normalization. The DAN aptitude is scrutinized on three repositories of varying domains, including FERET (faces), MNIST (handwritten digits), and CIFAR10 (natural objects). The empirical results unveil that DAN escalates the SH baseline performance over a sufficiently deep layer.

negative: A Simple and Reliable PDMS and SU-8 Irreversible Bonding Method and Its Application on a Microfluidic-MEA Device for Neuroscience Research Polydimethylsiloxane (PDMS) and SU-8 are currently two very commonly used polymeric materials in the microfluidics field for biological applications. However, there is a pressing need to find a simple, reliable, irreversible bonding method between these two materials for their combined use in innovative integrated microsystems. In this paper, we attempt to investigate the aminosilane-mediated irreversible bonding method for PDMS and SU-8 with X-Ray Photoelectron Spectroscopy (XPS) surface analysis and bonding strength tests. Additionally, the selected bonding method was applied in fabricating a microelectrode array (MEA) device, including microfluidic features, which allows electrophysiological observations on compartmentalized neuronal cultures. As there is a growing trend towards microfluidic devices for neuroscience research, this type of integrated microdevice, which can observe functional alterations on compartmentalized neuronal culture, can potentially be used for neurodegenerative disease research and pharmaceutical development.

negative: A review of overview+detail, zooming, and focus+context interfaces There are many interface schemes that allow users to work at, and move between, focused and contextual views of a dataset. We review and categorize these schemes according to the interface mechanisms used to separate and blend views. The four approaches are overview+detail, which uses a spatial separation between focused and contextual views; zooming, which uses a temporal separation; focus+context, which minimizes the seam between views by displaying the focus within the context; and cue-based techniques which selectively highlight or suppress items within the information space. Critical features of these categories, and empirical evidence of their success, are discussed. The aim is to provide a succinct summary of the state-of-the-art, to illuminate both successful and unsuccessful interface strategies, and to identify potentially fruitful areas for further work.

negative: Measurements and Simulations of the Convective Heat Transfer Coefficients on the End Windings of an Electrical Machine The prediction of the thermal behavior of an electrical machine in the design process basically depends on the used boundary conditions such as the convective heat transfer coefficient. Due to the complicated shape of the end windings, the heat transfer coefficient has to be identified empirically by using thermopiles as heat flux sensors placed at certain positions on the end windings, because an analytical derivation is not feasible. This paper presents the results of the measured values of the heat transfer on the end windings of an electrical machine. The gained coefficients are compared to the results obtained by numerical simulations using commercial software for computational fluid dynamics. The simulation results are discussed according to the validity of the gained heat transfer coefficient. The configuration of the simulation has been tested on a cylindrical shape in a cross-flow.

negative: Modeling Credit Risk for SMEs : Evidence from the US Market Considering the fundamental role played by small and medium sized enterprises (SMEs) in the economy of many countries and the considerable attention placed on SMEs in the new Basel Capital Accord, we develop a distress prediction model specifically for the SME sector and to analyze its effectiveness compared to a generic corporate model. The behaviour of financial measures for SMEs is analyzed and the most significant variables in predicting the entities’ credit worthiness are selected in order to construct a default prediction model. Using a logit regression technique on panel data of over 2,000 US firms (with sales less than $65 million) over the period 1994-2002, we develop a one-year default prediction model. This model has an out-of-sample prediction power which is almost 30 percent higher than a generic corporate model. An associated objective is to observe our model’s ability to lower bank capital requirements considering the new Basel Capital Accord’s rules for SMEs.

negative:
Query optimization using column statistics in hive Hive is a data warehousing solution on top of the Hadoop MapReduce framework that has been designed to handle large amounts of data and store them in tables like a relational database management system or a conventional data warehouse while using the parallelization and batch processing functionalities of the Hadoop MapReduce framework to speed up the execution of queries. Data inserted into Hive is stored in the Hadoop FileSystem (HDFS), which is part of the Hadoop MapReduce framework. To make the data accessible to the user, Hive uses a query language similar to SQL, which is called HiveQL. When a query is issued in HiveQL, it is translated by a parser into a query execution plan that is optimized and then turned into a series of map and reduce iterations. These iterations are then executed on the data stored in the HDFS, writing the output to a file.
 The goal of this work is to to develop an approach for improving the performance of the HiveQL queries executed in the Hive framework. For that purpose, we introduce an extension to the Hive MetaStore which stores metadata that has been extracted on the column level of the user database. These column level statistics are then used for example in combination with join ordering algorithms which are adapted to the specific needs of the Hadoop MapReduce environment to improve the overall performance of the HiveQL query execution.

negative: Perspectives of granular computing Granular computing emerges as a new multi-disciplinary study and has received much attention in recent years. A conceptual framework is presented by extracting shared commonalities from many fields. The framework stresses multiple views and multiple levels of understanding in each view. It is argued that granular computing is more about a philosophical way of thinking and a practical methodology of problem solving. By effectively using levels of granularity, granular computing provides a systematic, natural way to analyze, understand, represent, and solve real world problems. With granular computing, one aims at structured thinking at the philosophical level, and structured problem solving at the practical level.

negative: Learning Attribute-to-Feature Mappings for Cold-Start Recommendations Cold-start scenarios in recommender systems are situations in which no prior events, like ratings or clicks, are known for certain users or items. To compute predictions in such cases, additional information about users (user attributes, e.g. gender, age, geographical location, occupation) and items (item attributes, e.g. genres, product categories, keywords) must be used. We describe a method that maps such entity (e.g. user or item) attributes to the latent features of a matrix (or higher-dimensional) factorization model. With such mappings, the factors of a MF model trained by standard techniques can be applied to the new-user and the new-item problem, while retaining its advantages, in particular speed and predictive accuracy. We use the mapping concept to construct an attribute-aware matrix factorization model for item recommendation from implicit, positive-only feedback. Experiments on the new-item problem show that this approach provides good predictive accuracy, while the prediction time only grows by a constant factor.

negative: Language and the Newborn Brain: Does Prenatal Language Experience Shape the Neonate Neural Response to Speech? Previous research has shown that by the time of birth, the neonate brain responds specially to the native language when compared to acoustically similar non-language stimuli. In the current study, we use near-infrared spectroscopy to ask how prenatal language experience might shape the brain response to language in newborn infants. To do so, we examine the neural response of neonates when listening to familiar versus unfamiliar language, as well as to non language stimuli. Twenty monolingual English-exposed neonates aged 0-3 days were tested. Each infant heard low-pass filtered sentences of forward English (familiar language), forward Tagalog (unfamiliar language), and backward English and Tagalog (non-language). During exposure, neural activation was measured across 12 channels on each hemisphere. Our results indicate a bilateral effect of language familiarity on neonates' brain response to language. Differential brain activation was seen when neonates listened to forward Tagalog (unfamiliar language) as compared to other types of language stimuli. We interpret these results as evidence that the prenatal experience with the native language gained in utero influences how the newborn brain responds to language across brain regions sensitive to speech processing.

negative: Progress and prospects in plant genome editing The emergence of sequence-specific nucleases that enable genome editing is revolutionizing basic and applied biology. Since the introduction of CRISPR–Cas9, genome editing has become widely used in transformable plants for characterizing gene function and improving traits, mainly by inducing mutations through non-homologous end joining of double-stranded breaks generated by CRISPR–Cas9. However, it would be highly desirable to perform precision gene editing in plants, especially in transformation-recalcitrant species. Recently developed Cas9 variants, novel RNA-guided nucleases and base-editing systems, and DNA-free CRISPR–Cas9 delivery methods now provide great opportunities for plant genome engineering. In this Review Article, we describe the current status of plant genome editing, focusing on newly developed genome editing tools and methods and their potential applications in plants. We also discuss the specific challenges facing plant genome editing, and future prospects.

