title: Video Classification With CNNs: Using the Codec as a Spatio-Temporal Activity Sensor

abstract: We investigate video classification via a two-stream convolutional neural network (CNN) design that directly ingests information extracted from compressed video bitstreams. Our approach begins with the observation that all modern video codecs divide the input frames into macroblocks (MBs). We demonstrate that selective access to MB motion vector (MV) information within compressed video bitstreams can also provide for selective, motion-adaptive, MB pixel decoding (a.k.a., MB texture decoding). This in turn allows for the derivation of spatio-temporal video activity regions at extremely high speed in comparison to conventional full-frame decoding followed by optical flow estimation. In order to evaluate the accuracy of a video classification framework based on such activity data, we independently train two CNN architectures on MB texture and MV correspondences and then fuse their scores to derive the final classification of each test video. Evaluation on two standard datasets shows that the proposed approach is competitive to the best two-stream video classification approaches found in the literature. At the same time: (i) a CPU-based realization of our MV extraction is over 977 times faster than GPU-based optical flow methods; (ii) selective decoding is up to 12 times faster than full-frame decoding; (iii) our proposed spatial and temporal CNNs perform inference at 5 to 49 times lower cloud computing cost than the fastest methods from the literature.


Refining Word Embeddings for Sentiment Analysis


title: Swing-attenuation for a quadrotor transporting a cable-suspended payload.

abstract: This paper presents the problem of safe and fast transportation of packages by an Unmanned Aerial Vehicle (UAV) kind quadrotor. A mathematical model and a control strategy for a special class of underactuated mechanical systems, composed of a quadrotor transporting a cable-suspended payload, are proposed. The Euler-Lagrange formulation is used to obtain the dynamic model of the system, where the integrated dynamics of the quadrotor, cable and payload are considered. An Interconnection and Damping Assignment-Passivity Based Control (IDA-PBC) is chosen because of its inherent robustness against parametric uncertainty and unmodeled dynamics. Two cases are considered to obtain two different control laws, in the first case, the designed control law depends on the swing angle of the payload, in the second case the control law does not depend on it. The control objective is to transport the payload from point to point, with swing reduction along trajectory. Experimental results using monocular vision based navigation are shown to evaluate the proposed control law.


title: Loss of osseointegration caused by occlusal load of oral implants. A clinical and radiographic study in monkeys.

abstract: The breakdown of bone around oral implants following excessive occlusal load or plaque accumulation was evaluated in monkeys. 5 screw type implants of pure titanium (Astra) were inserted in the mandible of 4 monkeys (Macaca Fascicularis). 2 implants were placed in each of the lateral segments and 1 in the frontal area. Each monkey was provided with 2 cemented splints covering the premolars and molars in the right and left side of the maxilla, respectively. 6 months after insertion of the fixtures, a fixed partial prosthesis was mounted on the 2 implants in one of the lateral segments. The prosthesis was in supra-occlusal contact with the antagonizing splint. Each prosthesis was replaced during the course of the experiment. The renewed prosthesis caused a lateral displacement of the mandible during occlusion, and therefore resulted in a lateral rather than axial excessive occlusal load. Implants retaining the prosthesis were brushed 1 x a week and subgingival cleaning was performed 1 x a month. The remaining implants were never cleaned and, additionally, a cotton cord was placed passively around each of these to promote plaque accumulation. 5 out of 8 implants with excessive occlusal load lost osseointegration (mobility and peri-implant radiolucency). The loss of osseointegration was observed 4.5 months to 15.5 months after the occlusal overload was commenced. None of the implants with plaque accumulation lost osseointegration, although an average loss of 1.8 mm in the radiographic bone level was assessed after 18 months.


title: Packing lines, planes, etc., packings in grassmannian spaces

abstract: This paper addresses the question: how should N n-dimensional subspaces of m-dimensional Euclidean space be arranged so that they are as far apart as possible? The results of extensive computations for modest values of N, n, m are described, as well as a reformulation of the problem that was suggested by these computations. The reformulation gives a way to describe n-dimensional subspaces of m-space as points on a sphere in dimension (m-1)(m+2)/2, which provides a (usually) lower-dimensional representation than the Pluecker embedding, and leads to a proof that many of the new packings are optimal. The results have applications to the graphical display of multi-dimensional data via Asimov's "Grand Tour" method.


title: The cobas® 6800/8800 System: a new era of automation in molecular diagnostics.

abstract: INTRODUCTION ::: Molecular diagnostics is a key component of laboratory medicine. Here, the authors review key triggers of ever-increasing automation in nucleic acid amplification testing (NAAT) with a focus on specific automated Polymerase Chain Reaction (PCR) testing and platforms such as the recently launched cobas® 6800 and cobas® 8800 Systems. The benefits of such automation for different stakeholders including patients, clinicians, laboratory personnel, hospital administrators, payers, and manufacturers are described. Areas Covered: The authors describe how molecular diagnostics has achieved total laboratory automation over time, rivaling clinical chemistry to significantly improve testing efficiency. Finally, the authors discuss how advances in automation decrease the development time for new tests enabling clinicians to more readily provide test results. Expert Commentary: The advancements described enable complete diagnostic solutions whereby specific test results can be combined with relevant patient data sets to allow healthcare providers to deliver comprehensive clinical recommendations in multiple fields ranging from infectious disease to outbreak management and blood safety solutions.


title: A probabilistic fuzzy logic system for modeling and control

abstract: In this paper, a probabilistic fuzzy logic system (PFLS) is proposed for the modeling and control problems. Similar to the ordinary fuzzy logic system (FLS), the PFLS consists of the fuzzification, inference engine and defuzzification operation to process the fuzzy information. Different to the FLS, it uses the probabilistic modeling method to improve the stochastic modeling capability. By using a three-dimensional membership function (MF), the PFLS is able to handle the effect of random noise and stochastic uncertainties existing in the process. A unique defuzzification method is proposed to simplify the complex operation. Finally, the proposed PFLS is applied to a function approximation problem and a robotic system. It shows a better performance than an ordinary FLS in stochastic circumstance.


title: Mentoring programs for medical students - a review of the PubMed literature 2000 - 2008

abstract: BackgroundAlthough mentoring is acknowledged as a key to successful and satisfying careers in medicine, formal mentoring programs for medical students are lacking in most countries. Within the framework of planning a mentoring program for medical students at Zurich University, an investigation was carried out into what types of programs exist, what the objectives pursued by such programs are, and what effects are reported.MethodsA PubMed literature search was conducted for 2000 - 2008 using the following keywords or their combinations: mentoring, mentoring program, medical student, mentor, mentee, protégé, mentorship. Although a total of 438 publications were identified, only 25 papers met the selection criteria for structured programs and student mentoring surveys.ResultsThe mentoring programs reported in 14 papers aim to provide career counseling, develop professionalism, increase students' interest in research, and support them in their personal growth. There are both one-to-one and group mentorships, established in the first two years of medical school and continuing through graduation. The personal student-faculty relationship is important in that it helps students to feel that they are benefiting from individual advice and encourages them to give more thought to their career choices. Other benefits are an increase in research productivity and improved medical school performance in general. Mentored students also rate their overall well-being as higher. - The 11 surveys address the requirements for being an effective mentor as well as a successful mentee. A mentor should empower and encourage the mentee, be a role model, build a professional network, and assist in the mentee's personal development. A mentee should set agendas, follow through, accept criticism, and be able to assess performance and the benefits derived from the mentoring relationship.ConclusionMentoring is obviously an important career advancement tool for medical students. In Europe, more mentoring programs should be developed, but would need to be rigorously assessed based on evidence of their value in terms of both their impact on the career paths of juniors and their benefit for the mentors. Medical schools could then be monitored with respect to the provision of mentorships as a quality characteristic.


title: An iterative regularization method for total variation-based image restoration

abstract: We introduce a new iterative regularization procedure for inverse problems based on the use of Bregman distances, with particular focus on problems arising in image processing. We are motivated by the problem of restoring noisy and blurry images via variational methods by using total variation regularization. We obtain rigorous convergence results and effective stopping criteria for the general procedure. The numerical results for denoising appear to give significant improvement over standard models, and preliminary results for deblurring/denoising are very encouraging.


title: Two Market Models for Demand Response in Power Networks

abstract: In this paper, we consider two abstract market models for designing demand response to match power supply and shape power demand, respectively. We characterize the resulting equilibria in competitive as well as oligopolistic markets, and propose distributed demand response algorithms to achieve the equilibria. The models serve as a starting point to include the appliance-level details and constraints for designing practical demand response schemes for smart power grids.


title: Ant Colony Fuzzy Clustering Algorithm Applied to SAR Image Segmentation

abstract: A method of dynamic fuzzy clustering analysis based on ant colony algorithm for SAR image segmentation is proposed. The method confirms dynamically the clustering number and center by the stronger fuzzy clustering ability of ant colony algorithm. Texture feature of SAR image is calculated according to gray level co-occurrence matrix (GLCM), and the proper feature vector is selected through statistic analysis. The measurement SAR image segmentation experiment indicates that the algorithm can segment the target fast and exactly, and is an effective SAR image segmentation method.


