title: The Internet of Things: Opportunities and Challenges for Distributed Data Analysis

abstract: Nowadays, data is created by humans as well as automatically collected by physical things, which embed electronics, software, sensors and network connectivity. Together, these entities constitute the Internet of Things (IoT). The automated analysis of its data can provide insights into previously unknown relationships between things, their environment and their users, facilitating an optimization of their behavior. Especially the real-time analysis of data, embedded into physical systems, can enable new forms of autonomous control. These in turn may lead to more sustainable applications, reducing waste and saving resources IoT's distributed and dynamic nature, resource constraints of sensors and embedded devices as well as the amounts of generated data are challenging even the most advanced automated data analysis methods known today. In particular, the IoT requires a new generation of distributed analysis methods. Many existing surveys have strongly focused on the centralization of data in the cloud and big data analysis, which follows the paradigm of parallel high-performance computing. However, bandwidth and energy can be too limited for the transmission of raw data, or it is prohibited due to privacy constraints. Such communication-constrained scenarios require decentralized analysis algorithms which at least partly work directly on the generating devices. After listing data-driven IoT applications, in contrast to existing surveys, we highlight the differences between cloudbased and decentralized analysis from an algorithmic perspective. We present the opportunities and challenges of research on communication-efficient decentralized analysis algorithms. Here, the focus is on the difficult scenario of vertically partitioned data, which covers common IoT use cases. The comprehensive bibliography aims at providing readers with a good starting point for their own work


title: Analysis of User-generated Content for Improving YouTube Video Recommendation

abstract: Everyday video-sharing websites such as YouTube collect large amounts of new multimedia resources. Comments left by viewers often provide valuable information to describe sentiments, opinions and tastes of users. For this reason, we propose a novel re-ranking approach that takes into consideration that information in order to provide better recommendations of related videos. Early experiments indicate an improvement in the recommendation performance.


CamelParser: A system for Arabic Syntactic Analysis and Morphological Disambiguation


title: Essential oils and distilled straws of lavender and lavandin: a review of current use and potential application in white biotechnology

abstract: The Lavandula genus, which includes lavender (Lavandula angustifolia) and lavandin (L. angustifolia × Lavandula latifolia), is cultivated worldwide for its essential oils, which find applications in perfumes, cosmetics, food processing and, more recently, in aromatherapy products. The chemical composition of lavender and lavandin essential oils, usually produced by steam distillation from the flowering stems, is characterized by the presence of terpenes (e.g. linalool and linalyl acetate) and terpenoids (e.g. 1,8-cineole), which are mainly responsible for their characteristic flavour and their biological and therapeutic properties. Lavender and lavandin distilled straws, the by-products of oil extraction, were traditionally used for soil replenishment or converted to a fuel source. They are mineral- and carbon-rich plant residues and, therefore, a cheap, readily available source of valuable substances of industrial interest, especially aroma and antioxidants (e.g. terpenoids, lactones and phenolic compounds including coumarin, herniarin, α-bisabolol, rosmarinic and chlorogenic acids). Accordingly, recent studies have emphasized the possible uses of lavender and lavandin straws in fermentative or enzymatic processes involving various microorganisms, especially filamentous fungi, for the production of antimicrobials, antioxidants and other bioproducts with pharmaceutical and cosmetic activities, opening up new challenging perspectives in white biotechnology applications.


Autonomous Adaptive Modification of Unstructured Environments


title: Mechanisms and Materials of Flexible and Stretchable Skin Sensors

abstract: Wearable technology has attracted significant public attention and has generated huge societal and economic impact, leading to changes of both personal lifestyles and formats of healthcare. An important type of devices in wearable technology is flexible and stretchable skin sensors used primarily for biophysiological signal sensing and biomolecule analysis on skin. These sensors offer mechanical compatibility to human skin and maximum compliance to skin morphology and motion, demonstrating great potential as promising alternatives to current wearable electronic devices based on rigid substrates and packages. The mechanisms behind the design and applications of these sensors are numerous, involving profound knowledge about the physical and chemical properties of the sensors and the skin. The corresponding materials are diverse, featuring thin elastic films and unique stretchable structures based on traditional hard or ductile materials. In addition, the fabrication techniques that range from complementary metal-oxide semiconductor (CMOS) fabrication to innovative additive manufacturing have led to various sensor formats. This paper reviews mechanisms, materials, fabrication techniques, and representative applications of flexible and stretchable skin sensors, and provides perspective of future trends of the sensors in improving biomedical sensing, human machine interfacing, and quality of life.