negative: Material-based Design Computation An Inquiry into Digital Simulation of Physical Material Properties as Design Generators The paper demonstrates the association between geometry and material behavior, specifically the elastic properties of resin impregnated latex membranes, by means of homogenizing protocols which translate physical properties into geometrical functions. Resin-impregnation patterns are applied to 2-D pre-stretched form-active tension systems to induce 3-D curvature upon release.This method enables form-finding based on material properties, organization and behavior. Some theoretical foundations for material-computation are outlined. A digital tool developed in the Processing (JAVA coded) environment demonstrates the simulation of material behavior and its prediction under specific environmental conditions. Finally, conclusions are drawn from the physical and digital explorations which redefine generative material-based design computation, supporting a synergetic approach to design integrating form, structure, material and environment.

negative: Cooperative Multi-Sensor Video Surveillance Takeo Kanade, Robert T. Collins, and Alan J. Lipton Carnegie Mellon University, Pittsburgh, PA e-mail: fkanade,rcollins,ajlg@cs.cmu.edu homepage: http://www.cs.cmu.edu/ vsam P. Anandan, Peter Burt, and Lambert Wixson David Sarno Research Center, Princeton, NJ e-mail: fpanandan,pburt,lwixsong@sarno .com Abstract Carnegie Mellon University (CMU) and the David Sarno Research Center (Sarno ) have begun a joint, integrated feasibility demonstration in the area of Video Surveillance and Monitoring (VSAM). The objective is to develop a cooperative, multi-sensor video surveillance system that provides continuous coverage over large battle eld areas. Image Understanding (IU) technologies will be developed to: 1) coordinate multiple sensors to seamlessly track moving targets over an extended area, 2) actively control sensor and platform parameters to track multiple moving targets, 3) integrate multisensor output with collateral data to maintain an evolving, scene-level representation of all targets and platforms, and 4) monitor the scene for unusual \trigger" events and activities. These technologies will be integrated into an experimental testbed to support evaluation, data collection, and demonstration of other VSAM technologies developed within the DARPA IU community.

negative: Test Case Selection and Prioritization Using Multiple Criteria Regression testing activities such as test case selection and test case prioritization are ordinarily based on the criteria which focused around code coverage, code modifications and test execution costs. The approach mainly based on the multiple criteria of code coverage which performs efficient selection of test case. The method mainly aims to maximize the coverage size by executing the test cases effectively. The selected test cases are then prioritized based on the priority which depends on the code coverage to achieve the desired results. Keywords—Test case selection, Test case prioritization, Cod coverage, Jaccard distance, Greedy algorithm.

negative: Studying weak central coherence at low levels: children with autism do not succumb to visual illusions. A research note. While anecdotal reports of abnormal perceptual experiences in autism abound, there have been to date no experimental studies showing fundamental perceptual peculiarities. The present paper reports results from a first study of low-level visual integration in autism. Twenty-five subjects with autism, 21 normal 7- and 8-year-olds, and 26 children with learning difficulties were asked to make simple judgements about six well-known visual illusions. Two conditions were used, in an attempt to explore group differences; standard two-dimensional black and white line drawings, and the same figures augmented with raised coloured lines. The subjects with autism were less likely to succumb to the two-dimensional illusions than were the other groups, and were less aided by the three-dimensional 'disembedded' condition. These striking results are discussed with reference to the 'central coherence' account of autism.

negative: On Throughput Maximization of Time Division Multiple Access With Energy Harvesting Users In this paper, we consider a multiple-access channel, where multiple users equipped with energy harvesting batteries communicate to an access point. To avoid consuming extra energy on competition for the channel, the users are supposed to share the channel via time division multiple access (TDMA). In many existing works, it is commonly assumed that the users' energy harvesting processes and storage status are known to all the users before transmissions. In practice, such knowledge may not be readily available. To avoid excessive overhead for real-time information exchange, we consider the scenario where the users schedule their individual transmissions according to the users' statistical energy harvesting profiles. We first study the optimal transmission scheme in the case where each node has an infinite-capacity battery. By optimization theory, we show that, to maximize the average system throughput, all the users should transmit at an identical optimal power, which solely depends on the energy harvesting rate per time slot. We then study the equal-power TDMA scheme in the case where each node is equipped with a battery of finite capacity. The system is formulated as a polling system consisting of multiple energy queues and one server. By the Markov chain modeling method, we derive the performance of equal-power TDMA in this case, in terms of the energy loss ratio and average system throughput. In addition, we develop an algorithm to efficiently compute the optimal transmission power in the finite-capacity battery case. We also consider an equal-time TDMA scheme, which assigns equal-length subslots to each user, and analyze its system performance. It is found that equal-power TDMA always outperforms equal-time TDMA in the infinite-capacity battery case, whereas equal-time TDMA exhibits compatible or even slightly better performance in some scenarios when the batteries have finite capacities.

negative: Predictive habitat distribution models in ecology With the rise of new powerful statistical techniques and GIS tools, the development of predictive habitat distribution models has rapidly increased in ecology. Such models are static and probabilistic in nature, since they statistically relate the geographical distribution of species or communities to their present environment. A wide array of models has been developed to cover aspects as diverse as biogeography, conservation biology, climate change research, and habitat or species management. In this paper, we present a review of predictive habitat distribution modeling. The variety of statistical techniques used is growing. Ordinary multiple regression and its generalized form (GLM) are very popular and are often used for modeling species distributions. Other methods include neural networks, ordination and classification methods, Bayesian models, locally weighted approaches (e.g. GAM), environmental envelopes or even combinations of these models. The selection of an appropriate method should not depend solely on statistical considerations. Some models are better suited to reflect theoretical findings on the shape and nature of the species’ response (or realized niche). Conceptual considerations include e.g. the trade-off between optimizing accuracy versus optimizing generality. In the field of static distribution modeling, the latter is mostly related to selecting appropriate predictor variables and to designing an appropriate procedure for model selection. New methods, including threshold-independent measures (e.g. receiver operating characteristic (ROC)-plots) and resampling techniques (e.g. bootstrap, cross-validation) have been introduced in ecology for testing the accuracy of predictive models. The choice of an evaluation measure should be driven primarily by the goals of the study. This may possibly lead to the attribution of different weights to the various types of prediction errors (e.g. omission, commission or confusion). Testing the model in a wider range of situations (in space and time) will permit one to define the range of applications for which the model predictions are suitable. In turn, the qualification of the model depends primarily on the goals of the study that define the qualification criteria and on the usability of the model, rather than on statistics alone. © 2000 Elsevier Science B.V. All rights reserved.