title: Nurses’ responses to workplace verbal abuse: ascenario study of the impact of situational andindividual factors

abstract: Verbal abuse in the workplace represents a costly problem for human resource management, with implications for staff productivity and retention. Nurses are a profession exposed to extremely high levels of verbal abuse, particularly from patients, but also from colleagues, making these abusive behaviours, and reactions to them, of vital significance to hospital personnel management ::: professionals. The present study investigated the extent to which responses to verbal abuse scenarios varied as a function of the role and gender of the perpetrator, the situation in which the verbal abuse occurred, and the participant’s level of assertiveness, with a view to establishing key contributors for management attention. Participants were 62 female registered nurses. Results ::: showed evidence of variation in cognitive, affective and behavioural attitudes toward the verbal abuse described, particularly as a function of situation. In particular, verbal abuse was reported to be more frequent, considered less acceptable and elicited more affective arousal in non emergency than in emergency situations. These findings were consistent with predictions that were based on ::: attribution theory. Responses to verbal abuse also varied in relation to the nurse’s assertiveness, which is a finding suggesting that assertiveness training is a potential strategy for protecting employees against the effects of verbal abuse. This study has important implications for managers in hospitals, and indeed, other organisational settings in that dealing with verbal abuse requires cognisance of the need for the implementation of both organisational and individual levels of intervention, within a framework that acknowledges the complexity of responses to verbal abuse in the workplace.


title: Using Mutation Analysis for Assessing and Comparing Testing Coverage Criteria

abstract: The empirical assessment of test techniques plays an important role in software testing research. One common practice is to seed faults in subject software, either manually or by using a program that generates all possible mutants based on a set of mutation operators. The latter allows the systematic, repeatable seeding of large numbers of faults, thus facilitating the statistical analysis of fault detection effectiveness of test suites; however, we do not know whether empirical results obtained this way lead to valid, representative conclusions. Focusing on four common control and data flow criteria (block, decision, C-use, and P-use), this paper investigates this important issue based on a middle size industrial program with a comprehensive pool of test cases and known faults. Based on the data available thus far, the results are very consistent across the investigated criteria as they show that the use of mutation operators is yielding trustworthy results: generated mutants can be used to predict the detection effectiveness of real faults. Applying such a mutation analysis, we then investigate the relative cost and effectiveness of the above-mentioned criteria by revisiting fundamental questions regarding the relationships between fault detection, test suite size, and control/data flow coverage. Although such questions have been partially investigated in previous studies, we can use a large number of mutants, which helps decrease the impact of random variation in our analysis and allows us to use a different analysis approach. Our results are then; compared with published studies, plausible reasons for the differences are provided, and the research leads us to suggest a way to tune the mutation analysis process to possible differences in fault detection probabilities in a specific environment


title: More Content- Less Control: Access Control in the Web 2.0

abstract: The popularity of social-networking sites, blogging and other content-sharing sites has exploded, resulting in more personal information and opinions being available with less access control than ever before [5]. Many content-sharing sites provide only the most rudimentary access control: a document can be either completely private or completely public. Other sites offer the slightly more flexible private/friends/public access-control model, but this still fails to support natural distinctions users need, such as separating real-world friends from online friends. The traditional response to these privacy concerns is to post anonymously or pseudonymously, but recent psychological research shows that some Internet users do not establish separate, online personae, but instead consider their online identity as an extension of their real-life self [3]. And although privacy expectations that users desire are easy to state, there is a large gap between the users’ mental models and the policy languages of traditional access-control systems [2]. The consequences of poor access control are welldocumented in the news media. Bloggers have lost their jobs when their employer discovered the employee’s personal blog [9]. Sexual predators use social-networking sites to find victims [7]. Bloggers have been stalked based on the opinions and personal information placed on their blog [8]. Universities have disciplined students using photographs published on social-networking sites [1]. For all these reasons, we advocate that blogs and social networks need a policy mechanism that supports high-level policies that can be expressed succinctly, applied automatically, and updated easily. Current access-control systems fail to meet these goals. Users manually enforce and manage their policies, users, groups, and roles of the system. Furthermore, these systems lack intuitive tools and interfaces for policy generation. We propose to solve all these problems by specifying access-control policies in terms of the content being mediated, e.g. “Blog posts about my home-town are visible to my high school friends.” The system will then automatically infer the posts that are subject to policy rules based on the posts’ contents. Similarly, the system can infer relationships and interests of the users based on the content of objects they create (see Section 3). Such policies will be intuitive and easy to specify, greatly enhancing usability for non-technical users. We first discuss the current state of access control on content-driven sites and analyze approaches proposed in literature for implementing access control for the web. We then describe our proposed method of access control for content-sharing sites.


title: Regression testing in the presence of non-code changes

abstract: Regression testing is an important activity performed to validate modified software, and one of its key tasks is regression test selection (RTS) -- selecting a subset of existing test cases to run on the modified software. Most existing RTS techniques focus on changes made to code components and completely ignore non-code elements, such as configuration files and databases, which can also change and affect the system behavior. To address this issue, we present a new RTS technique that performs accurate test selection in the presence of changes to non-code components. To do this, our technique computes traceability between test cases and the external data accessed by an application, and uses this information to perform RTS in the presence of changes to non-code elements. We present our technique, a prototype implementation of our technique, and a set of preliminary empirical results that illustrate the feasibility, effectiveness, and potential usefulness of our approach.


title: Analysis of a CDN–P2P hybrid architecture for cost-effective streaming media distribution

abstract: To distribute video and audio data in real-time streaming mode, two different technologies --- Content Distribution Network (CDN) and Peer-to-Peer (P2P) --- have been proposed. However, both technologies have their own limitations: CDN servers are expensive to deploy and maintain, and consequently incur a cost for media providers and/or clients for server capacity reservation. On the other hand, a P2P-based architecture requires sufficient number of seed supplying peers to jumpstart the distribution process. Compared with a CDN server, a peer usually offers much lower out-bound streaming rate and hence multiple peers must jointly stream a media data to a requesting peer. Furthermore, it is not clear how to determine how much a peer should contribute back to the system after receiving the media data, in order to sustain the overall media distribution capacity. ::: ::: In this paper, we propose and analyze a novel hybrid architecture that integrates both CDN- and P2P-based streaming media distribution. The architecture is highly cost-effective: it significantly lowers the cost of CDN capacity reservation, without compromising the media quality delivered. In particular, we propose and compare different limited contribution policies for peers that request a media data, so that the streaming capacity of each peer can be exploited on a fair and limited basis. We present: (1) in-depth analysis of the proposed architecture under different contribution policies, and (2) extensive simulation results which validate the analysis. Our analytical and simulation results form a rigorous basis for the planning and dimensioning of the hybrid architecture.


title: Collaborative Filtering with Recurrent Neural Networks

abstract: We show that collaborative filtering can be viewed as a sequence prediction problem, and that given this interpretation, recurrent neural networks offer very competitive approach. In particular we study how the long short-term memory (LSTM) can be applied to collaborative filtering, and how it compares to standard nearest neighbors and matrix factorization methods on movie recommendation. We show that the LSTM is competitive in all aspects, and largely outperforms other methods in terms of item coverage and short term predictions.


title: Keyphrase Extraction Using Knowledge Graphs

abstract: Extracting keyphrases from documents automatically is an important and interesting task since keyphrases provide a quick summarization for documents. Although lots of efforts have been made on keyphrase extraction, most of the existing methods (the co-occurrence based methods and the statistic-based methods) do not take semantics into full consideration. The co-occurrence based methods heavily depend on the co-occurrence relations between two words in the input document, which may ignore many semantic relations. The statistic-based methods exploit the external text corpus to enrich the document, which introduces more unrelated relations inevitably. In this paper, we propose a novel approach to extract keyphrases using knowledge graphs, based on which we could detect the latent relations of two keyterms (i.e., noun words and named entities) without introducing many noises. Extensive experiments over real data show that our method outperforms the state-of-art methods including the graph-based co-occurrence methods and statistic-based clustering methods.


title: Moving obstacle detection in highly dynamic scenes

abstract: We address the problem of vision-based multi-person tracking in busy pedestrian zones using a stereo rig mounted on a mobile platform. Specifically, we are interested in the application of such a system for supporting path planning algorithms in the avoidance of dynamic obstacles. The complexity of the problem calls for an integrated solution, which extracts as much visual information as possible and combines it through cognitive feedback. We propose such an approach, which jointly estimates camera position, stereo depth, object detections, and trajectories based only on visual information. The interplay between these components is represented in a graphical model. For each frame, we first estimate the ground surface together with a set of object detections. Based on these results, we then address object interactions and estimate trajectories. Finally, we employ the tracking results to predict future motion for dynamic objects and fuse this information with a static occupancy map estimated from dense stereo. The approach is experimentally evaluated on several long and challenging video sequences from busy inner-city locations recorded with different mobile setups. The results show that the proposed integration makes stable tracking and motion prediction possible, and thereby enables path planning in complex and highly dynamic scenes.


title: GAIA Map: A Tool for Visual Ranking Analysis in Spatial Multicriteria Problems

abstract: Spatial multicriteria decision problems are decision problems where one needs to take multiple conflicting criteria as well as geographical knowledge into account. In such a context, exploratory spatial analysis is known to provide tools to visualize as much data as possible on maps but does not integrate multicriteria aspects. Also, none of the tools provided by multicriteria analysis were initially destined to be used in a geographical context.In this paper, we propose an application of the PROMETHEE and GAIA ranking methods to Geographical Information Systems (GIS). The aim is to help decision makers obtain rankings of geographical entities and understand why such rankings have been obtained. To do that, we make use of the visual approach of the GAIA method and adapt it to display the results on geographical maps. This approach is then extended to cover several weaknesses of the adaptation. Finally, it is applied to a study of the region of Brussels as well as an evaluation of the Human Development Index (HDI) in Europe.


