title: Variational Learning for Switching State-Space Models

abstract: We introduce a new statistical model for time series that iteratively segments data into regimes with approximately linear dynamics and learns the parameters of each of these linear regimes. This model combines and generalizes two of the most widely used stochastic time-series models— hidden Markov models and linear dynamical systems—and is closely related to models that are widely used in the control and econometrics literatures. It can also be derived by extending the mixture of experts neural network (Jacobs, Jordan, Nowlan, & Hinton, 1991) to its fully dynamical version, in which both expert and gating networks are recurrent. Inferring the posterior probabilities of the hidden states of this model is computationally intractable, and therefore the exact expectation maximization (EM) algorithm cannot be applied. However, we present a variational approximation that maximizes a lower bound on the log-likelihood and makes use of both the forward and backward recursions for hidden Markov models and the Kalman filter recursions for linear dynamical systems. We tested the algorithm on artificial data sets and a natural data set of respiration force from a patient with sleep apnea. The results suggest that variational approximations are a viable method for inference and learning in switching state-space models.


title: Hypocholesterolemic effect of an enteric-coated garlic supplement.

abstract: OBJECTIVE ::: To evaluate the hypocholesterolemic effect of an enteric-coated garlic supplement standardized for allicin-releasing potential in mild to moderate hypercholesterolemic patients. ::: ::: ::: METHODS ::: A double-blind randomized, placebo-controlled intervention study was conducted in 46 hypercholesterolemic subjects who had failed or were not compliant with drug therapy. Each subject was given dietary counseling to lower fat intake and enteric-coated Australian garlic powder tablets with 9.6 mg allicin-releasing potential or matching placebo tablets. ::: ::: ::: RESULTS ::: After 12 weeks, the garlic supplement group (n=22) had a significant reduction in total cholesterol (TC, -0.36 mmol/L. -4.2%) and LDL-cholesterol (LDL-C, -0.44 mmol/L, -6.6%) while the placebo group (n=24) had a non-significant increase in TC (0.13 mmol/L, 2.0%) and LDL-C (0.18 mmol/L, 3.7%). HDL-cholesterol was significantly increased in the placebo group (0.09 mmol/L, 9.1%), compared to the garlic group (-0.02 mmol/L, -0.9%). and no significant difference in triglycerides or in LDL/HDL ratio was observed between groups. ::: ::: ::: CONCLUSIONS ::: The study demonstrates that enteric-coated garlic powder supplements with 9.6 mg allicin-releasing potential may have value in mild to moderate hypercholesterolemic patients when combined with a low fat diet. Taken with other evidence, the efficacy of garlic for lipoprotein metabolism might require allicin bioavailability to be enhanced through the use of, for example, an enteric-coated dose form. If this is the case, the possibility remains that greater hypocholesterolemic efficacy may be evident at a higher allicin dose. Also noteworthy in this study was a small reduction in energy intake with garlic compared with placebo, attributable to reduction in fat, carbohydrate and alcohol intakes. This may also have contributed to the effects on blood lipids. This study suggests that garlic supplementation has a cholesterol-lowering effect, which may be mediated by direct action of a biologically active compound or compounds and in part through the effect on food and nutrient intake.


title: The generalized discontinuous PWM scheme for three-phase voltage source inverters

abstract: This paper presents analytical techniques for the determination of the expressions for the modulation signals used in the carrier-based sinusoidal and generalized discontinuous pulse-width modulation schemes for two-level, three-phase voltage source inverters. The proposed modulation schemes are applicable to inverters generating balanced or unbalanced phase voltages. Some results presented in this paper analytically generalize the several expressions for the modulation signals already reported in the literature and new ones are set forth for generating unbalanced three-phase voltages. Confirmatory experimental and simulation results are provided to illustrate the analyses.


title: GANosaic: Mosaic Creation with Generative Texture Manifolds

abstract: This paper presents a novel framework for generating texture mosaics with convolutional neural networks. Our method is called GANosaic and performs optimization in the latent noise space of a generative texture model, which allows the transformation of a content image into a mosaic exhibiting the visual properties of the underlying texture manifold. To represent that manifold, we use a state-of-the-art generative adversarial method for texture synthesis, which can learn expressive texture representations from data and produce mosaic images with very high resolution. This fully convolutional model generates smooth (without any visible borders) mosaic images which morph and blend different textures locally. In addition, we develop a new type of differentiable statistical regularization appropriate for optimization over the prior noise space of the PSGAN model.


title: Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models

abstract: Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retraining the model. By post-hoc learning latent constraints, value functions that identify regions in latent space that generate outputs with desired attributes, we can conditionally sample from these regions with gradient-based optimization or amortized actor functions. Combining attribute constraints with a universal "realism" constraint, which enforces similarity to the data distribution, we generate realistic conditional images from an unconditional variational autoencoder. Further, using gradient-based optimization, we demonstrate identity-preserving transformations that make the minimal adjustment in latent space to modify the attributes of an image. Finally, with discrete sequences of musical notes, we demonstrate zero-shot conditional generation, learning latent constraints in the absence of labeled data or a differentiable reward function. Code with dedicated cloud instance has been made publicly available (this https URL).


title: Extrinsic calibration of a single line scanning lidar and a camera

abstract: Lidar and visual imagery have been broadly utilized in computer vision and mobile robotics applications because these sensors provide complementary information. However, in order to convert data between the local coordinate systems, we must estimate the rigid body transformation between the sensors. In this paper, we propose a robust-weighted extrinsic calibration algorithm that is implemented easily and has small calibration error. The extrinsic calibration parameters are estimated by minimizing the distance between corresponding features projected onto the image plane. The features are edge and centerline features on a v-shaped calibration target. The proposed algorithm contributes two ways to improve the calibration accuracy. First, we use different weights to distance between a point and a line feature according to the correspondence accuracy of the features. Second, we apply a penalizing function to exclude the influence of outliers in the calibration data sets. We conduct several experiments to evaluate the performance of our extrinsic calibration algorithm, such as comparison of the RMS distance of the ground truth and the projected points, the effect of the number of lidar scan and image, and the effect of pose and range of the calibration target. In the experiments, we show our extrinsic calibration algorithm has calibration accuracy over 50% better than an existing state of the art approach. To evaluate the generality of our algorithm, we also colorize point clouds with different pairs of lidars and cameras calibrated by our algorithm.


title: Commonsense Reasoning in and Over Natural Language

abstract: ConceptNet is a very large semantic network of commonsense knowledge suitable for making various kinds of practical inferences over text. ConceptNet captures a wide range of commonsense concepts and relations like those in Cyc, while its simple semantic network structure lends it an ease-of-use comparable to WordNet. To meet the dual challenge of having to encode complex higher-order concepts, and maintaining ease-of-use, we introduce a novel use of semi-structured natural language fragments as the knowledge representation of commonsense concepts. In this paper, we present a methodology for reasoning flexibly about these semi-structured natural language fragments. We also examine the tradeoffs associated with representing commonsense knowledge in formal logic versus in natural language. We conclude that the flexibility of natural language makes it a highly suitable representation for achieving practical inferences over text, such as context finding, inference chaining, and conceptual analogy.


title: LSTM-Based ECG Classification for Continuous Monitoring on Personal Wearable Devices

abstract: Objective : A novel electrocardiogram (ECG) classification algorithm is proposed for continuous cardiac monitoring on wearable devices with limited processing capacity. Methods : The proposed solution employs a novel architecture consisting of wavelet transform and multiple long short-term memory (LSTM) recurrent neural networks (see Fig. 1 ). Results : Experimental evaluations show superior ECG classification performance compared to previous works. Measurements on different hardware platforms show the proposed algorithm meets timing requirements for continuous and real-time execution on wearable devices. Conclusion : In contrast to many compute-intensive deep-learning based approaches, the proposed algorithm is lightweight, and therefore, brings continuous monitoring with accurate LSTM-based ECG classification to wearable devices. Significance : The proposed algorithm is both accurate and lightweight. The source code is available online at http://lis.ee.sharif.edu .


title: Implementing effective software metrics programs

abstract: Increasingly organisations are foregoing an ad hoc approach to metrics in favor of complete metrics programs. The authors identify consensus requirements for metric program success and examine how programs in two organisations measured up.


title: An Enhanced Visualization Process Model for Incremental Visualization

abstract: With today's technical possibilities, a stable visualization scenario can no longer be assumed as a matter of course, as underlying data and targeted display setup are much more in flux than in traditional scenarios. Incremental visualization approaches are a means to address this challenge, as they permit the user to interact with, steer, and change the visualization at intermediate time points and not just after it has been completed. In this paper, we put forward a model for incremental visualizations that is based on the established Data State Reference Model, but extends it in ways to also represent partitioned data and visualization operators to facilitate intermediate visualization updates. In combination, partitioned data and operators can be used independently and in combination to strike tailored compromises between output quality, shown data quantity, and responsiveness—i.e., frame rates. We showcase the new expressive power of this model by discussing the opportunities and challenges of incremental visualization in general and its usage in a real world scenario in particular.


title: Bifurcation analysis of a Morris–Lecar neuron model

abstract: In this paper, we investigate the dynamical behaviors of a Morris---Lecar neuron model. By using bifurcation methods and numerical simulations, we examine the global structure of bifurcations of the model. Results are summarized in various two-parameter bifurcation diagrams with the stimulating current as the abscissa and the other parameter as the ordinate. We also give the one-parameter bifurcation diagrams and pay much attention to the emergence of periodic solutions and bistability. Different membrane excitability is obtained by bifurcation analysis and frequency-current curves. The alteration of the membrane properties of the Morris---Lecar neurons is discussed.


title: The amygdala as a hub in brain networks that support social life

abstract: A growing body of evidence suggests that the amygdala is central to handling the demands of complex social life in primates. In this paper, we synthesize extant anatomical and functional data from rodents, monkeys, and humans to describe the topography of three partially distinct large-scale brain networks anchored in the amygdala that each support unique functions for effectively managing social interactions and maintaining social relationships. These findings provide a powerful componential framework for parsing social behavior into partially distinct neural underpinnings that differ among healthy people and disintegrate or fail to develop in neuropsychiatric populations marked by social impairment, such as autism, antisocial personality disorder, and frontotemporal dementia.


title: Coarse-grain Fine-grain Coattention Network for Multi-evidence Question Answering

abstract: End-to-end neural models have made significant progress in question answering, however recent studies show that these models implicitly assume that the answer and evidence appear close together in a single document. In this work, we propose the Coarse-grain Fine-grain Coattention Network (CFC), a new question answering model that combines information from evidence across multiple documents. The CFC consists of a coarse-grain module that interprets documents with respect to the query then finds a relevant answer, and a fine-grain module which scores each candidate answer by comparing its occurrences across all of the documents with the query. We design these modules using hierarchies of coattention and self-attention, which learn to emphasize different parts of the input. On the Qangaroo WikiHop multi-evidence question answering task, the CFC obtains a new state-of-the-art result of 70.6% on the blind test set, outperforming the previous best by 3% accuracy despite not using pretrained contextual encoders.


title: Denoising Dictionary Learning Against Adversarial Perturbations

abstract: We propose denoising dictionary learning (DDL), a simple yet effective technique as a protection measure against adversarial perturbations. We examined denoising dictionary learning on MNIST and CIFAR10 perturbed under two different perturbation techniques, fast gradient sign (FGSM) and jacobian saliency maps (JSMA). We evaluated it against five different deep neural networks (DNN) representing the building blocks of most recent architectures indicating a successive progression of model complexity of each other. We show that each model tends to capture different representations based on their architecture. For each model we recorded its accuracy both on the perturbed test data previously misclassified with high confidence and on the denoised one after the reconstruction using dictionary learning. The reconstruction quality of each data point is assessed by means of PSNR (Peak Signal to Noise Ratio) and Structure Similarity Index (SSI). We show that after applying (DDL) the reconstruction of the original data point from a noisy


title: Bayesian Multitask Classification With Gaussian Process Priors

abstract: We present a novel approach to multitask learning in classification problems based on Gaussian process (GP) classification. The method extends previous work on multitask GP regression, constraining the overall covariance (across tasks and data points) to factorize as a Kronecker product. Fully Bayesian inference is possible but time consuming using sampling techniques. We propose approximations based on the popular variational Bayes and expectation propagation frameworks, showing that they both achieve excellent accuracy when compared to Gibbs sampling, in a fraction of time. We present results on a toy dataset and two real datasets, showing improved performance against the baseline results obtained by learning each task independently. We also compare with a recently proposed state-of-the-art approach based on support vector machines, obtaining comparable or better results.


title: Bucket ORAM: Single Online Roundtrip, Constant Bandwidth Oblivious RAM.

abstract: Known Oblivious RAM (ORAM) constructions achieve either optimal bandwidth blowup or optimal latency (as measured by online roundtrips), but not both. We are the first to demonstrate an ORAM scheme, called Bucket ORAM, which attains the best of both worlds. Bucket ORAM simultaneously achieves a single online roundtrip as well as constant overall bandwidth blowup.


title: Antioxidants in Wild Mushrooms

abstract: Maintenance of equilibrium between free radical production and antioxidant defences (enzymatic and non enzymatic) is an essential condition for normal organism functioning. When this equilibrium has a tendency for the production of free radicals we say that the organism is in oxidative stress. In this situation, excess free radicals may damage cellular lipids, proteins and DNA, affecting normal function and leading to various diseases. In aerobic organisms, the free radicals are constantly produced during the normal cellular metabolism, mainly in the form of Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS). Exposition of the organism to free radicals has led to the development of endogenous defence mechanisms to eliminate them. These defences were the response of evolution to the inevitability of ROS production in aerobic conditions. Natural products with antioxidant activity may help the endogenous defence system. In this perspective the antioxidants present in the diet assume a major importance as possible protector agents reducing oxidative damage. Particularly, the antioxidant properties of wild mushrooms have been extensively studied by our research group and by others, and many antioxidant compounds extracted from these sources have been identified, such as phenolic compounds, tocopherols, ascorbic acid, and carotenoids. We will review the compounds identified so far in mushrooms, as well as the mechanism of action involved in their antioxidant properties. Wild mushrooms might be used directly in diet and promote health, taking advantage of the additive and synergistic effects of all the bioactive compounds present.


title: On Extracting Structured Knowledge from Unstructured Business Documents

abstract: Efficient management of text data is a major concern of business organizations. In this direction, we propose a novel approach to extract structured knowledge from large corpora of unstructured business documents. This knowledge is represented in the form of object instances, which are common ways of organizing the available information about entities, and are modeled here using document templates. The approach itself is based on the observation that a significant fraction of these documents are created using the cut-copy-paste method, and thus, it is important to factor this observation into business document analysis projects. Correspondingly, our approach solves the problem of object instance extraction in two steps, namely similarity search and then extraction of object instances from the selected documents. Early qualitative results on a couple of carefully selected document corpora indicate the effective applicability of the approach for solving an important component of the efficient text management problem.


An Experimental Study of Genetic Crossover Operators for the Job Shop Scheduling Problem


title: Supermarket commodity identification using convolutional neural networks

abstract: In recent years, with the rapid development of deep learning, it has achieved great success in the field of image recognition. In this paper, we applied the convolution neural network (CNN) on supermarket commodity identification, contributing to the study of supermarket commodity identification. Different from the QR code identification of supermarket commodity, our work applied the CNN using the collected images of commodity as input. This method has the characteristics of fast and non-contact. In this paper, we mainly did the following works: 1. Collected a small dataset of supermarket goods. 2. Built Different convolutional neural network frameworks in caffe and trained the dataset using the built networks. 3. Improved train methods by finetuning the trained model.


title: Peak fronting in reversed-phase high-performance liquid chromatography: a study of the chromatographic behavior of oxycodone hydrochloride.

abstract: Abstract Severe peak asymmetry —fronting— was observed for oxycodone during elution at 30°C from a C 18 HPLC column using a mobile phase consisting of 14.9% MeOH, 84.5% 0.05 M KH 2 PO 4 (pH 3.0), 0.5% MTBE, and 0.1% TEA. Investigation using deuterium-labeled oxycodone and analysis by LC/MS showed that gem–diol and hemiketal adducts of oxycodone formed as a result of the equilibrium addition of water and methanol to the C-6 ketone on oxycodone. As a result of slow equilibrium kinetics at room temperature in aqueous solution, the gem–diol and methyl hemiketal eluted as an unresolved broad band in front of the oxycodone peak. Decreasing the column temperature to 0°C decreased the rates of interconversion and allowed the resolution and separation of these species from each other and from oxycodone. Increasing the column temperature to 60°C increased the rates of interconversion with the result that the three species eluted as a single, homogenous peak with greatly improved peak symmetry.(Abstract is approximately 157 words)


title: Parental Control vs. Teen Self-Regulation: Is there a middle ground for mobile online safety?

abstract: We conducted a structured, qualitative feature analysis of 75 Android mobile apps designed for the purpose of promoting adolescent online safety. Through this analysis we identified 42 unique features that mapped to a theoretically derived conceptual framework of teen online safety strategies balanced between parental control strategies (through monitoring, restriction, and active mediation) and teen self-regulation strategies (through self-monitoring, impulse control, and risk-coping). We found that the apps strongly favored features that promote parental control through monitoring and restricting teens' online behaviors over teen self-regulation or more communicative and collaborative practices between parents and teens. We use the lens of value sensitive design to discuss the implications of our results and identify opportunities for designing mobile apps for online safety that embed more positive family values.


Adaptive BFT protocols


Empirical Studies on Online Information Privacy Concerns: Literature Review and an Integrative Framework


title: A Common Grounded Z-Source DC–DC Converter With High Voltage Gain

abstract: In this paper, a common grounded Z-source dc–dc converter with high voltage gain is proposed for photovoltaic (PV) applications, which require a relatively high output–input voltage conversion ratio. Compared with the traditional Z-source dc–dc converter, the proposed converter, which employs a conventional Z-source network, can obtain higher voltage gain and provide the common ground for the input and output without any additional components, which results in low cost and small size. Moreover, the proposed converter features low voltage stresses of the switch and diodes. Therefore, the efficiency and reliability of the proposed converter can be improved. The operating principle, parameters design, and comparison with other converters are analyzed. Simulation and experimental results are given to verify the aforementioned characteristics and theoretical analysis of the proposed converter.


title: Regression Models for Categorical Dependent Variables Using Stata

abstract: Preface PART I GENERAL INFORMATION Introduction What is this book about? Which models are considered? Whom is this book for? How is the book organized? What software do you need? Where can I learn more about the models? Introduction to Stata The Stata interface Abbreviations How to get help The working directory Stata file types Saving output to log files Using and saving datasets Size limitations on datasets Do-files Using Stata for serious data analysis Syntax of Stata commands Managing data Creating new variables Labeling variables and values Global and local macros Graphics A brief tutorial Estimation, Testing, Fit, and Interpretation Estimation Postestimation analysis Testing estat command Measures of fit Interpretation Confidence intervals for prediction Next steps PART II MODELS FOR SPECIFIC KINDS OF OUTCOMES Models for Binary Outcomes The statistical model Estimation using logit and probit Hypothesis testing with test and lrtest Residuals and influence using predict Measuring fit Interpretation using predicted values Interpretation using odds ratios with listcoef Other commands for binary outcomes Models for Ordinal Outcomes The statistical model Estimation using ologit and oprobit Hypothesis testing with test and lrtest Scalar measures of fit using fitstat Converting to a different parameterization The parallel regression assumption Residuals and outliers using predict Interpretation Less common models for ordinal outcomes Models for Nominal Outcomes with Case-Specific Data The multinomial logit model Estimation using mlogit Hypothesis testing of coefficients Independence of irrelevant alternatives Measures of fit Interpretation Multinomial probit model with IIA Stereotype logistic regression Models for Nominal Outcomes with Alternative-Specific Data Alternative-specific data organization The conditional logit model Alternative-specific multinomial probit The sturctural covariance matrix Rank-ordered logistic regression Conclusions Models for Count Outcomes The Poisson distribution The Poisson regression model The negative binomial regression model Models for truncated counts The hurdle regression model Zero-inflated count models Comparisons among count models Using countfit to compare count models More Topics Ordinal and nominal independent variables Interactions Nonlinear models Using praccum and forvalues to plot predictions Extending SPost to other estimation commands Using Stata more efficiently Conclusions Appendix A Syntax for SPost Commands Appendix B Description of Datasets References Author Index Subject Index


Aggressive language in an online hacking forum.


title: Learning Covariant Feature Detectors

abstract: Local covariant feature detection, namely the problem of extracting viewpoint invariant features from images, has so far largely resisted the application of machine learning techniques. In this paper, we propose the first fully general formulation for learning local covariant feature detectors. We propose to cast detection as a regression problem, enabling the use of powerful regressors such as deep neural networks. We then derive a covariance constraint that can be used to automatically learn which visual structures provide stable anchors for local feature detection. We support these ideas theoretically, proposing a novel analysis of local features in term of geometric transformations, and we show that all common and many uncommon detectors can be derived in this framework. Finally, we present empirical results on translation and rotation covariant detectors on standard feature benchmarks, showing the power and flexibility of the framework.


title: An Adjacent Differential Statistics Method for IRFPA Nonuniformity Correction

abstract: In this paper, a novel scene-based nonuniformity correction (SBNUC) method using statistics between adjacent detectors for infrared focal-plane array (IRFPA) is proposed. Differential operation is designed as a decorrelation process to minimize the scene information remained into the nonuniformity parameters updating. Then, a global transfer matrix is established to construct the nonuniformity compensation matrix for the entire image. The adjacent differential statistics are demonstrated to be more effective and sufficient to estimate the nonuniformity when temporal variation is lacking. In addition, the proposed method exhibits the more superior capability of converging and does better in correcting all spatial components of nonuniformity. Application in simulated imagery and real infrared image shows the proposed method's excellent performance in convergence and elimination of ghosting artifacts.


title: Being overweight has limited effect on SCARF osteotomy outcome for hallux valgus correction

abstract: PURPOSE ::: The purpose of this study was to investigate the association between body mass index (BMI) and the results of SCARF osteotomy of the first metatarsal for hallux valgus (HV) correction, as the literature on this is scant. ::: ::: ::: METHODS ::: This prospective study was carried out between 2011 and 2015. One hundred and thirty-three patients diagnosed with moderate to severe HV underwent a SCARF corrective osteotomy. We divided the patients into two groups according to their BMI: normal and overweight. Postoperative follow-up was two years. All patients were examined twice by two medical doctors simultaneously: pre-operatively and post-operatively at two years' follow-up. Data collected included biometrical records, X-rays [HV angle (HVA), intermetatarsal angle (IMA), American Orthopaedic Foot and Ankle Society Hallux Metatarsophalangeal Index (AOFAS-HMI) and visual analogue scale (VAS) for pain and satisfaction]. ::: ::: ::: RESULTS ::: There was a significant difference between patient age (p = 0.001), age at onset (p < 0.001) and AOFAS-HMI (p = 0.035) at follow-up. Other parameters were similar in both groups. ::: ::: ::: CONCLUSION ::: Regardless of BMI, the radiological outcome was comparable. Despite a significant difference in AOFAS-HMI results, pain and satisfaction level were similar. The authors agreed that high BMI has protective role in the prevalence of HV.


title: A deep learning approach for on-site plant leaf detection

abstract: Plant diseases are the major problem in the worldwide agriculture sector. Therefore, the early detection is essential for reducing economic losses and mitigating the seriousness of the global food problem. Some fast and accurate computer-based methods have been applied to detect plant diseases. However, as far as our best knowledge, all those methodologies only accept a narrow range image, typically one or limited number of target(s) are in the image frame as their input. Thus, they are time-consuming and difficult to be applied for on-site wide range images (e.g. images or videos from stationary surveillance camera). In this paper, we propose leaf localization method from on-site wide-angle images with a deep learning approach. Our method achieves a detection performance of 78.0% in F1-measure at 2.0 fps.


title: Applying data mining techniques to address critical process optimization needs in advanced manufacturing

abstract: Advanced manufacturing such as aerospace, semi-conductor, and flat display device often involves complex production processes, and generates large volume of production data. In general, the production data comes from products with different levels of quality, assembly line with complex flows and equipments, and processing craft with massive controlling parameters. The scale and complexity of data is beyond the analytic power of traditional IT infrastructures. To achieve better manufacturing performance, it is imperative to explore the underlying dependencies of the production data and exploit analytic insights to improve the production process. However, few research and industrial efforts have been reported on providing manufacturers with integrated data analytical solutions to reveal potentials and optimize the production process from data-driven perspectives. In this paper, we design, implement and deploy an integrated solution, named PDP-Miner, which is a data analytics platform customized for process optimization in Plasma Display Panel (PDP) manufacturing. The system utilizes the latest advances in data mining technologies and Big Data infrastructures to create a complete analytical solution. Besides, our proposed system is capable of supporting automatically configuring and scheduling analysis tasks, and balancing heterogeneous computing resources. The system and the analytic strategies can be applied to other advanced manufacturing fields to enable complex data analysis tasks. Since 2013, PDP-Miner has been deployed as the data analysis platform of ChangHong COC. By taking the advantages of our system, the overall PDP yield rate has increased from 91% to 94%. The monthly production is boosted by 10,000 panels, which brings more than 117 million RMB of revenue improvement per year.


title: Data-driven particle Filters for particle Markov Chain Monte Carlo

abstract: This paper proposes new automated proposal distributions for sequential Monte Carlo algorithms, including particle filtering and related sequential importance sampling methods. The wrights for these proposal distributions are easily established, as is the unbiasedness property of the resultant likelihood estimators, so that the methods may be used within a particle Markov chain Monte Carlo (PMCMC) inferential setting. Simulation exercises, based on a range of state space models, are used to demonstrate the linkage between the signal-to-noise ratio of the system and the performance of the new particle filters, in comparison with existing filters. In particular, we demonstrate that one of our proposed filters performs well in a high signal-to-noise ratio setting, that is, when the observation is informative in identifying the location of the unobserved state. A second filter, deliberately designed to draw proposals that are informed by both the current observation and past states, is shown to work well across a range of signal-to noise ratios and to be much more robust than the auxiliary particle filter, which is often used as the default choice. We then extend the study to explore the performance of the PMCMC algorithm using the new filters to estimate the likelihood function, once again in comparison with existing alternatives. Taking into consideration robustness to the signal-to-noise ratio, computation time and the efficiency of the chain, the second of the new filters is again found to be the best-performing method. Application of the preferred filter to a stochastic volatility model for weekly Australian/US exchange rate returns completes the paper.


title: Image Restoration Using Space-Variant Gaussian Scale Mixtures in Overcomplete Pyramids

abstract: In recent years, Bayes least squares-Gaussian scale mixtures (BLS-GSM) has emerged as one of the most powerful methods for image restoration. Its strength relies on providing a simple and, yet, very effective local statistical description of oriented pyramid coefficient neighborhoods via a GSM vector. This can be viewed as a fine adaptation of the model to the signal variance at each scale, orientation, and spatial location. Here, we present an enhancement of the model by introducing a coarser adaptation level, where a larger neighborhood is used to estimate the local signal covariance within every subband. We formulate our model as a BLS estimator using space-variant GSM. The model can be also applied to image deconvolution, by first performing a global blur compensation, and then doing local adaptive denoising. We demonstrate through simulations that the proposed method, besides being model-based and noniterative, it is also robust and efficient. Its performance, measured visually and in L2-norm terms, is significantly higher than the original BLS-GSM method, both for denoising and deconvolution.


title: Covariance Matrix Adaptation Revisited – the CMSA Evolution Strategy –

abstract: The covariance matrix adaptation evolution strategy (CMA-ES) rates among the most successful evolutionary algorithms for continuous parameter optimization. Nevertheless, it is plagued with some drawbacks like the complexity of the adaptation process and the reliance on a number of sophisticatedly constructed strategy parameter formulae for which no or little theoretical substantiation is available. Furthermore, the CMA-ES does not work well for large population sizes. In this paper, we propose an alternative --- simpler --- adaptation step of the covariance matrix which is closer to the "traditional" mutative self-adaptation. We compare the newly proposed algorithm, which we term the CMSA-ES, with the CMA-ES on a number of different test functions and are able to demonstrate its superiority in particular for large population sizes.


title: Perceived Maternal Control and Responsiveness to Distress as Predictors of Young Adults’ Empathic Responses

abstract: Two studies examined the relations between young adults’ empathic responding and their perceptions of two maternal behaviors. As predicted from self-determination theory, perceived maternal control had unique negative associations with empathic support of one’s romantic partner (indicated by both self-reports and partner reports) and with empathic concern for others in general, and a unique positive association with personal distress in response to others in need. Perceived maternal responsiveness to distress was a unique positive predictor of empathic concern. The findings suggest that the experience of one’s mother as controlling is likely to interfere with one’s empathic responding and that high levels of perceived maternal responsiveness do not cancel the negative effects of the experience of controlling parenting. Furthermore, the findings suggest that high levels of perceived maternal responsiveness might exacerbate the negative relations between perceived maternal control and personal distress in r...


title: Building Change Detection Using Old Aerial Images and New LiDAR Data

abstract: Building change detection is important for urban area monitoring, disaster assessment and updating geo-database. 3D information derived from image dense matching or airborne light detection and ranging (LiDAR) is very effective for building change detection. However, combining 3D data from different sources is challenging, and so far few studies have focused on building change detection using both images and LiDAR data. This study proposes an automatic method to detect building changes in urban areas using aerial images and LiDAR data. First, dense image matching is carried out to obtain dense point clouds and then co-registered LiDAR point clouds using the iterative closest point (ICP) algorithm. The registered point clouds are further resampled to a raster DSM (Digital Surface Models). In a second step, height difference and grey-scale similarity are calculated as change indicators and the graph cuts method is employed to determine changes considering the contexture information. Finally, the detected results are refined by removing the non-building changes, in which a novel method based on variance of normal direction of LiDAR points is proposed to remove vegetated areas for positive building changes (newly building or taller) and nEGI (normalized Excessive Green Index) is used for negative building changes (demolish building or lower). To evaluate the proposed method, a test area covering approximately 2.1 km2 and consisting of many different types of buildings is used for the experiment. Results indicate 93% completeness with correctness of 90.2% for positive changes, while 94% completeness with correctness of 94.1% for negative changes, which demonstrate the promising performance of the proposed method.


Detailed Autonomic Nervous System Analysis of Microcurrent Point Stimulation Applied to Battlefield Acupuncture Protocol


title: Firm-Created Word-of-Mouth Communication: Evidence from a Field Test ∗

abstract: In this paper, we investigate the effectiveness of a firm's proactive management of customer-to-customer communication. We are particularly interested in understanding how, if at all, the firm should go about effecting meaningful word-of-mouth (WOM) communications. To tackle this problem, we collect data from two sources: (1) we implement a large-scale field test in which a national firm created word of mouth through two populations: customers and noncustomers, and (2) we collect data from an online experiment. We break our theoretical problem into two subproblems. First, we ask: “What kind of WOM drives sales?” Motivated by previous research, we hypothesize that for a product with a low initial awareness level, WOM that is most effective at driving sales is created by less loyal (not highly loyal) customers and occurs between acquaintances (not friends). We find support for this in the field test as well as in an experimental setting. Hence, we demonstrate the potential usefulness of exogenously created WOM: conversations are created where none would naturally have occured otherwise. Then, we ask: “Which agents are most effective at creating this kind of WOM?” In particular, we are interested in evaluating the effectiveness of the commonly used opinion leader designation. We find that although opinion leadership is useful in identifying potentially effective spreaders of WOM among very loyal customers, it is less useful for the sample of less loyal customers.


title: An Embedded Modbus Compliant Interactive Operator Interface for a Variable Frequency Drive Using Rs 485

abstract: The paper proposes the architecture and software design of a Modbus Compliant Operator Interface Panel (MCOIP) for a high speed Variable Frequency Drive (VFD) – a state of the art embedded design that offers several key advantages over the existing proprietary industrial models in use today. The use of serial Modbus RTU communication over RS485 allows an economically feasible, open source, vendor neutral, feature laden, robust and safe operating model. Through the use of an ARM based RISC microcontroller, the low response time of the design makes the human machine interface more real-time and interactive.


title: Learning Deep Sigmoid Belief Networks with Data Augmentation

abstract: Deep directed generative models are developed. The multi-layered model is designed by stacking sigmoid belief networks, with sparsity-encouraging priors placed on the model parameters. Learning and inference of layer-wise model parameters are implemented in a Bayesian setting. By exploring the idea of data augmentation and introducing auxiliary P olya-Gamma variables, simple and ecient Gibbs sampling and meaneld variational Bayes (VB) inference are implemented. To address large-scale datasets, an online version of VB is also developed. Experimental results are presented for three publicly available datasets: MNIST, Caltech 101 Silhouettes and OCR letters.


title: Consumer trust, perceived security and privacy policy: Three basic elements of loyalty to a web site

abstract: Purpose – The purpose of this paper is to analyze the effect of privacy and perceived security on the level of trust shown by the consumer in the internet. It also aims to reveal and test the close relationship between the trust in a web site and the degree of loyalty to it.Design/methodology/approach – First, there is an explanation of the main attributes of the concepts examined, with special attention being paid to the multi‐dimensional nature of the variables and the relationships between them. This is followed by an examination of the validation processes of the measuring instruments.Findings – Specifically, the study reveals that an individual's loyalty to a web site is closely linked to the levels of trust. Thus, the development of trust not only affects the intention to buy, as shown by previous researchers, but it also directly affects the effective purchasing behavior, in terms of preference, cost and frequency of visits, and therefore, the level of profitability provided by each consumer. In ad...


title: A systematic review of text stemming techniques

abstract: Stemming is a program that matches the morphological variants of the word to its root word. Stemming is extensively used as a pre-processing tool in the field of natural language processing, information retrieval, and language modeling. Though a lot of advancements have been made in the field, yet organized arrangement of the previous work and efforts are lacking in this field. In this paper, we present a review of the text stemming theory, algorithms, and applications. It first describes the existing literature relevant to text stemming by classifying it according to certain key parameters; then it describes the deep analysis of some well-known stemming algorithms on standard data sets. In the end, the current state-of-the-art and certain open issues related to unsupervised stemming are presented. The main aim of this paper is to provide an extensive and useful understanding of the important aspects of text stemming. The open issues and analysis of the current stemming techniques will help the researchers to think of new lines to conduct research in future.


Is Palmomental Reflex Actually A Part of Feeding Behavior ?


TIPS 1346 No . of Pages 10 Review Wiping Out CGRP : Potential Cardiovascular Risks


title: Use of Attack Graphs in Security Systems

abstract: Attack graphs have been used to model the vulnerabilities of the systems and their potential exploits. The successful exploits leading to the partial/total failure of the systems are subject of keen security interest. Considerable effort has been expended in exhaustive modeling, analyses, detection, and mitigation of attacks. One prominent methodology involves constructing attack graphs of the pertinent system for analysis and response strategies. This not only gives the simplified representation of the system, but also allows prioritizing the security properties whose violations are of greater concern, for both detection and repair. We present a survey and critical study of state-of-the-art technologies in attack graph generation and use in security system. Based on our research, we identify the potential, challenges, and direction of the current research in using attack graphs.


title: Compact and broadband circular polarized Microstrip Antenna with wideband Axial-Ratio bandwidth

abstract: In this paper, a novel stacked microstrip antenna (MSA) is proposed for wideband circular polarization. The antenna consists of two rectangular patches with two orthogonal slots at the center of each patch. The lower patch is excited with a coaxial line. The normalized bandwidths of the input impedance and axial ratio (AR) of the proposed antenna are found to be 17% and 13% respectively. Also, comparing the proposed antenna with a similar structure but without any slots, it is found to be 10% more compact. The proposed antenna is simulated using commercial electromagnetic software IE3D which is based on the method of moment (MoM).


title: Designing robust network topologies for wireless sensor networks in adversarial environments

abstract: Abstract In this paper, we address the problem of deploying sink nodes in a wireless sensor network such that the resulting network topology be robust. In order to measure network robustness, we propose a new metric, called persistence, which better captures the notion of robustness than the widely known connectivity based metrics. We study two variants of the sink deployment problem: sink selection and sink placement. We prove that both problems are NP-hard, and show how the problem of sink placement can be traced back to the problem of sink selection using an optimal search space reduction technique, which may be of independent interest. To solve the problem of sink selection, we propose efficient heuristic algorithms. Finally, we provide experimental results on the performance of our proposed algorithms.