negative: MOBILE LEARNING FOR EDUCATION : BENEFITS AND CHALLENGES Education and training is the process by which the wisdom, knowledge and skills of one generation are passed on to the next. Today there are two form s f education and training: conventional education and distance education. Mobile learning, or "M-Lear ning", offers modern ways to support learning process through mobile devices, such as handheld an d tablet computers, MP3 players, smart phones and mobile phones. This document introduces the sub ject of mobile learning for education purposes. It examines what impact mobile devices have had on teaching and learning practices and goes on to look at the opportunities presented by the use of d igital media on mobile devices. The main purpose of this paper is to describe the current state of mobi le learning, benefits, challenges, and it’s barrier s to support teaching and learning. Data for this paper w re collected through bibliographic and internet research from January to March 2013. Four key areas will be addressed in this paper: 1. An analysis of Mobile Learning. 2. Differentiating E-Learning from Mobile Learning 3. Value and Benefits of Mobile Learning 4. Challenges and Barriers of M obile Learning: Study showed that M-Learning as a Distance learning brought great benefits to socie ty include : Training when it is needed, Training a t any time; Training at any place; Learner-centred co ntent; Avoidance of re-entry to work problems; Training for taxpayers, and those fully occupied du ring university lectures and sessions at training centres; and The industrialisation of teaching and learning. And also, notebooks, mobile Tablets, iPod touch, and iPads are very popular devices for mobil e learning because of their cost and availability o f apps. --------------------------------

negative: Gaming frequency and academic performance There are numerous claims that playing computer and video games may be educationally beneficial, but there has been little formal investigation into whether or not the frequency of exposure to such games actually affects academic performance. This paper explores the issue by analysing the relationships between gaming frequency – measured as the amount of time undergraduate students spend playing games in their free time – and their academic performance as measured by their examination marks. Using a sample of 713 students, correlation analyses between gaming frequency and examination performance were conducted for students of varying gaming frequency, study discipline, gender, and general attitudes towards gaming and study. The results reveal that examination marks are in fact negatively correlated with gaming frequency – i.e. frequent gamers generally achieve lower marks than less frequent gamers.

negative: Evaluation of mobile app paradigms The explosion of mobile applications both in number and variety raises the need of shedding light on their architecture, composition and quality. Indeed, it is crucial to understand which mobile application paradigm fits better to what type of application and usage. Such understanding has direct consequences on the user experience, the development cost and sale revenues of mobile apps. In this paper, we identified four main mobile application paradigms and evaluated them from the developer, user and service provider viewpoints. To ensure objectivity and soundness we start by defining high level criteria and then breaking down in finer-grained criteria. After a theoretical evaluation an implementation was carried out as a practical verification. The selected application is object recognition app, which is both exciting and challenging to develop.

negative: Spectral conversion based on maximum likelihood estimation considering global variance of converted parameter The paper describes a novel spectral conversion method for voice transformation. We perform spectral conversion between speakers using a Gaussian mixture model (GMM) on the joint probability density of source and target features. A smooth spectral sequence can be estimated by applying maximum likelihood (ML) estimation to the GMM-based mapping using dynamic features. However, there is still degradation of the converted speech quality due to an over-smoothing of the converted spectra, which is inevitable in conventional ML-based parameter estimation. In order to alleviate the over-smoothing, we propose an ML-based conversion taking account of the global variance of the converted parameter in each utterance. Experimental results show that the performance of the voice conversion can be improved by using the global variance information. Moreover, it is demonstrated that the proposed algorithm is more effective than spectral enhancement by postfiltering.

negative: Can Structural Models Price Default Risk ? Evidence from Bond and Credit Derivative Markets ∗ Using a set of structural models, we evaluate the price of default protection for a sample of US corporations. In contrast to previous evidence from corporate bond data, CDS premia are not systematically underestimated. In fact, one of our studied models has little difficulty on average in predicting their level. For robustness, we perform the same exercise for bond spreads by the same issuers on the same trading date. As expected, bond spreads relative to the Treasury curve are systematically underestimated. This is not the case when the swap curve is used as a benchmark, suggesting that previously documented underestimation results may be sensitive to the choice of risk free rate. ∗We are indebted to seminar participants at Wilfrid Laurier University, Carnegie Mellon University, the Bank of Canada, McGill University and Queen’s University for helpful discussions. In addition, we are grateful to participants of the 2005 EFA in Moscow, the 2005 NFA in Vancouver, the 2006 CFP Vallendar conference and the 2006 Moody’s NYU Credit Risk Conference. We are particularly grateful to Stephen Schaefer for comments. †Faculty of Management, McGill University, 1001 Sherbrooke Street West, Montreal QC, H3A 1G5 Canada. Tel +1 514 398-3186, Fax +1 514 398-3876, Email jan.ericsson@mcgill.ca. ‡Stockholm School of Economics, Department of Finance, Box 6501, S-113 83 Stockholm, Sweden. Tel: +46 8 736 9143, fax +46 8 312327. 1

negative: Transfer Learning Improves Supervised Image Segmentation Across Imaging Protocols The variation between images obtained with different scanners or different imaging protocols presents a major challenge in automatic segmentation of biomedical images. This variation especially hampers the application of otherwise successful supervised-learning techniques which, in order to perform well, often require a large amount of labeled training data that is exactly representative of the target data. We therefore propose to use transfer learning for image segmentation. Transfer-learning techniques can cope with differences in distributions between training and target data, and therefore may improve performance over supervised learning for segmentation across scanners and scan protocols. We present four transfer classifiers that can train a classification scheme with only a small amount of representative training data, in addition to a larger amount of other training data with slightly different characteristics. The performance of the four transfer classifiers was compared to that of standard supervised classification on two magnetic resonance imaging brain-segmentation tasks with multi-site data: white matter, gray matter, and cerebrospinal fluid segmentation; and white-matter-/MS-lesion segmentation. The experiments showed that when there is only a small amount of representative training data available, transfer learning can greatly outperform common supervised-learning approaches, minimizing classification errors by up to 60%.

negative: Learning to lip read words by watching videos Our aim is to recognise the words being spoken by a talking face, given only the video but not the audio. Existing works in this area have focussed on trying to recognise a small number of utterances in controlled environments (e.g. digits and alphabets), partially due to the shortage of suitable datasets. We make three novel contributions: first, we develop a pipeline for fully automated data collection from TV broadcasts. With this we have generated a dataset with over a million word instances, spoken by over a thousand different people; second, we develop a two-stream convolutional neural network that learns a joint embedding between the sound and the mouth motions from unlabelled data. We apply this network to the tasks of audio-to-video synchronisation and active speaker detection; third, we train convolutional and recurrent networks that are able to effectively learn and recognize hundreds of words from this large-scale dataset. In lip reading and in speaker detection, we demonstrate results that exceed the current state-of-the-art on public benchmark datasets.

negative:
Older adults' views of "successful aging"--how do they compare with researchers' definitions? OBJECTIVES
To determine whether older adults have thought about aging and aging successfully and to compare their perceptions of successful aging with attributes of successful aging identified in the published literature.

DESIGN
A cross-sectional, mailed survey.

SETTING
King County, Washington.

PARTICIPANTS
Nondemented participants from two cohorts. The first cohort, referred to as Kame, which means turtle, a symbol of longevity for Japanese, enrolled 1,985 Japanese Americans aged 65 and older and was established in 1992-94. The second cohort, Adult Changes in Thought, enrolled 2,581 white men and women aged 65 and older from a health maintenance organization and was established in 1994-96.

MEASUREMENTS
Respondents were asked whether they had ever thought about aging and aging successfully and whether these thoughts had changed over the previous 20 years and about how important specific attributes, originating from the published literature, were in characterizing successful aging.

RESULTS
Overall, 90% had previously thought about aging and aging successfully, and approximately 60% said their thoughts had changed over the previous 20 years. The Japanese-American group rated 13 attributes as important to successful aging; the white group rated the same 13 as important and added one additional attribute, learning new things.

