title: COMPUTATIONAL METHODS FOR SHAPE MANIPULATION IN GENERATION A LITERATURE REVIEW

abstract: In this paper we will present a state of the art of the descriptive and generative models for shape. We will present several different approaches for the manipulation of shape in computational systems: numerical models, graph models, descriptive models. This investigation will lead to a discussion regarding the use of these models for supporting the generation of shapes in the early phases of the design process.


title: Text2Action: Generative Adversarial Synthesis from Language to Action

abstract: In this paper, we propose a generative model which learns the relationship between language and human action in order to generate a human action sequence given a sentence describing human behavior. The proposed generative model is a generative adversarial network (GAN), which is based on the sequence to sequence (SEQ2SEQ) model. Using the proposed generative network, we can synthesize various actions for a robot or a virtual agent using a text encoder recurrent neural network (RNN) and an action decoder RNN. The proposed generative network is trained from 29,770 pairs of actions and sentence annotations extracted from MSR-Video-to-Text (MSR-VTT), a large-scale video dataset. We demonstrate that the network can generate human-like actions which can be transferred to a Baxter robot, such that the robot performs an action based on a provided sentence. Results show that the proposed generative network correctly models the relationship between language and action and can generate a diverse set of actions from the same sentence.


title: Circuit recognition with deep learning

abstract: Identifying properties (features) of circuits and applying proper algorithms are helpful for solving various computer-aided design problems. For hardware security inspection, there is a demand for reverse engineering, the process of extracting high-level components from bit-level designs. Given a suspected circuit block, a common approach is to find a set of candidate functions and then to apply formal methods to identify it. Identifying useful features of high-level functions and collecting suggested candidates of an unknown block are important steps. Convolutional neural networks (CNNs) have been used extensively in machine learning because often pre-defined features are not required. Deep networks with multiple processing layers have been shown to be capable of learning concealed structures of objects during a training process. This paper discusses requirements for representing logic circuits for CNN processing. A new circuit representation (data format) is developed for the proposed circuit-based convolution operation with dynamic pooling. Based on this data format, a deep learning framework using CNNs to recognize circuit functionalities was built. Compared to reference methods based on support vector machines (SVM), experiments demonstrate the effectiveness of the proposed CNN method for both circuit classification as well as function detection and location. With proper training data, e.g. a set of circuits with hidden Trojans, the proposed framework can be used to train a model to help detect and locate malware in hardware designs.


title: The magic carpet: physical sensing for immersive environments

abstract: An interactive environment has been developed that uses a pair of Doppler radars to measure upper-body kinematics (velocity, direction of motion, amount of motion) and a grid of piezoelectric wires hidden under a 6 x 10 foot carpet to monitor dynamic foot position and pressure. This system has been used in an audio installation, where users launch and modify complex musical sounds and sequences as they wander about the carpet. This paper describes the floor and radar systems, quantifies their performance, and outlines the musical application.


title: Design of absolute encoder disk coding based on affine n digit N-ary gray code

abstract: This paper presents a proposal to design the absolute encoder disk based on n digit N-ary cyclic gray code. The n digit N-ary cyclic gray code is generated using affine transformation. Based on the concept of the n digit N-ary cyclic gray code, two types of the coded tracks were designed, namely, Nary cyclic color coded track and N-ary cyclic gray scale coded track. The resolution of the proposed coded disk is compared with the traditional gray coded disk. A 5 digit 5-ary color coded disk gives approximately 100 times better resolution than the traditional binary gray coded track with same number of coded tracks and sensors.


title: Aircraft Control via Variable Cant-Angle Winglets

abstract: This paper investigates a novel method for the control of “morphing” aircraft. The concept consists of a pair of winglets with adjustable cant angle, independently actuated and mounted at the tips of a baseline flying wing. The generalphilosophybehindtheconceptwasthatforspecific flightconditionssuchasacoordinatedturn,theuseoftwo control devices would be sufficient for adequate control. Computations with a vortex lattice model and subsequent wind-tunnel tests demonstrate the viability of the concept, with individual and/or dual winglet deflection producing multi-axis coupled control moments. Comparisons between the experimental and computational results showed reasonable to good agreement, with the major discrepancies thought to be due to wind-tunnel model aeroelastic effects.


title: Full-wave design of broad-band compact waveguide step-twists

abstract: Rectangular waveguide twists are components that provide an on-axis rotation of the transverse waveguide section. In this letter, a step twist configuration, obtained by the direct connection of rotated rectangular waveguide lengths, is considered. The advantages of this configuration are the easy manufacturing with tight control on the relative rotation angles and the reduction of the overall dimensions. By means of a full-wave synthesis approach various wide-band devices were designed and reported in the letter. In particular, a couple of 45/spl deg/ twists was manufactured and integrated in the Ka-band OMT realized for the SPOrt project. The experimental results match the excellent predicted data.


title: Seroprevalence of Toxocara infection in middle-class and disadvantaged children in northern Spain (Gipuzkoa, Basque Country)

abstract: The prevalence of human toxocarosis in disadvantaged socioeconomic population in Europe is unknown. In Gipuzkoa the seroprevalence in middle-class children 2- to 5-years-old, and 6- to 16-year-olds, was zero (n = 135) and 4.4% (n = 320), respectively. In contrast, among socially and economically disadvantaged children the figures were 37% (n = 27) and 65.7% (n = 64). This high prevalence obliges us to consider toxocarosis as a public health problem.


title: Evolution of ERP Systems in the Cloud: A Study on System Updates

abstract: Cloud-based enterprise resource planning (ERP) systems emerged around the new millennium, and since then there has been a lack of research regarding the evolution and update processes of these systems. From the users’ perspective, updates in a traditional on-premise ERP system are carried at their own request; while cloud-based ERPs are compulsory updated. Through an established ERP lifecycle framework, this study investigates how the process of updates is conducted in a cloud ERP context, from both the users’ and vendors’ perspectives. A multiple case study was conducted in Norway at 10 client organizations, as well as a cloud ERP vendor. Our main findings suggest that the vendor and the users view the process of updates differently. The main challenges with the process of updates from the users’ perspective are the size and date of the updates, lack of information and communication during the process, and extinction of certain functionalities. Yet, the main advantages are that all system users will always have the same version of the system, users do not need to spend time on updating the system and paying attention to the ERP market, which leads to more focus on their core competences instead.


title: A Study on Apparent Age Estimation

abstract: Age estimation from facial images is an important problem in computer vision and pattern recognition. Typically the goal is to predict the chronological age of a person given his or her face picture. It is seldom to study a related problem, that is, how old does a person look like from?the face photo? It is called apparent age estimation. A key difference between apparent age estimation and the traditional age estimation is that the age labels are annotated by human assessors rather than the real chronological age. The challenge for apparent age estimation is that there are?not many face images available with annotated age labels. Further, the annotated age labels for each face photo may not be consistent among different assessors. We study the problem of apparent age estimation by addressing the issues from different aspects, such as how to utilize a large number of face images without apparent age labels to learn a face representation using the deep neural networks, how to tune the deep networks using a limited number of examples with apparent age labels, and how well the machine learning methods can perform to estimate apparent ages. The apparent age data is from the ChaLearn Looking At People (LAP) challenge 2015. Using the protocol and time frame given by the challenge competition, we have achieved an error of 0.294835 on the final evaluation, and our result has been ranked the 3rd place in this competition.


title: Group Selection Clone Immune Model of Microgrid Optimization

abstract: In this paper, we have proposed a Group Selection Clone Immune Model for energy resource scheduling of microgrid, which consists of integrated microgrids and lumped loads. Multiple objectives are considered for load balancing among the feeders, minimization of the operating cost, minimizing the emission, minimizing active power losses. The agent represents message of unit and constitutes an autonomic unit. By understanding properties of messages, the agent use projection-join closure to capture such message situations. The network is achieved by the evolution of the agent based on the semantic negotiation. Based on the objectives evaluated by membership functions respectively, we propose a Group Selection Clone Immune algorithm to solve it. Simulation results demonstrated that the proposed method is effective in improving performance and management of micro-sources.


title: Vector Space Representations of Documents in Classifying Finnish Social Media Texts

abstract: Computational analysis of linguistic data requires that texts are transformed into numeric representations. The aim of this research is to evaluate different methods for building vector representations of text documents from social media. The methods are compared in respect to their performance in a classification task. Namely, traditional count-based term frequency-inverse document frequency (TFIDF) is compared to the semantic distributed word embedding representations. Unlike previous research, we investigate document representations in the context of morphologically rich Finnish. Based on the results, we suggest a framework for building vector space representations of texts in social media, applicable to language technologies for morphologically rich languages. In the current study, lemmatization of tokens increased classification accuracy, while lexical filtering generally hindered performance. Finally, we report that distributed embeddings and TFIDF perform at comparable levels with our data.


title: Ceramic Based Small LTE MIMO Handset Antenna

abstract: A compact multiband antenna for handset and tablet applications is presented, covering most LTE Bands (698-960 and 1710-2690 MHz). This study utilizes a new ceramic material suitable for the internal antenna design which gives a shorter guided wavelength and wider bandwidth for each resonant mode to achieve antenna size reduction, sufficient space utilization, and very low correlation coefficient as MIMO antennas. The occupied volume of the antenna radiator is only 35 × 11 × 5 mm3, equal to 0.082 λm × 0.026λm × 0.012λm (λm is the wavelength at 700 MHz) whose radiation aperture is only λm/11 so the antenna is a good candidate as an internal antenna.