title: Treatment of Rhinitis Sicca Anterior with Ectoine Containing Nasal Spray

abstract: Objectives. The safety and efficacy of ectoine nasal spray and ectoine nasal spray with dexpanthenol in the treatment of rhinitis sicca were evaluated in two studies. Design and Methods. Two noninterventional observational studies were performed to evaluate the efficacy and safety of a nasal spray containing ectoine (study 1) and ectoine/dexpanthenol (study 2) over a period of two weeks including comparable numbers of patients suffering from rhinitis sicca anterior. Patients and physicians were asked to rate the efficacy in reducing symptoms and the tolerability over the treatment phase. Results. The treatment in both studies resulted in a clinical and statistical significant reduction of the main diagnosis parameters, nasal airway obstruction, and crust formation. There was also a significant reduction in the secondary diagnosis parameters in both studies. Importantly, the tolerability was very good. During the whole observational study, neither patients nor doctors stopped the medication due to unwanted effects. Conclusion. Rhinitis sicca could be successfully treated with a nasal spray containing ectoine and a nasal spray combining ectoine with dexpanthenol. The combination of both substances led to slight advantages.


GATE: A Framework And Graphical Development Environment For Robust NLP Tools And Applications


title: GraphIn: An Online High Performance Incremental Graph Processing Framework

abstract: The massive explosion in social networks has led to a significant growth in graph analytics and specifically in dynamic, time-varying graphs. Most prior work processes dynamic graphs by first storing the updates and then repeatedly running static graph analytics on saved snapshots. To handle the extreme scale and fast evolution of real-world graphs, we propose a dynamic graph analytics framework, GraphIn, that incrementally processes graphs on-the-fly using fixed-sized batches of updates. As part of GraphIn, we propose a novel programming model called I-GAS based on gather-apply-scatter programming paradigm that allows for implementing a large set of incremental graph processing algorithms seamlessly across multiple CPU cores. We further propose a property-based, dual-path execution model to choose between incremental or static computation. Our experiments show that for a variety of graph inputs and algorithms, GraphIn achieves upi?źto 9.3 million updates/sec and over 400$$\times $$ speedup when compared to static graph recomputation.


title: Analyzing Evolutionary Optimization in Noisy Environments

abstract: Many optimization tasks must be handled in noisy environments, where the exact evaluation of a solution cannot be obtained, only a noisy one. For optimization of noisy tasks, evolutionary algorithms EAs, a type of stochastic metaheuristic search algorithm, have been widely and successfully applied. Previous work mainly focuses on the empirical study and design of EAs for optimization under noisy conditions, while the theoretical understandings are largely insufficient. In this study, we first investigate how noisy fitness can affect the running time of EAs. Two kinds of noise-helpful problems are identified, on which the EAs will run faster with the presence of noise, and thus the noise should not be handled. Second, on a representative noise-harmful problem in which the noise has a strong negative effect, we examine two commonly employed mechanisms dealing with noise in EAs: reevaluation and threshold selection. The analysis discloses that using these two strategies simultaneously is effective for the one-bit noise but ineffective for the asymmetric one-bit noise. Smooth threshold selection is then proposed, which can be proved to be an effective strategy to further improve the noise tolerance ability in the problem. We then complement the theoreticali? analysis by experiments on both synthetic problems as well as two combinatorial problems, the minimum spanning tree and the maximum matching. The experimental results agree with the theoretical findings and also show that the proposed smooth threshold selection can deal with the noise better.


title: Snake-Based Segmentation of Teeth from Virtual Dental Casts

abstract: During the past years several innovative and technological developments have been made in oral surgery. Today, digitized dental casts are common for simulation and planning of orthodontic interventions. In order to work with these models, knowing the exact position of the teeth is of high importance. In this paper, we present a new method for tooth segmentation with minimal user interaction. At the beginning, an initial estimate for the separating curve between the teeth and the gum is computed and optimized by use of an active contour. The second step calculates the dental arch and the interstices between the teeth. In order to detect each tooth surface exactly, we finally position a snake around the cusp of each tooth.


title: A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM

abstract: We introduce the Imperial College London and ::: National University of Ireland Maynooth (ICL-NUIM) dataset ::: for the evaluation of visual odometry, 3D reconstruction and ::: SLAM algorithms that typically use RGB-D data. We present ::: a collection of handheld RGB-D camera sequences within ::: synthetically generated environments. RGB-D sequences with ::: perfect ground truth poses are provided as well as a ground ::: truth surface model that enables a method of quantitatively ::: evaluating the final map or surface reconstruction accuracy. ::: Care has been taken to simulate typically observed real-world ::: artefacts in the synthetic imagery by modelling sensor noise in ::: both RGB and depth data. While this dataset is useful for the ::: evaluation of visual odometry and SLAM trajectory estimation, ::: our main focus is on providing a method to benchmark the ::: surface reconstruction accuracy which to date has been missing ::: in the RGB-D community despite the plethora of ground truth ::: RGB-D datasets available.


title: A Connection-Centric Survey of Recommender Systems Research

abstract: Recommender systems attempt to reduce information overload and retain customers by selecting a subset of items from a universal set based on user preferences. While research in recommender systems grew out of information retrieval and filtering, the topic has steadily advanced into a legitimate and challenging research area of its own. Recommender systems have traditionally been studied from a content-based filtering vs. collaborative design perspective. Recommendations, however, are not delivered within a vacuum, but rather cast within an informal community of users and social context. Therefore, ultimately all recommender systems make connections among people and thus should be surveyed from such a perspective. This viewpoint is under-emphasized in the recommender systems literature. We therefore take a connection-oriented viewpoint toward recommender systems research. We posit that recommendation has an inherently social element and is ultimately intended to connect people either directly as a result of explicit user modeling or indirectly through the discovery of relationships implicit in extant data. Thus, recommender systems are characterized by how they model users to bring people together: explicitly or implicitly. Finally, user modeling and the connection-centric viewpoint raise broadening and social issues--such as evaluation, targeting, and privacy and trust--which we also briefly address.


title: 21.6 A 1.2cm2 2.4GHz self-oscillating rectifier-antenna achieving −34.5dBm sensitivity for wirelessly powered sensors

abstract: Ubiquitous Internet-of-Everything (IoE) applications require low-cost, miniature sensors with long lifetimes. Wirelessly-powered ICs that harvest energy from an RF beacon or from existing wireless signals can address challenges associated with battery size, capacity and replacements [1–5]. Given FCC transmit power limits, the operating range of wirelessly-powered sensors is limited by rectifier sensitivity rather than by communication link budgets. In this work, we present area-constrained antenna-rectifier architectures that leverage electrically-small antennas and rectifier circuits to achieve −34.5dBm sensitivity for 1.6V output with 1.8MΩ load (CC topology) and −24dBm sensitivity for 2.5V output with 100KΩ load (CP topology), representing a >5× improvement in sensitivity (>2× improvement in range) over current state-of-the-art and >1V output voltage [1]. Notably, this improvement is obtained even for cold-start with the typical rectifier storage capacitor as the only off-chip component, resulting in a very compact 1.2cm2 complete 2.4GHz antenna-rectifier system area.


The Pseudo P Pulmonale


title: Microcontroller based PMDC motor control for driving 0.5KW scooter

abstract: In the recent years, the use of clean energy to reduce pollution and hence to save the environment is an urgent need to keep the communities against the dangers that began to show with huge negative consequences. This paper illustrates with full details how to built DC scooter which can be used with solar cells to charge the DC batteries used to drive the scooter safely, economically and with negligible environmental effects. The old variable speed drives, had various limitations, such as poor efficiencies, larger space, lower speeds, etc., however, the development of power electronic devices such as power MOSFETs, IGBTs beside Micro-Controllers, overturned the scene completely, today we can built a variable speed drive systems with better comparable characteristics such as smaller in size, higher efficiency, more reliable and meeting all demands requirements for modern control applications. The aim of this paper is to drive a scooter, by using a Permanent Magnet DC Motor (PMDC), by using PIC16F877 microcontroller to produce variable duty cycle pulse width modulation PWM through up and down button to increase or decrease the speed with less-losses and with very low price. Also we intend to control the mechanical speed, starting, braking, and 4-Quadrant directions of the used PMDC motor safely and economically to increase the half life of the battery used and to increase the working time compared by other type of traditional controlled scooters. Also we explain the starting of the scooter with and without starting resistors to decrease the losses more and more.


title: Learning Compact Binary Descriptors with Unsupervised Deep Neural Networks

abstract: In this paper, we propose a new unsupervised deep learning approach called DeepBit to learn compact binary descriptor for efficient visual object matching. Unlike most existing binary descriptors which were designed with random projections or linear hash functions, we develop a deep neural network to learn binary descriptors in an unsupervised manner. We enforce three criterions on binary codes which are learned at the top layer of our network: 1) minimal loss quantization, 2) evenly distributed codes and 3) uncorrelated bits. Then, we learn the parameters of the networks with a back-propagation technique. Experimental results on three different visual analysis tasks including image matching, image retrieval, and object recognition clearly demonstrate the effectiveness of the proposed approach.


title: STA: Spatial-Temporal Attention for Large-Scale Video-based Person Re-Identification

abstract: In this work, we propose a novel Spatial-Temporal Attention (STA) approach to tackle the large-scale person reidentification task in videos. Different from the most existing methods, which simply compute representations of video clips using frame-level aggregation (e.g. average pooling), the proposed STA adopts a more effective way for producing robust clip-level feature representation. Concretely, our STA fully exploits those discriminative parts of one target person in both spatial and temporal dimensions, which results in a 2-D attention score matrix via inter-frame regularization to measure the importances of spatial parts across different frames. Thus, a more robust clip-level feature representation can be generated according to a weighted sum operation guided by the mined 2-D attention score matrix. In this way, the challenging cases for video-based person re-identification such as pose variation and partial occlusion can be well tackled by the STA. We conduct extensive experiments on two large-scale benchmarks, i.e. MARS and DukeMTMCVideoReID. In particular, the mAP reaches 87.7% on MARS, which significantly outperforms the state-of-the-arts with a large margin of more than 11.6%.


title: Pediatric Pain Syndromes and Noninflammatory Musculoskeletal Pain

abstract: Chronic musculoskeletal pain (CMP) is one of the main reasons for referral to a pediatric rheumatologist and is the third most common cause of chronic pain in children and adolescents. Causes of CMP include amplified musculoskeletal pain, benign limb pain of childhood, hypermobility, overuse syndromes, and back pain. CMP can negatively affect physical, social, academic, and psychological function so it is essential that clinicians know how to diagnose and treat these conditions. This article provides an overview of the epidemiology and impact of CMP, the steps in a comprehensive pain assessment, and the management of the most common CMPs.


title: PRIVACY ON REDDIT? TOWARDS LARGE-SCALE USER CLASSIFICATION

abstract: Reddit is a social news website that aims to provide user privacy by encouraging them to use pseudonyms and refraining from any kind of personal data collection. However, users are often not aware of possibilities to indirectly gather a lot of information about them by analyzing their contributions and behaviour on this site. In order to investigate the feasibility of large-scale user classification with respect to the attributes social gender and citizenship this article provides and evaluates several data mining techniques. First, a large text corpus is collected from Reddit and annotations are derived using lexical rules. Then, a discriminative approach on classification using support vector machines is undertaken and extended by using topics generated by a latent Dirichlet allocation as features. Based on supervised latent Dirichlet allocation, a new generative model is drafted and implemented that captures Reddit’s specific structure of organizing information exchange. Finally, the presented techniques for user classification are evaluated and compared in terms of classification performance as well as time efficiency. Our results indicate that large-scale user classification on Reddit is feasible, which may raise privacy concerns among its community.


title: Sentiment Classification of Arabic Documents: Experiments with multi-type features and ensemble algorithms

abstract: Document sentiment classification is often processed by applying machine learning techniques, in particular supervised learning which consists basically of two major steps: feature extraction and training the learning model. In the literature, most existing researches rely on n-grams as selected features, and on a simple basic classifier as learning model. In the context of our work, we try to improve document classification findings in Arabic sentiment analysis by combining different types of features such as opinion and discourse features; and by proposing an ensemble-based classifier to investigate its contribution in Arabic sentiment classification. Obtained results attained 85.06% in terms of macro-averaged Fmeasure, and showed that discourse features have moderately improved Fmeasure by approximately 3% or 4%.


title: Analysis of digital bang-bang clock and data recovery for multi-gigabit/s serial transceivers

abstract: A Harmonic Balance method for analyzing digital bang-bang clock and data recovery (CDR) is proposed in this paper. The jitter tolerance performance of the CDR is predicted by a function with variables that can be easily correlated to design parameters. A 6.25Gb/s serial transceiver was fabricated in 90nm CMOS technology. Measurements show that the jitter tolerance performance can be accurately predicted by the proposed method.


title: Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs

abstract: Deep learning frameworks have been widely deployed on GPU servers for deep learning applications in both academia and industry. In the training of deep neural networks (DNNs), there are many standard processes or algorithms, such as convolution and stochastic gradient descent (SGD), but the running performance of different frameworks might be different even running the same deep model on the same GPU hardware. In this paper, we evaluate the running performance of four state-of-the-art distributed deep learning frameworks (i.e., Caffe-MPI, CNTK, MXNet and TensorFlow) over single-GPU, multi-GPU and multi-node environments. We first build performance models of standard processes in training DNNs with SGD, and then we benchmark the running performance of these frameworks with three popular convolutional neural networks (i.e., AlexNet, GoogleNet and ResNet-50), after that we analyze what factors that results in the performance gap among these four frameworks. Through both analytical and experimental analysis, we identify bottlenecks and overheads which could be further optimized. The main contribution is two-fold. First, the testing results provide a reference for end users to choose the proper framework for their own scenarios. Second, the proposed performance models and the detailed analysis provide further optimization directions in both algorithmic design and system configuration.


title: Reinforcement Learning And Its Application To Control

abstract: Learning control involves modifying a controller's behavior to improve its performance as measured by some predefined index of performance (IP). If control actions that improve performance as measured by the IP are known, supervised learning methods, or methods for learning from examples, can be used to train the controller. But when such control actions are not known a priori, appropriate control behavior has to be inferred from observations of the IP. One can distinguish between two classes of methods for training controllers under such circumstances. Indirect methods involve constructing a model of the problem's IP and using the model to obtain training information for the controller. On the other hand, direct, or model-free, methods obtain the requisite training information by observing the effects of perturbing the controlled process on the IP. Despite its reputation for inefficiency, we argue that for certain types of problems the latter approach, of which reinforcement learning is an example, can yield faster, more reliable learning. Using several control problems as examples, we illustrate how the complexity of model construction can often exceed that of solving the original control problem using direct reinforcement learning methods, making indirect methods relatively inefficient. These results indicate the importance of considering direct reinforcement learning methods as tools for learning to solve control problems. We also present several techniques for augmenting the power of reinforcement learning methods. These include (1) the use of local models to guide assigning credit to the components of a reinforcement learning system, (2) implementing a procedure from experimental psychology called "shaping" to improve the efficiency of learning, thereby making more complex problems amenable to solution, and (3) implementing a multi-level learning architecture designed for exploiting task decomposability by using previously-learned behaviors as primitives for learning more complex tasks.


GFDM- Generalized Frequency Division Multiplexing Invited Paper


title: 3D integration technologies for a planar dual band active array in Ka-band

abstract: In this paper, the concept and 3D integration technologies for a dual band e-scan antenna system for mobile satellite communications are presented. The antenna architecture is based on a low-profile (1 cm) and low cost approach using multifunctional Silicon Germanium Bipolar Complementary Metal-Oxide Semiconductor (SiGe BiCMOS) chip-sets and dual band planar antennas, covering both Ka-band operational frequencies, up-link at 30 GHz and down-link at 20 GHz. The key 3D integration methods for the RF distribution network and vertical transitions have been developed. First measurements show for different paths of the 36 way RF network an Insertion Loss (IL) between 2.5 dB and 7.5 dB and a Return Loss (RL) better than 10 dB. For vertical transitions, IL is smaller than 0.5 dB and RL better than 10 dB.


title: Loving Yourself Abundantly: Relationship of the Narcissistic Personality to Self- and Other Perceptions of Workplace Deviance, Leadership, and Task and Contextual Performance

abstract: The authors report results from 2 studies assessing the extent to which narcissism is related to self- and other ratings of leadership, workplace deviance, and task and contextual performance. Study 1 results revealed that narcissism was related to enhanced self-ratings of leadership, even when controlling for the Big Five traits. Study 2 results also revealed that narcissism was related to enhanced leadership self-perceptions; indeed, whereas narcissism was significantly positively correlated with self-ratings of leadership, it was significantly negatively related to other ratings of leadership. Study 2 also revealed that narcissism was related to more favorable self-ratings of workplace deviance and contextual performance compared to other (supervisor) ratings. Finally, as hypothesized, narcissism was more strongly negatively related to contextual performance than to task performance. All that is lovely in himself he loves, and in his witless way he wants himself: he who approves is equally approved; he seeks, is sought, he burns and he is burnt. But why, O foolish boy, so vainly catching at this flitting form? The cheat that you are seeking has no place. Avert your gaze and you will lose your love, for this that holds your eyes is nothing save the image of yourself reflected back to you. It comes and waits with you; it has no life it will depart if you will only go. ‐ Publius Ovidius Naso (“Ovid”) (B. More [trans.])


title: Reusable dialog component framework for rapid voice application development

abstract: Voice application development requires specialized speech related skills besides the general programming ability. Encapsulating the speech specific behavior and complexities in prepackaged, configurable User Interface (UI) components will ease and expedite the voice application development. These components can be used across applications and are called as Reusable Dialog Components (RDCs). In this paper we propose a programming model and the framework for developing reusable dialog components. Our framework facilitates the development of voice applications via the encapsulation of interaction mechanisms, the encapsulation of best-of-breed practices (ie. grammars, prompts, and configuration parameters), a modular design and through pluggable dialog management strategies. The framework extends the standard J2EE/JSP based programming model to make it suitable for voice applications.


title: Electrocardiographic characterization of the QTc interval in patients with advanced solid tumors: pharmacokinetic- pharmacodynamic evaluation of sunitinib.

abstract: PURPOSE ::: To evaluate the effects of sunitinib, a multitargeted tyrosine kinase inhibitor, on the QT interval in patients with cancer. ::: ::: ::: EXPERIMENTAL DESIGN ::: Patients received sunitinib loading doses (150-200 mg) on days 3 and 9 and maintenance doses (50 mg/d) on days 4 to 8. Moxifloxacin (day 1), placebo (day 2), and granisetron [with placebo (day 2) or sunitinib (days 3 and 9)] were also administered. Treatment effects were evaluated by time-matched, serial electrocardiograms, and manually overread. ::: ::: ::: RESULTS ::: Twenty-four of 48 patients were QT/PK evaluable. Moxifloxacin produced a time-matched, maximum mean placebo-adjusted corrected QT interval (QT(c)F) of 5.6 ms [90% confidence interval (CI), 1.9-9.3]. Sunitinib QT(c)F changes correlated with exposure, but not T(max). Maximum mean time-matched, placebo-adjusted QT(c)F was 9.6 ms (90% CI, 4.1-15.1) at steady state/therapeutic concentrations (day 3) and 15.4 ms (90% CI, 8.4-22.4) at supratherapeutic concentrations (day 9). No patient had a QT(c)F >500 ms. Concomitant granisetron produced no significant QT(c)F prolongation. Sunitinib-related adverse events were as previously described. ::: ::: ::: CONCLUSIONS ::: Sunitinib has a dose-dependent effect on QT interval. The increased risk of ventricular arrhythmias must be weighed against the therapeutic benefit sunitinib provides to patients with advanced cancer.


title: Translating Scientific Opportunity Into Public Health Impact: A Strategic Plan for Research on Mental Illness

abstract: Context Research has transformed many areas of medicine, with profound effects on morbidity and mortality. Exciting advances in neuroscience and genomics have transformed research but have not yet been translated to public health impact in psychiatry. Current treatments are necessary but not sufficient for most patients. Objectives To improve outcomes we will need to (1) identify the neural circuitry of mental disorders, (2) detect the earliest manifestations of risk or illness even before cognition or behavior appear abnormal, (3) personalize care based on individual responses, and (4) implement broader use of effective psychosocial interventions. Results To address these objectives, NIMH, working with its many stakeholders, developed a strategic plan for research. The plan calls for research that will (1) define the pathophysiology of disorders from genes to behavior, (2) map the trajectory of illness to determine when, where, and how to intervene to preempt disability, (3) develop new interventions based on a personalized approach to the diverse needs and circumstances of people with mental illnesses, and (4) strengthen the public health impact of NIMH-supported research by focusing on dissemination science and disparities in care. Conclusions The NIMH is shifting its funding priorities to close the gap between basic biological knowledge and effective mental health care, paving the way for prevention, recovery, and cure.


title: Beyond Catmull–Clark? A Survey of Advances in Subdivision Surface Methods

abstract: Subdivision surfaces allow smooth free-form surface modelling without topological constraints. They have become a fundamental representation for smooth geometry, particularly in the animation and entertainment industries. This survey summarizes research on subdivision surfaces over the last 15 years in three major strands: analysis, integration into existing systems and the development of new schemes. We also examine the reason for the low adoption of new schemes with theoretical advantages, explain why Catmull–Clark surfaces have become a de facto standard in geometric modelling, and conclude by identifying directions for future research. © 2012 Wiley Periodicals, Inc.


title: Geocoding crime and a first estimate of a minimum acceptable hit rate

abstract: Spatial crime analysis relies not only on accurate geocoding but also the achievement of a high level of geocoding success. Geocoding is the task of converting locations, such as the addresses of burglary victims, into grid coordinates and is a task performed regularly by many crime analysts. Data sources include police offence and incident databases where the quality of geographical references can vary. The reality of dealing with this real world data means that achieving a completely successful geocoding process is rare and few crime analysts can get a hit rate (the percentage measure of success) of 100%. This paper seeks the answer to a seemingly simple question: what is an ‘acceptable’ minimum geocoding hit rate for crime data? This paper uses a number of different crime patterns and Monte Carlo simulation to replicate a declining geocoding hit rate to answer this question. Reduced crime rates of mapped points, aggregated to census boundaries, are compared for a statistically significant difference. T...