CONCLUSION
Older adults' definition of successful aging is multidimensional, encompassing physical, functional, psychological, and social health. In contrast, none of the published work describing attributes of successful aging includes all four dimensions. Future work would benefit from an expanded definition to adequately reflect the perceptions of older adults.

negative: Cyclic deadlock prediction and avoidance for zone-controlled AGV system Automated guided vehicles (AGVs) are now becoming popular in automated materials handling systems, flexible manufacturing systems and even containers handling applications at seaports. Its performance affects the efficiency of the entire system. Deadlock formation is a serious problem as it stalls the AGVS. The objective of this paper is to develop an efficient AGV deadlock prediction and avoidance algorithm for container port operations. Deadlock in a broad sense is a situation in which at least a part of the system stalls. There are a lot of situations in which the system may stall and most of these situations can be avoided through the control and navigation system. This paper discusses the development of an efficient strategy for predicting and avoiding the deadlocks in a large scale AGVS A variety of deadlock-detecting algorithms are available, but these methods work mainly for manufacturing system where the network layout is simple and uses only a small number of AGVs. The AGVS under study has complex layout and involves close to 80 AGVs. The proposed solution is implemented using the Automod simulation software, and the performance of the technique is presented. Simulation shows that all the potential deadlock situations can be detected and avoided via this methodology. Also, the proposed strategy is computationally efficient and is able to provide the movement decision to the vehicles within the 1.5-s sampling time. r 2002 Published by Elsevier Science B.V.

negative: Fast 3D mapping in highly dynamic environments using normal distributions transform occupancy maps Autonomous vehicles operating in real-world industrial environments have to overcome numerous challenges, chief among which is the creation and maintenance of consistent 3D world models. This paper focuses on a particularly important challenge: mapping in dynamic environments. We introduce several improvements to the recently proposed Normal Distributions Transform Occupancy Map (NDT-OM) aimed for efficient mapping in dynamic environments. A careful consistency analysis is given based on convergence and similarity metrics specifically designed for evaluation of NDT maps in dynamic environments. We show that in the context of mapping with known poses the proposed method results in improved consistency and in superior runtime performance, when compared against 3D occupancy grids at the same size and resolution. Additionally, we demonstrate that NDT-OM features real-time performance in a highly dynamic 3D mapping and tracking scenario with centimeter accuracy over a 1.5km trajectory.

negative: Map API - scalable decentralized map building for robots Large scale, long-term, distributed mapping is a core challenge to modern field robotics. Using the sensory output of multiple robots and fusing it in an efficient way enables the creation of globally accurate and consistent metric maps. To combine data from multiple agents into a global map, most existing approaches use a central entity that collects and manages the information from all agents. Often, the raw sensor data of one robot needs to be made available to processing algorithms on other agents due to the lack of computational resources on that robot. Unfortunately, network latency and low bandwidth in the field limit the generality of such an approach and make multi-robot map building a tedious task. In this paper, we present a distributed and decentralized back-end for concurrent and consistent robotic mapping. We propose a set of novel approaches that reduce the bandwidth usage and increase the effectiveness of inter-robot communication for distributed mapping. Instead of locking access to the map during operations, we define a version control system which allows concurrent and consistent access to the map data. Updates to the map are then shared asynchronously with agents which previously registered notifications. A technique for data lookup is provided by state-of-the-art algorithms from distributed computing. We validate our approach on real-world datasets and demonstrate the effectiveness of the proposed algorithms.

negative: A Strategic Empty Container Logistics Optimization in a Major Shipping Company In this paper, we present a system that Compañía Sud Americana de Vapores (CSAV), one of the world’s largest shipping companies, developed to support its decisions for repositioning and stocking empty containers. CSAV’s main business is shipping cargo in containers to clients worldwide. It uses a fleet of about 700,000 TEU containers of different types, which are carried by both CSAV-owned and third-party ships. Managing the container fleet is complex; CSAV must make thousands of decisions each day. In particular, imbalances exist among the regions. For example, China often has a deficit of empty containers and is a net importer; Saudi Arabia often has a surplus and is a net exporter. CSAV and researchers from the University of Chile developed the Empty Container Logistics Optimization System (ECO) to manage this imbalance. ECO’s multicommodity, multiperiod model manages the repositioning problem, whereas an inventory model determines the safety stock required at each location. CSAV uses safety stock to ensure high service levels despite uncertainties, particularly in the demand for containers. A hybrid forecasting system supports both the inventory and the multicommodity network flow model. Major improvements in data gathering, real-time communications, and automation of data handling were needed as input to the models. A collaborative Web-based optimization framework allows agents from different zones to interact in decision making. The use of ECO led to direct savings of $81 million for CSAV, a reduction in inventory stock of 50 percent, and an increase in container turnover of 60 percent. Moreover, the system helped CSAV to become more efficient and to overcome the 2008 economic crisis.

negative: Robotics-logistics: Challenges for automation of logistic processes Robotics-logistics can be understood as the field of activities in which applications of industrial robot-technologies are offered and demanded in order to ensure the optimization of internal material flows. According to a survey conducted by BIBA in 2007, the area of robotics-logistics has great need for modernization. Robotics-Logistics activities can be classified in several scenarios as unloading / loading of goods and palletizing / depalletizing of goods. Possible scenarios for research and development activities within theses fields are given.

negative: Semi-global matching based disparity estimate using fast Census transform The Census transform is very useful in stereo matching algorithms. But the traditional Census transform cost too much computation, and its accuracy and robustness are not satisfying. In this paper, a modified semi-global matching (SGM) algorithm based on the fast Census transform is proposed to improve the quality of the range map and decrease the computation. In addition, we modify the disparity refinement to enhance the quality of the estimated range map. Our experiments show that the proposed method performs better than the original SGM algorithm in terms of the accuracy and the anti-noise capability.

negative: SEQUENCE BASED HIERARCHICAL CONFLICT-FREE ROUTING STRATEGY OF BI-DIRECTIONAL AUTOMATED GUIDED VEHICLES Conflict-free routing is an important problem to be addressed in AGV Systems, in particular when they are bi-directional. This paper considers the problem of routing AGVs in the presence of contingencies. The importance of this work stems from the realization that path-planning approaches are ill suited to AGVSs subject to contingencies. In this paper, a two stage closed-loop control strategy is proposed. In the first control stage, a pre-planning method proposed by Kim and Tanchoco (1991) is used to establish the fastest conflict-free routes for AGVs. The objective of the second stage is the avoidance of deadlocks in the presence of interruptions while maintaining the planned routes. Copyright © 2005 IFAC


query: Design of a UHF RFID metal tag for long reading range using a cavity structure

positive: Design criteria for the RF section of UHF and microwave passive RFID transponders A set of design criteria for the radio-frequency (RF) section of long-range passive RF identification (RFID) transponders operating in the 2.45-GHz or 868-MHz industrial, scientific, and medical (ISM) frequency ranges is derived in this paper, focusing in particular on the voltage multiplier, the power-matching network, and the backscatter modulation. The paper discusses the design tradeoffs between the error probability at the reader receiver and the converted RF-dc power at the transponder, determining the regions of the design space that allow optimization of the operating range and the data rate of the RFID system.

positive: Low-Profile RFID Tag Antenna Using Compact AMC Substrate for Metallic Objects A low-profile (aplambda/100) passive radio frequency identification (RFID) tag antenna incorporated with a compact artificial magnetic conductor (AMC) substrate is proposed. A novel modified rectangular patch type AMC substrate with offset vias is employed to significantly reduce the planar dimensions of a tag antenna, which is about 0.1lambda times 0.2lambda. The effect of a ground plane size and the number of AMC unit cells on the antenna performance such as return loss and gain is investigated with a view of tag installation. It is demonstrated through experiments that the proposed tag antenna shows relatively tolerant reading distances from 2.4 to 4.8 m under various platform environments such as a conductive plate or a plastic water container.

