Query: ZVS range extension of 10A 15kV SiC MOSFET based 20kW Dual Active Half Bridge (DHB) DC-DC converter

Relevant passages:
10-kV SiC MOSFET-Based Boost Converter <=SEP=> 10-kV silicon carbide (SiC) MOSFETs are currently being developed by a number of organizations in the U.S. with prospective applications in high-voltage and high-frequency power-electronic systems. The aim of this paper is to demonstrate the high-frequency and high-temperature capability of 10-kV SiC MOSFETs in the application of a dc/dc boost converter. In this study, 10-kV SiC MOSFET and junction barrier Schottky (JBS) diode were characterized and modeled in SPICE. Following this, a dc/dc boost converter based on a 10-kV 10-A MOSFET and a 10-kV 5-A JBS diode was designed and tested under continuous operation for frequencies up to 25 kHz. The boost converter had an output voltage of 4 kV, an output power of 4 kW, and operated with a junction temperature of 174degC for the SiC MOSFET. The fast-switching speed, low losses, and high-temperature operation capability of 10-kV SiC MOSFETs demonstrated in the dc/dc boost converter make them attractive for high-frequency and high-voltage power-conversion applications.

Analytical loss model of power MOSFET <=SEP=> An accurate analytical model is proposed in this paper to calculate the power loss of a metal-oxide semiconductor field-effect transistor. The nonlinearity of the capacitors of the devices and the parasitic inductance in the circuit, such as the source inductor shared by the power stage and driver loop, the drain inductor, etc., are considered in the model. In addition, the ringing is always observed in the switching power supply, which is ignored in the traditional loss model. In this paper, the ringing loss is analyzed in a simple way with a clear physical meaning. Based on this model, the circuit power loss could be accurately predicted. Experimental results are provided to verify the model. The simulation results match the experimental results very well, even at 2-MHz switching frequency.

Design And Application Guide For High Speed MOSFET Gate Drive Circuits By <=SEP=> The main purpose of this paper is to demonstrate a systematic approach to design high performance gate drive circuits for high speed switching applications. It is an informative collection of topics offering a “one-stop-shopping” to solve the most common design challenges. Thus it should be of interest to power electronics engineers at all levels of experience. The most popular circuit solutions and their performance are analyzed, including the effect of parasitic components, transient and extreme operating conditions. The discussion builds from simple to more complex problems starting with an overview of MOSFET technology and switching operation. Design procedure for ground referenced and high side gate drive circuits, AC coupled and transformer isolated solutions are described in great details. A special chapter deals with the gate drive requirements of the MOSFETs in synchronous rectifier applications. Several, step-by-step numerical design examples complement the paper.

A High Efficiency Synchronous Buck VRM with Current Source Gate Driver <=SEP=> In this paper, a new current source gate drive circuit is proposed for high efficiency synchronous buck VRMs. The proposed circuit achieves quick turn-on and turn-off transition times to reduce switching loss and conduction loss in power MOSFETS. The driver circuit consists of two sets of four control switches and two very small inductors (typically 50 nH-300 nH each at 1 MHz). It drives both the control MOSFET and synchronous MOSFET in synchronous buck VRMs. An analysis, design procedure, optimization procedure and experimental results are presented for the proposed circuit. Experimental results demonstrate an efficiency of 86.6% at 15 A load and 81.9% at 30 A load for 12 V input and 1.3 V output at 1 MHz.

Tapped-inductor buck converter for high-step-down DC-DC conversion <=SEP=> The narrow duty cycle in the buck converter limits its application for high-step-down dc-dc conversion. With a simple structure, the tapped-inductor buck converter shows promise for extending the duty cycle. However, the leakage inductance causes a huge turn-off voltage spike across the top switch. Also, the gate drive for the top switch is not simple due to its floating source connection. This paper solves all these problems by modifying the tapped-inductor structure. A simple lossless clamp circuit can effectively clamp the switch turn-off voltage spike and totally recover the leakage energy. Experimental results for 12V-to-1.5V and 48V-to-6V dc-dc conversions show significant improvements in efficiency.

