title: Altering mindset can enhance motor learning in older adults.

abstract: Beliefs about personal capability have been shown to affect performance. Lowered ability expectations due to older age may themselves contribute to a decline in performance. In the present study, we investigated whether enhancing older adults’ performance expectancies would facilitate the learning of a novel balance task. In Experiment 1, providing older women (71 years) with fabricated feedback indicating that their performance was above average reduced their ability-related concerns and nervousness, and resulted in more effective balance learning, compared with a control group. In Experiment 2, also involving older women (64 years), a simple statement made at the beginning of practice, suggesting that their peers usually do well on that task, enhanced participants’ self-efficacy and learning of the task. These results demonstrate that motor performance and learning in older age can be influenced quickly and positively by enhancing individuals’ ability perceptions.


title: Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education

abstract: Abstract The purpose of this study is to provide conceptual order and a tool for the use of computer-mediated communication (CMC) and computer conferencing in supporting an educational experience. Central to the study introduced here is a model of community inquiry that constitutes three elements essential to an educational transaction—cognitive presence, social presence, and teaching presence. Indicators (key words/phrases) for each of the three elements emerged from the analysis of computer-conferencing transcripts. The indicators described represent a template or tool for researchers to analyze written transcripts, as well as a guide to educators for the optimal use of computer conferencing as a medium to facilitate an educational transaction. This research would suggest that computer conferencing has considerable potential to create a community of inquiry for educational purposes.


title: Analysis of Possible Sexual Assault or Abuse in a 67-Year-Old Female With Early Dementia Post–Brain Attack

abstract: : The case analysis explores an emergency department visit by a 67-year-old female with early dementia post-brain attack, with complaints of possible sexual assault or abuse. This patient resides in a long-term skilled nursing facility. The case outlines the forensic care provided by the Advanced Practice Forensic Nurse (APFN) once medically cleared by the emergency department Advanced Practice Nurse (APN). The case discussion includes issues related to sexuality in aging populations, consent, sexual abuse or assault in institutions, and forensic care of older persons. Expected genital injuries in older women that result from sexual assault and abuse are also explored. The case discussion will analyze the key elements for critical thinking and clinical reasoning and demonstrate standards of care for the APFN and APN practice. Language: en


title: Reward, Addiction, and Emotion Regulation Systems Associated With Rejection in Love

abstract: Romantic rejection causes a profound sense of loss and negative affect. It can induce clinical depression and in extreme cases lead to suicide and/or homicide. To begin to identify the neural systems associated with this natural loss state, we used functional magnetic resonance imaging to study 10 women and 5 men who had recently been rejected by a partner but reported they were still intensely “in love.” Participants alternately viewed a photograph of their rejecting beloved and a photograph of a familiar, individual, interspersed with a distraction-attention task. Their responses while looking at their rejecter included love, despair, good, and bad memories, and wondering why this happened. Activation specific to the image of the beloved occurred in areas associated with gains and losses, craving and emotion regulation and included the ventral tegmental area (VTA) bilaterally, ventral striatum, medial and lateral orbitofrontal/prefrontal cortex, and cingulate gyrus. Compared with data from happily-in-lo...


title: A Wireless Reflectance Pulse Oximeter With Digital Baseline Control for Unfiltered Photoplethysmograms

abstract: Pulse oximeters are central to the move toward wearable health monitoring devices and medical electronics either hosted by, e.g., smart phones or physically embedded in their design. This paper presents a small, low-cost pulse oximeter design appropriate for wearable and surface-based applications that also produces quality, unfiltered photo-plethysmograms (PPGs) ideal for emerging diagnostic algorithms. The design's “filter-free” embodiment, which employs only digital baseline subtraction as a signal compensation mechanism, distinguishes it from conventional pulse oximeters that incorporate filters for signal extraction and noise reduction. This results in high-fidelity PPGs with thousands of peak-to-peak digitization levels that are sampled at 240 Hz to avoid noise aliasing. Electronic feedback controls make these PPGs more resilient in the face of environmental changes (e.g., the device can operate in full room light), and data stream in real time across either a ZigBee wireless link or a wired USB connection to a host. On-board flash memory is available for store-and-forward applications. This sensor has demonstrated an ability to gather high-integrity data at fingertip, wrist, earlobe, palm, and temple locations from a group of 48 subjects (20 to 64 years old).


title: THE FUNCTIONAL APPROACH TO THE STUDY OF ATTITUDES

abstract: At the psychological level the reasons for holding or for changing attitudes are found in the functions they perform for the individual, specifically the functions of adjustment, ego defense, value expression, and knowledge. The conditions necessary to arouse or modify an attitude vary according to the motivational basis of the attitude. Ego-defensive attitudes, for example, can be aroused by threats, appeals to hatred and repressed impulses, and authoritarian suggestion, and can be changed by removal of threat, catharsis, and self-insight. Expressive attitudes are aroused by cues associated with the individual's values and by the need to reassert his self-image and can be changed by showing the appropriateness of the new or modified beliefs to the self-concept. Brain washing is primarily directed at the value-expressive function and operates by controlling all environmental supports of old values. Changing attitudes may involve generalization of change to related areas of belief and feeling. Minimal generalization seems to be the rule among adults; for example, in politics voting for an opposition candidate does not have much effect upon party identification. The author is Professor of Psychology at the University of Michigan, former president of the Society for the Psychological Study of Social Issues, and coeditor of Research Methods in the Behavioral Sciences and Public Opinion and Propaganda.


title: Design of a poly silicon MEMS microphone for high signal-to-noise ratio

abstract: This paper reports on the state of the art silicon micromachined microphone utilizing a dual poly silicon membrane system. MEMS chips from 1.4mm down to 1.0mm side length are applied for mobile communication. Design aspects related with key performance parameters such as sensitivity, signal to noise ration and distortion are discussed. Sensitivity of − 38BV/Pa is achieved for different microphone membrane diameters. A maximum signal to noise ration of 66dB(A) for the largest system could be achieved. The perfect fit of simulation versus measurements enables deeper analysis and balancing of noise contributors. Environmental noise suppression of 5dB by acoustical high pass design is demonstrated. ()


A review of functional Near-Infrared Spectroscopy measurements in naturalistic environments


title: A Multimedia Retrieval Framework Based on Semi-Supervised Ranking and Relevance Feedback

abstract: We present a new framework for multimedia content analysis and retrieval which consists of two independent algorithms. First, we propose a new semi-supervised algorithm called ranking with Local Regression and Global Alignment (LRGA) to learn a robust Laplacian matrix for data ranking. In LRGA, for each data point, a local linear regression model is used to predict the ranking scores of its neighboring points. A unified objective function is then proposed to globally align the local models from all the data points so that an optimal ranking score can be assigned to each data point. Second, we propose a semi-supervised long-term Relevance Feedback (RF) algorithm to refine the multimedia data representation. The proposed long-term RF algorithm utilizes both the multimedia data distribution in multimedia feature space and the history RF information provided by users. A trace ratio optimization problem is then formulated and solved by an efficient algorithm. The algorithms have been applied to several content-based multimedia retrieval applications, including cross-media retrieval, image retrieval, and 3D motion/pose data retrieval. Comprehensive experiments on four data sets have demonstrated its advantages in precision, robustness, scalability, and computational efficiency.


title: Comparative assessment of large-scale data sets of protein–protein interactions

abstract: Comprehensive protein–protein interaction maps promise to reveal many aspects of the complex regulatory network underlying cellular function. Recently, large-scale approaches have predicted many new protein interactions in yeast. To measure their accuracy and potential as well as to identify biases, strengths and weaknesses, we compare the methods with each other and with a reference set of previously reported protein interactions.


title: Planning by Probabilistic Inference

abstract: This paper presents and demonstrates a new approach to the problem of planning under uncertainty. Actions are treated as hidden variables, with their own prior distributions, in a probabilistic generative model involving actions and states. Planning is done by computing the posterior distribution over actions, conditioned on reaching the goal state within a specified number of steps. Under the new formulation, the toolbox of inference techniques be brought to bear on the planning problem. This paper focuses on problems with discrete actions and states, and discusses some extensions.