positive: Power reflection coefficient analysis for complex impedances in RFID tag design Kurokawa's method of calculating the power reflection coefficient from the Smith chart in the situation when one complex impedance is directly connected to another is applied to passive RFID tag design, where power reflection is important, as it determines the tag characteristics. The performance analysis of a specific RFID tag is presented together with experimental data, which is in close agreement with the theory.

positive: High-Impedance Electromagnetic Surfaces with a Forbidden Frequency Band A new type of metallic electromagnetic structure has been developed that is characterized by having high surface impedance. Although it is made of continuous metal, and conducts dc currents, it does not conduct ac currents within a forbidden frequency band. Unlike normal conductors, this new surface does not support propagating surface waves, and its image currents are not phase reversed. The geometry is analogous to a corrugated metal surface in which the corrugations have been folded up into lumped-circuit elements, and distributed in a two-dimensional lattice. The surface can be described using solid-state band theory concepts, even though the periodicity is much less than the free-space wavelength. This unique material is applicable to a variety of electromagnetic problems, including new kinds of low-profile antennas.

negative: Combining natural language processing and network analysis to examine how advocacy organizations stimulate conversation on social media. Social media sites are rapidly becoming one of the most important forums for public deliberation about advocacy issues. However, social scientists have not explained why some advocacy organizations produce social media messages that inspire far-ranging conversation among social media users, whereas the vast majority of them receive little or no attention. I argue that advocacy organizations are more likely to inspire comments from new social media audiences if they create "cultural bridges," or produce messages that combine conversational themes within an advocacy field that are seldom discussed together. I use natural language processing, network analysis, and a social media application to analyze how cultural bridges shaped public discourse about autism spectrum disorders on Facebook over the course of 1.5 years, controlling for various characteristics of advocacy organizations, their social media audiences, and the broader social context in which they interact. I show that organizations that create substantial cultural bridges provoke 2.52 times more comments about their messages from new social media users than those that do not, controlling for these factors. This study thus offers a theory of cultural messaging and public deliberation and computational techniques for text analysis and application-based survey research.

negative: On the momentum term in gradient descent learning algorithms A momentum term is usually included in the simulations of connectionist learning algorithms. Although it is well known that such a term greatly improves the speed of learning, there have been few rigorous studies of its mechanisms. In this paper, I show that in the limit of continuous time, the momentum parameter is analogous to the mass of Newtonian particles that move through a viscous medium in a conservative force field. The behavior of the system near a local minimum is equivalent to a set of coupled and damped harmonic oscillators. The momentum term improves the speed of convergence by bringing some eigen components of the system closer to critical damping. Similar results can be obtained for the discrete time case used in computer simulations. In particular, I derive the bounds for convergence on learning-rate and momentum parameters, and demonstrate that the momentum term can increase the range of learning rate over which the system converges. The optimal condition for convergence is also analyzed.

negative: A sparse-response deep belief network based on rate distortion theory Deep belief networks (DBNs) are currently the dominant technique for modeling the architectural depth of brain, and can be trained efficiently in a greedy layer-wise unsupervised learning manner. However, DBNs without a narrow hidden bottleneck typically produce redundant, continuous-valued codes and unstructured weight patterns. Taking inspiration from rate distortion (RD) theory, which encodes original data using as few bits as possible, we introduce in this paper a variant of DBN, referred to as sparse-response DBN (SR-DBN). In this approach, Kullback–Leibler divergence between the distribution of data and the equilibrium distribution defined by the building block of DBN is considered as a distortion function, and the sparse response regularization induced by L1-norm of codes is used to achieve a small code rate. Several experiments by extracting features from different scale image datasets show that our approach SR-DBN learns codes with small rate, extracts features at multiple levels of abstraction mimicking computations in the cortical hierarchy, and obtains more discriminative representation than PCA and several basic algorithms of DBNs. & 2014 Elsevier Ltd. All rights reserved.

negative: Pl@ntNet mobile app Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.

negative: Resilient Cell-Based Architecture for Time-to-Digital Converter This paper proposes a resilient Time-to-Digital Converter (TDC) that lends itself to cell-based design automation. We adopt a shrinking-based architecture with a number of distinctive techniques. First of all, a specialized on-chip re-calibration scheme is developed so that the real-time transfer function of the TDC in silicon (which maps an input pulse-width to its corresponding output code) can be derived on the chip and thereby the absolute value (instead of just a relative code) of an input pulse-width under measurement can be reported. Secondly, the sampling errors stemming from the jitters of training clocks used in the calibration scheme are mitigated by the principle of multi sampling. Thirdly, a flexible coarse-shrinking block is adopted and an automatic adjustment scheme is employed so that the coarse-shrinking block can adjust itself when operated under different input pulse-width ranges.

negative: Measuring User Productivity in Machine Translation Enhanced Computer Assisted Translation This paper addresses the problem of reliably measuring productivity gains by professional translators working with a machine translation enhanced computer assisted translation tool. In particular, we report on a field test we carried out with a commercial CAT tool in which translation memory matches were supplemented with suggestions from a commercial machine translation engine. The field test was conducted with 12 professional translators working on real translation projects. Productivity of translators were measured with two indicators, post-editing speed and post-editing effort, on two translation directions, English–Italian and English–German, and two linguistic domains, legal and information technology. Besides a detailed statistical analysis of the experimental results, we also discuss issues encountered in running the test.

negative: A Deep Learning Architecture for Image Representation, Visual Interpretability and Automated Basal-Cell Carcinoma Cancer Detection This paper presents and evaluates a deep learning architecture for automated basal cell carcinoma cancer detection that integrates (1) image representation learning, (2) image classification and (3) result interpretability. A novel characteristic of this approach is that it extends the deep learning architecture to also include an interpretable layer that highlights the visual patterns that contribute to discriminate between cancerous and normal tissues patterns, working akin to a digital staining which spotlights image regions important for diagnostic decisions. Experimental evaluation was performed on set of 1,417 images from 308 regions of interest of skin histopathology slides, where the presence of absence of basal cell carcinoma needs to be determined. Different image representation strategies, including bag of features (BOF), canonical (discrete cosine transform (DCT) and Haar-based wavelet transform (Haar)) and proposed learned-from-data representations, were evaluated for comparison. Experimental results show that the representation learned from a large histology image data set has the best overall performance (89.4% in F-measure and 91.4% in balanced accuracy), which represents an improvement of around 7% over canonical representations and 3% over the best equivalent BOF representation.

negative: Defining Media Enjoyment as the Satisfaction of Intrinsic Needs This article presents a model of enjoyment rooted in self-determination theory (Deci & Ryan, 1985) that includes the satisfaction of three needs related to psychological wellbeing: autonomy, competence, and relatedness. In an experiment designed to validate this conceptualization of enjoyment, we manipulate video game characteristics related to the satisfaction of these needs and examine their relative effects on enjoyment. The validated model explains 51% of the variance in enjoyment, even without including needs usually studied in relation to enjoyment such as pleasure seeking. Results indicate the utility of defining enjoyment as need satisfaction. These results are discussed in terms of a broader conceptualization of enjoyment represented as the satisfaction of a comprehensive set of functional needs.

negative: A Short-term Traffic Flow Forecasting Method Based on the Hybrid PSO-SVR Accurate short-term flow forecasting is important for the real-time traffic control, but due to its complex nonlinear data pattern, getting a high precision is difficult. The support vector regression model (SVR) has been widely used to solve nonlinear regression and time series predicting problems. To get a higher precision with less learning time, this paper presents a Hybrid PSO-SVR forecasting method, which uses particle swarm optimization (PSO) to search optimal SVR parameters. In order to find a PSO that is more proper for SVR parameters searching, this paper proposes three kinds of strategies to handle the particles flying out of the searching space Through the comparison of three strategies, we find one of the strategies can make PSO get the optimal parameters more quickly. The PSO using this strategy is called fast PSO. Furthermore, aiming at the problem about the decrease of prediction accuracy caused by the noises in the original data, this paper proposes a hybrid PSO-SVR method with historical momentum based on the similarity of historical short-term flow data. The results of extensive comparison experiments indicate that the proposed model can get more accurate forecasting results than other state-of-the-art algorithms, and when the data contain noises, the method with historical momentum still gets accurate forecasting results.

