title: Custom Pictorial Structures for Re-identification

text: We propose a novel methodology for re-identification, based on Pictorial Structures (PS). Whenever face or other biometric information is missing, humans recognize an individual by selectively focusing on the body parts, looking for part-to-part correspondences. We want to take inspiration from this strategy in a re-identification context, using PS to achieve this objective. For single image re-identification, we adopt PS to localize the parts, extract and match their descriptors. When multiple images of a single individual are available, we propose a new algorithm to customize the fit of PS on that specific person, leading to what we call a Custom Pictorial Structure (CPS). CPS learns the appearance of an individual, improving the localization of its parts, thus obtaining more reliable visual characteristics for re-identification. It is based on the statistical learning of pixel attributes collected through spatio-temporal reasoning. The use of PS and CPS leads to state-of-the-art results on all the available public benchmarks, and opens a fresh new direction for research on re-identification.


title: Feature Mining for Image Classification

text: The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).


title: Unsupervised Salience Learning for Person Re-identification

text: Human eyes can recognize person identities based on some small salient regions. However, such valuable salient information is often hidden when computing similarities of images with existing approaches. Moreover, many existing approaches learn discriminative features and handle drastic viewpoint change in a supervised way and require labeling new training data for a different pair of camera views. In this paper, we propose a novel perspective for person re-identification based on unsupervised salience learning. Distinctive features are extracted without requiring identity labels in the training procedure. First, we apply adjacency constrained patch matching to build dense correspondence between image pairs, which shows effectiveness in handling misalignment caused by large viewpoint and pose variations. Second, we learn human salience in an unsupervised manner. To improve the performance of person re-identification, human salience is incorporated in patch matching to find reliable and discriminative matched patches. The effectiveness of our approach is validated on the widely used VIPeR dataset and ETHZ dataset.


title: Person Re-identification: What Features Are Important?

text: State-of-the-art person re-identification methods seek robust person matching through combining various feature types. Often, these features are implicitly assigned with a single vector of global weights, which are assumed to be universally good for all individuals, independent to their different appearances. In this study, we show that certain features play more important role than others under different circumstances. Consequently, we propose a novel unsupervised approach for learning a bottom-up feature importance, so features extracted from different individuals are weighted adaptively driven by their unique and inherent appearance attributes. Extensive experiments on two public datasets demonstrate that attribute-sensitive feature importance facilitates more accurate person matching when it is fused together with global weights obtained using existing methods.


title: Salient Color Names for Person Re-identification

text:  A novel and effective salient color names (SCNCD) based color descriptor is proposed for person re-identification;  Background information is exploited to enrich the feature representation which is of good robustness against background interference and partial occlusions;  To tackle different types of illumination changes, color names distribution and color histograms are fused in four color spaces. Experimental Results


title: A Beginner’s Guide to the Mathematics of Neural Networks

text: In this paper I try to describe both the role of mathematics in shaping our understanding of how neural networks operate, and the curious new mathematical concepts generated by our attempts to capture neural networks in equations. My target reader being the non-expert, I will present a biased selection of relatively simple examples of neural network tasks, models and calculations, rather than try to give a full encyclopedic review-like account of the many mathematical developments in this eld.


title: When and how to develop domain-specific languages

text: Domain-specific languages (DSLs) are languages tailored to a specific application domain. They offer substantial gains in expressiveness and ease of use compared with general-purpose programming languages in their domain of application. DSL development is hard, requiring both domain knowledge and language development expertise. Few people have both. Not surprisingly, the decision to develop a DSL is often postponed indefinitely, if considered at all, and most DSLs never get beyond the application library stage.Although many articles have been written on the development of particular DSLs, there is very limited literature on DSL development methodologies and many questions remain regarding when and how to develop a DSL. To aid the DSL developer, we identify patterns in the decision, analysis, design, and implementation phases of DSL development. Our patterns improve and extend earlier work on DSL design patterns. We also discuss domain analysis tools and language development systems that may help to speed up DSL development. Finally, we present a number of open problems.


title: Local sleep spindle modulations in relation to specific memory cues

text: Sleep spindles have been connected to memory processes in various ways. In addition, spindles appear to be modulated at the local cortical network level. We investigated whether cueing specific memories during sleep leads to localized spindle modulations in humans. During learning of word-location associations, words presented in the left and right visual hemifields were paired with different odors. By presenting a single odor during a subsequent nap, we aimed to selectively reactivate a subset of the studied material in sleeping subjects. During sleep, we observed topographically restricted spindle responses to memory cues, suggesting successful reactivation of specific memory traces. In particular, we found higher amplitude and greater incidence of fast spindles over posterior brain areas involved in visuospatial processing, contralateral to the visual field being cued. These results suggest that sleep spindles in different cortical areas reflect the reprocessing of specific memory traces.


title: Automatic Opinion Question Generation

text: We study the problem of opinion question generation from sentences with the help of community-based question answering systems. For this purpose, we use a sequence to sequence attentional model, and we adopt coverage mechanism to prevent sentences from repeating themselves. Experimental results on the Amazon question/answer dataset show an improvement in automatic evaluation metrics as well as human evaluations from the state-of-theart question generation systems.


title: Predicting IMDB Movie Ratings Using Social Media

text: We predict IMDb movie ratings and consider two sets of features: surface and textual features. For the latter, we assume that no social media signal is isolated and use data from multiple channels that are linked to a particular movie, such as tweets from Twitter and comments from YouTube. We extract textual features from each channel to use in our prediction model and we explore whether data from either of these channels can help to extract a better set of textual feature for prediction. Our best performing model is able to rate movies very close to the observed values.


title: Facial expression recognition based on local region specific features and support vector machines

text: Facial expressions are one of the most powerful, natural and immediate means for human being to communicate their emotions and intensions. Recognition of facial expression has many applications including human-computer interaction, cognitive science, human emotion analysis, personality development etc. In this paper, we propose a new method for the recognition of facial expressions from single image frame that uses combination of appearance and geometric features with support vector machines classification. In general, appearance features for the recognition of facial expressions are computed by dividing face region into regular grid (holistic representation). But, in this paper we extracted region specific appearance features by dividing the whole face region into domain specific local regions. Geometric features are also extracted from corresponding domain specific regions. In addition, important local regions are determined by using incremental search approach which results in the reduction of feature dimension and improvement in recognition accuracy. The results of facial expressions recognition using features from domain specific regions are also compared with the results obtained using holistic representation. The performance of the proposed facial expression recognition system has been validated on publicly available extended Cohn-Kanade (CK+) facial expression data sets.


title: Statistical physics of vaccination

text: Historically, infectious diseases caused considerable damage to human societies, and they continue to do so today. To help reduce their impact, mathematical models of disease transmission have been studied to help understand disease dynamics and inform prevention strategies. Vaccination–one of the most important preventive measures of modern times–is of great interest both theoretically and empirically. And in contrast to traditional approaches, recent research increasingly explores the pivotal implications of individual behavior and heterogeneous contact patterns in populations. Our report reviews the developmental arc of theoretical epidemiology with emphasis on vaccination, as it led from classical models assuming homogeneously mixing (mean-field) populations and ignoring human behavior, to recent models that account for behavioral feedback and/or population spatial/social structure. Many of the methods used originated in statistical physics, such as lattice and network models, and their associated analytical frameworks. Similarly, the feedback loop between vaccinating behavior and disease propagation forms a coupled nonlinear system with analogs in physics. We also review the new paradigm of digital epidemiology, wherein sources of digital data such as online social media are mined for high-resolution information on epidemiologically relevant individual behavior. Armed with the tools and concepts of statistical physics, and further assisted by new sources of digital data, models that capture nonlinear interactions between behavior and disease dynamics offer a novel way of modeling real-world phenomena, and can help improve health outcomes. We conclude the review by discussing open problems in the field and promising directions for future research.


title: A Study of Various Feature Extraction Methods on a Motor Imagery Based Brain Computer Interface System

text:
INTRODUCTION
Brain Computer Interface (BCI) systems based on Movement Imagination (MI) are widely used in recent decades. Separate feature extraction methods are employed in the MI data sets and classified in Virtual Reality (VR) environments for real-time applications.

METHODS
This study applied wide variety of features on the recorded data using Linear Discriminant Analysis (LDA) classifier to select the best feature sets in the offline mode. The data set was recorded in 3-class tasks of the left hand, the right hand, and the foot motor imagery.

RESULTS
The experimental results showed that Auto-Regressive (AR), Mean Absolute Value (MAV), and Band Power (BP) features have higher accuracy values,75% more than those for the other features.