Recognition of Tables and Forms


Automatically Recognizing On-Ball Screens


title: Code generation using tree matching and dynamic programming

abstract: Compiler-component generators, such as lexical analyzer generators and parser generators, have long been used to facilitate the construction of compilers. A tree-manipulation language called twig has been developed to help construct efficient code generators. Twig transforms a tree-translation scheme into a code generator that combines a fast top-down tree-pattern matching algorithm with dynamic programming. Twig has been used to specify and construct code generators for several experimental compilers targeted for different machines.


title: Random search for hyper-parameter optimization

abstract: Grid search and manual search are the most widely used strategies for hyper-parameter optimization. This paper shows empirically and theoretically that randomly chosen trials are more efficient for hyper-parameter optimization than trials on a grid. Empirical evidence comes from a comparison with a large previous study that used grid search and manual search to configure neural networks and deep belief networks. Compared with neural networks configured by a pure grid search, we find that random search over the same domain is able to find models that are as good or better within a small fraction of the computation time. Granting random search the same computational budget, random search finds better models by effectively searching a larger, less promising configuration space. Compared with deep belief networks configured by a thoughtful combination of manual search and grid search, purely random search over the same 32-dimensional configuration space found statistically equal performance on four of seven data sets, and superior performance on one of seven. A Gaussian process analysis of the function from hyper-parameters to validation set performance reveals that for most data sets only a few of the hyper-parameters really matter, but that different hyper-parameters are important on different data sets. This phenomenon makes grid search a poor choice for configuring algorithms for new data sets. Our analysis casts some light on why recent "High Throughput" methods achieve surprising success--they appear to search through a large number of hyper-parameters because most hyper-parameters do not matter much. We anticipate that growing interest in large hierarchical models will place an increasing burden on techniques for hyper-parameter optimization; this work shows that random search is a natural baseline against which to judge progress in the development of adaptive (sequential) hyper-parameter optimization algorithms.


title: Data quality: A survey of data quality dimensions

abstract: Nowadays, activities and decisions making in an organization is based on data and information obtained from data analysis, which provides various services for constructing reliable and accurate process. As data are significant resources in all organizations the quality of data is critical for managers and operating processes to identify related performance issues. Moreover, high quality data can increase opportunity for achieving top services in an organization. However, identifying various aspects of data quality from definition, dimensions, types, strategies, techniques are essential to equip methods and processes for improving data. This paper focuses on systematic review of data quality dimensions in order to use at proposed framework which combining data mining and statistical techniques to measure dependencies among dimensions and illustrate how extracting knowledge can increase process quality.


title: A 3D Point Cloud Segmentation Method Based on Local Convexity and Dimension Features

abstract: Segmentation of 3D point clouds is an essential part of automatic tasks, such as object classification, recognition, and localization. The segmentation results pose a direct impact on the further processing. In this paper, we present an improved region-growing algorithm based on local convexity and dimension features for 3D point clouds segmentation. The point clouds on tabletop is removed from the original dataset by using RANSAC algorithm. Then the seed point and growing rules are set according to the local convexity and dimension features. Our method can reduce the uncorrect segmentation to some extent, and reduce the impact from the selection of seed points on the segmentation results. Experiments are provided to demonstrate that the proposed algorithm outperforms the traditional region-growing one from the perspective of segmenting the adjacent objects.


title: A comparison of the noise sensitivity of nine QRS detection algorithms

abstract: The noise sensitivities of nine different QRS detection algorithms were measured for a normal, single-channel, lead-II, synthesized ECG corrupted with five different types of synthesized noise: electromyographic interference, 60-Hz power line interference, baseline drift due to respiration, abrupt baseline shift, and a composite noise constructed from all of the other noise types. The percentage of QRS complexes detected, the number of false positives, and the detection delay were measured. None of the algorithms were able to detect all QRS complexes without any false positives for all of the noise types at the highest noise level. Algorithms based on amplitude and slope had the highest performance for EMG-corrupted ECG. An algorithm using a digital filter had the best performance for the composite-noise-corrupted data. >


title: Social media analytics for competitive advantage

abstract: Big Data Analytics is getting a great deal of attention in the business and government communities. If it lives up to its name, visual analytics will be a prime path by which visualization competes successfully in this arena. This paper discusses some fundamental work we have done in this area through integration of interactive visualization and automated analysis methods and the applications that have resulted.


title: DropMax: Adaptive Variational Softmax

abstract: We propose DropMax, a stochastic version of softmax classifier which at each iteration drops non-target classes according to dropout probabilities adaptively decided for each instance. Specifically, we overlay binary masking variables over class output probabilities, which are input-adaptively learned via variational inference. This stochastic regularization has an effect of building an ensemble classifier out of exponentially many classifiers with different decision boundaries. Moreover, the learning of dropout rates for non-target classes on each instance allows the classifier to focus more on classification against the most confusing classes. We validate our model on multiple public datasets for classification, on which it obtains significantly improved accuracy over the regular softmax classifier and other baselines. Further analysis of the learned dropout probabilities shows that our model indeed selects confusing classes more often when it performs classification.


title: A Framework for Deep Constrained Clustering -- Algorithms and Advances

abstract: The area of constrained clustering has been extensively explored by researchers and used by practitioners. Constrained clustering formulations exist for popular algorithms such as k-means, mixture models, and spectral clustering but have several limitations. We explore a deep learning formulation of constrained clustering and in particular explore how it can extend the field of constrained clustering. We show that our formulation can not only handle standard together/apart constraints without the well documented negative effects reported but can also model instance level constraints (level-of-difficulty), cluster level constraints (balancing cluster size) and triplet constraints. The first two are new ways for domain experts to enforce guidance whilst the later importantly allows generating ordering constraints from continuous side-information.


title: Self-Attention Networks for Connectionist Temporal Classification in Speech Recognition

abstract: The success of self-attention in NLP has led to recent applications in end-to-end encoder-decoder architectures for speech recognition. Separately, connectionist temporal classification (CTC) has matured as an alignment-free, non-autoregressive approach to sequence transduction, either by itself or in various multitask and decoding frameworks. We propose SAN-CTC, a deep, fully self-attentional network for CTC, and show it is tractable and competitive for end-to-end speech recognition. SAN-CTC trains quickly and outperforms existing CTC models and most encoder-decoder models, with character error rates (CERs) of 4.7% in 1 day on WSJ eval92 and 2.8% in 1 week on LibriSpeech test-clean, with a fixed architecture and one GPU. Similar improvements hold for WERs after LM decoding. We motivate the architecture for speech, evaluate position and down-sampling approaches, and explore how label alphabets (character, phoneme, subword) affect attention heads and performance.


title: Background modeling for generative image models

abstract: Discussion of the implicit but unavoidable background model in generative image models.Analysis of common practical strategies to deal with the problem and their drawbacks.Explicit background models are proposed as a fundamental solution.The background model is introduced through an efficient likelihood ratio correction.The background correction clearly improves on face pose estimation and recognition. Face image interpretation with generative models is done by reconstructing the input image as well as possible. A comparison between the target and the model-generated image is complicated by the fact that faces are surrounded by background. The standard likelihood formulation only compares within the modeled face region. Through this restriction an unwanted but unavoidable background model appears in the likelihood. This implicitly present model is inappropriate for most backgrounds and leads to artifacts in the reconstruction, ranging from pose misalignment to shrinking of the face. We discuss the problem in detail for a probabilistic 3D Morphable Model and propose to use explicit image-based background models as a simple but fundamental solution. We also discuss common practical strategies which deal with the problem but suffer from a limited applicability which inhibits the fully automatic adaption of such models. We integrate the explicit background model through a likelihood ratio correction of the face model and thereby remove the need to evaluate the complete image. The background models are generic and do not need to model background specifics. The corrected 3D Morphable Model directly leads to more accurate pose estimation and image interpretations at large yaw angles with strong self-occlusion.


title: An Efficient Memristor-based Distance Accelerator for Time Series Data Mining on Data Centers

abstract: The rapid development of Internet-of-Things (IoT) is yielding a huge volume of time series data, the real-time mining of which becomes a major load for data centers. The computation bottleneck in time series data mining is the distance function, which has been tackled by various software optimization and hardware acceleration techniques recently. However, each of these techniques is only designed or optimized for a specific distance function. To address this problem, in this paper we propose an efficient and reconfigurable memristor-based distance accelerator for real-time and energy-efficient data mining with time series on data centers. Common circuit structure is extracted to save chip areas, and the circuit can be configured to any specific distance functions. Experimental results show that compared with existing works, our work has achieved a speedup of 3.5x-376x on performance and an improvement of 1-3 orders of magnitude on energy efficiency.


title: Bidding optimally in concurrent second-price auctions of perfectly substitutable goods

abstract: We derive optimal bidding strategies for a global bidding agent that participates in multiple, simultaneous second-price auctions with perfect substitutes. We first consider a model where all other bidders are local and participate in a single auction. For this case, we prove that, assuming free disposal, the global bidder should always place non-zero bids in all available auctions, irrespective of the local bidders' valuation distribution. Furthermore, for non-decreasing valuation distributions, we prove that the problem of finding the optimal bids reduces to two dimensions. These results hold both in the case where the number of local bidders is known and when this number is determined by a Poisson distribution. This analysis extends to online markets where, typically, auctions occur both concurrently and sequentially. In addition, by combining analytical and simulation results, we demonstrate that similar results hold in the case of several global bidders, provided that the market consists of both global and local bidders. Finally, we address the efficiency of the overall market, and show that information about the number of local bidders is an important determinant for the way in which a global bidder affects efficiency.


title: Role of intestinal microbiota and metabolites on gut homeostasis and human diseases

abstract: A vast diversity of microbes colonizes in the human gastrointestinal tract, referred to intestinal microbiota. Microbiota and products thereof are indispensable for shaping the development and function of host innate immune system, thereby exerting multifaceted impacts in gut health. This paper reviews the effects on immunity of gut microbe-derived nucleic acids, and gut microbial metabolites, as well as the involvement of commensals in the gut homeostasis. We focus on the recent findings with an intention to illuminate the mechanisms by which the microbiota and products thereof are interacting with host immunity, as well as to scrutinize imbalanced gut microbiota (dysbiosis) which lead to autoimmune disorders including inflammatory bowel disease (IBD), Type 1 diabetes (T1D) and systemic immune syndromes such as rheumatoid arthritis (RA). In addition to their well-recognized benefits in the gut such as occupation of ecological niches and competition with pathogens, commensal bacteria have been shown to strengthen the gut barrier and to exert immunomodulatory actions within the gut and beyond. It has been realized that impaired intestinal microbiota not only contribute to gut diseases but also are inextricably linked to metabolic disorders and even brain dysfunction. A better understanding of the mutual interactions of the microbiota and host immune system, would shed light on our endeavors of disease prevention and broaden the path to our discovery of immune intervention targets for disease treatment.