negative: Fully Parallel Stochastic LDPC Decoders Stochastic decoding is a new approach to iterative decoding on graphs. This paper presents a hardware architecture for fully parallel stochastic low-density parity-check (LDPC) decoders. To obtain the characteristics of the proposed architecture, we apply this architecture to decode an irregular state-of-the-art (1056,528) LDPC code on a Xilinx Virtex-4 LX200 field-programmable gate-array (FPGA) device. The implemented decoder achieves a clock frequency of 222 MHz and a throughput of about 1.66 Gb/s at Eb/N0=4.25 dB (a bit error rate of 10-8). It provides decoding performance within 0.5 and 0.25 dB of the floating-point sum-product algorithm with 32 and 16 iterations, respectively, and similar error-floor behavior. The decoder uses less than 40% of the lookup tables, flip-flops, and IO ports available on the FPGA device. The results provided in this paper validate the potential of stochastic LDPC decoding as a practical and competitive fully parallel decoding approach.

negative: Epigenetics and the embodiment of race: developmental origins of US racial disparities in cardiovascular health. The relative contribution of genetic and environmental influences to the US black-white disparity in cardiovascular disease (CVD) is hotly debated within the public health, anthropology, and medical communities. In this article, we review evidence for developmental and epigenetic pathways linking early life environments with CVD, and critically evaluate their possible role in the origins of these racial health disparities. African Americans not only suffer from a disproportionate burden of CVD relative to whites, but also have higher rates of the perinatal health disparities now known to be the antecedents of these conditions. There is extensive evidence for a social origin to prematurity and low birth weight in African Americans, reflecting pathways such as the effects of discrimination on maternal stress physiology. In light of the inverse relationship between birth weight and adult CVD, there is now a strong rationale to consider developmental and epigenetic mechanisms as links between early life environmental factors like maternal stress during pregnancy and adult race-based health disparities in diseases like hypertension, diabetes, stroke, and coronary heart disease. The model outlined here builds upon social constructivist perspectives to highlight an important set of mechanisms by which social influences can become embodied, having durable and even transgenerational influences on the most pressing US health disparities. We conclude that environmentally responsive phenotypic plasticity, in combination with the better-studied acute and chronic effects of social-environmental exposures, provides a more parsimonious explanation than genetics for the persistence of CVD disparities between members of socially imposed racial categories.

negative: Sugihara causality analysis of scalp EEG for detection of early Alzheimer's disease Recently, Sugihara proposed an innovative causality concept, which, in contrast to statistical predictability in Granger sense, characterizes underlying deterministic causation of the system. This work exploits Sugihara causality analysis to develop novel EEG biomarkers for discriminating normal aging from mild cognitive impairment (MCI) and early Alzheimer's disease (AD). The hypothesis of this work is that scalp EEG based causality measurements have different distributions for different cognitive groups and hence the causality measurements can be used to distinguish between NC, MCI, and AD participants. The current results are based on 30-channel resting EEG records from 48 age-matched participants (mean age 75.7 years) - 15 normal controls (NCs), 16 MCI, and 17 early-stage AD. First, a reconstruction model is developed for each EEG channel, which predicts the signal in the current channel using data of the other 29 channels. The reconstruction model of the target channel is trained using NC, MCI, or AD records to generate an NC-, MCI-, or AD-specific model, respectively. To avoid over fitting, the training is based on the leave-one-out principle. Sugihara causality between the channels is described by a quality score based on comparison between the reconstructed signal and the original signal. The quality scores are studied for their potential as biomarkers to distinguish between the different cognitive groups. First, the dimension of the quality scores is reduced to two principal components. Then, a three-way classification based on the principal components is conducted. Accuracies of 95.8%, 95.8%, and 97.9% are achieved for resting eyes open, counting eyes closed, and resting eyes closed protocols, respectively. This work presents a novel application of Sugihara causality analysis to capture characteristic changes in EEG activity due to cognitive deficits. The developed method has excellent potential as individualized biomarkers in the detection of pathophysiological changes in early-stage AD.

negative: Multidisciplinary Instruction with the Natural Language Toolkit The Natural Language Toolkit ( NLTK ) is widely used for teaching natural language processing to students majoring in linguistics or computer science. This paper describes the design ofNLTK , and reports on how it has been used effectively in classes that involve different mixes of linguistics and computer science students. We focus on three key issues: getting started with a course, delivering interactive demonstrations in the classroom, and organizing assignments and projects. In each case, we report on practical experience and make recommendations on how to useNLTK to maximum effect.

negative: A 2.4 GHz Interferer-Resilient Wake-Up Receiver Using A Dual-IF Multi-Stage N-Path Architecture A 2.4 GHz interferer-resilient wake-up receiver for ultra-low power wireless sensor nodes uses an uncertain-IF dualconversion topology, combining a distributed multi-stage N-path filtering technique with an unlocked low-Q resonator-referred local oscillator. This structure provides narrow-band selectivity and strong immunity against interferers, while avoiding expensive external resonant components such as BAW resonators or crystals. The 65 nm CMOS receiver prototype provides a sensitivity of -97 dBm and a carrier-to-interferer ratio better than -27 dB at 5 MHz offset, for a data rate of 10 kb/s at a 10-3 bit error rate, while consuming 99 μW from a 0.5 V voltage supply under continuous operation.

negative: Understanding choice overload in recommender systems Even though people are attracted by large, high quality recommendation sets, psychological research on choice overload shows that choosing an item from recommendation sets containing many attractive items can be a very difficult task. A web-based user experiment using a matrix factorization algorithm applied to the MovieLens dataset was used to investigate the effect of recommendation set size (5 or 20 items) and set quality (low or high) on perceived variety, recommendation set attractiveness, choice difficulty and satisfaction with the chosen item. The results show that larger sets containing only good items do not necessarily result in higher choice satisfaction compared to smaller sets, as the increased recommendation set attractiveness is counteracted by the increased difficulty of choosing from these sets. These findings were supported by behavioral measurements revealing intensified information search and increased acquisition times for these large attractive sets. Important implications of these findings for the design of recommender system user interfaces will be discussed.

negative: Deep Rewiring: Training very sparse deep networks Neuromorphic hardware tends to pose limits on the connectivity of deep networks that one can run on them. But also generic hardware and software implementations of deep learning run more efficiently on sparse networks. Several methods exist for pruning connections of a neural network after it was trained without connectivity constraints. We present an algorithm, DEEP R, that enables us to train directly a sparsely connected neural network. DEEP R automatically rewires the network during supervised training so that connections are there where they are most needed for the task, while its total number is all the time strictly bounded. We demonstrate that DEEP R can be used to train very sparse feedforward and recurrent neural networks on standard benchmark tasks with just a minor loss in performance. DEEP R is based on a rigorous theoretical foundation that views rewiring as stochastic sampling of network configurations from a posterior.

