abstract: Valenti M, Pacchiarotti I, Rosa AR, Bonnin CM, Popovic D, Nivoli AMA, Murru A, Grande I, Colom F, Vieta E. Bipolar mixed episodes and antidepressants: a cohort study of bipolar I disorder patients. Bipolar Disord 2011: 13: 145–154. © 2011 The Authors. Journal compilation © 2011 John Wiley & Sons A/S. ::: ::: ::: ::: Objectives: The aim of this study was to elucidate the factors associated with the occurrence of mixed episodes, characterized by the presence of concomitant symptoms of both affective poles, during the course of illness in bipolar I disorder patients treated with an antidepressant, as well as the role of antidepressants in the course and outcome of the disorder. ::: ::: ::: ::: Method: We enrolled a sample of 144 patients followed for up to 20 years in the referral Barcelona Bipolar Disorder Program and compared subjects who had experienced at least one mixed episode during the follow-up (n = 60) with subjects who had never experienced a mixed episode (n = 84) regarding clinical variables. ::: ::: ::: ::: Results: Nearly 40% of bipolar I disorder patients treated with antidepressants experienced at least one mixed episode during the course of their illness; no gender differences were found between two groups. Several differences regarding clinical variables were found between the two groups, but after performing logistic regression analysis, only suicide attempts (p < 0.001), the use of serotonin norepinephrine reuptake inhibitors (p = 0.041), switch rates (p = 0.010), and years spent ill (p = 0.022) were significantly associated with the occurrence of at least one mixed episode during follow-up. ::: ::: ::: ::: Conclusions: The occurrence of mixed episodes is associated with a tendency to chronicity, with a poorer outcome, a higher number of depressive episodes, and greater use of antidepressants, especially serotonin norepinephrine reuptake inhibitors.


title: Truth revelation in approximately efficient combinatorial auctions

abstract: Some important classical mechanisms considered in Microeconomics and Game Theory require the solution of a difficult optimization problem. This is true of mechanisms for combinatorial auctions, which have in recent years assumed practical importance, and in particular of the gold standard for combinatorial auctions, the Generalized Vickrey Auction (GVA). Traditional analysis of these mechanisms - in particular, their truth revelation properties - assumes that the optimization problems are solved precisely. In reality, these optimization problems can usually be solved only in an approximate fashion. We investigate the impact on such mechanisms of replacing exact solutions by approximate ones. Specifically, we look at a particular greedy optimization method. We show that the GVA payment scheme does not provide for a truth revealing mechanism. We introduce another scheme that does guarantee truthfulness for a restricted class of players. We demonstrate the latter property by identifying natural properties for combinatorial auctions and showing that, for our restricted class of players, they imply that truthful strategies are dominant. Those properties have applicability beyond the specific auction studied.


title: An In-body communication link based on 400 MHz MICS band wireless body area networks

abstract: Implant wireless body area networks (WBANs) are becoming increasingly significant for numerous applications in the healthcare and medical sectors. Here an in-body communication model is proposed, based on a 1 mm resolution mesh kneeling position inhomogeneous 30-year male body model in the 400 MHz medical implant communication service (MICS) band. Firstly, semi-empirical path loss models for numerous homogeneous tissues and a heterogeneous human body model are proposed. Then we investigate the bit error rate (BER) performance using the heterogeneous human body channel model. Coupled with link margin analysis, this model can be applied to future implementations and system design research work.


title: Inferring Employee Engagement from Social Media

abstract: Employees increasingly are expressing ideas and feelings through enterprise social media. Recent work in CHI and CSCW has applied linguistic analysis towards understanding employee experiences. In this paper, we apply dictionary based linguistic analysis to measure 'Employee Engagement'. Employee engagement is a measure of employee willingness to apply discretionary effort towards organizational goals, and plays an important role in organizational outcomes such as financial or operational results. Organizations typically use surveys to measure engagement. This paper describes an approach to model employee engagement based on word choice in social media. This method can potentially complement surveys, thus providing more real-time insights into engagement and allowing organizations to address engagement issues faster. Our results predicting engagement scores on a survey by combining demographics with social media text demonstrate that social media text has significant predictive power compared to demographic data alone. We also find that engagement may be a state than a stable trait since social media posts closer to the administration of the survey had the most predictive power. We further identify the minimum number of social media posts required per employee for the best prediction.


Learning Discriminative Projections for Text Similarity Measures


title: Predicting future hourly residential electrical consumption: A machine learning case study

abstract: Abstract Traditional whole building energy modeling suffers from several factors, including the large number of inputs required for building characterization, simplifying assumptions, and the gap between the as-designed and as-built building. Prior work has attempted to mitigate these problems by using sensor-based machine learning approaches to statistically model energy consumption, applying the techniques primarily to commercial building data, which makes use of hourly consumption data. It is unclear, however, whether these techniques can translate to residential buildings, since the energy usage patterns may vary significantly. Until now, most residential modeling research only had access to monthly electrical consumption data. In this article, we report on the evaluation of seven different machine learning algorithms applied to a new residential data set that contains sensor measurements collected every 15 min, with the objective of determining which techniques are most successful for predicting next hour residential building consumption. We first validate each learner's correctness on the ASHRAE Great Energy Prediction Shootout, confirming existing conclusions that Neural Network-based methods perform best on commercial buildings. However, our additional results show that these methods perform poorly on residential data, and that Least Squares Support Vector Machines perform best – a technique not previously applied to this domain.


title: Research Article Benign Violations: Making Immoral Behavior Funny

abstract: Humor is an important, ubiquitous phenomenon; however, seemingly disparate conditions seem to facilitate humor. We integrate these conditions by suggesting that laughter and amusement result from violations that are simultaneously seen as benign. We investigated three conditions that make a violation benign and thus humorous: (a) the presence of an alternative norm suggesting that the situation is acceptable, (b) weak commitment to the violated norm, and (c) psychological distance from the violation. We tested the benign-violation hypothesis in the domain of moral psychology, where there is a strong documented association between moral violations and negative emotions, particularly disgust. Five experimental studies show that benign moral violations tend to elicit laughter and amusement in addition to disgust. Furthermore, seeing a violation as both wrong and not wrong mediates behavioral displays of humor. Our account is consistent with evolutionary accounts of laughter, explains humor across many domains, and suggests that humor can accompany negative emotion.


A molecular switch in immunodominant HIV-1-specific CD8 T-cell epitopes shapes differential HLA-restricted escape


title: Gender differences in the relationship between internet addiction and depression: A cross-lagged study in Chinese adolescents

abstract: The present study explored the role of gender in the association between Internet addiction and depression. Three-wave longitudinal panel data were collected from self-reported questionnaires that were completed by 1715 adolescents in grades 6-8 in China. Cross-lagged structural equation modeling was used to examine the relationship between Internet addiction and depression. In male adolescents, depression was found to significantly predict subsequent Internet addiction, suggesting that depression was the cause of Internet addiction and supporting the mood enhancement hypothesis. In female adolescents, Internet addiction was found to significantly predict subsequent depression, indicating that Internet addiction leads to depression and supporting the social displacement hypothesis. These results indicate that the relationship between Internet addiction and depression depended on gender. In addition, it was found that males and females exhibit different behavioral patterns and motivations of Internet usage. Males were more likely to use the Internet for pleasure and less likely to surf the Internet to search for information, compared with females. Although both males and females were prone to surfing the Internetźalone, males were more likely to go online with friends compared with females. These findings suggest that gender-specific preventative and interventional strategies should be developed to reduce Internet addiction. Males and females had different behavioral patterns and motivations of Internet usage.Males were more likely to overindulge in the Internet because of depression.Females were prone to depression because of Internet addiction.The relationship between Internet addiction and depression depended on gender.These findings could inform prevention and intervention strategies for Internet addiction.


title: An Omnidirectional Circularly Polarized Antenna Array

abstract: An omnidirectional circularly polarized (CP) antenna array is proposed. The antenna array is composed of four identical CP antenna elements and one parallel strip-line feeding network. Each of CP antenna elements comprises a dipole and a zero-phase-shift (ZPS) line loop. The in-phase fed dipole and the ZPS line loop generate vertically and horizontally polarized omnidirectional radiation, respectively. Furthermore, the vertically polarized dipole is positioned in the center of the horizontally polarized ZPS line loop. The size of the loop is designed such that a 90° phase difference is realized between the two orthogonal components because of the spatial difference and, therefore, generates CP omnidirectional radiation. A ${1} \times {4}$ antenna array at 900 MHz is prototyped and targeted to ultra-high frequency (UHF) radio frequency identification (RFID) applications. The measurement results show that the antenna array achieves a 10-dB return loss over a frequency range of 900–935 MHz and 3-dB axial-ratio (AR) from 890 to 930 MHz. At the frequency of 915 MHz, the measured maximum AR of 1.53 dB, maximum gain of 5.4 dBic, and an omnidirectionality of $\pm {1}\;{\text{dB}}$ are achieved.