title: Learning Social Affordance for Human-Robot Interaction

abstract: In this paper, we present an approach for robot learning of social affordance from human activity videos. We consider the problem in the context of human-robot interaction: Our approach learns structural representations of human-human (and human-object-human) interactions, describing how body-parts of each agent move with respect to each other and what spatial relations they should maintain to complete each sub-event (i.e., sub-goal). This enables the robot to infer its own movement in reaction to the human body motion, allowing it to naturally replicate such interactions. ::: We introduce the representation of social affordance and propose a generative model for its weakly supervised learning from human demonstration videos. Our approach discovers critical steps (i.e., latent sub-events) in an interaction and the typical motion associated with them, learning what body-parts should be involved and how. The experimental results demonstrate that our Markov Chain Monte Carlo (MCMC) based learning algorithm automatically discovers semantically meaningful interactive affordance from RGB-D videos, which allows us to generate appropriate full body motion for an agent.


title: Speech emotion recognition using DWT

abstract: Emotion recognition from speech helps us in improving the effectiveness of human-machine interaction. This paper presents a method to identify suitable features in DWT domain and improve good accuracy. In this work, 7 emotions (Berlin Database) are recognized using Support Vector Machine (SVM) classifier. Entropy of Teager Energy operated Discrete Wavelet Transform (DWT) coefficients, Linear Predictive Cepstral Coefficients(LPCC), Mel Energy Spectral Dynamic Coefficients(MEDC), Zero Crossing Rate (ZCR), shimmer, spectral roll off, spectral flux, spectral centroid, pitch, short time energy and Harmonic to Noise Ratio (HNR) are considered as features. The obtained average accuracy is 82.14 % Earlier work done on emotion recognition using DWT coefficients yielded an accuracy of 63.63 % and 68.5% for 4 emotions on Berlin and Malayalam databases respectively. The proposed algorithm shows a significant increase in accuracy of about 15% to 20% for 7 emotions on Berlin database. Also, 100% efficiency has been achieved for four emotions with Simple Logistic classifier of WEKA 3.6 tool.


title: The Schroeder Frequency of Furnished and Unfurnished Spaces

abstract: This paper considers the situation in which an empty room is furnished. Hence, the Schroeder frequency of an empty room is compared with the Schroeder frequency of the same room when it is furnished. It is concluded that the Schroeder frequency of empty spaces is more accurate compared with the Schroeder frequency of furnished spaces.


title: Adaptive integer-precision Lagrange multiplier selection for high performance AVS video coding

abstract: In AVS and H.264/AVC, Lagrangian Rate distortion (RD) optimization techniques are widely adopted for coding mode selection and displacement vector estimation. The optimal Lagrange multipliers in these two cases are both floating-point values. If RD optimized video encoder is implemented on computation-constrained fixed-point platform such as FPGA and ASIC, fixed-point Lagrange multiplier selection is an important problem to trade-off the RD performance and computation complexity. This work focuses on fixed-point Lagrange multiplier selection for RD mode decision. Adaptive scaling matrix is used to trade-off complexity and RD performance. Also, intensive simulation results and analysis on precision, hardware cost, and RD performance are given. The proposed approach is also well-suited for RD optimized motion estimation for computation-constrained video coding.


title: Etiologies and prognostic factors of leukocytoclastic vasculitis with skin involvement: A retrospective study in 112 patients

abstract: In this study, outcomes of patients with leukocytoclastic vasculitis (LCV) were analyzed focusing on clinical, histopathology and laboratory findings, relapses, and survival. ::: ::: Data from patients with cutaneous vasculitis diagnosed between January 1, 2000, and December 31, 2010, at Dijon University Hospital (France) were retrospectively reviewed. LCV was defined as perivascular neutrophilic infiltrate, endothelial cell nuclear swelling, extravasation of red blood cells, and/or fibrin deposition in vessels. Patients were classified according to the 2012 Chapel Hill Consensus Conference. Relapses were defined as the recurrence of vasculitis symptoms after a period of remission >1 month. Time to relapse and/or death was calculated from the date of diagnosis. Univariate and multivariate (Cox model) analyses were performed. ::: ::: A total of 112 patients (57 males and 55 females), with a mean age of 60 ± 19 (18–98) years, were analyzed. Overall follow-up was 61 ± 38 months. At diagnosis, all patients had skin lesions, purpura being the most common (n = 83). Lesions were associated with systemic involvement in 55 (51%) patients. Only 41 (36.6%) patients received specific treatment: glucocorticoids in 29 of 41 (70.7%) and immunosuppressants in 9 of 41 (22%). Sixty-two patients (55%) had LCV due to underlying causes, 29 (25.9%) had single-organ cutaneous small vessel vasculitis (SoCSVV), and 21 (18.8%) had unclassifiable LCV. Twenty patients of the cohort (18%) experienced relapse, 14 ± 13 (1–40) months after the diagnosis of LCV. None of the 29 patients with SoCSVV relapsed. Independent risk factors for relapse were vascular thrombosis in the biopsy [hazard ratio (HR) = 4.9; P = 0.017], peripheral neuropathy (HR = 9.8; P = 0.001), hepatitis (HR = 3.1; P = 0.004), and positive antineutrophil cytoplasm antibodies (ANCA, HR = 5.9 P = 0.005). In contrast, SoCSVV was a protective factor for relapse (HR = 0.12; P = 0.043). ::: ::: The 1-, 3-, and 6-year overall survival rates were 99%, 83%, and 71%, respectively, with no difference between relapsers and nonrelapsers (P = 0.960) or between SoCSVV and unclassifiable LCV (P = 0.588). ::: ::: This study demonstrates that global survival for LCV patients is good but relapses remain frequent, especially when the cutaneous biopsy shows vascular thrombosis, or in patients with peripheral neuropathy or hepatitis. Conversely, SoCSVV is a protective factor for relapse.


Modeling and Control Design of Continuous Stirred Tank Reactor System


Towards a Theoretical Model for Human Resource Management Information Systems, Government Policy and Organizational Performance: A Research Agenda


Employee Turnover in banking sector : Empirical evidence 1


Large-Scale Hypoconnectivity Between Resting-State Functional Networks in Unmedicated Adolescent Major Depressive Disorder


title: Bluetooth Low Energy based Communication Framework for Intra Vehicle Wireless Sensor Networks

abstract: In modern vehicles Controller Area Network (CAN) bus has significant importance to transfer data from sensors to Engine Control Unit (ECU) by employing two wire system. CAN bus is very efficient in terms of throughput and reliability but it requires very careful sensor deployment and wiring. In this paper, we propose low cost and energy efficient communication between sensor nodes and ECU using Bluetooth Low Energy (BLE) within Vehicular Ad Hoc Network by employing Intra Vehicle Wireless Sensor Network. BLE can be readily employed in densely urban environment for inter vehicular (up to 50m range) and intra vehicular (up to 10m range) wireless communication. We have compared BLE and CAN bus for communicating actual engine data between ECUs and sensors, and ECU and Onboard Unit (OBU), in terms of energy efficiency, throughput, latency and coverage area. Proposed BLE based system exhibited very high successful packet delivery ratios for static and driving scenarios with acceptable average RSSI values of at least -10dBm. During extensive tests, BLE CC2540/CC2541 based system also achieved very low current consumption (in microamperes) and longer battery life time (around six months on a standard coin battery).


title: Public reporting and market area exit decisions by home health agencies.

abstract: OBJECTIVE ::: To examine whether home health agencies selectively discontinue services to areas with socio-economically disadvantaged people after the introduction of Home Health Compare (HHC), a public reporting program initiated by Medicare in 2003. ::: ::: ::: STUDY DESIGN /METHODS ::: We focused on agencies' initial responses to HHC and examined selective market-area exits by agencies between 2002 and 2004. We measured HHC effects by the percentage of quality indicators reported in public HHC data in 2003. Socio-economic status was measured by per capita income and percent college-educated at the market-area level. ::: ::: ::: DATA SOURCES ::: 2002 and 2004 Outcome and Assessment Information Set (OASIS); 2000 US Census file; 2004 Area Resource File; and 2002 Provider of Service File. ::: ::: ::: PRINCIPAL FINDINGS ::: WE FOUND A SMALL AND WEAK EFFECT OF PUBLIC REPORTING ON SELECTIVE EXITS: a 10-percent increase in reporting (reporting one more indicator) increased the probability of leaving an area with less-educated people by 0.3 percentage points, compared with leaving an area with high education. ::: ::: ::: CONCLUSION ::: The small level of market-area exits under public reporting is unlikely to be practically meaningful, suggesting that HHC did not lead to a disruption in access to home health care through selective exits during the initial year of the program.


title: Answer Sequence Learning with Neural Networks for Answer Selection in Community Question Answering

abstract: In this paper, the answer selection problem in community question answering (CQA) is regarded as an answer sequence labeling task, and a novel approach is proposed based on the recurrent architecture for this problem. Our approach applies convolution neural networks (CNNs) to learning the joint representation of question-answer pair firstly, and then uses the joint representation as input of the long short-term memory (LSTM) to learn the answer sequence of a question for labeling the matching quality of each answer. Experiments conducted on the SemEval 2015 CQA dataset shows the effectiveness of our approach.


