title: Static and dynamic 3D facial expression recognition: A comprehensive survey

text: a r t i c l e i n f o Keywords: Facial behaviour analysis Facial expression recognition 3D facial surface 3D facial surface sequences (4D faces) Automatic facial expression recognition constitutes an active research field due to the latest advances in computing technology that make the user's experience a clear priority. The majority of work conducted in this area involves 2D imagery, despite the problems this presents due to inherent pose and illumination variations. In order to deal with these problems, 3D and 4D (dynamic 3D) recordings are increasingly used in expression analysis research. In this paper we survey the recent advances in 3D and 4D facial expression recognition. We discuss developments in 3D facial data acquisition and tracking, and present currently available 3D/4D face databases suitable for 3D/4D facial expressions analysis as well as the existing facial expression recognition systems that exploit either 3D or 4D data in detail. Finally, challenges that have to be addressed if 3D facial expression recognition systems are to become a part of future applications are extensively discussed. Automatic human behaviour understanding has attracted a great deal of interest over the past two decades, mainly because of its many applications spanning various fields such as psychology, computer technology , medicine and security. It can be regarded as the essence of next-generation computing systems as it plays a crucial role in affective computing technologies (i.e. proactive and affective user interfaces), learner-adaptive tutoring systems, patient-profiled personal wellbeing technologies, etc. [1]. Facial expression is the most cogent, naturally preeminent means for humans to communicate emotions, to clarify and give emphasis, to signal comprehension disagreement, to express intentions and, more generally, to regulate interactions with the environment and other people [2]. These facts highlight the importance of automatic facial behaviour analysis, including facial expression of emotion and facial action unit (AU) recognition, and justify the interest this research area has attracted, in the past twenty years [3,4]. Until recently, most of the available data sets of expressive faces were of limited size containing only deliberately posed affective displays, mainly of the prototypical expressions of six basic emotions (i.e. anger, disgust, fear, happiness, sadness and surprise), recorded under highly controlled conditions. Recent efforts focus on the recognition of complex and spontaneous emotional phenomena (e.g. boredom or lack of attention, frustration , stress, etc.) rather than on the recognition of deliberately displayed prototypical expressions of emotions [5,4,6,7]. However, most …


title: Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment

text: This paper presents the application of an effective EEG-based brain-computer interface design for binary control in a visually elaborate immersive 3D game. The BCI uses the steady-state visual evoked potential (SSVEP) generated in response to phasereversing checkerboard patterns. Two power-spectrum estimation methods were employed for feature extraction in a series of offline classification tests. Both methods were also implemented during real-time game play. The performance of the BCI was found to be robust to distracting visual stimulation in the game and relatively consistent across six subjects, with 41 of 48 games successfully completed. For the best performing feature extraction method, the average real-time control accuracy across subjects was 89%. The feasibility of obtaining reliable control in such a visually rich environment using SSVEPs is thus demonstrated and the impact of this result is discussed.


title: Brainport: an alternative input to the brain.

text: Brain Computer Interface (BCI) technology is one of the most rapidly developing areas of modern science; it has created numerous significant crossroads between Neuroscience and Computer Science. The goal of BCI technology is to provide a direct link between the human brain and a computerized environment. The objective of recent BCI approaches and applications have been designed to provide the information flow from the brain to the computerized periphery. The opposite or alternative direction of the flow of information (computer to brain interface, or CBI) remains almost undeveloped. The BrainPort is a CBI that offers a complementary technology designed to support a direct link from a computerized environment to the human brain - and to do so non-invasively. Currently, BrainPort research is pursuing two primary goals. One is the delivery of missing sensory information critical for normal human behavior through an additional artificial sensory channel around the damaged or malfunctioning natural sensory system. The other is to decrease the risk of sensory overload in human-machine interactions by providing a parallel and supplemental channel for information flow to the brain. In contrast, conventional CBI strategies (e.g., Virtual Reality), are usually designed to provide additional or substitution information through pre-existing sensory channels, and unintentionally aggravate the brain overload problem.


title: A general framework for brain-computer interface design

text: The Brain-Computer Interface (BCI) research community has acknowledged that researchers are experiencing difficulties when they try to compare the BCI techniques described in the literature. In response to this situation, the community has stressed the need for objective methods to compare BCI technologies. Suggested improvements have included the development and use of benchmark applications and standard data sets. However, as a young, multidisciplinary research field, the BCI community lacks a common vocabulary. As a result, this deficiency leads to poor intergroup communication, which hinders the development of the desired methods of comparison. One of the principle reasons for the lack of common vocabulary is the absence of a common functional model of a BCI System. This paper proposes a new functional model for BCI System design. The model supports many features that facilitate the comparison of BCI technologies with other BCI and non-BCI user interface technologies. From this model, taxonomy for BCI System design is developed. Together the model and taxonomy are considered a general framework for BCI System design. The representational power of the proposed framework was evaluated by applying it to a set of existing BCI technologies. The framework could effectively describe all of the BCI System designs tested.


title: The thought translation device (TTD) for completely paralyzed patients.

text: The thought translation device trains locked-in patients to self-regulate slow cortical potentials (SCP's) of their electroencephalogram (EEG). After operant learning of SCP self-control, patients select letters, words or pictograms in a computerized language support program. Results of five respirated, locked-in-patients are described, demonstrating the usefulness of the thought translation device as an alternative communication channel in motivated totally paralyzed patients with amyotrophic lateral sclerosis.


title: Brain-computer interfaces based on the steady-state visual-evoked response.

text: The Air Force Research Laboratory has implemented and evaluated two brain-computer interfaces (BCI's) that translate the steady-state visual evoked response into a control signal for operating a physical device or computer program. In one approach, operators self-regulate the brain response; the other approach uses multiple evoked responses.


title: The Struggle for District-based Health Information Systems in South Africa

text: This article describes the initial period (1994–2001) of an ongoing action research project to develop health information systems to support district management in South Africa. The reconstruction of the health sector in postapartheid South Africa striving for equity in health service delivery and building of a decentralized structure based on health districts. In terms of information systems (IS) development, this reform process translates into standardization of health data in ways that inscribe the goals of the new South Africa by enhancing local control and integration of information handling. We describe our approach to action research and use concepts from actor-network and structuration theories in analyzing the case material. In the detailed description and analysis of the process of IS development provided, we focus on the need to balance standardization and local  exibility (localization); standardization is thus seen as bottom-up alignment of an array of heterogeneous actors. Building on a social system model of information systems, we conceptualize the IS design strategy developed and used as the cultivation of processes whereby these actors are translating and aligning their interests. We develop a modular hierarchy of global and local datasets as a framework within which the tensions between standardization and localization may be understood and addressed. Finally, we discuss the possible relevance of the results of the research in other countries.


title: Dynamic Conditional Random Fields for Joint Sentence Boundary and Punctuation Prediction

text: The use of dynamic conditional random fields (DCRF) has been shown to outperform linear-chain conditional random fields (LCRF) for punctuation prediction on conversational speech texts [1]. In this paper, we combine lexical, prosodic, and modified n-gram score features into the DCRF framework for a joint sentence boundary and punctuation prediction task on TDT3 English broadcast news. We show that the joint prediction method outperforms the conventional two-stage method using LCRF or maximum entropy model (MaxEnt). We show the importance of various features using DCRF, LCRF, MaxEnt, and hidden-event n-gram model (HEN) respectively. In addition, we address the practical issue of feature explosion by introducing lexical pruning, which reduces model size and improves the F1-measure. We adopt incremental local training to overcome memory size limitation without incurring significant performance penalty. Our results show that adding prosodic and n-gram score features gives about 20% relative error reduction in all cases. Overall, DCRF gives the best accuracy, followed by LCRF, MaxEnt, and HEN.


title: Toward Understanding How Early-Life Stress Reprograms Cognitive and Emotional Brain Networks

text: Vulnerability to emotional disorders including depression derives from interactions between genes and environment, especially during sensitive developmental periods. Adverse early-life experiences provoke the release and modify the expression of several stress mediators and neurotransmitters within specific brain regions. The interaction of these mediators with developing neurons and neuronal networks may lead to long-lasting structural and functional alterations associated with cognitive and emotional consequences. Although a vast body of work has linked quantitative and qualitative aspects of stress to adolescent and adult outcomes, a number of questions are unclear. What distinguishes ‘normal’ from pathologic or toxic stress? How are the effects of stress transformed into structural and functional changes in individual neurons and neuronal networks? Which ones are affected? We review these questions in the context of established and emerging studies. We introduce a novel concept regarding the origin of toxic early-life stress, stating that it may derive from specific patterns of environmental signals, especially those derived from the mother or caretaker. Fragmented and unpredictable patterns of maternal care behaviors induce a profound chronic stress. The aberrant patterns and rhythms of early-life sensory input might also directly and adversely influence the maturation of cognitive and emotional brain circuits, in analogy to visual and auditory brain systems. Thus, unpredictable, stress-provoking early-life experiences may influence adolescent cognitive and emotional outcomes by disrupting the maturation of the underlying brain networks. Comprehensive approaches and multiple levels of analysis are required to probe the protean consequences of early-life adversity on the developing brain. These involve integrated human and animal-model studies, and approaches ranging from in vivo imaging to novel neuroanatomical, molecular, epigenomic, and computational methodologies. Because early-life adversity is a powerful determinant of subsequent vulnerabilities to emotional and cognitive pathologies, understanding the underlying processes will have profound implications for the world’s current and future children.


title: Mutations in EZH2 cause Weaver syndrome.

text: We used trio-based whole-exome sequencing to analyze two families affected by Weaver syndrome, including one of the original families reported in 1974. Filtering of rare variants in the affected probands against the parental variants identified two different de novo mutations in the enhancer of zeste homolog 2 (EZH2). Sanger sequencing of EZH2 in a third classically-affected proband identified a third de novo mutation in this gene. These data show that mutations in EZH2 cause Weaver syndrome.


title: CapStones and ZebraWidgets: sensing stacks of building blocks, dials and sliders on capacitive touch screens

text: Recent research proposes augmenting capacitive touch pads with tangible objects, enabling a new generation of mobile applications enhanced with tangible objects, such as game pieces and tangible controllers. In this paper, we extend the concept to capacitive tangibles consisting of multiple parts, such as stackable gaming pieces and tangible widgets with moving parts. We achieve this using a system of wires and connectors inside each block that causes the capacitance of the bottom-most block to reflect the entire assembly. We demonstrate three types of tangibles, called CapStones, Zebra Dials and Zebra Sliders that work with current consumer hardware and investigate what designs may become possible as touchscreen hardware evolves.


title: Sufficient Sample Sizes for Multilevel Modeling

text: An important problem in multilevel modeling is what constitutes a sufficient sample size for accurate estimation. In multilevel analysis, the major restriction is often the higher-level sample size. In this paper, a simulation study is used to determine the influence of different sample sizes at the group level on the accuracy of the estimates (regression coefficients and variances) and their standard errors. In addition, the influence of other factors, such as the lowest-level sample size and different variance distributions between the levels (different intraclass correlations), is examined. The results show that only a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second-level standard errors. In all of the other simulated conditions the estimates of the regression coefficients, the variance components, and the standard errors are unbiased and accurate.


title: How Many Trees in a Random Forest?

text: Random Forest is a computationally efficient technique that can operate quickly over large datasets. It has been used in many recent research projects and real-world applications in diverse domains. However, the associated literature provides almost no directions about how many trees should be used to compose a Random Forest. The research reported here analyzes whether there is an optimal number of trees within a Random Forest, i.e., a threshold from which increasing the number of trees would bring no significant performance gain, and would only increase the computational cost. Our main conclusions are: as the number of trees grows, it does not always mean the performance of the forest is significantly better than previous forests (fewer trees), and doubling the number of trees is worthless. It is also possible to state there is a threshold beyond which there is no significant gain, unless a huge computational environment is available. In addition, it was found an experimental relationship for the AUC gain when doubling the number of trees in any forest. Furthermore, as the number of trees grows, the full set of attributes tend to be used within a Random Forest, which may not be interesting in the biomedical domain. Additionally, datasets’ density-based metrics proposed here probably capture some aspects of the VC dimension on decision trees and low-density datasets may require large capacity machines whilst the opposite also seems to be true.


title: Security in IPv 6-enabled Wireless Sensor Networks : An Implementation of TLS / DTLS for the Contiki Operating System

text: During the last several years the advancements in technology made it possible for small sensor nodes to communicate wirelessly with the rest of the Internet. With this achievement the question of securing such IP-enabled Wireless Sensor Networks (IP-WSNs) emerged and has been an important research topic since. In this thesis we discuss our implementation of TLS and DTLS protocols using a pre-shared key cipher suite (TLS PSK WITH AES 128 CCM 8) for the Contiki operating system. Apart from simply adding a new protocol to the set of protocols supported by the Contiki OS, this project allows us to evaluate how suitable the transport-layer security and pre-shared key management schemes are for IP-WSNs.


title: Arbuscular mycorrhizal fungi in alleviation of salt stress: a review.

text:
BACKGROUND
Salt stress has become a major threat to plant growth and productivity. Arbuscular mycorrhizal fungi colonize plant root systems and modulate plant growth in various ways.

SCOPE
This review addresses the significance of arbuscular mycorrhiza in alleviation of salt stress and their beneficial effects on plant growth and productivity. It also focuses on recent progress in unravelling biochemical, physiological and molecular mechanisms in mycorrhizal plants to alleviate salt stress.

CONCLUSIONS
The role of arbuscular mycorrhizal fungi in alleviating salt stress is well documented. This paper reviews the mechanisms arbuscular mycorrhizal fungi employ to enhance the salt tolerance of host plants such as enhanced nutrient acquisition (P, N, Mg and Ca), maintenance of the K(+) : Na(+) ratio, biochemical changes (accumulation of proline, betaines, polyamines, carbohydrates and antioxidants), physiological changes (photosynthetic efficiency, relative permeability, water status, abscissic acid accumulation, nodulation and nitrogen fixation), molecular changes (the expression of genes: PIP, Na(+)/H(+) antiporters, Lsnced, Lslea and LsP5CS) and ultra-structural changes. Theis review identifies certain lesser explored areas such as molecular and ultra-structural changes where further research is needed for better understanding of symbiosis with reference to salt stress for optimum usage of this technology in the field on a large scale. This review paper gives useful benchmark information for the development and prioritization of future research programmes.


title: Efficient Object Localization and Pose Estimation with 3D Wireframe Models

text: We propose a new and efficient method for 3D object localization and fine-grained 3D pose estimation from a single 2D image. Our approach follows the classical paradigm of matching a 3D model to the 2D observations. Our first contribution is a 3D object model composed of a set of 3D edge primitives learned from 2D object blueprints, which can be viewed as a 3D generalization of HOG features. This model is used to define a matching cost obtained by applying a rigid-body transformation to the 3D object model, projecting it onto the image plane, and matching the projected model to HOG features extracted from the input image. Our second contribution is a very efficient branch-and-bound algorithm for finding the 3D pose that maximizes the matching score. For this, 3D integral images of quantized HOGs are employed to evaluate in constant time the maximum attainable matching scores of individual model primitives. We applied our method to three different datasets of cars and achieved promising results with testing times as low as less than half a second.


title: Didier Dubey Suarez Medina Assessment of Web-based Information Security Awareness Courses

text: Information security awareness web-based courses are commonly recommended in cyber security strategies to help build a security culture capable of addressing information systems breaches caused by user mistakes whose negligence or ignorance of policies may endanger information systems assets. A research gap exists on the impact of Information Security Awareness Web-Based Courses: these are failing in changing to a significant degree the behavior of participants regarding compliance and diligence, which translates into continuous vulnerabilities. The aim of this work is to contribute with a theoretical and empirical analysis on the potential strengths and weaknesses of Information Security Awareness Web-Based Courses and with two practical tools readily applicable for designers and reviewers of web-based or mediatized courses on information security awareness and education. The research design seeks to respond two research questions. The first on the formulation of a minimum set of criteria that could be applied to Information Security Awareness Web-Based Courses, to support their real impact on employee’s diligence and compliance, resulting in eleven criteria for courses’ assessment and a checklist. The second, about a controlled experiment to explore the actual impact of an existing course, in respect to diligence and compliance using phishing emails as educational tools, that reaffirms the theoretical assumptions arrived to earlier. The development of minimum criteria and their systematic implementation pursue behavioral change, emphasizes the importance of disciplinary integration in cyber security research, and advocates for the development of a solid security culture of diligence and compliance, capable of supporting the protection of organizations from information system threats. The results gathered in this study suggest that achieving positive results in the existing information security tests that follow security awareness courses does not necessarily imply that diligence or information security policies compliance are affected. These preliminary findings accumulate evidence on the importance of implementing the recommendations formulated in this work.


title: An Analysis of Time-Dependent Planning

text: This paper presents a framework for exploring issues in time-dependent planning: planning in which the time available to respond to predicted events varies, and the decision making required to formulate effective responses is complex. Our analysis of time-dependent planning suggests an approach based on a class of algorithms that we call anytime algorithms. Anytime algorithms can be interrupted at any point during computation to return a result whose utility is a function of computation time. We explore methods for solving time-dependent planning problems based on the properties of anytime algorithms. Time-dependent planning is concerned with determining how best to respond to predicted events when the time available to make such determinations varies from situation to situation. In order to program a robot to react appropriately over a range of situations, we have to understand how to design effective algorithms for time-dependent planning. In this paper, we will be concerned primarily with understanding the properties of such algorithms, and providing a precise characterization of time-dependent planning. The issues we are concerned with arise either because the number of events that the robot has to contend with varies, and, hence, the time allotted to deliberating about any one event varies, or the observations that allow us to predict events precede the events they herald by varying amounts of time. The range of planning problems in which such complications occur is quite broad. Almost any situation that involves tracking objects of differing velocities will involve time-dependent planning (e.g., vehicle monitoring [Lesser and Corkill, 1983; Durfee, 19871, signal processing [Chung et al., 19871, and juggling [Donner and Jameson, 19861). S t t i ua ions where a system has to dynamically reevaluate its options [Fox and Smith, 1985; Dean, 19871 or delay committing to specific options until critical information arrives [Fox and Kempf, 19851 generally can be cast as time-dependent planning problems. To take a specific example, consider the problem faced by a stationary robot assigned the task of recognizing and intercepting or rerouting objects on a moving conveyor *This work was supported in part by the National Science Foundation under grant IRI-8612644 and by an IBM faculty development award. belt. Suppose that the robot’s view of the conveyor is obscured at some point by a partition, and that someone on the other side of this partition places objects on the conveyor at irregular intervals. The robot’s task requires that, between the time each object clears the partition and the time it reaches the end of the conveyor, it must classify the object and react appropriately. We assume that classification is computationally intensive, and that the longer the robot spends in analyzing an image, the more likely it is to make a correct classification. One can imagine a variety of reactions. The robot might simply have to push a button to direct each object into a bin intended for objects of a specific class; the time required for this sort of reaction is negligible. Alternatively, the robot might have to reach out and grasp certain objects and assemble them; the time required to react in this case will depend upon many factors. One can also imagine variations that exacerbate the timedependent aspects of the problem. For instance, it might take more time to classify certain objects, the number of objects placed on the conveyor might vary throughout the day, or the conveyor might speed up or slow down according to production demands. The important thing to note is, if the robot is to make optimal use of its time, it should be prepared to make decisions in situations where there is very little time to decide as well as to take advantage of situations where there is more than average time to decide. This places certain constraints on the design of the algorithms for performing classification, determining assembly sequences, and handling other inferential tasks. Traditional computer science concerns itself primarily with the complexity and correctness of algorithms. In most planning situations, however, there is no one correct answer, and having the right answer too late is tantamount to not having it at all. In dealing with potentially intractable problems, computer scientists are sometimes content with less than guaranteed solutions (e.g., answers that are likely correct and guaranteed computed in polynomial time (Monte Carlo algorithms), answers that are guaranteed correct and likely computed in polynomial time (Las Vegas algorithms), answers that are optimal within some factor and computed in polynomial time (approximation algorithms). While we regard this small concession to reality as encouraging, it doesn’t begin to address the problems in time-dependent planning. For many planning tasks, polynomial performance is not sufficient; we need algorithms that compute the best answers they can in the time they have available. Planning is concerned with reasoning about whether to act and how. Scheduling is concerned with reasoning about Dean and Boddy 49 From: AAAI-88 Proceedings. Copyright ©1988, AAAI (www.aaai.org). All rights reserved.


title: LSTM-CF: Unifying Context Modeling and Fusion with LSTMs for RGB-D Scene Labeling

text: Our model achieves the state-of-the-art performance on SUNRGBD, NYUdepth v2. It can be leveraged to improve the groundtruth annotations of newly captured 3943 RGB-D images in the SUNRGBD dataset. 2.2% improvement over previous state-of-the-art average accuracy and best on 15 categories Photometric data is vital for scene labeling while depth is an auxiliary information source. LSMT-CF model is important for fusing context features from different sources.


title: RGB-D Based Action Recognition with Light-weight 3D Convolutional Networks

text: Different from RGB videos, depth data in RGB-D videos provide key complementary information for tristimulus visual data which potentially could achieve accuracy improvement for action recognition. However, most of the existing action recognition models solely using RGB videos limit the performance capacity. Additionally, the state-of-the-art action recognition models, namely 3D convolutional neural networks (3D-CNNs) contain tremendous parameters suffering from computational inefficiency. In this paper, we propose a series of 3D lightweight architectures for action recognition based on RGB-D data. Compared with conventional 3D-CNN models, the proposed lightweight 3D-CNNs have considerably less parameters involving lower computation cost, while it results in favorable recognition performance. Experimental results on two public benchmark datasets show that our models can approximate or outperform the state-of-the-art approaches. Specifically, on the RGB+DNTU (NTU) dataset, we achieve 93.2% and 97.6% for crosssubject and cross-view measurement, and on the NorthwesternUCLA Multiview Action 3D (N-UCLA) dataset, we achieve 95.5% accuracy of cross-view.


title: Learning from Ambiguously Labeled Face Images

text: Learning a classifier from ambiguously labeled face images is challenging since training images are not always explicitly-labeled. For instance, face images of two persons in a news photo are not explicitly labeled by their names in the caption. We propose a Matrix Completion for Ambiguity Resolution (MCar) method for predicting the actual labels from ambiguously labeled images. This step is followed by learning a standard supervised classifier from the disambiguated labels to classify new images. To prevent the majority labels from dominating the result of MCar, we generalize MCar to a weighted MCar (WMCar) that handles label imbalance. Since WMCar outputs a soft labeling vector of reduced ambiguity for each instance, we can iteratively refine it by feeding it as the input to WMCar. Nevertheless, such an iterative implementation can be affected by the noisy soft labeling vectors, and thus the performance may degrade. Our proposed Iterative Candidate Elimination (ICE) procedure makes the iterative ambiguity resolution possible by gradually eliminating a portion of least likely candidates in ambiguously labeled faces. We further extend MCar to incorporate the labeling constraints among instances when such prior knowledge is available. Compared to existing methods, our approach demonstrates improvements on several ambiguously labeled datasets.


title: Using Generative Adversarial Networks to Design Shoes : The Preliminary Steps

text: In this paper, we envision a Conditional Generative Adversarial Network (CGAN) designed to generate shoes according to an input vector encoding desired features and functional type. Though we do not build the CGAN, we lay the foundation for its completion by exploring 3 areas. Our dataset is the UT-Zap50K dataset, which has 50,025 images of shoes categorized by functional type and with relative attribute comparisons. First, we experiment with several models to build a stable Generative Adversarial Network (GAN) trained on just athletic shoes. Then, we build a classifier based on GoogLeNet that is able to accurately categorize shoe images into their respective functional types. Finally, we explore the possibility of creating a binary classifier for each attribute in our dataset, though we are ultimately limited by the quality of the attribute comparisons provided. The progress made by this study will provide a robust base to create a conditional GAN that generates customized shoe designs.


title: A confirmatory factor analysis of IS employee motivation and retention

text: It is widely recognized that the relationships between organizations and their IS departments are changing. This trend threatens to undermine the retention of IS workers and the productivity of IS operations. In the study reported here, we examine IS employees' motivation and intent to remain using structural equation modeling. A survey was conducted among existing IS employees and analyzed with LISREL VIII. Results showed that latent motivation has an impact on latent retention, with job satisfaction and perceptions of management on career development as indicator variables for the former, and burnout, loyalty, and turnover intent as indicator variables for the latter. Implications for management in developing strategies for the retention of IS employees are provided. # 2001 Elsevier Science B.V. All rights reserved.


title: Living with Seal Robots in a Care House - Evaluations of Social and Physiological Influences

text: Robot therapy for elderly residents in a care house has been conducted since June 2005. Two therapeutic seal robots were introduced and activated for over 9 hours every day to interact with the residents. This paper presents the results of this experiment. In order to investigate the psychological and social effects of the robots, the activities of the residents in public areas were recorded by video cameras during daytime hours (8:30-18:00) for over 2 months. In addition, the hormones 17-Ketosteroid sulfate (17-KS-S) and 17-hydroxycorticosteroids (17-OHCS) in the residents' urine were obtained and analyzed. The results indicate that their social interaction was increased through interaction with the seal robots. Furthermore, the results of the urinary tests showed that the reactions of the subjects' vital organs to stress improved after the introduction of the robots


title: Guest Editorial Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique

text: D EEP learning is a growing trend in general data analysis and has been termed one of the 10 breakthrough technologies of 2013 [1]. Deep learning is an improvement of artificial neural networks, consisting of more layers that permit higher levels of abstraction and improved predictions from data [2]. To date, it is emerging as the leading machine-learning tool in the general imaging and computer vision domains. In particular, convolutional neural networks (CNNs) have proven to be powerful tools for a broad range of computer vision tasks. Deep CNNs automatically learn mid-level and high-level abstractions obtained from raw data (e.g., images). Recent results indicate that the generic descriptors extracted from CNNs are extremely effective in object recognition and localization in natural images. Medical image analysis groups across the world are quickly entering the field and applying CNNs and other deep learning methodologies to a wide variety of applications. Promising results are emerging. In medical imaging, the accurate diagnosis and/or assessment of a disease depends on both image acquisition and image interpretation. Image acquisition has improved substantially over recent years, with devices acquiring data at faster rates and increased resolution. The image interpretation process, however, has only recently begun to benefit from computer technology. Most interpretations of medical images are performed by physicians; however, image interpretation by humans is limited due to its subjectivity, large variations across interpreters, and fatigue. Many diagnostic tasks require an initial search process to detect abnormalities, and to quantify measurements and changes over time. Computerized tools, specifically image analysis and machine learning, are the key enablers to improve diagnosis, by facilitating identification of the findings that require treatment and to support the expert’s workflow. Among these tools, deep learning is rapidly proving to be the state-of-the-art foundation, leading to improved accuracy. It has also opened up new frontiers in data analysis with rates of progress not before experienced.


title: Privacy in e-commerce: examining user scenarios and privacy preferences

text: Privacy is a necessary concern in electronic commerce. It is difficult, if not impossible, to complete a transaction without revealing some personal data – a shipping address, billing information, or product preference. Users may be unwilling to provide this necessary information or even to browse online if they believe their privacy is invaded or threatened. Fortunately, there are technologies to help users protect their privacy. P3P (Platform for Privacy Preferences Project) from the World Wide Web Consortium is one such technology. However, there is a need to know more about the range of user concerns and preferences about privacy in order to build usable and effective interface mechanisms for P3P and other privacy technologies. Accordingly, we conducted a survey of 381 U.S. Net users, detailing a range of commerce scenarios and examining the participants' concerns and preferences about privacy. This paper presents both the findings from that study as well as their design implications.