title: Hyperglycemia and Adverse Pregnancy Outcomes

abstract: BACKGROUND ::: It is controversial whether maternal hyperglycemia less severe than that in diabetes mellitus is associated with increased risks of adverse pregnancy outcomes. ::: ::: ::: METHODS ::: A total of 25,505 pregnant women at 15 centers in nine countries underwent 75-g oral glucose-tolerance testing at 24 to 32 weeks of gestation. Data remained blinded if the fasting plasma glucose level was 105 mg per deciliter (5.8 mmol per liter) or less and the 2-hour plasma glucose level was 200 mg per deciliter (11.1 mmol per liter) or less. Primary outcomes were birth weight above the 90th percentile for gestational age, primary cesarean delivery, clinically diagnosed neonatal hypoglycemia, and cord-blood serum C-peptide level above the 90th percentile. Secondary outcomes were delivery before 37 weeks of gestation, shoulder dystocia or birth injury, need for intensive neonatal care, hyperbilirubinemia, and preeclampsia. ::: ::: ::: RESULTS ::: For the 23,316 participants with blinded data, we calculated adjusted odds ratios for adverse pregnancy outcomes associated with an increase in the fasting plasma glucose level of 1 SD (6.9 mg per deciliter [0.4 mmol per liter]), an increase in the 1-hour plasma glucose level of 1 SD (30.9 mg per deciliter [1.7 mmol per liter]), and an increase in the 2-hour plasma glucose level of 1 SD (23.5 mg per deciliter [1.3 mmol per liter]). For birth weight above the 90th percentile, the odds ratios were 1.38 (95% confidence interval [CI], 1.32 to 1.44), 1.46 (1.39 to 1.53), and 1.38 (1.32 to 1.44), respectively; for cord-blood serum C-peptide level above the 90th percentile, 1.55 (95% CI, 1.47 to 1.64), 1.46 (1.38 to 1.54), and 1.37 (1.30 to 1.44); for primary cesarean delivery, 1.11 (95% CI, 1.06 to 1.15), 1.10 (1.06 to 1.15), and 1.08 (1.03 to 1.12); and for neonatal hypoglycemia, 1.08 (95% CI, 0.98 to 1.19), 1.13 (1.03 to 1.26), and 1.10 (1.00 to 1.12). There were no obvious thresholds at which risks increased. Significant associations were also observed for secondary outcomes, although these tended to be weaker. ::: ::: ::: CONCLUSIONS ::: Our results indicate strong, continuous associations of maternal glucose levels below those diagnostic of diabetes with increased birth weight and increased cord-blood serum C-peptide levels.


title: Protractor: a fast and accurate gesture recognizer

abstract: Protractor is a novel gesture recognizer that can be easily implemented and quickly customized for different users. Protractor uses a nearest neighbor approach, which recognizes an unknown gesture based on its similarity to each of the known gestures, e.g., training samples or examples given by a user. In particular, it employs a novel method to measure the similarity between gestures, by calculating a minimum angular distance between them with a closed-form solution. As a result, Protractor is more accurate, naturally covers more gesture variation, runs significantly faster and uses much less memory than its peers. This makes Protractor suitable for mobile computing, which is limited in processing power and memory. An evaluation on both a previously published gesture data set and a newly collected gesture data set indicates that Protractor outperforms its peers in many aspects.


title: Salient Object Detection: A Survey

abstract: Detecting and segmenting salient objects from natural scenes, often referred to as salient object detection, has attracted great interest in computer vision. While many models have been proposed and several applications have emerged, a deep understanding of achievements and issues remains lacking. We aim to provide a comprehensive review of recent progress in salient object detection and situate this field among other closely related areas such as generic scene segmentation, object proposal generation, and saliency for fixation prediction. Covering 228 publications, we survey i) roots, key concepts, and tasks, ii) core techniques and main modeling trends, and iii) datasets and evaluation metrics for salient object detection. We also discuss open problems such as evaluation metrics and dataset bias in model performance, and suggest future research directions.


title: Time Series Prediction Using DBN and ARIMA

abstract: Time series data analyze and prediction is very important to the study of nonlinear phenomenon. Studies of time series prediction have a long history since last century, linear models such as autoregressive integrated moving average (ARIMA) model, and nonlinear models such as multi-layer perceptron (MLP) are well-known. As the state-of-art method, a deep belief net (DBN) using multiple Restricted Boltzmann machines (RBMs) was proposed recently. In this study, we propose a novel prediction method which composes not only a kind of DBN with RBM and MLP but also ARIMA. Prediction experiments for the time series of the actual data and chaotic time series were performed, and results showed the effectiveness of the proposed method.


title: Extracting relevant knowledge for the detection of sarcasm and nastiness in the social web

abstract: Automatic detection of emotions like sarcasm or nastiness in online written conversation is a difficult task. It requires a system that can manage some kind of knowledge to interpret that emotional language is being used. In this work, we try to provide this knowledge to the system by considering alternative sets of features obtained according to different criteria. We test a range of different feature sets using two different classifiers. Our results show that the sarcasm detection task benefits from the inclusion of linguistic and semantic information sources, while nasty language is more easily detected using only a set of surface patterns or indicators.


title: Broadband biquad UHF antenna array for DOA

abstract: In this contribution a broadband biquad antenna array for DOA applications is investigated. Simulation and measurement results of the single broadband biquad antenna element and results of antenna array configurations for vertical polarisation, horizontal polarisation or polarisation independant direction of arrival estimation using different algorithms are shown.


title: Efficiency, internationalization and market positioning in textiles fast fashion

abstract: Purpose ::: ::: ::: ::: – The purpose of this paper is to investigate the competitive position of the company Inditex in the period 1990-2013 as a case study, identified by academics and professionals as a successful company. The analysis has focussed on the comparison of Inditex with its competitive environment. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: – The methodology used to achieve the objectives was: data envelopment analysis for the analysis of efficiency and for the second the Tobit regression to determine the factors explaining efficiency. The authors have used additional methodologies such as social networks or cluster analysis. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: – The individual company analysis reveals that the average efficiency level by years for the period 1990-2013, is relatively high 88.8 percent. The determinants of efficiency have been; the resources of the company in terms of assets whose relationship with is U-shaped curvilinear, where the minimum value (trend change) is produced between years 2002 and 2003. The degree of internationalization of the firm, is positively related to efficiency. As the company increases its expansion, experience and skills, increases efficiency. Finally, the effect of liberalization of textile trade in 2005 had no influence on the efficiency levels. ::: ::: ::: ::: ::: Research limitations/implications ::: ::: ::: ::: – The limitations involving the methodology, in terms of representativeness, possible generalizations and type of secondary information used, can be offset by the ability to provide good vision and establish alternatives for possible studies. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: – This paper contributes to the fast-fashion retail industry literature by emphasizing the importance of the case study.


title: Customer shopping pattern prediction: A recurrent neural network approach

abstract: Customer relationship management is a popular and strategic topic in marketing and quality of service. The availability of big transactions data as well as computing systems have provided a great opportunity to model and predict customer behaviour. However, there is a lack of modern modelling and analytical methods to perform analysis on such data. Deep learning techniques can assist marketing decision makers to provide more reliable and practical marketing strategic plans. In this paper, we propose a customer behaviour prediction model using recurrent neural networks (RNNs) based on the client loyalty number (CLN), recency, frequency, and monetary (RFM) variables. The experiment results show that RNNs can predict RFM values of customers efficiently. This model can be later used in recommender systems for exclusive promotional offers and loyalty programs management.


AUTOMATIC RETINAL IMAGE REGISTRATION


title: "Beacons of hope" in decentralized coordination: learning from on-the-ground medical twitterers during the 2010 Haiti earthquake

abstract: We examine the public, social media communications of 110 emergency medical response teams and organizations in the immediate aftermath of the January 12, 2010 Haiti earthquake. We found the teams through an inductive analysis of Twitter communications acquired over the three-week emergency period from 89,114 Twitterers. We then analyzed the teams' Twitter streams, as well as all digital media they generated and pointed to in their streams - blog posts, photographs, videos, status updates and field reports - to understand the medical coordination challenges they faced from pre-deployment readiness to on-the-ground action. Here we identify opportunities for improving coordination in a decentralized and distributed environment where staffing, disease trajectories, and other circumstances rapidly change. We extrapolate from these findings to theorize about how "beaconing" behavior is a sign of latent potential for coordination upon which mechanisms of coordination can capitalize.


title: Jointly Learning to Label Sentences and Tokens

abstract: Learning to construct text representations in end-to-end systems can be difficult, as natural languages are highly compositional and task-specific annotated datasets are often limited in size. Methods for directly supervising language composition can allow us to guide the models based on existing knowledge, regularizing them towards more robust and interpretable representations. In this paper, we investigate how objectives at different granularities can be used to learn better language representations and we propose an architecture for jointly learning to label sentences and tokens. The predictions at each level are combined together using an attention mechanism, with token-level labels also acting as explicit supervision for composing sentence-level representations. Our experiments show that by learning to perform these tasks jointly on multiple levels, the model achieves substantial improvements for both sentence classification and sequence labeling.