Strengthening customer loyalty through intimacy and passion : Roles of customer – firm affection and customer – staff relationships in services


title: A Substrate-Integrated Evanescent-Mode Waveguide Filter With Nonresonating Node in Low-Temperature Co-Fired Ceramic

abstract: A cross-coupled substrate-integrated evanescent-mode waveguide filter is proposed with a quasi-elliptic frequency response. It is realized with a coplanar waveguide as a nonresonating node to provide an equivalent negative coupling coefficient. The filter prototype is developed with folded and ridge substrate-integrated waveguides (SIWs) in low-temperature co-fired ceramic. Since evanescent-mode and cross-coupling techniques are used in the design of filters, about 90% area reduction and more than 60% volume reduction are achieved, in comparison with conventional planar cavity-coupled SIW filters. Their unloaded Q factor is also improved. In particular, the spurious suppression characteristic of the direct-coupled evanescent-mode waveguide filter is kept by the cross-coupled filter with the nonresonating node structure. All these merits are demonstrated numerically, as well as experimentally, with good agreement obtained between the measured and simulated S-parameters.


title: Linear Programming SVM-ARMA $_{\rm 2K}$ With Application in Engine System Identification

abstract: As an emerging non-parametric modeling technique, the methodology of support vector regression blazed a new trail in identifying complex nonlinear systems with superior generalization capability and sparsity. Nevertheless, the conventional quadratic programming support vector regression can easily lead to representation redundancy and expensive computational cost. In this paper, by using the l1 norm minimization and taking account of the different characteristics of autoregression (AR) and the moving average (MA), an innovative nonlinear dynamical system identification approach, linear programming SVM-ARMA2K, is developed to enhance flexibility and secure model sparsity in identifying nonlinear dynamical systems. To demonstrate the potential and practicality of the proposed approach, the proposed strategy is applied to identify a representative dynamical engine model.


title: 5G service requirements and operational use cases: Analysis and METIS II vision

abstract: One of the objectives of METIS-II project is to facilitate discussion on scenarios, use cases, KPIs and requirements for 5G, building upon the comprehensive work conducted in the METIS-I project and taking the work of other European projects as well as other bodies such as ITU-R, NGMN, etc. into account. This paper analyses the landscape of 5G use cases and presents METIS-II 5G use cases that cover the main 5G services, have stringent requirements and whose technical solutions are expected to serve other similar use cases as well. It also links these use cases to the business cases defined by 5G PPP so that requirements of vertical industries can be taken into account when designing the 5G Radio Access Network (RAN).


title: Wind Turbine Active Power Control Based on Multi-Model Adaptive Control

abstract: This paper proposes a multiple model adaptive control method of wind turbine active power that considers the complexity of the control model, nonlinear and strong coupling. The model is designed to reduce the negative influences of wind turbines in the process of active power control caused by different uncertain factors. We first build the multiple model of turbine operating by using subtracting cluster algorithm， based on data of a 1.5MW doubly-fed inductor generators (DFIGs) in a wind farm in Gansu, China. We use recursive least squares (RLS) algorithm to identify local model parameters. In addition， the controller is designed by adopting online optimal control model which based on a weighted index of output matching switching strategy. The controller is to realize multi-model adaptive control (MMAC). Results show that the proposed method has good control performance. The method can effectively solve the problems of wind turbines nonlinear modeling and active power control in operation.


title: Ternary Residual Networks

abstract: Sub-8-bit representation of DNNs incur some noticeable loss of accuracy despite rigorous (re)training at low-precision. Such loss of accuracy essentially makes them equivalent to a much shallower counterpart, diminishing the power of being deep networks. To address this problem of accuracy drop we introduce the notion of \textit{residual networks} where we add more low-precision edges to sensitive branches of the sub-8-bit network to compensate for the lost accuracy. Further, we present a perturbation theory to identify such sensitive edges. Aided by such an elegant trade-off between accuracy and model size, the 8-2 architecture (8-bit activations, ternary weights), enhanced by residual ternary edges, turns out to be sophisticated enough to achieve similar accuracy as 8-8 representation ($\sim 1\%$ drop from our FP-32 baseline), despite $\sim 1.6\times$ reduction in model size, $\sim 26\times$ reduction in number of multiplications , and potentially $\sim 2\times$ inference speed up comparing to 8-8 representation, on the state-of-the-art deep network ResNet-101 pre-trained on ImageNet dataset. Moreover, depending on the varying accuracy requirements in a dynamic environment, the deployed low-precision model can be upgraded/downgraded on-the-fly by partially enabling/disabling residual connections. For example, disabling the least important residual connections in the above enhanced network, the accuracy drop is $\sim 2\%$ (from our FP-32 baseline), despite $\sim 1.9\times$ reduction in model size, $\sim 32\times$ reduction in number of multiplications, and potentially $\sim 2.3\times$ inference speed up comparing to 8-8 representation. Finally, all the ternary connections are sparse in nature, and the residual ternary conversion can be done in a resource-constraint setting without any low-precision (re)training and without accessing the data.


title: A survey of the state-of-the-art in patch-based synthesis

abstract: This paper surveys the state-of-the-art of research in patch-based synthesis. Patch-based methods synthesize output images by copying small regions from exemplar imagery. This line of research originated from an area called “texture synthesis”, which focused on creating regular or semi-regular textures from small exemplars. However, more recently, much research has focused on synthesis of larger and more diverse imagery, such as photos, photo collections, videos, and light fields. Additionally, recent research has focused on customizing the synthesis process for particular problem domains, such as synthesizing artistic or decorative brushes, synthesis of rich materials, and synthesis for 3D fabrication. This report investigates recent papers that follow these themes, with a particular emphasis on papers published since 2009, when the last survey in this area was published. This survey can serve as a tutorial for readers who are not yet familiar with these topics, as well as provide comparisons between these papers, and highlight some open problems in this area.


title: AQBox: An air quality measuring box from COTS gas sensors

abstract: In this demo, we demonstrate a functional prototype of an air quality monitoring box (AQBox) built from cheap/commodity off- the-shelf (COTS) sensors. We use a set of MQ gas sensors, a temperature and humidity sensor, a dust sensor and a GPS. We instrument the box, powered by an on-board battery, with a 3G cellular connection to upload sensed data to the cloud. The box is suitable for deploying in developing countries where other means to monitor air quality, such as large expensive environmental sensors affixed to certain locations (such as at weather stations) and use of satellite, is not available or not viable. We shall demonstrate the construction and function of the box as well as the collection and analysis of captured data (both in real-time and offline). Built and deployed in large numbers, we believe, these boxes can be a cheap solution to perpetual air quality monitoring for modern cities.


title: Measuring student learning with item response theory

abstract: We investigate short-term learning from hints and feedback in a Web-based physics tutoring system. Both the skill of students and the difficulty and discrimination of items were determined by applying item response theory IRT to the first answers of students who are working on for-credit homework items in an introductory Newtonian physics course. We show that after tutoring a shifted logistic item response function with lower discrimination fits the students’ second responses to an item previously answered incorrectly. Student skill decreased by 1.0 standard deviation when students used no tutoring between their incorrect first and second attempts, which we attribute to “item-wrong bias.” On average, using hints or feedback increased students’ skill by 0.8 standard deviation. A skill increase of 1.9 standard deviation was observed when hints were requested after viewing, but prior to attempting to answer, a particular item. The skill changes measured in this way will enable the use of IRT to assess students based on their second attempt in a tutoring environment.


title: Simulation model of beacon enabled 802.15.4 networks with OPNET modeler

abstract: This IEEE 802.15.4 comes upon the LR-WPAN family which has low cost low power and the devices are battery driven with several applications. Here we analyze the MAC layer performance of such networks which use slotted CSMA/CA channel access mechanism. Our analysis is based on a OPNET simulation model, designed under star topology with a PAN coordinator and n no of transmitting nodes. Simulation has been done for different MAC parameter settings and for different number of n. Impact of hidden node is also considered. We also analyze the probability of receiving packet with the variation of different parameters.


title: Fluorescent porous carbon nanocapsules for two-photon imaging, NIR/pH dual-responsive drug carrier, and photothermal therapy

abstract: Abstract An efficient nanomedical platform that can combine two-photon cell imaging, near infrared (NIR) light and pH dual responsive drug delivery, and photothermal treatment was successfully developed based on fluorescent porous carbon-nanocapsules (FPC-NCs, size ∼100 nm) with carbon dots (CDs) embedded in the shell. The stable, excitation wavelength (λ ex )-tunable and upconverted fluorescence from the CDs embedded in the porous carbon shell enable the FPC-NCs to serve as an excellent confocal and two-photon imaging contrast agent under the excitation of laser with a broad range of wavelength from ultraviolet (UV) light (405 nm) to NIR light (900 nm). The FPC-NCs demonstrate a very high loading capacity (1335 mg g −1 ) toward doxorubicin drug benefited from the hollow cavity structure, porous carbon shell, as well as the supramolecular π stacking and electrostatic interactions between the doxorubicin molecules and carbon shell. In addition, a responsive release of doxorubicin from the FPC-NCs can be activated by lowering the pH to acidic (from 7.4 to 5.0) due to the presence of pH-sensitive carboxyl groups on the FPC-NCs and amino groups on doxorubicin molecules. Furthermore, the FPC-NCs can absorb and effectively convert the NIR light to heat, thus, manifest the ability of NIR-responsive drug release and combined photothermal/chemo-therapy for high therapeutic efficacy.


