Query: Mobile Shopping Consumers' Behavior: An Exploratory Study and Review

Relevant passages:
Mobile commerce adoption in China and the United States: a cross-cultural study <=SEP=> Mobile communication technologies have penetrated consumer markets throughout the world. Mobile commerce is likely to make a strong influence on business activities, consumer behavior, as well as national and global markets. Thus the identification of factors that influence mobile commerce adoption has significant value. In a global context, this study identified nine factors affecting mobile commerce adoption based on published research in MIS. These factors were investigated in China and the United States, and a comparative examination was conducted. A survey was conducted on 190 individual mobile commerce users in China and USA. Results show that there are several significant differences among the antecedents and their impacts on consumer intention to use mobile commerce in the two cultural settings. The study provides a number of practical insights and informs vendors seeking to enter the Chinese and the US marketplace with specific information on user perspectives.

The effects of perceived risk and technology type on users' acceptance of technologies <=SEP=> Previous studies on technology adoption disagree regarding the relative magnitude of the effects of perceived usefulness and perceived ease of use. However these studies did not consider moderating variables. We investigated four potential moderating variables – perceived risk, technology type, user experience, and gender – in users’ technology adoption. Their moderating effects were tested in an empirical study of 161 subjects. Results showed that perceived risk, technology type, and gender were significant moderating variables. However the effects of user experience were marginal after the variance of errors was removed. # 2007 Published by Elsevier B.V. www.elsevier.com/locate/im Available online at www.sciencedirect.com Information & Management 45 (2008) 1–9

Understanding continued information technology usage behavior: A comparison of three models in the context of mobile internet <=SEP=> This study examines the utility of three prospective models for understanding the continued IT usage behavior. The three models include: Expectation-Confirmation Model in IT Domain (ECM-IT), Technology Acceptance Model (TAM), and a hybrid model integrating TAM and ECM-IT (extended ECM-IT). Based on a survey of 1826 mobile Internet users, the LISREL analysis shows that all three models meet the various goodness-of-fit criteria. When compared using special indices for differentiating among alternative good models, TAM has the best fit to the data followed by ECM-IT, and the extended ECM-IT. In terms of variance explained for intention to continue IT usage, the extended ECM-IT has the highest R (67%) followed by TAM (63%), and ECM-IT (50%). We conclude that TAM is the most parsimonious and generic model that can be used to study both initial and continued IT adoption; the extended ECM-IT explains continued IT usage behavior as well as TAM; and both the ECM-IT and extended ECM-IT models provide additional information to increase our understanding of continued IT usage. © 2006 Elsevier B.V. All rights reserved.

Trust and TAM in Online Shopping: An Integrated Model <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at . http://www.jstor.org/page/info/about/policies/terms.jsp. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology <=SEP=> Valid measurement scales for predicting user acceptance of computers are in short supply. Most subjective measures used in practice are unvalidated, and their relationship to system usage is unknown. The present research develops and validates new scales for two specific variables, perceived usefulness and perceived ease of use, which are hypothesized to be fundamental determinants of user acceptance. Definitions for these two variables were used to develop scale items that were pretested for content validity and then tested for reliability and construct validity in two studies involving a total of 152 users and four application programs. The measures were refined and streamlined, resulting in two six-item scales with reliabilities of .98 for usefulness and .94 for ease of use. The scales exhibited high convergent, discriminant, and factorial validity. Perceived usefulness was significantly correlated with both selfreported current usage (r=.63, Study 1) and self-predicted future usage (r =.85, Study 2). Perceived ease of use was also significantly correlated with current usage (r= .45, Study 1) and future usage (r =.59, Study 2). In both studies, usefulness had a significantly greater correlation with usage behavior than did ease of use. Regression analyses suggest that perceived ease of use may actually be a causal antecedent to perceived usefulness, as opposed to a parallel, direct determinant of system usage. Implications are drawn for future research on user acceptance.

A Practical Approach to Classify Evolving Data Streams: Training with Limited Amount of Labeled Data <=SEP=> Recent approaches in classifying evolving data streams are based on supervised learning algorithms, which can be trained with labeled data only. Manual labeling of data is both costly and time consuming. Therefore, in a real streaming environment, where huge volumes of data appear at a high speed, labeled data may be very scarce. Thus, only a limited amount of training data may be available for building the classification models, leading to poorly trained classifiers. We apply a novel technique to overcome this problem by building a classification model from a training set having both unlabeled and a small amount of labeled instances. This model is built as micro-clusters using semi-supervised clustering technique and classification is performed with kappa-nearest neighbor algorithm. An ensemble of these models is used to classify the unlabeled data. Empirical evaluation on both synthetic data and real botnet traffic reveals that our approach, using only a small amount of labeled data for training, outperforms state-of-the-art stream classification algorithms that use twenty times more labeled data than our approach.

Chess Q & A : Question Answering on Chess Games <=SEP=> We introduce a new dataset1 for the evaluation of models addressing reasoning tasks. For a position in a chess game, we provide question and answer pairs, an image of the board, and the sequence of moves up to that position. We hope this synthetic task will improve our understanding in memory based Deep Learning with posed challenges.

NEUROSCIENTIFIC INVE STIGATIONS OF MUSICAL RHYTHM <=SEP=> Music occurs in every human society, unfolds over time, and enables synchronized movements. The neural mechanisms underlying the perception, cognition, and production of musical rhythm have been investigated using a variety of methods. FMRI studies in particular have shown that the motor system is crucially involved in rhythm and beat perception. Studies using other methods demonstrate that oscillatory neural activity entrains to regularities in musical rhythm, and that motor system excitability is modulated by listening to musical rhythm. This review paper describes some of the recent neuroscientific findings regarding musical rhythm, and especially the perception of a regular beat. INTRODUCTION The temporal structure of music enables synchronized movement, such as tapping one’s foot, clapping, or dancing to the ‘beat’ of musical rhythms. Such movement is precisely timed to align with the periodic, salient beats in the music, and with the movements of other individuals. Given this relationship between musical rhythm and movement, it is perhaps unsurprising that the brain’s motor system is heavily involved in the neural processing of auditory rhythms. However, it is a relatively recent discovery that the motor system is involved even in the absence of movement – subtle differences in the temporal structure or context of an auditory rhythm can elicit robust differences in motor system activity. These discoveries are the topic of this review paper, with a focus on findings from functional magnetic resonance imaging (fMRI). FMRI measures the change in oxygenated blood levels following neural activity [see 1, 2]. This ‘blood-oxygen-level dependent’ (or BOLD) signal is considered to be an indirect measure of brain activity, and therefore increases in BOLD are termed ‘activations’ in this review. Findings from patient studies, as well as electroencephalography (EEG), magnetoencephalography (MEG), and transcranial magnetic stimulation (TMS) studies will also be discussed. Although much theoretic and empirical work has sought to explain why certain temporal patterns elicit movement (e.g., dancing) while others do not [3-5], and the evolutionary basis for human sensitivity to musical rhythm [6-7], this review will focus on the neural substrates of rhythm perception and the role of individual differences, expertise, and sensory modality. RHYTHM AND BEAT IN THE BRAIN When human participants listen to rhythms (i.e., auditory sequences) with or without a beat, widespread activity is observed in the cortical motor system, especially in the supplementary motor area (SMA) and premotor cortex (PMC), as well as subcortical regions such as the basal ganglia and cerebellum [8-14]. Rhythms that are composed of intervals that are integer ratios of one another, and have accents occurring at regular intervals, tend to elicit the perception of a regular, emphasized beat, and beats are usually organized in a metre (a temporal structure determined by the cyclical pattern of strong and weak beats; see Figure 1). Compared to rhythms without a beat, listening to beat-based rhythms elicits more activity in the SMA and the basal ganglia [10]. The importance of the basal ganglia in beat perception was highlighted in a study demonstrating that patients with Parkinson’s disease have impaired

Image Retrieval by Examples <=SEP=> A currently relevant research field in information sciences is the management of nontraditional distributed multimedia databases. Two related key issues are to achieve an efficient content-basedquery by exampleretrieval and a fast response time. This paper presents the architecture of a distributed image retrieval system which provides novel solutions to these key issues. In particular, a way to quantify the effectiveness of low level visual descriptors in database query tasks is presented. The results are also used to improve the system response time, which is an important issue when querying very large databases. A new mechanism to adapt system query strategies to user behavior is also introduced in order to improve the effectiveness of relevance feedback and overall system response time. Finally, the issue of browsing multiple distributed databases is considered and a solution is proposed using multidimensional scaling techniques.