title: Radiological evaluation by magnetic resonance of the ‘new anatomy’ of transsexual patients undergoing male to female sex reassignment surgery

abstract: Magnetic resonance (MR) is the best way to assess the new anatomy of the pelvis after male to female (MtF) sex reassignment surgery. The aim of the study was to evaluate the radiological appearance of the small pelvis after MtF surgery and to compare it with the normal women's anatomy. Fifteen patients who underwent MtF surgery were subjected to pelvic MR at least 6 months after surgery. The anthropometric parameters of the small pelvis were measured and compared with those of ten healthy women (control group). Our personal technique (creation of the mons Veneris under the pubic skin) was performed in all patients. In patients who underwent MtF surgery, the mean neovaginal depth was slightly superior than in women (P=0.009). The length of the inferior pelvic aperture and of the inlet of pelvis was higher in the control group (P<0.005). The inclination between the axis of the neovagina and the inferior pelvis aperture, the thickness of the mons Veneris and the thickness of the rectovaginal septum were comparable between the two study groups. MR consents a detailed assessment of the new pelvic anatomy after MtF surgery. The anthropometric parameters measured in our patients were comparable with those of women.


title: Self-enucleation in a young schizophrenic patient--a case report.

abstract: Self-enucleation represents an extreme but fortunately rare form of deliberate self-harm. Case reports of patients who self-enucleate reveal some common features. A case of auto-enucleation in a young schizophrenic patient and a short discussion on deliberate self-harm are presented.


title: Demultiplexing activities of daily living in IoT enabled smarthomes

abstract: Powered by the emergence of the Internet of Things, smart homes containing a variety of sensors and actuators are expected to monitor and react to the activities of the residents with the goal of improving convenience, comfort and safety. However, in typical home settings, each human Activity of Daily Living (ADL) generates events from multiple sensors, and each sensor is triggered by multiple ADLs. Consequently, achieving high detection accuracy in these complex environments requires large amounts of training data for every possible multiplexing scenario, making it a complex problem. In this paper, we propose a data driven three-step de-multiplexing approach that simplifies the ADL recognition problem by first segmenting the event stream into periods of interest, before feeding to a classifier. We mine datasets to identify salient features which allow us to achieve a good segmentation. Extensive evaluation on ten public datasets shows that our approach achieves upto 77% segmentation accuracy, and a activity detection accuracy within 91% of the best possible.


title: Unified Power Control for PMSG-Based WECS Operating Under Different Grid Conditions

abstract: A unified power control strategy is proposed for the permanent magnet synchronous generator-based wind energy conversion system (WECS) operating under different grid conditions. In the strategy, the generator-side converter is used to control the dc-link voltage and the grid-side converter is responsible for the control of power flow injected into the grid. The generator-side controller has inherent damping capability of the torsional oscillations caused by drive-train characteristics. The grid-side control is utilized to satisfy the active and reactive current (power) requirements defined in the grid codes, and at the same time mitigates the current distortions even with unsymmetrical grid fault. During grid faults, the generator-side converter automatically reduces the generator current to maintain the dc voltage and the resultant generator acceleration is counteracted by pitch regulation. Compared with the conventional strategy, the dc chopper, which is intended to assist the fault ride through of the WECS, can be eliminated if the proposed scheme is employed. Compared with the variable-structured control scheme, the proposed strategy has quicker and more precise power responses, which is beneficial to the grid recovery. The simulation results verify the effectiveness of the proposed strategy.


title: Broadband design of a low-profile, circularly polarized crossed dipole antenna on an AMC surface

abstract: A crossed dipole antenna is incorporated with an AMC surface to achieve low-profile and broadband characteristics. Interactions between the crossed dipole and the AMC surface are meticulously considered for optimum design. The antenna yields an impedance bandwidth of 18.8% for |S 11 | < −10 dB and a 3-dB AR bandwidth of 10.9%.


title: Model-based Deep Hand Pose Estimation

abstract: Previous learning based hand pose estimation methods does not fully exploit the prior information in hand model geometry. Instead, they usually rely a separate model fitting step to generate valid hand poses. Such a post processing is inconvenient and sub-optimal. In this work, we propose a model based deep learning approach that adopts a forward kinematics based layer to ensure the geometric validity of estimated poses. For the first time, we show that embedding such a non-linear generative process in deep learning is feasible for hand pose estimation. Our approach is verified on challenging public datasets and achieves state-of-the-art performance.


title: RFID security

abstract: Radio Frequency Identification (RFID) systems have become popular for automated identification and supply chain applications. This article describes the technical fundamentals of RFID systems and the associated standards. Specifically, we address the security and privacy aspects of this relatively new and heterogeneous radio technology. We discuss the related security requirements, the threats and the implemented mechanisms. Then the current security and privacy proposals and their enhancements are presented. Finally we discuss the role of this technology in Ubiquitous Computing.


title: R.: Linking data to ontologies

abstract: Many organizations nowadays face the problem of accessing existing data sources by means of flexible mechanisms that are both powerful and efficient. Ontologies are widely considered as a suitable formal tool for sophisticated data access. The ontology expresses the domain of interest of the information system at a high level of abstraction, and the relationship between data at the sources and instances of concepts and roles in the ontology is expressed by means of mappings. In this paper we present a solution to the problem of designing effective systems for ontology-based data access. Our solution is based on three main ingredients. First, we present a new ontology language, based on Description Logics, that is particularly suited to reason with large amounts of instances. The second ingredient is a novel mapping language that is able to deal with the so-called impedance mismatch problem, i.e., the problem arising from the difference between the basic elements managed by the sources, namely data, and the elements managed by the ontology, namely objects. The third ingredient is the query answering method, that combines reasoning at the level of the ontology with specific mechanisms for both taking into account the mappings and efficiently accessing the data at the sources.


title: Women's Partnered Orgasm Consistency Is Associated with Greater Duration of Penile–Vaginal Intercourse but Not of Foreplay

abstract: ABSTRACT Introduction It has been asserted that women's likelihood or consistency of partnered orgasm (her orgasm as a result of sexual activities with a partner) is determined by duration of foreplay, but not by duration of penile–vaginal intercourse. Aims The objective was to examine the extent to which women's likelihood or consistency of partnered orgasm is associated with duration of foreplay, duration of penile–vaginal intercourse, and age. Methods In a representative sample of the Czech population, 2,360 women reported their consistency of orgasm with a partner (from “never” to “almost every time”), and estimates of their typical durations of foreplay and of penile–vaginal intercourse. Main Outcome Measures The association of consistency of partnered orgasm with typical durations of both foreplay and penile–vaginal intercourse. Results In univariate analyses, consistency of partnered orgasm was more associated with penile–vaginal intercourse duration than with foreplay duration (consistency also correlated negatively with age). In multivariate analysis, foreplay ceased to be a significant correlate of partnered orgasm consistency (the exclusion of respondents reporting a penile–vaginal intercourse duration of 1 minute or less did not alter the results). Conclusions When both sexual activity categories are examined in tandem on a population level, women's likelihood or consistency of partnered orgasm is associated with penile–vaginal intercourse duration, but not with foreplay duration. In contrast to the assumptions of many sex therapists and educators, more attention should be given to improve the quality and duration of penile–vaginal intercourse rather than foreplay. Weiss P, and Brody S. Women's partnered orgasm consistency is associated with greater duration of penile–vaginal intercourse but not of foreplay.


title: Effects of Selected Polysorbate and Sucrose Ester Emulsifiers on the Physicochemical Properties of Astaxanthin Nanodispersions

abstract: The effects of selected nonionic emulsifiers on the physicochemical characteristics of astaxanthin nanodispersions produced by an emulsification/evaporation technique were studied. The emulsifiers used were polysorbates (Polysorbate 20, Polysorbate 40, Polysorbate 60 and Polysorbate 80) and sucrose esters of fatty acids (sucrose laurate, palmitate, stearate and oleate). The mean particle diameters of the nanodispersions ranged from 70 nm to 150 nm, depending on the emulsifier used. In the prepared nanodispersions, the astaxanthin particle diameter decreased with increasing emulsifier hydrophilicity and decreasing carbon number of the fatty acid in the emulsifier structure. Astaxanthin nanodispersions with the smallest particle diameters were produced with Polysorbate 20 and sucrose laurate among the polysorbates and the sucrose esters, respectively. We also found that the Polysorbate 80- and sucrose oleate-stabilized nanodispersions had the highest astaxanthin losses (i.e., the lowest astaxanthin contents in the final products) among the nanodispersions. This work demonstrated the importance of emulsifier type in determining the physicochemical characteristics of astaxanthin nano-dispersions.