negative: Stability Analysis and Stabilization methods of DC Microgrid with Multiple Parallel-Connected DC-DC Converters loaded -Constant power loads (CPLs) may yield instability due to the well-known negative impedance characteristic. This study analyzes the factors which cause instability of a DC microgrid with multiple DC-DC converters. Two stabilization methods are presented for two operation modes: constant voltage source mode and droop mode. And sufficient conditions for the stability of the DC microgrid are obtained by identifying the eigenvalues of the Jacobian matrix. The key is to transform the eigenvalue problem to a quadratic eigenvalue problem (QEP). When applying the methods in practical engineering, the salient feature is that the stability parameter domains can be estimated by the available constraints, such as the values of capacities, inductances, maximum load power and distances of the cables. Compared with some classical methods, the proposed methods have wider stability region. The simulation results based on MATLAB /simulink platform verify the feasibility of the methods.

negative: Building Enterprise Systems Infrastructure Flexibility as Enabler of Organisational Agility: Empirical Evidence Enterprise systems (ES) that capture the most advanced developments of information technology are becoming common fixtures in most organisations. However, how ES affect organizational agility (OA) has been less researched and the existing research remains equivocal. From the perspective that ES can positively contribute to OA, this research via theory-based model development and rigorous empirical investigation of the proposed model, has bridged significant research gaps and provided empirical evidence for, and insights into, the effect of ES on OA. The empirical results based on data collected from 179 large organizations in Australia and New Zealand which have implemented and used ES for at least one year show that organisations can achieve agility out of their ES in two ways: by developing ES technical competences to build ES-enabled capabilities that digitise their key sensing and responding processes; and when ES-enabled sensing and responding capabilities are aligned in relatively turbulent environment.

negative: Power grids reliability appraisal with intrusion tolerant capability in SCADA systems By intruding into the substations and control center of the supervisory control and data acquisition (SCADA) system, trip commands can be sent to intelligent electronic devices (IEDs) which control the system breakers. Reliability of the power system can be impacted through the cyber attacks. In this paper, a modified semi-Markov process (SMP) model is used to describe the procedures of normal and penetration attacks against the intrusion tolerant system. By modeling the transition probabilities between the SMP states and the sojourn time of each SMP state, the mean times to compromise (MTTCs) of the normal and penetration attacks are calculated. With increased probabilities of breaker trips resulted from the cyber attacks, the loss of load probabilities (LOLP) are evaluated based on IEEE reliability test system 79 (RTS79). When the level of attack increases or the level of defense in the system decreases, the simulation results demonstrate that the power system becomes less reliable.

negative: FiDO: A Community-based Web Browsing Agent and CDN for Challenged Network Environments Homes located on tribal lands, particularly in rural areas of the United States, continue to lack access to broadband Internet and cellular connectivity [19]. Inspired by previous observations of community content similarity in tribal networks, we propose FiDO, a community-based Web browsing and content delivery system that takes advantage of user mobility, opportunistic connectivity, and collaborative filtering to provide relevant Web content to members of disconnected households via opportunistic contact with cellular base stations during a daily commute. We evaluate FiDO using trace-driven simulations with network usage data collected from a tribal-operated ISP that serves the Coeur d’Alene Indian Reservation in Western Idaho. By collecting data about household Web preferences and applying a collaborative filtering technique based on the Web usage patterns of the surrounding reservation community, we are able to opportunistically browse the Web on behalf of members of disconnected households, providing an average of 69.4 Web pages (all content from a specific URL, e.g., “http://gis.cdatribe-nsn.gov/LandBuyBack/”) crawled from 73% of their top 10 most visited Web domains (e.g., “cdatribe-nsn.gov” or “cnn.com/”) per day. Moreover, this content is able to be fetched and pushed to users even when the opportunistic data rate is limited to an average of only 0.99 Mbps (σ = 0.24 Mbps) and the daily opportunistic connection time is an average of 45.9 minutes (σ = 2.3 minutes). Additionally, we demonstrate a hybrid “search and browse” approach that allocates a percentage of opportunistic resources to the download of user-specified content. By dedicating only 10% of opportunistic windows of connectivity to the download of social media content, 51% of households were able to receive all of their daily expected social media content in addition to an average of 55.3 Web pages browsed on their behalf from an average of 4 different Web domains. Critically, we demonstrate the feasibility of a collaborative and community-based Web browsing model that extends access to Web content across the last mile(s) using existing infrastructure and rural patterns of mobility.

negative: Actions and Attributes from Wholes and Parts We investigate the importance of parts for the tasks of action and attribute classification. We develop a part-based approach by leveraging convolutional network features inspired by recent advances in computer vision. Our part detectors are a deep version of poselets and capture parts of the human body under a distinct set of poses. For the tasks of action and attribute classification, we train holistic convolutional neural networks and show that adding parts leads to top-performing results for both tasks. We observe that for deeper networks parts are less significant. In addition, we demonstrate the effectiveness of our approach when we replace an oracle person detector, as is the default in the current evaluation protocol for both tasks, with a state-of-the-art person detection system.

negative: 基於對照表以及語言模型之簡繁字體轉換 (Chinese Characters Conversion System based on Lookup Table and Language Model) [In Chinese] The character sets used in China and Taiwan are both Chinese, but they are divided into simplified and traditional Chinese characters. There are large amount of information exchange between China and Taiwan through books and Internet. To provide readers a convenient reading environment, the character conversion between simplified and traditional Chinese is necessary. The conversion between simplified and traditional Chinese characters has two problems: one-to-many ambiguity and term usage problems. Since there are many traditional Chinese characters that have only one corresponding simplified character, when converting simplified Chinese into traditional Chinese, the system will face the one-to-many ambiguity. Also, there are many terms that have different usages between the two Chinese societies. This paper focus on designing an extensible conversion system, that can take the advantage of community knowledge by accumulating lookup tables through Wikipedia to tackle the term usage problem and can integrate language model to disambiguate the one-to-many ambiguity. The system can reduce the cost of proofreading of character conversion for books, e-books, or online publications. The extensible architecture makes it easy to improve the system with new training data.

negative: Evaluating abdominal core muscle fatigue: Assessment of the validity and reliability of the prone bridging test. The aims of this study were to research the amplitude and median frequency characteristics of selected abdominal, back, and hip muscles of healthy subjects during a prone bridging endurance test, based on surface electromyography (sEMG), (a) to determine if the prone bridging test is a valid field test to measure abdominal muscle fatigue, and (b) to evaluate if the current method of administrating the prone bridging test is reliable. Thirty healthy subjects participated in this experiment. The sEMG activity of seven abdominal, back, and hip muscles was bilaterally measured. Normalized median frequencies were computed from the EMG power spectra. The prone bridging tests were repeated on separate days to evaluate inter and intratester reliability. Significant differences in normalized median frequency slope (NMFslope ) values between several abdominal, back, and hip muscles could be demonstrated. Moderate-to-high correlation coefficients were shown between NMFslope values and endurance time. Multiple backward linear regression revealed that the test endurance time could only be significantly predicted by the NMFslope of the rectus abdominis. Statistical analysis showed excellent reliability (ICC=0.87-0.89). The findings of this study support the validity and reliability of the prone bridging test for evaluating abdominal muscle fatigue.

negative: ProfileDroid: multi-layer profiling of android applications The Android platform lacks tools for assessing and monitoring apps in a systematic way. This lack of tools is particularly problematic when combined with the open nature of Google Play, the main app distribution channel. As our key contribution, we design and implement ProfileDroid, a comprehensive, multi-layer system for monitoring and profiling apps. Our approach is arguably the first to profile apps at four layers: (a) static, or app specification, (b) user interaction, (c) operating system, and (d) network. We evaluate 27 free and paid Android apps and make several observations: (a) we identify discrepancies between the app specification and app execution, (b) free versions of apps could end up costing more than their paid counterparts, due to an order of magnitude increase in traffic, (c) most network traffic is not encrypted, (d) apps communicate with many more sources than users might expect---as many as 13, and (e) we find that 22 out of 27 apps communicate with Google during execution. ProfileDroid is the first step towards a systematic approach for (a) generating cost-effective but comprehensive app profiles, and (b) identifying inconsistencies and surprising behaviors.