title: Hyperspectral image classification and dimensionality reduction: an orthogonal subspace projection approach

text: Abstruct-Most applications of hyperspectral imagery require processing techniques which achieve two fundamental goals: 1) detect and classify the constituent materials for each pixel in the scene; 2) reduce the data volumeldimensionality, without loss of critical information, so that it can be processed efficiently and assimilated by a human analyst. In this paper, we describe a technique which simultaneously reduces the data dimensionality, suppresses undesired or interfering spectral signatures, and detects the presence of a spectral signature of interest. The basic concept is to project each pixel vector onto a subspace which is orthogonal to the undesired signatures. This operation is an optimal interference suppression process in the least squares sense. Once the interfering signatures have been nulled, projecting the residual onto the signature of interest maximizes the signal-to-noise ratio and results in a single component image that represents a classification for the signature of interest. The orthogonal subspace projection (OSP) operator can be extended to k signatures of interest, thus reducing the dimensionality of k and classifying the hyperspectral image simultaneously. The approach is applicable to both spectrally pure as well as mixed pixels.


title: Flow Wars: Systemizing the Attack Surface and Defenses in Software-Defined Networks

text: Emerging software defined network SDN stacks have introduced an entirely new attack surface that is exploitable from a wide range of launch points. Through an analysis of the various attack strategies reported in prior work, and through our own efforts to enumerate new and variant attack strategies, we have gained two insights. First, we observe that different SDN controller implementations, developed independently by different groups, seem to manifest common sets of pitfalls and design weakness that enable the extensive set of attacks compiled in this paper. Second, through a principled exploration of the underlying design and implementation weaknesses that enables these attacks, we introduce a taxonomy to offer insight into the common pitfalls that enable SDN stacks to be broken or destabilized when fielded within hostile computing environments. This paper first captures our understanding of the SDN attack surface through a comprehensive survey of existing SDN attack studies, which we extend by enumerating 12 new vectors for SDN abuse. We then organize these vulnerabilities within the well-known confidentiality, integrity, and availability model, assess the severity of these attacks by replicating them in a physical SDN testbed, and evaluate them against three popular SDN controllers. We also evaluate the impact of these attacks against published SDN defense solutions. Finally, we abstract our findings to offer the research and development communities with a deeper understanding of the common design and implementation pitfalls that are enabling the abuse of SDN networks.


title: Psychopathy and the DSM-IV criteria for antisocial personality disorder.

text: The Axis II Work Group of the Task Force on DSM-IV has expressed concern that antisocial personality disorder (APD) criteria are too long and cumbersome and that they focus on antisocial behaviors rather than personality traits central to traditional conceptions of psychopathy and to international criteria. We describe an alternative to the approach taken in the rev. 3rd ed. of the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R; American Psychiatric Association, 1987), namely, the revised Psychopathy Checklist. We also discuss the multisite APD field trials designed to evaluate and compare four criteria sets: the DSM-III-R criteria, a shortened list of these criteria, the criteria for dyssocial personality disorder from the 10th ed. of the International Classification of Diseases (World Health Organization, 1990), and a 10-item criteria set for psychopathic personality disorder derived from the revised Psychopathy Checklist.


title: Annular-ring CMUT arrays for forward-looking IVUS: transducer characterization and imaging

text: In this study, a 64-element, 1.15-mm diameter annular-ring capacitive micromachined ultrasonic transducer (CMUT) array was characterized and used for forward-looking intravascular ultrasound (IVUS) imaging tests. The array was manufactured using low-temperature processes suitable for CMOS electronics integration on a single chip. The measured radiation pattern of a 43 /spl times/ 140-/spl mu/m array element depicts a 40/spl deg/ view angle for forward-looking imaging around a 15-MHz center frequency in agreement with theoretical models. Pulse-echo measurements show a -10-dB fractional bandwidth of 104% around 17 MHz for wire targets 2.5 mm away from the array in vegetable oil. For imaging and SNR measurements, RF A-scan data sets from various targets were collected using an interconnect scheme forming a 32-element array configuration. An experimental point spread function was obtained and compared with simulated and theoretical array responses, showing good agreement. Therefore, this study demonstrates that annular-ring CMUT arrays fabricated with CMOS-compatible processes are capable of forward-looking IVUS imaging, and the developed modeling tools can be used to design improved IVUS imaging arrays.


title: Interactive Robots as Social Partners and Peer Tutors for Children: A Field Trial

text: Robots increasingly have the potential to interact with people in daily life. It is believed that, based on this ability, they will play an essential role in human society in the not-so-distant future. This article examined the proposition that robots could form relationships with children and that children might learn from robots as they learn from other children. In this article, this idea is studied in an 18-day field trial held at a Japanese elementary school. Two English-speaking “Robovie” robots interacted with firstand sixth-grade pupils at the perimeter of their respective classrooms. Using wireless identification tags and sensors, these robots identified and interacted with children who came near them. The robots gestured and spoke English with the children, using a vocabulary of about 300 sentences for speaking and 50 words for recognition. The children were given a brief picture–word matching English test at the start of the trial, af62 KANDA, HIRANO, EATON, ISHIGURO


title: The role of physical embodiment in human-robot interaction

text: Autonomous robots are agents with physical bodies that share our environment. In this work, we test the hypothesis that physical embodiment has a measurable effect on performance and perception of social interactions. Support of this hypothesis would suggest fundamental differences between virtual agents and robots from a social standpoint and have significant implications for human-robot interaction. We measure task performance and perception of a robot's social abilities in a structured but open-ended task based on the Towers of Hanoi puzzle. Our experiment compares aspects of embodiment by evaluating: (1) the difference between a physical robot and a simulated one; (2) the effect of physical presence through a co-located robot versus a remote tele-present robot. We present data from a pilot study with 12 subjects showing interesting differences in perception of remote physical robot's and simulated agent's attention to the task, and task enjoyment


title: Representations in Distributed Cognitive Tasks

text: In this paper we propose a theoretical framework of distributed representations and a methodology of representational analysis for the study of distributed cognitive tasksÑtasks that require the processing of information distributed across the internal mind and the external environment. The basic principle of distributed representations is that the representational system of a distributed cognitive task is a set of internal and external representations, which together represent the abstract structure of the task. The basic strategy of representational analysis is to decompose the representation of a hierarchical task into its component levels so that the representational properties at each level can be independently examined. The theoretical framework and the methodology are used to analyze the hierarchical structure of the Tower of Hanoi problem. Based on this analysis, four experiments are designed to examine the representational properties of the Tower of Hanoi. Finally, the nature of external representations is discussed. This research was supported by a grant to Donald Norman and Edwin Hutchins from the Ames Research Center of the National Aeronautics & Space Agency, Grant NCC 2-591 in the Aviation Safety/Automation Program, technical monitor, Everett Palmer. Additional support was provided by funds from the Apple Computer Company and the Digital Equipment Corporation to the Affiliates of Cognitive Science at


title: The usability of everyday technology: emerging and fading opportunities

text: Current work in the field of usability tends to focus on snapshots of use as the basis for evaluating designs. However, giving due consideration to the fact that everyday use of technology involves a process of evolution, we set out to investigate how the design of the technology may be used to support this. Based on a long-term empirical study of television use in the homes of two families, we illustrate how use continuously develops in a complex interplay between the users' expectations---as they are formed and triggered by the design---and the needs and context of use per se. We analyze the empirical data from the perspective of activity theory. This framework serves to highlight how use develops, and it supports our analysis and discussion about how design, the users' backgrounds, previous experience, and needs, and the specific context of use supports or hinders the development of use. Moreover, we discuss how the characteristics of the home settings, in which the televisions studied were situated, represent a challenge to usability work. The concluding discussion leads to a set of hypotheses relevant to designers and researchers who wish to tackle some of the aspects of usability of particular importance to development in the use of home technology.


title: Are Robots Embodied ?

text: Embodiment has become an important concept in many areas of cognitive science. There are, however, very different notions of exactly what embodiment is and what kind of body is required for what kind of embodied cognition. Hence, while many would agree that humans are embodied cognizers, there is much less agreement on what kind of artefact could be considered as embodied. This paper identifies and contrasts five different notions of embodiment which can roughly be characterized as (1) structural coupling between agent and environment, (2) historical embodiment as the result of a history of structural coupling, (3) physical embodiment, (4) ‘organismoid’ embodiment, i.e. organism-like bodily form (e.g., humanoid robots), and (5) organismic embodiment of autopoietic, living systems.


title: Single Cesium Lead Halide Perovskite Nanocrystals at Low Temperature: Fast Single-Photon Emission, Reduced Blinking, and Exciton Fine Structure.

text: Metal-halide semiconductors with perovskite crystal structure are attractive due to their facile solution processability, and have recently been harnessed very successfully for high-efficiency photovoltaics and bright light sources. Here, we show that at low temperature single colloidal cesium lead halide (CsPbX3, where X = Cl/Br) nanocrystals exhibit stable, narrow-band emission with suppressed blinking and small spectral diffusion. Photon antibunching demonstrates unambiguously nonclassical single-photon emission with radiative decay on the order of 250 ps, representing a significant acceleration compared to other common quantum emitters. High-resolution spectroscopy provides insight into the complex nature of the emission process such as the fine structure and charged exciton dynamics.