FUNCTIONAL UNCERTAINTY IN THE MANDARIN NOMINAL : A UNIFIED ANALYSIS OF RELATIVE CLAUSES AND LOCATIVE STRUCTURES


title: FeedMe: a collaborative alert filtering system

abstract: As the number of alerts generated by collaborative applications grows, users receive more unwanted alerts. FeedMe is a general alert management system based on XML feed protocols such as RSS and ATOM. In addition to traditional rule-based alert filtering, FeedMe uses techniques from machine-learning to infer alert preferences based on user feedback. In this paper, we present and evaluate a new collaborative naive Bayes filtering algorithm. Using FeedMe, we collected alert ratings from 33 users over 29 days. We used the data to design and verify the accuracy of the filtering algorithm and provide insights into alert prediction.


title: Debugging reinvented: asking and answering why and why not questions about program behavior

abstract: When software developers want to understand the reason for a program's behavior, they must translate their questions about the behavior into a series of questions about code, speculating about the causes in the process. The Whyline is a new kind of debugging tool that avoids such speculation by instead enabling developers to select a question about program output from a set of why did and why didn't questions derived from the program's code and execution. The tool then finds one or more possible explanations for the output in question, using a combination of static and dynamic slicing, precise call graphs, and new algorithms for determining potential sources of values and explanations for why a line of code was not reached. Evaluations of the tool on one task showed that novice programmers with the Whyline were twice as fast as expert programmers without it. The tool has the potential to simplify debugging in many software development contexts.


title: Machine learning on FPGAs to face the IoT revolution

abstract: FPGAs have been rapidly adopted for acceleration of Deep Neural Networks (DNNs) with improved latency and energy efficiency compared to CPU and GPU-based implementations. High-level synthesis (HLS) is an effective design flow for DNNs due to improved productivity, debugging, and design space exploration ability. However, optimizing large neural networks under resource constraints for FPGAs is still a key challenge. In this paper, we present a series of effective design techniques for implementing DNNs on FPGAs with high performance and energy efficiency. These include the use of configurable DNN IPs, performance and resource modeling, resource allocation across DNN layers, and DNN reduction and re-training. We showcase several design solutions including Long-term Recurrent Convolution Network (LRCN) for video captioning, Inception module for FaceNet face recognition, as well as Long Short-Term Memory (LSTM) for sound recognition. These and other similar DNN solutions are ideal implementations to be deployed in vision or sound based IoT applications.


A Survey on ADPLL Components and their effects upon Power , Frequency and Resolution


EVOLVING THE NARROW LANGUAGE FACULTY : WAS RECURSION THE PIVOTAL STEP ?


title: Voltage Tunable Magnetoelectric Inductors With Improved Operational Frequency and Quality Factor for Power Electronics

abstract: Inductors find widespread use in various applications. Inductors with a large tunability of inductance and quality factor would lead to new paradigm on circuit designs. In this paper, we investigated the effect of ferromagnetic layer thickness on the performance of a magnetoelectric tunable inductor. Using chemical etching, the thickness of Metglas ribbons (2605SA1) can be reduced from 24 to about 8 μm, which dramatically reduces the eddy-current loss at higher frequencies. With the same total thickness, compared with inductor constructed with unthinned Metglas layers (2605CO), the maximum quality factor can be increased for 80%, and the operational frequency range can be extended for 10× to the >10 MHz range. An inductance tunable range of L ::: max ::: /L ::: min ::: = 370% was also achieved together with a significantly enhanced quality factor by ~3. Furthermore, with an inductor constructed with single thin Metglas (2605HB1M) layer, the maximum quality factor can be increased by 150% with single PZT slab and by 235% with double PZT slabs, and the operational frequency range can be extended by 100× with single slab and by 200× with double slabs, which makes these metallic magnetic ribbons tunable inductors competitive with ferrite materials in adaptive inductive power supply systems.


Dermoscopy of necrobiosis lipoidica.


title: A Literature Study on Privacy Patterns Research

abstract: Context: Facing the implementation of the EU General Data Protection Regulation in May 2018, many commercial software providers will soon need to adapt their products to new privacy-related constraints. Privacy patterns defined for different aspects of the software engineering process promise to be a useful concept for this task. In this situation, it seems valuable to characterize the state of the research related to privacy patterns.Objective: To identify, characterize and classify the contributions made by published research results related to patterns in the context of considering privacy concerns in engineering software. Method: A literature review in form of a mapping study of scientific articles was performed. The resulting map structures the relevant body of work into multiple dimensions, illustrating research focuses and gaps.Results: Results show that empirical evidence in this field is scarce and that holistic approaches to engineering privacy into software based on patterns are lacking. This potentially hinders industrial adoption.Conclusion: Based on these results, we recommend to empirically validate existing privacy patterns, to consolidate them in pattern catalogues and languages, and to move towards seamless approaches from engineering privacy requirements to implementation.


title: Forensic anthropometry from voice: An articulatory-phonetic approach

abstract: This paper addresses a problem that is of paramount importance in solving crimes wherein voice may be key evidence, or the only evidence: that of describing the perpetrator. The term Forensic anthropometry from voice refers to the deduction of the speaker's physical dimensions from voice. There are multiple studies in the literature that approach this problem in different ways, many of which depend on the availability of sufficient volumes of speech for analysis. However, in the case of many voice-based crimes, the voice evidence available may be limited. In such cases it is especially advantageous to regard the recorded signal as comprising multiple pieces of evidence. In this paper, we show how this can be done. We explain why, for any anthropometric measurement from speech, it makes sense to consider the contributions of each articulatory-phonetic unit independently of others, and to aggregate the deductions from them only in the aftermath. This approach is based on the hypothesis that the relative evidence given by different compositional units of speech can be more indicative of the anthropometric factor being deduced, than the evidence derived from the aggregate voice signal. We explain the applicability of this approach through experiments on standard speech databases.


title: Is Design Theory Possible?

abstract: ABSTRACT Because design is a practice, a theory of design is not possible in the same sense as a theory of chemistry is. Socio-psychological theories of what designers do are not design theories. Knowledge of how to design cannot be reduced to theory, for reasons that grow out of the philosophy of science. This paper suggests, however, that there can be a theory of design in the sense of supporting theory, drawn from other disciplines, that employs a level or style of analysis that is uniquely suited to design practice. In particular, design theory might involve incompletely described objects or teleological explanation, since these are key features of design, and since the design process can be understood as moving from a functional description to a physical description of an object. INTRODUCTION The notion of a theory of design is problematic because design, like medicine or management, is a practice. Whereas chemistry or physics is defined by a set of phenomena it is assigned to study, design is defined by a task it is assigned to do. Chemical theory is clearly possible because one can organize one's knowledge of chemical phenomena in a systematic way. But it is not obvious that one can reduce knowledge of design practice to theory. I will argue that it is impossible to do this, for reasons that derive from a body of thought in the philosophy of science. I do not dispute that one can theorize about design practice in the sense that one can theoretically understand the socio-psychological phenomenon of design. But this, I will argue, does not issue in design theory. My claim is that one cannot theoretically organize one's knowledge of how to design. Despite this negative conclusion, I will identify a sense in which design theory is possible. Practical arts like design, medicine, and engineering are typically assisted by supporting theories that provide techniques and a basic understanding of the phenomenon one is trying to influence. These theories are generally part of other sciences. But a supporting theory may investigate a phenomenon, or employ a level or style of analysis, that is unique to the practice in question. At an extreme, one could say that even chemical theory is a supporting theory uniquely suited to the practice of chemistry, albeit one that does not reduce to theory all the knowledge required to practice chemistry. The possibility of design theory, then, turns on whether there are theoretical styles or subject matters that support design practice and are uniquely associated with it. I will suggest that there are. But on this understanding, design theory is in its infancy, and my rumination about what it might become must be treated as speculation. I begin below with a brief discussion of what is meant by "theory." After an even briefer definition of "design," I argue that a socio-psychological understanding of the design process is important but is not design theory. I then argue at some length that one cannot reduce practical design knowledge to theory. But this leaves open the possibility that there are supporting theories unique to design, and I suggest ways in which these can arise from the need to understand the behavior of incompletely described objects, which are characteristic of design. In particular, I discuss the potential for design theory in computational models, design representations, optimization models, and empirical investigations. I also point out that since part of what it means to design is to concoct a functional or teleological explanation of the resulting artifact, teleological theories can play a special role in design. I defend the notion of teleological explanation and suggest how it might lead to design theories. I make no attempt to survey systematically the literature that might be relevant to design theory. Several useful surveys can be found in the literature (Cross, Naughton and Walker 1980; Hubka 1980; Simon 1969; Suhl 990). …


title: Game design principles in everyday fitness applications

abstract: The global obesity epidemic has prompted our community to explore the potential for technology to play a stronger role in promoting healthier lifestyles. Although there are several examples of successful games based on focused physical interaction, persuasive applications that integrate into everyday life have had more mixed results. This underscores a need for designs that encourage physical activity while addressing fun, sustainability, and behavioral change. This note suggests a new perspective, inspired in part by the social nature of many everyday fitness applications and by the successful encouragement of long term play in massively multiplayer online games. We first examine the game design literature to distill a set of principles for discussing and comparing applications. We then use these principles to analyze an existing application. Finally, we present Kukini, a design for an everyday fitness game.


title: XBotCore: A Real-Time Cross-Robot Software Platform

abstract: In this work we introduce XBotCore (Cross-Bot-Core), a light-weight, Real-Time (RT) software platform for EtherCAT-based robots. XBotCore is open-source and is designed to be both an RT robot control framework and a software middleware. It satisfies hard RT requirements, while ensuring 1 kHz control loop even in complex Multi-Degree-Of-Freedom systems. It provides a simple and easy-to-use middleware Application Programming Interface (API), for both RT and non-RT control frameworks. This API is completely flexible with respect to the framework a user wants to utilize. Moreover it is possible to reuse the code written using XBotCore API with different robots (cross-robot feature). In this paper, the XBotCore design and architecture will be described and experimental results on the humanoid robot WALK-MAN [17], developed at the Istituto Italiano di Tecnologia (IIT), will be presented.


title: A multi-loop low-dropout FVF voltage regulator with enhanced load regulation

abstract: This paper introduces a multi-loop fast transient response flipped voltage follower (FVF) low-dropout (LDO) voltage regulator suitable for system-on-chip (SOC) applications. While typical FVF-based LDOs exhibit fast transient response, which is critical for SOC applications, their output DC accuracy is limited due to low loop gain of the FVF. In this work, we introduce a multi-loop design aimed at improving the DC accuracy while preserving the transient performance. The LDO is implemented in a 180-nm CMOS process to provide an output voltage of 1.5V at a maximum load current of 10mA from an input line voltage of 1.8V. The designed LDO's quiescent current is 53μA at minimum load. Simulation results showcase the advantages of the multi-loop design with a transient response time of 0.73ns and a figure of merit (FOM) of 3.9ps.


title: Dynamic Path Planning and Movement Control in Pedestrian Simulation

abstract: Modeling and simulation of pedestrian behavior is used in applications such as planning large buildings, disaster management, or urban planning. Realistically simulating pedestrian behavior is challenging, due to the complexity of individual behavior as well as the complexity of interactions of pedestrians with each other and with the environment. This work-in-progress paper addresses the tactical (path planning) and the operational level (movement control) of pedestrian simulation from the perspective of multiagent-based modeling. We propose (1) an novel extension of the JPS routing algorithm for tactical planning, and (2) an architecture how path planning can be integrated with a social-force based movement control. The architecture is inspired by layered architectures for robot planning and control. We validate correctness and efficiency of our approach through simulation runs.


title: Magnetically actuated and guided milli-gripper for medical applications

abstract: This paper presents the design, kinematics, fabrication, and magnetic manipulation of a milli-gripper for medical applications. The design employs a permanent magnet for two purposes. It actuates the compliant gripper and allows for maneuverability of the milli-gripper in an externally applied magnetic field generated by an electromagnetic manipulation system. The modular milli-gripper can be manipulated directly or attached to the distal tip of a magnetically steered catheter. Experiments show successful actuation of the gripper and guidance of the device with the integrated gripper in both the tethered and untethered configuration.


title: A Riemannian Framework for Matching Point Clouds Represented by the Schrödinger Distance Transform

abstract: In this paper, we cast the problem of point cloud matching as a shape matching problem by transforming each of the given point clouds into a shape representation called the Schrodinger distance transform (SDT) representation. This is achieved by solving a static Schrodinger equation instead of the corresponding static Hamilton-Jacobi equation in this setting. The SDT representation is an analytic expression and following the theoretical physics literature, can be normalized to have unit 2 norm -- making it a square-root density, which is identified with a point on a unit Hilbert sphere, whose intrinsic geometry is fully known. The Fisher-Rao metric, a natural metric for the space of densities leads to analytic expressions for the geodesic distance between points on this sphere. In this paper, we use the well known Riemannian framework never before used for point cloud matching, and present a novel matching algorithm. We pose point set matching under rigid and non-rigid transformations in this framework and solve for the transformations using standard nonlinear optimization techniques. Finally, to evaluate the performance of our algorithm -- dubbed SDTM -- we present several synthetic and real data examples along with extensive comparisons to state-of-the-art techniques. The experiments show that our algorithm outperforms state-of the-art point set registration algorithms on many quantitative metrics.


title: Risk estimation and risk prediction using machine-learning methods

abstract: After an association between genetic variants and a phenotype has been established, further study goals comprise the classification of patients according to disease risk or the estimation of disease probability. To accomplish this, different statistical methods are required, and specifically machine-learning approaches may offer advantages over classical techniques. In this paper, we describe methods for the construction and evaluation of classification and probability estimation rules. We review the use of machine-learning approaches in this context and explain some of the machine-learning algorithms in detail. Finally, we illustrate the methodology through application to a genome-wide association analysis on rheumatoid arthritis.


title: Project Adam: building an efficient and scalable deep learning training system

abstract: Large deep neural network models have recently demonstrated state-of-the-art accuracy on hard visual recognition tasks. Unfortunately such models are extremely time consuming to train and require large amount of compute cycles. We describe the design and implementation of a distributed system called Adam comprised of commodity server machines to train such models that exhibits world-class performance, scaling and task accuracy on visual recognition tasks. Adam achieves high efficiency and scalability through whole system co-design that optimizes and balances workload computation and communication. We exploit asynchrony throughout the system to improve performance and show that it additionally improves the accuracy of trained models. Adam is significantly more efficient and scalable than was previously thought possible and used 30x fewer machines to train a large 2 billion connection model to 2x higher accuracy in comparable time on the ImageNet 22,000 category image classification task than the system that previously held the record for this benchmark. We also show that task accuracy improves with larger models. Our results provide compelling evidence that a distributed systems-driven approach to deep learning using current training algorithms is worth pursuing.


CIO Leadership Profiles: Implications of Matching CIO Authority and Leadership Capability on IT Impact


title: Blog Community Discovery and Evolution Based on Mutual Awareness Expansion

abstract: There are information needs involving costly decisions that cannot be efficiently satisfied through conventional web search engines. Alternately, community centric search can provide multiple viewpoints to facilitate decision making. We propose to discover and model the temporal dynamics of thematic communities based on mutual awareness, where the awareness arises due to observable blogger actions and the expansion of mutual awareness leads to community formation. Given a query, we construct a directed action graph that is time-dependent, and weighted with respect to the query. We model the process of mutual awareness expansion using a random walk process and extract communities based on the model. We propose an interaction space based representation to quantify community dynamics. Each community is represented as a vector in the interaction space and its evolution is determined by a novel interaction correlation method. We have conducted experiments with a real-world blog dataset and have promising results for detection as well as insightful results for community evolution.


title: Application-aware prioritization mechanisms for on-chip networks

abstract: Network-on-Chips (NoCs) are likely to become a critical shared resource in future many-core processors. The challenge is to develop policies and mechanisms that enable multiple applications to efficiently and fairly share the network, to improve system performance. Existing local packet scheduling policies in the routers fail to fully achieve this goal, because they treat every packet equally, regardless of which application issued the packet. This paper proposes prioritization policies and architectural extensions to NoC routers that improve the overall application-level throughput, while ensuring fairness in the network. Our prioritization policies are application-aware, distinguishing applications based on the stall-time criticality of their packets. The idea is to divide processor execution time into phases, rank applications within a phase based on stall-time criticality, and have all routers in the network prioritize packets based on their applications' ranks. Our scheme also includes techniques that ensure starvation freedom and enable the enforcement of system-level application priorities. We evaluate the proposed prioritization policies on a 64-core CMP with an 8x8 mesh NoC, using a suite of 35 diverse applications. For a representative set of case studies, our proposed policy increases average system throughput by 25.6% over age-based arbitration and 18.4% over round-robin arbitration. Averaged over 96 randomly-generated multiprogrammed workload mixes, the proposed policy improves system throughput by 9.1% over the best existing prioritization policy, while also reducing application-level unfairness.


The Beta Policy for Continuous Control Reinforcement Learning


title: Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things

abstract: Currently, a large number of industrial data, usually referred to big data, are collected from Internet of Things (IoT). Big data are typically heterogeneous, i.e., each object in big datasets is multimodal, posing a challenging issue on the convolutional neural network (CNN) that is one of the most representative deep learning models. In this paper, a deep convolutional computation model (DCCM) is proposed to learn hierarchical features of big data by using the tensor representation model to extend the CNN from the vector space to the tensor space. To make full use of the local features and topologies contained in the big data, a tensor convolution operation is defined to prevent overfitting and improve the training efficiency. Furthermore, a high-order backpropagation algorithm is proposed to train the parameters of the deep convolutional computational model in the high-order space. Finally, experiments on three datasets, i.e., CUAVE, SNAE2, and STL-10 are carried out to verify the performance of the DCCM. Experimental results show that the deep convolutional computation model can give higher classification accuracy than the deep computation model or the multimodal model for big data in IoT.


title: Discrete laplace operator on meshed surfaces

abstract: In recent years a considerable amount of work in graphics and geometric optimization used tools based on the Laplace-Beltrami operator on a surface. The applications of the Laplacian include mesh editing, surface smoothing, and shape interpolations among others. However, it has been shown [13, 24, 26] that the popular cotangent approximation schemes do not provide convergent point-wise (or even L2) estimates, while many applications rely on point-wise estimation. Existence of such schemes has been an open question [13]. In this paper we propose the first algorithm for approximating the Laplace operator of a surface from a mesh with point-wise convergence guarantees applicable to arbitrary meshed surfaces. We show that for a sufficiently fine mesh over an arbitrary surface, our mesh Laplacian is close to the Laplace-Beltrami operator on the surface at every point of the surface. Moreover, the proposed algorithm is simple and easily implementable. Experimental evidence shows that our algorithm exhibits convergence empirically and compares favorably with cotangentbased methods in providing accurate approximation of the Laplace operator for various meshes.


title: Memristor-based memory: The sneak paths problem and solutions

abstract: In this paper, we investigate the read operation of memristor-based memories. We analyze the sneak paths problem and provide a noise margin metric to compare the various solutions proposed in the literature. We also analyze the power consumption associated with these solutions. Moreover, we study the effect of the aspect ratio of the memory array on the sneak paths. Finally, we introduce a new technique for solving the sneak paths problem by gating the memory cell using a three-terminal memistor device.


title: Precision patterning of PDMS membranes and applications

abstract: This paper presents a method for precision patterning of polydimethylsiloxane (PDMS) membranes based on parylene C lift-off. The process permits the construction of PDMS membranes either with a highly flat, uniform top surface or with a controlled curvature. Effects of varying processing parameters on the geometrical characteristics of the PDMS membranes are described. The paper also demonstrates the application of the PDMS precision patterning method to the construction of PDMS microlens arrays, which require curved top surfaces, and a 3-axis electrostatic positioning stage that uses PDMS membranes with flat surfaces as a bonding material as well as a precisely defined spacer. (Some figures in this article are in colour only in the electronic version)


title: Speech Feature Extraction and Classification: A Comparative Review

abstract: This paper gives a brief survey on speech recognition and presents an overview for various techniques used at various stages of speech recognition systems. Researchers has been working in this research area for many years however accuracy for speech recognition still attention for variation of context, speaker’s variability, environment conditions .The development of speech recognition system requires certain concepts to be included-Defining different classes of speech, techniques for speech feature extraction, speech classification modeling and measuring system performance .The main aim of this paper is to discuss and compare different approaches used for feature extraction and classification stages in speech recognition system.


title: Addressing Overfitting on Pointcloud Classification using Atrous XCRF

abstract: Advances in techniques for automated classification of pointcloud data introduce great opportunities for many new and existing applications. However, with a limited number of labeled points, automated classification by a machine learning model is prone to overfitting and poor generalization. The present paper addresses this problem by inducing controlled noise (on a trained model) generated by invoking conditional random field similarity penalties using nearby features. The method is called Atrous XCRF and works by forcing a trained model to respect the similarity penalties provided by unlabeled data. In a benchmark study carried out using the ISPRS 3D labeling dataset, our technique achieves 84.97% in term of overall accuracy, and 71.05% in term of F1 score. The result is on par with the current best model for the benchmark dataset and has the highest value in term of F1 score.


title: Contribution of intravestibular sensory conflict to motion sickness and dizziness in migraine disorders

abstract: The present study examines the relationship between migraine, motion sickness, and vertigo and the mechanisms used by the brain to resolve conflict between the two types of vestibular information provided by the labyrinth (semicircular canals and otolith organs). We demonstrate specific differences in eye movement responses between normal and migraine subjects when tested with a motion paradigm that generates canal-otolith conflict and interpret these findings in the context of normal and aberrant processing in the vestibulocerebellum.


title: Determinants of urinary 8-hydroxy-2′-deoxyguanosine in Chinese children with acute leukemia

abstract: The 8-hydroxy-2′-deoxyguanosine (8-OHdG), an oxidized nucleoside of DNA, not only is a widely used biomarker for the measurement of endogenous oxidative DNA damage, but might also be a risk factor for many diseases including cancer. Elevated level of urinary 8-OHdG has been detected in patients with various malignancies. In the present study, the level of urinary 8-OHdG was examined in 116 Chinese children with acute leukemia (94 acute lymphoid leukemia, ALL, 22 acute myeloid leukemia, AML), and its correlation with urinary metal elements was investigated. Our result showed that the level of urinary 8-OHdG in children with acute leukemia before treatment was significantly elevated compared with that in normal controls (11.92 ± 15.42 vs. 4.03 ± 4.70 ng/mg creatinine, P < 0.05). In particular, urinary 8-OHdG was higher in children with acute leukemia aged under 3 years (20.86 ± 21.75 ng/mg creatinine) than in those aged 3–15 years (8.09 ± 9.65 ng/mg creatinine), whereas no differences were shown in terms of gender, parental smoking and education, household income, place of residence, and use of paracetamol. In addition, urinary 8-OHdG levels were similar among different subtypes of acute lymphoid leukemia (ALL) patients. Furthermore, linear regression analysis revealed a significant correlation between urinary 8-OHdG and urinary Cr, but not Fe or As, in group aged <3 years compared with group aged 3–15 years (P = 0.041), indicating that the metal elements may be involved in increasing urinary 8-OHdG level in younger children with acute leukemia. Our results suggest that children with acute leukemia undergo an increased risk of oxidative DNA damage, which may be correlated with high level of Cr exposure in Chinese children with acute leukemia. © 2008 Wiley Periodicals, Inc. Environ Toxicol, 2009.


title: Do Adolescents Prefer Electronic Books to Paper Books

abstract: While electronic books offer a range of benefits and may be supposed to be more appealing to young people than paper books, this assumption is often treated as fact by educational researchers. Understanding adolescents’ true current preferences is essential, as incorrect assumptions can lead to decisions which restrict adolescent access to their preferred book mode. The belief that adolescents prefer electronic books to paper books has already led to some school libraries being expunged of paper books. As adolescents show a higher level of aliteracy than younger children, and regular reading offers a broad range of benefits for young people, it is imperative that school’s decisions around providing access to books are responsive to adolescent students’ genuine preferences. This paper analyses the current and relevant academic research around adolescent preferences for book modes, finding that, at present, the contention that adolescents prefer electronic books is not supported by the available research. In addition, there are a number of issues identified that make analyzing the findings in this area problematic. Future studies in this area are needed before an adolescent preference for electronic books can be unequivocally substantiated.


title: Multi-classification of skin diseases for dermoscopy images using deep learning

abstract: Skin diseases are very common in our daily life. Due to the similar appearance of skin diseases, automatic classification through lesion images is quite a challenging task. In this paper, a novel multi-classification method based on convolutional neural network (CNN) is proposed for dermoscopy images. A CNN network with nested residual structure is designed first, which can learn more information than the original residual structure. Then, the designed network are trained through transfer learning. With the trained network, 6 kinds of lesion diseases are classified, including nevus, seborrheic keratosis, psoriasis, seborrheic dermatitis, eczema and basal cell carcinoma. The experiments are conducted on six-classification and two-classification tasks, and with the accuracies of 65.8% and 90% respectively, our method greatly outperforms other 4 state-of-the-art networks and the average of 149 professional dermatologists.


title: Pseudobulbar affect: the spectrum of clinical presentations, etiologies and treatments.

abstract: Pseudobulbar affect (PBA) consists of uncontrollable outbursts of laughter or crying inappropriate to the patient's external circumstances and incongruent with the patient's internal emotional state. Recent data suggest disruption of cortico-pontine-cerebellar circuits, reducing the threshold for motor expression of emotion. Disruption of the microcircuitry of the cerebellum itself may likewise impair its ability to act as a gate-control for emotional expression. Current evidence also suggests that serotonergic and glutamatergic neurotransmission play key roles. Although antidepressants have shown benefit, the supportive clinical data have often derived from small numbers of patients and unvalidated measures of PBA severity. Dextromethorphan/quinidine, the first FDA-approved PBA medication, is a novel therapy with antiglutamatergic actions. As life expectancy lengthens and the neurologic settings of PBA become more common, the need for treatment can be expected to increase.


Report on tooth detection and numbering in panoramic radiographs using CNNs


title: Semantic Role Labeling

abstract: This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary


title: The Nature of LoBAL QSOs: I. SEDs and mid-infrared spectral properties

abstract: We have obtained Spitzer Infrared Spectrograph spectra and MIPS 24, 70, and 160 μm photometry for a volume-limited sample of 22 Sloan Digital Sky Survey selected low-ionization broad absorption line (LoBALs) QSOs at 0.5 1012 L ☉). We estimate star formation rates (SFRs) corrected for the AGN contribution to the FIR flux and find that LoBALs have comparable levels of SF activity to non-LoBALs when considering the entire samples. However, the SFRs of the IR-luminous LoBALs are 80% higher than those of their counterparts in the control sample. The median contribution of SF to the total far-infrared flux in LoBALs and in non-LoBALs is estimated to be 40%-50%, in agreement with previous results for Palomar-Green (PG) QSOs. Overall, our results show that there is no strong evidence from the mid- and far-IR properties that LoBALs are drawn from a different parent population than non-LoBALs.


title: Evaluation of Atomic, Physical and Thermal Properties of Tellurium Powder: Impact of Biofield Energy Treatment

abstract: Tellurium has gained significant attention due to its photoconductivity, piezoelectricity, and thermo conductivity properties. The aim of this study was to evaluate the effect of biofield energy treatment on thermal, physical and atomic properties of tellurium powder. The tellurium powder was equally divided in two parts: control and treated (T). The treated part was subjected to Mr. Trivedi’s biofield energy treatment, whereas the control part was remained untreated. Subsequently, the control and treated samples were characterized using differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and Fourier transform infrared (FT-IR) spectroscopy. The DSC data showed that latent heat of fusion was decreased by 14.13, 21.90, and 5.55% in treated samples T1, T2, and T3, respectively as compared to the control. However, the melting temperature did not show any change in treated samples as compared to the control. The TGA data showed that the peak width (difference in onset and endset) was increased from 213.67°C (control) to 234.82°C in treated tellurium sample. Besides, XRD results exhibited an alteration in lattice parameter, unit cell volume, density, atomic weight and nuclear charge volume of the treated tellurium powder as compared to the control. In addition, the crystallite sizes were significantly changed on crystalline plane (102) and (110) as 146.05→48.67 nm and 63.01→88.21 nm, respectively in the treated tellurium. The FT-IR spectra did not show any significant change in absorption frequencies in treated sample as compared to the control. Therefore, DSC, TGA and XRD data suggested that Mr. Trivedi’s biofield energy treatment has significantly altered the thermal and physical properties of tellurium powder. Thus, biofield energy treatment could be applied to modulate the thermal and physical properties in semiconductor and chalcogenide glass industries.