title: Cartesian impedance control of redundant manipulators for human-robot co-manipulation

abstract: This paper addresses the problem of controlling a robot arm executing a cooperative task with a human who guides the robot through direct physical interaction. This problem is tackled by allowing the end effector to comply according to an impedance control law defined in the Cartesian space. While, in principle, the robot’s dynamics can be fully compensated and any impedance behaviour can be imposed by the control, the stability of the coupled human-robot system is not guaranteed for any value of the impedance parameters. Moreover, if the robot is kinematically or functionally redundant, the redundant degrees of freedom play an important role. The idea proposed here is to use redundancy to ensure a decoupled apparent inertia at the end effector. Through an extensive experimental study on a 7-DOF KUKA LWR4 arm, we show that inertial decoupling enables a more flexible choice of the impedance parameters and improves the performance during manual guidance.


title: A Simple Way to Initialize Recurrent Networks of Rectified Linear Units

abstract: Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated optimization techniques and network architectures. In this paper, we propose a simpler solution that use recurrent neural networks composed of rectified linear units. Key to our solution is the use of the identity matrix or its scaled version to initialize the recurrent weight matrix. We find that our solution is comparable to LSTM on our four benchmarks: two toy problems involving long-range temporal structures, a large language modeling problem and a benchmark speech recognition problem.


title: An introduction to recursive partitioning: Rationale, application, and characteristics of classification and regression trees, bagging, and random forests.

abstract: Recursive partitioning methods have become popular and widely used tools for nonparametric regression and classification in many scientific fields. Especially random forests, which can deal with large numbers of predictor variables even in the presence of complex interactions, have been applied successfully in genetics, clinical medicine, and bioinformatics within the past few years. High-dimensional problems are common not only in genetics, but also in some areas of psychological research, where only a few subjects can be measured because of time or cost constraints, yet a large amount of data is generated for each subject. Random forests have been shown to achieve a high prediction accuracy in such applications and to provide descriptive variable importance measures reflecting the impact of each variable in both main effects and interactions. The aim of this work is to introduce the principles of the standard recursive partitioning methods as well as recent methodological improvements, to illustrate their usage for low and high-dimensional data exploration, but also to point out limitations of the methods and potential pitfalls in their practical application. Application of the methods is illustrated with freely available implementations in the R system for statistical computing.


title: Coherent intrinsic images from photo collections

abstract: An intrinsic image is a decomposition of a photo into an illumination layer and a reflectance layer, which enables powerful editing such as the alteration of an object's material independently of its illumination. However, decomposing a single photo is highly under-constrained and existing methods require user assistance or handle only simple scenes. In this paper, we compute intrinsic decompositions using several images of the same scene under different viewpoints and lighting conditions. We use multi-view stereo to automatically reconstruct 3D points and normals from which we derive relationships between reflectance values at different locations, across multiple views and consequently different lighting conditions. We use robust estimation to reliably identify reflectance ratios between pairs of points. From these, we infer constraints for our optimization and enforce a coherent solution across multiple views and illuminations. Our results demonstrate that this constrained optimization yields high-quality and coherent intrinsic decompositions of complex scenes. We illustrate how these decompositions can be used for image-based illumination transfer and transitions between views with consistent lighting.


title: A 2.5-Gb/s half-rate clock and data recovery circuit with a digital quadricorrelator frequency detector

abstract: A 2.5-Gb/s clock and data recovery (CDR) circuit, which incorporates dual loop architecture with half-rate linear phase detector and digital quadricorrelator frequency detector (DQFD) was present in this paper. The circuit is implemented under 0.13 mum CMOS process with the core chip area of 350 mum*110 mum. The HSPICE simulation results show that the center frequency of the voltage-controlled oscillator (VCO) is 1.25 GHz, the lock time is less than 5 mus, the operation range is from 2.20 Gbps to 2.83 Gbps, and the power consumption is about 15.2 mW at supply voltage of 1.2 V.


title: LEGO ® Therapy and the Social Use of Language Programme: An Evaluation of Two Social Skills Interventions for Children with High Functioning Autism and Asperger Syndrome

abstract: LEGO® therapy and the Social Use of Language Programme (SULP) were evaluated as social skills interventions for 6–11 year olds with high functioning autism and Asperger Syndrome. Children were matched on CA, IQ, and autistic symptoms before being randomly assigned to LEGO or SULP. Therapy occurred for 1 h/week over 18 weeks. A no-intervention control group was also assessed. Results showed that the LEGO therapy group improved more than the other groups on autism-specific social interaction scores (Gilliam Autism Rating Scale). Maladaptive behaviour decreased significantly more in the LEGO and SULP groups compared to the control group. There was a non-significant trend for SULP and LEGO groups to improve more than the no-intervention group in communication and socialisation skills.


title: Locus of control and cell phone use: Implications for sleep quality, academic performance, and subjective well-being

abstract: A path model examined how locus of control (LoC) is related to cell phone use and behavioral outcomes.Behavioral outcomes include sleep quality, academic performance and subjective well-being.LoC is not related to total daily cell phone use, but to use at inopportune times and circumstances.Greater external LoC: less control of use at bedtime, in class, and while studying.More inopportune use: lower in sleep quality, academic performance and subjective well-being. This study centers on the following hypothesis: that individuals with an external locus of control, in comparison to individuals with an internal locus of control, have less control over their cell phone use (i.e., more likely to use at bedtime; more likely to use in class and while studying) and are consequently more vulnerable to the negative outcomes associated with excessive cell phone use (i.e., poor sleep quality, reduced academic performance, and reduced subjective well-being). Methods: Undergraduate college students (N=516) participated in the study by completing validated surveys assessing their cell phone use, locus of control, sleep quality, academic performance, and subjective well-being. A path model was used to examine how locus of control relates to students' cell phone use and the key outcome variables. Results: The model exhibited reasonable model fit with all paths being statistically significant and in the hypothesized direction. Conclusion: By enabling an individual to better control cell phone use at inopportune times, a greater internal locus of control may mitigate some of the negative outcomes associated with high frequency cell phone use; conversely, an individual with a greater external locus of control may have difficulty controlling use at inopportune times and the negative effects associated with high frequency use may be exacerbated.


title: Maximum Entropy Models For Named Entity Recognition

abstract: In this paper, we describe a system that applies maximum entropy (ME) models to the task of named entity recognition (NER). Starting with an annotated corpus and a set of features which are easily obtainable for almost any language, we first build a baseline NE recognizer which is then used to extract the named entities and their context information from additional non-annotated data. In turn, these lists are incorporated into the final recognizer to further improve the recognition accuracy.


Above the Din : Identification of Human Voice in Noisy Signals


title: Cyber Bullying Prevention: Intervention in Taiwan

abstract: Background ::: This study aimed to explore the effectiveness of the cyber bullying prevention WebQuest course implementation. ::: ::: ::: Methodology/Findings ::: The study adopted the quasi-experimental design with two classes made up of a total of 61 junior high school students of seventh grade. The study subjects comprised of 30 students from the experimental group and 31 students from the control group. The experimental group received eight sessions (total 360 minutes) of the teaching intervention for four consecutive weeks, while the control group did not engage in any related courses. The self-compiled questionnaire for the student’s knowledge, attitudes, and intentions toward cyber bullying prevention was adopted. Data were analysed through generalized estimating equations to understand the immediate results on the student’s knowledge, attitudes, and intentions after the intervention. The results show that the WebQuest course immediately and effectively enhanced the knowledge of cyber bullying, reduced the intentions, and retained the effects after the learning. But it produced no significant impact on the attitude toward cyber bullying. ::: ::: ::: Conclusions/Significance ::: The intervention through this pilot study was effective and positive for cyber bulling prevention. It was with small number of students. Therefore, studies with large number of students and long experimental times, in different areas and countries are warranted.


title: Differentiating User Authentication Graphs

abstract: Authentication using centralized methods is a primary trust mechanism within most large-scale, enterprise computer networks. This paper proposes using graphs to represent user authentication activity within the network. Using this mechanism over a real enterprise network dataset, we find that non-privileged users and users with system administration privileges have distinguishable graph attributes in terms of size and complexity. In addition, we find that user authentication graphs provide intuitive insights into network user behavior. We believe that understanding these differences in even greater detail will lead to improved user behavior profiling and the elusive detection of authentication credential misuse.


title: The HiBench benchmark suite: Characterization of the MapReduce-based data analysis

abstract: The MapReduce model is becoming prominent for the large-scale data analysis in the cloud. In this paper, we present the benchmarking, evaluation and characterization of Hadoop, an open-source implementation of MapReduce. We first introduce HiBench, a new benchmark suite for Hadoop. It consists of a set of Hadoop programs, including both synthetic micro-benchmarks and real-world Hadoop applications. We then evaluate and characterize the Hadoop framework using HiBench, in terms of speed (i.e., job running time), throughput (i.e., the number of tasks completed per minute), HDFS bandwidth, system resource (e.g., CPU, memory and I/O) utilizations, and data access patterns.


title: Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf

abstract: Athletes in any sports can greatly benefit from feedback systems for improving the quality of their training. In this paper, we present a golf swing training system which incorporates wearable motion sensors to obtain inertial information and provide feedback on the quality of movements. The sensors are placed on a golf club and athlete's body at positions which capture the unique movements of a golf swing. We introduce a quantitative model which takes into consideration signal processing techniques on the collected data and quantifies the correctness of the performed actions. We evaluate the effectiveness of our framework on data obtained from four subjects and discuss ongoing research.


title: A proactive approach based on online reliability prediction for adaptation of service-oriented systems

abstract: Abstract Service computing is an emerging technology in System of Systems Engineering (SoS Engineering or SoSE), which regards System as a Service (i.e. SaaS), and aims to construct a robust and value-added complex system by outsourcing external component systems through service composition technology. A service-oriented SoS runs under a dynamic and uncertain environment. To successfully deploy SoS’s run-time quality assurance, online reliability time series prediction, which aims to predict the reliability in near future for a service-oriented SoS,arises as a grand challenge in SoS research. In this paper, we tackle the prediction challenge by exploiting two novel prediction models. We adopt motifs-based Dynamic Bayesian Networks (or m_DBNs) model to perform one-step-ahead time series prediction, and propose a multi-steps trajectories DBNs (or multi_DBNs) model to further revise the future reliability prediction. Finally, a proactive adaption strategy is achieved based on the reliability prediction results. Extensive experiments conducted on real-world Web services demonstrate that our models outperform other well-known approaches consistently.