title: Abstract Structures in Spatial Cognition

text: Structures in Spatial Cognition Christopher Habel and Carola Eschenbach University of Hamburg Abstract. The importance of studying spatial cognition in cognitive science is enforced by the fact that the applicability of spatial concepts and spatial expressions is not limited to the spatial domain. We claim that common structures underlying both concrete, physical space and other domains are the basis for using spatial expressions, e.g., prepositions like between, with respect to space as well as time or other domains. This claim opposes the thesis that the common use is based upon an analogy between concrete space and other domains. The development of geometry from Euclid’s Elements to more differentiated systems of diverse geometries and topologies can be perceived of as an example of the transfer from modeling concrete space towards describing abstract spatial structures. The importance of studying spatial cognition in cognitive science is enforced by the fact that the applicability of spatial concepts and spatial expressions is not limited to the spatial domain. We claim that common structures underlying both concrete, physical space and other domains are the basis for using spatial expressions, e.g., prepositions like between, with respect to space as well as time or other domains. This claim opposes the thesis that the common use is based upon an analogy between concrete space and other domains. The development of geometry from Euclid’s Elements to more differentiated systems of diverse geometries and topologies can be perceived of as an example of the transfer from modeling concrete space towards describing abstract spatial structures. 1 The Current Interest in Spatial Cognition: Spatial Representations and Spatial Concepts Human behavior is anchored in space and time. Spatial information, i.e., information about spatial properties of the entities in our environment, about spatial constellations in our surrounding, and about the spatial properties and relations of our bodies with respect to this surrounding, has a central position for human cognition. In the recognition of objects and events by different sensory channels, i.e., in visual, haptic or auditory perception, spatial information is involved. Motor behavior, i.e., locomotion and the movement of the body, is based on such information as well. Beyond perception and motor action, some higher cognitive activities that interact indirectly with the spatial environment are coupled with spatial information, for instance, memory, problem solving and planning (cf. Eilan et al. 1993). The interaction of spatial cognition and other cognitive faculties is also exemplified by the ability to communicate information about spatial properties of the external world, especially about objects or constellations not directly perceivable (cf. Freksa & Habel 1990). The cognitive science method of investigating and explaining cognition based on computation and representation has led to increasing research activities focusing on spatial representations and processes on such representations: * Parts of this paper are based on Habel & Eschenbach (1995). The research reported in this paper was carried out in connection to the project ‘Axiomatik räumlicher Konzepte’ (Ha 1237/7) supported by the Deutsche Forschungsgemeinschaft (DFG). Thanks to an anonymous referee for comments and suggestions for improvements. Address: FB Informatik (AB WSV) and Graduiertenkolleg Kognitionswissenschaft, Universität Hamburg, Vogt-Kölln-Str. 30, D-22527 Hamburg. {habel, eschenbach}@informatik.uni-hamburg.de. • In cognitive psychology spatial concepts are basic for thinking about objects and situations in physical space and therefore the necessary constituents for the integration of the central higher cognitive faculties with sensory, motor and linguistic faculties (cf. Miller 1978, Landau & Jackendoff 1993). • In linguistics spatial concepts are discussed as basic ingredients of the – mental – lexicon; the linguistic approach of cognitive grammar – with Lakoff, Langacker and Talmy as its most influential advocates – is committed to space as the foundation for semantics and for the general grammatical system. • In Artificial Intelligence spatial concepts are the basis for developing representational formalisms for processing spatial knowledge; for example, calculi of Qualitative Spatial Reasoning differ with respect to what their primitive terms are and which spatial expressions are definable on this basis (See, e.g., Freksa 1992, Hernández 1994, and Randell et al. 1992, Schlieder 1995a, b). In the cognitive grammar approach as well as in most other discussions on spatial information the question what the basis of using the term spatial is seems to allow a simple answer: Space is identified with three-dimensional physical space. And by this, the concrete, physical space of our environment is seen as the conceptual and semantic basis for a wide range of linguistic and non-linguistic cognition. Accordingly, spatial concepts concern size, shape or relative location of objects in three-dimensional physical space. This view is based on the judgment that direct interaction with concrete, physical space is the core of our experience and therefore of our knowledge. Our spatial experience leads to groupings among spatial concepts depending on geometrical types of spatial characterizations. Examples of such groups, each of them corresponding to types of experience, are: topological concepts • based on relations between regions and their boundaries • invariant with respect to elastic transformations concepts of ordering • based on relations between objects or regions with respect to the relative position in a spatial constellation. • independent of the extensions and distances of the objects and regions in question metric concepts • include measures of distance and size of objects and regions. 1 Following Miller (1978) we assume that the conceptual structure includes different types of concepts, e.g. concepts for objects, properties and relations. In this sense, spatial relations like touching or betweenness correspond to relational concepts, while shape properties like being round or angular correspond to predicative concepts. 2 See, e.g., Lakoff (1987), Langacker (1986), Talmy (1983). Note that in the initial phase of this framework the term space grammar was used (Langacker 1982). 3 Although it is often claimed that physical space is not concrete but an abstraction based on spatial properties and relations of material bodies, we will use the term concrete space to refer to physical space in contrast to abstract spatial structures referring to less restricted structures (such as topological, ordering or metric spaces) underlying physical space. Aspects of this division are reflected by the contrast between qualitative and quantitative spatial reasoning. The means of qualitative spatial reasoning are in many cases restricted to topological terms (e.g., Randell et al. 1992) and terms of ordering (e.g., Schlieder 1995a, b). An independent dimension of analysis concerns the distinction between concept types and types of spatial entities: On the one hand we deal with spatial relations between objects, their location or relative position. Characteristically these relations are independent of shape and extension, such that they can apply to points idealizing the place of the objects. On the other hand we are concerned with shape properties of extended objects or regions independent of their location. Shape properties are coined by the relative position of object parts among themselves. Concepts of object orientation combine both, shape of extended entities and spatial relations of their parts and other objects. These types of spatial concepts can be subdivided according to the dimension of geometrical type. As a third dimension in classifying spatial concepts, we distinguish between static and dynamic concepts. Whereas the former concern properties and relations without consideration of time and change, the latter reflect the possibility of changes of spatial properties and relations over time. Since trajectories or paths of locomotion processes are extended spatial entities, spatial concepts are applicable to them as well as to material or geographical entities. (See Table 1 for an exemplification of some sections according to the three-dimensional classification scheme).


title: DA-GAN: Instance-Level Image Translation by Deep Attention Generative Adversarial Networks

text: Unsupervised image translation, which aims in translating two independent sets of images, is challenging in discovering the correct correspondences without paired data. Existing works build upon Generative Adversarial Networks (GANs) such that the distribution of the translated images are indistinguishable from the distribution of the target set. However, such set-level constraints cannot learn the instance-level correspondences (e.g. aligned semantic parts in object transfiguration task). This limitation often results in false positives (e.g. geometric or semantic artifacts), and further leads to mode collapse problem. To address the above issues, we propose a novel framework for instance-level image translation by Deep Attention GAN (DA-GAN). Such a design enables DA-GAN to decompose the task of translating samples from two sets into translating instances in a highly-structured latent space. Specifically, we jointly learn a deep attention encoder, and the instance-level correspondences could be consequently discovered through attending on the learned instances. Therefore, the constraints could be exploited on both set-level and instance-level. Comparisons against several state-of-the-arts demonstrate the superiority of our approach, and the broad application capability, e.g, pose morphing, data augmentation, etc., pushes the margin of domain translation problem.1


title: Automatic detection and rapid determination of earthquake magnitude by wavelet multiscale analysis of the primary arrival

text: Earthquake early warning systems must save lives. It is of great importance that networked systems of seismometers be equipped with reliable tools to make rapid determinations of earthquake magnitude in the few to tens of seconds before the damaging ground motion occurs. A new fully automated algorithm based on the discrete wavelet transform detects as well as analyzes the incoming first arrival with great accuracy and precision, estimating the final magnitude to within a single unit from the first few seconds of the P wave. © 2006 Elsevier B.V. All rights reserved.


title: High-Accuracy Tracking Control of Hydraulic Rotary Actuators With Modeling Uncertainties

text: Structured and unstructured uncertainties are the main obstacles in the development of advanced controllers for high-accuracy tracking control of hydraulic servo systems. For the structured uncertainties, nonlinear adaptive control can be employed to achieve asymptotic tracking performance. But modeling errors, such as nonlinear frictions, always exist in physical hydraulic systems and degrade the tracking accuracy. In this paper, a robust integral of the sign of the error controller and an adaptive controller are synthesized via backstepping method for motion control of a hydraulic rotary actuator. In addition, an experimental internal leakage model of the actuator is built for precise model compensation. The proposed controller accounts for not only the structured uncertainties (i.e., parametric uncertainties), but also the unstructured uncertainties (i.e., nonlinear frictions). Furthermore, the controller theoretically guarantees asymptotic tracking performance in the presence of various uncertainties, which is very important for high-accuracy tracking control of hydraulic servo systems. Extensive comparative experimental results are obtained to verify the high-accuracy tracking performance of the proposed control strategy.


title: Computationally efficient leakage inductance calculation for a high-frequency core-type transformer

text: Leakage inductance is a critical design attribute of high-frequency transformers. In this paper, a new method to estimate core-type transformer leakage inductance is proposed. The magnetic analysis uses a combination of analytical and numerical analysis. A two-dimensional magnetic analysis using Biot-Savart law and the method of images is introduced. Using the same field analysis, expressions for mean squared field intensity values necessary for estimating proximity effect loss in the windings are also derived.


title: View-Independent Recognition of Hand Postures

text: In Proc. of IEEE Conf. on CVPR’2000, Vol.II, pp.88-94, Hilton Head Island, SC, 2000 Since human hand is highly articulated and deformable, hand posture recognition is a challenging example in the research of view-independent object recognition. Due to the difficulties of the modelbased approach, the appearance-based learning approach is promising to handle large variation in visual inputs. However, the generalization of many proposed supervised learning methods to this problem often suffers from the insufficiency of labeled training data. This paper describes an approach to alleviate this difficulty by adding a large unlabeled training set. Combining supervised and unsupervised learning paradigms, a novel and powerful learning approach, the Discriminant-EM (D-EM) algorithm, is proposed in this paper to handle the case of small labeled training set. Experiments show that D-EM outperforms many other learning methods. Based on this approach, we implement a gesture interface to recognize a set of predefined gesture commands, and it is also extended to hand detection. This algorithm can also apply to other object recognition tasks.


title: A Survey of CPU-GPU Heterogeneous Computing Techniques

text: As both CPUs and GPUs become employed in a wide range of applications, it has been acknowledged that both of these Processing Units (PUs) have their unique features and strengths and hence, CPU-GPU collaboration is inevitable to achieve high-performance computing. This has motivated a significant amount of research on heterogeneous computing techniques, along with the design of CPU-GPU fused chips and petascale heterogeneous supercomputers. In this article, we survey Heterogeneous Computing Techniques (HCTs) such as workload partitioning that enable utilizing both CPUs and GPUs to improve performance and/or energy efficiency. We review heterogeneous computing approaches at runtime, algorithm, programming, compiler, and application levels. Further, we review both discrete and fused CPU-GPU systems and discuss benchmark suites designed for evaluating Heterogeneous Computing Systems (HCSs). We believe that this article will provide insights into the workings and scope of applications of HCTs to researchers and motivate them to further harness the computational powers of CPUs and GPUs to achieve the goal of exascale performance.


title: Software engineering for big data projects: Domains, methodologies and gaps

text: Context: Big data has become the new buzzword in the information and communication technology industry. Researchers and major corporations are looking into big data applications to extract the maximum value from the data available to them. However, developing and maintaining stable and scalable big data applications is still a distant milestone. Objective: To look at existing research on how software engineering concepts, namely the phases of the software development project life cycle (SDPLC), can help build better big data application projects. Method: A literature survey was performed. A manual search covered papers returned by search engines resulting in approximately 2,000 papers being searched and 170 papers selected for review. Results: The search results helped in identifying data rich application projects that have the potential to utilize big data successfully. The review helped in exploring SDPLC phases in the context of big data applications and performing a gap analysis of the phases that have yet to see detailed research efforts but deserve attention.


title: Biology of language-The epistemology of reality

text: 2. What processes take place in a linguistic interaction that permit an organism (us) to describe and to predict events that it may experience? This is my way of honoring the memory of Eric H. Lenneberg, if one honors the memory of another scientist by speaking about one’s own work Whatever the case, I wish to honor his memory not only because of his great accomplishments, but also because he was capable of inspiring his students, as the symposium on which this book is based revealed. The only way I can do this is to accept the honor of presenting my views about biology, language, and reality. I shall, accordingly, speak about language as a biologist. In doing so, I shall use language, notwithstanding that this use of language to speak about language is within the core of the problem I wish to consider.


title: Statistical significance versus clinical relevance.

text: In March this year, the American Statistical Association (ASA) posted a statement on the correct use of P-values, in response to a growing concern that the P-value is commonly misused and misinterpreted. We aim to translate these warnings given by the ASA into a language more easily understood by clinicians and researchers without a deep background in statistics. Moreover, we intend to illustrate the limitations of P-values, even when used and interpreted correctly, and bring more attention to the clinical relevance of study findings using two recently reported studies as examples. We argue that P-values are often misinterpreted. A common mistake is saying that P < 0.05 means that the null hypothesis is false, and P ≥0.05 means that the null hypothesis is true. The correct interpretation of a P-value of 0.05 is that if the null hypothesis were indeed true, a similar or more extreme result would occur 5% of the times upon repeating the study in a similar sample. In other words, the P-value informs about the likelihood of the data given the null hypothesis and not the other way around. A possible alternative related to the P-value is the confidence interval (CI). It provides more information on the magnitude of an effect and the imprecision with which that effect was estimated. However, there is no magic bullet to replace P-values and stop erroneous interpretation of scientific results. Scientists and readers alike should make themselves familiar with the correct, nuanced interpretation of statistical tests, P-values and CIs.


title: Derivation of Human Trophoblast Stem Cells.

text: Trophoblast cells play an essential role in the interactions between the fetus and mother. Mouse trophoblast stem (TS) cells have been derived and used as the best in vitro model for molecular and functional analysis of mouse trophoblast lineages, but attempts to derive human TS cells have so far been unsuccessful. Here we show that activation of Wingless/Integrated (Wnt) and EGF and inhibition of TGF-β, histone deacetylase (HDAC), and Rho-associated protein kinase (ROCK) enable long-term culture of human villous cytotrophoblast (CT) cells. The resulting cell lines have the capacity to give rise to the three major trophoblast lineages, which show transcriptomes similar to those of the corresponding primary trophoblast cells. Importantly, equivalent cell lines can be derived from human blastocysts. Our data strongly suggest that the CT- and blastocyst-derived cell lines are human TS cells, which will provide a powerful tool to study human trophoblast development and function.


title: A Bidirectional-Switch-Based Wide-Input Range High-Efficiency Isolated Resonant Converter for Photovoltaic Applications

text: Modular photovoltaic (PV) power conditioning systems (PCSs) require a high-efficiency dc-dc converter stage capable of regulation over a wide input voltage range for maximum power point tracking. In order to mitigate ground leakage currents and to be able to use a high-efficiency, nonisolated grid-tied inverter, it is also desirable for this microconverter to provide galvanic isolation between the PV module and the inverter. This paper presents a novel, isolated topology that is able to meet the high efficiency over a wide input voltage range requirement. This topology yields high efficiency through low circulating currents, zero-voltage switching (ZVS) and low-current switching of the primary side devices, ZCS of the output diodes, and direct power transfer to the load for the majority of switching cycle. This topology is also able to provide voltage regulation through basic fixed-frequency pulsewidth modulated (PWM) control. These features are able to be achieved with the simple addition of a secondary-side bidirectional ac switch to the isolated series resonant converter. Detailed analysis of the operation of this converter is discussed along with a detailed design procedure. Experimental results of a 300-W prototype are given. The prototype reached a peak power stage efficiency of 98.3% and a California Energy Commission (CEC) weighted power stage efficiency of 98.0% at the nominal input voltage.


title: Detection of Red Tomato on Plants using Image Processing Techniques

text: Tomatoes are the best-known grown fruit in greenhouses that have been recently attempted to be picked up automatically. Tomato is a plant which its fruit does not ripe simultaneously, therefore it is necessary to develop an algorithm to distinguish red tomatoes. In the current study, a new segmentation algorithm based on region growing was proposed for guiding a robot to pick up red tomatoes. For this purpose, several colour images of tomato plants were acquired in a greenhouse. The colour images of tomato were captured under natural light, without any artificial lighting equipment. To recognize red tomatoes form non-red ones, at first background of images were removed. For removing the background, subtraction of red and green components (R-G) was applied. Usually tomatoes touch together, so separating touching tomatoes was next step. In this step, the watershed algorithm was used that was followed by improving process. Afterwards, red tomato was detected by the region growing approach. Results obtained from testing the developed algorithm showed an encouraging accuracy (82.38%) to develop an expert system for online recognition of red


title: A Novel Metal-Plate Monopole Antenna for DTV Application

text: In this paper, a novel metal-plate monopole antenna is presented for indoor digital television (DTV) signal coverage in the 468-880MHz band. The proposed antenna consists of two orthorhombic metal-plates with symmetrical bevels, a top-uploaded metal-plate and a ground plane. Simulation results show that the antenna achieves a bandwidth ranging from 468MHz to 880MHz which covers the DTV band (470-860MHZ) and shows stable radiation patterns in three coordinate planes.


title: Plot Induction and Evolutionary Search for Story Generation

text: In this paper we develop a story generator that leverages knowledge inherent in corpora without requiring extensive manual involvement. A key feature in our approach is the reliance on a story planner which we acquire automatically by recording events, their participants, and their precedence relationships in a training corpus. Contrary to previous work our system does not follow a generate-and-rank architecture. Instead, we employ evolutionary search techniques to explore the space of possible stories which we argue are well suited to the story generation task. Experiments on generating simple children’s stories show that our system outperforms previous data-driven approaches.


title: , GT-GOLEM-2011-001 , 2011 Ach : IPC for Real-Time Robot Control

text: We present a new Inter-Process Communication (IPC) mechanism and library. Ach is uniquely suited for coordinating perception, control drivers, and algorithms in real-time systems that sample data from physical processes. Ach eliminates the Head-of-Line Blocking problem for applications that always require access to the newest message. Ach is efficient, robust, and formally verified. It has been tested and demonstrated on a variety of physical robotic systems. Finally, the source code for Ach is available under an Open Source BSD-style license.


title: CNNPred: CNN-based stock market prediction using several data sources

text: Feature extraction from financial data is one of the most important problems in market prediction domain for which many approaches have been suggested. Among other modern tools, convolutional neural networks (CNN) have recently been applied for automatic feature selection and market prediction. However, in experiments reported so far, less attention has been paid to the correlation among different markets as a possible source of information for extracting features. In this paper, we suggest a CNN-based framework with specially designed CNNs, that can be applied on a collection of data from a variety of sources, including different markets, in order to extract features for predicting the future of those markets. The suggested framework has been applied for predicting the next days direction of movement for the indices of S&P 500, NASDAQ, DJI, NYSE, and RUSSELL markets based on various sets of initial features. The evaluations show a significant improvement in predictions performance compared to the state of the art baseline algorithms.


title: Deep Learning for Multi-label Classification

text: In multi-label classification, the main focus has been to develop ways of learning the underlying dependencies between labels, and to take advantage of this at classification time. Developing better feature-space representations has been predominantly employed to reduce complexity, e.g., by eliminating non-helpful feature attributes from the input space prior to (or during) training. This is an important task, since many multilabel methods typically create many different copies or views of the same input data as they transform it, and considerable memory can be saved by taking advantage of redundancy. In this paper, we show that a proper development of the feature space can make labels less interdependent and easier to model and predict at inference time. For this task we use a deep learning approach with restricted Boltzmann machines. We present a deep network that, in an empirical evaluation, outperforms a number of competitive methods from the literature.


title: A 69.8 dB SNDR 3rd-order Continuous Time Delta-Sigma Modulator with an Ultimate Low Power Tuning System for a Worldwide Digital TV-Receiver

text: This paper presents a 3rd-order continuous time delta-sigma modulator for a worldwide digital TV-receiver whose SNDR is 69.8 dB. An ultimate low power tuning system using RC-relaxation oscillator is developed in order to achieve high yield against PVT variations. A 3rd-order modulator with modified single opamp resonator contributes to cost reduction by realizing very compact circuit. The mechanism to occur 2nd-order harmonic distortion at current feedback DAC was analyzed and a reduction scheme of the distortion enabled the modulator to achieved FOM of 0.18 pJ/conv-step.


title: A comparison of spotlight synthetic aperture radar image formation techniques

text: Spotlight synthetic aperture radar images can be formed from the complex phase history data using two main techniques: 1) polar-to-Cartesian interpolation followed by twodimensional inverse Fourier transform (2DFFT) , and 2) convolution backprojection (CBP) . CBP has been widely used to reconstruct medical images in computer aided tomography, and only recently has been applied to form synthetic aperture radar imagery. It is alleged that CBP yields higher quality images because 1) all the Fourier data are used and 2) the polar formatted data is used directly to form a 2D Cartesian image and therefore 2D interpolation is not required. This report compares the quality of images formed by CBP and several modified versions of the 2DFFT method. We show from an image quality point of view that CBP is equivalent to first windowing the phase history data and then interpolating to an exscribed rectangle. From a mathematical perspective, we should expect this conclusion since the same Fourier data are used to form the SAR image. We next address the issue of parallel implementation of each algorithm. We dispute previous claims that CBP is more readily parallelizable than the 2DFFT method. Our conclusions are supported by comparing execution times between massively parallel implementations of both algorithms, showing that both experience similar decreases in computation time, but that CBP takes significantly longer to form an image. This page intentionally left blank


title: A Decision Framework for Blockchain Platforms for IoT and Edge Computing

text: Blockchains started as an enabling technology in the area of digital currencies with the introduction of Bitcoin. However, blockchains have emerged as a technology that goes beyond financial transactions by providing a platform supporting secure and robust distributed public ledgers. We think that the Internet of Things (IoT) can also benefit from blockchain technology, especially in the areas of security, privacy, fault tolerance, and autonomous behavior. Here we present a decision framework to help practitioners systematically evaluate the potential use of blockchains in an IoT context.


title: Brain Activity during Simulated Deception: An Event-Related Functional Magnetic Resonance Study

text: TheGuilty Knowledge Test (GKT) has been used extensively to model deception. An association between the brain evoked response potentials and lying on the GKT suggests that deception may be associated with changes in other measures of brain activity such as regional blood flow that could be anatomically localized with event-related functional magnetic resonance imaging (fMRI). Blood oxygenation level-dependent fMRI contrasts between deceptive and truthful responses were measured with a 4 Tesla scanner in 18 participants performing the GKT and analyzed using statistical parametric mapping. Increased activity in the anterior cingulate cortex (ACC), the superior frontal gyrus (SFG), and the left premotor, motor, and anterior parietal cortex was specifically associated with deceptive responses. The results indicate that: (a) cognitive differences between deception and truth have neural correlates detectable by fMRI, (b) inhibition of the truthful response may be a basic component of intentional deception, and (c) ACC and SFG are components of the basic neural circuitry for deception.


title: Central sensitization in carpal tunnel syndrome with extraterritorial spread of sensory symptoms

text: Extraterritorial spread of sensory symptoms is frequent in carpal tunnel syndrome (CTS). Animal models suggest that this phenomenon may depend on central sensitization. We sought to obtain psychophysical evidence of sensitization in CTS with extraterritorial symptoms spread. We recruited 100 unilateral CTS patients. After selection to rule out concomitant upper-limb causes of pain, 48 patients were included. The hand symptoms distribution was graded with a diagram into median and extramedian pattern. Patients were asked on proximal pain. Quantitative sensory testing (QST) was performed in the territory of injured median nerve and in extramedian territories to document signs of sensitization (hyperalgesia, allodynia, wind-up). Extramedian pattern and proximal pain were found in 33.3% and 37.5% of patients, respectively. The QST profile associated with extramedian pattern includes: (1) thermal and mechanic hyperalgesia in the territory of the injured median nerve and in those of the uninjured ulnar and radial nerves and (2) enhanced wind-up. No signs of sensitization were found in patients with the median distribution and those with proximal symptoms. Different mechanisms may underlie hand extramedian and proximal spread of symptoms, respectively. Extramedian spread of symptoms in the hand may be secondary to spinal sensitization but peripheral and supraspinal mechanisms may contribute. Proximal spread may represent referred pain. Central sensitization may be secondary to abnormal activity in the median nerve afferents or the consequence of a predisposing trait. Our data may explain the persistence of sensory symptoms after median nerve surgical release and the presence of non-anatomical sensory patterns in neuropathic pain.


title: PsychoPy—Psychophysics software in Python

text: The vast majority of studies into visual processing are conducted using computer display technology. The current paper describes a new free suite of software tools designed to make this task easier, using the latest advances in hardware and software. PsychoPy is a platform-independent experimental control system written in the Python interpreted language using entirely free libraries. PsychoPy scripts are designed to be extremely easy to read and write, while retaining complete power for the user to customize the stimuli and environment. Tools are provided within the package to allow everything from stimulus presentation and response collection (from a wide range of devices) to simple data analysis such as psychometric function fitting. Most importantly, PsychoPy is highly extensible and the whole system can evolve via user contributions. If a user wants to add support for a particular stimulus, analysis or hardware device they can look at the code for existing examples, modify them and submit the modifications back into the package so that the whole community benefits.


title: Learning realistic human actions from movies

text: The aim of this paper is to address recognition of natural human actions in diverse and realistic video settings. This challenging but important subject has mostly been ignored in the past due to several problems one of which is the lack of realistic and annotated video datasets. Our first contribution is to address this limitation and to investigate the use of movie scripts for automatic annotation of human actions in videos. We evaluate alternative methods for action retrieval from scripts and show benefits of a text-based classifier. Using the retrieved action samples for visual learning, we next turn to the problem of action classification in video. We present a new method for video classification that builds upon and extends several recent ideas including local space-time features, space-time pyramids and multi-channel non-linear SVMs. The method is shown to improve state-of-the-art results on the standard KTH action dataset by achieving 91.8% accuracy. Given the inherent problem of noisy labels in automatic annotation, we particularly investigate and show high tolerance of our method to annotation errors in the training set. We finally apply the method to learning and classifying challenging action classes in movies and show promising results.


title: DeepFace: Closing the Gap to Human-Level Performance in Face Verification

text: In modern face recognition, the conventional pipeline consists of four stages: detect => align => represent => classify. We revisit both the alignment step and the representation step by employing explicit 3D face modeling in order to apply a piecewise affine transformation, and derive a face representation from a nine-layer deep neural network. This deep network involves more than 120 million parameters using several locally connected layers without weight sharing, rather than the standard convolutional layers. Thus we trained it on the largest facial dataset to-date, an identity labeled dataset of four million facial images belonging to more than 4, 000 identities. The learned representations coupling the accurate model-based alignment with the large facial database generalize remarkably well to faces in unconstrained environments, even with a simple classifier. Our method reaches an accuracy of 97.35% on the Labeled Faces in the Wild (LFW) dataset, reducing the error of the current state of the art by more than 27%, closely approaching human-level performance.


title: A Multi-scale Approach to Gesture Detection and Recognition

text: We propose a generalized approach to human gesture recognition based on multiple data modalities such as depth video, articulated pose and speech. In our system, each gesture is decomposed into large-scale body motion and local subtle movements such as hand articulation. The idea of learning at multiple scales is also applied to the temporal dimension, such that a gesture is considered as a set of characteristic motion impulses, or dynamic poses. Each modality is first processed separately in short spatio-temporal blocks, where discriminative data-specific features are either manually extracted or learned. Finally, we employ a Recurrent Neural Network for modeling large-scale temporal dependencies, data fusion and ultimately gesture classification. Our experiments on the 2013 Challenge on Multimodal Gesture Recognition dataset have demonstrated that using multiple modalities at several spatial and temporal scales leads to a significant increase in performance allowing the model to compensate for errors of individual classifiers as well as noise in the separate channels.


title: Unsupervised Learning of Invariant Feature Hierarchies with Applications to Object Recognition

text: We present an unsupervised method for learning a hierarchy of sparse feature detectors that are invariant to small shifts and distortions. The resulting feature extractor consists of multiple convolution filters, followed by a feature-pooling layer that computes the max of each filter output within adjacent windows, and a point-wise sigmoid non-linearity. A second level of larger and more invariant features is obtained by training the same algorithm on patches of features from the first level. Training a supervised classifier on these features yields 0.64% error on MNIST, and 54% average recognition rate on Caltech 101 with 30 training samples per category. While the resulting architecture is similar to convolutional networks, the layer-wise unsupervised training procedure alleviates the over-parameterization problems that plague purely supervised learning procedures, and yields good performance with very few labeled training samples.


title: Efficient model-based 3D tracking of hand articulations using Kinect

text: The 3D tracking of the articulation of human hands is a theoretically interesting and challenging problem with numerous and diverse applications [1]. Several attempts have been made to address the problem by considering markerless visual data. Existing approaches can be categorized into modeland appearance-based [1]. In this work, we propose a model-based approach to the problem (Fig. 1). We formulate it as an optimization problem that minimizes the discrepancy between the 3D structure and appearance of hypothesized 3D hand model instances, and actual visual observations. Optimization is performed with a variant of an existing stochastic optimization method (Particle Swarm Optimization PSO) [2]. The most computationally demanding parts of the process have been implemented to run efficiently on a GPU [3]. The resulting system performs hand articulations tracking at a rate of 15Hz.


title: DeepCas: An End-to-end Predictor of Information Cascades

text: Information cascades, effectively facilitated by most social network platforms, are recognized as a major factor in almost every social success and disaster in these networks. Can cascades be predicted? While many believe that they are inherently unpredictable, recent work has shown that some key properties of information cascades, such as size, growth, and shape, can be predicted by a machine learning algorithm that combines many features. These predictors all depend on a bag of hand-crafting features to represent the cascade network and the global network structures. Such features, always carefully and sometimes mysteriously designed, are not easy to extend or to generalize to a different platform or domain. Inspired by the recent successes of deep learning in multiple data mining tasks, we investigate whether an end-to-end deep learning approach could effectively predict the future size of cascades. Such a method automatically learns the representation of individual cascade graphs in the context of the global network structure, without hand-crafted features or heuristics. We find that node embeddings fall short of predictive power, and it is critical to learn the representation of a cascade graph as a whole. We present algorithms that learn the representation of cascade graphs in an end-to-end manner, which significantly improve the performance of cascade prediction over strong baselines including feature based methods, node embedding methods, and graph kernel methods. Our results also provide interesting implications for cascade prediction in general.


title: Applications of Probabilistic Programming (Master's thesis, 2015)

text: This thesis describes work on two applications of probabilistic programming: the learning of probabilistic program code given specifications, in particular program code of one-dimensional samplers; and the facilitation of sequential Monte Carlo inference with help of data-driven proposals. The latter is presented with experimental results on a linear Gaussian model and a non-parametric dependent Dirichlet process mixture of objects model for object recognition and tracking. We begin this work by providing a brief introduction to probabilistic programming. In the second Chapter we present an approach to automatic discovery of samplers in the form of probabilistic programs. Specifically, we learn the procedure code of samplers for one-dimensional distributions. We formulate a Bayesian approach to this problem by specifying a grammar-based prior over probabilistic program code. We use an approximate Bayesian computation method to learn the programs, whose executions generate samples that statistically match observed data or analytical characteristics of distributions of interest. In our experiments we leverage different probabilistic programming systems, including Anglican and Probabilistic C, to perform Markov chain Monte Carlo sampling over the space of programs. Experimental results have demonstrated that, using the proposed methodology, we can learn approximate and even some exact samplers. Finally, we show that our results are competitive with regard to genetic programming methods.


title: Low-Level Software Security: Attacks and Defenses

text: This tutorial paper considers the issues of low-level software security from a language-based perspective, with the help of concrete examples. Four examples of low-level software attacks are covered in full detail; these examples are representative of the major types of attacks on C and C++ software that is compiled into machine code. Six examples of practical defenses against those attacks are also covered in detail; these defenses are selected because of their effectiveness, wide applicability, and low enforcement overhead.


title: Towards integrated safety analysis and design

text: There are currently many problems with the development and assessment of software intensive safety-critical systems. In this paper we describe the problems, and introduce a novel approach to their solution, based around goal-structuring concepts, which we believe will ameliorate some of the difficulties. We discuss the use of modified and new forms of safety assessment notations to provide evidence of safety, and the use of data derived from such notations as a means of providing quantified input into the design assessment process. We then show how the design assessment can be partially automated, and from this develop some ideas on how we might move from analytical to synthetic approaches, using safety criteria and evidence as a fitness function for comparing alternative automatically-generated designs.


title: Hand Gesture Recognition Based on Perceptual Shape Decomposition with a Kinect Camera

text: In this paper, we propose the Perceptual Shape Decomposition (PSD) to detect fingers for a Kinect-based hand gesture recognition system. The PSD is formulated as a discrete optimization problem by removing all negative minima with minimum cost. Experiments show that our PSD is perceptually relevant and robust against distortion and hand variations, and thus improves the recognition system performance. key words: Kinect camerta, hand gesture recognition, perceptual decomposition, finger detection


title: Convergence guarantees for RMSProp and ADAM in non-convex optimization and their comparison to Nesterov acceleration on autoencoders

text: RMSProp and ADAM continue to be extremely popular algorithms for training neural nets but their theoretical foundations have remained unclear. In this work we make progress towards that by giving proofs that these adaptive gradient algorithms are guaranteed to reach criticality for smooth non-convex objectives and we give bounds on the running time. We then design experiments to compare the performances of RMSProp and ADAM against Nesterov Accelerated Gradient method on a variety of autoencoder setups. Through these experiments we demonstrate the interesting sensitivity that ADAM has to its momentum parameter β1. We show that in terms of getting lower training and test losses, at very high values of the momentum parameter (β1 = 0.99) (and large enough nets if using mini-batches) ADAM outperforms NAG at any momentum value tried for the latter. On the other hand, NAG can sometimes do better when ADAM’s β1 is set to the most commonly used value: β1 = 0.9. We also report experiments on different autoencoders to demonstrate that NAG has better abilities in terms of reducing the gradient norms and finding weights which increase the minimum eigenvalue of the Hessian of the loss function.


title: Input-series output-parallel AC/AC converter

text: Input-series and output-parallel (ISOP) converters are suitable for the high input voltage and low output voltage conversion applications. An ISOP current mode AC/AC converter with high frequency link is proposed. The control strategy and operation principle of the ISOP AC/AC converter are investigated. The control strategy ensures the proper sharing of the input voltage and the proper sharing of the output current among the ISOP AC/AC converter. By simulation, the correctness of the operation principle and the control strategy of the ISOP AC/AC converter are proved.


title: Visual Dynamics: Probabilistic Future Frame Synthesis via Cross Convolutional Networks

text: We study the problem of synthesizing a number of likely future frames from a single input image. In contrast to traditional methods, which have tackled this problem in a deterministic or non-parametric way, we propose to model future frames in a probabilistic manner. Our probabilistic model makes it possible for us to sample and synthesize many possible future frames from a single input image. To synthesize realistic movement of objects, we propose a novel network structure, namely a Cross Convolutional Network; this network encodes image and motion information as feature maps and convolutional kernels, respectively. In experiments, our model performs well on synthetic data, such as 2D shapes and animated game sprites, as well as on real-world video frames. We also show that our model can be applied to visual analogy-making, and present an analysis of the learned network representations.


title: High efficiency Ka-band Gallium Nitride power amplifier MMICs

text: The design and performance of two high efficiency Ka-band power amplifier MMICs utilizing a 0.15μm GaN HEMT process technology is presented. Measured in-fixture continuous wave (CW) results for the 3-stage balanced amplifier demonstrates up to 11W of output power and 30% power added efficiency (PAE) at 30GHz. The 3-stage single-ended design produced over 6W of output power and up to 34% PAE. The die size for the balanced and single-ended MMICs are 3.24×3.60mm2 and 1.74×3.24mm2 respectively.


title: A prioritized grid long short-term memory RNN for speech recognition

text: Recurrent neural networks (RNNs) are naturally suitable for speech recognition because of their ability of utilizing dynamically changing temporal information. Deep RNNs have been argued to be able to model temporal relationships at different time granularities, but suffer vanishing gradient problems. In this paper, we extend stacked long short-term memory (LSTM) RNNs by using grid LSTM blocks that formulate computation along not only the temporal dimension, but also the depth dimension, in order to alleviate this issue. Moreover, we prioritize the depth dimension over the temporal one to provide the depth dimension more updated information, since the output from it will be used for classification. We call this model the prioritized Grid LSTM (pGLSTM). Extensive experiments on four large datasets (AMI, HKUST, GALE, and MGB) indicate that the pGLSTM outperforms alternative deep LSTM models, beating stacked LSTMs with 4% to 7% relative improvement, and achieve new benchmarks among uni-directional models on all datasets.