title: Coarse-to-fine optical flow estimation with image structure tensor

abstract: This paper proposes a novel coarse-to-fine optical flow method based on the image local structure tensor. The data term of the optical flow model is projected by combining the proposed structure tensor constancy assumption and the grey value constancy assumption with the robust penalty function, and an isotropic nonlinear function is planned to smoothing term. With the coarse-to-fine multiscale warping strategy, a linear iteration scheme is established. Several experimental results of the Middlebury sequences show that the presented method is high accuracy and good robustness.


title: Mining social networks using heat diffusion processes for marketing candidates selection

abstract: Social Network Marketing techniques employ pre-existing social networks to increase brands or products awareness through word-of-mouth promotion. Full understanding of social network marketing and the potential candidates that can thus be marketed to certainly offer lucrative opportunities for prospective sellers. Due to the complexity of social networks, few models exist to interpret social network marketing realistically. We propose to model social network marketing using Heat Diffusion Processes. This paper presents three diffusion models, along with three algorithms for selecting the best individuals to receive marketing samples. These approaches have the following advantages to best illustrate the properties of real-world social networks: (1) We can plan a marketing strategy sequentially in time since we include a time factor in the simulation of product adoptions; (2) The algorithm of selecting marketing candidates best represents and utilizes the clustering property of real-world social networks; and (3) The model we construct can diffuse both positive and negative comments on products or brands in order to simulate the complicated communications within social networks. Our work represents a novel approach to the analysis of social network marketing, and is the first work to propose how to defend against negative comments within social networks. Complexity analysis shows our model is also scalable to very large social networks.


title: Bayes Merging of Multiple Vocabularies for Scalable Image Retrieval

abstract: In the Bag-of-Words (BoW) model, the vocabulary is of key importance. Typically, multiple vocabularies are generated to correct quantization artifacts and improve recall. However, this routine is corrupted by vocabulary correlation, i.e., overlapping among different vocabularies. Vocabulary correlation leads to an over-counting of the indexed features in the overlapped area, or the intersection set, thus compromising the retrieval accuracy. In order to address the correlation problem while preserve the benefit of high recall, this paper proposes a Bayes merging approach to down-weight the indexed features in the intersection set. Through explicitly modeling the correlation problem in a probabilistic view, a joint similarity on both image- and feature-level is estimated for the indexed features in the intersection set. We evaluate our method on three benchmark datasets. Albeit simple, Bayes merging can be well applied in various merging tasks, and consistently improves the baselines on multi-vocabulary merging. Moreover, Bayes merging is efficient in terms of both time and memory cost, and yields competitive performance with the state-of-the-art methods.


title: From space syntax to space semantics: a behaviorally and perceptually oriented methodology for the efficient description of the geometry and topology of environments

abstract: Human spatial behavior and experience cannot be investigated independently from the shape and configuration of environments. Therefore, comparative studies in architectural psychology and spatial cognition would clearly benefit from operationalizations of space that provide a common denominator for capturing its behavioral and psychologically relevant properties. This paper presents theoretical and methodological issues arising from the practical application of isovist-based graphs for the analysis of architectural spaces. Based on recent studies exploring the influence of spatial form and structure on behavior and experience in virtual environments, the following topics are discussed: (1) the derivation and empirical verification of meaningful descriptor variables on the basis of classic qualitative theories of environmental psychology relating behavior and experience to spatial properties; (2) methods to select reference points for the analysis of architectural spaces at a local level; furthermore, based on two experiments exploring the phenomenal conception of the spatial structure of architectural environments, formalized strategies for (3) the selection of reference points at a global level, and for (4), their integration into a sparse yet plausible comprehensive graph structure, are proposed. Taken together, a well formalized and psychologically oriented methodology for the efficient description of spatial properties of environments at the architectural scale level is outlined. This method appears useful for a wide range of applications, ranging from abstract architectural analysis over behavioral experiments to studies on mental representations in cognitive science.


title: Motor Skill Acquisition

abstract: INTRODUCTION ......... .... . . . . . . . . . . . . . . . . . .. ... . . . . .... 2 1 3 A GENERAL LAW O F MOTOR LEARNING. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1 5 Motor Learning a s a Power Law..... .... ... . . . . . . . . .. . . . . . . . . . . ....... 2 1 6 The Power Law-A Special Case of Motor Learning? ...... . . .. .. 217 WHAT IS LEARNED WITH PRACTICE . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1 9 One-To-One Recall-Recognition Processes ... 2 1 9 Schema Representation o f Action 221 Problems with Prescriptive Accounts of Motor Learning .. . . . 223 Motor Skill Acquisition as a Search Strategy 224 INFORMATION AND MOTOR SKILL ACQUISITION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 Information as a Prescription 226 Information as Feedback.. .. .. . ...... . .. . ....... . . ... . . .... .. . . . .... ....... 227 Information to Channel the Search ... ... 231 CONCLUDING REMARKS .. . .. .. ...... 233


title: Emotional Intelligence Is Synergy in Intelligence

abstract: Three emotional intelligence models are widely discussed in the literature: the ability model, trait model, and mixed model. This study introduces a model for understanding emotional intelligence: the synergy model. The emotional intelligence model presented in this study is simple and easy to understand. This study suggests that emotional intelligence is the synergy present in intelligence, where total intelligence is the sum of the intelligence quotient, emotional quotient, and unexplained quotient. The model shows three possible outcomes of emotional intelligence. It also suggests that the best way to understand and measure the impact of emotional intelligence is to listen, observe, and feel (LOF) it.


title: Constraint-induced movement therapy as a rehabilitation intervention for upper extremity in stroke patients: systematic review and meta-analysis.

abstract: Constraint-induced movement therapy (CIMT) is a neurorehabilitation technique designed to improve upper extremity motor functions after stroke. This review aimed to investigate evidence of the effect of CIMT on upper extremity in stroke patients and to identify optimal methods to apply CIMT. Four databases (MEDLINE, EMBASE, CINHAL, and PEDro) and reference lists of relevant articles and reviews were searched. Randomized clinical trials that studied the effect of CIMT on upper extremity outcomes in stroke patients compared with other rehabilitative techniques, usual care, or no intervention were included. Methodological quality was assessed using the PEDro score. The following data were extracted for each trial: patients' characteristics, sample size, eligibility criteria, protocols of CIMT and control groups, outcome measurements, and the PEDro score. A total of 38 trials were identified according to the inclusion criteria. The trials included were heterogeneous in CIMT protocols, time since stroke, and duration and frequency of treatment. The pooled meta-analysis of 36 trials found a heterogeneous significant effect of CIMT on upper extremity. There was no significant effect of CIMT at different durations of follow-up. The majority of included articles did not fulfill powered sample size and quality criteria. The effect of CIMT changed in terms of sample size and quality features of the articles included. These meta-analysis findings indicate that evidence for the superiority of CIMT in comparison with other rehabilitative interventions is weak. Information on the optimal dose of CIMT and optimal time to start CIMT is still limited.


title: EcoLexiCAT: a Terminology-enhanced Translation Tool for Texts on the Environment

abstract: Although machine translation and computer assisted translation (CAT) are now a reality in the workflow of professional translators, terminology management is still considered complex and time-consuming and is often not seamlessly integrated into the translation process. Most terminographic resources are not designed to take into account the real search behavior of end users such as translators (Tudhope et al., 2006), and in many cases CAT tools do not provide ::: terminological modules that go beyond a simple glossary with interlinguistic equivalents. Furthermore, corpus consultation is rarely possible in most CAT tools, despite the fact that the phraseological information extracted from a corpus is of great help for translators. To address these issues, we created a web-based tool for the terminology-enhanced translation of specialized environmental texts for the language combination English-Spanish-English. EcoLexiCAT uses the open source version of th ::: e web-based CAT tool MateCat and enriches a source text with information from: (i) EcoLexicon, a multimodal and multilingual terminological knowledge base on the environment (Faber et al., 2014; Faber et al., 2016); (ii) BabelNet, an automatically constructed multilingual encyclopedic dictionary and semantic ::: network (Navigli & Ponzetto, 2012); (iii) and Sketch Engine, the well-known corpus query system (Kilgarriff et al., 2004).


title: The Social Influence of Brand Community: Evidence from European Car Clubs

abstract: Abstract The authors develop and estimate a conceptual model of how different aspects of customers' relationships with the brand community influence their intentions and behaviors. The authors describe how identification with the brand community leads to positive consequences, such as greater community engagement, and negative consequences, such as normative community pressure and (ultimately) reactance. They examine the moderating effects of customers' brand knowledge and the brand community's size and test their hypotheses by estimating a structural equation model with survey data from a sample of European car club members.


title: Brain correlates of autonomic modulation: Combining heart rate variability with fMRI

abstract: Abstract The central autonomic network (CAN) has been described in animal models but has been difficult to elucidate in humans. Potential confounds include physiological noise artifacts affecting brainstem neuroimaging data, and difficulty in deriving non-invasive continuous assessments of autonomic modulation. We have developed and implemented a new method which relates cardiac-gated fMRI timeseries with continuous-time heart rate variability (HRV) to estimate central autonomic processing. As many autonomic structures of interest are in brain regions strongly affected by cardiogenic pulsatility, we chose to cardiac-gate our fMRI acquisition to increase sensitivity. Cardiac-gating introduces T1-variability, which was corrected by transforming fMRI data to a fixed TR using a previously published method [Guimaraes, A.R., Melcher, J.R., et al., 1998. Imaging subcortical auditory activity in humans. Hum. Brain Mapp. 6(1), 33–41]. The electrocardiogram was analyzed with a novel point process adaptive-filter algorithm for computation of the high-frequency (HF) index, reflecting the time-varying dynamics of efferent cardiovagal modulation. Central command of cardiovagal outflow was inferred by using the resample HF timeseries as a regressor to the fMRI data. A grip task was used to perturb the autonomic nervous system. Our combined HRV-fMRI approach demonstrated HF correlation with fMRI activity in the hypothalamus, cerebellum, parabrachial nucleus/locus ceruleus, periaqueductal gray, amygdala, hippocampus, thalamus, and dorsomedial/dorsolateral prefrontal, posterior insular, and middle temporal cortices. While some regions consistent with central cardiovagal control in animal models gave corroborative evidence for our methodology, other mostly higher cortical or limbic-related brain regions may be unique to humans. Our approach should be optimized and applied to study the human brain correlates of autonomic modulation for various stimuli in both physiological and pathological states.


title: Positional language models for information retrieval

abstract: Although many variants of language models have been proposed for information retrieval, there are two related retrieval heuristics remaining "external" to the language modeling approach: (1) proximity heuristic which rewards a document where the matched query terms occur close to each other; (2) passage retrieval which scores a document mainly based on the best matching passage. Existing studies have only attempted to use a standard language model as a "black box" to implement these heuristics, making it hard to optimize the combination parameters. In this paper, we propose a novel positional language model (PLM) which implements both heuristics in a unified language model. The key idea is to define a language model for each position of a document, and score a document based on the scores of its PLMs. The PLM is estimated based on propagated counts of words within a document through a proximity-based density function, which both captures proximity heuristics and achieves an effect of "soft" passage retrieval. We propose and study several representative density functions and several different PLM-based document ranking strategies. Experiment results on standard TREC test collections show that the PLM is effective for passage retrieval and performs better than a state-of-the-art proximity-based retrieval model.


Magnetic Treatment of Irrigation Water and its Effect on Water Salinity


title: Robust text classification under confounding shift

abstract: As statistical classifiers become integrated into real-world applications, it is important to consider not only their accuracy but also their robustness to changes in the data distribution. Although identifying and controlling for confounding variables Z-correlated with both the input X of a classifier and its output Y-has been assiduously studied in empirical social science, it is often neglected in text classification. This can be understood by the fact that, if we assume that the impact of confounding variables does not change between the time we fit a model and the time we use it, then prediction accuracy should only be slightly affected. We show in this paper that this assumption often does not hold and that when the influence of a confounding variable changes from training time to prediction time (i.e. under confounding shift), the classifier accuracy can degrade rapidly. We use Pearl's back-door adjustment as a predictive framework to develop a model robust to confounding shift under the condition that Z is observed at training time. Our approach does not make any causal conclusions but by experimenting on 6 datasets, we show that our approach is able to outperform baselines 1) in controlled cases where confounding shift is manually injected between fitting time and prediction time 2) in natural experiments where confounding shift appears either abruptly or gradually 3) in cases where there is one or multiple confounders. Finally, we discuss multiple issues we encountered during this research such as the effect of noise in the observation of Z and the importance of only controlling for confounding variables.


title: BICO: BIRCH Meets Coresets for k-Means Clustering

abstract: We design a data stream algorithm for the k-means problem, called BICO, that combines the data structure of the SIGMOD Test of Time award winning algorithm BIRCH [27] with the theoretical concept of coresets for clustering problems. The k-means problem asks for a set C of k centers minimizing the sum of the squared distances from every point in a set P to its nearest center in C. In a data stream, the points arrive one by one in arbitrary order and there is limited storage space.


title: Sequencing and scheduling : algorithms and complexity

abstract: Sequencing and scheduling as a research area is motivated by questions that arise in production planning, in computer control, and generally in all situations in which scarce resources have to be allocated to activities over time. In this survey, we concentrate on the area of deterministic machine scheduling. We review complexity results and optimization and approximation algorithms for problems involving a single machine, parallel machines, open shops, flow shops and job shops. We also pay attention to two extensions of this area: resource-constrained project scheduling and stochastic machine scheduling.


title: A Practical Methodology for Speech Source Localization With Microphone Arrays

abstract: Abstract Electronically steerable arrays of microphones have a variety of uses in speech data acquisition systems. Applications include teleconferencing, speech recognition and speaker identification, sound capture in adverse environments, and biomedical devices for the hearing impaired. An array of microphones has a number of advantages over a single-microphone system. It may be electronically aimed to provide a high-quality signal from a desired source location while simultaneously attenuating interfering talkers and ambient noise, does not necessitate local placement of transducers or encumber the talker with a hand-held or head-mounted microphone, and does not require physical movement to alter its direction of reception. Additionally, it has capabilities that a single microphone does not; namely automatic detection, localization and tracking of active talkers in its receptive area. This paper addresses the specific application of source localization algorithms for estimating the position of speech sources in a real-room environment given limited computational resources. The theoretical foundations of a speech source localization system are presented. This includes the development of a source–sensor geometry for talkers and sensors in the near-field environment as well as the evaluation of several error criteria available to the problem. Several practical algorithms necessary for real-time implementation are developed, specifically the derivation and evaluation of an appropriate time-delay estimator and a novel closed-form locator. Finally, results obtained from a real system are presented to illustrate the effectiveness of the proposed source localization techniques as well as to confirm the practicality of the theoretical models.


title: The precedence effect.

abstract: In a reverberant environment, sounds reach the ears through several paths. Although the direct sound is followed by multiple reflections, which would be audible in isolation, the first-arriving wavefront dominates many aspects of perception. The "precedence effect" refers to a group of phenomena that are thought to be involved in resolving competition for perception and localization between a direct sound and a reflection. This article is divided into five major sections. First, it begins with a review of recent work on psychoacoustics, which divides the phenomena into measurements of fusion, localization dominance, and discrimination suppression. Second, buildup of precedence and breakdown of precedence are discussed. Third measurements in several animal species, developmental changes in humans, and animal studies are described. Fourth, recent physiological measurements that might be helpful in providing a fuller understanding of precedence effects are reviewed. Fifth, a number of psychophysical models are described which illustrate fundamentally different approaches and have distinct advantages and disadvantages. The purpose of this review is to provide a framework within which to describe the effects of precedence and to help in the integration of data from both psychophysical and physiological experiments. It is probably only through the combined efforts of these fields that a full theory of precedence will evolve and useful models will be developed.


title: Stochastic optimization for PCA and PLS

abstract: We study PCA, PLS, and CCA as stochastic optimization problems, of optimizing a population objective based on a sample. We suggest several stochastic approximation (SA) methods for PCA and PLS, and investigate their empirical performance.


title: Airport Detection Based on a Multiscale Fusion Feature for Optical Remote Sensing Images

abstract: Automatically detecting airports from remote sensing images has attracted significant attention due to its importance in both military and civilian fields. However, the diversity of illumination intensities and contextual information makes this task difficult. Moreover, auxiliary features both within and surrounding the regions of interest are usually ignored. To address these problems, we propose a novel method that uses a multiscale fusion feature to represent the complementary information of each region proposal, which is extracted by constructing a GoogleNet with a light feature module model that has an additional light fully connected layer. Then, the fusion feature is input to a support vector machine whose performance is enhanced using a hard negative mining method. Finally, a simplified localization method is applied to tackle the problem of box redundancy and to optimize the locations of airports. An experiment demonstrates that the fusion feature outperforms other features on airport detection tasks from remote sensing images containing complicated contextual information.


title: Transfer Learning to Infer Social Ties across Heterogeneous Networks

abstract: Interpersonal ties are responsible for the structure of social networks and the transmission of information through these networks. Different types of social ties have essentially different influences on people. Awareness of the types of social ties can benefit many applications, such as recommendation and community detection. For example, our close friends tend to move in the same circles that we do, while our classmates may be distributed into different communities. Though a bulk of research has focused on inferring particular types of relationships in a specific social network, few publications systematically study the generalization of the problem of predicting social ties across multiple heterogeneous networks. In this work, we develop a framework referred to as TranFG for classifying the type of social relationships by learning across heterogeneous networks. The framework incorporates social theories into a factor graph model, which effectively improves the accuracy of predicting the types of social relationships in a target network by borrowing knowledge from a different source network. We also present several active learning strategies to further enhance the inferring performance. To scale up the model to handle really large networks, we design a distributed learning algorithm for the proposed model. We evaluate the proposed framework (TranFG) on six different networks and compare with several existing methods. TranFG clearly outperforms the existing methods on multiple metrics. For example, by leveraging information from a coauthor network with labeled advisor-advisee relationships, TranFG is able to obtain an F1-score of 90p (8p--28p improvements over alternative methods) for predicting manager-subordinate relationships in an enterprise email network. The proposed model is efficient. It takes only a few minutes to train the proposed transfer model on large networks containing tens of thousands of nodes.


title: The OWL API: A Java API for Working with OWL 2 Ontologies

abstract: This paper presents the OWL API a high level Application Programming Interface (API) for working with OWL 2 ontologies. The API is closely aligned with the OWL 2 structural specification. It supports parsing and rendering in the syntaxes defined in the W3C specification, namely, the Functional Syntax, RDF/XML, OWL/XML and the Manchester OWL Syntax. Finally, the reference implementation of the API, which is written in Java, includes validators for the various OWL 2 profiles - OWL 2 QL, OWL 2 EL and OWL 2 RL.


title: [Treatment of cough in respiratory tract infections - the effect of combining the natural active compounds with thymol].

abstract: Cough is one of the characteristic symptoms of upper respiratory tract infections (URI). Herbal medicines are often used in the treatment of the cough associated with infection and to accelerate recovery or support the immune system. An example of such products are extracts of thyme and primrose, and also their combination with thymol. Thymus vulgaris is a spasmolytic, antimicrobial, anti-inflammatory, immunomodulatory and antioxidant agent. The most important component responsible for the activity of thyme is thymol contained in the volatile thyme oil. Primrose is, most of all, a saponine agent with expectorant and secretolytic activity, showing also spasmolytic, anti-inflammatory and antimicrobial effects. Because of the additive effects of the mentioned extracts and their wide activity profile they are often used as a combination drug. The use of this combination was assessed in the URI with the effects of alleviation of cough and dyspnea, and shortening the length of the disease. The effectiveness of the drug containing the extracts of thyme and primrose with the addition of thymol (Bronchosol®) was comparable to synthetic ambroxol and its safety has been proved. Moreover, the in-vitro antibacterial and antifungal effects of this drug have been evaluated.


title: System-Level Simulation of a Self-Powered Sensor with Piezoelectric Energy Harvesting

abstract: This paper presents a complete system simulation of a self-powered communication module. The components are described with the Verilog-A language, that allows to merge the electrical and mechanical models of the system. The self-powered sensor system is composed by an energy harvesting piezoelectric generator that powers a RF transmitter. The simulations here presented compare between the case of a battery-less and battery-powered system. The results obtained with the simulation model implemented allow to show how design choices of the system change the periodicity of the transmission and the ability to recharge the battery.


title: Transferred Deep Learning for Anomaly Detection in Hyperspectral Imagery

abstract: In this letter, a novel anomaly detection framework with transferred deep convolutional neural network (CNN) is proposed. The framework is designed by considering the following facts: 1) a reference data with labeled samples are utilized, because no prior information is available about the image scene for anomaly detection and 2) pixel pairs are generated to enlarge the sample size, since the advantage of CNN can be realized only if the number of training samples is sufficient. A multilayer CNN is trained by using difference between pixel pairs generated from the reference image scene. Then, for each pixel in the image for anomaly detection, difference between pixel pairs, constructed by combining the center pixel and its surrounding pixels, is classified by the trained CNN with the result of similarity measurement. The detection output is simply generated by averaging these similarity scores. Experimental performance demonstrates that the proposed algorithm outperforms the classic Reed-Xiaoli and the state-of-the-art representation-based detectors, such as sparse representation-based detector (SRD) and collaborative representation-based detector.


title: Detecting spammers on social networks

abstract: Social networking has become a popular way for users to meet and interact online. Users spend a significant amount of time on popular social network platforms (such as Facebook, MySpace, or Twitter), storing and sharing a wealth of personal information. This information, as well as the possibility of contacting thousands of users, also attracts the interest of cybercriminals. For example, cybercriminals might exploit the implicit trust relationships between users in order to lure victims to malicious websites. As another example, cybercriminals might find personal information valuable for identity theft or to drive targeted spam campaigns. In this paper, we analyze to which extent spam has entered social networks. More precisely, we analyze how spammers who target social networking sites operate. To collect the data about spamming activity, we created a large and diverse set of "honey-profiles" on three large social networking sites, and logged the kind of contacts and messages that they received. We then analyzed the collected data and identified anomalous behavior of users who contacted our profiles. Based on the analysis of this behavior, we developed techniques to detect spammers in social networks, and we aggregated their messages in large spam campaigns. Our results show that it is possible to automatically identify the accounts used by spammers, and our analysis was used for take-down efforts in a real-world social network. More precisely, during this study, we collaborated with Twitter and correctly detected and deleted 15,857 spam profiles.


Neural Net Adaptive Flight Control Stability , Verification and Validation Challenges , and Future Research


title: Enterprise Architecture Development Based on Enterprise Ontology

abstract: Enterprises choose Enterprise Architecture (EA) solution, in order to overcome dynamic business challenges and in order to coordinate various enterprise elements. In this article, a solution is suggested for the Enterprise Architecture based on a conceptual model of Enterprise Ontology (EO). Enterprise Ontology provides a common structure for data collection. First, conceptual model of Enterprise Ontology based on the Zachman Framework is presented. Then, the Enterprise Architecture development process based on Enterprise Ontology is proposed. Using this solution, Staffs, stakeholders, users, architects, and systems achieved a common understanding of enterprise concepts and relationships and therefore, architecture data are collected in a correct way. The primary focus is collecting accurate architecture data instead of developing architecture artifacts; also meet the decision maker's needs by fit for purpose modeling. Finally, we demonstrate our solution in a case study and show the appropriate results and conclusions.


title: Optimizing the Convolution Operation to Accelerate Deep Neural Networks on FPGA

abstract: As convolution contributes most operations in convolutional neural network (CNN), the convolution acceleration scheme significantly affects the efficiency and performance of a hardware CNN accelerator. Convolution involves multiply and accumulate operations with four levels of loops, which results in a large design space. Prior works either employ limited loop optimization techniques, e.g., loop unrolling, tiling, and interchange, or only tune some of the design variables after the accelerator architecture and dataflow are already fixed. Without fully studying the convolution loop optimization before the hardware design phase, the resulting accelerator can hardly exploit the data reuse and manage data movement efficiently. This paper overcomes these barriers by quantitatively analyzing and optimizing the design objectives (e.g., memory access) of the CNN accelerator based on multiple design variables. Then, we propose a specific dataflow of hardware CNN acceleration to minimize the data communication while maximizing the resource utilization to achieve high performance. The proposed CNN acceleration scheme and architecture are demonstrated by implementing end-to-end CNNs including NiN, VGG-16, and ResNet-50/ResNet-152 for inference. For VGG-16 CNN, the overall throughputs achieve 348 GOPS and 715 GOPS on Intel Stratix V and Arria 10 FPGAs, respectively.


title: Spectrum sensing in cognitive radio using Labview and NI USRP

abstract: Cognitive Radio(CR) is an adaptive radio that detects empty or white bands available in wireless spectrum and sets transmission parameters to utilize radio spectrum more efficiently. In this paper the spectrum sensing or presence of primary user (PU) is detected using a Labview code and universal software radio peripheral (USRP). We have used NI USRP-2943R device as a transceiver. It has two channels and frequency range of both transmitter and receiver is 1.2 GHz to 6 GHz. The method which is used to detect primary user is called Energy Detection. In energy detection method we set a threshold energy level which depends upon noise floor and then we compare the energy detector output with it to detect the presence of primary user. To implement the cognitive radio we have made use of ISM spectrum which is a free spectrum and by performing several experiments we have set a threshold value to compare with detectors output. We have used power spectrum which shows the distribution of power levels of signals corresponding to their frequencies. Ultimately depending upon whether the PU is present or not the secondary user gets to utilize a channel.


title: Ambient and Unobtrusive Cardiorespiratory Monitoring Techniques

abstract: Monitoring vital signs through unobtrusive means is a goal which has attracted a lot of attention in the past decade. This review provides a systematic and comprehensive review over the current state of the field of ambient and unobtrusive cardiorespiratory monitoring. To this end, nine different sensing modalities which have been in the focus of current research activities are covered: capacitive electrocardiography, seismo- and ballistocardiography, reflective photoplethysmography (PPG) and PPG imaging, thermography, methods relying on laser or radar for distance-based measurements, video motion analysis, as well as methods using high-frequency electromagnetic fields. Current trends in these subfields are reviewed. Moreover, we systematically analyze similarities and differences between these methods with respect to the physiological and physical effects they sense as well as the resulting implications. Finally, future research trends for the field as a whole are identified.


title: Exploiting Semantic Annotations for Entity-based Information Retrieval

abstract: In this paper, we propose a new approach to entity-based information retrieval by exploiting semantic annotations of documents. With the increased availability of structured knowledge bases and semantic annotation techniques, we can capture documents and queries at their semantic level to avoid the high semantic ambiguity of terms and to bridge the language barrier between queries and documents. Based on various semantic interpretations, users can refine the queries to match their intents. By exploiting the semantics of entities and their relations in knowledge bases, we propose a novel ranking scheme to address the information needs of users.


title: Structure-based Clustering of Novels

abstract: To date, document clustering by genres or authors has been performed mostly by means of stylometric and content features. With the premise that novels are societies in miniature, we build social networks from novels as a strategy to quantify their plot and structure. From each social network, we extract a vector of features which characterizes the novel. We perform clustering over the vectors obtained, and the resulting groups are contrasted in terms of author and genre.


title: An Assessment of Incomplete-LU Preconditioners for Nonsymmetric Linear Systems

abstract: We report on an extensive experiment to compare an iterative solver preconditioned by several versions of incomplete LU factorization with a sparse direct solver using LU factorization with partial pivoting. Our test suite is 24 nonsymmetric matrices drawn from benchmark sets in the literature. On a few matrices, the best iterative method is more than 5 times as fast and more than 10 times as memory-efficient as the direct method. Nonetheless, in most cases the iterative methods are slower; in many cases they do not save memory; and in general they are less reliable. Our primary conclusion is that a direct method is currently more appropriate than an iterative method for a general-purpose black-box nonsymmetric linear solver. We draw several other conclusions about these nonsymmetric problems: pivoting is even more important for incomplete than for complete factorizations; the best iterative solutions almost always take only 8 to 16 iterations; a drop-tolerance strategy is superior to a column-count strategy; and column MMD ordering is superior to RCM ordering. The reader is advised to keep in mind that our conclusions are drawn from experiments with 24 matrices; other test suites might have given somewhat different results. Nonetheless, we are not aware of any other studies more extensive than ours.


title: The comparison of soil sensors for integrated creation of IOT-based Wetting front detector (WFD) with an efficient irrigation system to support precision farming

abstract: This study investigates a prototyping of integrated system of Internet of Things based Wetting front detector (IOT-WFD) which focuses on how to enhance the IOT based Wetting front detector design for smart irrigation system. The empirical study was conducted with 2 sensors type to detect the wetting fronts which are the Frequency Domain Reflectrometry sensor (FDR) and Resistor-based sensor (RB) integrated and design with low-cost WFD. The results of this study point toward the IOT-WFD as an appropriated technology providing real time wetting front information in soil positively for application in terms of agricultural water management, with precision agriculture and efficient irrigation domain with a related decision knowledge that matches with the technology trend and smart farmer requirements. Evidence of positive results of this prototyping summary has been provided.


title: Painting Outside the Box: Image Outpainting with GANs

abstract: The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion). Accordingly, we present a deep learning approach based on Iizuka et al. for adversarially training a network to hallucinate past image boundaries. We use a three-phase training schedule to stably train a DCGAN architecture on a subset of the Places365 dataset. In line with Iizuka et al., we also use local discriminators to enhance the quality of our output. Once trained, our model is able to outpaint $128 \times 128$ color images relatively realistically, thus allowing for recursive outpainting. Our results show that deep learning approaches to image outpainting are both feasible and promising.


title: Uncovering Discriminative Knowledge-Guided Medical Concepts for Classifying Coronary Artery Disease Notes

abstract: Text classification is a challenging task for allocating each document to the correct predefined class. Most of the time, there are irrelevant features which make noise in the learning step and reduce the precision of prediction. Hence, more efficient methods are needed to select or extract meaningful features to avoid noise and overfitting. In this work, an ontology-guided method utilizing the taxonomical structure of the Unified Medical Language System (UMLS) is proposed. This method extracts concepts of appeared phrases in the documents which relate to diseases or symptoms as features. The efficiency of this method is evaluated on the 2010 Informatics for Integrating Biology and the Bedside (i2b2) data set. The obtained experimental results show significant improvement by the proposed ontology-based method on the accuracy of classification.


title: The influence of social media use on body image concerns

abstract: Internet-based media and especially social networking sites differ from traditional media in that they allow individuals to interact with their friends in their networks. Moreover, Internet-based media are easily available on devices such as smartphones or tablets. Previous research has demonstrated that the mass media are a powerful contributor to individual’s body dissatisfaction. To date, research on the effects related to exposure to ‘newer’ forms of media, in particular the Internet social media is scarce. The purpose of the current study is to review the extant body of research dealing with the influence of social media on body image concerns, especially among adolescents. Adolescents connect on Internet to get access to different kind of Internet-based media such as social media (among which social networking sites). Our results document the importance of idealized social media models—especially thin-ideal models for girls and muscular-ideal models for boys—in shaping the body perceptions of adolescents. However, the effects of pressure from the social media on body image concerns in men need to be further investigated both in clinical and community samples. Keywords: body image dissatisfaction; social media; social networking; adolescents


title: Performance analysis of a ultra-compact low-power rectenna in paper substrate for RF energy harvesting

abstract: In this paper the experimental results of a compact low-power rectenna in paper substrate, designed to operate in the Wi-Fi band, are presented. The complete prototype, based on an annular slot antenna and a single-diode rectifier, features a weight of 1.5 grams and shows an RF-to-dc conversion efficiency in the design band of about 40 % for a −10 dBm available input power, of about 28 % at −15 dBm, and in the range [10, 22] % at −20 dBm, corresponding to an output DC voltage in the order of 320, 240 and 60 mV respectively. Additionally, the rectenna features an efficiency higher then 7 % in the whole band 1.8–2.7 dBm for a power density estimated around 3 µW/cm2.


title: Link prediction in social networks based on hypergraph

abstract: In recent years, online social networks have undergone a significant growth and attracted much attention. In these online social networks, link prediction is a critical task that not only offers insights into the factors behind creation of individual social relationship but also plays an essential role in the whole network growth. In this paper, we propose a novel link prediction method based on hypergraph. In contrast with conventional methods that using ordinary graph, we model the social network as a hypergraph, which can fully capture all types of objects and either the pair wise or high-order relations among these objects in the network. Then the link prediction task is formulated as a ranking problem on this hypergraph. Experimental results on Sina-Weibo dataset have demonstrated the effectiveness of our methods.


LipNet: Sentence-level Lipreading


title: A re-examination of relevance: toward a dynamic, situational definition

abstract: Abstract Although relevance judgments are fundamental to the design and evaluation of all information retrieval systems, information scientists have not reached a consensus in defining the central concept of relevance. In this paper we ask two questions: What is the meaning of relevance? and What role does relevance play in information behavior? We attempt to address these questions by reviewing literature over the last 30 years that presents various views of relevance as topical, user-oriented, multidimensional, cognitive, and dynamic. We then discuss traditional assumptions on which most research in the field has been based and begin building a case for an approach to the problem of definition based on alternative assumptions. The dynamic, situational approach we suggest views the user — regardless of system — as the central and active determinant of the dimensions of relevance. We believe that relevance is a multidimensional concept; that it is dependent on both internal (cognitive) and external (situational) factors; that it is based on a dynamic human judgment process; and that it is a complex but systematic and measurable phenomenon.


Smart Indoor Positioning/Location and Navigation: A Lightweight Approach


Understanding the exploding gradient problem


title: Visualization of large-scale customer satisfaction surveys using a parallel coordinate tree

abstract: Satisfaction surveys are an important measurement tool in fields such as market research or human resources management. Serious studies consist of numerous questions and contain answers from large population samples. Aggregation on both sides, the questions asked as well as the answers received, turns the multidimensional problem into a complex system of interleaved hierarchies. Traditional ways of presenting the results are limited to one-dimensional charts and cross-tables. We developed a visualization method called the Parallel Coordinate Tree that combines multidimensional analysis with a tree structure representation. Distortion-oriented focus+context techniques are used to facilitate interaction with the visualization. In this paper we present a design study of a commercial application that we built, using this method to analyze and communicate results from large-scale customer satisfaction surveys.


title: Content-addressable memory (CAM) circuits and architectures: a tutorial and survey

abstract: We survey recent developments in the design of large-capacity content-addressable memory (CAM). A CAM is a memory that implements the lookup-table function in a single clock cycle using dedicated comparison circuitry. CAMs are especially popular in network routers for packet forwarding and packet classification, but they are also beneficial in a variety of other applications that require high-speed table lookup. The main CAM-design challenge is to reduce power consumption associated with the large amount of parallel active circuitry, without sacrificing speed or memory density. In this paper, we review CAM-design techniques at the circuit level and at the architectural level. At the circuit level, we review low-power matchline sensing techniques and searchline driving approaches. At the architectural level we review three methods for reducing power consumption.


title: Application of CRISPR-Cas9 Based Genome-Wide Screening Approaches to Study Cellular Signalling Mechanisms

abstract: The cellular signalling process is a highly complex mechanism, involving multiple players, which together orchestrate the cell’s response to environmental changes and perturbations. Given the multitude of genes that participate in the process of cellular signalling, its study in a genome-wide manner has proven challenging. Recent advances in gene editing technologies, including clustered regularly-interspaced short palindromic repeats/Cas9 (CRISPR/Cas9) approaches, have opened new opportunities to investigate global regulatory signalling programs of cells in an unbiased manner. In this review, we focus on how the application of pooled genetic screening approaches using the CRISPR/Cas9 system has contributed to a systematic understanding of cellular signalling processes in normal and disease contexts.


title: Nonnegative Matrix Tri-factorization Based High-Order Co-clustering and Its Fast Implementation

abstract: The fast growth of Internet and modern technologies has brought data involving objects of multiple types that are related to each other, called as Multi-Type Relational data. Traditional clustering methods for single-type data rarely work well on them, which calls for new clustering techniques, called as high-order co-clustering (HOCC), to deal with the multiple types of data at the same time. A major challenge in developing HOCC methods is how to effectively make use of all available information contained in a multi-type relational data set, including both inter-type and intra-type relationships. Meanwhile, because many real world data sets are often of large sizes, clustering methods with computationally efficient solution algorithms are of great practical interest. In this paper, we first present a general HOCC framework, named as Orthogonal Nonnegative Matrix Tri-factorization (O-NMTF), for simultaneous clustering of multi-type relational data. The proposed O-NMTF approach employs Nonnegative Matrix Tri-Factorization (NMTF) to simultaneously cluster different types of data using the inter-type relationships, and incorporate intra-type information through manifold regularization, where, different from existing works, we emphasize the importance of the orthogonal ties of the factor matrices of NMTF. Based on O-NMTF, we further develop a novel Fast Nonnegative Matrix Tri-Factorization (F-NMTF) approach to deal with large-scale data. Instead of constraining the factor matrices of NMTF to be nonnegative as in existing methods, F-NMTF constrains them to be cluster indicator matrices, a special type of nonnegative matrices. As a result, the optimization problem of the proposed method can be decoupled, which results in sub problems of much smaller sizes requiring much less matrix multiplications, such that our new algorithm scales well to real world data of large sizes. Extensive experimental evaluations have demonstrated the effectiveness of our new approaches.


title: Synergistic Effects of Plant Growth Promoting Rhizobacteria and Chitosan on In Vitro Seeds Germination, Greenhouse Growth, and Nutrient Uptake of Maize (Zea mays L.)

abstract: This study aimed to assess the effects of three plant growth promoting rhizobacteria (PGPR) and chitosan either singly or in combination on maize seeds germination and growth and nutrient uptake. Maize seeds were treated with chitosan and bacterial solution. The germination and growth tests were carried out in square Petri dishes and plastic pots. The combination chitosan-A. lipoferum-P. fluorescens has increased the seeds vigor index up to 36.44% compared to the control. In comparison to the control, P. putida has significantly improved root weight (44.84%) and germinated seed weight (31.39%) whereas chitosan-P. putida has increased the shoot weight (65.67%). For the growth test, the maximal heights (17.66%) were obtained by plants treated with the combination A. lipoferum-P. fluorescens-P. putida. Chitosan-P. fluorescens induced the highest increases of leaves per plant (50.09%), aerial (84.66%), and underground biomass (108.77%) production. The plants inoculated with A. lipoferum had the large leaf areas with an increase of 54.08%, while combinations P. fluorescens-P. putida and chitosan-A. lipoferum improved the aerial and underground dry matter of plants to 26.35% and 18.18%. The nitrogen content of the plants was increased by chitosan-A. lipoferum-P. fluorescens-P. putida with an increasing of 41.61%. The combination of chitosan and PGPR can be used as biological fertilizers to increase maize production.


title: Mutuality in leader–subordinate empowerment expectation: Its impact on role ambiguity and intrinsic motivation☆

abstract: Abstract Drawing on leader role set theory, we examine the relationship between the congruence of leaders' and subordinates' empowerment expectations and subordinates' experiences of role ambiguity and intrinsic motivation. Based on cross-level polynomial regression analysis using 168 subordinates and 33 leaders, the results indicated that the relationship between congruence and role ambiguity and intrinsic motivation vary depending on whether leaders misevaluate subordinate empowerment expectations, as well as whether the expectations match. Specifically, subordinates had low role ambiguity and low intrinsic motivation when leaders' and subordinates' empowerment expectations matched at low levels and when leaders underestimated subordinates' empowerment expectations. However, subordinates had low role ambiguity and high intrinsic motivation when expectations matched at high levels. Furthermore, role ambiguity was high and intrinsic motivation was low when the leaders overestimated subordinates' empowerment expectations. Theoretical and practical implications are discussed.


title: Surface Polaritons at the Planar Interface of Twinned Dielectric Gyrotropic Media

abstract: Abstract The closed-form dispersion equation for surface polaritons localized near the planar interface of the semi-infinite halves of the same transparent uniaxial dielectric gyrotropic medium was derived for the case when the gyration vectors of the contacting materials are perpendicular to the interface and oppositely directed to each other. Surface electromagnetic waves can be excited only if the medium is both anisotropic and gyrotropic. Numerical analysis of the phase velocity, decay coefficients, and polarization of the surface polaritons as functions of the gyration parameter was undertaken. It turned out that, for a weakly gyrotropic medium, the characteristic penetration depth of the surface polaritons is inversely proportional to the squared gyration parameter.


title: Bayesian Estimation of Beta Mixture Models with Variational Inference

abstract: Bayesian estimation of the parameters in beta mixture models (BMM) is analytically intractable. The numerical solutions to simulate the posterior distribution are available, but incur high computational cost. In this paper, we introduce an approximation to the prior/posterior distribution of the parameters in the beta distribution and propose an analytically tractable (closed form) Bayesian approach to the parameter estimation. The approach is based on the variational inference (VI) framework. Following the principles of the VI framework and utilizing the relative convexity bound, the extended factorized approximation method is applied to approximate the distribution of the parameters in BMM. In a fully Bayesian model where all of the parameters of the BMM are considered as variables and assigned proper distributions, our approach can asymptotically find the optimal estimate of the parameters posterior distribution. Also, the model complexity can be determined based on the data. The closed-form solution is proposed so that no iterative numerical calculation is required. Meanwhile, our approach avoids the drawback of overfitting in the conventional expectation maximization algorithm. The good performance of this approach is verified by experiments with both synthetic and real data.


title: Incorporating label and attribute information for enhanced network representation learning

abstract: Recently, network representation learning has shown its super performance in many network analysis tasks. Traditional network representation learning algorithms mainly mine unilateral network structure information. However, as we move to the age of big data, we can obtain rich auxiliary information from many real-world networks. In this paper, we propose an enhanced network representation learning algorithm by incorporating label and attribute Information. Inspired by natural language model, we propose two novel models: label enhanced attribute information representing model and label enhanced structure information representing model. Then, through parameter sharing strategy and jointly training based on a unified optimization objective function, we can obtain representations of nodes in network preserving network structure, attribute information and label information simultaneously. Experimental results demonstrate that representation vectors obtained by our model outperform state-of-art network representation learning methods on two real-world networks.


title: IoT devices and applications based on LoRa/LoRaWAN

abstract: Internet of Things (IoT) has revolutionized the traditional Internet where only human-centric services were offered. It has enabled objects to have the ability to connect and communicate through the Internet. IoT has several applications such as smart water management systems. However, they require high energy-efficient sensor nodes that are able to communicate across long distance. This motivates the development of many Low-Power Wide Area Networks (LPWAN) technologies, such as LoRa, to fulfill these requirements. Therefore, in this paper, we survey IoT devices and different applications based on LoRa and LoRaWAN in order understand the current stream of devices used. The objective is to contribute toward the realization of LoRa as a viable communication technology for applications that needs long-range links and deployed in a distributed manner. We highlighted the device parameter settings and the output of each experiment surveyed.


title: Three-Phase (LC)(L)-Type Series-Resonant Converter With Capacitive Output Filter

abstract: This paper presents a three-phase (LC)(L)-type dc-dc series-resonant converter with capacitive output filter. Operation of the converter has been presented using the operating waveforms and equivalent-circuit diagrams during different intervals. An approximate analysis is used, and design procedure is presented with a design example. Intusoft simulation results for the designed converter are given for input voltage and load variations. Experimental results obtained with a 300-W converter are presented. Major advantages of this converter are the leakage and magnetizing inductances of the high-frequency transformer that are used as a part of resonant circuit, and the output-rectifier voltage is clamped to the output voltage. Also, the converter operates in soft switching for the inverter switches with a narrow frequency control range and the tank current decreases with the load current.


title: Fog Based Intelligent Transportation Big Data Analytics in The Internet of Vehicles Environment: Motivations, Architecture, Challenges, and Critical Issues

abstract: The intelligent transportation system (ITS) concept was introduced to increase road safety, manage traffic efficiently, and preserve our green environment. Nowadays, ITS applications are becoming more data-intensive and their data are described using the “5Vs of Big Data”. Thus, to fully utilize such data, big data analytics need to be applied. The Internet of vehicles (IoV) connects the ITS devices to cloud computing centres, where data processing is performed. However, transferring huge amount of data from geographically distributed devices creates network overhead and bottlenecks, and it consumes the network resources. In addition, following the centralized approach to process the ITS big data results in high latency which cannot be tolerated by the delay-sensitive ITS applications. Fog computing is considered a promising technology for real-time big data analytics. Basically, the fog technology complements the role of cloud computing and distributes the data processing at the edge of the network, which provides faster responses to ITS application queries and saves the network resources. However, implementing fog computing and the lambda architecture for real-time big data processing is challenging in the IoV dynamic environment. In this regard, a novel architecture for real-time ITS big data analytics in the IoV environment is proposed in this paper. The proposed architecture merges three dimensions including intelligent computing (i.e. cloud and fog computing) dimension, real-time big data analytics dimension, and IoV dimension. Moreover, this paper gives a comprehensive description of the IoV environment, the ITS big data characteristics, the lambda architecture for real-time big data analytics, several intelligent computing technologies. More importantly, this paper discusses the opportunities and challenges that face the implementation of fog computing and real-time big data analytics in the IoV environment. Finally, the critical issues and future research directions section discusses some issues that should be considered in order to efficiently implement the proposed architecture.


Analyzing the Relevance of SOA Patterns for Microservice-Based Systems


title: Deep reinforcement learning from human preferences

abstract: For sophisticated reinforcement learning (RL) systems to interact usefully with real-world environments, we need to communicate complex goals to these systems. In this work, we explore goals defined in terms of (non-expert) human preferences between pairs of trajectory segments. We show that this approach can effectively solve complex RL tasks without access to the reward function, including Atari games and simulated robot locomotion, while providing feedback on less than one percent of our agent's interactions with the environment. This reduces the cost of human oversight far enough that it can be practically applied to state-of-the-art RL systems. To demonstrate the flexibility of our approach, we show that we can successfully train complex novel behaviors with about an hour of human time. These behaviors and environments are considerably more complex than any that have been previously learned from human feedback.


title: Holography of Wi-fi Radiation

abstract: Wireless data transmission systems such as WiFi or Bluetooth emit coherent light - electromagnetic waves with precisely known amplitude and phase. Propagating in space, this radiation forms a hologram - a two-dimensional wavefront encoding a three-dimensional view of all objects traversed by the light beam. Here we demonstrate a scheme to record this hologram in a phase-coherent fashion across a meter-sized imaging region. We recover three-dimensional views of objects and emitters by feeding the resulting data into digital reconstruction algorithms. Employing a digital implementation of dark field propagation to suppress multipath reflection we significantly enhance the quality of the resulting images.


How are Ethnic Hairstyles Really Viewed in the Workplace


title: Approximate Thin Plate Spline Mappings

abstract: The thin plate spline (TPS) is an effective tool for modeling coordinate transformations that has been applied successfully in several computer vision applications. Unfortunately the solution requires the inversion of a p × p matrix, where p is the number of points in the data set, thus making it impractical for large scale applications. As it turns out, a surprisingly good approximate solution is often possible using only a small subset of corresponding points. We begin by discussing the obvious approach of using the subsampled set to estimate a transformation that is then applied to all the points, and we show the drawbacks of this method. We then proceed to borrow a technique from the machine learning community for function approximation using radial basis functions (RBFs) and adapt it to the task at hand. Using this method, we demonstrate a significant improvement over the naive method. One drawback of this method, however, is that is does not allow for principal warp analysis, a technique for studying shape deformations introduced by Bookstein based on the eigenvectors of the p × p bending energy matrix. To address this, we describe a third approximation method based on a classic matrix completion technique that allows for principal warp analysis as a by-product. By means of experiments on real and synthetic data, we demonstrate the pros and cons of these different approximations so as to allow the reader to make an informed decision suited to his or her application.


title: External and Intrinsic Plagiarism Detection Using Vector Space Models

abstract: Plagiarism detection can be divided in external and intrinsic methods. Naive external plagiarism analysis suffers from computationally demanding full near- est neighbor searches within a reference corpus. We present a conceptually simple space partitioning approach to achieve search times sub linear in the number of ref- erence documents, trading precision for speed. We focus on full duplicate searches while achieving acceptable results in the near duplicate case. Intrinsic plagiarism analysis tries to find plagiarized passages within a document without any exter- nal knowledge. We use several topic independent stylometric features from which a vector space model for each sentence of a suspicious document is constructed. Plagiarized passages are detected by an outlier analysis relative to the document mean vector. Our system was created for the first PAN competition on plagiarism detection in 2009. The evaluation was performed on the challenge's development


title: An interactive ambient visualization for code smells

abstract: Code smells are characteristics of software that indicate that code may have a design problem. Code smells have been proposed as a way for programmers to recognize the need for restructuring their software. Because code smells can go unnoticed while programmers are working, tools called smell detectors have been developed to alert programmers to the presence of smells in their code, and to help them understand the cause of those smells. In this paper, we propose a novel smell detector called Stench Blossom that provides an interactive ambient visualization designed to first give programmers a quick, high-level overview of the smells in their code, and then, if they wish, to help in understanding the sources of those code smells. We also describe a laboratory experiment with 12 programmers that tests several hypotheses about our tool. Our findings suggest that programmers can use our tool effectively to identify smells and to make refactoring judgements. This is partly because the tool serves as a memory aid, and partly because it is more reliable and easier to use than heuristics for analyzing smells.


title: Avatar-independent scripting for real-time gesture animation

abstract: When animation of a humanoid figure is to be generated at run-time, instead of by replaying pre-composed motion clips, some method is required of specifying the avatar's movements in a form from which the required motion data can be automatically generated. This form must be of a more abstract nature than raw motion data: ideally, it should be independent of the particular avatar's proportions, and both writable by hand and suitable for automatic generation from higher-level descriptions of the required actions. ::: We describe here the development and implementation of such a scripting language for the particular area of sign languages of the deaf, called SiGML (Signing Gesture Markup Language), based on the existing HamNoSys notation for sign languages. ::: We conclude by suggesting how this work may be extended to more general animation for interactive virtual reality applications.


title: JOURNAL OF ALGORITHMS 7,425-440 (1986) Algorithms for Maximum independent Sets J.M.

abstract: Abstract An algorithm is presented which finds (the size of) a maximum independent set of an n vertex graph in time O (2 0.276 n ) improving on a previous bound of O(2 n 3 ) . The improvement comes principally from three sources: first, a modified recursive algorithm based on a more detailed study of the possible subgraphs around a chosen vertex; second, an improvement, not in the algorithm but in the time bound proved, by an argument about connected regular graphs; third, a time-space trade-off which can speed up recursive algorithms from a fairly wide class.


title: Substrate Integrated Waveguide Leaky-Wave Antenna With H-Shaped Slots

abstract: A new leaky-wave antenna based on substrate integrated waveguide (SIW) with H-shaped slots is proposed and investigated. The SIW with H-shaped slots is analyzed as a rectangular waveguide with H-shaped slots. Using an aperture magnetic field integral equation (MFIE), it is found that the SIW with H-shaped slots supports a leaky waveguide mode, a surface-wave mode and a proper waveguide mode depending on frequency. The radiation property is also evaluated using the MFIE. The leaky-wave antenna based on the SIW with H-shaped slots has a narrow beam that scans from broadside to endfire with frequency. The antenna produces an elliptical polarization, including circular polarization. Measured results are consistent with the simulation from HFSS and the theoretical analysis from the MFIE.


title: A novel method of text representation on hybrid neural networks

abstract: Text representation is one of the fundamental problems in text analysis tasks. The key of text representation is to extract and express the semantic and syntax feature of texts. The order-sensitive sequence models based on neural networks have achieved great progress in text representation. Bidirectional Long Short-Term Memory (BiLSTM) Neural Networks, as an extension of Recurrent Neural Networks (RNN), not only can deal with variable-length texts, capture the long-term dependencies in texts, but also model the forward and backward sequence contexts. Moreover, typical neural networks, Convolutional Neural Networks (CNN), can extract more semantic and structural information from texts, because of their convolution and pooling operations. The paper proposes a hybrid model, which combines the BiLSTM with 2-dimensial convolution and 1-dimensial pooling operations. In other words, the model firstly captures the abstract representation vector of texts by the BiLSTM, and then extracts text semantic features by 2-dimensial convolutional and 1-dimensial pooling operations. Experiments on text classification tasks show that our method obtains preferable performances compared with the state-of-the-art models when applied on the MR1 sentence polarity dataset.


title: TraNNsformer: Neural Network Transformation for Memristive Crossbar based Neuromorphic System Design

abstract: Implementation of Neuromorphic Systems using post Complementary Metal-Oxide-Semiconductor (CMOS) technology based Memristive Crossbar Array (MCA) has emerged as a promising solution to enable low-power acceleration of neural networks. However, the recent trend to design Deep Neural Networks (DNNs) for achieving human-like cognitive abilities poses significant challenges towards the scalable design of neuromorphic systems (due to the increase in computation/storage demands). Network pruning [7] is a powerful technique to remove redundant connections for designing optimally connected (maximally sparse) DNNs. However, such pruning techniques induce irregular connections that are incoherent to the crossbar structure. Eventually they produce DNNs with highly inefficient hardware realizations (in terms of area and energy). In this work, we propose TraNNsformer - an integrated training framework that transforms DNNs to enable their efficient realization on MCA-based systems. TraNNsformer first prunes the connectivity matrix while forming clusters with the remaining connections. Subsequently, it retrains the network to fine tune the connections and reinforce the clusters. This is done iteratively to transform the original connectivity into an optimally pruned and maximally clustered mapping. We evaluated the proposed framework by transforming different Multi-Layer Perceptron (MLP) based Spiking Neural Networks (SNNs) on a wide range of datasets (MNIST, SVHN and CIFAR10) and executing them on MCA-based systems to analyze the area and energy benefits. Without accuracy loss, TraNNsformer reduces the area (energy) consumption by 28% - 55% (49% - 67%) with respect to the original network. Compared to network pruning, TraNNsformer achieves 28% - 49% (15% - 29%) area (energy) savings. Furthermore, TraNNsformer is a technology-aware framework that allows mapping a given DNN to any MCA size permissible by the memristive technology for reliable operations.


title: Guanfacine, But Not Clonidine, Improves Planning and Working Memory Performance in Humans

abstract: The present study compares, using a double-blind, placebo controlled design the effects of two α2-agonists, clonidine (0.5, 2, and 5 μg/kg) and guanfacine (7 and 29 μg/kg) on spatial working memory, planning and attentional set-shifting, functions thought to be dependent on the “central executive” of the prefrontal cortex. Blood pressure and the subjective feeling of sedation were affected equally by clonidine and guanfacine. The 0.5 μg/kg and 5 μg/kg doses of clonidine disrupted spatial working memory, but the medium dose had no effect. The 0.5 and 2 μg/kg doses of clonidine increased impulsive responding in the planning test. The 5 μg/kg dose of clonidine slowed responding at effortful levels of planning and attentional set-shifting tests. The 29 μg/kg dose of guanfacine improved spatial working memory and planning. Guanfacine had no effect on attentional set-shifting. These data indicate that guanfacine improved planning and spatial working memory, but clonidine dose-dependently disrupted performance. It is possible that the greater selectivity of guanfacine for α2A-adrenoceptor subtype may underlie its differences from clonidine.


title: A Sentiment Analysis Method of Capsule Network Based on BiLSTM

abstract: Nowadays, capsule network model is widely used in image processing, whose feature engineering is not suitable for sentiment analysis based on texts obviously. In this paper, we propose a capsule network model with BiLSTM named caps-BiLSTM for sentiment analysis to solve the problem, and introduce the experimental results on different datasets. At the beginning of caps-BiLSTM, a convolution layer is used to transform the instance to hide vector. Then the capsule module constructs the capsule representation to the n-gram model. The state probability of a certain capsule is calculated by the capsule model. If the state probability of a given instance is the largest among all capsules, a higher coupling coefficient is assigned. Finally, in order to fusion the data features, the output of the capsule enters into a BiLSTM structure, which is used as a decoder to get the probability representation. Experimental results based on MR, IMDB and SST datasets show that the proposed method can achieve better performances than the traditional machine learning methods and the compared deeping learning models.


A Distributed Cloud Robotics Middleware Architecture for Trust-minimized Storage and Computation


title: Mean-Variance Portfolia Optimization When Means and Covariances are Unknown

abstract: Markowitz's celebrated mean--variance portfolio optimization theory assumes that the means and covariances of the underlying asset returns are known. In practice, they are unknown and have to be estimated from historical data. Plugging the estimates into the efficient frontier that assumes known parameters has led to portfolios that may perform poorly and have counter-intuitive asset allocation weights; this has been referred to as the "Markowitz optimization enigma." After reviewing different approaches in the literature to address these difficulties, we explain the root cause of the enigma and propose a new approach to resolve it. Not only is the new approach shown to provide substantial improvements over previous methods, but it also allows flexible modeling to incorporate dynamic features and fundamental analysis of the training sample of historical data, as illustrated in simulation and empirical studies.


title: Pathological pain and the neuroimmune interface

abstract: Here, the authors describe the immune mechanisms that are involved in pain, one of the key features of inflammation. They explain how the immune and nervous systems interact to initiate and propagate pain, and discuss the immune components that can be targeted for alleviating pathological pain in patients.


title: ResumeNet: A Learning-Based Framework for Automatic Resume Quality Assessment

abstract: Recruitment of appropriate people for certain positions is critical for any companies or organizations. Manually screening to select appropriate candidates from large amounts of resumes can be exhausted and time-consuming. However, there is no public tool that can be directly used for automatic resume quality assessment (RQA). This motivates us to develop a method for automatic RQA. Since there is also no public dataset for model training and evaluation, we build a dataset for RQA by collecting around 10K resumes, which are provided by a private resume management company. By investigating the dataset, we identify some factors or features that could be useful to discriminate good resumes from bad ones, e.g., the consistency between different parts of a resume. Then a neural-network model is designed to predict the quality of each resume, where some text processing techniques are incorporated. To deal with the label deficiency issue in the dataset, we propose several variants of the model by either utilizing the pair/triplet-based loss, or introducing some semi-supervised learning technique to make use of the abundant unlabeled data. Both the presented baseline model and its variants are general and easy to implement. Various popular criteria including the receiver operating characteristic (ROC) curve, F-measure and ranking-based average precision (AP) are adopted for model evaluation. We compare the different variants with our baseline model. Since there is no public algorithm for RQA, we further compare our results with those obtained from a website that can score a resume. Experimental results in terms of different criteria demonstrate the effectiveness of the proposed method. We foresee that our approach would transform the way of future human resources management.


title: BlasterJS: A novel interactive JavaScript visualisation component for BLAST alignment results

abstract: This work was supported by the Spanish “Programa Estatal de Investigacion, Desarrollo e Inovacion Orientada a los Retos de la Sociedad” (grant AGL2013-44039R to BS and AL); and the Asociacion Espanola Contra el Cancer ("Obtencio ´n de pe ´pti-dosbioactivos contra el Cancer ColoRectal a partir de secuencias gene ´ticas de microbiomas intestinales", grant PS-2016 to BS and ABM). This study was also supportedby the PortugueseFoundationfor Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit(AL) and COMPETE2020 (POCI-01-0145-FEDER-006684 to AL). SING group thanks CITI (Centro de Investigacion, Transferencia e Innovacion) from University of Vigo for hosting its IT infrastructure. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


title: Entailment above the word level in distributional semantics

abstract: We introduce two ways to detect entailment using distributional semantic representations of phrases. Our first experiment shows that the entailment relation between adjective-noun constructions and their head nouns (big cat|= cat), once represented as semantic vector pairs, generalizes to lexical entailment among nouns (dog|= animal). Our second experiment shows that a classifier fed semantic vector pairs can similarly generalize the entailment relation among quantifier phrases (many dogs|= some dogs) to entailment involving unseen quantifiers (all cats|= several cats). Moreover, nominal and quantifier phrase entailment appears to be cued by different distributional correlates, as predicted by the type-based view of entailment in formal semantics.


title: A Review of the Design Issues and Techniques for Radial-Flux Brushless Surface and Internal Rare-Earth Permanent-Magnet Motors

abstract: This paper reviews many design issues and analysis techniques for the brushless permanent-magnet machine. It reviews the basic requirements for the use of both ac and dc machines and issues concerning the selection of pole number, winding layout, rotor topology, drive strategy, field weakening, and cooling. These are key issues in the design of a motor. Leading-edge design techniques are illustrated. This paper is aimed as a tutor for motor designers who may be unfamiliar with this particular type of machine.


title: Mosses of southwest Ethiopian montane forests – notes on their occurrence pattern and many new country records

abstract: One hundred and thirty-nine taxa of mosses are reported from the montane forest zone of southwest Ethiopia. Of these, 53 taxa are new country records and Meteoriopsis reclinata (Mull.Hal.) M.Fleisch. ex Broth. is, in addition, new to Africa. Most of the new records are of species also found in Uganda, Kenya or Tanzania, but there are some surprises, such as Fabronia perciliata Mull.Hal. with the closest location in South Africa and Namibia, Entodontella cameruniae Broth. previously known only from West Africa and Bryomaltaea obtusifolia (Hook.) Goffinet, otherwise reported only from the Democratic Republic of the Congo. We list all taxa and give a short account of their frequency and in which habitat and on which substrate they grow. That such a large proportion of the species are new to the country suggests that Ethiopia is bryologically very under-explored, and that further exploration will reveal many additional species. In particular, the remnant forests of southwest Ethiopia are likely to be of great...


title: Computer simulation of bilayer ice: Structures and thermodynamics

abstract: A series of molecular dynamics simulations is performed in order to examine in more detail the results of a previous simulation which shows that a thin film of water, when confined to a hydrophobic slit nanopore, freezes into a bilayer ice crystal composed of two layers of hexagonal rings. Three simulations are carried out and each starts with a different initial configuration but has the same number of molecules and the area density. Using a previously introduced solid-like cluster definition, we monitor the dynamic process of crystallization. We find that only in one case the confined water completely freezes into perfect bilayer of ice whereas in other two cases, an imperfect crystalline structure consisting of hexagons of slightly different shapes is observed and this imperfection apparently hinders the growth of perfect bilayer of crystal. After adjusting the area density to match spatial arrangements of molecules, the latter two systems are able to crystallize completely. As a result, we obtain three forms of bilayer crystal differing in the area density and hexagonal rings alignment. Further analyses of these bilayer crystals provide more insightful explanation on the influence of the boundary condition and the simulation-cell size on the diversity of possible crystallographic structures.


title: A Shortest Path Dependency Kernel For Relation Extraction

abstract: We present a novel approach to relation extraction, based on the observation that the information required to assert a relationship between two named entities in the same sentence is typically captured by the shortest path between the two entities in the dependency graph. Experiments on extracting top-level relations from the ACE (Automated Content Extraction) newspaper corpus show that the new shortest path dependency kernel outperforms a recent approach based on dependency tree kernels.


title: Beyond self-management: Antecedents and consequences of team empowerment.

abstract: We examined the antecedents, consequences, and mediational role of team empowerment using 111 work teams in four organizations. The results indicated that the actions of external leaders, the production/service responsibilities given to teams, team-based human resources policies, and the social structure of teams all worked to enhance employee team empowerment experiences. More empowered teams were also more productive and proactive than less empowered teams and had higher levels of customer service, job satisfaction, and organizational and team commitment.


title: Cooperation of the basal ganglia, cerebellum, sensory cerebrum and hippocampus: possible implications for cognition, consciousness, intelligence and creativity

abstract: Abstract It is suggested that the anatomical structures which mediate consciousness evolved as decisive embellishments to a (non-conscious) design strategy present even in the simplest unicellular organisms. Consciousness is thus not the pinnacle of a hierarchy whose base is the primitive reflex, because reflexes require a nervous system, which the single-celled creature does not possess. By postulating that consciousness is intimately connected to self-paced probing of the environment, also prominent in prokaryotic behavior, one can make mammalian neuroanatomy amenable to dramatically straightforward rationalization. Muscular contraction is the nervous system's only externally directed product, and the signaling routes which pass through the various brain components must ultimately converge on the motor areas. The function of several components is still debatable, so it might seem premature to analyze the global operation of the circuit these routes constitute. But such analysis produces a remarkably simple picture, and it sheds new light on the roles of the individual components. The underlying principle is conditionally permitted movement, some components being able to veto muscular contraction by denying the motor areas sufficient activation. This is true of the basal ganglia (BG) and the cerebellum (Cb), which act in tandem with the sensory cerebrum, and which can prevent the latter's signals to the motor areas from exceeding the threshold for overt movement. It is also true of the anterior cingulate, which appears to play a major role in directing attention. In mammals, the result can be mere thought, provided that a second lower threshold is exceeded. The veto functions of the BG and the Cb stem from inhibition, but the countermanding disinhibition develops at markedly different rates in those two key components. It develops rapidly in the BG, control being exercised by the amygdala, which itself is governed by various other brain regions. It develops over time in the Cb, thereby permitting previously executed movements that have proved advantageous. If cognition is linked to overt or covert movement, intelligence becomes the ability to consolidate individual motor elements into more complex patterns, and creativity is the outcome of a race-to-threshold process which centers on the motor areas. Amongst the ramifications of these ideas are aspects of cortical oscillations, phantom limb sensations, amyotrophic lateral sclerosis (ALS) the difficulty of self-tickling and mirror neurons.


title: Enabling evidence-based retail marketing with the use of payment data - the Mobile Payment Reference Model 2.0

abstract: Although the extent of e-commerce and m-commerce is continually increasing, the vast majority of the payment transaction potential remains in real-world scenarios such as customary retail POS. However, without adding substantial value, current business models rely on simple transaction-based revenues. The aim of this paper is to evaluate and exploit the freedom degrees of the m-payment business model framework, with special regard to new developments connected to the smartphone economy age. Therefore, we examine methods of data collection and usage to analyse customer preferences and enable evidence-based marketing both theoretically and by case study research. To generate new revenues from third parties, we extend the mobile payment reference model MPRM with new business model elements and roles. For the shift from existing simple transaction-based systems to upcoming modern m-payment procedures built around the central value of customer data, the MPRM 2.0 provides a theoretical background and according reference value architecture as well as an analysis tool to be used in research and practice.


title: A Survey of Digital Image Processing Techniques in Character Recognition

abstract: The digital image processing (DIP) has been employed in a number of areas, particularly for feature extraction and to obtain patterns of digital images. Recognition of characters is a novel problem, and although, currently there are widelyavailable digital image processing algorithms and implementations that are able to detect characters from images, selection of an appropriate technique that can straightforwardly acclimatize to diverse types of images, that are very specific or complex is very important. This paper presents a brief overview of digital image processing techniques such as image restoration, image enhancements, and feature extraction, a framework for processing images and aims at presenting an adaptable digital image processing method for recognition of characters in digital images.


title: Cellulosic ethanol production via consolidated bioprocessing by a novel thermophilic anaerobic bacterium isolated from a Himalayan hot spring

abstract: Background ::: Cellulose-degrading thermophilic anaerobic bacterium as a suitable host for consolidated bioprocessing (CBP) has been proposed as an economically suited platform for the production of second-generation biofuels. To recognize the overall objective of CBP, fermentation using co-culture of different cellulolytic and sugar-fermenting thermophilic anaerobic bacteria has been widely studied as an approach to achieving improved ethanol production. We assessed monoculture and co-culture fermentation of novel thermophilic anaerobic bacterium for ethanol production from real substrates under controlled conditions.


title: On QoS management in SDN by multipath routing

abstract: The Quality of Service (QoS) management is one of the urgent problems in networking which doesn't have an acceptable solution yet. In the paper the approach to this problem based on multipath routing protocol in SDN is considered. The proposed approach is compared with other QoS management methods. A structural and operation schemes for its practical implementation is proposed.


title: Percutaneous Hallux Valgus Surgery Without Distal Metatarsal Articular Angle Correction

abstract: BACKGROUND ::: Many factors are considered predictors of recurrence after hallux valgus (HV) surgery, including preoperative distal metatarsal articular angle (DMAA). The restoration of the bone and joint alignment would be more important than realigning the cartilaginous surface of the metatarsal head. Therefore, is DMAA correction essential for a good clinical and radiological results after HV surgery? This study aims to illustrate the results of percutaneous forefoot surgery (PFS) for correction of HV deformity without DMAA correction. ::: ::: ::: MATERIAL AND METHODS ::: A prospective single-center study of 74 patients (89 feet), with mild-to-moderate hallux valgus deformity, who underwent PFS. The mean latest follow-up was 57.3 months. ::: ::: ::: RESULTS ::: Preoperative median visual analog scale was 7 points and AOFAS scores were 52 points. At the mean latest follow up both scores improved to 0 points and 90 points, respectively. Median HV angle and intermetatarsal angle changed from 30° and 12° preoperatively, to 21° and 11° at mean latest follow-up. Overall, 80% of the patients were satisfied or very satisfied. Recurrence of medial first metatarsal head pain occurred in 12 cases (13.5%). ::: ::: ::: CONCLUSIONS ::: PFS, without DMAA correction, is a valid procedure for surgical correction in patients with HV, despite the slightly worse radiographic results in our study. ::: ::: ::: LEVELS OF EVIDENCE ::: Level II: Prospective study.


title: ABNE: An Attention-Based Network Embedding for User Alignment Across Social Networks

abstract: User alignment across social networks can facilitate more information/knowledge transferring across networks and thereby benefit several applications, including social link prediction, cross-domain recommendation, and information diffusion. Several works try to learn a common subspace for networks by preserving the structural proximities, such that different contribution weights of neighbors are ignored as users were always connected by unweighted edges. In this paper, we propose an attention-based network embedding model that exploits the social structures for user alignment. In particular, two main components are contained in our model framework: a masked graph attention mechanism which tries to learn the alignment task driven attention weights by the supervision of pre-aligned user pairs, and an embedding algorithm tries to learn a common vector space by explicitly modeling the weighted contribution probabilities between follower-ships and followee-ships. With the learned weights and embeddings transferring between these two components, we construct a unified model for user embedding and alignment. Stochastic gradient descent and negative sampling are adopted for efficient learning and scalability. The extensive experiments on real-world social network data sets demonstrate the effectiveness and robustness of the proposed model compared with several state-of-the-art methods.


title: Accelerated Life Testing in Reliability Evaluation of Power Electronics Assemblies

abstract: Role of reliability tests in assessment of service life in power electronics product life cycle is investigated. Paper reviews basic principies of highly accelerated life test and multiple environment over stress tests. Failure types revealed during the accelerated life tests are discussed as well as practical examples of the tests from industry.


title: Critical sizing of LRU caches with dependent requests

abstract: It was recently proved by Jelenkovic and Radovanovic (2004) that the least-recently-used (LRU) caching policy, in the presence of semi-Markov-modulated requests that have a generalized Zipf's law popularity distribution, is asymptotically insensitive to the correlation in the request process. However, since the previous result is asymptotic, it remains unclear how small the cache size can become while still retaining the preceding insensitivity property. In this paper, assuming that requests are generated by a nearly completely decomposable Markov-modulated process, we characterize the critical cache size below which the dependency of requests dominates the cache performance. This critical cache size is small relative to the dynamics of the modulating process, and in fact is sublinear with respect to the sojourn times of the modulated chain that determines the dependence structure.


title: Adult hippocampal neurogenesis in depression

abstract: The development of new treatments for depression is predicated upon identification of neural substrates and mechanisms that underlie its etiology and pathophysiology. The heterogeneity of depression indicates that its origin may lie in dysfunction of multiple brain regions. Here we evaluate adult hippocampal neurogenesis as a candidate mechanism for the etiology of depression and as a substrate for antidepressant action. Current evidence indicates that adult hippocampal neurogenesis may not be a major contributor to the development of depression, but may be required for some of the behavioral effects of antidepressants. We next revisit the functional differentiation of the hippocampus along the septo-temporal axis within the context of adult hippocampal neurogenesis and suggest that neurogenesis in the ventral dentate gyrus may be preferentially involved in regulation of emotion. Finally, we speculate on how increased adult hippocampal neurogenesis may modulate dentate gyrus function to confer the behavioral effects of antidepressants.


Customer Segmentation Using Clustering and Data Mining Techniques


title: A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things

abstract: Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation.


title: (−)-Epigallocatechin-3-gallate protects PC12 cells against corticosterone-induced neurotoxicity via the hedgehog signaling pathway

abstract: It has been acknowledged that environmental stress is a risk factor for developing mental disorders. Chronic stress may contribute to the hyperactivation of the hypothalamic-pituitary-adrenal (HPA) axis and a sustained rise in the levels of glucocorticoids (GCs). A high concentration of corticosterone (CORT) damages neuronal PC12 cells. It has been reported that (-)-Epigallocatechin-3-gallate (EGCG), a major component of green tea, exhibits neuroprotective activity. However, the protective effect of EGCG on neuronal cells injured by CORT remains to be elucidated. The present study aimed to identify the effects of EGCG on CORT-injured neuronal PC12 cells and its associated mechanisms of action. CORT-injured PC12 cells were pretreated with EGCG with or without cyclopamine. Cell viability was assessed using an MTT assay, changes in cell morphology were observed using phase-contrast microscopy, cellular apoptosis was assessed by Hoechst 33342 staining, cell proliferation was measured using a cell counting kit-8 assay, mRNA levels were measured by reverse transcription-quantitative polymerase chain reaction and protein expression was assessed using western blot analysis. The current study demonstrated that exposure to high concentrations of CORT induced cytotoxicity and downregulated the Sonic hedgehog pathway (Shh) in PC12 cells. These effects were attenuated by EGCG. However, the EGCG-mediated neuroprotective effects, as well as upregulation of the Shh pathway were all attenuated by the Shh signaling inhibitor cyclopamine. These results indicate that EGCG protects PC12 cells from CORT-induced neurotoxicity via activation of the Shh signaling pathway.