title: Convolutional Two-Stream Network Fusion for Video Action Recognition

abstract: Recent applications of Convolutional Neural Networks (ConvNets) for human action recognition in videos have proposed different solutions for incorporating the appearance and motion information. We study a number of ways of fusing ConvNet towers both spatially and temporally in order to best take advantage of this spatio-temporal information. We make the following findings: (i) that rather than fusing at the softmax layer, a spatial and temporal network can be fused at a convolution layer without loss of performance, but with a substantial saving in parameters, (ii) that it is better to fuse such networks spatially at the last convolutional layer than earlier, and that additionally fusing at the class prediction layer can boost accuracy, finally (iii) that pooling of abstract convolutional features over spatiotemporal neighbourhoods further boosts performance. Based on these studies we propose a new ConvNet architecture for spatiotemporal fusion of video snippets, and evaluate its performance on standard benchmarks where this architecture achieves state-of-the-art results.


title: Autonomous Scanning for Endomicroscopic Mosaicing and 3D Fusion

abstract: Robot-assisted minimally invasive surgery can benefit from the automation of common, repetitive or well-defined but ergonomically difficult tasks. One such task is the scanning of a pick-up endomicroscopy probe over a complex, undulating tissue surface to enhance the effective field-of-view through video mosaicing. In this paper, the da Vinci® surgical robot, through the dVRK framework, is used for autonomous scanning and 2D mosaicing over a user-defined region of interest. To achieve the level of precision required for high quality mosaic generation, which relies on sufficient overlap between consecutive image frames, visual servoing is performed using a combination of a tracking marker attached to the probe and the endomicroscopy images themselves. The resulting sub-millimetre accuracy of the probe motion allows for the generation of large mosaics with minimal intervention from the surgeon. Images are streamed from the endomicroscope and overlaid live onto the surgeons view, while 2D mosaics are generated in real-time, and fused into a 3D stereo reconstruction of the surgical scene, thus providing intuitive visualisation and fusion of the multi-scale images. The system therefore offers significant potential to enhance surgical procedures, by providing the operator with cellular-scale information over a larger area than could typically be achieved by manual scanning.


title: Context-Aware Correlation Filter Tracking

abstract: Correlation filter (CF) based trackers have recently gained a lot of popularity due to their impressive performance on benchmark datasets, while maintaining high frame rates. A significant amount of recent research focuses on the incorporation of stronger features for a richer representation of the tracking target. However, this only helps to discriminate the target from background within a small neighborhood. In this paper, we present a framework that allows the explicit incorporation of global context within CF trackers. We reformulate the original optimization problem and provide a closed form solution for single and multi-dimensional features in the primal and dual domain. Extensive experiments demonstrate that this framework significantly improves the performance of many CF trackers with only a modest impact on frame rate.


title: Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates

abstract: In large-scale distributed learning, security issues have become increasingly important. Particularly in a decentralized environment, some computing units may behave abnormally, or even exhibit Byzantine failures---arbitrary and potentially adversarial behavior. In this paper, we develop distributed learning algorithms that are provably robust against such failures, with a focus on achieving optimal statistical performance. A main result of this work is a sharp analysis of two robust distributed gradient descent algorithms based on median and trimmed mean operations, respectively. We prove statistical error rates for three kinds of population loss functions: strongly convex, non-strongly convex, and smooth non-convex. In particular, these algorithms are shown to achieve order-optimal statistical error rates for strongly convex losses. To achieve better communication efficiency, we further propose a median-based distributed algorithm that is provably robust, and uses only one communication round. For strongly convex quadratic loss, we show that this algorithm achieves the same optimal error rate as the robust distributed gradient descent algorithms.


title: Authorship Attribution Using Word Network Features

abstract: In this paper, we explore a set of novel features for authorship attribution of documents. These features are derived from a word network representation of natural language text. As has been noted in previous studies, natural language tends to show complex network structure at word level, with low degrees of separation and scale-free (power law) degree distribution. There has also been work on authorship attribution that incorporates ideas from complex networks. The goal of our paper is to explore properties of these complex networks that are suitable as features for machine-learning-based authorship attribution of documents. We performed experiments on three different datasets, and obtained promising results.


Masters of disguise : Super-recognisers ’ superior memory for concealed faces


title: Predicting defects using network analysis on dependency graphs

abstract: In software development, resources for quality assurance are limited by time and by cost. In order to allocate resources effectively, managers need to rely on their experience backed by code complexity metrics. But often dependencies exist between various pieces of code over which managers may have little knowledge. These dependencies can be construed as a low level graph of the entire system. In this paper, we propose to use network analysis on these dependency graphs. This allows managers to identify central program units that are more likely to face defects. In our evaluation on Windows Server 2003, we found that the recall for models built from network measures is by 10% points higher than for models built from complexity metrics. In addition, network measures could identify 60% of the binaries that the Windows developers considered as critical-twice as many as identified by complexity metrics.


title: Characterizing the sparseness of neural codes.

abstract: It is often suggested that efficient neural codes for natural visual information should be 'sparse'. However, the term 'sparse' has been used in two different ways--firstly to describe codes in which few neurons are active at any time ('population sparseness'), and secondly to describe codes in which each neuron's lifetime response distribution has high kurtosis ('lifetime sparseness'). Although these ideas are related, they are not identical, and the most common measure of lifetime sparseness--the kurtosis of the lifetime response distributions of the neurons--provides no information about population sparseness. We have measured the population sparseness and lifetime kurtosis of several biologically inspired coding schemes. We used three measures of population sparseness (population kurtosis, Treves-Rolls sparseness and 'activity sparseness'), and found them to be in close agreement with one another. However, we also measured the lifetime kurtosis of the cells in each code. We found that lifetime kurtosis is uncorrelated with population sparseness for the codes we used. Lifetime kurtosis is not, therefore, a useful measure of the population sparseness of a code. Moreover, the Gabor-like codes, which are often assumed to have high population sparseness (since they have high lifetime kurtosis), actually turned out to have rather low population sparseness. Surprisingly, principal components filters produced the codes with the highest population sparseness.


title: Calreticulin inhibits commitment to adipocyte differentiation

abstract: Calreticulin, an endoplasmic reticulum (ER) resident protein, affects many critical cellular functions, including protein folding and calcium homeostasis. Using embryonic stem cells and 3T3-L1 preadipocytes, we show that calreticulin modulates adipogenesis. We find that calreticulin-deficient cells show increased potency for adipogenesis when compared with wild-type or calreticulin-overexpressing cells. In the highly adipogenic crt−/− cells, the ER lumenal calcium concentration was reduced. Increasing the ER lumenal calcium concentration led to a decrease in adipogenesis. In calreticulin-deficient cells, the calmodulin–Ca2+/calmodulin-dependent protein kinase II (CaMKII) pathway was up-regulated, and inhibition of CaMKII reduced adipogenesis. Calreticulin inhibits adipogenesis via a negative feedback mechanism whereby the expression of calreticulin is initially up-regulated by peroxisome proliferator–activated receptor γ (PPARγ). This abundance of calreticulin subsequently negatively regulates the expression of PPARγ, lipoprotein lipase, CCAAT enhancer–binding protein α, and aP2. Thus, calreticulin appears to function as a Ca2+-dependent molecular switch that regulates commitment to adipocyte differentiation by preventing the expression and transcriptional activation of critical proadipogenic transcription factors.


title: Using Neo4j for Mining Protein Graphs: A Case Study

abstract: Using graph databases becomes increasingly popular in domains where data can be modeled as a set of connected objects. Graph databases enable to query such data using graphbased queries in a relatively simple manner in comparison to the classical relational databases. In this paper, we show how one of the most popular graph databases, Neo4j, can be applied to the bioinformatics problem of protein-protein interface (PPI) identification. The goal of the PPI identification task is, given a protein structure, to identify amino acids which are responsible for binding of the structure to other proteins. Each protein structure consists of a set of amino acid molecules which can be conceived as a graph and multitude of methods for analysis of such protein graphs have been established. We introduce here a knowledge-based approach which can enhance the quality of these methods by utilizing existing protein structure knowledge stored in the Protein Data Bank (PDB). We show how to transform information about protein complexes from PDB into Neo4j where they can be stored as a set of independent protein graphs. The resulting graph database contains about 14 millions labeled nodes and 38 millions edges. In the PPI identification phase, this database is queried using exact subgraph matching and the results are aggregated to improve an existing PPI identification method. We show the pros and cons of using Neo4j for such endeavor with respect to the size of the database and complexity of the queries in comparison to using a relational database (Microsoft SQL Server). We conclude that using Neo4j is a viable option for specific, rather small, subgraph query types. However, we have encountered performance limitations, especially for larger query graphs in terms of number of edges.


title: Fingerprint Reconstruction: From Minutiae to Phase

abstract: Fingerprint matching systems generally use four types of representation schemes: grayscale image, phase image, skeleton image, and minutiae, among which minutiae-based representation is the most widely adopted one. The compactness of minutiae representation has created an impression that the minutiae template does not contain sufficient information to allow the reconstruction of the original grayscale fingerprint image. This belief has now been shown to be false; several algorithms have been proposed that can reconstruct fingerprint images from minutiae templates. These techniques try to either reconstruct the skeleton image, which is then converted into the grayscale image, or reconstruct the grayscale image directly from the minutiae template. However, they have a common drawback: Many spurious minutiae not included in the original minutiae template are generated in the reconstructed image. Moreover, some of these reconstruction techniques can only generate a partial fingerprint. In this paper, a novel fingerprint reconstruction algorithm is proposed to reconstruct the phase image, which is then converted into the grayscale image. The proposed reconstruction algorithm not only gives the whole fingerprint, but the reconstructed fingerprint contains very few spurious minutiae. Specifically, a fingerprint image is represented as a phase image which consists of the continuous phase and the spiral phase (which corresponds to minutiae). An algorithm is proposed to reconstruct the continuous phase from minutiae. The proposed reconstruction algorithm has been evaluated with respect to the success rates of type-I attack (match the reconstructed fingerprint against the original fingerprint) and type-II attack (match the reconstructed fingerprint against different impressions of the original fingerprint) using a commercial fingerprint recognition system. Given the reconstructed image from our algorithm, we show that both types of attacks can be successfully launched against a fingerprint recognition system.


Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation


title: Horse racing prediction using artificial neural networks

abstract: Artificial Neural Networks (ANNs) have been applied to predict many complex problems. In this paper ANNs are applied to horse racing prediction. We employed Back-Propagation, Back-Propagation with Momentum, Quasi-Newton, Levenberg-Marquardt and Conjugate Gradient Descent learning algorithms for real horse racing data and the performances of five supervised NN algorithms were analyzed. Data collected from AQUEDUCT Race Track in NY, include 100 actual races from 1 January to 29 January 2010. The experimental results demonstrate that NNs are appropriate methods in horse racing prediction context. Results show that BP algorithm performs slightly better than other algorithms but it needs a longer training time and more parameter selection. Furthermore, LM algorithm is the fastest.


title: The control of the false discovery rate in multiple testing under dependency

abstract: Benjamini and Hochberg suggest that the false discovery rate may be the appropriate error rate to control in many applied multiple testing problems. A simple procedure was given there as an FDR controlling procedure for independent test statistics and was shown to be much more powerful than comparable procedures which control the traditional familywise error rate. We prove that this same procedure also controls the false discovery rate when the test statistics have positive regression dependency on each of the test statistics corresponding to the true null hypotheses. This condition for positive dependency is general enough to cover many problems of practical interest, including the comparisons of many treatments with a single control, multivariate normal test statistics with positive correlation matrix and multivariate t. Furthermore, the test statistics may be discrete, and the tested hypotheses composite without posing special difficulties. For all other forms of dependency, a simple conservative modification of the procedure controls the false discovery rate. Thus the range of problems for which a procedure with proven FDR control can be offered is greatly increased. 1.1. Simultaneous hypotheses testing. The control of the increased type I error when testing simultaneously a family of hypotheses is a central issue in the area of multiple comparisons. Rarely are we interested only in whether all hypotheses are jointly true or not, which is the test of the intersection null hypothesis. In most applications, we infer about the individual hypotheses, realizing that some of the tested hypotheses are usually true—we hope not all—and some are not. We wish to decide which ones are not true, indicating (statistical) discoveries. An important such problem is that of multiple endpoints in a clinical trial: a new treatment is compared with an existing one in terms of a large number of potential benefits (endpoints).


title: Data and Information Leakage Prevention Within the Scope of Information Security

abstract: Incidents involving data breaches are ever-present in the media since several years. In order to overcome this threat, organizations apply enterprise content-aware data leakage prevention (DLP) solutions to monitor and control data access and usage. However, this paper argues that current solutions are not able to reliably protect information assets. The analyses of data breaches reported in 2014 reveal a significant number of data leakage incidents that are not within the focus of the DLP solutions. Furthermore, these analyses indicate that the classification of the provided data breach records is not qualified for detailed investigations. Therefore, advanced criteria for characterizing data leakage incidents are introduced, and the reported records are extended. The resulting analyses illustrate that DLP and information leakage prevention (ILP) demand various information security (IS) measures to be established in order to reduce the risk of technologically based data breaches. Furthermore, the effectiveness of DLP and information leakage prevention (ILP) measures is significantly influenced by non-technological aspects, such as the human factor. Therefore, this paper presents a concept for establishing DLP and ILP within the scope of IS.


Advances in Artificial Intelligence: From Theory to Practice


title: The role of RFID in agriculture: Applications, limitations and challenges

abstract: The recent advances in RFID offer vast opportunities for research, development and innovation in agriculture. The aim of this paper is to give readers a comprehensive view of current applications and new possibilities, but also explain the limitations and challenges of this technology. RFID has been used for years in animal identification and tracking, being a common practice in many farms. Also it has been used in the food chain for traceability control. The implementation of sensors in tags, make possible to monitor the cold chain of perishable food products and the development of new applications in fields like environmental monitoring, irrigation, specialty crops and farm machinery. However, it is not all advantages. There are also challenges and limitations that should be faced in the next years. The operation in harsh environments, with dirt, extreme temperatures; the huge volume of data that are difficult to manage; the need of longer reading ranges, due to the reduction of signal strength due to propagation in crop canopy; the behavior of the different frequencies, understanding what is the right one for each application; the diversity of the standards and the level of granularity are some of them.


title: Probabilistic reasoning in practical system analysis

abstract: This article presents an application of the Bayesian networks (BN) to reliability analysis. First part of the article describes BN used to construct simple nodes, second shows the methodology used in constructing models of complex systems. A combination of path analysis with the Bayesian networks to enhance the construction of the latter is also presented. There are three examples of complex systems analysed with the results compared to the outcomes obtained using the minimal cuts method.


title: Challenges for Static Analysis of Java Reflection - Literature Review and Empirical Study

abstract: The behavior of software that uses the Java Reflection API is fundamentally hard to predict by analyzing code. Only recent static analysis approaches can resolve reflection under unsound yet pragmatic assumptions. We survey what approaches exist and what their limitations are. We then analyze how real-world Java code uses the Reflection API, and how many Java projects contain code challenging state-of-the-art static analysis. Using a systematic literature review we collected and categorized all known methods of statically approximating reflective Java code. Next to this we constructed a representative corpus of Java systems and collected descriptive statistics of the usage of the Reflection API. We then applied an analysis on the abstract syntax trees of all source code to count code idioms which go beyond the limitation boundaries of static analysis approaches. The resulting data answers the research questions. The corpus, the tool and the results are openly available. We conclude that the need for unsound assumptions to resolve reflection is widely supported. In our corpus, reflection can not be ignored for 78% of the projects. Common challenges for analysis tools such as non-exceptional exceptions, programmatic filtering meta objects, semantics of collections, and dynamic proxies, widely occur in the corpus. For Java software engineers prioritizing on robustness, we list tactics to obtain more easy to analyze reflection code, and for static analysis tool builders we provide a list of opportunities to have significant impact on real Java code.


title: A Fully Attention-Based Information Retriever

abstract: Recurrent neural networks are now the state-of-the-art in natural language processing because they can build rich contextual representations and process texts of arbitrary length. However, recent developments on attention mechanisms have equipped feedforward networks with similar capabilities, hence enabling faster computations due to the increase in the number of operations that can be parallelized. We explore this new type of architecture in the domain of question-answering and propose a novel approach that we call Fully Attention Based Information Retriever (FABIR). We show that FABIR achieves competitive results in the Stanford Question Answering Dataset (SQuAD) while having fewer parameters and being faster at both learning and inference than rival methods.


title: Specifying and Enforcing the Principle of Least Privilege in Role-Based Access Control

abstract: The principle of least privilege in role-based access control is an important area of research. There are two crucial issues related to it: the specification and the enforcement. We believe that the existing least privilege specification schemes are not comprehensive enough and few of the enforcement methods are likely to scale well. In this paper, we formally define the basic principle of least privilege problem and present different variations, called the delta-approx principle of least privilege problem and the minimizing-approx principle of least privilege problem. Since there may be more than one result to enforce the same principle of least privilege, we introduce the notation about weights of permissions and roles to optimize the results. Then we prove that all least privilege problems are NP-complete. As an important contribution of the paper, we show that the principle of least privilege problem can be reduced to minimal cost set covering (MCSC) problem. We can borrow the existing solutions of MCSC to solve the principle of least privilege problems. Finally, different algorithms are designed to solve the proposed least privilege problems. Experiments on performance study prove the superiority of our algorithms. Copyright © 2011 John Wiley & Sons, Ltd.


title: In vivo dendritic calcium imaging with a fiberoptic periscope system

abstract: Dendritic recordings in freely moving animals present great challenges using the current approaches. Here we present in detail a microendoscopic technique (the 'periscope' method) for measuring intracellular calcium activity directly from the apical dendrites of L5 pyramidal neurons from the pia down to depths of ~700 μm in anesthetized and freely moving rats. This method gives high signal-to-noise dendritic fluorescence responses to sensory stimuli, and has been proven to be inexpensive, straightforward and reliable, allowing essentially unrestricted behavior. We describe refinements and practical optimizations of procedures aimed at achieving dendritic Ca2+ imaging in freely moving animals. The periscope imaging technique presented here is also ideal for combining with other in vivo recording techniques. The protocol, from the beginning of anesthesia to starting dendritic imaging, can be completed in 5 h.


title: Pronunciation Adaptation For Disordered Speech Recognition Using State-Specific Vectors of Phone-Cluster Adaptive Training

abstract: Pronunciation variation is a major problem in disordered speech recognition. This paper focus on handling the pronunciation variations in dysarthric speech by forming speaker-specific lexicons. A novel approach is proposed for identifying mispronunciations made by each dysarthric speaker, using state-specific vector (SSV) of phone-cluster adaptive training (Phone-CAT) acoustic model. SSV is low-dimensional vector estimated for each tied-state where each element in a vector denotes the weight of a particular monophone. The SSV indicates the pronounced phone using its dominant weight. This property of SSV is exploited in adapting the pronunciation of a particular dysarthric speaker using speaker-specific lexicons. Experimental validation on Nemours database showed an average relative improvement of 9% across all the speakers compared to the system built with canonical lexicon. Index Terms: Dysarthric speech recognition, phone-CAT, lexical modeling, pronunciations, phone confusion matrix


Dimensions Of Qualitative Spatial Reasoning


title: Compressive Sensing (CS) Assisted Low-Complexity Beamspace Hybrid Precoding for Millimeter-Wave MIMO Systems

abstract: Hybrid analog/digital precoding is a promising technique to reduce the hardware cost of radio-frequency components compared with the conventional full-digital precoding approach in millimeter-wave multiple-input multiple output systems. However, the large antenna dimensions of the hybrid precoder design makes it difficult to acquire an optimal full-digital precoder. Moreover, it also requires matrix inversion, which leads to high complexity in the hybrid precoder design. In this paper, we propose a low-complexity optimal full-digital precoder acquisition algorithm, named beamspace singular value decomposition (SVD) that saves power for the base station and user equipment. We exploit reduced-dimension beamspace channel state information (CSI) given by compressive sensing (CS) based channel estimators. Then, we propose a CS-assisted beamspace hybrid precoding (CS-BHP) algorithm that leverages CS-based CSI. Simulation results show that the proposed beamspace-SVD reduces complexity by 99.4% compared with an optimal full-digital precoder acquisition using full-dimension SVD. Furthermore, the proposed CS-BHP reduces the complexity of the state-of-the-art approach by 99.6% and has less than 5% performance loss compared with an optimal full-digital precoder.


title: Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images

abstract: Convolutional neural networks (CNNs) achieved impressive recognition rates in image classification tasks recently. In order to exploit those capabilities, we trained CNNs on a database of photometric stereo images of metal surface defects, i.e. rail defects. Those defects are cavities in the rail surface and are indication for further surface degradation right up to rail break. Due to security issues, defects have to be recognized early in order to take countermeasures in time. By means of differently colored light-sources illuminating the rail surfaces from different and constant directions, those cavities are made visible in a photometric dark-field setup. So far, a model-based approach has been used for image classification, which expressed the expected reflection properties of surface defects in contrast to non-defects. In this work, we experimented with classical CNNs trained in pure supervised manner and also explored the impact of regularization methods such as unsupervised layer-wise pre-training and training data-set augmentation. The classical CNN already distinctly outperforms the model-based approach. Moreover, regularization methods yet yield further improvements.


title: Tight bounds for rumor spreading in graphs of a given conductance

abstract: We study the connection between the rate at which a rumor spreads throughout a graph and the conductance of the graph—a standard measure of a graph’s expansion properties. We show that for any n-node graph with conductance , the classical PUSH-PULL algorithm distributes a rumor to all nodes of the graph in O( 1 logn) rounds with high probability (w.h.p.). This bound improves a recent result of Chierichetti, Lattanzi, and Panconesi [6], and it is tight in the sense that there exist graphs where ( 1 logn) rounds of the PUSH-PULL algorithm are required to distribute a rumor w.h.p. We also explore the PUSH and the PULL algorithms, and derive conditions that are both necessary and sucient for the above upper bound to hold for those algorithms as well. An


title: Syntactically Guided Neural Machine Translation

abstract: We investigate the use of hierarchical phrase-based SMT lattices in end-to-end neural machine translation (NMT). Weight pushing transforms the Hiero scores for complete translation hypotheses, with the full translation grammar score and full n-gram language model score, into posteriors compatible with NMT predictive probabilities. With a slightly modified NMT beam-search decoder we find gains over both Hiero and NMT decoding alone, with practical advantages in extending NMT to very large input and output vocabularies.


title: Efficient Classifier for Classification of Prognostic Breast Cancer Data through Data Mining Techniques

abstract: Data mining involves the process of recovering related, significant and credential information from a large collection of aggregated data. A major area of current research in data mining is the field of clinical investigations that involve disease diagnosis, prognosis and drug therapy. The objective of this paper is to identify an efficient classifier for prognostic breast cancer data. This research work involves designing a data mining framework that incorporates the task of learning patterns and rules that will facilitate the formulation of decisions in new cases. The machine learning techniques employed to train the proposed system are based on feature relevance analysis and classification algorithms. Wisconsin Prognostic Breast Cancer (WPBC) data from the UCI machine learning repository is utilized by means of data mining techniques to completely train the system on 198 individual cases, each comprising of 33 predictor values. This paper highlights the performance of feature reduction and classification algorithms on the training dataset. We evaluate the number of attributes for split in the Random tree algorithm and the confidence level and minimum size of the leaves in the C4.5 algorithm to produce 100 percent classification accuracy. Our results demonstrate that Random Tree and Quinlan's C4.5 classification algorithm produce 100 percent accuracy in the training and test phase of classification with proper evaluation of algorithmic parameters.


title: Disability and schizophrenia: a systematic review of experienced psychosocial difficulties

abstract: Schizophrenia is a significantly disabling disease that affects all major areas of life. There is a lack of comprehensive synthesis of research findings on the full extent of psychosocial difficulties (PSDs) experienced by people living with schizophrenia. This paper provides a systematic review of the literature concerning PSDs and their associated factors in schizophrenia. PSDs were conceptualized in accordance with the International Classification of Functioning, Disability and Health (ICF) as disabilities, in particular impairments of mental functions, activity limitations and participation restrictions. An electronic search using MEDLINE and PsychINFO plus a manual search of the literature was performed for qualitative and longitudinal studies published in English between 2005 and 2010 that examined PSDs in persons with schizophrenia. The ICF was used as a conceptual framework. A total of 104 papers were included. The most frequent PSDs addressed in the literature were not specific ones, directly linkable to the ICF categories of mental functions, activity limitations or participation restrictions, but broad areas of psychosocial functioning, such as psychopathological symptoms (53% of papers) or global disability and functioning (37%). Among mental functions, the most extensively studied were cognitive functions (27%) and emotional functions (27%). Within the domain of activities and participation, the most widely investigated were difficulties in relationships with others (31%) and employment (20%). Of the factors associated with the intensity or course of PSDs, the most commonly identified were treatment modalities (56%), psychopathological symptoms (26%), and socio-demographic variables (24%). Medication tended to improve the most relevant PSD, but at the same time was the only consistently reported determinant of onset of PSDs (emerging as unwanted side-effects). The present review illustrates the remarkably broad scope and diversity of psychosocial areas affected in schizophrenia and shows how these areas are interconnected and how they interact with contextual factors. The need for a shift in focus of schizophrenia research is suggested – from an excessive reliance on global measures of psychopathology and disability for defining outcomes to the creation of profiles of specific PSDs that have a more direct bearing on the disabling experience and real-world functioning of patients and can serve to guide interventions and monitoring over time.


title: A New Type of Periodically Loaded Half-Mode Substrate Integrated Waveguide and Its Applications

abstract: A new type of half-mode substrate integrated waveguide (SIW) periodically loaded with different lumped elements and structures is proposed in this paper. The propagation constants, Bloch impedances, and voltage distributions of the Floquet TE-modes are all characterized by solving the eigenvalues of the generalized transmission matrix of a single period. Several typical capacitor-loaded, inductor-loaded, corrugated, and electromagnetic-bandgap (EBG)-loaded half-mode SIWs were fabricated to demonstrate their modal dispersion behavior. The measured and simulated S -parameters, group delays, and simulated electric field distributions in these novel structures are presented, with good agreements obtained for all samples. In particular, their unique controllable cutoff frequencies, multiple bandgaps, and separated monomode regions are also illuminated numerically and experimentally. To further show their applications, a tunable phase shifter is developed using a varactor-loaded structure, with its 3-dB bandwidth of 50% and a maximal tunable range of 88°. In addition, a bandpass filter based on EBG-loaded half-mode SIW is also realized and presented with its special characteristics. It demonstrates that the filter has a sharp upper slope side and a good performance of spurious suppression.


title: Speaker adaptation of context dependent deep neural networks

abstract: There has been little work on examining how deep neural networks may be adapted to speakers for improved speech recognition accuracy. Past work has examined using a discriminatively trained affine transformation of the input features applied at a frame level or the re-training of the entire shallow network for a specific speaker. This work explores how deep neural networks may be adapted to speakers by re-training the input layer, the output layer or the entire network. We look at how L2 regularization using weight decay to the speaker independent model improves generalization. Other training factors are examined including the role momentum plays and stochastic mini-batch versus batch training. While improvements are significant for smaller networks, the largest show little gain from adaptation on a large vocabulary mobile speech recognition task.


title: Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction

abstract: This paper introduces an elemental building block which combines Dictionary Learning and Dimension Reduction (DRDL). We show how this foundational element can be used to iteratively construct a Hierarchical Sparse Representation (HSR) of a sensory stream. We compare our approach to existing models showing the generality of our simple prescription. We then perform preliminary experiments using this framework, illustrating with the example of an object recognition task using standard datasets. This work introduces the very first steps towards an integrated framework for designing and analyzing various computational tasks from learning to attention to action. The ultimate goal is building a mathematically rigorous, integrated theory of intelligence.


title: Internet Addiction on Campus: The Vulnerability of College Students

abstract: Use of the Internet on college campuses has increased dramatically in recent years, leading to pathological use, or Internet addiction, for some students. Internet addiction is defined as a psychological dependence on the Internet and is characterized by (a) an increasing investment of resources on Internet-related activities, (b) unpleasant feelings (e.g., anxiety, depression, emptiness) when offline, (c) an increasing tolerance to the effects of being online, and (d) denial of the problematic behaviors. Individuals exhibiting such symptoms often are dealing with underlying psychological issues. College students are particularly vulnerable to pathological Internet use due to several factors. These factors include (a) the psychological and developmental characteristics of late adolescence/young adulthood, (b) ready access to the Internet, and (c) an expectation of computer/Internet use. The nature of the computer medium and the sense of control experienced when engaged in computer activities can also cont...


title: XNOR-RRAM: A scalable and parallel resistive synaptic architecture for binary neural networks

abstract: Recent advances in deep learning have shown that Binary Neural Networks (BNNs) are capable of providing a satisfying accuracy on various image datasets with significant reduction in computation and memory cost. With both weights and activations binarized to +1 or −1 in BNNs, the high-precision multiply-and-accumulate (MAC) operations can be replaced by XNOR and bit-counting operations. In this work, we propose a RRAM synaptic architecture (XNOR-RRAM) with a bit-cell design of complementary word lines that implements equivalent XNOR and bit-counting operation in a parallel fashion. For large-scale matrices in fully connected layers or when the convolution kernels are unrolled in multiple channels, the array partition is necessary. Multi-level sense amplifiers (MLSAs) are employed as the intermediate interface for accumulating partial weighted sum. However, a low bit-level MLSA and intrinsic offset of MLSA may degrade the classification accuracy. We investigate the impact of sensing offsets on classification accuracy and analyze various design options with different sub-array sizes and sensing bit-levels. Experimental results with RRAM models and 65nm CMOS PDK show that the system with 128×128 sub-array size and 3-bit MLSA can achieve accuracies of 98.43% for MLP on MNIST and 86.08% for CNN on CIFAR-10, showing 0.34% and 2.39% degradation respectively compared to the accuracies of ideal BNN algorithms. The projected energy-efficiency of XNOR-RRAM is 141.18 TOPS/W, showing ∼33X improvement compared to the conventional RRAM synaptic architecture with sequential row-by-row read-out.


title: HyperPower: Power- and memory-constrained hyper-parameter optimization for neural networks

abstract: While selecting the hyper-parameters of Neural Networks (NNs) has been so far treated as an art, the emergence of more complex, deeper architectures poses increasingly more challenges to designers and Machine Learning (ML) practitioners, especially when power and memory constraints need to be considered. In this work, we propose HyperPower, a framework that enables efficient Bayesian optimization and random search in the context of power- and memory-constrained hyper-parameter optimization for NNs running on a given hardware platform. HyperPower is the first work (i) to show that power consumption can be used as a low-cost, a priori known constraint, and (ii) to propose predictive models for the power and memory of NNs executing on GPUs. Thanks to HyperPower, the number of function evaluations and the best test error achieved by a constraint-unaware method are reached up to 112.99x and 30.12x faster, respectively, while never considering invalid configurations. HyperPower significantly speeds up the hyper-parameter optimization, achieving up to 57.20x more function evaluations compared to constraint-unaware methods for a given time interval, effectively yielding significant accuracy improvements by up to 67.6%.


title: Exercise in an electrotactic flow chamber ameliorates age-related degeneration in Caenorhabditis elegans

abstract: Degeneration is a senescence process that occurs in all living organisms. Although tremendous efforts have been exerted to alleviate this degenerative tendency, minimal progress has been achieved to date. The nematode, Caenorhabditis elegans (C. elegans), which shares over 60% genetic similarities with humans, is a model animal that is commonly used in studies on genetics, neuroscience, and molecular gerontology. However, studying the effect of exercise on C. elegans is difficult because of its small size unlike larger animals. To this end, we fabricated a flow chamber, called "worm treadmill," to drive worms to exercise through swimming. In the device, the worms were oriented by electrotaxis on demand. After the exercise treatment, the lifespan, lipofuscin, reproductive capacity, and locomotive power of the worms were analyzed. The wild-type and the Alzheimer's disease model strains were utilized in the assessment. Although degeneration remained irreversible, both exercise-treated strains indicated an improved tendency compared with their control counterparts. Furthermore, low oxidative stress and lipofuscin accumulation were also observed among the exercise-treated worms. We conjecture that escalated antioxidant enzymes imparted the worms with an extra capacity to scavenge excessive oxidative stress from their bodies, which alleviated the adverse effects of degeneration. Our study highlights the significance of exercise in degeneration from the perspective of the simple life form, C. elegans.


title: Learning to predict slip for ground robots

abstract: In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do that, the information of terrain appearance and geometry regarding some location is correlated to the slip measured by the rover while this location is being traversed. This relationship is learned from previous experience, so slip can be predicted later at a distance from visual information only. The advantages of the approach are: 1) learning from examples allows the system to adapt to unknown terrains rather than using fixed heuristics or predefined rules; 2) the feedback about the observed slip is received from the vehicle's own sensors which can fully automate the process; 3) learning slip from previous experience can replace complex mechanical modeling of vehicle or terrain, which is time consuming and not necessarily feasible. Predicting slip is motivated by the need to assess the risk of getting trapped before entering a particular terrain. For example, a planning algorithm can utilize slip information by taking into consideration that a slippery terrain is costly or hazardous to traverse. A generic nonlinear regression framework is proposed in which the terrain type is determined from appearance and then a nonlinear model of slip is learned for a particular terrain type. In this paper we focus only on the latter problem and provide slip learning and prediction results for terrain types, such as soil, sand, gravel, and asphalt. The slip prediction error achieved is about 15% which is comparable to the measurement errors for slip itself


title: Optimal distribution of contact forces with inverse-dynamics control

abstract: The development of legged robots for complex environments requires controllers that guarantee both high tracking performance and compliance with the environment. More specifically the control of the contact interaction with the environment is of crucial importance to ensure stable, robust and safe motions. In this contribution we develop an inverse-dynamics controller for floating-base robots under contact constraints that can minimize any combination of linear and quadratic costs in the contact constraints and the commands. Our main result is the exact analytical derivation of the controller. Such a result is particularly relevant for legged robots as it allows us to use torque redundancy to directly optimize contact interactions. For example, given a desired locomotion behavior, we can guarantee the minimization of contact forces to reduce slipping on difficult terrains while ensuring high tracking performance of the desired motion. The main advantages of the controller are its simplicity, computational efficiency and robustness to model inaccuracies. We present detailed experimental results on simulated humanoid and quadruped robots as well as a real quadruped robot. The experiments demonstrate that the controller can greatly improve the robustness of locomotion of the robots.1


title: Decoupling light reflex from pupillary dilation to measure emotional arousal in videos

abstract: Predicting the exciting portions of a video is a widely relevant problem because of applications such as video summarization, searching for similar videos, and recommending videos to users. Researchers have proposed the use of physiological indices such as pupillary dilation as a measure of emotional arousal. The key problem with using the pupil to measure emotional arousal is accounting for pupillary response to brightness changes. We propose a linear model of pupillary light reflex to predict the pupil diameter of a viewer based only on incident light intensity. The residual between the measured pupillary diameter and the model prediction is attributed to the emotional arousal corresponding to that scene. We evaluate the effectiveness of this method of factoring out pupillary light reflex for the particular application of video summarization. The residual is converted into an exciting-ness score for each frame of a video. We show results on a variety of videos, and compare against ground truth as reported by three independent coders.


title: 10. Microvascular Complications and Foot Care: Standards of Medical Care in Diabetes—2018

abstract: The American Diabetes Association (ADA) "Standards of Medical Care in Diabetes" includes ADA's current clinical practice recommendations and is intended to provide the components of diabetes care, general treatment goals and guidelines, and tools to evaluate quality of care. Members of the ADA Professional Practice Committee, a multidisciplinary expert committee, are responsible for updating the Standards of Care annually, or more frequently as warranted. For a detailed description of ADA standards, statements, and reports, as well as the evidence-grading system for ADA's clinical practice recommendations, please refer to the Standards of Care Introduction Readers who wish to comment on the Standards of Care are invited to do so at professional.diabetes.org/SOC.


title: Altered Fingerprints: Analysis and Detection

abstract: The widespread deployment of Automated Fingerprint Identification Systems (AFIS) in law enforcement and border control applications has heightened the need for ensuring that these systems are not compromised. While several issues related to fingerprint system security have been investigated, including the use of fake fingerprints for masquerading identity, the problem of fingerprint alteration or obfuscation has received very little attention. Fingerprint obfuscation refers to the deliberate alteration of the fingerprint pattern by an individual for the purpose of masking his identity. Several cases of fingerprint obfuscation have been reported in the press. Fingerprint image quality assessment software (e.g., NFIQ) cannot always detect altered fingerprints since the implicit image quality due to alteration may not change significantly. The main contributions of this paper are: 1) compiling case studies of incidents where individuals were found to have altered their fingerprints for circumventing AFIS, 2) investigating the impact of fingerprint alteration on the accuracy of a commercial fingerprint matcher, 3) classifying the alterations into three major categories and suggesting possible countermeasures, 4) developing a technique to automatically detect altered fingerprints based on analyzing orientation field and minutiae distribution, and 5) evaluating the proposed technique and the NFIQ algorithm on a large database of altered fingerprints provided by a law enforcement agency. Experimental results show the feasibility of the proposed approach in detecting altered fingerprints and highlight the need to further pursue this problem.