negative: Playing Atari Games with Deep Reinforcement Learning and Human Checkpoint Replay This paper introduces a novel method for learning how to play the most difficult Atari 2600 games from the Arcade Learning Environment using deep reinforcement learning. The proposed method, called human checkpoint replay, consists in using checkpoints sampled from human gameplay as starting points for the learning process. This is meant to compensate for the difficulties of current exploration strategies, such as ε-greedy, to find successful control policies in games with sparse rewards. Like other deep reinforcement learning architectures, our model uses a convolutional neural network that receives only raw pixel inputs to estimate the state value function. We tested our method on Montezuma’s Revenge and Private Eye, two of the most challenging games from the Atari platform. The results we obtained show a substantial improvement compared to previous learning approaches, as well as over a random player. We also propose a method for training deep reinforcement learning agents using human gameplay experience, which we call human experience replay.

negative: 3-Dimensional Localization via RFID Tag Array In this paper, we propose 3DLoc, which performs 3-dimensional localization on the tagged objects by using the RFID tag arrays. 3DLoc deploys three arrays of RFID tags on three mutually orthogonal surfaces of each object. When performing 3D localization, 3DLoc continuously moves the RFID antenna and scans the tagged objects in a 2-dimensional space right in front of the tagged objects. It then estimates the object's 3D position according to the phases from the tag arrays. By referring to the fixed layout of the tag array, we use Angle of Arrival-based schemes to accurately estimate the tagged objects' orientation and 3D coordinates in the 3D space. To suppress the localization errors caused by the multipath effect, we use the linear relationship of the AoA parameters to remove the unexpected outliers from the estimated results. We have implemented a prototype system and evaluated the actual performance in the real complex environment. The experimental results show that 3DLoc achieves the mean accuracy of 10cm in free space and 15.3cm in the multipath environment for the tagged object.

negative: Substrate Integrated Waveguide Quasi-Elliptic Filter Using Slot-Coupled and Microstrip-Line Cross-Coupled Structures This paper proposes a quasi-elliptic filter with slot coupling and nonadjacent cross coupling based on the substrate integrated waveguide (SIW) cavity. The slots etched on the top metal plane of SIW cavity are used to produce electrical coupling, and the cross coupling is realized by the microstrip transmission line above the SIW cavity. The coupling strength is mainly controlled by the width and height of the slot. The length of the open-ended microstrip line controls the sign of the cross coupling. The cross coupling with different signs are used in the filter to produce a pair of transmission zeros (TZs) at both sides of the passband. In order to prove the validity, a fourth-order SIW quasi-elliptic filter with TZsat both sides of the passband is fabricated in a two-layer printed circuit board. The measured insertion loss at a center frequency of 3.7 GHz is 1.1 dB. The return loss within the passband is below -18 dB with a fractional bandwidth of 16%. The measured results are in good agreement with the simulated results.

negative: Electrically Large Zero-Phase-Shift Line Grid-Array UHF Near-Field RFID Reader Antenna A grid-array antenna using a zero-phase-shift (ZPS) line is proposed to enlarge the interrogation zone of a reader antenna for near-field ultra-high-frequency (UHF) radio frequency identification (RFID) system. The proposed grid-array antenna is composed of a number of grid cells and a double-sided parallel-strip line feeding network. Each grid cell, namely segmented loop constructed by the ZPS line, has uniform and single-direction flowing current along the line. By configuration of the cells sharing a common side with its adjacent grid cells which carry reverse-direction flowing current, a grid-array antenna is formed to generate a strong and uniform magnetic-field distribution over a large interrogation zone even when the perimeter of the interrogation zone reaches up to 3λ (where λ is the operating wavelength in free space) or larger. As an example, a grid-array antenna with 1 × 2 segmented ZPS line loop cells implemented onto a piece of FR4 printed board (PCB) is designed and prototyped. The results show that the grid-array antenna achieves the impedance matching over the frequency range from 790 to 1040 MHz and produces strong and uniform magnetic-field distribution over an interrogation zone of 308 mm × 150 mm.

negative: Attacking an AES-Enabled NFC Tag: Implications from Design to a Real-World Scenario Radio-frequency identification (RFID) technology is the enabler for applications like the future internet of things (IoT), where security plays an important role. When integrating security to RFID tags, not only the cryptographic algorithms need to be secure but also their implementation. In this work we present differential power analysis (DPA) and differential electromagnetic analysis (DEMA) attacks on a securityenabled RFID tag. The attacks are conducted on both an ASIC-chip version and on an FPGA-prototype version of the tag. The design of the ASIC version equals that of commercial RFID tags and has analog and digital part integrated on a single chip. Target of the attacks is an implementation of the Advanced Encryption Standard (AES) with 128-bit key length and DPA countermeasures. The countermeasures are shuffling of operations and insertion of dummy rounds. Our results illustrate that the effort for successfully attacking the ASIC chip in a real-world scenario is only 4.5 times higher than for the FPGA prototype in a laboratory environment. This let us come to the conclusion that the effort for attacking contactless devices like RFID tags is only slightly higher than that for contact-based devices. The results further underline that the design of countermeasures like the insertion of dummy rounds has to be done with great care, since the detection of patterns in power or electromagnetic traces can be used to significantly lower the attacking effort.

negative: Wireless Measurement of RFID IC Impedance Accurate knowledge of the input impedance of a radio-frequency identification (RFID) integrated circuit (IC) at its wake-up power is valuable as it enables the design of a performance-optimized tag for a specific IC. However, since the IC impedance is power dependent, few methods exist to measure it without advanced equipment. We propose and demonstrate a wireless method, based on electromagnetic simulation and threshold power measurement, applicable to fully assembled RFID tags, to determine the mounted IC's input impedance in the absorbing state, including any parasitics arising from the packaging and the antenna-IC connection. The proposed method can be extended to measure the IC's input impedance in the modulating state as well.

negative: A Passive RFID Information Grid for Location and Proximity Sensing for the Blind User We describe a navigation and location determination system for the blind using an RFID tag grid. Each RFID tag is programmed upon installation with spatial coordinates and information describing the surroundings. This allows for a self-describing, localized information system with no dependency on a centralized database or wireless infrastructure for communications. The system could be integrated into building code requirements as part of ADA (Americans with Disabilities Act) at a cost of less than $1 per square foot. With an established RFID grid infrastructure blind children and adults will gain the independence and freedom to explore and participate in activities without external assistance. An established RFID grid infrastructure will also enable advances in robotics which will benefit from knowing precise location. In this paper, we present an RFID based information grid system with a reader integrated into the user’s shoe, which is connected to the user PDA or cell phone via a Bluetooth. An emphasis is placed on the architecture and design to allow for a truly integrated pervasive environment.

negative: Material property of on-metal magnetic sheet attached on NFC/HF-RFID antenna and research of its proper pattern and size on When a payment system with NFC/HF-RFID is installed in smart-phone, their communication with 13.56MHz is blocked by such metal as battery, coil and PCB. To solve this problem, magnetic sheet with high permeability is set between NFC/HF-RFID antenna and near-by metal. So far thin compound magnetic sheet or thin sintered ferrite has been used. In order to make thickness of these materials thinner, we propose amorphous magnetic sheet. Besides this amorphous magnetic sheet causes to increase the transmission effect.