Visual Object Tracking for Unmanned Aerial Vehicles: a Benchmark and New Motion Models


Static Hand Gesture Recognition with 2 Kinect Sensors


title: Evaluating text visualization: An experiment in authorship analysis

abstract: Analyzing authorship of online texts is an important analysis task in security-related areas such as cybercrime investigation and counter-terrorism, and in any field of endeavor in which authorship may be uncertain or obfuscated. This paper presents an automated approach for authorship analysis using machine learning methods, a robust stylometric feature set, and a series of visualizations designed to facilitate analysis at the feature, author, and message levels. A testbed consisting of 506,554 forum messages, in English and Arabic, from 14,901 authors was first constructed. A prototype portal system was then developed to support feasibility analysis of the approach. A preliminary evaluation to assess the efficacy of the text visualizations was conducted. The evaluation showed that task performance with the visualization functions was more accurate and more efficient than task performance without the visualizations.


title: A fuzzy linguistic algorithm for adaptive test in Intelligent Tutoring System based on competences

abstract: The Computerized Adaptive Tests (CAT) are common tools for the diagnosis process in Intelligent Tutor System based on Competency education (ITS-C). The item selection process to form a CAT plays a key role because it must ensure the selection of the item that best contributes to student assessment at any time. The item selection mechanisms proposed in the literature present some limitations that decrease the efficiency of CAT and its adaptation to the student profile. This paper introduces a new item selection algorithm, based on a multi-criteria decision model that integrates experts' knowledge modeled by fuzzy linguistic information that overcomes previous limitations and enhances the accuracy of diagnosis and the adaptation of CAT to student's competence level. Finally, such an algorithm is deployed in a mobile tool for an ITS-C.


title: Software Engineering Challenges in Game Development

abstract: In Software Engineering (SE), video game development is unique yet similar to other software endeavors. It is unique in that it combines the work of teams covering multiple disciplines (art, music, acting, programming, etc.), and that engaging game play is sought after through the use of prototypes and iterations. With that, game development is faced with challenges that can be addressed using traditional SE practices. The industry needs to adopt sound SE practices for their distinct needs such as managing multimedia assets and finding the “fun” in game play. The industry must take on the challenges by evolving SE methods to meet their needs. This work investigates these challenges and highlights engineering practices to mitigate these challenges.


title: A complete and efficient CUDA-sharing solution for HPC clusters

abstract: We detail a complete and up-to-date GPGPU remote virtualization solution.We describe efficient mechanisms for remote GPU data transfers.We detail an optimized InfiniBand implementation of rCUDA communications.We provide an extensive experimental evaluation of remote GPU acceleration. In this paper we detail the key features, architectural design, and implementation of rCUDA, an advanced framework to enable remote and transparent GPGPU acceleration in HPC clusters. rCUDA allows decoupling GPUs from nodes, forming pools of shared accelerators, which brings enhanced flexibility to cluster configurations. This opens the door to configurations with fewer accelerators than nodes, as well as permits a single node to exploit the whole set of GPUs installed in the cluster. In our proposal, CUDA applications can seamlessly interact with any GPU in the cluster, independently of its physical location. Thus, GPUs can be either distributed among compute nodes or concentrated in dedicated GPGPU servers, depending on the cluster administrator's policy. This proposal leads to savings not only in space but also in energy, acquisition, and maintenance costs. The performance evaluation in this paper with a series of benchmarks and a production application clearly demonstrates the viability of this proposal. Concretely, experiments with the matrix-matrix product reveal excellent performance compared with regular executions on the local GPU; on a much more complex application, the GPU-accelerated LAMMPS, we attain up to 11x speedup employing 8 remote accelerators from a single node with respect to a 12-core CPU-only execution. GPGPU service interaction in compute nodes, remote acceleration in dedicated GPGPU servers, and data transfer performance of similar GPU virtualization frameworks are also evaluated.


title: High-voltage high-frequency Marx-bank type pulse generator using integrated power semiconductor half-bridges

abstract: This paper discusses the operation of an all silicon-based solution for the conventional Marx generator circuit, which has been developed for high-frequency (kHz), high-voltage (kV) applications needing rectangular pulses. The conventional Marx generator, for high-voltage pulsed applications, uses passive power components (inductors or resistors), to supply the energy storage capacitors. This solution has the disadvantages of cost, size, power losses and limited frequency operation. In the proposed circuit, the bulky passive power elements are replaced by power semiconductor switches, increasing the performance of the classical circuit, strongly reducing costs, losses and increasing the pulse repetition frequency. Also, the proposed topology enables the use of typical half-bridge semiconductor structures, and ensures that the maximum voltage blocked by the semiconductors equals the power supply voltage (i.e. the voltage of each capacitor), even with mismatches in the synchronized switching, and in fault conditions. A laboratory prototype with five stages, 5 kW peak power, of the proposed silicon-based Marx generator circuit, was constructed using 1200 V IGBTs and diodes, operating with 1000 V d-c input voltage and 10 kHz frequency, giving 5 kV/1 A pulses, with 10 mus width and 50 ns rise time


title: A 45nm 1.3GHz 16.7 double-precision GFLOPS/W RISC-V processor with vector accelerators

abstract: A 64-bit dual-core RISC-V processor with vector accelerators has been fabricated in a 45 nm SOI process. This is the first dual-core processor to implement the open-source RISC-V ISA designed at the University of California, Berkeley. In a standard 40 nm process, the RISC-V scalar core scores 10% higher in DMIPS/MHz than the Cortex-A5, ARM’s comparable single-issue in-order scalar core, and is 49% more area-efficient. To demonstrate the extensibility of the RISC-V ISA, we integrate a custom vector accelerator alongside each single-issue in-order scalar core. The vector accelerator is 1.8× more energy-efficient than the IBM Blue Gene/Q processor, and 2.6× more than the IBM Cell processor, both fabricated in the same process. The dual-core RISC-V processor achieves maximum clock frequency of 1.3 GHz at 1.2 V and peak energy efficiency of 16.7 doubleprecision GFLOPS/W at 0.65 V with an area of 3 mm.


title: MATLAB-based toolkit for an introductory course in SAR image processing

abstract: This paper documents the continuation of the work that started with a student paper presented at Radar04. The earlier paper was a description of a graphic user interface (GUI) for SAR data processing that was developed for classroom use, based on a previously published algorithm. This GUI and associated MATLAB toolbox has now been extended to include several other capabilities and has grown into a toolkit that can be used in an introductory course in SAR image processing. Besides the processing of raw SAR data, the GUI and toolbox now include additional teaching and research capabilities, to include additional types of speckle filters and image compression capability. Apart from this the user can choose from two different algorithms that help to extract the single look image from the raw SAR data. These algorithms were implemented in MATLAB, because of its wide availability, and "self documenting" capability. Thus, the resulting package is suitable for classroom use to illustrate the principles of SAR data processing. Results of the project is made available to the radar community for use by the other schools and institutions that desire a simple SAR processing capability. The MATLAB code has been extensively commented, so that it provides useful baseline for other class projects. Although the material presented is not cutting edge research, it does provide a much-needed tutorial introduction to SAR processing in the classroom. We have also begun developing C and Python based versions of this package so that users can make comparative performance measurements and understand how various implementations of the algorithms affect the end results. We would like to acknowledge the help received from Alaska Satellite Facility (ASF) and the Space Dynamics Laboratory (SDL) at Utah State University for this project.


title: Signal Detection Theory (SDT) 1 Overview

abstract: Signal Detection Theory (sdt) is used to analyze data coming from experiments where the task is to categorize ambiguous stimuli which can be generated either by a known process (called the signal) or be obtained by chance (called the noise in the sdt framework). In particular sdt is used to analyze experiments where a binary answer (e.g., “Yes” vs “No”) needs to be provided. For example, if we need to decide if an education program is effective or not, we can use sdt.