DISCUSSION
These features were selected for the designed real-time navigation. The corresponding results revealed the subject-specific nature of the MI-based BCI system; however, the Power Spectral Density (PSD) based α-BP feature had the highest averaged accuracy.


title: A Test Generation Strategy for Pairwise Testing

text: ÐPairwise testing is a specification-based testing criterion, which requires that for each pair of input parameters of a system, every combination of valid values of these two parameters be covered by at least one test case. In this paper, we propose a new test generation strategy for pairwise testing.


title: Spatial graphlet matching kernel for recognizing aerial image categories

text: This paper presents a method for recognizing aerial image categories based on matching graphlets(i.e., small connected subgraphs) extracted from aerial images. By constructing a Region Adjacency Graph (RAG) to encode the geometric property and the color distribution of each aerial image, we cast aerial image category recognition as RAG-to-RAG matching. Based on graph theory, RAG-to-RAG matching is conducted by matching all their respective graphlets. Towards an effective graphlet matching process, we develop a manifold embedding algorithm to transfer different-sized graphlets into equal length feature vectors and further integrate these feature vectors into a kernel. This kernel is used to train a SVM [8] classifier for aerial image categories recognition. Experimental results demonstrate our method outperforms several state-of-the-art object/scene recognition models.


title: A Study of Android Malware Detection Techniques and Machine Learning

text: Android OS is one of the widely used mobile Operating Systems. The number of malicious applications and adwares are increasing constantly on par with the number of mobile devices. A great number of commercial signature based tools are available on the market which prevent to an extent the penetration and distribution of malicious applications. Numerous researches have been conducted which claims that traditional signature based detection system work well up to certain level and malware authors use numerous techniques to evade these tools. So given this state of affairs, there is an increasing need for an alternative, really tough malware detection system to complement and rectify the signature based system. Recent substantial research focused on machine learning algorithms that analyze features from malicious application and use those features to classify and detect unknown malicious applications. This study summarizes the evolution of malware detection techniques based on machine learning algorithms focused


title: Selecting image pairs for SfM by introducing Jaccard similarity

text: We present a new approach for selecting image pairs that are more likely to match in Structure from Motion (SfM). We propose to use Jaccard Similarity (JacS) which shows how many different visual words is shared by an image pair. In our method, the similarity between images is evaluated using JacS of bag-of-visual-words in addition to tf-idf, which is popular for this purpose. To evaluate the efficiency of our method, we carry out experiments on our original datasets as well as on “Pantheon” dataset, which is derived from Flickr. The result of our method using both JacS and tf-idf is better than the results of a standard method using tf-idf only.


title: Collective List-Only Entity Linking: A Graph-Based Approach

text: List-only entity linking (EL) is the task of mapping ambiguous mentions in texts to target entities in a group of entity lists. Different from traditional EL task, which leverages rich semantic relatedness in knowledge bases to improve linking accuracy, the list-only EL can merely take advantage of co-occurrences information in entity lists. State-of-the-art work utilizes co-occurrences information to enrich entity descriptions which are further used to calculate local compatibility between mentions and entities to determine results. Nonetheless, entity coherence is also deemed to play an important part in EL, which is yet currently neglected. In this paper, in addition to local compatibility, we take into account global coherence among entities. Specifically, we propose to harness co-occurrences in entity lists for mining both explicit and implicit entity relations. The relations are then integrated into an entity graph, on which personalized PageRank is incorporated to compute entity coherence. The final results are derived by combining local mention-entity similarity and global entity coherence. The experimental studies validate the superiority of our method. Our proposal not only improves the performance of the list-only EL, but also opens up the bridge between the list-only EL and conventional EL solutions.


title: Keeping up to date: An academic researcher's information journey

text: Keeping up to date with research developments is a central activity of academic researchers, but researchers face difficulties in managing the rapid growth of available scientific information. This study examined how researchers stay up to date, using the information journey model as a framework for analysis and investigating which dimensions influence information behaviors. We designed a 2-round study involving semistructured interviews and prototype testing with 61 researchers with 3 levels of seniority (PhD student to professor). Data were analyzed following a semistructured qualitative approach. Five key dimensions that influence information behaviors were identified: level of seniority, information sources, state of the project, level of familiarity, and how well defined the relevant community is. These dimensions are interrelated and their values determine the flow of the information journey. Across all levels of professional expertise, researchers used similar hard (formal) sources to access content, while soft (interpersonal) sources were used to filter information. An important “pain point” that future information tools should address is helping researchers filter information at the point of need. Introduction


title: Aspects of anthocyanin absorption, metabolism and pharmacokinetics in humans.

text: Interest in the health-promoting properties of berry anthocyanins is intensifying; however, findings are primarily based on in vitro characteristics, leaving mechanisms associated with absorption, metabolism and pharmacokinetics largely unexplored. The present review integrates the available anthocyanin literature with that of similar flavonoids or polyphenols in order to form hypotheses regarding absorption, metabolism and clearance in humans. Of the limited available literature regarding the absorption and clearance kinetics of anthocyanins, maximum plasma concentrations are reported anywhere between 1.4 and 592 nmol/l and occur at 0.5-4 h post-consumption (doses; 68-1300 mg). Average urinary excretion is reported between 0.03 and 4 % of the ingested dose, having elimination half-lives of 1.5-3 h. In addition, much is unknown regarding the metabolism of anthocyanins. The most commonly cited conjugation reactions involved in the metabolism of other flavonoids include glucuronidation, methylation and sulfation. It is reasonable to suspect that anthocyanins are metabolised in much the same manner; however, until recently, there was little evidence to suggest that anthocyanins were metabolised to any significant extent. New evidence now suggests that anthocyanins are absorbed and transported in human serum and urine primarily as metabolites, with recent studies documenting as much as 68-80 % of anthocyanins as metabolised derivatives in human urine. Further research is required to resolve mechanisms associated with the absorption, metabolism and clearance of anthocyanins in order to establish their true biological activities and health effects. The presented evidence will hopefully focus future research, refining study design and propagating a more complete understanding of anthocyanins' biological significance in humans.


title: Mood Detection : Implementing a facial expression recognition system

text: Facial expressions play a significant role in human dialogue. As a result, there has been considerable work done on the recognition of emotional expressions and the application of this research will be beneficial in improving human-machine dialogue. One can imagine the improvements to computer interfaces, automated clinical (psychological) research or even interactions between humans and autonomous robots.


title: FRACTIONAL DIFFERENTIAL EQUATIONS IN BANACH SPACES

text: This paper deals with some impulsive fractional differential equations in Banach spaces. Utilizing the Leray-Schauder fixed point theorem and the impulsive nonlinear singular version of the Gronwall inequality, the existence of PC-mild solutions for some fractional differential equations with impulses are obtained under some easily checked conditions. At last, an example is given for demonstration.


title: Vehicle License Plate Recognition Based on Extremal Regions and Restricted Boltzmann Machines

text: This paper presents a vehicle license plate recognition method based on character-specific extremal regions (ERs) and hybrid discriminative restricted Boltzmann machines (HDRBMs). First, coarse license plate detection (LPD) is performed by top-hat transformation, vertical edge detection, morphological operations, and various validations. Then, character-specific ERs are extracted as character regions in license plate candidates. Followed by suitable selection of ERs, the segmentation of characters and coarse-to-fine LPD are achieved simultaneously. Finally, an offline trained pattern classifier of HDRBM is applied to recognize the characters. The proposed method is robust to illumination changes and weather conditions during 24 h or one day. Experimental results on thorough data sets are reported to demonstrate the effectiveness of the proposed approach in complex traffic environments.


title: A vehicle license plate recognition system based on analysis of maximally stable extremal regions

text: Vehicle License Plate Recognition (VLPR) system is a core module in Intelligent Transportation Systems (ITS). In this paper, a VLPR system is proposed. Considering that license plate localization is the most important and difficult part in VLPR system, we present an effective license plate localization method based on analysis of Maximally Stable Extremal Region (MSER) features. Firstly, MSER detector is utilized to extract candidate character regions. Secondly, the exact locations of license plates are inferred according to the arrangement of characters in standard license plates. The advantage of this license plate localization method is that less assumption of environmental illumination, weather and other conditions is made. After license plate localization, we continue to recognize the license plate characters and color to complete the whole VLPR system. Finally, the proposed VLPR system is tested on our own collected dataset. The experimental results show the availability and effectiveness of our VLPR system in locating and recognizing all the explicit license plates in an image.


title: A wideband unity-gain buffer in 0.13-μm CMOS

text: In this paper, an ultra wideband analog voltage-mode buffer is presented which can drive a load impedance of 50 Ω. The presented feedback-based buffer uses a compound amplifier which is a parallel combination of a high-DC gain operational amplifier and a operation transconductance amplifier to achieve a high unity gain bandwidth. A proof-of-concept prototype is designed and fabricated in a 0.13 μm CMOS process. The simulation and measurement results of the proposed buffer are in good agreement. The prototype buffer circuit consumes 7.34 mW from a 1.3-V supply, while buffering a 2 GHz sinusoidal input signal with a 0.4 V peak-to-peak (Vpp) amplitude and driving an AC-coupled 50-Ω load.


title: How can human motion prediction increase transparency?

text: A major issue in the field of human-robot interaction for assistance to manipulation is transparency. This basic feature qualifies the capacity for a robot to follow human movements without any human-perceptible resistive forces. In this paper we address the issue of human motion prediction in order to increase the transparency of a robotic manipulator. Our aim is not to predict the motion itself, but to study how this prediction can be used to improve the robot transparency. For this purpose, we have designed a setup for performing basic planar manipulation tasks involving movements that are demanded to the subject and thus easily predictable. Moreover, we have developed a general controller which takes a predicted trajectory (recorded from offline free motion experiments) as an input and feeds the robot motors with a weighted sum of three controllers: torque feedforward, variable stiffness control and force feedback control. Subjects were then asked to perform the same task but with or without the robot assistance (which was not visible to the subject), and with several sets of gains for the controller tuning. First results seems to indicate that when a predictive controller with open loop torque feedforward is used, in conjunction with force- feedback control, the interaction forces are minimized. Therefore, the transparency is increased.


title: Brain development and ADHD.

text: Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by excessive inattention, hyperactivity, and impulsivity, either alone or in combination. Neuropsychological findings suggest that these behaviors result from underlying deficits in response inhibition, delay aversion, and executive functioning which, in turn, are presumed to be linked to dysfunction of frontal-striatal-cerebellar circuits. Over the past decade, magnetic resonance imaging (MRI) has been used to examine anatomic differences in these regions between ADHD and control children. In addition to quantifying differences in total cerebral volume, specific areas of interest have been prefrontal regions, basal ganglia, the corpus callosum, and cerebellum. Differences in gray and white matter have also been examined. The ultimate goal of this research is to determine the underlying neurophysiology of ADHD and how specific phenotypes may be related to alterations in brain structure.


title: Media Framing and Public Attitudes Toward Biofuels

text: This study investigates the relationship between media framing and public opinion on the issue of biofuels—transportation fuels made from plants, animal products, or organic waste. First, the paper investigates how media framing of biofuels has changed since the issue regained national prominence in the early 2000s. Through a detailed content analysis of newspaper coverage, the paper documents an increase in negative frames between 1999 and 2008, especially frames focusing on the negative economic effects of biofuels on consumers. Second, using data from a 2010 Internet survey of a random sample of the U.S. public, the paper analyzes the relative influence of these new media frames on public attitudes toward biofuels compared with other common predictors of public opinion, such as party ID, regional economic interests, and personal identity as an environmentalist. In general, the results confirm that public attitudes toward biofuels appear to be shaped by these new media frames, especially among those who indicate a high degree of attention to the media, suggesting the relative importance of framing effects on policy attitudes for environmental and energy policies in general.


title: Looking Beyond the Visible Scene

text: A common thread that ties together many prior works in scene understanding is their focus on the aspects directly present in a scene such as its categorical classification or the set of objects. In this work, we propose to look beyond the visible elements of a scene; we demonstrate that a scene is not just a collection of objects and their configuration or the labels assigned to its pixels - it is so much more. From a simple observation of a scene, we can tell a lot about the environment surrounding the scene such as the potential establishments near it, the potential crime rate in the area, or even the economic climate. Here, we explore several of these aspects from both the human perception and computer vision perspective. Specifically, we show that it is possible to predict the distance of surrounding establishments such as McDonald's or hospitals even by using scenes located far from them. We go a step further to show that both humans and computers perform well at navigating the environment based only on visual cues from scenes. Lastly, we show that it is possible to predict the crime rates in an area simply by looking at a scene without any real-time criminal activity. Simply put, here, we illustrate that it is possible to look beyond the visible scene.


title: Single motor actuated peristaltic wave generator for a soft bodied worm robot

text: This paper presents the design and development of a single motor actuated peristaltic worm robot with three segments using a bio-inspired method of locomotion with one actuator that achieves optimised worm like peristaltic motion. Each segment consists of two solid circular disks that have a tendon connected through to the drive mechanism using a Bowden cable and a soft rubber skin that deforms under the compression of the tendon. Our hypothesis that a tuned peristaltic waveform can achieve improved performance of locomotion distance and clamping strength is proven using an initial test platform capable of demonstrating varying waveform types with multiple actuators. Three experiments were undertaken: (i) moving along a flat surface, (ii) moving through a confined tunnel (iii) moving through a confined tunnel whilst pulling a payload. Results from these experiments have identified the optimal parameters for a smart gearbox capable of achieving the same optimal peristaltic waveform signal as the more complex test platform but with only one actuator driving all three worm segments. Unlike other examples of peristaltic worm robots, this example uses a control method embedded within the mechanics of the design removing excessive number of actuators which contributes to miniaturisation, reduces power consumption and simplifies the overall design.


title: A Powerful Generative Model Using Random Weights for the Deep Image Representation

text: To what extent is the success of deep visualization due to the training? Could we do deep visualization using untrained, random weight networks? To address this issue, we explore new and powerful generative models for three popular deep visualization tasks using untrained, random weight convolutional neural networks. First we invert representations in feature spaces and reconstruct images from white noise inputs. The reconstruction quality is statistically higher than that of the same method applied on well trained networks with the same architecture. Next we synthesize textures using scaled correlations of representations in multiple layers and our results are almost indistinguishable with the original natural texture and the synthesized textures based on the trained network. Third, by recasting the content of an image in the style of various artworks, we create artistic images with high perceptual quality, highly competitive to the prior work of Gatys et al. on pretrained networks. To our knowledge this is the first demonstration of image representations using untrained deep neural networks. Our work provides a new and fascinating tool to study the representation of deep network architecture and sheds light on new understandings on deep visualization.


title: Analysis of Flux Leakage in a Segmented Core Brushless Permanent Magnet Motor

text: This paper presents an analytical model to analyze the leakage flux associated with a brushless permanent magnet motor that utilizes segmented stator core. Due to the presence of a segmented stator structure, the leakage of flux through the tooth tips becomes worthy of analysis and consideration in such a motor. A leakage parameter alpha is developed to show the effects of various design parameters on tooth-tip flux leakage that is essential in accurately predicting the air gap flux density and other output parameters in the motor. Finite-element analysis and experimental verifications are also done to validate the analytical model.


title: Double-Input Bidirectional DC/DC Converter Using Cell-Voltage Equalizer With Flyback Transformer

text: In this paper, a double-input bidirectional dc/dc converter that uses a rechargeable battery and an ultracapacitor (UC) is proposed. This converter is connected to a cell-voltage equalizer between the battery and UC. The cell-voltage equalizer enables cell-voltage equalization and energy transfer between the battery and UC. This converter has six operational modes. These modes are investigated by reduced-power-scale circuit experiment. In addition, the circuit operation under the combination of the six modes is verified using a PSIM simulator in a large power scale.


title: An Improved Multiobjective Optimization Evolutionary Algorithm Based on Decomposition for Complex Pareto Fronts

text: The multiobjective evolutionary algorithm based on decomposition (MOEA/D) has been shown to be very efficient in solving multiobjective optimization problems (MOPs). In practice, the Pareto-optimal front (POF) of many MOPs has complex characteristics. For example, the POF may have a long tail and sharp peak and disconnected regions, which significantly degrades the performance of MOEA/D. This paper proposes an improved MOEA/D for handling such kind of complex problems. In the proposed algorithm, a two-phase strategy (TP) is employed to divide the whole optimization procedure into two phases. Based on the crowdedness of solutions found in the first phase, the algorithm decides whether or not to delicate computational resources to handle unsolved subproblems in the second phase. Besides, a new niche scheme is introduced into the improved MOEA/D to guide the selection of mating parents to avoid producing duplicate solutions, which is very helpful for maintaining the population diversity when the POF of the MOP being optimized is discontinuous. The performance of the proposed algorithm is investigated on some existing benchmark and newly designed MOPs with complex POF shapes in comparison with several MOEA/D variants and other approaches. The experimental results show that the proposed algorithm produces promising performance on these complex problems.


title: Towards a truly mobile auditory brain-computer interface: exploring the P300 to take away.

text: In a previous study we presented a low-cost, small, and wireless 14-channel EEG system suitable for field recordings (Debener et al., 2012, psychophysiology). In the present follow-up study we investigated whether a single-trial P300 response can be reliably measured with this system, while subjects freely walk outdoors. Twenty healthy participants performed a three-class auditory oddball task, which included rare target and non-target distractor stimuli presented with equal probabilities of 16%. Data were recorded in a seated (control condition) and in a walking condition, both of which were realized outdoors. A significantly larger P300 event-related potential amplitude was evident for targets compared to distractors (p<.001), but no significant interaction with recording condition emerged. P300 single-trial analysis was performed with regularized stepwise linear discriminant analysis and revealed above chance-level classification accuracies for most participants (19 out of 20 for the seated, 16 out of 20 for the walking condition), with mean classification accuracies of 71% (seated) and 64% (walking). Moreover, the resulting information transfer rates for the seated and walking conditions were comparable to a recently published laboratory auditory brain-computer interface (BCI) study. This leads us to conclude that a truly mobile auditory BCI system is feasible.