title: Is there any role for physical therapy in chronic GvHD?

abstract: Chronic GvHD is the leading cause of non-relapse mortality in recipients of hematopoietic cell transplantation. Although the benefit of physical therapy (PT) has been reported in some GvHD studies, a literature gap is identified in demonstrating the exact role of different types of PT interventions and their impact on GvHD outcomes. An electronic search was undertaken using 13 peer-reviewed databases from 1994 to 2016. JADAD scoring method was used to score the quality of articles. PT interventions utilized for non-GvHD aspects of transplantation were excluded. Out of the 4775 articles on the electronic search, 297 articles were reviewed out of which 3 fulfilled the selection criteria. Moderately high evidence for effectiveness of supervised PT intervention was found, whereas moderate evidence for a self-administered exercise program was established. No safety concerns with PT were observed in any of the studies, however none of the studies were conducted to directly evaluate safety and effectiveness specifically in GvHD patients. PT is a safe but understudied therapy for GvHD. Limited evidence on the effectiveness of most PT interventions is available through randomized control trials. Well-designed trials are urgently needed for musculoskeletal GvHD especially with focused PT interventions.


title: ACCNet: Actor-Coordinator-Critic Net for"Learning-to-Communicate"with Deep Multi-agent Reinforcement Learning

abstract: Communication is a critical factor for the big multi-agent world to stay organized and productive. Typically, most previous multi-agent "learning-to-communicate" studies try to predefine the communication protocols or use technologies such as tabular reinforcement learning and evolutionary algorithm, which can not generalize to changing environment or large collection of agents. ::: In this paper, we propose an Actor-Coordinator-Critic Net (ACCNet) framework for solving "learning-to-communicate" problem. The ACCNet naturally combines the powerful actor-critic reinforcement learning technology with deep learning technology. It can efficiently learn the communication protocols even from scratch under partially observable environment. We demonstrate that the ACCNet can achieve better results than several baselines under both continuous and discrete action space environments. We also analyse the learned protocols and discuss some design considerations.


title: Spatio-Temporal Neural Networks for Space-Time Series Forecasting and Relations Discovery

abstract: We introduce a dynamical spatio-temporal model formalized as a recurrent neural network for forecasting time series of spatial processes, i.e. series of observations sharing temporal and spatial dependencies. The model learns these dependencies through a structured latent dynamical component, while a decoder predicts the observations from the latent representations. We consider several variants of this model, corresponding to different prior hypothesis about the spatial relations between the series. The model is evaluated and compared to state-of-the-art baselines, on a variety of forecasting problems representative of different application areas: epidemiology, geo-spatial statistics and car-traffic prediction. Besides these evaluations, we also describe experiments showing the ability of this approach to extract relevant spatial relations.


title: Friendly, Appealing or Both?: Characterising User Experience in Sponsored Search Landing Pages

abstract: Many of today's websites have recognised the importance of mobile friendly pages to keep users engaged and to provide a satisfying user experience. However, next to the experience provided by the sites themselves, advertisements, when clicked, present users with landing pages that are not necessarily mobile friendly. We explore what type of features are able to characterise the mobile friendliness of sponsored search ad landing pages. To have a complete understanding of the mobile ad experience in terms of layout and visual appearance, we also explore the notion of the ad page aesthetic appeal. We design and collect annotations for both dimensions on a large set of ads, and find that mobile friendliness and aesthetics represent different notions. We perform a comprehensive study of the effectiveness of over 120 features on the tasks of friendliness and aesthetics prediction. We find that next to general page size, HTML, and resource usage based features, several features based on the visual composition of landing pages are important to determine mobile friendliness and aesthetics. We demonstrate the additional benefit of these various types of features by comparing against the mobile friendliness guidelines provided by W3C. Finally, we use our models to determine the state of landing page mobile friendliness and aesthetics on a large sample of advertisements of a major internet company.


title: Parallel On-Demand Hierarchy Construction on Contemporary GPUs

abstract: We present the first parallel on-demand spatial hierarchy construction algorithm targeting ray tracing on many-core processors such as GPUs. The method performs simultaneous ray traversal and spatial hierarchy construction focused on the parts of the data structure being traversed. The method is based on a versatile framework built around a task pool and runs entirely on the GPU. We show that the on-demand construction can improve rendering times compared to full hierarchy construction. We evaluate our method on both object (BVH) and space (kd-tree) subdivision data structures and compare them mutually. The on-demand method is particularly beneficial for rendering large scenes with high occlusion. We also present SAH kd-tree builder that outperforms previous state-of-the-art builders running on the GPU.


title: Efficient Collision Detection Using Bounding Volume Hierarchies of k-DOPs

abstract: Collision detection is of paramount importance for many applications in computer graphics and visualization. Typically, the input to a collision detection algorithm is a large number of geometric objects comprising an environment, together with a set of objects moving within the environment. In addition to determining accurately the contacts that occur between pairs of objects, one needs also to do so at real-time rates. Applications such as haptic force feedback can require over 1000 collision queries per second. We develop and analyze a method, based on bounding-volume hierarchies, for efficient collision detection for objects moving within highly complex environments. Our choice of bounding volume is to use a discrete orientation polytope (k-DOP), a convex polytope whose facets are determined by halfspaces whose outward normals come from a small fixed set of k orientations. We compare a variety of methods for constructing hierarchies (BV-trees) of bounding k-DOPs. Further, we propose algorithms for maintaining an effective BV-tree of k-DOPs for moving objects, as they rotate, and for performing fast collision detection using BV-trees of the moving objects and of the environment. Our algorithms have been implemented and tested. We provide experimental evidence showing that our approach yields substantially faster collision detection than previous methods.


title: GRASS GIS : A multi-purpose open source GIS

abstract: The GIS software sector has developed rapidly over the last ten years. Open Source GIS applications are gaining relevant market shares in academia, business, and public administration. In this paper, we illustrate the history and features of a key Open Source GIS, the Geographical Resources Analysis Support System (GRASS). GRASS has been under development for more than 28 years, has strong ties into academia, and its review mechanisms led to the integration of well tested and documented algorithms into a joint GIS suite which has been used regularly for environmental modelling. The development is community-based with developers distributed globally. Through the use of an online source code repository, mailing lists and a Wiki, users and developers communicate in order to review existing code and develop new methods. In this paper, we provide a functionality overview of the more than 400 modules available in the latest stable GRASS software release. This new release runs natively on common operating systems (MS-Windows, GNU/Linux, Mac OSX), giving basic and advanced functionality to casual and expert users. In the second part, we review selected publications with a focus on environmental modelling to illustrate the wealth of use cases for this open and free GIS.


title: Living Well Alaska: An Evaluation of a Distance Delivery Project

abstract: Living Well Alaska is based on Stanford University’s Chronic Disease Self-Management Program (CDSMP). It is a six-week workshop designed to help Alaskans manage their chronic conditions. The program’s current infrastructure is based in Anchorage. The disadvantage of this situation is that workshops are less likely to be available to rural Alaskans. This Chronicle describes our efforts to bring this program to two remote regions of Alaska


title: Automated requirements engineering: Use case patterns-driven approach

abstract: Based on a novel multi-phases process, a new use case patterns catalogue is constructed. This catalogue is then utilised as a framework for a new use case patterns-driven approach for requirement engineering. The application of the proposed approach in a TestWarehouse environment showed promising results in saving up to 30% of the total software development project time, yet the resulted requirements models showed high, 85–95%, completeness percentage. Furthermore, the application of an automated version of the proposed approach saved an extra 43% of the time saved by its manual application. This had direct implications on improving requirements and design deliverables of agile software development processes. Nevertheless, users raised a number of concerns that have been considered to outline prospective phases of this research.


title: Contrary Positions about Modeling Knowledge Work

abstract: Modeling for knowledge management (KM) is fundamentally influenced by decisions about which approach, focus, perspectives or modeling language should be used. This paper characterizes three key decisions with regard to what modeling should focus on, which units of description are appropriate and what level of granularity is suited and relates them to a general framework that structures the quality of conceptual modeling. For each decision, contrary positions are highlighted and based on this investigation theses about modeling knowledge work are formulated. It is argued for a more flexible modeling of knowledge work by starting from hot spots of knowledge work with high potential for improvement, using situations as basic units of description and modeling on task level to provide starting points for KM support. The theses developed are related to the concept of knowledge stance that seems to be a promising approach to describe recurring situations of knowledge work.


title: Hemodynamic Assessment of Pre- and Post-operative Aortic Coarctation from MRI

abstract: Coarctation of the aorta (CoA), is a congenital defect characterized by a severe narrowing of the aorta, usually distal to the aortic arch. The treatment options include surgical repair, stent implantation, and balloon angioplasty. In order to evaluate the physiological significance of the pre-operative coarctation and to assess the post-operative results, the hemodynamic analysis is usually performed by measuring the pressure gradient (deltaP) across the coarctation site via invasive cardiac catheterization. The measure of success is reduction of the (deltaP > 20 mmHg) systolic blood pressure gradient. In this paper, we propose a non-invasive method based on Computational Fluid Dynamics and MR imaging to estimate the pre- and post-operative hemodynamics for both native and recurrent coarctation patients. High correlation of our results and catheter measurements is shown on corresponding pre- and post-operative examination of 5 CoA patients.


title: Hand-Drawn Face Sketch Recognition by Humans and a PCA-Based Algorithm for Forensic Applications

abstract: Because face sketches represent the original faces in a very concise yet recognizable form, they play an important role in criminal investigations, human visual perception, and face biometrics. In this paper, we compared the performances of humans and a principle component analysis (PCA)-based algorithm in recognizing face sketches. A total of 250 sketches of 50 subjects were involved. All of the sketches were drawn manually by five artists (each artist drew 50 sketches, one for each subject). The experiments were carried out by matching sketches in a probe set to photographs in a gallery set. This study resulted in the following findings: 1) A large interartist variation in terms of sketch recognition rate was observed; 2) fusion of the sketches drawn by different artists significantly improved the recognition accuracy of both humans and the algorithm; 3) human performance seems mildly correlated to that of PCA algorithm; 4) humans performed better in recognizing the caricature-like sketches that show various degrees of geometrical distortion or deviation, given the particular data set used; 5) score level fusion with the sum rule worked well in combining sketches, at least for a small number of artists; and 6) the algorithm was superior with the sketches of less distinctive features, while humans seemed more efficient in handling tonality (or pigmentation) cues of the sketches that were not processed with advanced transformation functions.


title: Deep Variational Canonical Correlation Analysis

abstract: We present deep variational canonical correlation analysis (VCCA), a deep multi-view learning model that extends the latent variable model interpretation of linear CCA~\citep{BachJordan05a} to nonlinear observation models parameterized by deep neural networks (DNNs). Computing the marginal data likelihood, as well as inference of the latent variables, are intractable under this model. We derive a variational lower bound of the data likelihood by parameterizing the posterior density of the latent variables with another DNN, and approximate the lower bound via Monte Carlo sampling. Interestingly, the resulting model resembles that of multi-view autoencoders~\citep{Ngiam_11b}, with the key distinction of an additional sampling procedure at the bottleneck layer. We also propose a variant of VCCA called VCCA-private which can, in addition to the ``common variables'' underlying both views, extract the ``private variables'' within each view. We demonstrate that VCCA-private is able to disentangle the shared and private information for multi-view data without hard supervision.


title: Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos

abstract: We present a scalable approach to automatically suggest relevant clothing products, given a single image without metadata. We formulate the problem as cross-scenario retrieval: the query is a real-world image, while the products from online shopping catalogs are usually presented in a clean environment. We divide our approach into two main stages: a) Starting from articulated pose estimation, we segment the person area and cluster promising image regions in order to detect the clothing classes present in the query image. b) We use image retrieval techniques to retrieve visually similar products from each of the detected classes. We achieve clothing detection performance comparable to the state-of-the-art on a very recent annotated dataset, while being more than 50 times faster. Finally, we present a large scale clothing suggestion scenario, where the product database contains over one million products.


title: Religious and ethnic group in uences on beliefs about mental illness : A qualitative interview study

abstract: An in-depth qualitative interview study is reported, with respondents ( N=52; all female) from the following urban-dwelling religious groups: White Christian, Pakistani Muslim, Indian Hindu, Orthodox Jewish and Afro-Caribbean Christian. Qualitative thematic analysis of open-ended interview responses revealed that the degree to which religious coping strategies were perceived to be effective in the face of depressive and schizophrenic symptoms, varied across the groups, with prayer being perceived as particularly effective among Afro-Caribbean Christian and Pakistani Muslim groups. Across all non-white groups, and also for the Jewish group, there was fear of being misunderstood by outgroup health professionals, and among Afro-Caribbean Christian and Pakistani Muslim participants, evidence of a community stigma associated with mental illness, leading to a preference for private coping strategies. The results lend further support to recent calls for ethnic-specie c mental health service provision and highlight the utility of qualitative methodology for exploring the link between religion and lay beliefs about mental illness.


title: Generative Adversarial Active Learning for Unsupervised Outlier Detection

abstract: Outlier detection is an important topic in machine learning and has been used in a wide range of applications. In this paper, we approach outlier detection as a binary-classification issue by sampling potential outliers from a uniform reference distribution. However, due to the sparsity of data in high-dimensional space, a limited number of potential outliers may fail to provide sufficient information to assist the classifier in describing a boundary that can separate outliers from normal data effectively. To address this, we propose a novel Single-Objective Generative Adversarial Active Learning (SO-GAAL) method for outlier detection, which can directly generate informative potential outliers based on the mini-max game between a generator and a discriminator. Moreover, to prevent the generator from falling into the mode collapsing problem, the stop node of training should be determined when SO-GAAL is able to provide sufficient information. But without any prior information, it is extremely difficult for SO-GAAL. Therefore, we expand the network structure of SO-GAAL from a single generator to multiple generators with different objectives (MO-GAAL), which can generate a reasonable reference distribution for the whole dataset. We empirically compare the proposed approach with several state-of-the-art outlier detection methods on both synthetic and real-world datasets. The results show that MO-GAAL outperforms its competitors in the majority of cases, especially for datasets with various cluster types or high irrelevant variable ratio. The experiment codes are available at: https://github.com/leibinghe/GAAL-based-outlier-detection


title: Multi-Feature Based Emotion Recognition for Video Clips

abstract: In this paper, we present our latest progress in Emotion Recognition techniques, which combines acoustic features and facial features in both non-temporal and temporal mode. This paper presents the details of our techniques used in the Audio-Video Emotion Recognition subtask in the 2018 Emotion Recognition in the Wild (EmotiW) Challenge. After the multimodal results fusion, our final accuracy in Acted Facial Expression in Wild (AFEW) test dataset achieves 61.87%, which is 1.53% higher than the best results last year. Such improvements prove the effectiveness of our methods.


title: Towards edge-caching for image recognition

abstract: With the available sensors on mobile devices and their improved CPU and storage capability, users expect their devices to recognize the surrounding environment and to provide relevant information and/or content automatically and immediately. For such classes of real-time applications, user perception of performance is key. To enable a truly seamless experience for the user, responses to requests need to be provided with minimal user-perceived latency. Current state-of-the-art systems for these applications require offloading requests and data to the cloud. This paper proposes an approach to allow users' devices and their onboard applications to leverage resources closer to home, i.e., resources at the edge of the network. We propose to use edge-servers as specialized caches for image-recognition applications. We develop a detailed formula for the expected latency for such a cache that incorporates the effects of recognition algorithms' computation time and accuracy. We show that, counter-intuitively, large cache sizes can lead to higher latencies. To the best of our knowledge, this is the first work that models edge-servers as caches for compute-intensive recognition applications.


title: Embodiment enables the spinal engine in quadruped robot locomotion

abstract: The biological hypothesis of spinal engine states that locomotion is mainly achieved by the spine, while the legs may serve as assistance. Inspired by this hypothesis, a compliant, multiple degree-of-freedom, biologically-inspired spine has been embedded into a quadruped robot, named Kitty, which has no actuation on the legs. In this paper, we demonstrate how versatile behaviors (bounding, trotting, and turning) can be generated exclusively by the spine's movements through dynamical interaction between the controller, the body, and the environment, known as embodiment. Moreover, we introduce information theoretic approach to quantitatively study the spine internal dynamics and its effect on the bounding gait based on three spinal morphologies. These three morphologies differ in the position of virtual spinal joint where the spine is easier to get bent. The experimental results reveal that locomotion can be enhanced by using the spine featuring a rear virtual spinal joint, which offers more freedom for the rear legs to move forward. In addition, the information theoretic analysis shows that, according to the morphological differences of the spine, the information structure changes. The relationship between the observed behavior of the robot and the corresponding information structure is discussed in detail.


title: Plastics recycling: challenges and opportunities

abstract: Plastics are inexpensive, lightweight and durable materials, which can readily be moulded into a variety of products that find use in a wide range of applications. As a consequence, the production of plastics has increased markedly over the last 60 years. However, current levels of their usage and disposal generate several environmental problems. Around 4 per cent of world oil and gas production, a non-renewable resource, is used as feedstock for plastics and a further 3-4% is expended to provide energy for their manufacture. A major portion of plastic produced each year is used to make disposable items of packaging or other short-lived products that are discarded within a year of manufacture. These two observations alone indicate that our current use of plastics is not sustainable. In addition, because of the durability of the polymers involved, substantial quantities of discarded end-of-life plastics are accumulating as debris in landfills and in natural habitats worldwide. Recycling is one of the most important actions currently available to reduce these impacts and represents one of the most dynamic areas in the plastics industry today. Recycling provides opportunities to reduce oil usage, carbon dioxide emissions and the quantities of waste requiring disposal. Here, we briefly set recycling into context against other waste-reduction strategies, namely reduction in material use through downgauging or product reuse, the use of alternative biodegradable materials and energy recovery as fuel. While plastics have been recycled since the 1970s, the quantities that are recycled vary geographically, according to plastic type and application. Recycling of packaging materials has seen rapid expansion over the last decades in a number of countries. Advances in technologies and systems for the collection, sorting and reprocessing of recyclable plastics are creating new opportunities for recycling, and with the combined actions of the public, industry and governments it may be possible to divert the majority of plastic waste from landfills to recycling over the next decades.


title: Axial-Ratio-Bandwidth Enhancement of a Microstrip-Line-Fed Circularly Polarized Annular-Ring Slot Antenna

abstract: The design of a new microstrip-line-fed wideband circularly polarized (CP) annular-ring slot antenna (ARSA) is proposed. Compared with existing ring slot antennas, the ARSAs designed here possess much larger CP bandwidths. The main features of the proposed design include a wider ring slot, a pair of grounded hat-shaped patches, and a deformed bent feeding microstrip line. The ARSAs designed using FR4 substrates in the L and S bands have 3-dB axial-ratio bandwidths (ARBWs) of as large as 46% and 56%, respectively, whereas the one using an RT5880 substrate in the L band, 65%. In these 3-dB axial-ratio bands, impedance matching with VSWR ≤ 2 is also achieved.


title: Integration of scheduling and advanced process control in semiconductor manufacturing: review and outlook

abstract: Scheduling in semiconductor manufacturing is of vital importance due to the impact on production performance indicators such as equipment utilization, cycle time, and delivery times. With the increasing complexity of semiconductor manufacturing, ever-new products and demanding customers, scheduling plans for efficient production control become crucial. Scheduling and control are mutually dependent as control requires information from scheduling, for example, where jobs are processed, and scheduling requires control information, for example, on which equipment operations can be processed. Based on a survey of the literature, this article proposes a review and an outlook for the potential improvements by binding scheduling decisions and information coming from advanced process control systems in semiconductor manufacturing.


title: Collaborative Filtering for Binary, Positiveonly Data

abstract: Traditional collaborative ltering assumes the availability of explicit ratings of users for items. However, in many cases these ratings are not available and only binary, positive-only data is available. Binary, positive-only data is typically associated with implicit feedback such as items bought, videos watched, ads clicked on, etc. However, it can also be the results of explicit feedback such as likes on social networking sites. Because binary, positive-only data contains no negative information, it needs to be treated differently than rating data. As a result of the growing relevance of this problem setting, the number of publications in this field increases rapidly. In this survey, we provide an overview of the existing work from an innovative perspective that allows us to emphasize surprising commonalities and key differences.


title: Pazl: A mobile crowdsensing based indoor WiFi monitoring system

abstract: WiFi in indoor environments exhibits spatio-temporal variations in terms of coverage and interference in typical WLAN deployments with multiple APs, motivating the need for automated monitoring to aid network administrators to adapt the WLAN deployment in order to match the user expectations. We develop Pazl, a mobile crowdsensing based indoor WiFi monitoring system that is enabled by a novel hybrid localization mechanism to locate individual measurements taken from participant phones. The localization mechanism in Pazl integrates the best aspects of two well known localization techniques, pedestrian dead reckoning and WiFi fingerprinting; it also relies on crowdsourcing for constructing the WiFi fingerprint database. Compared to existing WiFi monitoring systems based on static sniffers, Pazl is low cost and provides a user-side perspective. Pazl is significantly more automated than wireless site survey tools such as Ekahau Mobile Survey tool by drastically reducing the manual point-and-click based measurement location determination. We implement Pazl through a combination of Android mobile app and cloud backend application on the Google App Engine. Experimental evaluation of Pazl with a trial set of users shows that it yields similar results to manual site surveys but without the tedium.


title: Bootstrapping Labelled Dataset Construction for Cow Tracking and Behavior Analysis

abstract: This paper introduces a new approach to the long-term tracking of an object in a challenging environment. The object is a cow and the environment is an enclosure in a cowshed. Some of the key challenges in this domain are a cluttered background, low contrast and high similarity between moving objects which greatly reduces the efficiency of most existing approaches, including those based on background subtraction. Our approach is split into object localization, instance segmentation, learning and tracking stages. Our solution is compared to a range of semi-supervised object tracking algorithms and we show that the performance is strong and well suited to subsequent analysis. We present our solution as a first step towards broader tracking and behavior monitoring for cows in precision agriculture with the ultimate objective of early detection of lameness.


title: Statistical functions describing two-hop amplify and forward relay systems under Ricean fading

abstract: Motivated by the importance of the Ricean Statistical model in describing communication channels, in this paper we study statistical behavior of dual-hop amplify-and-forward relay transmission over non-identical Ricean fading channels. Fixed gain (FG) amplification at the relay is assumed. Two most important statistical functions describing total received signal-to-noise ratio are derived: probability density function (PDF) and cumulative distribution function (CDF). They are obtained in a form of rapidly converging infinite sum. The derived expressions are valid for different fading scenarios (different Ricean K factors and average SNRs), and coincide to previously published results for Rayleigh fading.


title: Social Networking Sites and Addiction: Ten Lessons Learned

abstract: Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.


title: Neutrosophic Logic Applied in the Statistical Indicators Theory

abstract: Neutrosophic numbers easily allow modeling uncertainties of prices universe, thus justifying the growing interest for theoretical and practical aspects of arithmetic generated by some special numbers in our work. At the beginning of this paper, we reconsider the importance in applied research of instrumental discernment, viewed as the main support of the final measurement validity.


title: Overview of the face recognition grand challenge

abstract: Over the last couple of years, face recognition researchers have been developing new techniques. These developments are being fueled by advances in computer vision techniques, computer design, sensor design, and interest in fielding face recognition systems. Such advances hold the promise of reducing the error rate in face recognition systems by an order of magnitude over Face Recognition Vendor Test (FRVT) 2002 results. The face recognition grand challenge (FRGC) is designed to achieve this performance goal by presenting to researchers a six-experiment challenge problem along with data corpus of 50,000 images. The data consists of 3D scans and high resolution still imagery taken under controlled and uncontrolled conditions. This paper describes the challenge problem, data corpus, and presents baseline performance and preliminary results on natural statistics of facial imagery.


title: The progression of online trust in the multi-channel retailer context and the role of product uncertainty

abstract: This research attempts to evaluate the effects of antecedents of online trust in the context of multi-channel retailers at different phases, taking into consideration the moderating effects of product types. The results reveal that multi-channel retailers' trust is transferred from the offline channel to the online channel. Secondly, the customers' initial interaction with the retailers ameliorates the effects of non-direct experience, such as Internet-based structural assurance and word-of-mouth. With increases in product uncertainty, the effects of word-of-mouth, offline trust, and efficacy of sanctions on online trust are greater for experience products than for search products. Highlights? We test the effects of antecedents of online trust in multi-channel retailer context. ? We test the moderating effects of experience products and search products. ? Multi-channel retailers' trust is moved from offline channel to online channel. ? Customers' initial interaction with the retailers forces the effects of experience. ? The effects on online trust are greater for experience products than search products.


title: Treatment of Criminals at Mari: A Survey

abstract: Professor Jack Sasson looks at crime and punishment within the justice system of the ancient city of Mari.


title: Deep Person Detection in 2D Range Data

abstract: Detecting humans is a key skill for mobile robots and intelligent vehicles in a large variety of applications. While the problem is well studied for certain sensory modalities such as image data, few works exist that address this detection task using 2D range data. However, a widespread sensory setup for many mobile robots in service and domestic applications contains a horizontally mounted 2D laser scanner. Detecting people from 2D range data is challenging due to the speed and dynamics of human leg motion and the high levels of occlusion and self-occlusion particularly in crowds of people. While previous approaches mostly relied on handcrafted features, we recently developed the deep learning based wheelchair and walker detector DROW. In this paper, we show the generalization to people, including small modifications that significantly boost DROW's performance. Additionally, by providing a small, fully online temporal window in our network, we further boost our score. We extend the DROW dataset with person annotations, making this the largest dataset of person annotations in 2D range data, recorded during several days in a real-world environment with high diversity. Extensive experiments with three current baseline methods indicate it is a challenging dataset, on which our improved DROW detector beats the current state-of-the-art.


title: An Optimization Design Method for Differential-Based Coupling Power-Slit Device

abstract: For a typical Full-hybrid electric vehicle (HEV) layout, such as Toyota Prius, the power-slit device (PSD) is a key component. Determination of volume or weight of a PSD is an important issue in preliminary design of power transmission applications. The purpose of this paper is to preliminary design of PSD gear drives by minimizing volume of gear trains. A stochastic approach Genetic Algorithm (GA) was applied to a differential-based coupling PSD design problem. Static and dynamic penalty functions were introduced to the objective function for handling the design constraints. The calculation of quadratic mean deviation and dynamic bounding parameter values such as determination of crossover and mutation parameter values were adopted to ensure the convergence of the fitness function. The optimized design method based on GA produced quite well results, which promptly supplied preliminary design parameters of gear drives for differential-based coupling PSD design.


title: "Will I always be not social?": Re-Conceptualizing Sociality in the Context of a Minecraft Community for Autism

abstract: Traditional face-to-face social interactions can be challenging for individuals with autism, leading some to perceive and categorize them as less social than their typically-developing peers. Individuals with autism may even see themselves as less social relative to their peers. Online communities can provide an alternative venue for social expression, enabling different types of communication beyond face-to-face, oral interaction. Using ethnographic methods, we studied the communication ecology that has emerged around a Minecraft server for children with autism and their allies. Our analysis shows how members of this community search for, practice, and define sociality through a variety of communication channels. These findings suggest an expansion in how sociality has traditionally been conceptualized for individuals with autism.


Picossecond testing and evaluation of three terminal devices


title: The Spanish version of the Body Image Scale (S-BIS): psychometric properties in a sample of breast and gynaecological cancer patients

abstract: Purpose ::: The aim of this study is to develop a Spanish version of the Body Image Scale (Hopwood et al. Eur J Cancer 37(2):189–197, 2001) and to analyze its psychometric properties in a sample of women with breast or gynaecological cancer.


title: Regularized linear discriminant analysis and its application in microarrays

abstract: SUMMARY In this paper, we introduce a modified version of linear discriminant analysis, called the “shrunken centroids regularized discriminant analysis” (SCRDA). This method generalizes the idea of the “nearest shrunken centroids” (NSC) (Tibshirani and others, 2003) into the classical discriminant analysis. The SCRDA method is specially designed for classification problems in high dimension low sample size situations, for example, microarray data. Through both simulated data and real life data, it is shown that this method performs very well in multivariate classification problems, often outperforms the PAM method (using the NSC algorithm) and can be as competitive as the support vector machines classifiers. It is also suitable for feature elimination purpose and can be used as gene selection method. The open source R package for this method (named “rda”) is available on CRAN (http://www.r-project.org) for download and testing.


title: Multi-Target Tracking for Swarm vs. Swarm UAV Systems

abstract: Abstract : Unmanned systems, including unmanned aerial vehicles (UAVs), are developing technologies that are becoming increasingly important. This thesis provides a model for generating a common operational picture (COP) for unmanned systems that is applicable in today's technology, and presents results and analysis based on simulation studies. This thesis specifically investigates a swarm versus swarm unmanned systems scenario in which opposing teams of UAVs approach each other. Different methodologies for generating a COP from the perspective of a given team are investigated, and a simulation is designed to explore the performance of the selected strategies for performing multi-target tracking. The results of the simulation show the performance of the presented approach where targets are assumed in the field of view of the tracking agents, false detections may or may not be present, and all entities maneuver according to nondeterministic motion models.


title: Topology control of multihop wireless networks using transmit power adjustment

abstract: We consider the problem of adjusting the transmit powers of nodes in a multihop wireless network (also called an ad hoc network) to create a desired topology. We formulate it as a constrained optimization problem with two constraints-connectivity and biconnectivity, and one optimization objective-maximum power used. We present two centralized algorithms for use in static networks, and prove their optimality. For mobile networks, we present two distributed heuristics that adaptively adjust node transmit powers in response to topological changes and attempt to maintain a connected topology using minimum power. We analyze the throughput, delay, and power consumption of our algorithms using a prototype software implementation, an emulation of a power-controllable radio, and a detailed channel model. Our results show that the performance of multihop wireless networks in practice can be substantially increased with topology control.


title: Integration of analytic hierarchy process and data envelopment analysis for assessment and optimization of personnel productivity in a large industrial bank

abstract: Research highlights? We assess and optimize productivity of personnel in a large private bank in Iran. ? An integrated algorithm based on data envelopment analysis and analytic hierarchy process is proposed for assessment and optimization purposes. ? It is concluded that the most branches inefficiencies are due to high teaching hours and lack of work quality. ? It is shown that number of personnel, discipline, personnel operation in the organization, and skills and capabilities are critical indicators so that even the smallest undesirable changes in these indicators have the most influences on the bank branches' efficiency. This paper presents an integration of analytical hierarchy process (AHP) and data envelopment analysis (DEA) for assessment and optimization of personnel productivity in a large private bank. In this algorithm the effective personnel operation indicators are evaluated by the management which is usually in qualitative forms and converted to quantitative forms by using AHP. Then, the ranking and efficiency of the organization will be assessed and optimized by DEA. Principal components analysis (PCA) and numeral taxonomy (NT) are applied to verify and validate the ranking results of the DEA method. We applied this algorithm in various branches of the Bank of Industry and Mine in Iran. The proposed framework may be used to study and optimize personnel productivity in large banks. This is the first study that integrated DEA and AHP for optimization of personnel productivity in large banks based on both qualitative and quantitative indicators.


title: Plasmonics simulations with the MNPBEM toolbox: Consideration of substrates and layer structures

abstract: Abstract Within the MNPBEM toolbox, developed for the simulation of plasmonic nanoparticles using a boundary element method approach, we show how to include substrate and layer structure effects. We develop the methodology for solving Maxwell’s equations using scalar and vector potentials within the inhomogeneous dielectric environment of a layer structure. We show that the implementation of our approach allows for fast and efficient simulations of plasmonic nanoparticles situated on top of substrates or embedded in layer structures. The new toolbox provides a number of demo files which can be also used as templates for other simulations. Program summary Program title: MNPBEM toolbox Catalogue identifier: AEKJ_v3_0 Program summary URL: http://cpc.cs.qub.ac.uk/summaries/AEKJ_v3_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 56958 No. of bytes in distributed program, including test data, etc.: 1258497 Distribution format: tar.gz Programming language: Matlab 8.3.0 (R2014a). Computer: Any which supports Matlab 8.3.0 (R2014a). Operating system: Any which supports Matlab 8.3.0 (R2014a). Has the code been vectorised or parallelized?: Yes RAM: ≥4 Gbyte Classification: 18. Catalogue identifier of previous version: AEKJ_v2_0 Journal reference of previous version: Comput. Phys. Comm. 185 (2014) 1177 Does the new version supersede the previous version?: Yes Nature of problem: Simulation of plasmonic nanoparticles placed on substrates or within layer structures. Solution method: Boundary element method using electromagnetic potentials Reasons for new version: Inclusion of substrate and layer structure effects Summary of revisions: • simulations with layer structures and substrates • simulation control through one options structure • refined boundary element integration • consideration of flat and curved particle boundaries • an improved plot function for particle objects • a new polygon3 class for the extrusion of 2d shapes • a new meshfield class for the computation of electric field maps Running time: Depending on surface discretization between seconds and hours.


title: Symmetry axis computation for almost-symmetrical and asymmetrical objects: Application to pigmented skin lesions

abstract: The detection of symmetry axes through the optimization of a given symmetry measure, computed as a function of the mean-square error between the original and reflected images, is investigated in this paper. A genetic algorithm and an optimization scheme derived from the self-organizing maps theory are presented. The notion of symmetry map is then introduced. This transform allows us to map an object into a symmetry space where its symmetry properties can be analyzed. The locations of the different axes that globally and locally maximize the symmetry value can be obtained. The input data are assumed to be vector-valued, which allow to focus on either shape. color or texture information. Finally, the application to skin cancer diagnosis is illustrated and discussed.


title: Compact Tapered Slot UWB Antenna With WLAN Band Rejection

abstract: This letter extends the design of a previously proposed uniplanar ultrawideband (UWB) antenna to include within its 3.110.6-GHz operation band a rejection of the wireless local area network (WLAN) band near 5.5 GHz. The antenna configuration is based on a combination of two crossed exponentially tapered slots plus a star-shaped slot printed on both sides of a 10-mils dielectric substrate. The band rejection is obtained with the addition of extra slots at the antenna back-face metallization. The radiation pattern and polarization are very stable over the entire UWB. Link measurements between two identical antennas demonstrate low pulse distortion over almost all the solid angle.


title: On the role of linguistic descriptions of data in the building of natural language generation systems

abstract: This paper explores the current state of the task of generating easily understandable information from data for people using natural language, which is currently addressed by two independent research fields: the natural language generation field - and, more specifically, the data-to-text sub-field - and the linguistic descriptions of data field. Both approaches are explained in a detailed description which includes: i) a methodological revision of both fields including basic concepts and definitions, models and evaluation procedures; ii) the most relevant systems, use cases and real applications described in the literature. Some reflections about the current state and future trends of each field are also provided, followed by several remarks that conclude by hinting at some potential points of mutual interest and convergence between both fields.


title: Retrieving documents with mathematical content

abstract: Many documents with mathematical content are published on the Web, but conventional search engines that rely on keyword search only cannot fully exploit their mathematical information. In particular, keyword search is insufficient when expressions in a document are not annotated with natural keywords or the user cannot describe her query with keywords. Retrieving documents by querying their mathematical content directly is very appealing in various domains such as education, digital libraries, engineering, patent documents, medical sciences, etc. Capturing the relevance of mathematical expressions also greatly enhances document classification in such domains. Unlike text retrieval, where keywords carry enough semantics to distinguish text documents and rank them, math symbols do not contain much semantic information on their own. In fact, mathematical expressions typically consist of few alphabetical symbols organized in rather complex structures. Hence, the structure of an expression, which describes the way such symbols are combined, should also be considered. Unfortunately, there is no standard testbed with which to evaluate the effectiveness of a mathematics retrieval algorithm. In this paper we study the fundamental and challenging problems in mathematics retrieval, that is how to capture the relevance of mathematical expressions, how to query them, and how to evaluate the results. We describe various search paradigms and propose retrieval systems accordingly. We discuss the benefits and drawbacks of each approach, and further compare them through an extensive empirical study.


History of Optimal Power Flow and Formulations Optimal Power Flow Paper 1