Caring for a child with autism spectrum disorder and parents' quality of life: application of the CarerQol. <=SEP=> This study describes the impact of caregiving on parents of children with autism spectrum disorders (ASDs). Secondly, we investigate construct validation of the care-related quality of life instrument (CarerQol) measuring impact of caregiving. Primary caregivers of children with ASDs were included. Many parents experienced considerable problems combining daily activities with care, had financial problems or suffered from depressive mood. Validity tests showed that a higher impact of caring on the CarerQol was positively associated with higher subjective burden and lower family quality of life. Most of the associations between CarerQol scores and background characteristics confirmed previous research. The CarerQol validly measures the impact of caregiving for children with ASDs on caregivers in our sample. The CarerQol may therefore be useful for including parent outcomes in research on ASDs.

Image recognition of plant diseases based on principal component analysis and neural networks <=SEP=> Plant disease identification based on image processing could quickly and accurately provide useful information for the prediction and control of plant diseases. In this study, 21 color features, 4 shape features and 25 texture features were extracted from the images of two kinds wheat diseases (wheat stripe rust and wheat leaf rust) and two kinds of grape diseases (grape downy mildew and grape powdery mildew), principal component analysis (PCA) was performed for reducing dimensions in feature data processing, and then neural networks including backpropagation (BP) networks, radial basis function (RBF) neural networks, generalized regression networks (GRNNs) and probabilistic neural networks (PNNs) were used as the classifiers to identify wheat diseases and grape diseases, respectively. The results showed that these neural networks could be used for image recognition of these diseases based on reducing dimensions using PCA and acceptable fitting accuracies and prediction accuracies could be obtained. For the two kinds of wheat diseases, the optimal recognition result was obtained when image recognition was conducted based on PCA and BP networks, and the fitting accuracy and the prediction accuracy were both 100%. For the two kinds of grape diseases, the optimal recognition results were obtained when GRNNs and PNNs were used as the classifiers after reducing the dimensions of feature data with PCA, and the prediction accuracies were 94.29% with the fitting accuracies equal to 100%.

Damage in the anca-associated vasculitides: long-term data from the European vasculitis study group (EUVAS) therapeutic trials. <=SEP=> OBJECTIVES
To describe short-term (up to 12 months) and long-term (up to 7 years) damage in patients with newly diagnosed antineutrophil-cytoplasm antibody-associated vasculitis (AAV).


METHODS
Data were combined from six European Vasculitis Study group trials (n=735). Long-term follow-up (LTFU) data available for patients from four trials (n=535). Damage accrued was quantified by the Vasculitis Damage Index (VDI). Sixteen damage items were defined a priori as being potentially treatment-related.


RESULTS
VDI data were available for 629 of 735 patients (85.6%) at baseline, at which time 217/629 (34.5%) had ≥1 item of damage and 32 (5.1%) ≥5 items, reflecting disease manifestations prior to diagnosis and trial enrolment. LTFU data were available for 467/535 (87.3%) at a mean of 7.3 years postdiagnosis. 302/535 patients (56.4%) had VDI data at LTFU, with 104/302 (34.4%) having ≥5 items and only 24 (7.9%) no items of damage. At 6 months and LTFU, the most frequent items were proteinuria, impaired glomerular filtration rate, hypertension, nasal crusting, hearing loss and peripheral neuropathy. The frequency of damage, including potentially treatment-related damage, rose over time (p<0.01). At LTFU, the most commonly reported items of treatment-related damage were hypertension (41.5%; 95% CI 35.6 to 47.4%), osteoporosis (14.1%; 9.9 to 18.2%), malignancy (12.6%; 8.6 to 16.6%), and diabetes (10.4%; 6.7 to 14.0%).


CONCLUSIONS
In AAV, renal, otolaryngological and treatment-related (cardiovascular, disease, diabetes, osteoporosis and malignancy) damage increases over time, with around one-third of patients having ≥5 items of damage at a mean of 7 years postdiagnosis.

A Software Defined Networking architecture for the Internet-of-Things <=SEP=> The growing interest in the Internet of Things (IoT) has resulted in a number of wide-area deployments of IoT subnetworks, where multiple heterogeneous wireless communication solutions coexist: from multiple access technologies such as cellular, WiFi, ZigBee, and Bluetooth, to multi-hop ad-hoc and MANET routing protocols, they all must be effectively integrated to create a seamless communication platform. Managing these open, geographically distributed, and heterogeneous networking infrastructures, especially in dynamic environments, is a key technical challenge. In order to take full advantage of the many opportunities they provide, techniques to concurrently provision the different classes of IoT traffic across a common set of sensors and networking resources must be designed. In this paper, we will design a software-defined approach for the IoT environment to dynamically achieve differentiated quality levels to different IoT tasks in very heterogeneous wireless networking scenarios. For this, we extend the Multinetwork INformation Architecture (MINA), a reflective (self-observing and adapting via an embodied Observe-Analyze-Adapt loop) middleware with a layered IoT SDN controller. The developed IoT SDN controller originally i) incorporates and supports commands to differentiate flow scheduling over task-level, multi-hop, and heterogeneous ad-hoc paths and ii) exploits Network Calculus and Genetic Algorithms to optimize the usage of currently available IoT network opportunities. We have applied the extended MINA SDN prototype in the challenging IoT scenario of wide-scale integration of electric vehicles, electric charging sites, smart grid infrastructures, and a wide set of pilot users, as targeted by the Artemis Internet of Energy and Arrowhead projects. Preliminary simulation performance results indicate that our approach and the extended MINA system can support efficient exploitation of the IoT multinetwork capabilities.

Multi-Task Label Embedding for Text Classification <=SEP=> Multi-task learning in text classification leverages implicit correlations among related tasks to extract common features and yield performance gains. However, most previous works treat labels of each task as independent and meaningless onehot vectors, which cause a loss of potential information and makes it difficult for these models to jointly learn three or more tasks. In this paper, we propose Multi-Task Label Embedding to convert labels in text classification into semantic vectors, thereby turning the original tasks into vector matching tasks. We implement unsupervised, supervised and semisupervised models of Multi-Task Label Embedding, all utilizing semantic correlations among tasks and making it particularly convenient to scale and transfer as more tasks are involved. Extensive experiments on five benchmark datasets for text classification show that our models can effectively improve performances of related tasks with semantic representations of labels and additional information from each other.

Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings <=SEP=> Web tables constitute valuable sources of information for various applications, ranging from Web search to Knowledge Base (KB) augmentation. An underlying common requirement is to annotate the rows of Web tables with semantically rich descriptions of entities published in Web KBs. In this paper, we evaluate three unsupervised annotation methods: (a) a lookup-based method which relies on the minimal entity context provided in Web tables to discover correspondences to the KB, (b) a semantic embeddings method that exploits a vectorial representation of the rich entity context in a KB to identify the most relevant subset of entities in the Web table, and (c) an ontology matching method, which exploits schematic and instance information of entities available both in a KB and a Web table. Our experimental evaluation is conducted using two existing benchmark data sets in addition to a new large-scale benchmark created using Wikipedia tables. Our results show that: 1) our novel lookup-based method outperforms state-of-theart lookup-based methods, 2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and 3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation. As a result, we propose a hybrid method that significantly outperforms individual methods on all the benchmarks.

WebRTC technology overview and signaling solution design and implementation <=SEP=> This paper describes the WebRTC technology and implementation of WebRTC client, server and signaling. Main parts of the WebRTC API are described and explained. Signaling methods and protocols are not specified by the WebRTC standards, therefore in this study we design and implement a novel signaling mechanism. The corresponding message sequence chart of the WebRTC communication behavior describes a communication flow between peers and the server. The server application is implemented as a WebSocket server. The client application demonstrates the use of the WebRTC API for achieving real-time communication. Benefits and future development of the WebRTC technology are mentioned.

Leadership for safety: industrial experience. <=SEP=> The importance of leadership for effective safety management has been the focus of research attention in industry for a number of years, especially in energy and manufacturing sectors. In contrast, very little research into leadership and safety has been carried out in medical settings. A selective review of the industrial safety literature for leadership research with possible application in health care was undertaken. Emerging findings show the importance of participative, transformational styles for safety performance at all levels of management. Transactional styles with attention to monitoring and reinforcement of workers' safety behaviours have been shown to be effective at the supervisory level. Middle managers need to be involved in safety and foster open communication, while ensuring compliance with safety systems. They should allow supervisors a degree of autonomy for safety initiatives. Senior managers have a prime influence on the organisation's safety culture. They need to continuously demonstrate a visible commitment to safety, best indicated by the time they devote to safety matters.

How to handle ARP in a software-defined network <=SEP=> The Address Resolution Protocol (ARP) enables communication between IP-speaking nodes in a local network by reconstructing the hardware (MAC) address associated with the IP address of an interface. This is not needed in a Software-Defined Network (SDN), because each device can forward packets without the need to learn this association. We tackle the interoperability problem arising between standard network devices (end systems, routers), that rely on ARP, and SDN datapaths, that do not handle ARP packets natively. In particular, we propose a general approach to handle ARP in a SDN, that is applicable in several network scenarios, is transparent for existing devices, and can coexist with any packet forwarding logic implemented in the controller. Our approach reduces ARP traffic by confining it to the edge of SDNs and requires a minimal set of flow entries in the datapaths. We argument about its applicability and confirm it with experiments performed on SDN datapaths from a range of different vendors.

RASCAL: A Domain Specific Language for Source Code Analysis and Manipulation <=SEP=> Many automated software engineering tools require tight integration of techniques for source code analysis and manipulation. State-of-the-art tools exist for both, but the domains have remained notoriously separate because different computational paradigms fit each domain best. This impedance mismatch hampers the development of new solutions because the desired functionality and scalability can only be achieved by repeated and ad hoc integration of different techniques. Rascal is a domain-specific language that takes away most of this boilerplate by integrating source code analysis and manipulation at the conceptual, syntactic, semantic and technical level. We give an overview of the language and assess its merits by implementing a complex refactoring.

Robust Audio Hashing for Content Identification <=SEP=> Nowadays most audio content identification systems are based on watermarking technology. In this paper we present a different technology, referred to as robust audio hashing. By extracting robust features and translating them into a bit string, we get an object called a robust hash. Content can then be identified by comparing hash values of a received audio clip with the hash values of previously stored original audio clips. A distinguishing feature of the proposed hash scheme is its ability to extract a bit string for every so many milliseconds. More precisely, for every windowed time interval a hash value of 32 bits is computed by thresholding energy differences of several frequency bands. A sequence of 256 hash values, corresponding to approximately 3 seconds of audio, can uniquely identify a song. Experimental results show that the proposed scheme is robust against severe compression, but bit errors do occur. This implies that searching and matching is a non-trivial task for large databases. For instance, a brute force search approach is already prohibitive for databases containing hash values of more than 100 songs. Therefore we propose a scheme that exploits a structured search algorithm that allows searching databases containing over 100,000 songs.

License plate detection and character recognition system for commercial vehicles based on morphological approach and template matching <=SEP=> Though license plate detection and recognition system has a lot of applications but a very little work has been done on Bangla license plate recognition. Variations among the license plate patterns and complex background of Bangladeshi license plates make it more difficult to use the existing algorithms. Because of this, we propose a solution for Bangla license plate detection and recognition. Firstly, the position of the vehicle is determined. In our country, commercial License Plates have the unique color (green) of its own. That's why; we select the portions of green color with the matching RGB intensity of the plate. A boundary based contour algorithm, area and aspect ratio have been proposed to track down the license plate in the vehicle region. License plate rows containing registration information have been separated by using horizontal projection with threshold. The characters and the digits of the rows have been segmented using vertical projection with threshold value. Finally, template matching has been used for recognizing the characters and the digits of the Bangla license plate. We tested our algorithms for over 180 still images captured from the road. We achieve93% success in license plate detection, 98.1% success in segmentation and 88.8% success rate in Bangla license plate recognition.

Illumination compensation and normalization for robust face recognition using discrete cosine transform in logarithm domain <=SEP=> This paper presents a novel illumination normalization approach for face recognition under varying lighting conditions. In the proposed approach, a discrete cosine transform (DCT) is employed to compensate for illumination variations in the logarithm domain. Since illumination variations mainly lie in the low-frequency band, an appropriate number of DCT coefficients are truncated to minimize variations under different lighting conditions. Experimental results on the Yale B database and CMU PIE database show that the proposed approach improves the performance significantly for the face images with large illumination variations. Moreover, the advantage of our approach is that it does not require any modeling steps and can be easily implemented in a real-time face recognition system.

ServerSwitch: A Programmable and High Performance Platform for Data Center Networks <=SEP=> As one of the fundamental infrastructures for cloud computing, data center networks (DCN) have recently been studied extensively. We currently use pure software-based systems, FPGA based platforms, e.g., NetFPGA, or OpenFlow switches, to implement and evaluate various DCN designs including topology design, control plane and routing, and congestion control. However, software-based approaches suffer from high CPU overhead and processing latency; FPGA based platforms are difficult to program and incur high cost; and OpenFlow focuses on control plane functions at present. In this paper, we design a ServerSwitch to address the above problems. ServerSwitch is motivated by the observation that commodity Ethernet switching chips are becoming programmable and that the PCI-E interface provides high throughput and low latency between the server CPU and I/O subsystem. ServerSwitch uses a commodity switching chip for various customized packet forwarding, and leverages the server CPU for control and data plane packet processing, due to the low latency and high throughput between the switching chip and server CPU. We have built our ServerSwitch at low cost. Our experiments demonstrate that ServerSwitch is fully programmable and achieves high performance. Specifically, we have implemented various forwarding schemes including source routing in hardware. Our in-network caching experiment showed high throughput and flexible data processing. Our QCN (Quantized Congestion Notification) implementation further demonstrated that ServerSwitch can react to network congestions in 23us. ∗Correspondent author. †This work was performed when Zhiqiang Zhou was a visiting student at Microsoft Research Asia.

First response fire combat: Deep leaning based visible fire detection <=SEP=> Visible spectrum video based fire detection using non-stationary cameras has been an overlooked research problem. While many authors have successfully developed algorithms to identify and measure the proportions of uncontrolled fire using thermal or stationary surveillance cameras, the development of non-stationary systems provides a much larger application scope. We present a deep learning based approach, based on Google's Inception v3 and evaluate how the use of different optimizers, learning rates and reduction functions impact the convergence time and the results obtained. The experiment used a balanced two-class dataset, composed of images from videos captured through hand-held and drone attached cameras on real fire hazard situations. The results show that using any choice of algorithms, the Inception v3 architecture is able to converge and obtain results that surpass the state-of-the-art fire detection methods. We show that using a proper choice of learning rate, the network is able to achieve a 99% accuracy in less than one hundred interactions.

Generalized velocity obstacles <=SEP=> We address the problem of real-time navigation in dynamic environments for car-like robots. We present an approach to identify controls that will lead to a collision with a moving obstacle at some point in the future. Our approach generalizes the concept of velocity obstacles, which have been used for navigation among dynamic obstacles, and takes into account the constraints of a car-like robot. We use this formulation to find controls that will allow collision free navigation in dynamic environments. Finally, we demonstrate the performance of our algorithm on a simulated car-like robot among moving obstacles.

Plant pathogens and integrated defence responses to infection <=SEP=> Plants cannot move to escape environmental challenges. Biotic stresses result from a battery of potential pathogens: fungi, bacteria, nematodes and insects intercept the photosynthate produced by plants, and viruses use replication machinery at the host's expense. Plants, in turn, have evolved sophisticated mechanisms to perceive such attacks, and to translate that perception into an adaptive response. Here, we review the current knowledge of recognition-dependent disease resistance in plants. We include a few crucial concepts to compare and contrast plant innate immunity with that more commonly associated with animals. There are appreciable differences, but also surprising parallels.

Trait conscientiousness and the personality meta-trait stability are associated with regional white matter microstructure <=SEP=> Establishing the neural bases of individual differences in personality has been an enduring topic of interest. However, while a growing literature has sought to characterize grey matter correlates of personality traits, little attention to date has been focused on regional white matter correlates of personality, especially for the personality traits agreeableness, conscientiousness and openness. To rectify this gap in knowledge we used a large sample (n > 550) of older adults who provided data on both personality (International Personality Item Pool) and white matter tract-specific fractional anisotropy (FA) from diffusion tensor MRI. Results indicated that conscientiousness was associated with greater FA in the left uncinate fasciculus (β = 0.17, P < 0.001). We also examined links between FA and the personality meta-trait 'stability', which is defined as the common variance underlying agreeableness, conscientiousness, and neuroticism/emotional stability. We observed an association between left uncinate fasciculus FA and stability (β = 0.27, P < 0.001), which fully accounted for the link between left uncinate fasciculus FA and conscientiousness. In sum, these results provide novel evidence for links between regional white matter microstructure and key traits of human personality, specifically conscientiousness and the meta-trait, stability. Future research is recommended to replicate and address the causal directions of these associations.

Paraplay: Exploring Playfulness Around Physical Console Gaming <=SEP=> We present the concept of paraplay: playful activities that take place within the context of an interactive game or other play activity, but outside the activity itself. By critically examining work related to gaming and play goals and motivations we argue that the concept of playfulness should have a stronger role in our understanding of gaming sessions, and particularly social gaming sessions. In order to further understand the role of playfulness in social gaming we conducted an empirical field study of physical console gaming. Six families with a total of 32 participants were provided with an Xbox 360 console, Kinect sensor, and three casual physical video games to play together for a period of approximately two weeks. Participants were instructed to record their social gaming sessions. We conducted video analysis on these recordings as well as interviews with many of the participants. We found numerous types and examples of playfulness within the gaming session even from those who were not actively participating in the game. Drawing on the results of this study we present a taxonomy of paraplay and discuss the ways that playfulness can be exhibited in a social play session. We show that participants in a game situation act within a wider context of playfulness, according to a variety of significant roles ranging from active player through to audience member. We explore these roles and their attributes to provide a rich account of paraplay and its importance in understanding playful activities broadly.

How Regulatory Fit Affects Value in Consumer Choices and Opinions <=SEP=> Vol. XLIII (February 2006), 1–10 1 © 2006, American Marketing Association ISSN: 0022-2437 (print), 1547-7193 (electronic) *Tamar Avnet is Assistant Professor of Business, Rotman School of Management, University of Toronto (e-mail: tavnet@rotman.utoronto.ca). E. Tory Higgins is Professor of Psychology, Department of Psychology, and Professor of Management, Columbia School of Business, Columbia University (e-mail: tory@psych.columbia.edu). The research reported in this article was supported by a grant (No. 39429) from the National Institute of Mental Health to E. Tory Higgins. TAMAR AVNET and E. TORY HIGGINS*

Elastic Distributed Bayesian Collaborative Filtering <=SEP=> In this paper, we consider learning a Bayesian collaborative filtering model on a shared cluster of commodity machines. Two main challenges arise: (1) How can we parallelize and distribute Bayesian collaborative filtering? (2) How can our distributed inference system handle elasticity events common in a shared, resource managed cluster, including resource ramp-up, preemption, and stragglers? To parallelize Bayesian inference, we adapt ideas from both matrix factorization partitioning schemes used with stochastic gradient descent and stale synchronous programming used with parameter servers. To handle elasticity events we offer a generalization of previous partitioning schemes that gives increased flexibility during system disruptions. We additionally describe two new scheduling algorithms to dynamically route work at runtime. In our experiments, we compare the effectiveness of both scheduling algorithms and demonstrate their robustness to system failure.