title: Exploring Recurrent Neural Networks for On-Line Handwritten Signature Biometrics

text: Systems based on deep neural networks have made a breakthrough in many different pattern recognition tasks. However, the use of these systems with traditional architectures seems not to work properly when the amount of training data is scarce. This is the case of the on-line signature verification task. In this paper, we propose a novel writer-independent on-line signature verification systems based on Recurrent Neural Networks (RNNs) with a Siamese architecture whose goal is to learn a dissimilarity metric from the pairs of signatures. To the best of our knowledge, this is the first time these recurrent Siamese networks are applied to the field of on-line signature verification, which provides our main motivation. We propose both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) systems with a Siamese architecture. In addition, a bidirectional scheme (which is able to access both past and future context) is considered for both LSTM- and GRU-based systems. An exhaustive analysis of the system performance and also the time consumed during the training process for each recurrent Siamese network is carried out in order to compare the advantages and disadvantages for practical applications. For the experimental work, we use the BiosecurID database comprised of 400 users who contributed a total of 11,200 signatures in four separated acquisition sessions. Results achieved using our proposed recurrent Siamese networks have outperformed the state-of-the-art on-line signature verification systems using the same database.


title: OpenHAR: A Matlab Toolbox for Easy Access to Publicly Open Human Activity Data Sets

text: This study introduces OpenHAR, a free Matlab toolbox to combine and unify publicly open data sets. It provides an easy access to accelerometer signals of ten publicly open human activity data sets. Data sets are easy to access as OpenHAR provides all the data sets in the same format. In addition, units, measurement range and labels are unified, as well as, body position IDs. Moreover, data sets with different sampling rates are unified using downsampling. What is more, data sets have been visually inspected to find visible errors, such as sensor in wrong orientation. OpenHAR improves re-usability of data sets by fixing these errors. Altogether OpenHAR contains over 65 million labeled data samples. This is equivalent to over 280 hours of data from 3D accelerometers. This includes data from 211 study subjects performing 17 daily human activities and wearing sensors in 14 different body positions.


title: Predicting Sequences of Clinical Events by Using a Personalized Temporal Latent Embedding Model

text: As a result of the recent trend towards digitization -- which increasingly affects evidence-based medicine, accountable care, personalized medicine, and medical "Big Data" analysis -- growing amounts of clinical data are becoming available for analysis. In this paper, we follow the idea that one can model clinical processes based on clinical data, which can then be the basis for many useful applications. We model the whole clinical evolution of each individual patient, which is composed of thousands of events such as ordered tests, lab results and diagnoses. Specifically, we base our work on a dataset provided by the Charité University Hospital of Berlin which is composed of patients that suffered from kidney failure and either obtained an organ transplant or are still waiting for one. These patients face a lifelong treatment and periodic visits to the clinic. Our goal is to develop a system to predict the sequence of events recorded in the electronic medical record of each patient, and thus to develop the basis for a future clinical decision support system. For modelling, we use machine learning approaches which are based on a combination of the embedding of entities and events in a multidimensional latent space, in combination with Neural Network predictive models. Similar approaches have been highly successful in statistical models for recommendation systems, language models, and knowledge graphs. We extend existing embedding models to the clinical domain, in particular with respect to temporal sequences, long-term memories and personalization. We compare the performance of our proposed models with standard approaches such as K-nearest neighbors method, Naïve Bayes classifier and Logistic Regression, and obtained favorable results with our proposed model.


title: A survey of video datasets for anomaly detection in automated surveillance

text: Automated anomaly detection in surveillance system demands to address various problems of real-time monitoring. Due to these challenges, the researchers from computer vision community focused on automation of the surveillance system. The research carried out in this domain has been proposed many datasets which are suitable for different applications. Though many prominent datasets are available, there is lack of description and categorization of these datasets according to the application demand. A survey on anomaly detection video datasets is necessary for researchers to understand and progress in this domain. This paper presents various datasets description briefly.


title: Logic and Artificial Intelligence

text: Nilsson, N.J., Logic and artificial intelligence, Artificial Intelligence 47 (1990) 31-56. The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.


title: Complete mesocolic excision with central vascular ligation produces an oncologically superior specimen compared with standard surgery for carcinoma of the colon.

text:
PURPOSE
The plane of surgery in colonic cancer has been linked to patient outcome although the optimal extent of mesenteric resection is still unclear. Surgeons in Erlangen, Germany, routinely perform complete mesocolic excision (CME) with central vascular ligation (CVL) and report 5-year survivals of higher than 89%. We aimed to further investigate the importance of CME and CVL surgery for colonic cancer by comparison with a series of standard specimens.

METHODS
The fresh photographs of 49 CME and CVL specimens from Erlangen and 40 standard specimens from Leeds, United Kingdom, for primary colonic adenocarcinoma were collected. Precise tissue morphometry and grading of the plane of surgery were performed before comparison to histopathologic variables.

RESULTS
CME and CVL surgery removed more tissue compared with standard surgery in terms of the distance between the tumor and the high vascular tie (median, 131 v 90 mm; P < .0001), the length of large bowel (median, 314 v 206 mm; P < .0001), and ileum removed (median, 83 v 63 mm; P = .003), and the area of mesentery (19,657 v 11,829 mm(2); P < .0001). In addition, CME and CVL surgery was associated with more mesocolic plane resections (92% v 40%; P < .0001) and a greater lymph node yield (median, 30 v 18; P < .0001).

CONCLUSION
Surgeons in Erlangen routinely practicing CME and CVL surgery remove more mesocolon and are more likely to resect in the mesocolic plane when compared with standard excisions. This, along with the associated greater lymph node yield, may partially explain the high 5-year survival rates reported in Erlangen.


title: Simple broadband Gysel combiner with a single coupled line

text: A coupled-Gysel broadband combiner/divider is proposed and demonstrated. The new concept relies on using a single coupled line segment in the design. Significant improvement in bandwidth are realized while maintaining low-loss, ease of design, and flexibility. The coupled-Gysel is demonstrated with a 2.5 - 8 GHz (105% fractional bandwidth) divider with 0.1 dB divider loss, and a 3.4 - 10.2 GHz (100% fractional bandwidth) with 0.2 dB divider loss.


title: A Deep Learning Approach for Population Estimation from Satellite Imagery

text: Knowing where people live is a fundamental component of many decision making processes such as urban development, infectious disease containment, evacuation planning, risk management, conservation planning, and more. While bottom-up, survey driven censuses can provide a comprehensive view into the population landscape of a country, they are expensive to realize, are infrequently performed, and only provide population counts over broad areas. Population disaggregation techniques and population projection methods individually address these shortcomings, but also have shortcomings of their own. To jointly answer the questions of "where do people live" and "how many people live there," we propose a deep learning model for creating high-resolution population estimations from satellite imagery. Specifically, we train convolutional neural networks to predict population in the USA at a 0.01°x0.01° resolution grid from 1-year composite Landsat imagery. We validate these models in two ways: quantitatively, by comparing our model's grid cell estimates aggregated at a county-level to several US Census county-level population projections, and qualitatively, by directly interpreting the model's predictions in terms of the satellite image inputs. We find that aggregating our model's estimates gives comparable results to the Census county-level population projections and that the predictions made by our model can be directly interpreted, which give it advantages over traditional population disaggregation methods. In general, our model is an example of how machine learning techniques can be an effective tool for extracting information from inherently unstructured, remotely sensed data to provide effective solutions to social problems.


title: High Voltage Pulsed Power Supply Using IGBT Stacks

text: High voltage pulsed power supply using IGBT (insulated gate bipolar transistor) stacks and pulse transformer for plasma source ion implantation is proposed. To increase voltage rating, twelve IGBTs are used in series at each IGBT stack and a step-up pulse transformer is utilized. To increase the current rating, the proposed system makes use of synchronized three pulse generator modules composed of diodes, capacitors and IGBT stacks. The proposed pulsed power supply uses semiconductor switches as main switches. Hence, the system is compact, and has semi-infinite lifetime. In addition, it has high flexibility in parameters such as voltage magnitude (10-60 kV), pulse repetition rate (PRR) (10-2000 pps), and pulse width (2-5 muS).


title: Super-resolution from image sequences-a review

text: Growing interest in super-resolution (SR) restoration of video sequences and the closely related problem of construction of SR still images from image sequences has led to the emergence of several competing methodologies. We review the state of the art of SR techniques using a taxonomy of existing techniques. We critique these methods and identify areas which promise performance improvements.


title: AppFusion: Interactive Appearance Acquisition Using a Kinect Sensor

text: We present an interactive material acquisition system for average users to capture the spatially-varying appearance of daily objects. While an object is being scanned, our system computes its appearance on-the-fly and provides quick visual feedback. We build the system entirely on low-end, off-the-shelf components: a Kinect, a mirror ball and printed markers. We exploit the Kinect infra-red emitter/receiver, originally designed for depth computation, as an active hand-held reflectometer, to segment the object into clusters of similar specular materials and estimate the roughness parameters of BRDFs simultaneously. Next, the diffuse albedo and specular intensity of the spatially-varying materials are rapidly computed in an inverse rendering framework, using data from the Kinect RGB camera. We demonstrate captured results of a range of materials, and physically validate our system. CR Categories: I.3.7 [Computer Graphics]: Three-Dimensional Graphics and Realism—Color, shading, shadowing, and texture


title: Gaussian Process Priors with Uncertain Inputs - Application to Multiple-Step Ahead Time Series Forecasting

text: We consider the problem of multi-step ahead prediction in time series analysis using the non-parametric Gaussian process model. -step ahead forecasting of a discrete-time non-linear dynamic system can be performed by doing repeated one-step ahead predictions. For a state-space model of the form , the prediction of at time is based on the point estimates of the previous outputs. In this paper, we show how, using an analytical Gaussian approximation, we can formally incorporate the uncertainty about intermediate regressor values, thus updating the uncertainty on the current prediction.