title: Desynchronization of diurnal rhythms in bipolar disorder and borderline personality disorder

abstract: It has long been proposed that diurnal rhythms are disturbed in bipolar disorder (BD). Such changes are obvious in episodes of mania or depression. However, detailed study of patients between episodes has been rare and comparison with other psychiatric disorders rarer still. Our hypothesis was that evidence for desynchronization of diurnal rhythms would be evident in BD and that we could test the specificity of any effect by studying borderline personality disorder (BPD). Individuals with BD (n = 36), BPD (n = 22) and healthy volunteers (HC, n = 25) wore a portable heart rate and actigraphy device and used a smart-phone to record self-assessed mood scores 10 times per day for 1 week. Average diurnal patterns of heart rate (HR), activity and sleep were compared within and across groups. Desynchronization in the phase of diurnal rhythms of HR compared with activity were found in BPD (+3 h) and BD (+1 h), but not in HC. A clear diurnal pattern for positive mood was found in all subject groups. The coherence between negative and irritable mood and HR showed a four-cycle per day component in BD and BPD, which was not present in HC. The findings highlight marked de-synchronisation of measured diurnal function in both BD but particularly BPD and suggest an increased association with negative and irritable mood at ultradian frequencies. These findings enhance our understanding of the underlying physiological changes associated with BPD and BD, and suggest objective markers for monitoring and potential treatment targets. Improved mood stabilisation is a translational objective for management of both patient groups.


title: Social street view: blending immersive street views with geo-tagged social media

abstract: This paper presents an immersive geo-spatial social media system for virtual and augmented reality environments. With the rapid growth of photo-sharing social media sites such as Flickr, Pinterest, and Instagram, geo-tagged photographs are now ubiquitous. However, the current systems for their navigation are unsatisfyingly one- or two-dimensional. In this paper, we present our prototype system, Social Street View, which renders the geo-tagged social media in its natural geo-spatial context provided by immersive maps, such as Google Street View. This paper presents new algorithms for fusing and laying out the social media in an aesthetically pleasing manner with geospatial renderings, validates them with respect to visual saliency metrics, suggests spatio-temporal filters, and presents a system architecture that is able to stream geo-tagged social media and render it across a range of display platforms spanning tablets, desktops, head-mounted displays, and large-area room-sized curved tiled displays. The paper concludes by exploring several potential use cases including immersive social storytelling, learning about culture and crowd-sourced tourism.


title: PointNetVLAD: Deep Point Cloud Based Retrieval for Large-Scale Place Recognition

abstract: Unlike its image based counterpart, point cloud based retrieval for place recognition has remained as an unexplored and unsolved problem. This is largely due to the difficulty in extracting local feature descriptors from a point cloud that can subsequently be encoded into a global descriptor for the retrieval task. In this paper, we propose the PointNetVLAD where we leverage on the recent success of deep networks to solve point cloud based retrieval for place recognition. Specifically, our PointNetVLAD is a combination/modification of the existing PointNet and NetVLAD, which allows end-to-end training and inference to extract the global descriptor from a given 3D point cloud. Furthermore, we propose the "lazy triplet and quadruplet" loss functions that can achieve more discriminative and generalizable global descriptors to tackle the retrieval task. We create benchmark datasets for point cloud based retrieval for place recognition, and the experimental results on these datasets show the feasibility of our PointNetVLAD. Our code and the link for the benchmark dataset downloads are available in our project website. this http URL


title: Learned graphical models for probabilistic planning provide a new class of movement primitives

abstract: BIOLOGICAL MOVEMENT GENERATION COMBINES THREE INTERESTING ASPECTS: its modular organization in movement primitives (MPs), its characteristics of stochastic optimality under perturbations, and its efficiency in terms of learning. A common approach to motor skill learning is to endow the primitives with dynamical systems. Here, the parameters of the primitive indirectly define the shape of a reference trajectory. We propose an alternative MP representation based on probabilistic inference in learned graphical models with new and interesting properties that complies with salient features of biological movement control. Instead of endowing the primitives with dynamical systems, we propose to endow MPs with an intrinsic probabilistic planning system, integrating the power of stochastic optimal control (SOC) methods within a MP. The parameterization of the primitive is a graphical model that represents the dynamics and intrinsic cost function such that inference in this graphical model yields the control policy. We parameterize the intrinsic cost function using task-relevant features, such as the importance of passing through certain via-points. The system dynamics as well as intrinsic cost function parameters are learned in a reinforcement learning (RL) setting. We evaluate our approach on a complex 4-link balancing task. Our experiments show that our movement representation facilitates learning significantly and leads to better generalization to new task settings without re-learning.


title: An eye fixation database for saliency detection in images

abstract: To learn the preferential visual attention given by humans to specific image content, we present NUSEF- an eye fixation database compiled from a pool of 758 images and 75 subjects. Eye fixations are an excellent modality to learn semantics-driven human understanding of images, which is vastly different from feature-driven approaches employed by saliency computation algorithms. The database comprises fixation patterns acquired using an eye-tracker, as subjects free-viewed images corresponding to many semantic categories such as faces (human and mammal), nudes and actions (look, read and shoot). The consistent presence of fixation clusters around specific image regions confirms that visual attention is not subjective, but is directed towards salient objects and object-interactions. ::: ::: We then show how the fixation clusters can be exploited for enhancing image understanding, by using our eye fixation database in an active image segmentation application. Apart from proposing a mechanism to automatically determine characteristic fixation seeds for segmentation, we show that the use of fixation seeds generated from multiple fixation clusters on the salient object can lead to a 10% improvement in segmentation performance over the state-of-the-art.


title: Detecting Non-compositional MWE Components using Wiktionary

abstract: We propose a simple unsupervised approach to detecting non-compositional components in multiword expressions based on Wiktionary. The approach makes use of the definitions, synonyms and translations in Wiktionary, and is applicable to any type of MWE in any language, assuming the MWE is contained in Wiktionary. Our experiments show that the proposed approach achieves higher F-score than state-of-the-art methods.


title: Design and Implementation of Energy Management System With Fuzzy Control for DC Microgrid Systems

abstract: This paper presents the design and implementation of an energy management system (EMS) with fuzzy control for a dc microgrid system. Modeling, analysis, and control of distributed power sources and energy storage devices with MATLAB/Simulink are proposed, and the integrated monitoring EMS is implemented with LabVIEW. To improve the life cycle of the battery, fuzzy control manages the desired state of charge. The RS-485/ZigBee network has been designed to control the operating mode and to monitor the values of all subsystems in the dc microgrid system.


title: Diet-Induced Dysbiosis of the Intestinal Microbiota and the Effects on Immunity and Disease

abstract: The gastrointestinal (GI) microbiota is the collection of microbes which reside in the GI tract and represents the largest source of non-self antigens in the human body. The GI tract functions as a major immunological organ as it must maintain tolerance to commensal and dietary antigens while remaining responsive to pathogenic stimuli. If this balance is disrupted, inappropriate inflammatory processes can result, leading to host cell damage and/or autoimmunity. Evidence suggests that the composition of the intestinal microbiota can influence susceptibility to chronic disease of the intestinal tract including ulcerative colitis, Crohn’s disease, celiac disease and irritable bowel syndrome, as well as more systemic diseases such as obesity, type 1 diabetes and type 2 diabetes. Interestingly, a considerable shift in diet has coincided with increased incidence of many of these inflammatory diseases. It was originally believed that the composition of the intestinal microbiota was relatively stable from early childhood; however, recent evidence suggests that diet can cause dysbiosis, an alteration in the composition of the microbiota, which could lead to aberrant immune responses. The role of the microbiota and the potential for diet-induced dysbiosis in inflammatory conditions of the GI tract and systemic diseases will be discussed.


title: Fighting the risk of developing Parkinson's disease; clinical counseling for first degree relatives of patients with Parkinson's disease

abstract: Abstract Background Parkinson's disease (PD) has a long pre-diagnosis phase that may span as long as 10–20 years. The ability to identify at risk populations raises the possibility of early intervention to delay or prevent the onset of motor symptoms. In Israel, there is a large group of Ashkenazi Jews at risk of developing PD due to high frequency of PD associated mutations in 2 genes (GBA and LRRK2). Objective To describe our unique experience with a clinical counseling service for 1st degree relatives of PD patients from the Ashkenazi origin who carry the G2019S mutation in the LRRK2 gene. Method One hundred, self declared, healthy, first degree relatives of PD patients who carry the G2019S mutation in the LRRK2 gene were tested clinically and genetically in a cross sectional study. Those who have requested information on their risk to develop PD were invited to a free of charge, clinical counseling session to provide information on their risk of developing PD and potential risk modifiers that can be applied. Genetic status was not disclosed and the counselor was unaware of the subjects' G2019S mutation status. Results 46 subjects (mean age 48.2 ± 10.7; 46% males) came for clinical counseling provided by a Movement Disorders specialist. Siblings and off-springs of the same proband were seen together. Counseling provided general information about the pre-diagnosis phase of PD, the concept of population at risk and habitual and behavioral recommendations that may delay PD motor symptoms onset. Conclusion A high percentage of individuals at risk for developing PD requested clinical counseling on modifiers of the risk to develop PD. Counseling provided information as well as recommendations for behavioral modifications and drug treatment. Screening population at risk increases the awareness as well as early diagnosis of PD. Prospective information is needed in order to improve our knowledge base and assess the long term impact of such a counseling service.


title: Detecting concept change in dynamic data streams A sequential approach based on reservoir sampling

abstract: In this research we present a novel approach to the concept change detection problem. Change detection is a fundamental issue with data stream mining as classification models generated need to be updated when significant changes in the underlying data distribution occur. A number of change detection approaches have been proposed but they all suffer from limitations with respect to one or more key performance factors such as high computational complexity, poor sensitivity to gradual change, or the opposite problem of high false positive rate. Our approach uses reservoir sampling to build a sequential change detection model that offers statistically sound guarantees on false positive and false negative rates but has much smaller computational complexity than the ADWIN concept drift detector. Extensive experimentation on a wide variety of datasets reveals that the scheme also has a smaller false detection rate while maintaining a competitive true detection rate to ADWIN.