title: Tapping the Potential: Secure Chunk-based Deduplication of Encrypted Data for Cloud Backup

abstract: We, in this work, investigate the problem of designing a secure chunk-based deduplication scheme in the enterprise backup storage setting. Most of the existing works focus on realizing file-level encrypted data deduplication or key/metadata management. Little attention is drawn to the practical chunk-level deduplication system. In particular, we identify that the information contained in a small-sized chunk is more susceptible to the brute-force attack compared with file-based deduplication. We propose a randomized oblivious key generation mechanism based on the inner workings of the backup service. In contrast with the current work that compromising one client will eventually expose all the clients’ storage, our scheme offers a counter-intuitive property of achieving security against multiclient compromise with minimal deduplication performance loss. In addition, we enforce a per-backup rate-limiting policy to slow down the online brute-force attack. We show that the proposed scheme is provably secure in the malicious model. We also calibrate the system design by taking into account the practical deduplication requirements to accomplish a comparable plaintext deduplication performance. Our experiment on the real-world dataset shows its efficiency, effectiveness, and practicality.


title: Food Addiction and Bulimia Nervosa: New Data Based on the Yale Food Addiction Scale 2.0

abstract: Previous research on ‘food addiction’ as measured with the Yale Food Addiction Scale (YFAS) showed a large overlap between addiction-like eating and bulimia nervosa. Most recently, a revised version of the YFAS has been developed according to the changes made in the diagnostic criteria for substance use disorder in the Diagnostic and Statistical Manual of Mental Disorders fifth edition. The current study examined prevalence and correlates of the YFAS2.0 in individuals with bulimia (n = 115) and controls (n = 341). Ninety-six per cent of participants with bulimia and 14% of controls received a YFAS2.0 diagnosis. A higher number of YFAS2.0 symptoms was associated with lower interoceptive awareness, higher depressiveness, and higher impulsivity in both groups. However, a higher number of YFAS2.0 symptoms was associated with higher body mass and weight suppression in controls only and not in participants with bulimia. The current study is the first to show a large overlap between bulimia and ‘food addiction’ as measured with the YFAS2.0, replicating and extending findings from studies, which used the previous version of the YFAS. Compensatory weight control behaviours in individuals with bulimia likely alleviate the association between addiction-like eating and higher body mass. Thus, the large overlap between bulimia and ‘food addiction’ should be taken into consideration when examining the role of addiction-like eating in weight gain and obesity. Copyright © 2016 John Wiley & Sons, Ltd and Eating Disorders Association.


title: The many methods of religious coping: Development and initial validation of the RCOPE

abstract: The purpose of this study was to develop and validate a new theoretically based measure that would assess the full range of religious coping methods, including potentially helpful and harmful religious expressions. The RCOPE was tested on a large sample of college students who were coping with a significant negative life event. Factor analysis of the RCOPE in the college sample yielded factors largely consistent with the conceptualization and construction of the subscales. Confirmatory factor analysis of the RCOPE in a large sample of hospitalized elderly patients was moderately supportive of the initial factor structure. Results of regression analyses showed that religious coping accounted for significant unique variance in measures of adjustment (stress-related growth, religious outcome, physical health, mental health, and emotional distress) after controlling for the effects of demographics and global religious measures (frequency of prayer, church attendance, and religious salience). Better adjustment was related to a number of coping methods, such as benevolent religious reappraisals, religious forgiveness/purification, and seeking religious support. Poorer adjustment was associated with reappraisals of God's powers, spiritual discontent, and punishing God reappraisals. The results suggest that the RCOPE may be useful to researchers and practitioners interested in a comprehensive assessment of religious coping and in a more complete integration of religious and spiritual dimensions in the process of counseling. © 2000 John Wiley & Sons, Inc. J Clin Psychol 56: 519–543, 2000.


title: Intelligent Techniques for Decision Support System in Human Resource Management

abstract: Nowadays, the evolution of information technology applications makes it an absolute obligation on behalf of the decision makers to continuously make the best decisions in the shortest possible time. Decision Support System (DSS) is a technology and application that assists managerial decision makers utilizing data and models to solve semi-structured and unstructured problems (Qian et al., 2004). This chapter discusses general issues on DSS technologies and an idea to apply DSS technologies into Human Resources Management (HRM) field. Recently, the collaboration between DSS technologies and Artificial Intelligent techniques has produced another type of DSS technology known as Active DSS, it is a technology that will take place in the new millennium era (Shim et al., 2002). Active DSS is an outcome of new DSS technologies and also known as a part of Intelligent System applications. Active DSS applications such as Expert System, Knowledge-based System, Adaptive DSS and Intelligent Decision Support System (IDSS) are categorized as part of Intelligent System studies. Expert systems technology, which was a crucial area for enterprise capital in 1985-1990, is now being replaced by the intelligent system applications (Faye et al., 1998). Intelligent systems are developed to fulfill the two main functions. Firstly, to screening, shifting and filtering the increasing overflow of data, information and knowledge. Secondly, as a supporter of an effective and productive decision making that is suitable to the user needs. Intelligent systems can be developed for these purposes; range from self-organizing maps to smart add-on modules to make the use of applications more effective and useful for the users (Shim et al., 2002). Human is important and a very valuable asset for an organization and managed by Human Resource professional. HRM system is an important element in the success of an organization, known as an integrated and interrelated approaches to managing human resources (DeNisi & Griffin, 2005). Activities in HRM involve a lot of unstructured processes such as staffing, training, motivation and maintenance (DeCenZo & Robbins, 2005). Besides that, decision making for unstructured processes in HRM usually depends on human judgment and preference. However, human decisions are subject to the limitation because sometimes people forget the crucial details of the problem, and besides, fairness and


Automated extraction of coastline from satellite imagery by integrating Canny edge detection and locally adaptive thresholding methods


title: Scheduling Hadoop Jobs to Meet Deadlines

abstract: User constraints such as deadlines are important requirements that are not considered by existing cloud-based data processing environments such as Hadoop. In the current implementation, jobs are scheduled in FIFO order by default with options for other priority based schedulers. In this paper, we extend real time cluster scheduling approach to account for the two-phase computation style of MapReduce. We develop criteria for scheduling jobs based on user specified deadline constraints and discuss our implementation and preliminary evaluation of a Deadline Constraint Scheduler for Hadoop which ensures that only jobs whose deadlines can be met are scheduled for execution.


title: Word Embeddings as Metric Recovery in Semantic Spaces

abstract: Continuous word representations have been remarkably useful across NLP tasks but remain poorly understood. We ground word embeddings in semantic spaces studied in the cognitive-psychometric literature, taking these spaces as the primary objects to recover. To this end, we relate log co-occurrences of words in large corpora to semantic similarity assessments and show that co-occurrences are indeed consistent with an Euclidean semantic space hypothesis. Framing word embedding as metric recovery of a semantic space unifies existing word embedding algorithms, ties them to manifold learning, and demonstrates that existing algorithms are consistent metric recovery methods given co-occurrence counts from random walks. Furthermore, we propose a simple, principled, direct metric recovery algorithm that performs on par with the state-of-the-art word embedding and manifold learning methods. Finally, we complement recent focus on analogies by constructing two new inductive reasoning datasets---series completion and classification---and demonstrate that word embeddings can be used to solve them as well.


title: Variational Autoencoder for Deep Learning of Images, Labels and Captions

abstract: A novel variational autoencoder is developed to model images, as well as associated labels or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of the latent image features, and a deep Convolutional Neural Network (CNN) is used as an image encoder; the CNN is used to approximate a distribution for the latent DGDN features/code. The latent code is also linked to generative models for labels (Bayesian support vector machine) or captions (recurrent neural network). When predicting a label/caption for a new image at test, averaging is performed across the distribution of latent codes; this is computationally efficient as a consequence of the learned CNN-based encoder. Since the framework is capable of modeling the image in the presence/absence of associated labels/captions, a new semi-supervised setting is manifested for CNN learning with images; the framework even allows unsupervised CNN learning, based on images alone.


title: Sairaan hauskaa! : nuorten kasvatuksellinen kapina folkloressa

abstract: Children's and young people's folklore, their own oral tradition, is an active and growing cultural area, despite the increasing urbanisation of the society. The main focus of the article is to analyse some digital folklore examples, which are not allowed for the ears of adults. This children's and youth folklore covers topics such as taboos, racism, sexual and forbidden themes. At the same time these jokes, urban legends, riddles, photo manipulations, tongue-twisters etc. offer us interesting insights into young people's way of coping with horrible happenings in the world like human suffering, wars, school shootings or catastrophes. They show us young people's own world, children's and young people's social area, without adults' custody or propriety. In some cases folklore may also be a revolt against moral norms in society and adults' way of thinking and living.


title: Exploiting Visibility Information in Surface Reconstruction to Preserve Weakly Supported Surfaces

abstract: We present a novel method for 3D surface reconstruction from an input cloud of 3D points augmented with visibility information. We observe that it is possible to reconstruct surfaces that do not contain input points. Instead of modeling the surface from input points, we model free space from visibility information of the input points. The complement of the modeled free space is considered full space. The surface occurs at interface between the free and the full space. We show that under certain conditions a part of the full space surrounded by the free space must contain a real object also when the real object does not contain any input points; that is, an occluder reveals itself through occlusion. Our key contribution is the proposal of a new interface classifier that can also detect the occluder interface just from the visibility of input points. We use the interface classifier to modify the state-of-the-art surface reconstruction method so that it gains the ability to reconstruct weakly supported surfaces. We evaluate proposed method on datasets augmented with different levels of noise, undersampling, and amount of outliers. We show that the proposed method outperforms other methods in accuracy and ability to reconstruct weakly supported surfaces.