title: Minimum Complexity Echo State Network

text: Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly with many successful applications. However, RC has been criticized for not being principled enough. Reservoir construction is largely driven by a series of randomized model-building stages, with both researchers and practitioners having to rely on a series of trials and errors. To initialize a systematic study of the field, we concentrate on one of the most popular classes of RC methods, namely echo state network, and ask: What is the minimal complexity of reservoir construction for obtaining competitive models and what is the memory capacity (MC) of such simplified reservoirs? On a number of widely used time series benchmarks of different origin and characteristics, as well as by conducting a theoretical analysis we show that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology. The (short-term) of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value.


title: The''echo state''approach to analysing and training recurrent neural networks

text: The report introduces a constructive learning algorithm for recurrent neural networks, which modifies only the weights to output units in order to achieve the learning task. key words: recurrent neural networks, supervised learning Zusammenfassung. Der Report führt ein konstruktives Lernverfahren für rekurrente neuronale Netze ein, welches zum Erreichen des Lernzieles lediglich die Gewichte der zu den Ausgabeneuronen führenden Verbindungen modifiziert. Stichwörter: rekurrente neuronale Netze, überwachtes Lernen


title: Reweighted Wake-Sleep

text: Training deep directed graphical models with many hidden variables and performing inference remains a major challenge. Helmholtz machines and deep belief networks are such models, and the wake-sleep algorithm has been proposed to train them. The wake-sleep algorithm relies on training not just the directed generative model but also a conditional generative model (the inference network) that runs backward from visible to latent, estimating the posterior distribution of latent given visible. We propose a novel interpretation of the wake-sleep algorithm which suggests that better estimators of the gradient can be obtained by sampling latent variables multiple times from the inference network. This view is based on importance sampling as an estimator of the likelihood, with the approximate inference network as a proposal distribution. This interpretation is confirmed experimentally, showing that better likelihood can be achieved with this reweighted wake-sleep procedure. Based on this interpretation, we propose that a sigmoid belief network is not sufficiently powerful for the layers of the inference network, in order to recover a good estimator of the posterior distribution of latent variables. Our experiments show that using a more powerful layer model, such as NADE, yields substantially better generative models.


title: Design and Synthesis of Synchronization Skeletons Using Branching Time Temporal Logic

text: We propose a method of constructing concurrent programs in which the synchronization skeleton of the program ~s automatically synthesized from a high-level (branching time) Temporal Logic specification. The synchronization skeleton is an abstraction of the actual program where detail irrelevant to synchronization is suppressed. For example, in the synchronization skeleton for a solution to the critical section problem each process's critical section may be viewed as a single node since the internal structure of the critical section is unimportant. Most solutions to synchronization problems in the literature are in fact given as synchronization skeletons. Because synchronization skeletons are in general finite state, the propositional version of Temporal Logic can be used to specify their properties. Our synthesis method exploits the (bounded) finite model property for an appropriate propositional Temporal Logic which asserts that if a formula of the logic is satisfiable, it is satisfiable in a finite model (of size bounded by a function of the length of the formula). Decision procedures have been devised which, given a formula of Temporal Logic, f, will decide whether f is satisfiable or unsatisfiable. If f is satisfiable, a finite model of f is constructed, in our application, unsatisfiability of f means that the specification is inconsistent (and must be reformulated). If the formula f is satisfiable, then the specification it expresses is consistent. A model for f with a finite number of states is constructed by the decision procedure. The synchronization skeleton of a program meeting the specification can be read from this model. The finite model property ensures that any program whose synchronization properties can be expressed in propositional Temporal Logic can be realized by a system of concurrently running processes, each of which is a finite state machine. initially, the synchronization skeletons we synthesize wi]] be for concurrent programs running in a shared-memory environment and for monitors. However, we believe that it is also possible to extend these techniques to synthesize distributed programs. One such application would be the automatic synthesis of network communication protocols from propositional Temporal Logic specifications. Previous efforts toward parallel program synthesis can be found in the work of [LA78] and iRK80]. [LA78] uses a specification language that is essentially predicate


title: Cross-Domain Perceptual Reward Functions

text: In reinforcement learning, we often define goals by specifying rewards within desirable states. One problem with this approach is that we typically need to redefine the rewards each time the goal changes, which often requires some understanding of the solution in the agent’s environment. When humans are learning to complete tasks, we regularly utilize alternative sources that guide our understanding of the problem. Such task representations allow one to specify goals on their own terms, thus providing specifications that can be appropriately interpreted across various environments. This motivates our own work, in which we represent goals in environments that are different from the agent’s. We introduce Cross-Domain Perceptual Reward (CDPR) functions, learned rewards that represent the visual similarity between an agent’s state and a cross-domain goal image. We report results for learning the CDPRs with a deep neural network and using them to solve two tasks with deep reinforcement learning.


title: Height and gradient from shading

text: The method described here for recovering the shape of a surface from a shaded image can deal with complex, wrinkled surfaces. Integrability can be enforced easily because both surface height and gradient are represented. (A gradient field is integrable if it is the gradient of some surface height function.) The robustness of the method stems in part from linearization of the reflectance map about the current estimate of the surface orientation at each picture cell. (The reflectance map gives the dependence of scene radiance on surface orientation.) The new scheme can find an exact solution of a given shape-from-shading problem even though a regularizing term is included. The reason is that the penalty term is needed only to stabilize the iterative scheme when it is far from the correct solution; it can be turned off as the solution is approached. This is a reflection of the fact that shape-from-shading problems are not ill posed when boundary conditions are available, or when the image contains singular points. This article includes a review of previous work on shape from shading and photoclinometry. Novel features of the new scheme are introduced one at a time to make it easier to see what each contributes. Included is a discussion of implementation details that are important if exact algebraic solutions of synthetic shape-from-shading problems are to be obtained. The hope is that better performance on synthetic data will lead to better performance on real data.


title: Origami Pop-up Card Generation from 3D Models Using a Directed Acyclic Graph

text: Origami is a paper art used to create three-dimensional (3D) objects by cutting and folding a single sheet of paper. Origami is labor-intensive and requires a high skill level to generate two-dimensional (2D) objects that pop-up into realistic 3D objects. However, this special feature makes designing an origami architecture procedure challenging. This paper provides a novel algorithm to create an origami paper card from a 3D model with a user-specified folding line. The algorithm segments the 2D shape from a 3D model and creates layers of the pop-up paper card using a directed acyclic graph. After applying layers to the layout, the algorithm creates connections between layers, and then outputs the origami layout. Based on a directed acyclic graph, the algorithm computes a class of paper architectures containing two sets of layers and connections that approximate the input geometry while guaranteeing that a pop up card is physically realizable. The proposed method is demonstrated with a number of paper pop-ups, and physical experimental results are presented.


title: Misplaced Trust ? Exploring the Structure of the E-Government-Citizen Trust Relationship

text: A growing body of research focuses on the relationship between e-government, the relatively new mode of citizen-to-government contact founded in information and communications technologies, and citizen trust in government. For many, including both academics and policy makers, e-government is seen as a potentially transformational medium, a mode of contact that could dramatically improve citizen perceptions of government service delivery and possibly reverse the long-running decline in citizen trust in government. To date, however, the literature has left significant gaps in our understanding of the e-government-citizen trust relationship. This study intends to fill some of these gaps. Using a cross-sectional sample of 787 end users of US federal government services, data from the American Customer Satisfaction Index study, and structural equation modeling statistical techniques, this study explores the structure of the e-government-citizen trust relationship. Included in the model are factors influencing the decision to adopt e-government, as well as prior expectations, overall satisfaction, and outcomes including both confidence in the particular agency experienced and trust in the federal government overall. The findings suggest that although e-government The authors would like to thank Professor Claes Fornell of the University ofMichigan for granting access to the ACSI data that made this study possible. Thanks also to Jason Joyner of CFI Group for his extensive and helpful comments on an early version of the article. Forrest V. Morgeson III is a Research Scientist at the ACSI in Ann Arbor, MI. He has a PhD in Political Science from the University of Pittsburgh. His recent research focuses on the financial impact of consumer satisfaction and citizen satisfaction with federal government services—both online and offline—and has recently been published or is forthcoming in journals such as Public Administration Review, Electronic Government: an International Journal, Journal of Marketing, Marketing Science, and the International Journal of Research in Marking. David VanAmburg is Director of the American Customer Satisfaction Index. Mr VanAmburg has acted as Director for the ACSI’s Federal government-wide satisfaction measurement since its inception in 1999 and of the ACSI as a whole since 2001. He has lectured extensively both in the United States and abroad on topics related to customer satisfaction, quality, customer loyalty, and shareholder value, and co-authored The American Customer Satisfaction Index at Ten Years, ACSI 1994–2004: A Summary of Findings: Implications for the Economy, Stock Returns andManagement. Sunil Mithas is an Assistant Professor at Robert H. Smith School of Business at University of Maryland. He has a PhD in Business from the Ross School of Business at the University of Michigan and an Engineering degree from Indian Institute of Technology, Roorkee, India. Prior to pursuing the PhD, he worked for about 10 years with the Tata group. His research focuses on strategic management and the impact of information technology resources and has appeared in journals that includeManagement Science, Information Systems Research, MIS Quarterly,Marketing Science, Journal of Marketing, and Production and Operations Management. His articles have won best article awards and best article nominations and have been featured in practice-oriented publications such as Harvard Business Review, Sloan Management Review, Bloomberg, Computerworld, and InformationWeek. Address correspondence to the author at morgeson@theacsi.org. doi:10.1093/jopart/muq006 a The Author 2010. Published by Oxford University Press on behalf of the Journal of Public Administration Research and Theory, Inc. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org Journal of Public Administration Research and Theory Advance Access published April 26, 2010 by on A ril 7, 2010 http://jpaordjournals.org D ow nladed fom may help improve citizens’ confidence in the future performance of the agency experienced, it does not yet lead to greater satisfaction with an agency interaction nor does it correlate with greater generalized trust in the federal government overall. Explanations for these findings, including an assessment of the potential of e-government to help rebuild trust in government in the future, are offered.


title: Death Due to Decapitation in Two Motorcyclists: A Description of a Unique Case and a Brief Review of the Literature.

text: Deaths due to decapitation, especially related to traffic accidents, are rarely found in forensic practice. The present case involves a man and a woman who died from decapitation due to an unusual mechanism while they were riding on a motorbike down a mountain road. The autopsy, which was completed as a physics study, allowed the accident to be reconstructed as follows: A plastic cable that had detached from a timber-transporting machine whipped the road and hit the two motorcyclists. The impact resulted in the complete severing of both riders' heads. Involving different scientists in this accident investigation was crucial to understanding the dynamics of the accident. In fact, in addition to scene inspection and autopsy, a physics study was carried out on the cable and the clamp involved, which led to an explanation for the abrupt movement of the cable and, thus, to a thorough reconstruction of the accident.


title: Revisiting Requirements Elicitation Techniques

text: The importance of Requirements Engineering (RE) has been well recognized by the research community in the last decade. There is no doubt to say that requirements phase is the foundation of the entire Software Development Life Cycle (SDLC) on which the entire software is built. With the proper management of various activities falling under requirements phase, a project can deliver the right solution within the time and budget. Requirement elicitation, requirement specification, and requirement validation are the important stages to assure the quality of requirement documentations. Out of these, elicitation is the first major activity, which requires proper attention by Requirement Engineers and other related stakeholders. Literature reveals various elicitation techniques, which can be used for the purpose depending upon the nature of project/s. In this paper, an attempt is made to cover all the major elicitation techniques along with their significant aspects at one place. Such a review would enable the concerned stakeholders to understand and select the most appropriate technique to be used for their project/s.


title: Video Gaming and Children’s Psychosocial Wellbeing: A Longitudinal Study

text: The effects of video games on children's psychosocial development remain the focus of debate. At two timepoints, 1 year apart, 194 children (7.27-11.43 years old; male = 98) reported their gaming frequency, and their tendencies to play violent video games, and to game (a) cooperatively and (b) competitively; likewise, parents reported their children's psychosocial health. Gaming at time one was associated with increases in emotion problems. Violent gaming was not associated with psychosocial changes. Cooperative gaming was not associated with changes in prosocial behavior. Finally, competitive gaming was associated with decreases in prosocial behavior, but only among children who played video games with high frequency. Thus, gaming frequency was related to increases in internalizing but not externalizing, attention, or peer problems, violent gaming was not associated with increases in externalizing problems, and for children playing approximately 8 h or more per week, frequent competitive gaming may be a risk factor for decreasing prosocial behavior. We argue that replication is needed and that future research should better distinguish between different forms of gaming for more nuanced and generalizable insight.


title: Classification of Substitution Ciphers using Neural Networks

text: Most of the time of a cryptanalyst is spent on finding the cipher technique used for encryption rather than the finding the key/ plaintext of the received cipher text. The Strength of the classical substitution cipher‟s success lie on the variety of characters employed to represent a single character. More, the characters employed more the complexity. Thus, in order to reduce the work of the cryptanalyst, neural network based identification is done based on the features of the cipher methods. In this paper, classical substitution ciphers, namely, Playfair, Vigenère and Hill ciphers are considered. The features of the cipher methods under consideration were extracted and a backpropagation neural network was trained. The network was tested for random texts with random keys of various lengths. The cipher text size was fixed as 1Kb. The results so obtained were encouraging.


title: Efficient Verification of Holograms Using Mobile Augmented Reality

text: Paper documents such as passports, visas and banknotes are frequently checked by inspection of security elements. In particular, optically variable devices such as holograms are important, but difficult to inspect. Augmented Reality can provide all relevant information on standard mobile devices. However, hologram verification on mobiles still takes long and provides lower accuracy than inspection by human individuals using appropriate reference information. We aim to address these drawbacks by automatic matching combined with a special parametrization of an efficient goal-oriented user interface which supports constrained navigation. We first evaluate a series of similarity measures for matching hologram patches to provide a sound basis for automatic decisions. Then a re-parametrized user interface is proposed based on observations of typical user behavior during document capture. These measures help to reduce capture time to approximately 15 s with better decisions regarding the evaluated samples than what can be achieved by untrained users.


title: Rethinking Feelings: An fMRI Study of the Cognitive Regulation of Emotion

text: The ability to cognitively regulate emotional responses to aversive events is important for mental and physical health. Little is known, however, about neural bases of the cognitive control of emotion. The present study employed functional magnetic resonance imaging to examine the neural systems used to reappraise highly negative scenes in unemotional terms. Reappraisal of highly negative scenes reduced subjective experience of negative affect. Neural correlates of reappraisal were increased activation of the lateral and medial prefrontal regions and decreased activation of the amygdala and medial orbito-frontal cortex. These findings support the hypothesis that prefrontal cortex is involved in constructing reappraisal strategies that can modulate activity in multiple emotion-processing systems.


title: Metacognitive Control of the Spacing of Study Repetitions

text: Rememberers play an active role in learning, not only by committing material more or less faithfully to memory, but also by selecting judicious study strategies (or not). In three experiments, subjects chose whether to mass or space the second presentation of to-be-learned paired-associate terms that were either normatively difficult or easy to remember, under the constraint that subjects needed to space exactly half of the items (and mass the other half). In contrast with recent findings that implemented no such constraint (Son, 2004), subjects chose to space more of the difficult pairs (in Experiments 1 and 2). Reduction in exposure time eliminated but did not reverse this effect (Experiment 3). Subjects who spaced more of the difficult pairs were more likely to exhibit superior memory performance, but, because subjects who made spacing selections that had no effect on the actual scheduling of items also showed this effect (Experiment 2), that enhancement in performance is more likely to reflect subject selection than strategy efficacy. Overall, these results suggest that choice constraints strongly elicit a discrepancy-reduction approach (Dunlosky & Hertzog, 1998) to strategic decisionmaking, but that reduced study time can eliminate this effect. Metacognitive control of spacing 3 The question of how people use their metacognitive knowledge to regulate their behaviors has been of much interest in recent years, particularly with regard to the implementation of study strategies. Metacognition plays an integral role in tasks such as selfdirected learning (Koriat & Goldsmith, 1996; Nelson & Narens, 1990), and understanding the means by which metacognitions guide learning processes is essential to facilitate and optimize the learning process itself (see Bjork, 1994). This article pursues that question by examining the strategies that subjects employ in the scheduling of learning events. Recent evidence revealed conditions under which subjects prefer to space easier materials and mass more difficult ones (Son, 2004). That result is fascinating because it either reveals that subjects choose to apply more effective study conditions to easier materials—a result in conflict with the vast majority of findings from study-time allocation experiments—or it reveals a fundamental misappreciation of the greater effectiveness of spacing in promoting learning (e.g., Baddeley & Longman, 1978). However, the present experiments reveal the opposite effect—subjects choose to space difficult and mass easy items. These results thus suggest that, under some conditions, subjects do understand the beneficial effects of spacing and also choose to selectively utilize them with difficult materials. Self-regulation of learning Theories of self-regulated study claim that active learners use assessments of item difficulty and their own degree of learning in deciding whether to allocate further cognitive resources toward study of that item or to move on to other items (e.g. Dunlosky & Hertzog, 1998; Mazzoni, Cornoldi, & Marchitelli, 1990; Metcalfe, 2002; Nelson & Leonesio, 1988; Nelson & Narens, 1990; Thiede & Dunlosky, 1999). There is some debate, however, with regard to how difficulty and resource allocation – specifically, study time allotment – are related. Metacognitive control of spacing 4 Discrepancy reduction. One theory emphasizes a discrepancy-reduction mechanism (Dunlosky & Hertzog, 1998). According to this theory, the learner compares their perceived degree of learning of a to-be-learned item to the their desired level of mastery for that item, also known as the norm of study (Le Ny, Denhiere, & Le Taillanter, 1972; see also Nelson & Narens, 1990), and if the degree of learning does not reach that criterion, additional study of that item ensues. Therefore, in reducing the discrepancy between an item’s current and desired degree of learning, the model predicts an inverse relationship between the perceived (prior) degree of learning and study time, and hence suggests that people will allot more study time to judgeddifficult than judged-easy items (Dunlosky & Hertzog, 1998; see also Thiede & Dunlosky, 1999). Indeed, a multitude of experiments have shown that people tend to study more difficult items for longer than they study easier items. In a comprehensive review, Son and Metcalfe (2000) reported that, of 46 treatment combinations in 19 published experiments in which subjects controlled the allocation of study time, 35 revealed a strategy of devoting more time to difficult items, and none showed the opposite strategy of devoting more time to easy items. These studies included subjective as well as objective measures of difficulty, and the results were consistently found across age groups and study materials. Proximal learning. However, Son and Metcalfe (2000) showed that total time constraints caused subjects to apportion more study time to judged-easy items than to judged-difficult items. That is, when the total study time allotted was likely insufficient to master all items, subjects chose to allocate their limited time to items that individually take less time to master, rather than slowly learn fewer difficult items. Similarly, Thiede and Dunlosky (1999) found that if their task Metacognitive control of spacing 5 was to remember only a small portion of the to-be-learned items, rather than the full set, subjects devoted more study time to easy items. Metcalfe (2002) surmised that the discrepancy-reduction model adequately accounted for subjects’ allocation strategies only under certain conditions, and forwarded a more comprehensive model to incorporate the newer data. She argued that study time should be devoted to those items that are just beyond the current grasp of the individual, in the region of proximal learning (Metcalfe, 2002; see also Metcalfe & Kornell, 2003). In the case that those just-unlearned items are the most difficult to-be-learned items, the discrepancy-reduction and region of proximal learning models agree on what the appropriate strategy should be. However, in cases where easy items are still unlearned, the predictions of the two theories are in opposition. Whereas the discrepancy-reduction model suggests that learners will always devote more time to the difficult items, the proximal learning hypothesis implies that individual differences in expertise within a domain should influence study-time allocation. Metcalfe (2002) demonstrated this effect using English-Spanish vocabulary pairs. To monolingual speakers, even relatively easy items can be difficult to learn, and thus those speakers allocated more study time to those easy pairs accordingly. Experts, on the other hand, spent more time studying the difficult word pairs, and Metcalfe (2002) attributed the group differences in item selection to the difference between the two groups’ regions of proximal learning. Novices chose to spend more time studying the easy, yet still unlearned, items before moving on to more difficult items, a result that is not predicted by the discrepancy-reduction model. Effects of strategy choice. In addition to identifying the strategies used in allocating study time, determining which strategy ultimately leads to superior performance on subsequent Metacognitive control of spacing 6 recall tests is of importance as well. The actual quality of any study strategy, after all, can only be evaluated by the outcome it produces on the subsequent test. As Son and Metcalfe (2000) pointed out, even though much of the previous literature suggests a tendency for subjects to study the difficult items for longer than the easy items, there are no data showing that subjects who employ such a strategy outperform subjects who spend equal amounts of time on easy and difficult items. While it is intuitive that increased duration of study should lead to higher recall performance for any given item, previous findings have suggested that once study time for an item has been sufficient for initial acquisition, continued immediate study of that item leads to little or no increase in the probability of future recall. This null increase in performance despite substantial increases in study time has been termed the “labor-in-vain effect” (Nelson & Leonesio, 1988, p. 680). Metcalfe and Kornell (2003) systematically investigated the effects of allocating study time to a particular subset of items on later recall performance. Their results showed that allocating study time to items of medium difficulty was more helpful than was allocating study time to easy or difficult items, presumably because learning of the easy items had already plateaued and would not benefit from additional study, whereas learning of the difficult items would require even larger amounts of additional study time to reach a level sufficient to improve later recall. The plateau occurs sooner for easy items than for medium items, and likewise sooner for medium items than for difficult items. If an optimal strategy of study-time allocation is one in which study of a particular item should be discontinued at the point when further study is not as advantageous as study of another item, a critical question for the metacognizer is where (or when) that point is. If information uptake has slowed sufficiently for an item, immediate further study of that item at the cost of a Metacognitive control of spacing 7 different item may be detrimental to overall recall performance, whereas further study at some later time may boost one’s memory for that item and may benefit future recall. Such a strategy has already been shown in the context of semantic retrieval tasks in which subjects tried to name as many items as possible from two semantic categories in a short time. When retrieval for items in one category slowed, and the other category became relatively more appealing, subjects switched to the second category (Young, 2004). To test whether subjects employ this type of strategy in a learning task, a pa


title: Undefined By Data: A Survey of Big Data Definitions

text: The term big data has become ubiquitous. Owing to a shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term.


title: Explaining the Poor Performance of Consumption-Based Asset Pricing Models

text: The poor performance of consumption-based asset pricing models relative to traditional portfoliobased asset pricing models is one of the great disappointments of the empirical asset pricing literature. We show that the external habit-formation model economy of Campbell and Cochrane (1999) can explain this puzzle. Though artificial data from that economy conform to a consumption-based model by construction, the CAPM and its extensions are much better approximate models than is the standard power utility specification of the consumption-based model. Conditioning information is the central reason for this result. The model economy has one shock, so when returns are measured at sufficiently high frequency the consumption-based model and the CAPM are equivalent and perfect conditional asset pricing models. However, the model economy also produces time-varying expected returns, tracked by the dividend-price ratio. Portfolio-based models capture some of this variation in state variables, which a state-independent function of consumption cannot capture, and so portfolio-based models are better approximate unconditional asset pricing models. John Y. Campbell John H. Cochrane Department of Economics Graduate School of Business Harvard University University of Chicago Littauer Center 213 1101 E. 58th. St. Cambridge, MA 02138 Chicago, IL 60637 and NBER and NBER john_campbell@harvard.edu john.cochrane@gsb.uchicago.edu


title: Assessing Organizational Capabilities: Reviewing and Guiding the Development of Maturity Grids

text: Managing and improving organizational capabilities is a significant and complex issue for many companies. To support management and enable improvement, performance assessments are commonly used. One way of assessing organizational capabilities is by means of maturity grids. While maturity grids may share a common structure, their content differs and very often they are developed anew. This paper presents both a reference point and guidance for developing maturity grids. This is achieved by reviewing 24 existing maturity grids and by suggesting a roadmap for their development. The review places particular emphasis on embedded assumptions about organizational change in the formulation of the maturity ratings. The suggested roadmap encompasses four phases: planning, development, evaluation, and maintenance. Each phase discusses a number of decision points for development, such as the selection of process areas, maturity levels, and the delivery mechanism. An example demonstrating the roadmap's utility in industrial practice is provided. The roadmap can also be used to evaluate existing approaches. In concluding the paper, implications for management practice and research are presented.


title: Modelling out-of-vocabulary words for robust speech recognition

text: This thesis concerns the problem of unknown or out-of-vocabulary (OOV) words in continuous speech recognition. Most of today's state-of-the-art speech recognition systems can recognize only words that belong to some predefined finite word vocabulary. When encountering an OOV word, a speech recognizer erroneously substitutes the OOV word with a similarly sounding word from its vocabulary. Furthermore, a recognition error due to an OOV word tends to spread errors into neighboring words; dramatically degrading overall recognition performance. In this thesis we propose a novel approach for handling OOV words within a single-stage recognition framework. To achieve this goal, an explicit and detailed model of OOV words is constructed and then used to augment the closed-vocabulary search space of a standard speech recognizer. This OOV model achieves open-vocabulary recognition through the use of more flexible subword units that can be concatenated during recognition to form new phone sequences corresponding to potential new words. Examples of such subword units are phones, syllables, or some automatically-learned multi-phone sequences. Subword units have the attractive property of being a closed set, and thus are able to cover any new words, and can conceivably cover most utterances with partially spoken words as well. The main challenge with such an approach is ensuring that the OOV model does not absorb portions of the speech signal corresponding to in-vocabulary (IV) words. In dealing with this challenge, we explore several research issues related to designing the subword lexicon, language model, and topology of the OOV model. We present a dictionary-based approach for estimating subword language models. Such language models are utilized within the subword search space to help recognize the underlying phonetic transcription of OOV words. We also propose a data-driven iterative bottom-up procedure for automatically creating a multi-phone subword inventory. Starting with individual phones, this procedure uses the maximum mutual information principle to successively merge phones to obtain longer subword units. The thesis also extends this OOV approach to modelling multiple classes of OOV words. Instead of augmenting the word search space with a single model, we add several models, one for each class of words. We present two approaches for designing the OOV word classes. The first approach relies on using common part-of-speech tags. The second approach is a data-driven two-step clustering procedure, where the first step uses agglomerative clustering to derive an initial class assignment, while the second step uses iterative clustering to move words from one class to another in order to reduce the model perplexity.


title: A Digital Input Controller for Audio Class-D Amplifiers with 100W 0.004% THD+N and 113dB DR

text: A digital input controller for audio class-D amplifiers is presented. The controller utilizes specially configured integrated DAC and power stage feedback loop to suppress distortion components coming from power-stage switching with digital input capability. The class-D amplifier system with the controller and an existing power stage achieves 113dB DR, 0.0018% THD+N with 10W output power, and 0.004% THD+N with 100W output power into 4Omega load


title: Understanding Latency Variation in Modern DRAM Chips: Experimental Characterization, Analysis, and Optimization

text:
Long DRAM latency is a critical performance bottleneck in current systems. DRAM access latency is defined by three fundamental operations that take place within the DRAM cell array: (i) activation of a memory row, which opens the row to perform accesses; (ii) precharge, which prepares the cell array for the next memory access; and (iii) restoration of the row, which restores the values of cells in the row that were destroyed due to activation. There is significant latency variation for each of these operations across the cells of a single DRAM chip due to irregularity in the manufacturing process. As a result, some cells are inherently faster to access, while others are inherently slower. Unfortunately, existing systems do not exploit this variation.
 The goal of this work is to (i) experimentally characterize and understand the latency variation across cells within a DRAM chip for these three fundamental DRAM operations, and (ii) develop new mechanisms that exploit our understanding of the latency variation to reliably improve performance. To this end, we comprehensively characterize 240 DRAM chips from three major vendors, and make several new observations about latency variation within DRAM. We find that (i) there is large latency variation across the cells for each of the three operations; (ii) variation characteristics exhibit significant spatial locality: slower cells are clustered in certain regions of a DRAM chip; and (iii) the three fundamental operations exhibit different reliability characteristics when the latency of each operation is reduced.
 Based on our observations, we propose Flexible-LatencY DRAM (FLY-DRAM), a mechanism that exploits latency variation across DRAM cells within a DRAM chip to improve system performance. The key idea of FLY-DRAM is to exploit the spatial locality of slower cells within DRAM, and access the faster DRAM regions with reduced latencies for the fundamental operations. Our evaluations show that FLY-DRAM improves the performance of a wide range of applications by 13.3%, 17.6%, and 19.5%, on average, for each of the three different vendors' real DRAM chips, in a simulated 8-core system. We conclude that the experimental characterization and analysis of latency variation within modern DRAM, provided by this work, can lead to new techniques that improve DRAM and system performance.


title: Autoimmune disorders: nail signs and therapeutic approaches.

text: Systemic sclerosis (scleroderma, SSc) is an autoimmune disease that targets small and medium-sized arteries and arterioles in the involved tissues, resulting in a fibrotic vasculopathy and tissue fibrosis. Several prominent nail and periungual changes are apparent in scleroderma. Examination of the nail fold capillaries can reveal the nature and extent of microvascular pathology in patients with collagen vascular disease and Raynaud's phenomenon. Among the complications stemming from Raynaud's phenomenon can be painful ischemic digital ulcers. This can be managed, and potentially prevented, through pharmacologic and nonpharmacologic means. Whereas oral calcium channel blockers remain the most convenient therapy, oral endothelin receptor antagonists and intravenous prostaglandins may be important therapeutic advances for ischemic digital vascular lesions.


title: An introduction to convex optimization for communications and signal processing

text: Convex optimization methods are widely used in the design and analysis of communication systems and signal processing algorithms. This tutorial surveys some of recent progress in this area. The tutorial contains two parts. The first part gives a survey of basic concepts and main techniques in convex optimization. Special emphasis is placed on a class of conic optimization problems, including second-order cone programming and semidefinite programming. The second half of the survey gives several examples of the application of conic programming to communication problems. We give an interpretation of Lagrangian duality in a multiuser multi-antenna communication problem; we illustrate the role of semidefinite relaxation in multiuser detection problems; we review methods to formulate robust optimization problems via second-order cone programming techniques


title: Fading correlation and its effect on the capacity of multielement antenna systems

text: We investigate the effects of fading correlations in multielement antenna (MEA) communication systems. Pioneering studies showed that if the fades connecting pairs of transmit and receive antenna elements are independently, identically distributed, MEA’s offer a large increase in capacity compared to single-antenna systems. An MEA system can be described in terms of spatial eigenmodes, which are single-input single-output subchannels. The channel capacity of an MEA is the sum of capacities of these subchannels. We will show that the fading correlation affects the MEA capacity by modifying the distributions of the gains of these subchannels. The fading correlation depends on the physical parameters of MEA and the scatterer characteristics. In this paper, to characterize the fading correlation, we employ an abstract model, which is appropriate for modeling narrow-band Rayleigh fading in fixed wireless systems.


title: Shifting the MIMO Paradigm

text: Multi-user MIMO (MU-MIMO) networks reveal the unique opportunities arising from a joint optimization of antenna combining techniques with resource allocation protocols. Furthermore, it brings robustness with respect to multipath richness, allowing for compact antenna spacing at the BS and, crucially, yielding the diversity and multiplexing gains without the need for multiple antenna user terminals. To realize these gains, however, the BS should be informed with the user's channel coefficients, which may limit practical application to TDD or low-mobility settings. To circumvent this problem and reduce feedback load, combining MU-MIMO with opportunistic scheduling seems a promising direction. The success for this type of scheduler is strongly traffic and QoS-dependent, however.


title: On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas

text: This paper is motivated by the need for fundamental understanding of ultimate limits of bandwidth efficient delivery of higher bit-rates in digital wireless communications and to also begin to look into how these limits might be approached. We examine exploitation of multi-element array (MEA) technology, that is processing the spatial dimension (not just the time dimension) to improve wireless capacities in certain applications. Specifically, we present some basic information theory results that promise great advantages of using MEAs in wireless LANs and building to building wireless communication links. We explore the important case when the channel characteristic is not available at the transmitter but the receiver knows (tracks) the characteristic which is subject to Rayleigh fading. Fixing the overall transmitted power, we express the capacity offered by MEA technology and we see how the capacity scales with increasing SNR for a large but practical number, , of antenna elements at bothtransmitter and receiver. We investigate the case of independent Rayleigh faded paths between antenna elements and find that with high probability extraordinary capacity is available. Compared to the baseline n = 1 case, which by Shannon’s classical formula scales as one more bit/cycle for every 3 dB of signal-to-noise ratio (SNR) increase, remarkably with MEAs, the scaling is almost like n more bits/cycle for each 3 dB increase in SNR. To illustrate how great this capacity is, even for small n, take the cases n = 2, 4 and 16 at an average received SNR of 21 dB. For over 99% of the channels the capacity is about 7, 19 and 88 bits/cycle respectively, while if n = 1 there is only about 1.2 bit/cycle at the 99% level. For say a symbol rate equal to the channel bandwith, since it is the bits/symbol/dimension that is relevant for signal constellations, these higher capacities are not unreasonable. The 19 bits/cycle for n = 4 amounts to 4.75 bits/symbol/dimension while 88 bits/cycle for n = 16 amounts to 5.5 bits/symbol/dimension. Standard approaches such as selection and optimum combining are seen to be deficient when compared to what will ultimately be possible. New codecs need to be invented to realize a hefty portion of the great capacity promised.


Detecting A Continuum Of Compositionality In Phrasal Verbs


title: Growth and morphological development of laboratory-reared larval and juvenile giant gourami Osphronemus goramy (Perciformes: Osphronemidae)

text: Morphological development in laboratory-reared larval and juvenile Osphronemus goramy is described. Body lengths were 4.8 ± 0.1 (mean ± SD) mm just after hatching, and reached 9.8 ± 0.8 mm by day 16 and 15.3 ± 0.6 mm by day 40. Aggregate fin ray numbers attained full complements by 11.9 mm BL on day 22. All larvae began feeding by day 5 (ca. 6.7 mm BL). Conical jaw teeth appeared on day 7 (ca. 7.0 mm BL). Notochord flexion began on day 3 (ca. 6.5 mm BL), the yolk being absorbed during days 14–15 (ca. 8.0–10.0 mm BL). Pale melanophores, initially visible on the upper yolksac surface during days 0 and 1, became more intense and numerous with growth, and covered almost the entire body in juveniles. Seven or eight vertical bars appeared on the body on day 13 (ca. 9.0 mm BL), thereafter becoming more discernible. Pre-anal length, body depth, and eye diameter proportions became relatively constant after yolk absorption, with those of the head/snout/upper jaw lengths subsequently increasing. The labyrinth organ began to develop in juveniles larger than ca. 14.5 mm BL (day 30), and air-breathing behavior was first observed on days 35–40.


title: Optimal power allocation for DL NOMA systems

text: Non-orthogonal multiple access (NOMA) is an important candidate for 5G radio access technology. In NOMA transmitter, different users' signals are superposed on the same radio resource with different power allocation factors. The receiver removes other users' signals from the received signal before decoding its own signal. In this work, an iterative gradient ascent-based power allocation method is proposed for downlink NOMA transmitter. It maximizes the geometric mean of the throughputs of users who share the same radio resource to provide proportional fairness between users. Simulation results show that the method achieves theoretical best results in terms of the suggested metric. Also it is shown that it increases the efficiency as much as 80% when compared to orthogonal multiple access (OMA) and it gives better results than NOMA that uses fixed and fractional power allocation methods.