title: Amnesia, memory and brain systems

abstract: Bilateral damage to either the medial temporal lobe or the diencephalic midline causes an amnesic syndrome, i.e. a global impairment in the ability to acquire new memories regardless of sensory modality, and a loss of some memories, especially recent ones, from the period before amnesia began. The memory deficit can occur against a background of intact intellectual and perceptual functions. Two themes have been prominent in recent work. First, the amnesic syndrome is narrower than once believed in the sense that a number of learning and memory abilities are preserved (e.g. skill and habit learning, simple forms of conditioning and the phenomenon of priming). Second, the brain system damaged in amnesia has only a temporary role in memory. As time passes after learning, memory is reorganized and consolidated within neocortex, such that eventually medial temporal lobe and diencephalic structures are not needed for storage or retrieval.


title: Training a convolutional neural network for multi-class object detection using solely virtual world data

abstract: Convolutional neural networks are a popular choice for current object detection and classification systems. Their performance improves constantly but for effective training, large, hand-labeled datasets are required. We address the problem of obtaining customized, yet large enough datasets for CNN training by synthesizing them in a virtual world, thus eliminating the need for tedious human interaction for ground truth creation. We developed a CNN-based multi-class detection system that was trained solely on virtual world data and achieves competitive results compared to state-of-the-art detection systems.


title: Experiments toward an human-like Adaptive Cruise Control

abstract: In this work some experiments are made in order to assess the feasibility of a human-like ACC (adaptive cruise control) system. The proposed system is able to understand driverpsilas attitudes and driving-styles by means of a self-calibration process that can be (re)initialized on request. Three different speed-control logics have been tested: one tries to learn from actual driverspsila behaviors by using an artificial neural network (ANN) approach, the second is based on the calibration of a linear function aimed to be mimic of the driver response to stimuli, the third is based on the calibration of a polynomial function instead of a linear one. A microscopic traffic model, accurately calibrated and validated for different aims and in a previous work, has been adapted and used in order to generate a long car-following trajectory on which the speed control logics have been calibrated and compared. This has allowed for a sufficient amount of accurate laboratory data at a relatively low cost. Comparison of the tested speed-control logics show that a fully adaptive human-like ACC system is feasible and worth further more costly developments.


title: A memory soft error measurement on production systems

abstract: Memory state can be corrupted by the impact of particles causing single-event upsets (SEUs). Understanding and dealing with these soft (or transient) errors is important for system reliability. Several earlier studies have provided field test measurement results on memory soft error rate, but no results were available for recent production computer systems. We believe the measurement results on real production systems are uniquely valuable due to various environmental effects. This paper presents methodologies for memory soft error measurement on production systems where performance impact on existing running applications must be negligible and the system administrative control might or might not be available. ::: ::: We conducted measurements in three distinct system environments: a rack-mounted server farm for a popular Internet service (Ask.com search engine), a set of office desktop computers (Univ. of Rochester), and a geographically distributed network testbed (PlanetLab). Our preliminary measurement on over 300 machines for varying multi-month periods finds 2 suspected soft errors. In particular, our result on the Internet servers indicates that, with high probability, the soft error rate is at least two orders of magnitude lower than those reported previously. We provide discussions that attribute the low error rate to several factors in today's production system environments. As a contrast, our measurement unintentionally discovers permanent (or hard) memory faults on 9 out of 212 Ask.com machines, suggesting the relative commonness of hard memory faults.


title: Church: a language for generative models

abstract: Formal languages for probabilistic modeling enable re-use, modularity, and descriptive clarity, and can foster generic inference techniques. We introduce Church, a universal language for describing stochastic generative processes. Church is based on the Lisp model of lambda calculus, containing a pure Lisp as its deterministic subset. The semantics of Church is defined in terms of evaluation histories and conditional distributions on such histories. Church also includes a novel language construct, the stochastic memoizer, which enables simple description of many complex non-parametric models. We illustrate language features through several examples, including: a generalized Bayes net in which parameters cluster over trials, infinite PCFGs, planning by inference, and various non-parametric clustering models. Finally, we show how to implement query on any Church program, exactly and approximately, using Monte Carlo techniques.


title: Rail-to-Rail, Linear Hot-Electron Injection Programming of Floating-Gate Voltage Bias Generators at 13-Bit Resolution

abstract: Hot-electron injection is widely used for accurate programming of on-chip floating-gate voltage and current references. The conventional programming approach involves adapting the duration and magnitude of the injection pulses based on a predictive model which is estimated by using measured data. However, varying the pulse-widths or amplitudes introduces nonlinearity in the injection process which complicates the modeling, calibration and programming procedure. In this paper, we propose a linear hot-electron injection technique which significantly simplifies the programming procedure, and can achieve programming accuracy greater than 13-b which is limited by the thermal noise from the injection process. The procedure employs an active feedback circuit which ensures that all the nonlinear factors affecting the hot-electron injection process are held constant, thus achieving a stable and controllable injection rate. Measured results using an array of floating-gate voltage reference prototyped in a 0.5-μm standard CMOS process demonstrate that the injection rates can be controlled from 0.1 to 4.1 V for the programmable voltage range. Using 50-ms injection pulses, we show that the average injection rate can be adapted from 6.9 to 250 μV/cycle.


Adaptive Hamiltonian and Riemann Manifold Monte Carlo Samplers


title: Multi-driver agent-based traffic simulation systems for evaluating the effects of advanced driver assistance systems on road traffic accidents

abstract: A recent investigation revealed that there is a substantiated need for the development of a micro-simulation system designed for traffic safety assessment. This paper describes the development of a road traffic simulation system, which uses a ‘nanoscopic model’ of driver behaviour and an integrated analysis-evaluation system designed for traffic safety assessment. The primary focus is on estimating the effects of an advanced driver assistance system thereby reducing traffic accidents. The effectiveness and validity of the present system are demonstrated through comparison with measured traffic data. This paper also proposes algorithms embedded in a ‘driver-agent’, for recognising driver’s intentions regarding choosing steering-control modes, lateral control tasks, and the driving mood. This is because the driver assistance systems need to recognise the driver’s intention when choosing steering-control. The results of a simulation study, using the data drawn from actual driving, show that the systems would achieve a high recognition capability. As an example of how driving mood recognition applies to driver assistance systems, an advanced steering system and the adaptability to the driver’s mood, have also been presented.


title: All models are wrong : reflections on becoming a systems scientist †

abstract: Thoughtful leaders increasingly recognize that we are not only failing to solve the persistent problems we face, but are in fact causing them. System dynamics is designed to help avoid such policy resistance and identify high-leverage policies for sustained improvement. What does it take to be an effective systems thinker, and to teach system dynamics fruitfully? Understanding complex systems requires mastery of concepts such as feedback, stocks and flows, time delays, and nonlinearity. Research shows that these concepts are highly counterintuitive and poorly understood. It also shows how they can be taught and learned. Doing so requires the use of formal models and simulations to test our mental models and develop our intuition about complex systems. Yet, though essential, these concepts and tools are not sufficient. Becoming an effective systems thinker also requires the rigorous and disciplined use of scientific inquiry skills so that we can uncover our hidden assumptions and biases. It requires respect and empathy for others and other viewpoints. Most important, and most difficult to learn, systems thinking requires understanding that all models are wrong and humility about the limitations of our knowledge. Such humility is essential in creating an environment in which we can learn about the complex systems in which we are embedded and work effectively to create the world we truly desire. The paper is based on the talk the author delivered at the 2002 International System Dynamics Conference upon presentation of the Jay W. Forrester Award. Copyright  2002 John Wiley & Sons,


title: Unilateral Breast Edema: Spectrum of Etiologies and Imaging Appearances

abstract: Breast edema is defined as a mammographic pattern of skin thickening, increased parenchymal density, and interstitial marking. It can be caused by benign or malignant diseases, as a result of a tumor in the dermal lymphatics of the breast, lymphatic congestion caused by breast, lymphatic drainage obstruction, or by congestive heart failure. ::: ::: Here we describe several conditions, that cause unilateral breast edema with the aim of familiarizing radiologists with these disease entities.


title: A Novel Method for Data and Power Transmission Through Metallic Structures

abstract: This paper discusses the implementation of a system that utilizes inductive coupling and single-conductor techniques in order to enable power and data transmission through a metallic structure. The power and data transfer occur simultaneously over the same structure. The source of power is connected to a double-ended coil that is coupled with a single-ended resonator through a resonant inductive coupling mechanism. This second coil is attached to the single conductor responsible to transfer power to another device. At the receiver side of the system, a similar couple of coils is used to transmit energy to the receiver device. The system operates according to the Qi standard for wireless power transfer, working at the frequency of 300 kHz. These characteristics ensure the portability, reliability, and interoperability of the system. A model is developed for the proposed system. The system is implemented over several arbitrary metallic structures and different combinations of coils and resonators. The measured power efficiency as high as 75% is achieved. The adaptive matching technique is proposed to maintain the performance of the system over a variety of operating conditions.


title: Expert recommendation based on social drivers, social network analysis, and semantic data representation

abstract: Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration.