title: Initialization of K-modes clustering using outlier detection techniques

abstract: We considered the initialization of K-modes clustering from the view of outlier detection.We proposed an initialization algorithm for K-modes clustering via the distance-based outlier detection technique.We presented a partition entropy-based outlier detection technique, and designed an initialization algorithm via it.We proposed a new distance metric - weighted matching distance metric.The effectiveness of our initialization algorithms was shown on several UCI data sets. The K-modes clustering has received much attention, since it works well for categorical data sets. However, the performance of K-modes clustering is especially sensitive to the selection of initial cluster centers. Therefore, choosing the proper initial cluster centers is a key step for K-modes clustering. In this paper, we consider the initialization of K-modes clustering from the view of outlier detection. We present two different initialization algorithms for K-modes clustering, where the first is based on the traditional distance-based outlier detection technique, and the second is based on the partition entropy-based outlier detection technique. By using the above two outlier detection techniques to calculate the degree of outlierness of each object, our algorithms can guarantee that the chosen initial cluster centers are not outliers. Moreover, during the process of initialization, we adopt a new distance metric - weighted matching distance metric, to calculate the distance between two objects described by categorical attributes. Experimental results on several UCI data sets demonstrate the effectiveness of our initialization algorithms for K-modes clustering.


title: Manufacturing cheap, resilient, and stealthy opaque constructs

abstract: It has become common to distribute software in forms that are isomorphic to the original source code. An important example is Java bytecode. Since such codes are easy to decompile, they increase the risk of malicious reverse engineering attacks.In this paper we describe the design of a Java code obfuscator, a tool which - through the application of code transformations - converts a Java program into an equivalent one that is more difficult to reverse engineer.We describe a number of transformations which obfuscate control-flow. Transformations are evaluated with respect to potency (To what degree is a human reader confused?), resilience (How well are automatic deobfuscation attacks resisted?), cost (How much time/space overhead is added?), and stealth (How well does obfuscated code blend in with the original code?).The resilience of many control-altering transformations rely on the resilience of opaque predicates. These are boolean valued expressions whose values are known to the obfuscator but difficult to determine for an automatic deobfuscator. We show how to construct resilient, cheap, and stealthy opaque predicates based on the intractability of certain static analysis problems such as alias analysis.


title: Cyano Metabolite as a Biomarker of Nitrofurazone in Channel Catfish

abstract: The use of nitrofuran drugs in food-producing animals continues to attract international concern as a food safety issue. Methods for monitoring nitrofuran residues have been directed to the intact side chain of tissue-bound metabolites. Semicarbazide, the side chain of nitrofurazone (NFZ), can enter food products from non-NFZ sources, suggesting the need for an alternative biomarker for confirmatory purposes. We characterized a cyano derivative as a major metabolite of NFZ in channel catfish (Ictalurus punctatus). The depletion of cyano metabolite was examined in the muscle of channel catfish after oral dosing (10 mg of NFZ/kg of body weight). Parent NFZ was rapidly eliminated in muscle, with a half-life of 6.3 h. The cyano metabolite was detected for up to 2 weeks, with an elimination half-life of 81 h. The cyano metabolite represents an alternative biomarker for confirming the use of NFZ in channel catfish.


title: Neural Block Sampling

abstract: Efficient Monte Carlo inference often requires manual construction of model-specific proposals. We propose an approach to automated proposal construction by training neural networks to provide fast approximations to block Gibbs conditionals. The learned proposals generalize to occurrences of common structural motifs both within a given model and across models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required. We explore several applications including open-universe Gaussian mixture models, in which our learned proposals outperform a hand-tuned sampler, and a real-world named entity recognition task, in which our sampler's ability to escape local modes yields higher final F1 scores than single-site Gibbs.


Fundamentals of deep learning


The Circumstantial Event Ontology (CEO)


title: Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease

abstract: We propose an automatic method using dilated convolutional neural networks (CNNs) for segmentation of the myocardium and blood pool in cardiovascular MR (CMR) of patients with congenital heart disease (CHD). Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge. A dilated CNNwith a receptive field of 131×131 voxels was trained for myocardium and blood pool segmentation in axial, sagittal and coronal image slices. Performance was evaluated within the HVSMR challenge. Automatic segmentation of the test scans resulted in Dice indices of 0.80 ± 0.06 and 0.93 ± 0.02, average distances to boundaries of 0.96 ± 0.31 and 0.89 ± 0.24 mm, and Hausdorff distances of 6.13 ± 3.76 and 7.07 ± 3.01mm for the myocardium and blood pool, respectively. Segmentation took 41.5 ± 14.7 s per scan. In conclusion, dilated CNNs trained on a small set of CMR images of CHD patients showing large anatomical variability provide accurate myocardium and blood pool segmentations.


title: Self-Published Books: An Empirical "Snapshot"

abstract: The number of books published by authors using fee-based publication services, such as Lulu and AuthorHouse, is overtaking the number of books published by mainstream publishers, according to Bowker’s 2009 annual data. Little empirical research exists on self-published books. This article presents the results of an investigation of a random sample of 348 books from a total population of 385,173 titles available from ninety-three fee-based publication services in 2008. Major findings include publication patterns within the nontraditional publishing industry, availability of self-published titles online and in libraries, state of bibliographic and cataloging control, and subject content of self-published books. The findings provide a baseline “snapshot” of books available from publication services in 2008, a time when the self-publishing industry was established but was still small enough to study overall. The article concludes with implications for librarians from this study of self-published books.


title: Optical Flow with Semantic Segmentation and Localized Layers

abstract: Existing optical flow methods make generic, spatially homogeneous, assumptions about the spatial structure of the flow. In reality, optical flow varies across an image depending on object class. Simply put, different objects move differently. Here we exploit recent advances in static semantic scene segmentation to segment the image into objects of different types. We define different models of image motion in these regions depending on the type of object. For example, we model the motion on roads with homographies, vegetation with spatially smooth flow, and independently moving objects like cars and planes with affine motion plus deviations. We then pose the flow estimation problem using a novel formulation of localized layers, which addresses limitations of traditional layered models for dealing with complex scene motion. Our semantic flow method achieves the lowest error of any published monocular method in the KITTI-2015 flow benchmark and produces qualitatively better flow and segmentation than recent top methods on a wide range of natural videos.


title: A New Framework for Quality Assessment of High-Resolution Fingerprint Images

abstract: The quality assessment of sets of features extracted from patterns of epidermal ridges on our fingers is a biometric challenge problem with implications on questions concerning security, privacy and identity fraud. In this work, we introduced a new methodology to analyze the quality of high-resolution fingerprint images containing sets of fingerprint pores. Our approach takes into account the spatial interrelationship between the considered features and some basic transformations involving point process and anisotropic analysis. We proposed two new quality index algorithms following spatial and structural classes of analysis. These algorithms have proved to be effective as a performance predictor and as a filter excluding low-quality features in a recognition process. The experiments using error reject curves show that the proposed approaches outperform the state-of-the-art quality assessment algorithm for high-resolution fingerprint recognition, besides defining a new method for reconstructing their friction ridge phases in a very consistent way.


title: Terahertz CMOS Frequency Generator Using Linear Superposition Technique

abstract: A low Terahertz (324 GHz) frequency generator is realized in 90 nm CMOS by linearly superimposing quadruple (N=4) phase shifted fundamental signals at one fourth of the output frequency (81 GHz). The developed technique minimizes the fundamental, second and third order harmonics without extra filtering and results in a high fundamental-to-4 th harmonic signal conversion ratio of 0.17 or -15.4 dB. The demonstrated prototype produces a calibrated -46 dBm output power when biased at 1 V and 12 mA with 4 GHz tuning range and extrapolated phase noise of -91 dBc/Hz at 10 MHz frequency offset. The linear superposition (LS) technique can be generalized for all even number cases (N=2k, where k=1,2,3,4,...,n) with different tradeoffs in output power and frequency. As CMOS continues to scale, we anticipate the LS N=4 VCO to generate signals beyond 2 Terahertz by using 22 nm CMOS and produce output power up to -1.5 dBm with 1.7% power added efficiency with an LS VCO + Class-B Power Amplifier cascaded circuit architecture.


title: A Model of Individual Keyword Performance in Paid Search Advertising

abstract: In paid search advertising on Internet search engines, advertisers bid for specific keywords, e.g. "Rental Cars LAX," to display a text ad in the sponsored section of the search results page. The advertiser is charged when a user clicks on the ad. Many of the keywords in paid search campaigns generate few, if any, sales conversions - even over several months. This sparseness makes it difficult to assess the profit performance of individual keywords and has led to the practice of managing large groups of keywords together or relying on easy-to-calculate heuristics such as click-through rate (CTR). The authors develop a model of individual keyword conversion that addresses the sparseness problem. Conversion rates are estimated using a hierarchical Bayes binary choice model. This enables conversion to be based on both word-level covariates and shrinkage across keywords. The model is applied to keyword-level paid search data containing daily information on impressions, clicks and reservations for a major lodging chain. The results show that including keyword-level covariates and heterogeneity significantly improves conversion estimates. A holdout comparison suggests that campaign management based on the model, i.e., estimated cost-per-sale on a keyword level, would outperform existing managerial strategies.


title: Comparison of Off-Chip Training Methods for Neuromemristive Systems

abstract: Neuromemristive systems offer an efficient platform for learning and modeling non-linear functions in real time. Specifically, they are effective tools for pattern classification. However, training these systems presents several challenges, especially when CMOS and memristor process variations are considered. In this paper, we propose two off-chip training methods for neuromemristive systems: weight programming and feature training. Detailed variation models are developed to study the effects of CMOS and memristor process variations on neuromemristive circuits, including neurons, synapses, and training circuits. We analyze the impact of those variations on the proposed off-chip training methods. Specifically, we train a neuromemristive system to classify handwritten digits. The results indicate that the feature training method is able to provide over 2× better classification accuracy per unit area than the weight programming method. However, the weight programming method is much faster, and may be more suitable when the network needs to be frequently re-trained.


title: Data Sharing with Fine-Grained Access Control for Multi-tenancy Cloud Storage System

abstract: Data sharing is one of the most significant applications of cloud computing. For security and privacy concerns, clients generally encrypt their data before upload them to the cloud. The existing data sharing schemes either entirely rely on the cloud to enforce access control or inevitably involve a trusted third party (TTP) to perform secret key distribution. This thesis proposes a secure data sharing scheme without TTP involved. Our scheme allows users to classify their data and achieves a fine-gained access authorization. The key-distribution is integrated with the user authorization and data sharing procedure. In terms of security, except for semi-honest cloud service provider and external adversary, we also take internal adversary into consideration and analysis security in this strong model.


title: Personality, motivation and job satisfaction: Hertzberg meets the Big Five

abstract: Purpose – The current study aims to investigate the extent to which personality and demographic variables contribute to motivation and job satisfaction as defined by the two‐factor theory.Design/methodology/approach – A total of 202 fulltime workers completed three questionnaires measuring their personality, work motivation and satisfaction.Findings – Results demonstrate that between 9 and 15 per cent of the variance in motivation is accounted for by demographic variables and the Big Five personality traits. In line with previous findings (Judge et al.), conscientiousness and job status were both significant predictors of job satisfaction, and between 11 and 13 per cent of the variance was accounted for by personality and other demographic variables.Research limitations/implications – This study was restricted to self‐report measure. It never took into consideration other potential confounds like a person's job history, level and responsibilities. It also showed personality factors accounted for very litt...


title: A Fast and Precise HOG-Adaboost Based Visual Support System Capable to Recognize Pedestrian and Estimate Their Distance

abstract: In this paper,we present a visual support system the visually impaired. Our detection algorithm is based on the well known Histograms of Oriented Gradients (HOG) method, due to its high detection rate and versatility [5]. However, the accuracy of object recognition rate is reduced because of high false detection rate. In order to solve that, multiple parts model and triple phase detection have been implemented. These additional filtering stages were conducted by separate action on different area of the sample, considering deformations and translations. We demonstrated that this approach has raised the accuracy and speed of calculation. Through an evaluation experiment based on a large dataset, we found that false detection has been improved by 18.9% in respect to standard HOG detectors. Experimental tests have also shown the system ability to estimate the distance of the pedestrian by the use of a simple perspective model. The system has been tested on several photographic datasets and have shown excellent performances also in ambiguous cases.


title: Macromolecular Helicity Induction and Memory in a Poly(biphenylylacetylene) Bearing an Ester Group and Its Application to a Chiral Stationary Phase for High-performance Liquid Chromatography

abstract: An optically inactive poly(biphenylylacetylene) bearing an ester group at the 4′-position of the pendants formed a preferred-handed helical conformation and biased axial chirality in the pendants t...


title: Random vector functional link network for short-term electricity load demand forecasting

abstract: Short-term electricity load forecasting plays an important role in the energy market as accurate forecasting is beneficial for power dispatching, unit commitment, fuel allocation and so on. This paper reviews a few single hidden layer network configurations with random weights (RWSLFN). The RWSLFN was extended to eight variants based on the presence or absence of input layer bias, hidden layer bias and direct input-output connections. In order to avoid mapping the weighted inputs into the saturation region of the enhancement nodes' activation function and to suppress the outliers in the input data, a quantile scaling algorithm to re-distribute the randomly weighted inputs is proposed. The eight variations of RWSLFN are assessed using six generic time series datasets and 12 load demand time series datasets. The result shows that the RWSLFNs with direct input-output connections (known as the random vector functional link network or RVFL network) have statistically significantly better performance than the RWSLFN configurations without direct input-output connections, possibly due to the fact that the direct input-output connections in the RVFL network emulate the time delayed finite impulse response (FIR) filter. However the RVFL network has simpler training and higher accuracy than the FIR based two stage neural network. The RVFL network is also compared with some reported forecasting methods. The RVFL network overall outperforms the non-ensemble methods, namely the persistence method, seasonal autoregressive integrated moving average (sARIMA), artificial neural network (ANN). In addition, the testing time of the RVFL network is the shortest while the training time is comparable to the other reported methods. Finally, possible future research directions are pointed out.


title: Behavior and properties of side chain thermotropic liquid crystal polymers

abstract: The phase behavior, ferroelectric and electrooptical properties and potential applications of ferroelectric liquid crystal polymers are reviewed. The molecular structure, mechanical properties and non linear optical properties of side chain liquid crystalline networks are also discussed


title: Text Mining (Applications Note) Text processing through Web services: Calling Whatizit

abstract: Motivation: Text-mining (TM) solutions are developing into efficient services to researchers in the biomedical research community. Such solutions have to scale with the growing number and size of resources (e.g. available controlled vocabularies), with the amount of literature to be processed (e.g. about 17 million documents in PubMed) and with the demands of the user community (e.g. different methods for fact extraction). These demands motivated the development of a server-based solution for literature analysis. ::: ::: Whatizit is a suite of modules that analyse text for contained information, e.g. any scientific publication or Medline abstracts. Special modules identify terms and then link them to the corresponding entries in bioinformatics databases such as UniProtKb/Swiss-Prot data entries and gene ontology concepts. Other modules identify a set of selected annotation types like the set produced by the EBIMed analysis pipeline for proteins. In the case of Medline abstracts, Whatizit offers access to EBI's in-house installation via PMID or term query. For large quantities of the user's own text, the server can be operated in a streaming mode ( http://www.ebi.ac.uk/webservices/whatizit). ::: ::: Contact: [email protected]


title: A hybrid approach to estimate the complex motions of clouds in sky images

abstract: Abstract Tracking the motion of clouds is essential to forecasting the weather and to predicting the short-term solar energy generation. Existing techniques mainly fall into two categories: variational optical flow, and block matching. In this paper, we summarize recent advances in estimating cloud motion using ground-based sky imagers and quantitatively evaluate state-of-the-art approaches. Then we propose a hybrid tracking framework to incorporate the strength of both block matching and optical flow models. To validate the accuracy of the proposed approach, we introduce a series of synthetic images to simulate the cloud movement and deformation, and thereafter comprehensively compare our hybrid approach with several representative tracking algorithms over both simulated and real images collected from various sites/imagers. The results show that our hybrid approach outperforms state-of-the-art models by reducing at least 30% motion estimation errors compared with the ground-truth motions in most of simulated image sequences. Moreover, our hybrid model demonstrates its superior efficiency in several real cloud image datasets by lowering at least 15% Mean Absolute Error (MAE) between predicted images and ground-truth images.


title: Plant Pathology and Information Technology: Opportunity for Management of Disease Outbreak and Applications in Regulation Frameworks

abstract: In many European rural areas, agriculture is not only an economic activity, but it is strictly linked to environmental and social characteristics of the area. Thus, sometimes, a pathogen can become a social threat, as in the case of Xylella fastidiosa and olive trees ( Olea europaea L.) in Salento. Fast and systemic response to threats represents the key to success in stopping pest invasions, and proves a great help in managing lots of data in a short time or coordinating large-scale monitoring coming from applying Information Technology tools. Regarding the field of applications, the advantages provided by new technologies are countless. However, is it the same in agriculture? Electronic identification tools can be applied for plant health management and certification. Treatments, agrochemical management or impact assessment may also be supported by dematerialization of data. Information Technology solution for urban forestry management or traceability of commodities belonging to “Food from Somewhere” regimes were analyzed and compared to protection from pests of a unique tree heritage such as olive trees in Salento.


title: Polaritons in van der Waals materials

abstract: BACKGROUND Light trapped at the nanoscale, deep below the optical wavelength, exhibits an increase in the associated electric field strength, which results in enhanced light-matter interaction. This leads to strong nonlinearities, large photonic forces, and enhanced emission and absorption probabilities. A practical approach toward nanoscale light trapping and manipulation is offered by interfaces separating media with permittivities of opposite signs. Such interfaces sustain hybrid light-matter modes involving collective oscillations of polarization charges in matter, hence the term polaritons. Surface plasmon polaritons, supported by electrons in metals, constitute a most-studied prominent example. Yet there are many other varieties of polaritons, including those formed by atomic vibrations in polar insulators, excitons in semiconductors, Cooper pairs in superconductors, and spin resonances in (anti)ferromagnets. Together, they span a broad region of the electromagnetic spectrum, ranging from microwave to ultraviolet wavelengths. We discuss polaritons in van der Waals (vdW) materials: layered systems in which individual atomic planes are bonded by weak vdW attraction (see the figure). This class of quantum materials includes graphene and other two-dimensional crystals. In artificial structures assembled from dissimilar vdW atomic layers, polaritons associated with different constituents can interact to produce unique optical effects by design. ADVANCES vdW materials host a full suite of different polaritonic modes with the highest degree of confinement among all known materials. Advanced near-field imaging methods allow the polaritonic waves to be launched and visualized as they travel along vdW layers or through multilayered heterostructures. Spectroscopic and nanoimaging experiments have identified multiple routes toward manipulation of nano-optical phenomena endowed by polaritons. A virtue of polaritons in vdW systems is their electrical tunability. Furthermore, in heterostructures assembled from dissimilar vdW layers, different brands of polaritons interact with each other, thus enabling unparalleled control of polaritonic response at the level of single atomic planes. New optoelectronic device concepts aimed at the detection, harvesting, emission, propagation, and modulation of light are becoming feasible as a result of combined synthesis, nanofabrication, and modeling of vdW systems. The extreme anisotropy of vdW systems leading to opposite signs of the in-plane and out-of-plane permittivities of the same layered crystal enables efficient polaritonic waveguides, which are instrumental for subdiffractional focusing and imaging. In addition to near-field optical probes facilitating nanoimaging, coupling to polaritons can be accomplished via electrical excitation and nonlinear wave mixing. OUTLOOK Potential outcomes of polariton exploration in vdW heterostructures go beyond nano-optical technologies. In particular, images of polaritonic standing and traveling waves contain rich insights into quantum phenomena occurring in the host material supporting polaritons. This line of inquiry into fundamental physics through polaritonic observations constitutes an approach toward optics-based materials research. In particular, the strong spatial confinement exhibited by vdW polaritons involves large optical-field gradients—or equivalently, large momenta—which allows regions of the dispersion relations of electrons, phonons, and other condensed-matter excitations to be accessed beyond what is currently possible with conventional optics. Additionally, polaritons created by short and intense laser pulses add femtosecond resolution to the study of these phenomena. Alongside future advances in the understanding of the physics and interactions of vdW polaritons, solutions to application challenges may be anticipated in areas such as loss compensation, nanoscale lasing, quantum optics, and nanomanipulation. The field of vdW polaritonics is ripe for exploring genuinely unique physical scenarios and exploiting these new phenomena in technology. van der Waals (vdW) materials consist of individual atomic planes bonded by weak vdW attraction. They display nearly all optical phenomena found in solids, including plasmonic oscillations of free electrons characteristic of metals, light emission/lasing and excitons encountered in semiconductors, and intense phonon resonances typical of insulators. These phenomena are embodied in confined light-matter hybrid modes termed polaritons—excitations of polarizable media, which are classified according to the origin of the polarization. The most studied varieties are plasmon, phonon, and exciton polaritons. In vdW materials, polaritons exhibit extraordinary properties that are directly affected by dimensionality and topology, as revealed by state-of-the-art imaging of polaritonic waves. vdW heterostructures provide unprecedented control over the polaritonic response, enabling new quantum phenomena and nanophotonics applications.


title: Community Detection in Networks with Node Attributes

abstract: Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Communities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statistically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community.


A KL Divergence and DNN-Based Approach to Voice Conversion without Parallel Training Sentences.


title: Frames: A Corpus for Adding Memory to Goal-Oriented Dialogue Systems

abstract: This paper presents the Frames dataset (Frames is available at http://datasets.maluuba.com/Frames), a corpus of 1369 human-human dialogues with an average of 15 turns per dialogue. We developed this dataset to study the role of memory in goal-oriented dialogue systems. Based on Frames, we introduce a task called frame tracking, which extends state tracking to a setting where several states are tracked simultaneously. We propose a baseline model for this task. We show that Frames can also be used to study memory in dialogue management and information presentation through natural language generation.


title: Face Recognition and Privacy in the Age of Augmented Reality

abstract: We investigate the feasibility of combining publicly available Web 2.0 data with off-the-shelf face recognition software for the purpose of large-scale, automated individual re-identification. Two experiments illustrate the ability of identifying strangers online (on a dating site where individuals protect their identities by using pseudonyms) and offline (in a public space), based on photos made publicly available on a social network site. A third proof-of-concept experiment illustrates the ability of inferring strangers' personal or sensitive information (their interests and Social Security numbers) from their faces, by combining face recognition, data mining algorithms, and statistical re-identification techniques. The results highlight the implications of the convergence of face recognition technology and increasing online self-disclosure, and the emergence of "personally predictable'' information, or PPI. They raise questions about the future of privacy in an "augmented'' reality world in which online and offline data will seamlessly blend.


title: Evaluation of a Context-Aware Voice Interface for Ambient Assisted Living: Qualitative User Study vs. Quantitative System Evaluation

abstract: This article presents an experiment with seniors and people with visual impairment in a voice-controlled smart home using the S weet -H ome system. The experiment shows some weaknesses in automatic speech recognition that must be addressed, as well as the need for better adaptation to the user and the environment. Users were disturbed by the rigid structure of the grammar and were eager to adapt it to their own preferences. Surprisingly, while no humanoid aspect was introduced in the system, the senior participants were inclined to embody the system. Despite these aspects to improve, the system has been favorably assessed as diminishing most participant fears related to the loss of autonomy.


title: STATISTICAL ANALYSIS ON SOFTWARE METRICS AFFECTING MODULARITY IN OPEN SOURCE SOFTWARE

abstract: Modularity has been identified by many researchers as one of the success factors of Open Source ::: Software (OSS) Projects. This modularity trait are influenced by some aspects of software metrics such ::: as size, complexity, cohesion, and coupling. In this research, we analyze the software metrics such as ::: Size Metrics (NCLOC, Lines, and Statements), Complexity Metrics (McCabe's Cyclomatic Complexity), ::: Cohesion Metrics (LCOM4), and Coupling Metrics (RFC, Afferent coupling and Efferent coupling) of 59 ::: Java-based OSS Projects from Sourceforge.net. By assuming that the number of downloads can be used ::: as the indication of success of these projects, the OSS Projects being selected are the projects which have ::: been downloaded more than 100,000 times. The software metrics reflecting the modularity of these ::: projects are collected using SONAR tool and then statistically analyzed using scatter graph, Pearson r ::: product-moment correlation, and least-square-fit linear approximation. It can be shown that there are ::: only three independent metrics reflecting modularity which are NCLOC, LCOM4, and Afferent Coupling, ::: whereas there is also one inconclusive result regarding Efferent Coupling


title: Analysis of a greedy active learning strategy

abstract: We abstract out the core search problem of active learning schemes, to better understand the extent to which adaptive labeling can improve sample complexity. We give various upper and lower bounds on the number of labels which need to be queried, and we prove that a popular greedy active learning rule is approximately as good as any other strategy for minimizing this number of labels.


title: Automated Experiments on Ad Privacy Settings

abstract: To partly address people’s concerns over web tracking, Google has created the Ad Settings webpage to provide information about and some choice over the profiles Google creates on users. We present AdFisher, an automated tool that explores how user behaviors, Google’s ads, and Ad Settings interact. Our tool uses a rigorous experimental design and analysis to ensure the statistical significance of our results. It uses machine learning to automate the selection of a statistical test. We use AdFisher to find that Ad Settings is opaque about some features of a user’s profile, that it does provide some choice on ads, and that these choices can lead to seemingly discriminatory ads. In particular, we found that visiting webpages associated with substance abuse will change the ads shown but not the settings page. We also found that setting the gender to female results in getting fewer instances of an ad related to high paying jobs than setting it to male.


title: FVRC2016: The 2nd Finger Vein Recognition Competition

abstract: Finger vein recognition is a newly developed and promising biometrics technology. In current researches, finger vein recognition algorithms are mostly evaluated on data collected in laboratory environment. Along with its development, this technology gradually transforms from laboratory to actual use. Finger vein images captured in operational systems are different from those obtained in the lab, due to its uncontrollable acquisition condition and users' behavior. The 2nd Finger Vein Recognition Competition (FVRC2016) was organized for the purpose of assessing and comparing finger vein recognition algorithms on such data. 3 new data sets collected in real application were prepared for evaluating and improving algorithms. 6 submitted algorithms were extensively tested on the data sets. Testing protocols and detailed analysis of the results are also presented in this paper.


title: An Efficient Automatic Attendance System using Fingerprint Verification Technique

abstract: The main aim of this paper is to develop an accurate, fast and very efficient automatic attendance system using fingerprint verification technique. We propose a system in which fingerprint verification is done by using extraction of minutiae technique and the system that automates the whole process of taking attendance, Manually which is a laborious and troublesome work and waste a lot of time, with its managing and maintaining the records for a period of time is also a burdensome task. For this purpose we use fingerprint verification system using extraction of minutiae techniques. The experimental result shows that our proposed system is highly efficient in verification of user fingerprint.


title: Trait Emotional Intelligence Theory

abstract: In addition to trait EI, Cherniss identifies three other EI models whose main limitations must be succinctly mentioned, not least because they provided the impetus for the development of the trait EI model. Bar-On’s (1997) model is predicated on the problematic assumption that emotional intelligence (or ‘‘ability’’ or ‘‘competence’’ or ‘‘skill’’ or ‘‘potential’’—terms that appear to be used interchangeably in his writings) can be validly assessed through self-report questions of the type ‘‘It is easy for me to understand my emotions.’’ Psychometrically, as pointed out in Petrides and Furnham (2001), this is not a viable position because such self-report questions can only be tapping into self-perceptions rather than into abilities or competencies. This poses a fundamental threat to the validity of this model, far more serious than the pervasive faking problem noted by several authors (e.g., Grubb & McDaniel, 2008). Goleman’s (1995) model is difficult to evaluate scientifically because of its reliance on


title: Towards the development of guidelines for educational evaluation of augmented reality tools

abstract: Augmented Reality (AR) is heralded to be a promising technology for education. Thus, it is important to properly evaluate it so practitioners feel more confident in its use. Considering the current lack of studies to evaluate AR based educational technology, this work aims to provide some guidelines in order to assist researchers when conducting educational evaluations. The proposed principles were based on both theoretical and practical research. The guidelines presented involve the use of multiple metrics, both quantitative and qualitative, high involvement of teachers and a comprehensive evaluation taking into account both formative and summative aspects. Two practical evaluation experiences were designed applying these principles and their findings are discussed in this paper.


title: Malunion of the Distal End of the Radius

abstract: Union with deformity continues to be the most common complication following fracture of the distal end of the radius.12,36,40 Malunion may be extraarticular, characterized by metaphyseal angulation and loss of radial length; intraarticular, involving distortion of the radiocarpal or radioulnar joints, or both; or a combination of the two.


title: Prior Swapping for Data-Independent Inference

abstract: While Bayesian methods are praised for their ability to incorporate useful prior knowledge, in practice, priors that allow for computationally convenient or tractable inference are more commonly used. In this paper, we investigate the following question: for a given model, is it possible to use any convenient prior to infer a false posterior, and afterwards, given some true prior of interest, quickly transform this result into the true posterior? We present a procedure to carry out this task: given an inferred false posterior and true prior, our algorithm generates samples from the true posterior. This transformation procedure, which we call"prior swapping"works for arbitrary priors. Notably, its cost is independent of data size. It therefore allows us, in some cases, to apply significantly less-costly inference procedures to more-sophisticated models than previously possible. It also lets us quickly perform any additional inferences, such as with updated priors or for many different hyperparameter settings, without touching the data. We prove that our method can generate asymptotically exact samples, and demonstrate it empirically on a number of models and priors.


title: On the design of 2D Gabor filtering of fingerprint images

abstract: The directional bandpass Gabor filter bank approach is one of the most effective and mathematically elegant techniques to date for fingerprint image enhancement. The filter output, however, is very sensitive to ridge orientation and the frequency that the filter is tuned to, and also to the spatial parameters of the Gaussian envelope. Unfortunately, filtering of a fingerprint image with an adaptive 2D Gabor filter bank is computationally expensive because ridge orientation and frequency vary significantly throughout the fingerprint. We propose to use an array of 8/spl times/4 two-dimensional Gabor filters tuned to eight directions and four ridge frequencies. Filtered fingerprint images at any combination of local ridge orientation and frequency can be calculated using a 2D interpolation algorithm The proposed technique produces a better image quality than current Gabor-based techniques. The results are compared using a goodness index measure of the reliability of the automatic minutiae detection.


title: InvisiMem: Smart Memory Defenses for Memory Bus Side Channel

abstract: A practically feasible low-overhead hardware design that provides strong defenses against memory bus side channel remains elusive. This paper observes that smart memory, memory with compute capability and a packetized interface, can dramatically simplify this problem. InvisiMem expands the trust base to include the logic layer in the smart memory to implement cryptographic primitives, which aid in addressing several memory bus side channel vulnerabilities efficiently. This allows the secure host processor to send encrypted addresses over the untrusted memory bus, and thereby eliminates the need for expensive address obfuscation techniques based on Oblivious RAM (ORAM). In addition, smart memory enables efficient solutions for ensuring freshness without using expensive Merkle trees, and mitigates memory bus timing channel using constant heart-beat packets. We demonstrate that InvisiMem designs have one to two orders of magnitude of lower overheads for performance, space, energy, and memory bandwidth, compared to prior solutions.


[Clinical experiences with the use of metronidazole in the radiotherapy of cancer patients].


title: Effects of Weighted Vests on the Engagement of Children With Developmental Delays and Autism

abstract: The use of weighted vests for children with autism spectrum disorders and developmental disabilities is a common practice as part of sensory integration therapy programs. The purpose of the current investigation was to extend the research on the use of weighted vests for children with autism and developmental delays in a methodologically rigorous study. The study was conducted using an alternating treatment design.This allowed the comparison of three different conditions: weighted vest, vest with no weight (which served as a placebo), and no vest (which served as a baseline). The results showed no differentiation in engagement between conditions for any of the participants. Implications for practice and future research are provided.


title: Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road Autonomous Driving

abstract: The mobile robotics community has traditionally addressed motion planning and navigation in terms of steering decisions. However, selecting the best speed is also important - beyond its relationship to stopping distance and lateral maneuverability. Consider a high-speed (35 mph) autonomous vehicle driving off-road through challenging desert terrain. The vehicle should drive slowly on terrain that poses substantial risk. However, it should not dawdle on safe terrain. In this paper we address one aspect of risk - shock to the vehicle. We present an algorithm for trading-off shock and speed in realtime and without human intervention. The trade-off is optimized using supervised learning to match human driving. The learning process is essential due to the discontinuous and spatially correlated nature of the control problem - classical techniques do not directly apply. We evaluate performance over hundreds of miles of autonomous driving, including performance during the 2005 DARPA Grand Challenge. This approach was the deciding factor in our vehicle's speed for nearly 20% of the DARPA competition - more than any other constraint except the DARPA-imposed speed limits - and resulted in the fastest finishing time.


title: Visible light communications for 5G wireless networking systems: from fixed to mobile communications

abstract: Visible light communication, considered as a potential access option for 5G wireless communications, is gaining extensive attention. VLC has strengths in energy efficiency and ultra wide bandwidth, but also has weakness in transmission range and obstacles in transmission paths. This article aims to provide a conclusive investigation of the latest progress in research on VLC, which can be used as part of 5G wireless communication systems. This work highlights the strengths and weaknesses of VLC in comparison with RF-based communications, especially in spectrum, spatial reuse, security and energy efficiency. The article also investigates various lighting sources proposed for VLC systems. It summarizes the literature work on VLC networking into two categories: fixed and mobile VLC communications.


title: Power Electronic Transformer-Based Railway Traction Systems: Challenges and Opportunities

abstract: In this paper, power electronic transformer (PET)-based railway traction systems are comprehensively reviewed according to the unique application features and requirements. By comparing PET and conventional line frequency transformer (LFT)-based systems, their pros and cons are summarized. By further reviewing all kinds of PET-based designs from the early concepts to the latest ones in the order of their publication dates, the developing trends are highlighted. By synthetically considering the requirements and the state of the art, the key challenges and opportunities are identified and discussed. It shows that although PET-based systems are still developing and far from mature, they are already superior to LFT-based systems in terms of system weight, efficiency, and functionalities especially for 15-kV/16.7-Hz applications. With the advancements on wide bandgap power devices, medium frequency transformers, and converters, PET systems will be even more promising and available for all types of railway tractions in the near future.


title: Sensor fusion for mobile robot navigation

abstract: We review techniques for sensor fusion in robot navigation, emphasizing algorithms for self-location. These find use when the sensor suite of a mobile robot comprises several different sensors, some complementary and some redundant. Integrating the sensor readings, the robot seeks to accomplish tasks such as constructing a map of its environment, locating itself in that map, and recognizing objects that should be avoided or sought. The review describes integration techniques in two categories: low-level fusion is used for direct integration of sensory data, resulting in parameter and state estimates; high-level fusion is used for indirect integration of sensory data in hierarchical architectures, through command arbitration and integration of control signals suggested by different modules. The review provides an arsenal of tools for addressing this (rather ill-posed) problem in machine intelligence, including Kalman filtering, rule-based techniques, behavior based algorithms, and approaches that borrow from information theory, Dempster-Shafer reasoning, fuzzy logic and neural networks.


title: MRI of hippocampal volume loss in early Alzheimer's disease in relation to ApoE genotype and biomarkers

abstract: Hippocampal volume change over time, measured with MRI, has huge potential as a marker for Alzheimer's disease. The objectives of this study were: (i) to test if constant and accelerated hippocampal loss can be detected in Alzheimer's disease, mild cognitive impairment and normal ageing over short periods, e.g. 6-12 months, with MRI in the large multicentre setting of the Alzheimer's Disease Neuroimaging Initiative (ADNI); (ii) to determine the extent to which the polymorphism of the apolipoprotein E (ApoE) gene modulates hippocampal change; and (iii) to determine if rates of hippocampal loss correlate with cerebrospinal fluid (CSF) biomarkers of Alzheimer's disease, such as the beta-amyloid (Abeta(1-42)) and tau proteins (tau). The MRI multicentre study included 112 cognitive normal elderly individuals, 226 mild cognitive impairment and 96 Alzheimer's disease patients who all had at least three successive MRI scans, involving 47 different imaging centres. The mild cognitive impairment and Alzheimer's disease groups showed hippocampal volume loss over 6 months and accelerated loss over 1 year. Moreover, increased rates of hippocampal loss were associated with presence of the ApoE allele epsilon4 gene in Alzheimer's disease and lower CSF Abeta(1-42) in mild cognitive impairment, irrespective of ApoE genotype, whereas relations with tau were only trends. The power to measure hippocampal change was improved by exploiting correlations statistically between successive MRI observations. The demonstration of considerable hippocampal loss in mild cognitive impairment and Alzheimer's disease patients over only 6 months and accelerated loss over 12 months illustrates the power of MRI to track morphological brain changes over time in a large multisite setting. Furthermore, the relations between faster hippocampal loss in the presence of ApoE allele epsilon4 and decreased CSF Abeta(1-42) supports the concept that increased hippocampal loss is an indicator of Alzheimer's disease pathology and a potential marker for the efficacy of therapeutic interventions in Alzheimer's disease.


title: Coprime sampling and the music algorithm

abstract: A new approach to super resolution line spectrum estimation in both temporal and spatial domain using a coprime pair of samplers is proposed. Two uniform samplers with sample spacings MT and NT are used where M and N are coprime and T has the dimension of space or time. By considering the difference set of this pair of sample spacings (which arise naturally in computation of second order moments), sample locations which are O(MN) consecutive multiples of T can be generated using only O(M + N) physical samples. In order to efficiently use these O(MN) virtual samples for super resolution spectral estimation, a novel algorithm based on the idea of spatial smoothing is proposed, which can be used for estimating frequencies of sinusoids buried in noise as well as for estimating Directions-of-Arrival (DOA) of impinging signals on a sensor array. This technique allows us to construct a suitable positive semidefinite matrix on which subspace based algorithms like MUSIC can be applied to detect O(MN) spectral lines using only O(M + N) physical samples.


title: Fairness in Package-to-Group Recommendations

abstract: Recommending packages of items to groups of users has several applications, including recommending vacation packages to groups of tourists, entertainment packages to groups of friends, or sets of courses to groups of students. In this paper, we focus on a novel aspect of package-to-group recommendations, that of fairness. Specifically, when we recommend a package to a group of people, we ask that this recommendation is fair in the sense that every group member is satisfied by a sufficient number of items in the package. We explore two definitions of fairness and show that for either definition the problem of finding the most fair package is NP-hard. We exploit the fact that our problem can be modeled as a coverage problem, and we propose greedy algorithms that find approximate solutions within reasonable time. In addition, we study two extensions of the problem, where we impose category or spatial constraints on the items to be included in the recommended packages. We evaluate the appropriateness of the fairness models and the performance of the proposed algorithms using real data from Yelp, and a user study.


title: Attribute-based authentication on the cloud for thin clients

abstract: We propose two new authentication schemes for the cloud that support private attribute-based authentication services. The basic scheme is non-anonymous attribute-based authentication scheme. The extended scheme of the basic scheme is fully anonymous attribute-based authentication scheme to realize full anonymity and unlinkability services. In the proposed schemes, a user is authenticated by the remote server if the intersection of the set of his/her assigned attributes and the server’s required attributes exceeds a satisfactory predefined level. Unlike existing attribute-based encryption and signature schemes that require the user to perform significant amount of elliptic curve bilinear pairings and modular exponentiations, and require the user to hold a significantly long decryption/signature key, in our schemes the user is not required to perform any bilinear pairings. With a fixed length private key, independent of the number of attributes, the cloud user performs only few exponentiations by which he/she is able to authenticate himself/herself to the remote server and establish a session key with the server with the condition that he/she satisfies a predefined level of the server’s attributes requirement. Therefore, our schemes are suitable for implementation on devices with limited resources. We provide the rigorous security of the proposed schemes and complexity analysis of our schemes. Finally, the security and performance comparisons of our schemes with the existing related schemes show that our schemes outperform other existing schemes.


title: A shadow detection method for remote sensing images using VHR hyperspectral and LIDAR data

abstract: In this paper, a shadow detection method combining hyperspectral and LIDAR data analysis is presented. First, a rough shadow image is computed through line-of-sight analysis on a Digital Surface Model (DSM), using an estimate of the position of the sun at the time of image acquisition. Then, large shadow and non-shadow areas in that image are detected and used for training a supervised classifier (a Support Vector Machine, SVM) that classifies every pixel in the hyperspectral image as shadow or non-shadow. Finally, small holes are filled through image morphological analysis. The method was tested on data including a 24 band hyperspectral image in the VIS/NIR domain (50 cm spatial resolution) and a DSM of 25 cm resolution. The results were in good accordance with visual interpretation. As the line-of-sight analysis step is only used for training, geometric mismatches (about 2 m) between LIDAR and hyperspectral data did not affect the results significantly, nor did uncertainties regarding the position of the sun.


title: Adaptive Cross-Modal Few-Shot Learning

abstract: Metric-based meta-learning techniques have successfully been applied to few-shot classification problems. In this paper, we propose to leverage cross-modal information to enhance metric-based few-shot learning methods. Visual and semantic feature spaces have different structures by definition. For certain concepts, visual features might be richer and more discriminative than text ones. While for others, the inverse might be true. Moreover, when the support from visual information is limited in image classification, semantic representations (learned from unsupervised text corpora) can provide strong prior knowledge and context to help learning. Based on these two intuitions, we propose a mechanism that can adaptively combine information from both modalities according to new image categories to be learned. Through a series of experiments, we show that by this adaptive combination of the two modalities, our model outperforms current uni-modality few-shot learning methods and modality-alignment methods by a large margin on all benchmarks and few-shot scenarios tested. Experiments also show that our model can effectively adjust its focus on the two modalities. The improvement in performance is particularly large when the number of shots is very small.


title: The documentary structure of source code

abstract: Abstract Many tools designed to help programmers view and manipulate source code exploit the formal structure of the programming language. Language-based tools use information derived via linguistic analysis to offer services that are impractical for purely text-based tools. In order to be effective, however, language-based tools must be designed to account properly for the documentary structure of source code: a structure that is largely orthogonal to the linguistic but no less important. Documentary structure includes, in addition to the language text, all extra-lingual information added by programmers for the sole purpose of aiding the human reader: comments, white space, and choice of names. Largely ignored in the research literature, documentary structure occupies a central role in the practice of programming. An examination of the documentary structure of programs leads to a better understanding of requirements for tool architectures.


title: Torque Ripple Reduction in BLDC Torque Motor With Nonideal Back EMF

abstract: In order to improve the speed precision and stabilization of the gimbal servo system of double gimbal magnetically suspended control moment gyro, a comprehensive analysis of the reason of electromagnetic torque ripples of brushless direct current motor with nonideal back electromotive force (EMF) drives in the conduction and commutation regions is presented. A novel automatic control method of torque is proposed. With this method, the current control rule is designed, and the duty cycle of pulse width modulation (PWM) is regulated in real time by measuring the wave function of back EMF. In order to eliminate the diode freewheeling of the inactive phase, the PWM_ON_PWM scheme is used. Simulation and experiment results are given to show that, compared with the conventional current control method, the proposed method can reduce the torque ripple effectively and improve the speed precision and stabilization as well.


title: Sequence-to-Sequence Models for Data-to-Text Natural Language Generation: Word- vs. Character-based Processing and Output Diversity

abstract: We present a comparison of word-based and character-based sequence-to sequence models for data-to-text natural language generation, which generate natural language descriptions for structured inputs. On the datasets of two recent generation challenges, our models achieve comparable or better automatic evaluation results than the best challenge submissions. Subsequent detailed statistical and human analyses shed light on the differences between the two input representations and the diversity of the generated texts. In a controlled experiment with synthetic training data generated from templates, we demonstrate the ability of neural models to learn novel combinations of the templates and thereby generalize beyond the linguistic structures they were trained on.


title: Tracing the locality of prisoners and workers at the Mausoleum of Qin Shi Huang: First Emperor of China (259-210 BC)

abstract: The mausoleum complex of the First Emperor of China, Qin Shi Huang (259-210 BC), is one of the most famous and important archaeological sites in China, yet questions remain as to how it was constructed and by whom. Here we present isotopic results of individuals from the Liyi (n = 146) and Shanren sites (n = 14), both associated with the mausoleum complex. Those buried at Liyi represent the local workers/inhabitants of the Qin population, and the δ(13)C (-8.7 ± 1.5%) and δ(15)N (10.3 ± 0.7%) values indicate that they consumed predominately millet and/or domestic animals fed millet. In contrast, the Shanren individuals were prisoners forced to construct the mausoleum (found buried haphazardly in a mass grave and some in iron leg shackles), and their δ(13)C (-15.4 ± 2.9%) and δ(15)N (8.0 ± 0.6%) results indicate a more mixed C3/C4 diet, with possibly less domestic animals and more wild game protein consumed. This pattern of decreased millet consumption is also characteristic of archaeological sites from southern China, and possible evidence the Shanren prisoners originated from this region (possibly the ancient Chu state located in modern day Hubei Province and parts of Hunan and Anhui Provinces). Further, this finding is in agreement with historical sources and is supported by previous ancient DNA evidence that the mausoleum workers had diverse origins, with many genetically related to southern Chinese groups.


title: From the internet of things to embedded intelligence

abstract: The Internet of Things (IoT) represents the future technology trend of sensing, computing, and communication. Under the Wisdom Web of Things (W2T) vision, the next-generation Internet will promote harmonious interaction among humans, computers, and things. Current research on IoT is primarily conducted from the perspective of identifying, connecting, and managing objects. In this paper, however, we attempt to enhance the IoT with intelligence and awareness under the W2T vision. By exploring the various interactions between humans and the IoT, we extract the "embedded" intelligence about individual, environment, and society, which can augment existing IoT systems with user, ambient, and social awareness. The characteristics, major applications, research issues, the reference architecture, as well as our ongoing efforts to embedded intelligence are also presented and discussed.


title: Using the Analytic Hierarchy Process to prioritise candidate improvements to a geovisualization application

abstract: Crime and disorder reduction (CDR) research analysts („analysts‟) in a UK local authority have generated candidate improvements for enhancing geovisualization prototypes designed using humancentred methods (Lloyd et al., 2007; Lloyd et al., 2008). Prioritising these is an important process and may require modification to established decision support techniques due to the nature of geovisualization. We explore this issue through the Analytic Hierarchy Process (AHP) (Saaty 1977) examining both the resulting priorities and their consistency.


title: Risk-aware trajectory generation with application to safe quadrotor landing

abstract: In navigation tasks, mobile robots often have to deal with substantial uncertainty due to imperfect actuators and noisy sensor measurements. In this paper, we consider the problem of online trajectory generation for safe navigation in the presence of state uncertainty and the resulting deviations from the desired trajectory. Our approach combines probabilistic estimation of the a priori collision risk with efficient trajectory generation, exploiting the differential flatness of many robotic systems in an explicitly constrained polynomial trajectory representation. Through trajectory optimization, our approach allows to flexibly trade off risk against, for example, the duration of the trajectory. It is computationally efficient because each optimization step has polynomial complexity. In contrast to other approaches, our method can also optimize the trajectory duration and supports cost functions that facilitate higher-order smoothness of the trajectory. Our experiments demonstrate the performance of the approach and show that our trajectories result in substantially lower collisions probabilities compared to minimum-snap trajectories in a quadrotor landing task.


title: StreamGlobe: Processing and Sharing Data Streams in Grid-Based P2P Infrastructures

abstract: Data stream processing is currently gaining importance due to the developments in novel application areas like e-science, e-health, and e-business (considering RFID, for example). Focusing on e-science, it can be observed that scientific experiments and observations in many fields, e. g., in physics and astronomy, create huge volumes of data which have to be interchanged and processed. With experimental and observational data coming in particular from sensors, online simulations, etc., the data has an inherently streaming nature. Furthermore, continuing advances will result in even higher data volumes, rendering storing all of the delivered data prior to processing increasingly impractical. Hence, in such e-science scenarios, processing and sharing of data streams will play a decisive role. It will enable new possibilities for researchers, since they will be able to subscribe to interesting data streams of other scientists without having to set up their own devices or experiments. This results in much better utilization of expensive equipment such as telescopes, satellites, etc. Further, processing and sharing data streams on-the-fly in the network helps to reduce network traffic and to avoid network congestion. Thus, even huge streams of data can be handled efficiently by removing unnecessary parts early on, e. g., by early filtering and aggregation, and by sharing previously generated data streams and processing results.


title: A real-time remote rendering system for interactive mobile graphics

abstract: Mobile devices are gradually changing people's computing behaviors. However, due to the limitations of physical size and power consumption, they are not capable of delivering a 3D graphics rendering experience comparable to desktops. Many applications with intensive graphics rendering workloads are unable to run on mobile platforms directly. This issue can be addressed with the idea of remote rendering: the heavy 3D graphics rendering computation runs on a powerful server and the rendering results are transmitted to the mobile client for display. However, the simple remote rendering solution inevitably suffers from the large interaction latency caused by wireless networks, and is not acceptable for many applications that have very strict latency requirements. In this article, we present an advanced low-latency remote rendering system that assists mobile devices to render interactive 3D graphics in real-time. Our design takes advantage of an image based rendering technique: 3D image warping, to synthesize the mobile display from the depth images generated on the server. The research indicates that the system can successfully reduce the interaction latency while maintaining the high rendering quality by generating multiple depth images at the carefully selected viewpoints. We study the problem of viewpoint selection, propose a real-time reference viewpoint prediction algorithm, and evaluate the algorithm performance with real-device experiments.


title: A Classification Refinement Strategy for Semantic Segmentation

abstract: Based on the observation that semantic segmentation errors are partially predictable, we propose a compact formulation using confusion statistics of the trained classifier to refine (re-estimate) the initial pixel label hypotheses. The proposed strategy is contingent upon computing the classifier confusion probabilities for a given dataset and estimating a relevant prior on the object classes present in the image to be classified. We provide a procedure to robustly estimate the confusion probabilities and explore multiple prior definitions. Experiments are shown comparing performances on multiple challenging datasets using different priors to improve a state-of-the-art semantic segmentation classifier. This study demonstrates the potential to significantly improve semantic labeling and motivates future work for reliable label prior estimation from images.


From ‘What?’ to ‘Why?’: The Social Uses of Personal Photos


A Review of Patient and Provider Satisfaction with Telemedicine


Can Simulator Sickness Be Avoided? A Review on Temporal Aspects of Simulator Sickness


title: CNN-based ranking for biomedical entity normalization

abstract: Background ::: Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities.


title: Stress aware layout optimization

abstract: Process-induced mechanical stress is used to enhance carrier transport and achieve higher drive currents in current CMOS technologies. In this paper, we study how stress-induced performance enhancements are affected by layout properties and suggest guidelines for improving layouts so that performance gains are maximized. All MOS devices in this work include STI and nitride stress liners as sources of stress. Additionally, the PMOS devices incorporate the stress effects caused by the embedded SiGe S/D layer common in today's processes. First, we study how stress and drive current depend on layout parameters such as active area length and contact placement. We develop an intuition for the drive current dependency on these parameters and propose simple guidelines to improve a layout while considering mechanical stress effects. We then use these guidelines to improve the standard cell layouts in a 65nm industrial library. Experimental results show that we can enhance NMOS and PMOS drive currents by ~5% and ~12%, respectively, while only increasing NMOS leakage current by 1.48X and PMOS leakage current by 3.78X. By applying our guidelines to a 3-input NOR gate and a 3-input NAND gate, we are able to achieve a ~13.5% PMOS drive current improvement in the NOR gate and a ~7% NMOS drive current improvement in the NAND gate, without increasing cell area in either case


title: Deep Learning Based Large-Scale Automatic Satellite Crosswalk Classification

abstract: High-resolution satellite imagery has been increasingly used on remote sensing classification problems. One of the main factors is the availability of this kind of data. Despite the high availability, very little effort has been placed on the zebra crossing classification problem. In this letter, crowdsourcing systems are exploited in order to enable the automatic acquisition and annotation of a large-scale satellite imagery database for crosswalks related tasks. Then, this data set is used to train deep-learning-based models in order to accurately classify satellite images that contain or not contain zebra crossings. A novel data set with more than 240000 images from 3 continents, 9 countries, and more than 20 cities was used in the experiments. The experimental results showed that freely available crowdsourcing data can be used to accurately (97.11%) train robust models to perform crosswalk classification on a global scale.


title: The effect of loneliness on malnutrition in elderly population.

abstract: INTRODUCTION ::: The clinical and epidemiological data show that proper nutrition plays an important role in maintaining health and combating the danger of developing some chronic diseases in the elderly population. Nutrition is an important factor in many physiological and pathological changes that accompany the aging process. More than 50% of elderly patients are suffering from malnutrition which is information that concerns. Due to various factors, older people are potentially vulnerable groups at risk of malnutrition. Loneliness, isolation from society and neglect of parents by children is a big problem to many people in old age. ::: ::: ::: OBJECTIVE ::: To determine differences in nutritional status of elderly people living alone compared to those who live in family surroundings. ::: ::: ::: SUBJECT AND METHODS ::: The study was conducted in the municipality of Tuzla in 2009-2010, in outpatient family medicine Simin Han. The survey covered a total of 200 elderly subjects (age >65 years). Subject group consisted of 45% of people living alone, and 55% control group consisted of elderly patients who live in traditional family surroundings. Questionnaires used in this study are General geriatric assessment questionnaire and Mini nutritional status. ::: ::: ::: RESULTS ::: The average age (+/- SD) was 75.4 +/- 6.2 years in subject group, while the same in the control group was 74.9 +/- 5.6 years. In subject group significantly more patients are on the verge of poverty. There are significant differences in the classification of financial status, according to the groups (p = 0.043). Members of subject groups have significantly lower BMI categories (p = 0.03) compared with the control group. In our study, people who live alone are at increased risk of malnutrition (p = 0030), have reduced the number of daily meals, significantly lower daily intake of protein, fruits and vegetables in the diet in relation to persons living in a family environment. Significantly more patients with loss of appetite live alone. According to the existence of self-reported food problems significantly more people are in subject group. There is a difference value score "Small assessment of nutrition" between the two groups (p = 0.001). About 22% of the total number of respondents said they have not so good health status compared to others. ::: ::: ::: CONCLUSIONS ::: Loneliness is a significant predictor of anorexia nervosa, the risk of malnutrition and malnutrition. Results indicate that it is necessary to work on improving the status and protection of elderly.


title: Implementation of Internet of Things for Home Automation

abstract: Internet of Things (IoT) is a concept that envisions all objects around us as part of internet. IoT coverage is very wide and includes variety of objects like smart phones, tablets, digital cameras and sensors. Once all these devices are connected to each other, they enable more and more smart processes and services that support our basic needs, environment and health. Such enormous number of devices connected to internet provides many kinds of services. They also produce huge amount of data and information. Cloud computing is one such model for on-demand access to a shared pool of configurable resources (computer, networks, servers, storage, applications, services, and software) that can be provisioned as infrastructures ,software and applications. Cloud based platforms help to connect to the things around us so that we can access anything at any time and any place in a user friendly manner using customized portals and in built applications. Hence, cloud acts as a front end to access IoT. Applications that interact with devices like sensors have special requirements of massive storage to store big data, huge computation power to enable the real time processing of the data, information and high speed network to stream audio or video. Here we have describe how Internet of Things and Cloud computing can work together can address the Big Data problems. We have also illustrated about Sensing as a service on cloud using few applications like Augmented Reality, Agriculture, Environment monitoring,etc. Finally, we propose a prototype model for providing sensing as a service on cloud.


title: Study on conditioning and feature extraction algorithm of photoplethysmography signal for physiological parameters detection

abstract: Photoplethysmography(PPG) signal can reflect many physiological parameters, such as heart function, blood vessel elasticity, blood viscosity and so on. It was a novel noninvasive method with the advantage of convenience and accuracy. It was important to find efficient pre-processing and feature extraction algorithms to deal with original PPG signal, which was interfered by many other factors. Many practical methods including median filtering and FIR filtering was used. A new algorithm based on wavelet transformation was proposed for eliminating the baseline drift. Feature points extraction was another key issue. An improved differential algorithm was used to solve this problem. All of these practical algorithms provided an effective platform for physiological parameters detection.


Variable Mini-Batch Sizing and Pre-Trained Embeddings


Augmented Reality


title: Sampling Student's T distribution – use of the inverse cumulative distribution function

abstract: With the current interest in copula methods, and fat-tailed or other non-normal distributions, it is appropriate to investigate technologies for managing marginal distributions of interest. We explore “Student’s” T distribution, survey its simulation, and present some new techniques for simulation. In particular, for a given real (not necessarily integer) value n of the number of degrees of freedom, we give a pair of power series approximations for the inverse, F−1 n , of the cumulative distribution function (CDF), Fn.We also give some simple and very fast exact and iterative techniques for defining this function when n is an even integer, based on the observation that for such cases the calculation of F−1 n amounts to the solution of a reduced-form polynomial equation of degree n − 1. We also explain the use of Cornish–Fisher expansions to define the inverse CDF as the composition of the inverse CDF for the normal case with a simple polynomial map. The methods presented are well adapted for use with copula and quasi-Monte-Carlo techniques.


title: A high efficient automotive roof-antenna concept for LTE, DAB-L, GNSS and SDARS with low mutual coupling

abstract: A combination of a new antenna for Long Term Evolution (LTE) technology and Digital Audio Broadcasting in L-band (DAB-L) together with compact antennas for Global Navigation Satellite System (GNSS) and Satellite Digital Audio Radio Services (SDARS) is presented. The antenna module is designed to be placed on the roof of a car underneath a shark-fin-formed housing. The LTE/DAB-L antenna operates at frequency bands - from 698 MHz to 960 MHz and from 1427.9 MHz to 2700 MHz - covering available LTE frequencies. This antenna has a high efficiency due to panel design and obtains monopole characteristics in all LTE frequencies. In combination with GNSS and SDARS ring antennas the structure yields a sufficient decoupling to these antennas. This is even true for the SDARS antenna sharing the same phase center with the LTE-antenna. The satellite antennas achieve the required characteristics at their center frequencies of 1575 MHz and 2332 MHz.


title: Ensemble Deep Learning for Skeleton-Based Action Recognition Using Temporal Sliding LSTM Networks

abstract: This paper addresses the problems of feature representation of skeleton joints and the modeling of temporal dynamics to recognize human actions. Traditional methods generally use relative coordinate systems dependent on some joints, and model only the long-term dependency, while excluding short-term and medium term dependencies. Instead of taking raw skeletons as the input, we transform the skeletons into another coordinate system to obtain the robustness to scale, rotation and translation, and then extract salient motion features from them. Considering that Long Shortterm Memory (LSTM) networks with various time-step sizes can model various attributes well, we propose novel ensemble Temporal Sliding LSTM (TS-LSTM) networks for skeleton-based action recognition. The proposed network is composed of multiple parts containing short-term, mediumterm and long-term TS-LSTM networks, respectively. In our network, we utilize an average ensemble among multiple parts as a final feature to capture various temporal dependencies. We evaluate the proposed networks and the additional other architectures to verify the effectiveness of the proposed networks, and also compare them with several other methods on five challenging datasets. The experimental results demonstrate that our network models achieve the state-of-the-art performance through various temporal features. Additionally, we analyze a relation between the recognized actions and the multi-term TS-LSTM features by visualizing the softmax features of multiple parts.


title: Nurse Moral Distress and Cancer Pain Management

abstract: BACKGROUND ::: The majority of cancer patients in low- and middle-income countries (LMICs) present with late-stage, incurable disease, and basic tools to alleviate patient suffering-such as morphine-are often absent. Oncology nurses must cope with many challenges and may experience moral distress (MD), yet little research has examined this experience in LMICs. ::: ::: ::: OBJECTIVE ::: This ethnographic study explored the experience of MD with oncology nurses (n = 37) and other providers (n = 22) in India and its potential relationship to opioid availability. ::: ::: ::: METHODS ::: Data (semistructured interviews and field observations) were collected at a 300-bed government cancer hospital in urban South India over 9 months. Dedoose v.4.5.91 supported analysis of transcripts using a coding schema that mapped to an Integrated Model of Nurse Moral Distress and concepts that emerged from field notes. ::: ::: ::: RESULTS ::: Primary themes included "We feel bad," "We are alone and afraid," "We are helpless," and "We leave it." A weak link between MD and opioid availability was found. ::: ::: ::: CONCLUSIONS ::: Participants described significant work-related distress, but the moral dimension to this distress was less clear as some key aspects of the Integrated Model of Nurse Moral Distress were not supported. The concept of MD may have limited applicability in settings where alternative courses of action are unknown, or not feasible, and where differing social and cultural norms influence moral sensitivity. ::: ::: ::: IMPLICATIONS FOR PRACTICE ::: Improving job-related conditions is prerequisite to creating an environment where MD can manifest. Educational initiatives in LMICs must account for the role of the oncology nurse and their contextual moral and professional obligations.


title: From query to usable code: an analysis of stack overflow code snippets

abstract: Enriched by natural language texts, Stack Overflow code snippets arean invaluable code-centric knowledge base of small units ofsource code. Besides being useful for software developers, theseannotated snippets can potentially serve as the basis for automatedtools that provide working code solutions to specific natural languagequeries. With the goal of developing automated tools with the Stack Overflowsnippets and surrounding text, this paper investigates the followingquestions: (1) How usable are the Stack Overflow code snippets? and(2) When using text search engines for matching on the naturallanguage questions and answers around the snippets, what percentage ofthe top results contain usable code snippets?A total of 3M code snippets are analyzed across four languages: C\#,Java, JavaScript, and Python. Python and JavaScript proved to be thelanguages for which the most code snippets are usable. Conversely,Java and C\# proved to be the languages with the lowest usabilityrate. Further qualitative analysis on usable Python snippets showsthe characteristics of the answers that solve the original question. Finally,we use Google search to investigate the alignment ofusability and the natural language annotations around code snippets, andexplore how to make snippets in Stack Overflow anadequate base for future automatic program generation.


title: D-HAZY: A dataset to evaluate quantitatively dehazing algorithms

abstract: Dehazing is an image enhancing technique that emerged in the recent years. Despite of its importance there is no dataset to quantitatively evaluate such techniques. In this paper we introduce a dataset that contains 1400+ pairs of images with ground truth reference images and hazy images of the same scene. Since due to the variation of illumination conditions recording such images is not feasible, we built a dataset by synthesizing haze in real images of complex scenes. Our dataset, called D-HAZY, is built on the Middelbury [1] and NYU Depth [2] datasets that provide images of various scenes and their corresponding depth maps. Due to the fact that in a hazy medium the scene radiance is attenuated with the distance, based on the depth information and using the physical model of a hazy medium we are able to create a corresponding hazy scene with high fidelity. Finally, using D-HAZY dataset, we perform a comprehensive quantitative evaluation of several state of the art single-image dehazing techniques.


title: Dataset on collecting volatile compounds produced by three bacteria and testing their efficacy against the pathogen Peronophythora litchii

abstract: Abstract This data article provides supporting information to a related research article “Identification of volatile organic compounds for the biocontrol of postharvest litchi fruit pathogen Peronophythora litchii” (Zheng et al., 2019) [1]. The litchi downy blight (LDB) caused by Peronophythora litchii is a major postharvest disease that can severely damage litchi trees and harvested litchi fruit. This data article describes the analysis of volatile compounds (VOCs) in three bacterial biological control agents (BCAs) of LDB (Bacillus amyloliquefaciens PP19, Bacillus pumilus PI26, and Exiguobacterium acetylicum SI17) via gas chromatography/mass spectrometry (GC–MS). Volatile compounds produced by the three BCAs were captured at five culture time of 24, 36, 48, 60 and 72 h by a solid-phase micro extraction method. The chemical compositions were identified and their retention times as well as relative peak areas were analyzed. Compounds commonly produced at more than one time points were then subjected to in vitro (on petri dish) and in vivo (litchi fruit and leaves) evaluations for their antagonistic activities against the pathogen Peronophythora litchii.


title: Classification and reassembly of archaeological fragments

abstract: Large number of archaeological fragments are mixed up when they are excavated, which brings difficulties for their reassembly. Computer aided restoration technologies have developed greatly in recent years, and approaches nowadays assist experts in a large scale. However, there still exist large number of fragments which are not reassembled and repaired. The current approaches have some disadvantages which affect the reassembly in large scale. This is because of the particular characteristics of archaeological fragments which affect the effectiveness of the current approaches. On the one hand, the archaeological fragments are piled up when they are excavated in archaeological sites. On the other hand, the fragments are changed during the long time because of abrasion and erosion. Therefore, innovative methods for the classification and reassembly of archaeological fragments are needed urgently. In this paper, the relate work on the classification and reassembly of archaeological fragments are first presented. Then, the problems when applying current approaches are summarized. At last, a feasible plan is given in order to classify and reassemble the fragments efficiently. By intact surface segmentation and partial matching with a complete model using discriminative feature descriptors, large number of fragments can be positioned with respect to a template model. The matching results provide the classification information and can assist the reassembly of fragments.


title: The ontogeny of trilobite segmentation: a comparative approach

abstract: Ontogenetic stages of trilobites have traditionally been recognized on the basis of the development of exoskeletal segmentation. The established protaspid, meraspid, and holaspid phas- es relate specifically to the development of articulated joints between exoskeletal elements. Tran- sitions between these phases were marked by the first and last appearances of new trunk segment articulations. Here we propose an additional and complementary ontogenetic scheme based on the generation of new trunk segments. It includes an anamorphic phase during which new trunk seg- ments appeared, and an epimorphic phase during which the number of segments in the trunk re- mained constant. In some trilobites an ontogenetic boundary can also be recognized at the first appearance of morphologically distinct posterior trunk segments. Comparison of the phase bound- aries of these different aspects of segment ontogeny highlights rich variation in the segmentation process among Trilobita. Cases in which the onset of the holaspid phase preceded onset of the epimorphic phase are here termed protarthrous, synchronous onset of both phases is termed syn- arthromeric, and onset of the epimorphic phase before onset of the holaspid phase is termed pro- tomeric. Although these conditions varied among close relatives and perhaps even intraspecifically in some cases, particular conditions may have been prevalent within some clades. Trilobites displayed hemianamorphic development that was accomplished over an extended series of juvenile and mature free-living instars. Although developmental schedules varied mark- edly among species, morphological transitions during trilobite development were generally reg- ular, limited in scope, and extended over a large number of instars when compared with those of many living arthropods. Hemianamorphic, direct development with modest change between instars is also seen among basal members of the Crustacea, basal myriapods, pycnogonids, and in some fossil chelicerates. This mode may represent the ancestral condition of euarthropod de- velopment.


title: Interactive volumetric lighting simulating scattering and shadowing

abstract: In this paper we present a volumetric lighting model, which simulates scattering as well as shadowing in order to generate high quality volume renderings. By approximating light transport in inhomogeneous participating media, we are able to come up with an efficient GPU implementation, in order to achieve the desired effects at interactive frame rates. Moreover, in many cases the frame rates are even higher as those achieved with conventional gradient-based shading. To evaluate the impact of the proposed illumination model on the spatial comprehension of volumetric objects, we have conducted a user study, in which the participants had to perform depth perception tasks. The results of this study show, that depth perception is significantly improved when comparing our illumination model to conventional gradient-based volume shading. Additionally, since our volumetric illumination model is not based on gradient calculation, it is also less sensitive to noise and therefore also applicable to imaging modalities incorporating a higher degree of noise, as for instance magnet resonance tomography or 3D ultrasound.


title: Agricultural Input Subsidies: The Recent Malawi Experience

abstract: This is an open access title available under the terms of a CC BY-NC-ND 3.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Agricultural input subsidies have been adopted on a large scale across different African countries in the last few years. However global experience with input subsidies has been mixed, and there is considerable concern that current input subsidies will turn out to be expensive political programmes with very limited development benefits. There is, however, also considerable enthusiasm for new, 'smart' approaches in subsidies' delivery and for their potential to raise the productivity of millions of poor smallholder farmers and lift them out of poverty while promoting wider food security. This book takes forward our understanding of agricultural input subsidies in low income countries. A review and extension of current thinking on the potential roles of such subsidies provides the basis for a broad examination of recent documented experience in different African countries and then for: a detailed examination of Malawi's current agricultural input subsidy programme, the main focus of the book. This large programme has been the subject a very considerable international debate, much of it unfortunately little informed by the substantial amount of information available on the programme. Drawing on their extensive involvement with the programme over many years and on a wide range of information sources, the authors provide a detailed analysis of the historical, political and agro-economic roots and context of the programme, and its implementation and impacts from 2005 to 2011. Of interest in its own right, this also provides critical insights into the potential benefits and risks with such programmes, and on political and technical issues that are critical in success or failure,.


Lifelong Robot Learning


title: Use of a peripheral perfusion index derived from the pulse oximetry signal as a noninvasive indicator of perfusion.

abstract: OBJECTIVE ::: Peripheral perfusion in critically ill patients frequently is assessed by use of clinical signs. Recently, the pulse oximetry signal has been suggested to reflect changes in peripheral perfusion. A peripheral perfusion index based on analysis of the pulse oximetry signal has been implemented in monitoring systems as an index of peripheral perfusion. No data on the variation of this index in the normal population are available, and clinical application of this variable in critically ill patients has not been reported. We therefore studied the variation of the peripheral perfusion index in healthy adults and related it to the central-to-toe temperature difference and capillary refill time in critically ill patients after changes in clinical signs of peripheral perfusion. ::: ::: ::: DESIGN ::: Prospective study. ::: ::: ::: SETTING ::: University-affiliated teaching hospital. ::: ::: ::: PATIENTS ::: One hundred eight healthy adult volunteers and 37 adult critically ill patients. ::: ::: ::: INTERVENTIONS ::: None. ::: ::: ::: MEASUREMENTS AND MAIN RESULTS ::: Capillary refill time, peripheral perfusion index, and arterial oxygen saturation were measured in healthy adults (group 1). Capillary refill time, peripheral perfusion index, arterial oxygen saturation, central-to-toe temperature difference, and hemodynamic variables were measured in critically ill patients (group 2) during different peripheral perfusion profiles. Poor peripheral perfusion was defined as a capillary refill time >2 secs and central-to-toe temperature difference > or = 7 degrees C. Peripheral perfusion index and arterial oxygen saturation were measured by using the Philips Medical Systems Viridia/56S monitor. In group 1, measurements were made before and after a meal. In group 2, two measurements were made, with the second measurement taken when the peripheral perfusion profile had changed. A total of 216 measurements were carried out in group 1. The distribution of the peripheral perfusion index was skewed and values ranged from 0.3 to 10.0, median 1.4 (inner quartile range, 0.7-3.0). Seventy-four measurements were carried out in group 2. A significant correlation between the peripheral perfusion index and the core-to-toe temperature difference was found (R2=.52; p <.001). A cutoff peripheral perfusion index value of 1.4 (calculated by constructing a receiver operating characteristic curve) best reflected the presence of poor peripheral perfusion in critically ill patients. Changes in peripheral perfusion index and changes in core-to-toe temperature difference correlated significantly (R =.52, p <.001). ::: ::: ::: CONCLUSIONS ::: The peripheral perfusion index distribution in the normal population is highly skewed. Changes in the peripheral perfusion index reflect changes in the core-to-toe temperature difference. Therefore, peripheral perfusion index measurements can be used to monitor peripheral perfusion in critically ill patients.


title: Combining BCI with Virtual Reality : Towards New Applications and Improved BCI

abstract: Brain–Computer Interfaces (BCI) are communication systems which can convey messages through brain activity alone. Recently BCIs were gaining interest among the virtual reality (VR) community since they have appeared as promising interaction devices for virtual environments (VEs). Especially these implicit interaction techniques are of great interest for the VR community, e.g., you are imaging the movement of your hand and the virtual hand is moving, or you can navigate through houses or museums by your thoughts alone or just by looking at some highlighted objects. Furthermore, VE can provide an excellent testing ground for procedures that could be adapted to real world scenarios, especially patients with disabilities can learn to control their movements or perform specific tasks in a VE. Several studies will highlight these interactions.


title: KL-divergence regularized deep neural network adaptation for improved large vocabulary speech recognition

abstract: We propose a novel regularized adaptation technique for context dependent deep neural network hidden Markov models (CD-DNN-HMMs). The CD-DNN-HMM has a large output layer and many large hidden layers, each with thousands of neurons. The huge number of parameters in the CD-DNN-HMM makes adaptation a challenging task, esp. when the adaptation set is small. The technique developed in this paper adapts the model conservatively by forcing the senone distribution estimated from the adapted model to be close to that from the unadapted model. This constraint is realized by adding Kullback-Leibler divergence (KLD) regularization to the adaptation criterion. We show that applying this regularization is equivalent to changing the target distribution in the conventional backpropagation algorithm. Experiments on Xbox voice search, short message dictation, and Switchboard and lecture speech transcription tasks demonstrate that the proposed adaptation technique can provide 2%-30% relative error reduction against the already very strong speaker independent CD-DNN-HMM systems using different adaptation sets under both supervised and unsupervised adaptation setups.


title: Parallel Multiscale Autoregressive Density Estimation

abstract: PixelCNN achieves state-of-the-art results in density estimation for natural images. Although training is fast, inference is costly, requiring one network evaluation per pixel; O(N) for N pixels. This can be sped up by caching activations, but still involves generating each pixel sequentially. In this work, we propose a parallelized PixelCNN that allows more efficient inference by modeling certain pixel groups as conditionally independent. Our new PixelCNN model achieves competitive density estimation and orders of magnitude speedup - O(log N) sampling instead of O(N) - enabling the practical generation of 512x512 images. We evaluate the model on class-conditional image generation, text-to-image synthesis, and action-conditional video generation, showing that our model achieves the best results among non-pixel-autoregressive density models that allow efficient sampling.