Robust and fast visual tracking via spatial kernel phase correlation filter <=SEP=> In this paper, we present a novel robust and fast object tracker called spatial kernel phase correlation based Tracker (SPC). Compared with classical correlation tracking which occupies all spectrums (including both phase spectrum and magnitude spectrum) in frequency domain, our SPC tracker only adopts the phase spectrum by implementing using phase correlation filter to estimate the object's translation. Thanks to circulant structure and kernel trick, we can implement dense sampling in order to train a high-quality phase correlation filter. Meanwhile, SPC learns the object's spatial context model by using new spatial response distribution, achieving superior performance. Given all these elaborate configurations, SPC is more robust to noise and cluster, and achieves more competitive performance in visual tracking. The framework of SPC can be briefly summarized as: firstly, phase correlation filter is well trained with all subwindows and is convoluted with a new image patch; then, the object's translation is calculated by maximizing spatial response; finally, to adapt to changing object, phase correlation filter is updated by reliable image patches. Tracking performance is evaluated by Peak-to-Sidelobe Ratio (PSR), aiming to resolve drifting problem by adaptive model updating. Owing to Fast Fourier Transform (FFT), the proposed tracker can track the object at about 50 frames/s. Numerical experiments demonstrate the proposed algorithm performs favorably against several state-of-the-art trackers in speed, accuracy and robustness. & 2016 Elsevier B.V. All rights reserved.

Menu-Match: Restaurant-Specific Food Logging from Images <=SEP=> Logging food and calorie intake has been shown to facilitate weight management. Unfortunately, current food logging methods are time-consuming and cumbersome, which limits their effectiveness. To address this limitation, we present an automated computer vision system for logging food and calorie intake using images. We focus on the "restaurant" scenario, which is often a challenging aspect of diet management. We introduce a key insight that addresses this problem specifically: restaurant plates are often both nutritionally and visually consistent across many servings. This insight provides a path to robust calorie estimation from a single RGB photograph: using a database of known food items together with restaurant-specific classifiers, calorie estimation can be achieved through identification followed by calorie lookup. As demonstrated on a challenging Menu-Match dataset and an existing third party dataset, our approach outperforms previous computer vision methods and a commercial calorie estimation app. Our Menu-Match dataset of realistic restaurant meals is made publicly available.

Modelling User Satisfaction with an Employee Portal <=SEP=> User satisfaction with general information system (IS) and certain types of information technology (IT) applications has been thoroughly studied in IS research. With the widespread and increasing use of portal technology, however, there is a need to conduct a user satisfaction study on portal use -in particular, the business-to-employee (b2e) portal. In this paper, we propose a conceptual model for determining b2e portal user satisfaction, which has been derived from an extensive literature review of user satisfaction scales and the b2e portal. Nine dimensions of b2e portal user satisfaction are identified and modeled: information content, ease of use, convenience of access, timeliness, efficiency, security, confidentiality, communication, and layout.

Indoor smartphone localization via fingerprint crowdsourcing: challenges and approaches <=SEP=> Nowadays, smartphones have become indispensable to everyone, with more and more built-in location-based applications to enrich our daily life. In the last decade, fingerprinting based on RSS has become a research focus in indoor localization, due to its minimum hardware requirement and satisfiable positioning accuracy. However, its time-consuming and labor-intensive site survey is a big hurdle for practical deployments. Fingerprint crowdsourcing has recently been promoted to relieve the burden of site survey by allowing common users to contribute to fingerprint collection in a participatory sensing manner. For its promising commitment, new challenges arise to practice fingerprint crowdsourcing. This article first identifies two main challenging issues, fingerprint annotation and device diversity, and then reviews the state of the art of fingerprint crowdsourcing-based indoor localization systems, comparing their approaches to cope with the two challenges. We then propose a new indoor subarea localization scheme via fingerprint crowdsourcing, clustering, and matching, which first constructs subarea fingerprints from crowdsourced RSS measurements and relates them to indoor layouts. We also propose a new online localization algorithm to deal with the device diversity issue. Our experiment results show that in a typical indoor scenario, the proposed scheme can achieve a 95 percent hit rate to correctly locate a smartphone in its subarea.

Optimizing Soft Real-Time Scheduling Performance for Virtual Machines with SRT-Xen <=SEP=> Multimedia applications are an important part of today's Internet. However, currently most virtualization solutions, including Xen, lack adequate support for soft real-time tasks. Soft real-time applications, e.g. media workloads, are impeded by components of virtualization, such as the increase of scheduling latency. This paper focuses on improving scheduling scheme to support soft real-time workloads in virtualization systems. In this paper, we present an enhanced scheduler SRT-Xen. SRT-Xen can promote the soft real-time domain's performance compared with Xen's existing scheduling. It focuses on not only bringing a new realtime-friendly scheduling framework with corresponding strategies but also improving the management of the virtual CPUs' queuing in order to implement a fair scheduling mechanism for both real-time and non-real-time tasks. Finally, we use PESQ (Perceptual Evaluation of Speech Quality) and other benchmarks to evaluate and compare SRT-Xen with some other works. The results show that SRT-Xen supports soft real-time domains well without penalizing non-real-time ones.

Scalable Edge Computing for Low Latency Data Dissemination in Topic-Based Publish/Subscribe <=SEP=> Advances in Internet of Things (IoT) give rise to a variety of latency-sensitive, closed-loop applications that reside at the edge. These applications often involve a large number of sensors that generate volumes of data, which must be processed and disseminated in real-time to potentially a large number of entities for actuation, thereby forming a closed-loop, publish-process-subscribe system. To meet the response time requirements of such applications, this paper presents techniques to realize a scalable, fog/edge-based broker architecture that balances data publication and processing loads for topic-based, publish-process-subscribe systems operating at the edge, and assures the Quality-of-Service (QoS), specified as the 90th percentile latency, on a per-topic basis. The key contributions include: (a) a sensitivity analysis to understand the impact of features such as publishing rate, number of subscribers, per-sample processing interval and background load on a topic's performance; (b) a latency prediction model for a set of co-located topics, which is then used for the latency-aware placement of topics on brokers; and (c) an optimization problem formulation for k-topic co-location to minimize the number of brokers while meeting each topic's QoS requirement. Here, k denotes the maximum number of topics that can be placed on a broker. We show that the problem is NP-hard for k >=3 and present three load balancing heuristics. Empirical results are presented to validate the latency prediction model and to evaluate the performance of the proposed heuristics.

Co-Training of Audio and Video Representations from Self-Supervised Temporal Synchronization <=SEP=> There is a natural correlation between the visual and auditive elements of a video. In this work, we use this correlation in order to learn strong and general features via cross-modal self-supervision with carefully chosen neural network architectures and calibrated curriculum learning. We suggest that this type of training is an effective way of pretraining models for further pursuits in video understanding, as they achieve on average 14.8% improvement over models trained from scratch. Furthermore, we demonstrate that these general features can be used for audio classification and perform on par with state-of-the-art results. Lastly, our work shows that using cross-modal self-supervision for pretraining is a good starting point for the development of multi-sensory models.

Machine Learning in Genomic Medicine: A Review of Computational Problems and Data Sets <=SEP=> In this paper, we provide an introduction to machine learning tasks that address important problems in genomic medicine. One of the goals of genomic medicine is to determine how variations in the DNA of individuals can affect the risk of different diseases, and to find causal explanations so that targeted therapies can be designed. Here we focus on how machine learning can help to model the relationship between DNA and the quantities of key molecules in the cell, with the premise that these quantities, which we refer to as cell variables, may be associated with disease risks. Modern biology allows high-throughput measurement of many such cell variables, including gene expression, splicing, and proteins binding to nucleic acids, which can all be treated as training targets for predictive models. With the growing availability of large-scale data sets and advanced computational techniques such as deep learning, researchers can help to usher in a new era of effective genomic medicine.

Measuring epistemic curiosity and its diversive and specific components. <=SEP=> A questionnaire constructed to assess epistemic curiosity (EC) and perceptual curiosity (PC) curiosity was administered to 739 undergraduates (546 women, 193 men) ranging in age from 18 to 65. The study participants also responded to the trait anxiety, anger, depression, and curiosity scales of the State-Trait Personality Inventory (STPI; Spielberger et al., 1979) and selected subscales of the Sensation Seeking (SSS; Zuckerman, Kolin, Price, & Zoob, 1964) and Novelty Experiencing (NES; Pearson, 1970) scales. Factor analyses of the curiosity items with oblique rotation identified EC and PC factors with clear simple structure. Subsequent analyses of the EC items provided the basis for developing an EC scale, with Diversive and Specific Curiosity subscales. Moderately high correlations of the EC scale and subscales with other measures of curiosity provided strong evidence of convergent validity. Divergent validity was demonstrated by minimal correlations with trait anxiety and the sensation-seeking measures, and essentially zero correlations with the STPI trait anger and depression scales. Male participants had significantly higher scores on the EC scale and the NES External Cognition subscale (effect sizes of r =.16 and.21, respectively), indicating that they were more interested than female participants in solving problems and discovering how things work. Male participants also scored significantly higher than female participants on the SSS Thrill-and-Adventure and NES External Sensation subscales (r =.14 and.22, respectively), suggesting that they were more likely to engage in sensation-seeking activities.

Gait planning and behavior-based control for statically-stable walking robots <=SEP=> A substantial portion of the Earth is inaccessible to any sort of wheeled mechanism— natural obstacles like large rocks, loose soil, deep ravines, and steep slopes conspire to render rolling locomotion ineffective. Hills, mountains, shores, seabeds, as well as the moon and other planets present similar terrain challenges. In many of these natural terrains, legs are well-suited. They can avoid small obstacles by making discrete contacts and passing up undesirable footholds. Legged mechanisms can climb over obstacles and step across ditches, surmounting terrain discontinuities of body-scale while staying level and stable. To achieve their potential, legged robots must coordinate their leg motions to climb over, step across and walk in natural terrain. These coordinated motions, which support and propel the robot, are called a gait. This thesis develops a new method of gait planning and control that enables statically-stable walking robots to produce a gait that is robust and productive in natural terrain. Independent task-achieving processes, called gait behaviors, establish a nominal gait, adapt it to the terrain, and react to disturbances like bumps and slips. Gait controlled in this way enabled the robot Dante II to walk autonomously in natural terrain, including the volcanic crater of Mount Spurr. This method extends to other walking robots as demonstrated by a generalized hexapod that performs the variety of gaits seen in six-legged insects, as well as aperiodic free gaits. The ability to change gait patterns on-the-fly with continuous, stable motion is a new development that enables robots to behave more like animals in adapting their gait to terrain. Finally, this thesis describes why walking robots need predictive plans as well as reflexive behaviors to walk effectively in the real world. It presents a method of guiding the behavior of a walking robot by planning distinct attributes of the desired gait. This partitioning of gait planning avoids the complexity of high degree-of-freedom motion planning. The ability to plan and foresee changes in gait improves performance while maintaining robust safety and stability. Abstract ii iii Acknowledgment

Developing a Taxonomy of Dark Triad Triggers at Work – A Grounded Theory Study Protocol <=SEP=> In past years, research and corporate scandals have evidenced the destructive effects of the dark triad at work, consisting of narcissism (extreme self-centeredness), psychopathy (lack of empathy and remorse) and Machiavellianism (a sense of duplicity and manipulativeness). The dark triad dimensions have typically been conceptualized as stable personality traits, ignoring the accumulating evidence that momentary personality expressions - personality states - may change due to the characteristics of the situation. The present research protocol describes a qualitative study that aims to identify triggers of dark triad states at work by following a grounded theory approach using semi-structured interviews. By building a comprehensive categorization of dark triad triggers at work scholars may study these triggers in a parsimonious and structured way and organizations may derive more effective interventions to buffer or prevent the detrimental effects of dark personality at work.

Web Engineering: A New Discipline for Development of Web-Based Systems <=SEP=> In most cases, development of Web-based systems has been ad hoc, lacking systematic approach and quality control and assurance procedures. Hence, there is now legitimate and growing concern about the manner in which Web-based systems are developed and their long-term quality and integrity. Web Engineering, an emerging new discipline, advocates a process and a systematic approach to development of high quality Web-based systems. It promotes the establishment and use of sound scientific, engineering and management principles, and disciplined and systematic approaches to development, deployment and maintenance of Web-based systems. This paper gives an introductory overview on Web Engineering. It presents the principles and roles of Web Engineering, assesses the similarities and differences between development of traditional software and Web-based systems, identifies key Web engineering activities and reviews some of the ongoing work in this area. It also highlights the prospects of Web engineering and the areas that need further study.

Negotiation and Cooperation in Multi-Agent Environments <=SEP=> Automated intelligent agents inhabiting a shared environment must coordinate their activities Cooperation not merely coordination may improve the performance of the individual agents or the overall behavior of the system they form Research in Distributed Arti cial Intelligence DAI addresses the problem of designing automated intelligent systems which interact e ectively DAI is not the only eld to take on the challenge of understanding cooperation and coordination There are a variety of other multi entity environments in which the entities coordinate their activity and cooperate Among them are groups of people animals particles and computers We argue that in order to address the challenge of building coordinated and collaborated intelligent agents it is bene cial to combine AI techniques with methods and techniques from a range of multi entity elds such as game theory operations research physics and philosophy To support this claim we describe some of our projects where we have successfully taken an interdisciplinary approach We demonstrate the bene ts in applying multi entity methodologies and show the adaptations modi cations and extensions necessary for solving the DAI problems This is an extended version of a lecture presented upon receipt of the Computers and Thought Award at the th International Joint Conference on Arti cial Intelligence in Montreal Canada August

Wikipedia Chemical Structure Explorer: substructure and similarity searching of molecules from Wikipedia <=SEP=> BACKGROUND
Wikipedia, the world's largest and most popular encyclopedia is an indispensable source of chemistry information. It contains among others also entries for over 15,000 chemicals including metabolites, drugs, agrochemicals and industrial chemicals. To provide an easy access to this wealth of information we decided to develop a substructure and similarity search tool for chemical structures referenced in Wikipedia.


RESULTS
We extracted chemical structures from entries in Wikipedia and implemented a web system allowing structure and similarity searching on these data. The whole search as well as visualization system is written in JavaScript and therefore can run locally within a web page and does not require a central server. The Wikipedia Chemical Structure Explorer is accessible on-line at www.cheminfo.org/wikipedia and is available also as an open source project from GitHub for local installation.


CONCLUSIONS
The web-based Wikipedia Chemical Structure Explorer provides a useful resource for research as well as for chemical education enabling both researchers and students easy and user friendly chemistry searching and identification of relevant information in Wikipedia. The tool can also help to improve quality of chemical entries in Wikipedia by providing potential contributors regularly updated list of entries with problematic structures. And last but not least this search system is a nice example of how the modern web technology can be applied in the field of cheminformatics. Graphical abstractWikipedia Chemical Structure Explorer allows substructure and similarity searches on molecules referenced in Wikipedia.

Word Embeddings and Their Use In Sentence Classification Tasks <=SEP=> This paper has two parts. In the first part we discuss word embeddings. We discuss the need for them, some of the methods to create them, and some of their interesting properties. We also compare them to image embeddings and see how word embedding and image embedding can be combined to perform different tasks. In the second part we implement a convolutional neural network trained on top of pre-trained word vectors. The network is used for several sentence-level classification tasks, and achieves state-of-art (or comparable) results, demonstrating the great power of pre-trainted word embeddings over random ones.

A critical review of blockchain and its current applications <=SEP=> Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the cryptocurrencies. Hitherto, it is used for the decentralization of markets more generally, not exclusively for the decentralization of money and payments. The decentralized transaction ledger of blockchain could be employed to register, confirm, and send all kinds of contracts to other parties in the network. In this paper, we thoroughly review state-of-the-art blockchain-related applications emerged in the literature. A number of published works were carefully included based on their contributions to the blockchain's body of knowledge. Several remarks are explored and discussed in the last section of the paper.

Hidden Voice Commands <=SEP=> Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.

Supply Chain Sourcing Under Asymmetric Information <=SEP=> We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer’s business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer’s business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier’s production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier’s and the manufacturer’s production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative-sourcing option for the big supplier (the manufacturer). We determine when an alternative-sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.

A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid <=SEP=> Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.

Secure and reconfigurable network design for critical information dissemination in the Internet of battlefield things (IoBT) <=SEP=> The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.

Multi-task learning of dish detection and calorie estimation <=SEP=> In recent years, a rise in healthy eating has led to various food management applications, which have image recognition to automatically record meals. However, most image recognition functions in existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network, which has improved accuracies of various kinds of image recognition tasks, such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates food calories while simultaneously detecting dishes by multi-task learning of food calorie estimation and food dish detection with a single CNN. It is expected to achieve high speed and save memory by simultaneous estimation in a single network. Currently, there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, so in this work, we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos with bounding-boxes attached and single-dish food photos with food calories. Our results show that our multi-task method achieved higher speed and a smaller network size than a sequential model of food detection and food calorie estimation.

Chernoff-Hoeffding Inequality and Applications <=SEP=> When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making “many simple estimates” of the full data set, and then judging them as a whole. Perhaps magically, these “many simple estimates” can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality [2, 6]. This document provides a simple form of this bound, and two examples of its use.

The Unbearable Automaticity of Being <=SEP=> What was noted by E. J. hanger (1978) remains true today: that much of contemporary psychological research is based on the assumption that people are consciously and systematically processing incoming information in order to construe and interpret their world and to plan and engage in courses of action. As did E. J. hanger, the authors question this assumption. First, they review evidence that the ability to exercise such conscious, intentional control is actually quite limited, so that most of moment-to-moment psychological life must occur through nonconscious means if it is to occur at all. The authors then describe the different possible mechanisms that produce automatic, environmental control over these various phenomena and review evidence establishing both the existence of these mechanisms as well as their consequences for judgments, emotions, and behavior. Three major forms of automatic self-regulation are identified: an automatic effect of perception on action, automatic goal pursuit, and a continual automatic evaluation of one's experience. From the accumulating evidence, the authors conclude that these various nonconscious mental systems perform the lion's share of the self-regulatory burden, beneficently keeping the individual grounded in his or her current environment.

Intrinsically Motivated Learning of Hierarchical Collections of Skills <=SEP=> Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies.

Biometric Gait Authentication Using Accelerometer Sensor <=SEP=> This paper presents a biometric user authentication based on a person’s gait. Unlike most previous gait recognition approaches, which are based on machine vision techniques, in our approach gait patterns are extracted from a physical device attached to the lower leg. From the output of the device accelerations in three directions: vertical, forward-backward, and sideways motion of the lower leg are obtained. A combination of these accelerations is used for authentication. Applying two different methods, histogram similarity and cycle length, equal error rates (EER) of 5% and 9% were achieved, respectively.