What Do Users Really Care About ? A C omparison of Usability Problems


title: Online Learning for Strong Branching Approximation in Branch-and-Bound

abstract: We present an online learning approach to variable branching in branch-and-bound for mixed-integer linear problems. Our approach consists in learning strong branching scores in an online fashion and in using them to take branching decisions. More specifically, numerical scores are used to rank the branching candidates. If, for a given variable, the learned approximation is deemed reliable, then the score for that variable is computed thanks to the learned function. If the approximation is not reliable yet, the real strong branching score is used instead. The scores that are computed through the real strong branching procedure are fed to the online learning algorithm in order to improve the approximated function. The experiments show promising results.


title: A GaN-based MHz active clamp flyback converter with adaptive dual edge dead time modulation for AC-DC adapters

abstract: The Active Clamp Flyback (ACF) converter utilizing Gallium Nitride (GaN) High Electron Mobility Transistor (HEMT) is expected to achieve high switching frequency and high efficiency for system miniaturization in AC-DC adapter applications. Due to high switching speed and large reverse conduction loss of GaN devices, the improper dead time length in a high-frequency ACF converter can lead to severe oscillation and efficiency decrease. This paper presents a high-frequency and high-efficiency ACF converter with an adaptive dual edge dead time modulation which can realize ideal Zero Voltage Switching (ZVS) ON for primary GaN FETs and Zero Current Switching (ZCS) for GaN synchronous rectifier. Designed by a high voltage 0.18um BCD process, the controller IC can enable a 65 W (19.5V/3.3A) ACF converter to operate at 1 MHz and automatically adjust the dead-time length under universal input (100VAC-260VAC) as well as wide load condition (0.33A-3.33A). With the proposed adaptive dual edge dead time control, the ACF converter can achieve the maximum efficiency of 94.3% and around 80% efficiency at the worst case. In addition, the converter based on this analog IC solution excludes any external active sampling devices, which will bring lower system cost.


title: A Modified P&O-MPPT based on Pythagorean Theorem and CV-MPPT for PV Systems

abstract: Maximum power point tracking MPPT techniques are a crucial part in photovoltaic PV system design to maximise the output power of a PV array which depends on weather conditions. Although several techniques have been developed, Perturb and Observe P&O is widely used for MPPT due to its low cost and simple implementation. However, the main drawbacks of this method are a low converging time, high oscillation around the maximum power point, and drift problem associated with changing irradiance rapidly. In this paper, a modified P&O-MPPT based on Pythagorean theorem and constant voltage CV-MPPT is presented. To assess this modification, conventional and modified P&O methods are simulated by a MATLAB-SIMULINK. Simulation results demonstrate that the modified P&O-MPTT has a highly effective to solve the problem of conventional P&O-MPPT.


title: Unsupervised Post-processing of Word Vectors via Conceptor Negation

abstract: Word vectors are at the core of many natural language processing tasks. Recently, there has been interest in post-processing word vectors to enrich their semantic information. In this paper, we introduce a novel word vector post-processing technique based on matrix conceptors (Jaeger 2014), a family of regularized identity maps. More concretely, we propose to use conceptors to suppress those latent features of word vectors having high variances. The proposed method is purely unsupervised: it does not rely on any corpus or external linguistic database. We evaluate the post-processed word vectors on a battery of intrinsic lexical evaluation tasks, showing that the proposed method consistently outperforms existing state-of-the-art alternatives. We also show that post-processed word vectors can be used for the downstream natural language processing task of dialogue state tracking, yielding improved results in different dialogue domains.


title: Device and technology evolution for Si-based RF integrated circuits

abstract: The relationships between device feature size and device performance figures of merit (FoMs) are more complex for radio frequency (RF) applications than for digital applications. Using the devices in the key circuit blocks for typical RF transceivers, we review and give trends for the FoMs that characterize active and passive RF devices. These FoMs include transit frequency at unity current gain f/sub T/, maximum frequency of oscillation f/sub MAX/ at unit power gain, noise, breakdown voltage, capacitor density, varactor and inductor quality, and the like. We use the specifications for wireless communications systems to show how different Si-based devices may achieve acceptable FoMs. We focus on Si complementary metal-oxide-semiconductor (CMOS), Si Bipolar CMOS, and Si bipolar devices, including SiGe heterojunction bipolar transistors, RF devices, and integrated circuits (ICs). We analyze trends in the FoMs for Si-based RF devices and ICs and show how these trends relate to the technology nodes of the 2003 International Technology Roadmap for Semiconductors. We also compare FoMs for the best reported performance of research devices and for the performance of devices manufactured in high volumes, typically more than 10 000 devices. Certain commercial equipment, instruments, or materials are identified in this article to specify adequately the experimental or theoretical procedures. Such identification does not imply recommendation by any of the host institutions of the authors, nor does it imply that the equipment or materials are necessarily the best available for the intended purpose.


title: Understanding Consumers' Acceptance of Online Purchasing

abstract: This paper examines previous Technology Acceptance Model (TAM)-related studies in order to provide an expanded model that explains consumers’ acceptance of online purchasing. Our model provides extensions to the original TAM by including constructs such as social influence and voluntariness; it also examines the impact of external variables including trust, privacy, risk, and e-loyalty. We surveyed consumers in the United States and Australia. Our findings suggest that our expanded model serves as a very good predictor of consumers’ online purchasing behaviors. The linear regression model shows a respectable amount of variance explained for Behavioral Intention (R 2 = .627). Suggestions are provided for the practitioner and ideas are presented for future research.


title: Evaluation and the Information Needs of Organizational Leaders.

abstract: The work of evaluators is constantly being reshaped in recognition of changes in the information needs of stakeholders. New thinking about the functioning of organizations poses a challenge for evaluators to provide information to make organizations more effective in terms of their structure, coordination, and delivery of programs and services. While the ways in which evaluation can serve the needs of organizational management have been well documented, far less attention has been given to the evaluation needs of leaders. This paper conceptualizes key differences between management and leadership as a basis for suggesting ways in which evaluators might operate to serve the decision making needs of organizational leaders.


title: EEG evidence for mirror neuron dysfunction in autism spectrum disorders

abstract: Abstract Autism spectrum disorders (ASD) are largely characterized by deficits in imitation, pragmatic language, theory of mind, and empathy. Previous research has suggested that a dysfunctional mirror neuron system may explain the pathology observed in ASD. Because EEG oscillations in the mu frequency (8–13 Hz) over sensorimotor cortex are thought to reflect mirror neuron activity, one method for testing the integrity of this system is to measure mu responsiveness to actual and observed movement. It has been established that mu power is reduced (mu suppression) in typically developing individuals both when they perform actions and when they observe others performing actions, reflecting an observation/execution system which may play a critical role in the ability to understand and imitate others' behaviors. This study investigated whether individuals with ASD show a dysfunction in this system, given their behavioral impairments in understanding and responding appropriately to others' behaviors. Mu wave suppression was measured in ten high-functioning individuals with ASD and ten age- and gender-matched control subjects while watching videos of (1) a moving hand, (2) a bouncing ball, and (3) visual noise, or (4) moving their own hand. Control subjects showed significant mu suppression to both self and observed hand movement. The ASD group showed significant mu suppression to self-performed hand movements but not to observed hand movements. These results support the hypothesis of a dysfunctional mirror neuron system in high-functioning individuals with ASD.


title: Toward automated driving in cities using close-to-market sensors: An overview of the V-Charge Project

abstract: Future requirements for drastic reduction of CO2 production and energy consumption will lead to significant changes in the way we see mobility in the years to come. However, the automotive industry has identified significant barriers to the adoption of electric vehicles, including reduced driving range and greatly increased refueling times. Automated cars have the potential to reduce the environmental impact of driving, and increase the safety of motor vehicle travel. The current state-of-the-art in vehicle automation requires a suite of expensive sensors. While the cost of these sensors is decreasing, integrating them into electric cars will increase the price and represent another barrier to adoption. The V-Charge Project, funded by the European Commission, seeks to address these problems simultaneously by developing an electric automated car, outfitted with close-to-market sensors, which is able to automate valet parking and recharging for integration into a future transportation system. The final goal is the demonstration of a fully operational system including automated navigation and parking. This paper presents an overview of the V-Charge system, from the platform setup to the mapping, perception, and planning sub-systems.


title: Botnet Communication Patterns

abstract: Malicious botnets have become a common threat and pervade large parts of the Internet today. Existing surveys and taxonomies focus on botnet topologies, command and control protocols, and botnet objectives. Building on these research results, network-based detection techniques have been proposed that are capable of detecting known botnets. Methods for botnet establishment and operation have evolved significantly over the past decade resulting in the need for detection methods that are capable of detecting new, previously unknown types of botnets. In this paper we present an in-depth analysis of all network communication aspects in botnet establishment and operation. We examine botnet topology, protocols, and analyze a large set of very different and highly sophisticated existing botnets from a network communication perspective. Based on our analysis, we introduce a novel taxonomy of generalized communication patterns for botnet communication using standardized unified modeling language sequence diagrams. We furthermore examine data exchange options and investigate the influence of encryption and hiding techniques. Our generalized communication patterns provide a useful basis for the development of sophisticated network-based botnet detection mechanisms and can offer a key component for building protocol- and topology-independent network-based detectors.


title: An RF Energy Harvester System Using UHF Micropower CMOS Rectifier Based on a Diode Connected CMOS Transistor

abstract: This paper presents a new type diode connected MOS transistor to improve CMOS conventional rectifier's performance in RF energy harvester systems for wireless sensor networks in which the circuits are designed in 0.18 μm TSMC CMOS technology. The proposed diode connected MOS transistor uses a new bulk connection which leads to reduction in the threshold voltage and leakage current; therefore, it contributes to increment of the rectifier's output voltage, output current, and efficiency when it is well important in the conventional CMOS rectifiers. The design technique for the rectifiers is explained and a matching network has been proposed to increase the sensitivity of the proposed rectifier. Five-stage rectifier with a matching network is proposed based on the optimization. The simulation results shows 18.2% improvement in the efficiency of the rectifier circuit and increase in sensitivity of RF energy harvester circuit. All circuits are designed in 0.18 μm TSMC CMOS technology.


title: P-Scheduler: adaptive hierarchical scheduling in apache storm

abstract: With ever-accelerating data creation rates in Big Data applications, there is a need for efficient stream processing engines. Apache Storm has been of interest in both academia and industry because of its real-time, distributed, scalable and reliable framework for stream processing. In this paper, we propose an adaptive hierarchical scheduler for the Storm framework, to allocate the resources more efficiently and improve performance. In our method, we consider the data transfer rate and traffic pattern between Storm's tasks and assign highly-communicating task pairs to the same computing node by dynamically employing two phases of graph partitioning. We also calculate the number of required computing nodes in the cluster based on the overall load of the application and use this information to reduce inter-node traffic. Our performance evaluation shows a significant improvement compared to the default scheduler provided by Storm, which evenly distributes the tasks across the cluster, ignoring the communication patterns between them.


title: Leveraging driverless car investment in next generation integrated modular avionics (IMA)

abstract: The market opportunity for driver assisted, driver piloted, and fully driverless cars is projected to reach 42 Billion by 2025. As a result, advanced “IMA like” automotive solutions are being explored and are focused on developing and maturing next generation transformational IMA technologies. Five future IMA dual use focus areas that have the potential to be transformational enablers for affordability, technology refresh, and new capabilities in both automotive and avionics IMA markets are: 1.) heterogeneous manycore processing, 2.) scalable autonomy and data fusion software components, 3.) hypervisor enabled mixed criticality software infrastructure, 4.) unified QoS networking, and 5.) Model Based Design (MBD). This paper builds on the authors' previous exploration into IMA dual use potential with these five specific roadmap focus areas analyzed for specific requirements, architecture alternatives being explored, potential payoff, and gaps requiring further research/development before successful mainstream insertion into avionics systems.


title: Addressing cloud computing security issues

abstract: The recent emergence of cloud computing has drastically altered everyone's perception of infrastructure architectures, software delivery and development models. Projecting as an evolutionary step, following the transition from mainframe computers to client/server deployment models, cloud computing encompasses elements from grid computing, utility computing and autonomic computing, into an innovative deployment architecture. This rapid transition towards the clouds, has fuelled concerns on a critical issue for the success of information systems, communication and information security. From a security perspective, a number of unchartered risks and challenges have been introduced from this relocation to the clouds, deteriorating much of the effectiveness of traditional protection mechanisms. As a result the aim of this paper is twofold; firstly to evaluate cloud security by identifying unique security requirements and secondly to attempt to present a viable solution that eliminates these potential threats. This paper proposes introducing a Trusted Third Party, tasked with assuring specific security characteristics within a cloud environment. The proposed solution calls upon cryptography, specifically Public Key Infrastructure operating in concert with SSO and LDAP, to ensure the authentication, integrity and confidentiality of involved data and communications. The solution, presents a horizontal level of service, available to all implicated entities, that realizes a security mesh, within which essential trust is maintained.


title: cor, a Novel Carbon Monoxide Resistance Gene, Is Essential for Mycobacterium tuberculosis Pathogenesis

abstract: Tuberculosis, caused by Mycobacterium tuberculosis, remains a devastating human infectious disease, causing two million deaths annually. We previously demonstrated that M. tuberculosis induces an enzyme, heme oxygenase (HO1), that pro- duces carbon monoxide (CO) gas and that M. tuberculosis adapts its transcriptome during CO exposure. We now demonstrate that M. tuberculosis carries a novel resistance gene to combat CO toxicity. We screened an M. tuberculosis transposon library for CO-susceptible mutants and found that disruption of Rv1829(carbon monoxide resistance, Cor) leads to marked CO sensitivity. Heterologous expression of Cor in Escherichia coli rescued it from CO toxicity. Importantly, the virulence of the cor mutant is attenuated in a mouse model of tuberculosis. Thus, Cor is necessary and sufficient to protect bacteria from host-derived CO. Taken together, this represents thefirst report of a role for HO1-derived CO in controlling infection of an intracellular pathogen and thefirst identification of a CO resistance gene in a pathogenic organism. IMPORTANCE Macrophages produce a variety of antimicrobial molecules, including nitric oxide (NO), hydrogen peroxide (H 2O2), and acid (H), that serve to kill engulfed bacteria. In addition to these molecules, human and mouse macrophages also produce carbon monoxide (CO) gas by the heme oxygenase (HO1) enzyme. We observed that, in contrast to other bacteria, my- cobacteria are resistant to CO, suggesting that this might be an evolutionary adaptation of mycobacteria for survival within mac- rophages. We screened a panel of ~2,500 M. tuberculosis mutants to determine which genes are required for survival of M. tu- berculosisin the presence of CO. Within this panel, we identified one such gene, cor, that specifically confers CO resistance. Importantly, we found that the ability of M. tuberculosis cells carrying a mutated copy of this gene to cause tuberculosis in a mouse disease model is significantly attenuated. This indicates that CO resistance is essential for mycobacterial survival in vivo.


title: Tuning Up the Old Brain with New Tricks: Attention Training via Neurofeedback

abstract: Neurofeedback is a form of biofeedback that uses real-time modulation of brain activity to enhance brain function and behavioral performance. Recent advances in Brain-Computer Interfaces (BCI) and cognitive training have provided new tools and evidence that neurofeedback improves cognitive functions, such as attention and working memory, beyond what is provided by traditional cognitive training. More published studies have demonstrated the efficacy of neurofeedback, particularly for treating attention deficit hyperactivity disorder (ADHD) in children. In contrast, there have been fewer studies done in older adults with or without cognitive impairment, with some notable exceptions. The focus of this review is to summarize current success in real-time neurofeedback training of older brains aiming to match those of younger brains during attention/working memory tasks. We also outline potential future advances in real-time brainwave-based neurofeedback for improving attention training in older populations. The rapid growth in wireless recording of brain activity, machine learning classification, and brain network analysis provides new tools for combating cognitive decline and brain aging in older adults. We optimistically conclude that neurofeedback, combined with new neuro-markers (event-related potentials and connectivity) and traditional features, promises to provide new hope for brain and cognitive training in the growing older population.


title: Towards a generic method of evaluating game levels

abstract: This paper addresses the problem of evaluating the quality of game levels across different games and even genres, which is of key importance for making procedural content generation and assisted game design tools more generally applicable. Three game design patterns are identified for having high generality while being easily quantifiable: area control, exploration and balance. Formulas for measuring the extent to which a level includes these concepts are proposed, and evaluation functions are derived for levels in two different game genres: mul-tiplayer strategy game maps and single-player roguelike dungeons. To illustrate the impact of these evaluation functions, and the similarity of impact across domains, sets of levels for each function are generated using a constrained genetic algorithm. The proposed measures can easily be extended to other game genres.


title: SenHint: A Joint Framework for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints

abstract: The state-of-the-art techniques for aspect-level sentiment analysis focus on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their practical performance may fall short of expectations due to semantic complexity of natural languages. Motivated by the observation that linguistic hints (e.g. explicit sentiment words and shift words) can be strong indicators of sentiment, we present a joint framework, SenHint, which integrates the output of deep neural networks and the implication of linguistic hints into a coherent reasoning model based on Markov Logic Network (MLN). In SenHint, linguistic hints are used in two ways: (1) to identify easy instances, whose sentiment can be automatically determined by machine with high accuracy; (2) to capture implicit relations between aspect polarities. We also empirically evaluate the performance of SenHint on both English and Chinese benchmark datasets. Our experimental results show that SenHint can effectively improve accuracy compared with the state-of-the-art alternatives.


title: Transcortical Selective Amygdalohippocampectomy in Temporal Lobe Epilepsy

abstract: Plusieurs etudes electrophysiologiques et neuropathologiques de l'epilepsie temporale ont demontre que les structures temporales mesiales sont le site d'origine des crises dans la majorite des cas. C'est la justification de l'approche selective transcorticale introduite par Niemeyer pour l'ablation de l'hippocampe et de l'amygdale. Des series de cas de plusieurs centres ont demontre l'efficacite de l'amygdalohippocampectomie selective comparee a une resection plus traditionnelle. La technique que nous decrivons ici et qui est utilisee a l'Institut neurologique de Montreal utilise une resection strictement endopiale de l'hippocampe et de l'amygdale sous guidage neuroradiologique pour effectuer l'intervention. Chez 90% des patients qui ont subi une amygdalohippocampectomie selective a l'Institut neurologique de Montreal les crises ont disparu ou ont diminue de plus de 90% avec des risques de complications minimes.


title: Prediction in Cyber Security: Complications and Consolations

abstract: Uncertainty, error, and similar complications add to the many challenges of cyber security. Various disciplines have developed methods for managing these complications, but applying these methods involves disambiguating overlapping terminology and determining a method's proper usage in the context of cyber security, which has unique properties. This process is here guided by the need for prediction, which is required for cyber security to become more like traditional sciences. A motivating malware analysis example is defined. A five-element ontology of complications for prediction is presented, and concepts from numerous disciplines are mapped to it in terms of the motivating example.


title: Self-determination theory and work motivation

abstract: Cognitive evaluation theory, which explains the effects of extrinsic motivators on intrinsic motivation, received some initial attention in the organizational literature. However, the simple dichotomy between intrinsic and extrinsic motivation made the theory difficult to apply to work settings. Differentiating extrinsic motivation into types that differ in their degree of autonomy led to self-determination theory, which has received widespread attention in the education, health care, and sport domains. This article describes self-determination theory as a theory of work motivation and shows its relevance to theories of organizational behavior. Copyright © 2005 John Wiley & Sons, Ltd.


Modified version FDI, Firm Heterogeneity and Exports An examination of evidence in India


title: Propeller Performance Data at Low Reynolds Numbers

abstract: While much research has been carried out on propellers for full-scale aircraft, not much data exists on propellers applicable to the ever growing number of UAVs. Many of these UAVs use propellers that must operate in the low Reynolds number range of 50,000 to 100,000 based on the propeller chord at the 75% propeller-blade station. Tests were performed at the University of Illinois at Urbana-Champaign (UIUC) to quantify the propeller efficiency at these conditions. In total, 79 propellers were tested and the majority fit in the 9- to 11-in diameter range. During the tests, the propeller speed (RPM) was fixed while changing the wind-tunnel speed to sweep over a range of advance ratios until reaching the windmill state (zero thrust). To examine Reynolds number effects, typically four RPM’s were tested in the range 1,500 to 7,500 RPM depending on the propeller diameter. Propeller efficiencies varied greatly from a peak near 0.65 (for an efficient pr opeller) to near 0.28 (for an exceptionally poor propeller). Thus, these results indicate that proper propeller selection for UAVs can have a dramatic effect on aircraft performance.


title: U-Net: a user-level network interface for parallel and distributed computing

abstract: The U-Net communication architecture provides processes with a virtual view of a network interface to enable userlevel access to high-speed communication devices. The architecture, implemented on standard workstations using offthe-shelf ATM communication hardware, removes the kernel from the communication path, while still providing full protection. The model presented by U-Net allows for the construction of protocols at user level whose performance is only limited by the capabilities of network. The architecture is extremely flexible in the sense that traditional protocols like TCP and UDP, as well as novel abstractions like Active Messages can be implemented efficiently. A U-Net prototype on an 8node ATM cluster of standard workstations offers 65 microseconds round-trip latency and 15 Mbytes/sec bandwidth. It achieves TCP performance at maximum network bandwidth and demonstrates performance equivalent to Meiko CS-2 and TMC CM-5 supercomputers on a set of Split-C benchmarks.


title: Gap plasmon-based metasurfaces for total control of reflected light

abstract: In the quest to miniaturise photonics, it is of paramount importance to control light at the nanoscale. We reveal the main physical mechanism responsible for operation of gap plasmon-based gradient metasurfaces, comprising a periodic arrangement of metal nanobricks, and suggest that two degrees of freedom in the nanobrick geometry allow one to independently control the reflection phases of orthogonal light polarisations. We demonstrate, both theoretically and experimentally, how orthogonal linear polarisations of light at wavelengths close to 800 nm can be manipulated independently, efficiently and in a broad wavelength range by realising polarisation beam splitters and polarisation-independent beam steering, showing at the same time the robustness of metasurface designs towards fabrication tolerances. The presented approach establishes a new class of compact optical components, viz., plasmonic metasurfaces with controlled gradient birefringence, with no dielectric counterparts. It can straightforwardly be adapted to realise new optical components with hitherto inaccessible functionalities.


title: Adversarial training and dilated convolutions for brain MRI segmentation

abstract: Convolutional neural networks (CNNs) have been applied to various automatic image segmentation tasks in medical image analysis, including brain MRI segmentation. Generative adversarial networks have recently gained popularity because of their power in generating images that are difficult to distinguish from real images.


title: Dead or Alive? the Development, Trajectory and Future of Technology Adoption Research

abstract: Research on individual-level technology adoption is one of the most mature streams of information systems (IS) research. In this paper, we compare the progress in the area of technology adoption with two widely-researched streams in psychology and organizational behavior: theory of planned behavior and job satisfaction. In addition to gauging the progress in technology adoption research, this allows us to identify some fruitful areas for future research. Based on our comparison, we conclude that there has been excellent progress in technology adoption research. However, as a next step, we call for research focused on interventions, contingencies, and alternative theoretical perspectives (to the largely social psychologybased technology adoption research. Also, we believe it would be important to use the comparisons discussed here as a basis to develop a framework-driven set of future research directions to guide further work in this area.


title: Deep tessellated retinal image detection using Convolutional Neural Networks

abstract: Tessellation in fundus is not only a visible feature for aged-related and myopic maculopathy but also confuse retinal vessel segmentation. The detection of tessellated images is an inevitable processing in retinal image analysis. In this work, we propose a model using convolutional neural network for detecting tessellated images. The input to the model is pre-processed fundus image, and the output indicate whether this photograph has tessellation or not. A database with 12,000 colour retinal images is collected to evaluate the classification performance. The best tessellation classifier achieves accuracy of 97.73% and AUC value of 0.9659 using pretrained GoogLeNet and transfer learning technique.


title: Small Molecule Immunosensing Using Surface Plasmon Resonance

abstract: Surface plasmon resonance (SPR) biosensors utilize refractive index changes to sensitively detect mass changes at noble metal sensor surface interfaces. As such, they have been extensively applied to immunoassays of large molecules, where their high mass and use of sandwich immunoassay formats can result in excellent sensitivity. Small molecule immunosensing using SPR is more challenging. It requires antibodies or high-mass or noble metal labels to provide the required signal for ultrasensitive assays. Also, it can suffer from steric hindrance between the small antigen and large antibodies. However, new studies are increasingly meeting these and other challenges to offer highly sensitive small molecule immunosensor technologies through careful consideration of sensor interface design and signal enhancement. This review examines the application of SPR transduction technologies to small molecule immunoassays directed to different classes of small molecule antigens, including the steroid hormones, toxins, drugs and explosives residues. Also considered are the matrix effects resulting from measurement in chemically complex samples, the construction of stable sensor surfaces and the development of multiplexed assays capable of detecting several compounds at once. Assay design approaches are discussed and related to the sensitivities obtained.


title: Variable clinical responses of a scrub typhus outbred mouse model to feeding by Orientia tsutsugamushi infected mites

abstract: Rodents are the natural hosts for Leptotrombidium mites that transmit Orientia tsutsugamushi, the causative agent of scrub typhus, a potentially fatal febrile human disease. Utilizing mite lines that included O. tsutsugamushi infected and non-infected Leptotrombidium species we investigated the varied infection response of outbred mice (ICR) exposed to L. chiangraiensis (Lc), L. imphalum (Li) and L. deliense (Ld). Each of six mite lines (Lc1, Lc5, Li3, Li4, Li7 and Ld) was separately placed in the inner ears of ICR mice either as a single individual (individual feeding, IF) or as a group of 2-4 individuals (pool feeding, PF). The species of infected chigger feeding on mice significantly affected mortality rates of the mice, with mite lines of Lc causing higher mean (±SE) mortality (90.7 ± 3.6 %) than mite lines of Li (62.9 ± 5.6 %) or Ld (53.6 ± 5.8 %). Mouse responses which included time to death, food consumption and total mice weight change depended on mite species and their O. tsutsugamushi genotype, more than on feeding procedure (IF vs. PF) except for mite lines within the Lc. Infected mite lines of Lc were the most virulent infected mites assessed whereas the infected Ld species was the least virulent for the ICR. Mice killed by various mite lines showed enlarged spleens and produced ascites. The results of this investigation of the clinical responses of ICR mice to feeding by various infected mite lines indicated that the different species of infected mites and their O. tsutsugamushi genotype produced different clinical presentations in ICR mice, a scrub typhus mouse model which mimics the natural transmission of O. tsutsugamushi that is critical for understanding scrub typhus disease in terms of natural transmission, host-pathogen-vector interaction and vaccine development.


title: Neural Net and Traditional Classifiers

abstract: Previous work on nets with continuous-valued inputs led to generative procedures to construct convex decision regions with two-layer perceptrons (one hidden layer) and arbitrary decision regions with three-layer perceptrons (two hidden layers). Here we demonstrate that two-layer perceptron classifiers trained with back propagation can form both convex and disjoint decision regions. Such classifiers are robust, train rapidly, and provide good performance with simple decision regions. When complex decision regions are required, however, convergence time can be excessively long and performance is often no better than that of k-nearest neighbor classifiers. Three neural net classifiers are presented that provide more rapid training under such situations. Two use fixed weights in the first one or two layers and are similar to classifiers that estimate probability density functions using histograms. A third "feature map classifier" uses both unsupervised and supervised training. It provides good performance with little supervised training in situations such as speech recognition where much unlabeled training data is available. The architecture of this classifier can be used to implement a neural net k-nearest neighbor classifier.