title: How social Q&A sites are changing knowledge sharing in open source software communities

abstract: Historically, mailing lists have been the preferred means for coordinating development and user support activities. With the emergence and popularity growth of social Q&A sites such as the StackExchange network (e.g., StackOverflow), this is beginning to change. Such sites offer different socio-technical incentives to their participants than mailing lists do, e.g., rich web environments to store and manage content collaboratively, or a place to showcase their knowledge and expertise more vividly to peers or potential recruiters. A key difference between StackExchange and mailing lists is gamification, i.e., StackExchange participants compete to obtain reputation points and badges. In this paper, we use a case study of R (a widely-used tool for data analysis) to investigate how mailing list participation has evolved since the launch of StackExchange. Our main contribution is the assembly of a joint data set from the two sources, in which participants in both the texttt{r-help} mailing list and StackExchange are identifiable. This permits their activities to be linked across the two resources and also over time. With this data set we found that user support activities show a strong shift away from texttt{r-help}. In particular, mailing list experts are migrating to StackExchange, where their behaviour is different. First, participants active both on texttt{r-help} and on StackExchange are more active than those who focus exclusively on only one of the two. Second, they provide faster answers on StackExchange than on texttt{r-help}, suggesting they are motivated by the emph{gamified} environment. To our knowledge, our study is the first to directly chart the changes in behaviour of specific contributors as they migrate into gamified environments, and has important implications for knowledge management in software engineering.


title: Video forgery detection using correlation of noise residue

abstract: We propose a new approach for locating forged regions in a video using correlation of noise residue. In our method, block-level correlation values of noise residual are extracted as a feature for classification. We model the distribution of correlation of temporal noise residue in a forged video as a Gaussian mixture model (GMM). We propose a two-step scheme to estimate the model parameters. Consequently, a Bayesian classifier is used to find the optimal threshold value based on the estimated parameters. Two video inpainting schemes are used to simulate two different types of forgery processes for performance evaluation. Simulation results show that our method achieves promising accuracy in video forgery detection.


title: How to Escape Saddle Points Efficiently

abstract: This paper shows that a perturbed form of gradient descent converges to a second-order stationary point in a number iterations which depends only poly-logarithmically on dimension (i.e., it is almost"dimension-free"). The convergence rate of this procedure matches the well-known convergence rate of gradient descent to first-order stationary points, up to log factors. When all saddle points are non-degenerate, all second-order stationary points are local minima, and our result thus shows that perturbed gradient descent can escape saddle points almost for free. Our results can be directly applied to many machine learning applications, including deep learning. As a particular concrete example of such an application, we show that our results can be used directly to establish sharp global convergence rates for matrix factorization. Our results rely on a novel characterization of the geometry around saddle points, which may be of independent interest to the non-convex optimization community.


title: The future of mental health care: peer-to-peer support and social media

abstract: Aims: People with serious mental illness are increasingly turning to popular social media, including Facebook, Twitter or YouTube, to share their illness experiences or seek advice from others with similar health conditions. This emerging form of unsolicited communication among self-forming online communities of patients and individuals with diverse health concerns is referred to as peer-to-peer support. We offer a perspective on how online peer-to-peer connections among people with serious mental illness could advance efforts to promote mental and physical wellbeing in this group. Methods: In this commentary, we take the perspective that when an individual with serious mental illness decides to connect with similar others online it represents a critical point in their illness experience. We propose a conceptual model to illustrate how online peer-to-peer connections may afford opportunities for individuals with serious mental illness to challenge stigma, increase consumer activation and access online interventions for mental and physical wellbeing. Results: People with serious mental illness report benefits from interacting with peers online from greater social connectedness, feelings of group belonging and by sharing personal stories and strategies for coping with day-to-day challenges of living with a mental illness. Within online communities, individuals with serious mental illness could challenge stigma through personal empowerment and providing hope. By learning from peers online, these individuals may gain insight about important health care decisions, which could promote mental health care seeking behaviours. These individuals could also access interventions for mental and physical wellbeing delivered through social media that could incorporate mutual support between peers, help promote treatment engagement and reach a wider demographic. Unforeseen risks may include exposure to misleading information, facing hostile or derogatory comments from others, or feeling more uncertain about one's health condition. However, given the evidence to date, the benefits of online peer-to-peer support appear to outweigh the potential risks. Conclusion: Future research must explore these opportunities to support and empower people with serious mental illness through online peer networks while carefully considering potential risks that may arise from online peer-to-peer interactions. Efforts will also need to address methodological challenges in the form of evaluating interventions delivered through social media and collecting objective mental and physical health outcome measures online. A key challenge will be to determine whether skills learned from peers in online networks translate into tangible and meaningful improvements in recovery, employment, or mental and physical wellbeing in the offline world.


title: Event-triggered communication for synchronization of Markovian jump delayed complex networks with partially unknown transition rates

abstract: The network model in this paper involves time delay and partly unknown transition rates, which is more practical than the models in 18,34,35,38.Different from the most existing results with partly unknown transition rates 24-26, the synchronization trajectory in this paper is dynamic, which will be adjusted according to the states of nodes.A randomly occurring event-triggered control strategy is proposed to study the synchronization issue of Markovian jump delayed complex networks with partly unknown transition rates.The positive lower bound of the event intervals is obtained which can exclude undesired accumulation of event instants (Zeno behaviors). In this paper, exponential synchronization problems are investigated for an array of Markovian jump delayed complex networks with partially unknown transition rates and discontinuous diffusions. To impel the array complex networks to achieve exponential synchronization, a new randomly occurring event-triggered control strategy is proposed. The idea of event-triggered control strategy is that the coupling term and controller update data only at the event-triggered instants, which can reduce the communication load and energy consumption. By constructing a novel stochastic Lyapunov-Krasovskii function, some exponential synchronization criteria are obtained in terms of LMIs and famous Halanay inequality. Furthermore, we obtain a positive lower bound of the event intervals which can exclude the Zeno behaviors. Finally, a simulation example is given to illustrate the effectiveness of the theoretical results.


title: Use of Data Mining in Various Field: A Survey Paper

abstract: Data mining is extracts the knowledge/ information from a large amount of data which stores in multiple heterogeneous data base. Knowledge /information are conveying the message through direct or indirect. This paper provides a survey of various data mining techniques. These techniques include association, correlation, clustering and neural network. This research paper also conducts a formal review of the application of data mining such as the education sector, marketing, fraud detection, manufacturing and telecommunication. This paper discusses the topic based on past research paper and also studies the data mining techniques. Keywords: Association, Clustering, Data mining, data mining application, knowledge discovery database,


Synthesis and properties of N-(2,3,5-tri-O-acetyl-D-ribofuranosyl)maleimide


title: Implementing Intelligent Traffic Control System for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection

abstract: This paper presents an intelligent traffic control system to pass emergency vehicles smoothly. Each individual vehicle is equipped with special radio frequency identification (RFID) tag (placed at a strategic location), which makes it impossible to remove or destroy. We use RFID reader, NSK EDK-125-TTL, and PIC16F877A system-on-chip to read the RFID tags attached to the vehicle. It counts number of vehicles that passes on a particular path during a specified duration. It also determines the network congestion, and hence the green light duration for that path. If the RFID-tag-read belongs to the stolen vehicle, then a message is sent using GSM SIM300 to the police control room. In addition, when an ambulance is approaching the junction, it will communicate to the traffic controller in the junction to turn ON the green light. This module uses ZigBee modules on CC2500 and PIC16F877A system-on-chip for wireless communications between the ambulance and traffic controller. The prototype was tested under different combinations of inputs in our wireless communication laboratory and experimental results were found as expected.


title: Weak supervision for detecting object classes from activities

abstract: The problem of detecting objects from weakly labeled activity videos is addressed.Multiple object instances from each video are inferred using a greedy approach.Combining object appearance with its functionality greatly improves performance.Object detection performance comparable to a fully supervised approach is achieved. Weakly supervised learning for object detection has been gaining significant attention in the recent past. Visually similar objects are extracted automatically from weakly labeled videos hence bypassing the tedious process of manually annotating training data. However, the problem as applied to small or medium sized objects is still largely unexplored. Our observation is that weakly labeled information can be derived from videos involving human-object interactions. Since the object is characterized neither by its appearance nor its motion in such videos, we propose a robust framework that taps valuable human context and models similarity of objects based on appearance and functionality. Furthermore, the framework is designed such that it maximizes the utility of the data by detecting possibly multiple instances of an object from each video. We show that object models trained in this fashion perform between 86% and 92% of their fully supervised counterparts on three challenging RGB and RGB-D datasets.