title: A Temporally-Aware Interpolation Network for Video Frame Inpainting

abstract: We propose the first deep learning solution to video frame inpainting, a more challenging but less ambiguous task than related problems such as general video inpainting, frame interpolation, and video prediction. We devise a pipeline composed of two modules: a bidirectional video prediction module and a temporally-aware frame interpolation module. The prediction module makes two intermediate predictions of the missing frames, each conditioned on the preceding and following frames respectively, using a shared convolutional LSTM-based encoder-decoder. The interpolation module blends the intermediate predictions, using time information and hidden activations from the video prediction module to resolve disagreements between the predictions. Our experiments demonstrate that our approach produces more accurate and qualitatively satisfying results than a state-of-the-art video prediction method and many strong frame inpainting baselines. Our code is available at https://github.com/sunxm2357/TAI_video_frame_inpainting.


title: Modeling Strategic Relationships for Process Reengineering

abstract: Existing models for describing a process (such as a business process or a software development process) tend to focus on the "what" or the "how" of the process. For example, a health insurance claim process would typically be described in terms of a number of steps for assessing and approving a claim. In trying to improve or redesign a process, however, one also needs to have an understanding of the "why"--for example, why do physicians submit treatment plans to insurance companies before giving treatment? and why do claims managers seek medical opinions when assessing treatment plans? An understanding of the motivations and interests of process participants is often crucial to the successful redesign of processes. ::: This thesis proposes a modelling framework i* (pronounced i-star) consisting of two modelling components. The Strategic Dependency (SD) model describes a process in terms of intentional dependency relationships among agents. Agents depended on each other for goals to be achieved, tasks to be performed, and resources to be furnished. Agents are intentional in that they have desires and wants, and strategic in that they are concerned about opportunities and vulnerabilities. The Strategic Rationale (SR) model describes the issues and concerns that agents have about existing processes and proposed alternatives, and how they might be addressed, in terms of a network of means-ends relationships. An agent's routines for carrying out a process can be analyzed for their ability, workability, viability and believability. Means-ends rules are used to suggest methods for addressing issues, related issues to be raised, and assumptions to be challenged. The models are represented in the conceptual modelling language Telos. The modelling concepts are axiomatically characterized. ::: The utility of the framework is illustrated each of four application areas: requirements engineering, business process reengineering, organizational impacts analysis, and software process modelling. Advantage of i* over existing modelling techniques in each of these areas are described.


title: Deep Linear Discriminant Analysis

abstract: We introduce Deep Linear Discriminant Analysis (DeepLDA) which learns linearly separable latent representations in an end-to-end fashion. Classic LDA extracts features which preserve class separability and is used for dimensionality reduction for many classification problems. The central idea of this paper is to put LDA on top of a deep neural network. This can be seen as a non-linear extension of classic LDA. Instead of maximizing the likelihood of target labels for individual samples, we propose an objective function that pushes the network to produce feature distributions which: (a) have low variance within the same class and (b) high variance between different classes. Our objective is derived from the general LDA eigenvalue problem and still allows to train with stochastic gradient descent and back-propagation. For evaluation we test our approach on three different benchmark datasets (MNIST, CIFAR-10 and STL-10). DeepLDA produces competitive results on MNIST and CIFAR-10 and outperforms a network trained with categorical cross entropy (same architecture) on a supervised setting of STL-10.


title: Adaptive navigation of an omni-drive autonomous mobile robot in unstructured dynamic environments

abstract: One of the challenges of Autonomous Systems navigating in real world is to deal with the large amounts of uncertainties which are inherent in such environment while maintaining stability. Higher order Fuzzy Logic Systems (FLS), such as Interval Type-2 Fuzzy Logic Systems (IT2FLS), that use type-2 fuzzy sets, can model and handle such uncertainties, and give good performances that outperform their Type-1 counterparts. However, the complexity and computational time of type-reduction process which is strongly related to Membership Functions (MFs) structure and the number of fuzzy rules limit their applications to simple cases in real-time. Artificial Potential Field (APF) approach due to its elegant mathematical analysis, simplicity and its possibility to take into account the dynamic of the system is widely used for autonomous mobile robots navigation. However, the potential field introduced exhibits local minima other than at the goal position of the robot. In this paper, a new real-time navigation approach where we combine the APF and IT2FL approaches is developed and implemented for an omnidrive mobile robot navigating in dynamic unstructured environments. The novelty of the approach is the association of IT2FL to APF and the way in which the two approaches are hybridized (rule base size reduction). The experiments carried out on an omnidrive mobile robot named Robotino show the effectiveness of our approach.


title: A novel Network Intrusion Detection System (NIDS) based on signatures search of data mining

abstract: Network security has been a very important issue, since the rising evolution of the Internet. There has been an increasing need for security systems against the external attacks from the hackers. One important type is the intrusion detection system (IDS). There are two major categories of the analysis techniques of IDS: the anomaly detection and the misuse detection. Here we forcus on misuse detection, the misuse detection collected the attack signatures in a database as the same as virus protection software to detect the relate attacks, we propose an algorithm to use the known signature to find the signature of the related attack quickly.


title: SWaT: a water treatment testbed for research and training on ICS security

abstract: This paper presents the SWaT testbed, a modern industrial control system (ICS) for security research and training. SWaT is currently in use to (a) understand the impact of cyber and physical attacks on a water treatment system, (b) assess the effectiveness of attack detection algorithms, (c) assess the effectiveness of defense mechanisms when the system is under attack, and (d) understand the cascading effects of failures in one ICS on another dependent ICS. SWaT consists of a 6-stage water treatment process, each stage is autonomously controlled by a local PLC. The local fieldbus communications between sensors, actuators, and PLCs is realized through alternative wired and wireless channels. While the experience with the testbed indicates its value in conducting research in an active and realistic environment, it also points to design limitations that make it difficult for system identification and attack detection in some experiments.


title: Construction site navigation for the autonomous excavator Thor

abstract: The paper at hand is part of the autonomous excavator project Thor (Terraforming Heavy Outdoor Robot) who's goal is the development of a construction machine which performs landscaping on a construction site without an operator. So far the project mainly focused on the local excavation on one position. Due to the high digging forces the machine permanently changes its position during excavation. Furthermore, the global goal is to shape the complete construction site. Therefore, a final test platform needs to permanently reposition itself on the site. Within this paper the construction site navigation function is described which guarantees safe traveling from one pose to another one. It is based on an extended A* path planning algorithm, executed on a 2D gridmap including region growing for obstacles, including forward and backward movement. In combination with an intelligent path following algorithm the machine proved to safely reach its position with the desired orientation.


title: The Integration of CBT, Multicultural and Feminist Psychotherapies With Latinas

abstract: Socorro was a 35-year-old Venezuelan woman who attempted suicide by overdose. The psychotherapeutic approach relied on identifying the social context, constructs, and working within Socorro's cultural framework. Cultural issues related to immigration, acculturation, and culturally based gender issue


title: Discrete spurious signals and background noise in direct frequency synthesizers

abstract: Some sources of output spurious signals in direct digital frequency synthesizers are discussed. First, the authors provide a closed-form solution for spurious signals generated by phase truncation. Then they consider the accuracy of sine look-up tables and arrive at an estimated level of background spurious signals generated this way. They also investigate the behavior of digital-to-analog convertors and their bit resolution, nonlinearity, and clock signal leakage. Finally, they investigate the eventual phase noise level. >


title: Experimental study of the valve-bend interaction in an aircraft ECS system

abstract: The authors acknowledge the support of the EU Seventh Framework Programme (FP7) under the Level 1 Collaborative Project IDEALVENT (GA 314066). The research of Herve ::: Denayer is funded by a fellowship of the Agency for ::: Innovation by Science and Technology in Flanders (IWT).


title: Iris recognition through machine learning techniques: A survey ☆

abstract: Abstract Iris recognition is one of the most promising fields in biometrics. Notwithstanding this, there are not so many research works addressing it by machine learning techniques. In this survey, we especially focus on recognition, and leave the detection and feature extraction problems in the background. However, the kind of features used to code the iris pattern may significantly influence the complexity of the methods and their performance. In other words, complexity affects learning, and iris patterns require relatively complex feature vectors, even if their size can be optimized. A cross-comparison of these two parameters, feature complexity vs. learning effectiveness, in the context of different learning algorithms, would require an unbiased common benchmark. Moreover, at present it is still very difficult to reproduce techniques and experiments due to the lack of either sufficient implementation details or reliable shared code.


title: Operation of a prototype microgrid system based on micro-sources quipped with fast-acting power electronics interfaces

abstract: The paper presents experimental results from the operation of a prototype microgrid system, installed in the National Technical University of Athens, which comprises a PV generator, battery energy storage, local load and a controlled interconnection to the LV grid. Both the battery unit and the PV generator are connected to the AC grid via fast-acting DC/AC power converters. The converters are suitably controlled to permit the operation of the system either interconnected to the LV network, or in stand-alone (island) mode, with a seamless transfer from the one mode to the other. The paper provides a technical description of the system components and the control concept implemented, along with extensive measurement results which demonstrate its capability to operate in the aforementioned way.


title: Can I Have Your Attention, Please?: An Empirical Investigation of Media Multitasking during University Lectures

abstract: The growing prevalence of continuous media use among university students in lecture environments has potential for detrimental effects. In this study the focus is placed upon the implications of digital media multitasking in a university lecture context for academic performance and learning. Previous studies reveal that students frequently engage with digital media whilst in a university lecture. Moreover, research has shown that multitasking imposes a cognitive cost, detrimental to learning and task execution. We propose, accordingly, that the constant distractions created by digital media interrupt the thought and communication processes of students and, subsequently, obstruct their ability to learn. To test this proposition we conduct a survey-based empirical investigation of digital media use and academic performance among undergraduate university students. A clear negative correlation was shown between frequency of media use and academic performance. This result confirms the hypothesis that there exists a negative correlation between digital media use during lectures and academic performance. The confirmation of this negative relationship suggests that media use poses a significant distraction to students.


title: Integrating image clustering and codebook learning

abstract: Image clustering and visual codebook learning are two fundamental problems in computer vision and they are tightly related. On one hand, a good codebook can generate effective feature representations which largely affect clustering performance. On the other hand, class labels obtained from image clustering can serve as supervised information to guide codebook learning. Traditionally, these two processes are conducted separately and their correlation is generally ignored. In this paper, we propose a Double Layer Gaussian Mixture Model (DLGMM) to simultaneously perform image clustering and codebook learning. In DLGMM, two tasks are seamlessly coupled and can mutually promote each other. Cluster labels and codebook are jointly estimated to achieve the overall best performance. To incorporate the spatial coherence between neighboring visual patches, we propose a Spatially Coherent DL-GMM which uses a Markov Random Field to encourage neighboring patches to share the same visual word label. We use variational inference to approximate the posterior of latent variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of two models.


title: Exploratory Search:Beyond the Query-Response Paradigm

abstract: As information becomes more ubiquitous and the demands that searchers have on search systems grow, there is a need to support search behaviors beyond simple lookup. Information seeking is the process or activity of attempting to obtain information in both human and technological contexts. Exploratory search describes an information-seeking problem context that is open-ended, persistent, and multifaceted, and information-seeking processes that are opportunistic, iterative, and multitactical. Exploratory searchers aim to solve complex problems and develop enhanced mental capacities. Exploratory search systems support this through symbiotic human-machine relationships that provide guidance in exploring unfamiliar information landscapes. Exploratory search has gained prominence in recent years. There is an increased interest from the information retrieval, information science, and human-computer interaction communities in moving beyond the traditional turn-taking interaction model support d by major Web search engines, and toward support for human intelligence amplification and information use. In this lecture, we introduce exploratory search, relate it to relevant extant research, outline the features of exploratory search systems, discuss the evaluation of these systems, and suggest some future directions for supporting exploratory search. Exploratory search is a new frontier in the search domain and is becoming increasingly important in shaping our future world. Table of Contents: Introduction / Defining Exploratory Search / Related Work / Features of Exploratory Search Systems / Evaluation of Exploratory Search Systems / Future Directions and concluding Remarks


title: Adversarial Feature Matching for Text Generation

abstract: The Generative Adversarial Network (GAN) has achieved great success in generating realistic (real-valued) synthetic data. However, convergence issues and difficulties dealing with discrete data hinder the applicability of GAN to text. We propose a framework for generating realistic text via adversarial training. We employ a long short-term memory network as generator, and a convolutional network as discriminator. Instead of using the standard objective of GAN, we propose matching the high-dimensional latent feature distributions of real and synthetic sentences, via a kernelized discrepancy metric. This eases adversarial training by alleviating the mode-collapsing problem. Our experiments show superior performance in quantitative evaluation, and demonstrate that our model can generate realistic-looking sentences.


title: Significance of Classification Techniques in Prediction of Learning Disabilities

abstract: The aim of this study is to show the importance of two classification techniques, viz. decision tree and clustering, in prediction of learning disabilities (LD) of school-age children. LDs affect about 10 percent of all children enrolled in schools. The problems of children with specific learning disabilities have been a cause of concern to parents and teachers for some time. Decision trees and clustering are powerful and popular tools used for classification and prediction in Data mining. Different rules extracted from the decision tree are used for prediction of learning disabilities. Clustering is the assignment of a set of observations into subsets, called clusters, which are useful in finding the different signs and symptoms (attributes) present in the LD affected child. In this paper, J48 algorithm is used for constructing the decision tree and K-means algorithm is used for creating the clusters. By applying these classification techniques, LD in any child can be identified.


An Intelligent Monitoring and Controlling of Greenhouse: Deployment of Wireless Sensor Networks and Internet-of-Things


title: Testing and evaluation of a wearable augmented reality system for natural outdoor environments

abstract: This paper describes performance evaluation of a wearable augmented reality system for natural outdoor environments. ::: Applied Research Associates (ARA), as prime integrator on the DARPA ULTRA-Vis (Urban Leader Tactical, ::: Response, Awareness, and Visualization) program, is developing a soldier-worn system to provide intuitive ‘heads-up’ ::: visualization of tactically-relevant geo-registered icons. Our system combines a novel pose estimation capability, a ::: helmet-mounted see-through display, and a wearable processing unit to accurately overlay geo-registered iconography ::: (e.g., navigation waypoints, sensor points of interest, blue forces, aircraft) on the soldier’s view of reality. We achieve ::: accurate pose estimation through fusion of inertial, magnetic, GPS, terrain data, and computer-vision inputs. We ::: leverage a helmet-mounted camera and custom computer vision algorithms to provide terrain-based measurements of ::: absolute orientation (i.e., orientation of the helmet with respect to the earth). These orientation measurements, which ::: leverage mountainous terrain horizon geometry and mission planning landmarks, enable our system to operate robustly ::: in the presence of external and body-worn magnetic disturbances. Current field testing activities across a variety of ::: mountainous environments indicate that we can achieve high icon geo-registration accuracy (<10mrad) using these ::: vision-based methods.


title: Dynamic Graph CNN for Learning on Point Clouds

abstract: Point clouds provide a flexible geometric representation suitable for countless applications in computer graphics; they also comprise the raw output of most 3D data acquisition devices. While hand-designed features on point clouds have long been proposed in graphics and vision, however, the recent overwhelming success of convolutional neural networks (CNNs) for image analysis suggests the value of adapting insight from CNN to the point cloud world. Point clouds inherently lack topological information, so designing a model to recover topology can enrich the representation power of point clouds. To this end, we propose a new neural network module dubbed EdgeConv suitable for CNN-based high-level tasks on point clouds, including classification and segmentation. EdgeConv acts on graphs dynamically computed in each layer of the network. It is differentiable and can be plugged into existing architectures. Compared to existing modules operating in extrinsic space or treating each point independently, EdgeConv has several appealing properties: It incorporates local neighborhood information; it can be stacked applied to learn global shape properties; and in multi-layer systems affinity in feature space captures semantic characteristics over potentially long distances in the original embedding. We show the performance of our model on standard benchmarks, including ModelNet40, ShapeNetPart, and S3DIS.


title: Fast unfolding of communities in large networks

abstract: We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.


title: A Syllable-based Technique for Word Embeddings of Korean Words

abstract: Word embedding has become a fundamental component to many NLP tasks such as named entity recognition and machine translation. However, popular models that learn such embeddings are unaware of the morphology of words, so it is not directly applicable to highly agglutinative languages such as Korean. We propose a syllable-based learning model for Korean using a convolutional neural network, in which word representation is composed of trained syllable vectors. Our model successfully produces morphologically meaningful representation of Korean words compared to the original Skip-gram embeddings. The results also show that it is quite robust to the Out-of-Vocabulary problem.


title: A Circuit-Compatible SPICE model for Enhancement Mode Carbon Nanotube Field Effect Transistors

abstract: This paper presents a circuit-compatible compact model for short channel length (5 nm~100 nm), quasi-ballistic single wall carbon nanotube field-effect transistors (CNFETs). For the first time, a universal circuit-compatible CNFET model was implemented with HSPICE. This model includes practical device non-idealities, e.g. the quantum confinement effects in both circumferential and channel length direction, the acoustical/optical phonon scattering in channel region and the resistive source/drain, as well as the real time dynamic response with a transcapacitance array. This model is valid for CNFET for a wide diameter range and various chiralities as long as the carbon nanotube (CNT) is semiconducting


title: FlexiWeb: Network-Aware Compaction for Accelerating Mobile Web Transfers

abstract: To reduce page load times and bandwidth usage for mobile web browsing, middleboxes that compress page content are commonly used today. Unfortunately, this can hurt performance in many cases; via an extensive measurement study, we show that using middleboxes to facilitate compression results in up to 28% degradation in page load times when the client enjoys excellent wireless link conditions. We find that benefits from compression are primarily realized under bad network conditions. Guided by our study, we design and implement FlexiWeb, a framework that determines both when to use a middlebox and how to use it, based on the client's network conditions. First, FlexiWeb selectively fetches objects on a web page either directly from the source or via a middlebox, rather than fetching all objects via the middlebox. Second, instead of simply performing lossless compression of all content, FlexiWeb performs network-aware compression of images by selecting from among a range of content transformations. We implement and evaluate a prototype of FlexiWeb using Google's open source Chromium mobile browser and our implementation of a modified version of Google's open source compression proxy. Our extensive experiments show that, across a range of scenarios, FlexiWeb reduces page load times for mobile clients by 35-42% compared to the status quo.


title: Autonomous door opening and plugging in with a personal robot

abstract: We describe an autonomous robotic system capable of navigating through an office environment, opening doors along the way, and plugging itself into electrical outlets to recharge as needed. We demonstrate through extensive experimentation that our robot executes these tasks reliably, without requiring any modification to the environment. We present robust detection algorithms for doors, door handles, and electrical plugs and sockets, combining vision and laser sensors. We show how to overcome the unavoidable shortcoming of perception by integrating compliant control into manipulation motions. We present a visual-differencing approach to high-precision plug-insertion that avoids the need for high-precision hand-eye calibration.


title: Choosing software metrics for defect prediction: an investigation on feature selection techniques

abstract: The selection of software metrics for building software quality prediction models is a search-based software engineering problem. An exhaustive search for such metrics is usually not feasible due to limited project resources, especially if the number of available metrics is large. Defect prediction models are necessary in aiding project managers for better utilizing valuable project resources for software quality improvement. The efficacy and usefulness of a fault-proneness prediction model is only as good as the quality of the software measurement data. This study focuses on the problem of attribute selection in the context of software quality estimation. A comparative investigation is presented for evaluating our proposed hybrid attribute selection approach, in which feature ranking is first used to reduce the search space, followed by a feature subset selection. A total of seven different feature ranking techniques are evaluated, while four different feature subset selection approaches are considered. The models are trained using five commonly used classification algorithms. The case study is based on software metrics and defect data collected from multiple releases of a large real-world software system. The results demonstrate that while some feature ranking techniques performed similarly, the automatic hybrid search algorithm performed the best among the feature subset selection methods. Moreover, performances of the defect prediction models either improved or remained unchanged when over 85were eliminated. Copyright © 2011 John Wiley & Sons, Ltd.


Graph Connectivity


title: A 10 ms-readout interface for the characterization of high-value wide-range experimental resistive sensors

abstract: Abstract Metal oxide gas sensors exhibit resistance values varying over a wide range, from tens of kilohms to tens of gigohms, depending on the chosen oxide and on the excitation parameters (voltage, temperature, gas exposure). Resistance-to-time converters (RTC) are widely used as electronic interfaces for such sensors, thanks to the low-cost, low-noise and high-range characteristics. RTC main limit is in the variable and long measuring time, ranging from microseconds (tens of kilohms) to several seconds (tens of gigohms), impeding a fine analysis of fast transients. This work proposes a new approach based on combination of the RTC method with a new technique based on the least mean square (LMS) algorithm. The implemented prototype allows the sensor resistance to be estimated with a fixed measuring time of 10 ms over the range 10 kΩ–10 GΩ with relative estimation error below 10% (below 1% in the range 47 kΩ–2 GΩ). It can furthermore estimate the parasitic capacitance of the sensor (in parallel with the resistive component), on the order of few picofarads with a linearity error less than 0.5% full scale (FS). Fast thermal transients of a SnO 2 nanowire sensor have been finely analyzed using the new interface system, demonstrating the suitability of the proposed method for accurate analysis of new experimental resistive sensors.


Automatic Fruit Recognition Based on DCNN for Commercial Source Trace System


title: Deep Understanding of a Document's Structure

abstract: Current language understanding approaches focus on small documents, such as newswire articles, blog posts, product reviews and discussion forum discussions. Understanding and extracting information from large documents like legal briefs, proposals, technical manuals and research articles is still a challenging task. We describe a framework that can analyze a large document and help people to locate desired information in it. We aim to automatically identify and classify different sections of documents and understand their purpose within the document. A key contribution of our research is modeling and extracting the logical structure of electronic documents using machine learning techniques, including deep learning. We also make available a dataset of information about a collection of scholarly articles from the arXiv eprints collection that includes a wide range of metadata for each article, including a table of contents, section labels, section summarizations and more. We hope that this dataset will be a useful resource for the machine learning and language understanding communities for information retrieval, content-based question answering and language modeling tasks.


title: Impact ionization in InAlAs/InGaAs HFET's

abstract: The presence of an energy barrier to the transfer of holes from the channel to the gate electrode of InAlAs/InGaAs HFET's prevents the gate current from being a reliable indicator of impact ionization. Consequently, we have used a specially designed sidegate structure to demonstrate that due to the narrow bandgap of InGaAs, impact ionization takes place in the channel of these devices under normal operating conditions. The ionization coefficient was found to follow a classic exponential dependence on the inverse electric field at the drain end of the gate, for over three orders of magnitude. >


title: Collaborative networked organizations - Concepts and practice in manufacturing enterprises

abstract: Participation in networks has nowadays become very important for any organization that strives to achieve a differentiated competitive advantage, especially if the company is small or medium sized. Collaboration is a key issue to rapidly answer market demands in a manufacturing company, through sharing competencies and resources. The collaborative networked organizations (CNO) area focuses on this type of organizational models that use ICT for supporting the development of collaborative business opportunities. This paper describes the key concepts related to CNOs, provides a high level classification of collaborative networks, and presents some application cases in the manufacturing industry. Finally a holistic research initiative addressing key challenges in the area is presented and a discussion of the CNO paradigm contribution to the challenges faced by manufacturing systems is made.


title: Clinical validation of robot simulation of toothbrushing - comparative plaque removal efficacy

abstract: BackgroundClinical validation of laboratory toothbrushing tests has important advantages. It was, therefore, the aim to demonstrate correlation of tooth cleaning efficiency of a new robot brushing simulation technique with clinical plaque removal.MethodsClinical programme: 27 subjects received dental cleaning prior to 3-day-plaque-regrowth-interval. Plaque was stained, photographically documented and scored using planimetrical index. Subjects brushed teeth 33–47 with three techniques (horizontal, rotating, vertical), each for 20s buccally and for 20s orally in 3 consecutive intervals. The force was calibrated, the brushing technique was video supported. Two different brushes were randomly assigned to the subject. Robot programme: Clinical brushing programmes were transfered to a 6-axis-robot. Artificial teeth 33–47 were covered with plaque-simulating substrate. All brushing techniques were repeated 7 times, results were scored according to clinical planimetry. All data underwent statistical analysis by t-test, U-test and multivariate analysis.ResultsThe individual clinical cleaning patterns are well reproduced by the robot programmes. Differences in plaque removal are statistically significant for the two brushes, reproduced in clinical and robot data. Multivariate analysis confirms the higher cleaning efficiency for anterior teeth and for the buccal sites.ConclusionsThe robot tooth brushing simulation programme showed good correlation with clinically standardized tooth brushing.This new robot brushing simulation programme can be used for rapid, reproducible laboratory testing of tooth cleaning.


title: Adult development and quality of life of transgender and gender nonconforming people.

abstract: PURPOSE OF REVIEW ::: Research on the health of transgender and gender nonconforming people has been limited with most of the work focusing on transition-related care and HIV. The present review summarizes research to date on the overall development and quality of life of transgender and gender nonconforming adults, and makes recommendations for future research. ::: ::: ::: RECENT FINDINGS ::: Pervasive stigma and discrimination attached to gender nonconformity affect the health of transgender people across the lifespan, particularly when it comes to mental health and well-being. Despite the related challenges, transgender and gender nonconforming people may develop resilience over time. Social support and affirmation of gender identity play herein a critical role. Although there is a growing awareness of diversity in gender identity and expression among this population, a comprehensive understanding of biopsychosocial development beyond the gender binary and beyond transition is lacking. ::: ::: ::: SUMMARY ::: Greater visibility of transgender people in society has revealed the need to understand and promote their health and quality of life broadly, including but not limited to gender dysphoria and HIV. This means addressing their needs in context of their families and communities, sexual and reproductive health, and successful aging. Research is needed to better understand what factors are associated with resilience and how it can be effectively promoted.


title: Heterotopic Ossification in the Middle Finger: A Case Report

abstract: A case of heterotopic ossification developed after traumatic laceration of the middle finger. It is extremely rare for heterotopic ossification to locate in the finger. The diagnosis was accomplished with plain radiographs as well as with MRI and confirmatory study with histopathological microscopic examination which demonstrated nubbin of bone surrounded by fibrovascular connective tissue, and the marrow was predominantly fatty with prominent ectatic vessels and patchy mild lymphocytic infiltrate and gross appearance of partly calcified bony tissue.


title: Regulation of necrotic cell death: p53, PARP1 and cyclophilin D-overlapping pathways of regulated necrosis?

abstract: In contrast to apoptosis and autophagy, necrotic cell death was considered to be a random, passive cell death without definable mediators. However, this dogma has been challenged by recent developments suggesting that necrotic cell death can also be a regulated process. Regulated necrosis includes multiple cell death modalities such as necroptosis, parthanatos, ferroptosis, pyroptosis, and mitochondrial permeability transition pore (MPTP)-mediated necrosis. Several distinctive executive molecules, particularly residing on the mitochondrial inner and outer membrane, amalgamating to form the MPTP have been defined. The c-subunit of the F1F0ATP synthase on the inner membrane and Bax/Bak on the outer membrane are considered to be the long sought components that form the MPTP. Opening of the MPTP results in loss of mitochondrial inner membrane potential, disruption of ATP production, increased ROS production, organelle swelling, mitochondrial dysfunction and consequent necrosis. Cyclophilin D, along with adenine nucleotide translocator and the phosphate carrier are considered to be important regulators involved in the opening of MPTP. Increased production of ROS can further trigger other necrotic pathways mediated through molecules such as PARP1, leading to irreversible cell damage. This review examines the roles of PARP1 and cyclophilin D in necrotic cell death. The hierarchical role of p53 in regulation and integration of key components of signaling pathway to elicit MPTP-mediated necrosis and ferroptosis is explored. In the context of recent insights, the indistinct role of necroptosis signaling in tubular necrosis after ischemic kidney injury is scrutinized. We conclude by discussing the participation of p53, PARP1 and cyclophilin D and their overlapping pathways to elicit MPTP-mediated necrosis and ferroptosis in acute kidney injury.


title: Low-Cost Wideband Microstrip Antenna Array for 60-GHz Applications

abstract: A low-cost single-layer wideband microstrip antenna array is presented for 60-GHz band applications. Each microstrip radiation element is fed by a modified L-shaped probe to enhance the impedance bandwidth. The proposed microstrip antenna array is excited by a novel coplanar waveguide (CPW) feed network, which not only has a simple structure by abandoning the traditional air bridges (wire bonds) above the CPW T-junctions but also provides pairs of broadband differential outputs. Experimentally, two 4 $\,\times\,$ 4 antenna arrays with different polarizations were designed and fabricated on the double-sided single-layer printed circuit board. The linearly polarized array exhibits an impedance bandwidth ( ${\hbox{SWR}}\leq 2$ ) of 25.5% and a gain of around 15.2 dBi. The circularly polarized array, employing the same CPW feed network to excite sequentially rotated circularly polarized elements, achieves an impedance bandwidth ( ${\hbox{SWR}}\leq 2$ ) of 17.8%, a 3-dB axial ratio bandwidth of 15.6%, and a gain of around 14.5 dBi.


title: Real-Time IGBT Open-Circuit Fault Diagnosis in Three-Level Neutral-Point-Clamped Voltage-Source Rectifiers Based on Instant Voltage Error

abstract: This paper presents a real-time diagnostic algorithm for insulated-gate bipolar transistor open-circuit faults in three-level neutral-point-clamped rectifiers. This algorithm is based on the instant voltage error in the converter and requires only signals already available to the control system, avoiding the use of additional hardware. The algorithm is independent from the load and from the used control strategy and provides very fast detection and identification of the fault, with diagnostic times as low as two sample periods (in favorable conditions). Experimental results are presented for the operation of the rectifier with two distinct control and modulation strategies and in different conditions. Results show the algorithm's speed, effectiveness, and robustness.


title: A survey of community gardens in upstate New York: implications for health promotion and community development.

abstract: Twenty community garden programs in upstate New York (representing 63 gardens) were surveyed to identify characteristics that may be useful to facilitate neighborhood development and health promotion. The most commonly expressed reasons for participating in gardens were access to fresh foods, to enjoy nature, and health benefits. Gardens in low-income neighborhoods (46%) were four times as likely as non low-income gardens to lead to other issues in the neighborhood being addressed; reportedly due to organizing facilitated through the community gardens. Additional research on community gardening can improve our understanding of the interaction of social and physical environments and community health, and eAective strategies for empowerment, development, and health promotion. 7 2000 Elsevier Science Ltd. All rights reserved.


title: A Low-Phase-Noise Wide-Tuning-Range Oscillator Based on Resonant Mode Switching

abstract: In this paper we will present a low-phase-noise wide-tuning-range oscillator suitable for scaled CMOS processes. It switches between the two resonant modes of a high-order LC resonator that consists of two identical LC tanks coupled by capacitor and transformer. The mode switching method does not add lossy switches to the resonator and thus doubles frequency tuning range without degrading phase noise performance. Moreover, the coupled resonator leads to 3 dB lower phase noise than a single LC tank, which provides a way of achieving low phase noise in scaled CMOS process. Finally, the novel way of using inductive and capacitive coupling jointly decouples frequency separation and tank impedances of the two resonant modes, and makes it possible to achieve balanced performance. The proposed structure is verified by a prototype in a low power 65 nm CMOS process, which covers all cellular bands with a continuous tuning range of 2.5-5.6 GHz and meets all stringent phase noise specifications of cellular standards. It uses a 0.6 V power supply and achieves excellent phase noise figure-of-merit (FoM) of 192.5 dB at 3.7 GHz and >; 188 dB across the entire tuning range. This demonstrates the possibility of achieving low phase noise and wide tuning range at the same time in scaled CMOS processes.


title: Do as you're told! Facets of agreeableness and early adult outcomes for inner-city boys

abstract: With data from the middle cohort of the Pittsburgh Youth Study, a prospective longitudinal study of inner-city boys, we examined whether Big Five agreeableness facets could be reliably recovered in this sample, and whether facets predicted educational, occupational, social, and antisocial life outcomes assessed a decade later. Caregivers described their adolescent boys’ personalities using the Common California Q-Set; twelve years later, participants were interviewed and court records were obtained. Factor analyses recovered two facets: compliance and compassion. Compliance predicted more schooling and lower risk of unemployment, teenage fatherhood, and crime; compassion related to longer committed relationships. Findings highlight the value of studying personality at the facet level.


title: ABSTRACT EFFICIENT CONSTRAINED PATH PLANNING VIA SEARCH IN STATE LATTICES

abstract: An addressable subscription television decoder includes an input stage including an IF amplifier for converting a scrambled RF television signal to a composite baseband video signal. The baseband video signal comprises either a nominal sync component or a sync component having suppressed horizontal sync pulses during video fields and having non-suppressed sync pulses during vertical intervals, the vertical intervals also including selected encoded data signals. An AGC voltage for controlling the gain of the IF amplifier is developed by a circuit exhibiting a relatively short time constant whenever the composite baseband video signal is characterized by nominal level sync pulses, the time constant being increased in response to suppressed horizontal sync pulses. The sync pulses produced during the vertical intervals of the composite baseband video signal are thereby not subjected to excessive IF gain for facilitating processing of the data signals by the decoder.


title: Excitatory or Inhibitory: A New Semantic Orientation Extracts Contradiction and Causality from the Web

abstract: We propose a new semantic orientation, Excitation, and its automatic acquisition method. Excitation is a semantic property of predicates that classifies them into excitatory, inhibitory and neutral. We show that Excitation is useful for extracting contradiction pairs (e.g., destroy cancer b develop cancer) and causality pairs (e.g., increase in crime ⇒ heighten anxiety). Our experiments show that with automatically acquired Excitation knowledge we can extract one million contradiction pairs and 500,000 causality pairs with about 70% precision from a 600 million page Web corpus. Furthermore, by combining these extracted causality and contradiction pairs, we can generate one million plausible causality hypotheses that are not written in any single sentence in our corpus with reasonable precision.


title: Stochastic Geometry for Modeling, Analysis, and Design of Multi-Tier and Cognitive Cellular Wireless Networks: A Survey

abstract: For more than three decades, stochastic geometry has been used to model large-scale ad hoc wireless networks, and it has succeeded to develop tractable models to characterize and better understand the performance of these networks. Recently, stochastic geometry models have been shown to provide tractable yet accurate performance bounds for multi-tier and cognitive cellular wireless networks. Given the need for interference characterization in multi-tier cellular networks, stochastic geometry models provide high potential to simplify their modeling and provide insights into their design. Hence, a new research area dealing with the modeling and analysis of multi-tier and cognitive cellular wireless networks is increasingly attracting the attention of the research community. In this article, we present a comprehensive survey on the literature related to stochastic geometry models for single-tier as well as multi-tier and cognitive cellular wireless networks. A taxonomy based on the target network model, the point process used, and the performance evaluation technique is also presented. To conclude, we discuss the open research challenges and future research directions.


title: Variable selection from random forests: application to gene expression data

abstract: Random forest is a classification algorithm well suited for microarray data: it shows excellent performance even when most predictive variables are noise, can be used when the number of variables is much larger than the number of observations, and returns measures of variable importance. Thus, it is important to understand the performance of random forest with microarray data and its use for gene selection. We first show the effects of changes in parameters of random forest on the prediction error. Then we present an approach for gene selection that uses measures of variable importance and error rate, and is targeted towards the selection of small sets of genes. Using simulated and real microarray data, we show that the gene selection procedure yields small sets of genes while preserving predictive accuracy. Availability: All code is available as an R package, varSelRF, from CRAN, http://cran.r-project.org/src/contrib/PACKAGES.html, or from the supplementary material page. Supplementary information: http://ligarto.org/rdiaz/Papers/rfVS/randomForestVarSel.html


title: Integrating Technologically-Enhanced Self-Regulated Strategies into Writing English as a Foreign Language Classes

abstract: This study aims to investigate the influence of technologically-enhanced (i.e., NetSupport School) self-regulated strategies (i.e., wrapper activity, think aloud and reciprocal teaching) on learners' achievement in the writing section of the TOEFL iBT (TOEFL Internet-based Test) and to explore the change in learners' self-regulated learning skill levels as a result of the technologically-enhanced self-regulated strategies training. Thirty-nine TOEFL iBT candidates who were studying TOEFL iBT writing preparation course were chosen as the participants of the study. The research design of this study is based on intervention in which pretests and posttests were administered before and after the technologically-enhanced self-regulated strategies training. Also, the Online Self-regulated Learning Questionnaire (OSLQ) was used to assess the effect of the intervention on students' self-regulated learning skill levels. Based on the data obtained, it was found that students' scores in the TOEFL iBT Writing Task and their self-regulated learning skills considerably improved as a result of the technologically-enhanced self-regulated strategies training. © 2015 IOJES. All rights reserved


title: Traffic dispersion graph based anomaly detection

abstract: Detecting and diagnosing anomalous traffic are important aspects of managing IP networks. In this paper, we propose a novel approach to detect anomalous network traffic based on graph theory concepts such as degree distribution, maximum degree and dK-2 distance. In this approach, we have used the traffic dispersion graphs (TDG) to model network traffic over time. We analyze differences of TDG graphs in time series to detect anomalies and introduce a method to identify attack patterns in anomalous traffic. The approach has been validated by using network traces from POSTECH and CAIDA.


title: Evaluation of wound healing activity of Allamanda cathartica. L. and Laurus nobilis. L. extracts on rats

abstract: BackgroundAllamanda cathartica. L. is a perennial shrub used in traditional medicine for treating malaria and jaundice. Laurus nobilis. L. is a tree and has been used for its astringent, healing and diuretic properties. The objective of this study was to investigate the aqueous extracts of Allamanda and Laurus nobilis to evaluate their wound healing activity in rats.MethodsExcision and incision wound models were used to evaluate the wound healing activity of both the extracts on Sprague Dawley rats. In each model, animals were divided into four groups of 10 animals each. In both the model, group 1 served as control and group 2 as reference standard. In an excision wound model, group 3 animals were treated with Allamanda (150 mg kg-1 day-1) and group 4 animals were treated with Laurus nobilis (200 mg kg-1 b.w day-1) for 14 days respectively. In the case of incision wound model, group 3 and 4 animals were treated with the extracts of Allamanda and Laurus respectively for 10 days. The effects of vehicles on the rate of wound healing were assessed by the rate of wound closure, period of epithelialisation, tensile strength, weights of the granulation tissue, hydroxyproline content and histopathology of the granulation tissue.ResultsThe aqueous extract of Allamanda promoted wound healing activity significantly in both the wound models studied. High rate of wound contraction (P < .001), decrease in the period of epithelialisation (10.2 ± 0.13), high skin breaking strength (440.0 ± 4.53), significant increase in the weight of the granulation tissue (P < .001) and hydroxyproline (P < .001) content were observed in animals treated with the aqueous extract of Allamanda. Histological studies of the granulation tissue from the Allamanda treated group showed the presence of a lesser number of inflammatory cells, and increased collagen formation than the control. In Laurus nobilis treated animals, the rate of wound contraction, weight of the granulation tissue and hydroxyproline content were moderately high (P < .05). The histological study of the granulation tissue of the Laurus nobilis treated animals showed larger number of inflammatory cells, and lesser collagen when compared with the Allamanda treated group of animals. However, it was better than the control group of animals.ConclusionThe data of this study indicated that the leaf extract of Allamanda possesses better wound healing activity than the Laurus nobilis and it can be used to treat different types of wounds in human beings too.


title: Robust and Efficient Subspace Segmentation via Least Squares Regression

abstract: This paper studies the subspace segmentation problem which aims to segment data drawn from a union of multiple linear subspaces. Recent works by using sparse representation, low rank representation and their extensions attract much attention. If the subspaces from which the data drawn are independent or orthogonal, they are able to obtain a block diagonal affinity matrix, which usually leads to a correct segmentation. The main differences among them are their objective functions. We theoretically show that if the objective function satisfies some conditions, and the data are sufficiently drawn from independent subspaces, the obtained affinity matrix is always block diagonal. Furthermore, the data sampling can be insufficient if the subspaces are orthogonal. Some existing methods are all special cases. Then we present the Least Squares Regression (LSR) method for subspace segmentation. It takes advantage of data correlation, which is common in real data. LSR encourages a grouping effect which tends to group highly correlated data together. Experimental results on the Hopkins 155 database and Extended Yale Database B show that our method significantly outperforms state-of-the-art methods. Beyond segmentation accuracy, all experiments demonstrate that LSR is much more efficient.


title: Distributed Kriged Kalman Filter for Spatial Estimation

abstract: This paper considers robotic sensor networks performing spatially-distributed estimation tasks. A robotic sensor network is deployed in an environment of interest, and takes successive point measurements of a dynamic physical process modeled as a spatio-temporal random field. Taking a Bayesian perspective on the Kriging interpolation technique from geostatistics, we design the distributed Kriged Kalman filter for predictive inference of the random field and of its gradient. The proposed algorithm makes use of a novel distributed strategy to compute weighted least squares estimates when measurements are spatially correlated. This strategy results from the combination of the Jacobi overrelaxation method with dynamic average consensus algorithms. As an application of the proposed algorithm, we design a gradient ascent cooperative strategy and analyze its convergence properties in the absence of measurement errors via stochastic Lyapunov functions. We illustrate our results in simulation.


title: Extracting Databases from Dark Data with DeepDive

abstract: DeepDive is a system for extracting relational databases from dark data: the mass of text, tables, and images that are widely collected and stored but which cannot be exploited by standard relational tools. If the information in dark data --- scientific papers, Web classified ads, customer service notes, and so on --- were instead in a relational database, it would give analysts access to a massive and highly-valuable new set of "big data" to exploit. DeepDive is distinctive when compared to previous information extraction systems in its ability to obtain very high precision and recall at reasonable engineering cost; in a number of applications, we have used DeepDive to create databases with accuracy that meets that of human annotators. To date we have successfully deployed DeepDive to create data-centric applications for insurance, materials science, genomics, paleontologists, law enforcement, and others. The data unlocked by DeepDive represents a massive opportunity for industry, government, and scientific researchers. DeepDive is enabled by an unusual design that combines large-scale probabilistic inference with a novel developer interaction cycle. This design is enabled by several core innovations around probabilistic training and inference.


title: Matlab-based flight control design scheme for UAVs

abstract: This work presents a systematic procedure for flight control design of fixed wing unmanned aerial vehicles using Matlab platform. The complex, non-linear, six degree-of-freedom dynamics of the vehicle is broken down into longitudinal and lateral dynamics after linearization about a flight condition. Primary control design task includes height, speed and heading control. A control assignment and control design structure is employed to simplify the design problem into multiple successive single-input-single-out (SISO) problems. The process is illustrated by autopilot design of a fixed-wing aerial vehicle. The overall design process is quite general in nature and has potential for application to other control assignments and control loop structures.


title: Ultra-wide stopband in a compact low pass filter using stepped impedance resonators and novel techniques

abstract: The design procedure of a compact ultra-wideband (UWB) stepped impedance resonator (SIR) low pass filter, is presented in this paper. A SIR LPF with 7 GHz cutoff frequency miniaturized by meander line technique is designed and it is shown that offsetting the stepped impedance resonators, as well as indenting the tap location greatly improve rejection bandwidth. Offsetting input lines further maximizes rejection bandwidth. The proposed filter is simulated in Sonnet EM software, fabricated, and tested on a high dielectric ceramic. The results show an improved stopband performance compared with similar topologies with a drastic size reduction in the filter.


title: Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions

abstract: This paper describes Tacotron 2, a neural network architecture for speech synthesis directly from text. The system is composed of a recurrent sequence-to-sequence feature prediction network that maps character embeddings to mel-scale spectrograms, followed by a modified WaveNet model acting as a vocoder to synthesize timedomain waveforms from those spectrograms. Our model achieves a mean opinion score (MOS) of $4.53$ comparable to a MOS of $4.58$ for professionally recorded speech. To validate our design choices, we present ablation studies of key components of our system and evaluate the impact of using mel spectrograms as the input to WaveNet instead of linguistic, duration, and $F_0$ features. We further demonstrate that using a compact acoustic intermediate representation enables significant simplification of the WaveNet architecture.


title: A Comprehensive Study on Social Network Mental Disorders Detection via Online Social Media Mining

abstract: The explosive growth in popularity of social networking leads to the problematic usage. An increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship Addiction, Information Overload, and Net Compulsion, have been recently noted. Symptoms of these mental disorders are usually observed passively today, resulting in delayed clinical intervention. In this paper, we argue that mining online social behavior provides an opportunity to actively identify SNMDs at an early stage. It is challenging to detect SNMDs because the mental status cannot be directly observed from online social activity logs. Our approach, new and innovative to the practice of SNMD detection, does not rely on self-revealing of those mental factors via questionnaires in Psychology. Instead, we propose a machine learning framework, namely, Social Network Mental Disorder Detection (SNMDD) , that exploits features extracted from social network data to accurately identify potential cases of SNMDs. We also exploit multi-source learning in SNMDD and propose a new SNMD-based Tensor Model (STM) to improve the accuracy. To increase the scalability of STM, we further improve the efficiency with performance guarantee. Our framework is evaluated via a user study with 3,126 online social network users. We conduct a feature analysis, and also apply SNMDD on large-scale datasets and analyze the characteristics of the three SNMD types. The results manifest that SNMDD is promising for identifying online social network users with potential SNMDs.


title: An evaluation of marketing strategies undertaken by Coca Cola Company as a multinational corporation in Nigeria

abstract: This research tend to evaluate the marketing strategies make use of by Coca Cola Company in Overseas market particularly Nigeria. The four (4) basic marketing strategies which are commonly called the 4ps which denote price, product, promotion and place are exceedingly used by Coca Cola Company in Nigeria. Coca Cola Company strategy of sales was price penetration where low price are charged and the company attends large market as possible. Unlike price skimming where higher price are charged. Product strategy was strongly applied such as the modification of its product as well as after sales services which all help to facilitate the marketing of the product. Promotion strategy which includes the sales promotion, technological change and advertisement were all used by coca cola Company in the marketing of its product in Nigeria. Finally place strategy which involve selection of distribution channels, transport arrangement etc which are very essential to the corporation was used. For example coca cola Company have twelve (12) factory plants, sixty (60) depots and over four hundred thousand (400,000) dealers or retailers of coca cola product in Nigeria.


title: A Brief Tutorial on Distributed and Concurrent Machine Learning

abstract: The area of machine learning has made considerable progress over the past decade, enabled by the widespread availability of large datasets, as well as by improved algorithms and models. Given the large computational demands of machine learning workloads, parallelism, implemented either through single-node concurrency or through multi-node distribution, has been a third key ingredient to advances in machine learning. The goal of this tutorial is to provide the audience with an overview of standard distribution techniques in machine learning, with an eye towards the intriguing trade-offs between synchronization and communication costs of distributed machine learning algorithms, on the one hand, and their convergence, on the other.The tutorial will focus on parallelization strategies for the fundamental stochastic gradient descent (SGD) algorithm, which is a key tool when training machine learning models, from classical instances such as linear regression, to state-of-the-art neural network architectures. The tutorial will describe the guarantees provided by this algorithm in the sequential case, and then move on to cover both shared-memory and message-passing parallelization strategies, together with the guarantees they provide, and corresponding trade-offs. The presentation will conclude with a broad overview of ongoing research in distributed and concurrent machine learning. The tutorial will assume no prior knowledge beyond familiarity with basic concepts in algebra and analysis.


title: Saber on ARM CCA-secure module lattice-based key encapsulation on ARM

abstract: The CCA-secure lattice-based post-quantum key encapsulation scheme Saber is a candidate in the NIST’s post-quantum cryptography standardization process. In this paper, we study the implementation aspects of Saber in resourceconstrained microcontrollers from the ARM Cortex-M series which are very popular for realizing IoT applications. In this work, we carefully optimize various parts of Saber for speed and memory. We exploit digital signal processing instructions and efficient memory access for a fast implementation of polynomial multiplication. We also use memory efficient Karatsuba and just-in-time strategy for generating the public matrix of the module lattice to reduce the memory footprint. We also show that our optimizations can be combined with each other seamlessly to provide various speed-memory trade-offs. Our speed optimized software takes just 1,147K, 1,444K, and 1,543K clock cycles on a Cortex-M4 platform for key generation, encapsulation and decapsulation respectively. Our memory efficient software takes 4,786K, 6,328K, and 7,509K clock cycles on an ultra resource-constrained Cortex-M0 platform for key generation, encapsulation, and decapsulation respectively while consuming only 6.2 KB of memory at most. These results show that lattice-based key encapsulation schemes are perfectly practical for securing IoT devices from quantum computing attacks.


title: Sematch: Semantic similarity framework for Knowledge Graphs

abstract: Abstract Sematch is an integrated framework for the development, evaluation and application of semantic similarity for Knowledge Graphs. The framework provides a number of similarity tools and datasets, and allows users to compute semantic similarity scores of concepts, words, and entities, as well as to interact with Knowledge Graphs through SPARQL queries. Sematch focuses on knowledge-based semantic similarity that relies on structural knowledge in a given taxonomy (e.g. depth, path length, least common subsumer), and statistical information contents. Researchers can use Sematch to develop and evaluate semantic similarity metrics and exploit these metrics in applications.


title: Methodological Quality and Outcome of Systematic Reviews Reporting on Orthopaedic Treatment for Class III Malocclusion: Overview of Systematic Reviews

abstract: OBJECTIVES ::: The aim of this overview of systematic reviews was to investigate methodological quality and outcome of current systematic reviews (SRs) reporting on orthopaedic treatment for class III malocclusion. ::: ::: ::: MATERIALS AND METHODS ::: Computerized and manual searches were performed in Medline, Google Scholar, Cochrane Library, Embase, LILACS, SciELO, American Journal of Orthodontics and Dentofacial Orthopaedics, Angle Orthodontist, European Journal of Orthodontics, Journal of Orthodontics, Conference abstracts and Grey literature. No restrictions were set on language or date of publication. The search covered the starting date of the relevant databases until 30 April 2015. MeSH terms and free-text terms included 'malocclusion', 'Angle class III', 'orthodontic appliances', 'functional', facemask, review and meta-analysis. Screening of eligible studies, assessment of the methodological quality of the SRs and data extraction were conducted in duplicate and independently by two reviewers. Methodological quality was assessed using AMSTAR (assessment of multiple systematic reviews). ::: ::: ::: RESULTS ::: A total of 222 studies were retrieved and after removal of duplicates, irrelevant studies, literature reviews and surgical approach treatments, 14 SRs and/or meta-analyses were included for qualitative synthesis. Mean AMSTAR score was 7.7/11 with a range of 3-10. There was evidence to demonstrate that face mask therapy can move the maxilla forward whilst causing a backward rotation of the mandible and increased facial height. There was also some evidence of mandibular growth retardation with chin cup therapy. ::: ::: ::: CONCLUSIONS ::: Orthopaedic appliances can improve a class III malocclusion in growing patients over the short-term; however, each appliance has a characteristic effect on the underlying skeletal pattern.


title: Software-Defined Networking (SDN) and Distributed Denial of Service (DDoS) Attacks in Cloud Computing Environments: A Survey, Some Research Issues, and Challenges

abstract: Distributed denial of service (DDoS) attacks in cloud computing environments are growing due to the essential characteristics of cloud computing. With recent advances in software-defined networking (SDN), SDN-based cloud brings us new chances to defeat DDoS attacks in cloud computing environments. Nevertheless, there is a contradictory relationship between SDN and DDoS attacks. On one hand, the capabilities of SDN, including software-based traffic analysis, centralized control, global view of the network, dynamic updating of forwarding rules, make it easier to detect and react to DDoS attacks. On the other hand, the security of SDN itself remains to be addressed, and potential DDoS vulnerabilities exist across SDN platforms. In this paper, we discuss the new trends and characteristics of DDoS attacks in cloud computing, and provide a comprehensive survey of defense mechanisms against DDoS attacks using SDN. In addition, we review the studies about launching DDoS attacks on SDN, as well as the methods against DDoS attacks in SDN. To the best of our knowledge, the contradictory relationship between SDN and DDoS attacks has not been well addressed in previous works. This work can help to understand how to make full use of SDN's advantages to defeat DDoS attacks in cloud computing environments and how to prevent SDN itself from becoming a victim of DDoS attacks, which are important for the smooth evolution of SDN-based cloud without the distraction of DDoS attacks.


title: A comparison of the transverse, axial and radial flux PM synchronous motors for electric vehicle

abstract: This paper presents the performance comparison among three different topologies of the permanent magnet synchronous machine, namely a transverse flux permanent magnet machine with flux concentration and claw pole structure, an axial flux machine with segmented armature torus structure and a conventional radial flux machine with embedded permanent magnets in the rotor. With the help of Finite Element Method, three electrical machines have been designed considering the required dimensions and permanent magnets mass. The complete results evaluation and comparisons are described in this paper. From the obtained results it can be concluded, the axial flux machine with segmented armature torus structure can be a competitive alternative compared with the conventional radial flux machine for applications with limited axial length, while the transverse flux machine is an attractive alternative for the high torque and low-speed applications due to its high pole number.


title: (Anti-)Egalitarianism Differentially Predicts Empathy for Members of Advantaged Versus Disadvantaged Groups

abstract: We explore the relationship between group-based egalitarianism and empathy for members of advantaged groups (e.g., corporate executives; state officials) versus disadvantaged groups (e.g., blue-collar workers; schoolteachers) subjected to harmful actions, events, or policies. Whereas previous research suggests that anti-egalitarians (vs. egalitarians) dispositionally exhibit less empathy for others, we propose that this relationship depends on the target’s position in the social hierarchy. We examined this question across eight studies (N = 3,154) conducted in the U.S. and the U.K., including online and in-person experiments and examining attitudinal and behavioral outcomes. We observed that (anti-)egalitarianism negatively predicted empathy for members of disadvantaged groups subjected to harmful situations, but positively predicted empathy for members of advantaged groups. This pattern held regardless of perceivers’ own membership in advantaged or disadvantaged groups (i.e., perceiver gender, race, or SES). (Anti-)egalitarianism’s differential effects on empathy for advantaged versus disadvantaged targets were attributable in part to differences in perceived degree of harm incurred (beyond roles for perceived value conflict and perceived deservingness): Egalitarians perceived the same action as more harmful than anti-egalitarians when it occurred to a disadvantaged target but less harmful than anti-egalitarians when it occurred to an advantaged target. We also explored how these patterns informed individuals’ downstream policy attitudes and policy-relevant behavior (e.g., willingness to sign a petition). Our findings enrich understanding of (anti-)egalitarianism by testing competing perspectives on the link between (anti-)egalitarianism and empathy, and by demonstrating when and why individuals’ preferences for social equality (vs. hierarchy) lead them to extend versus withhold empathy. (PsycINFO Database Record (c) 2018 APA, all rights reserved)


title: Emotion and the motivational brain

abstract: Psychophysiological and neuroscience studies of emotional processing undertaken by investigators at the University of Florida Laboratory of the Center for the Study of Emotion and Attention (CSEA) are reviewed, with a focus on reflex reactions, neural structures and functional circuits that mediate emotional expression. The theoretical view shared among the investigators is that expressed emotions are founded on motivational circuits in the brain that developed early in evolutionary history to ensure the survival of individuals and their progeny. These circuits react to appetitive and aversive environmental and memorial cues, mediating appetitive and defensive reflexes that tune sensory systems and mobilize the organism for action and underly negative and positive affects. The research reviewed here assesses the reflex physiology of emotion, both autonomic and somatic, studying affects evoked in picture perception, memory imagery, and in the context of tangible reward and punishment, and using the electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI), explores the brain’s motivational circuits that determine human emotion.