abstract: Signature is widely used and developed area of research for personal verification and authentication. In this paper Off-line Signature Verification Based on Fusion of Grid and Global Features Using Neural Networks (SVFGNN) is presented. The global and grid features are fused to generate set of features for the verification of signature. The test signature is compared with data base signatures based on the set of features and match/non match of signatures is decided with the help of Neural Network. The performance analysis is conducted on random, unskilled and skilled signature forgeries along with genuine signatures. It is observed that FAR and FRR results are improved in the proposed method compared to the existing algorithm. Biometrics is the science of automatic recognition of individual depending on their physiological and behavioral attributes. The expansion of networked society and increased use of some personal portable devices like tablet PCs, PDAs, mobile phones and authorization of access to sensitive data, is demanding the most reliable personal identification and authentication systems. Among the different forms of biometric recognition systems such as fingerprint, iris, face, voice, palm etc., signature will be most widely used. The applications like government and legal financial transaction, bank cheques use signature as one of the personal identification system. The financial transactions and shopping using debit cards and credit cards require a bill to be confirmed by handwritten signature. But this leads to increased risk of financial loss due to attempted forgeries. This problem may be resolved by introducing automatic recognition systems which are being successfully used effectively to analyse large quantities of biometric data. Since olden days handwritten signature has been most widely used and accepted individual attributes for recognition. The design and development of signature recognition system is really big challenge because of the increased dependence of personal identification systems. Signature recognition system is divided into On-line or dynamic and off-line or static recognition. On-line recognition refers to a process where the signer uses a special pen called stylus to create his or her signature, producing the pen locations, speed and pressure, where as off-line recognition deals with signature images acquired by a scanner or a digital camera. In general, off-line signature recognition is a challenging problem, unlike the on-line signature where dynamic aspects of the signing action are captured directly as the handwriting trajectory. Contribution: In this paper, the grid and global features of signature are fused to generate final feature vector of signature. The Neural Network (NN) is used as a classifier for the verification of signatures.


title: Visual Analysis of Movement Behavior Using Web Data for Context Enrichment

abstract: With increasing use of GPS devices more and more location-based information is accessible. Thus not only more movements of people are tracked but also POI (point of interest) information becomes available in increasing geo-spatial density. To enable analysis of movement behavior, we present an approach to enrich trajectory data with semantic POI information and show how additional insights can be gained. Using a density-based clustering technique we extract 1.215 frequent destinations of ~150.000 user movements from a large e-mobility database. We query available context information from Foursquare, a popular location-based social network, to enrich the destinations with semantic background. As GPS measurements can be noisy, often more then one possible destination is found and movement patterns vary over time. Therefore we present highly interactive visualizations that enable an analyst to cope with the inherent geospatial and behavioral uncertainties.


title: Development of a cross-platform ubiquitous language learning service via mobile phone and interactive television*

abstract: This paper describes the development processes for a cross-platform ubiquitous language learning service via interactive television (iTV) and mobile phone. Adapting a learner-centred design methodology, a number of requirements were gathered from multiple sources that were subsequently used in TAMALLE (television and mobile phone assisted language learning environment) development. A number of issues that arise in the context of cross-platform user interface design and architecture for ubiquitous language learning were tackled. Finally, we discuss a multi-method evaluation regime to gauge usability, perceived usefulness and desirability of TAMALLE system. The result broadly revealed an overall positive response from language learners. Although, there were some reported difficulties in reading text and on-screen display mainly on the iTV side of the interface, TAMALLE was perceived to be a usable, useful and desirable tool to support informal language learning and also for gaining new contextual and cultural knowledge.


title: Assessing the intention to use technology among pre-service teachers in Singapore and Malaysia : A multigroup invariance analysis of the Technology Acceptance Model ( TAM )

abstract: This study assesses the pre-service teachers' self-reported future intentions to use technology in Singapore and Malaysia. A survey was employed to validate items from past research. Using the Technology Acceptance Model (TAM) as a research framework, 495 pre-service teachers from Singapore and Malaysia responded to an 11-item questionnaires containing four constructs: intention to use (ITU), attitude towards computer use (ATCU), perceived usefulness (PU), and perceived ease of use (PEU). Structural equation modelling (SEM) was employed as the main method of analysis in this study. A multi-group analysis of invariance was performed on the two samples. The results show that configural and metric invariance were fully supported while scalar and factor variance invariance were partially supported, suggesting that the 11-item measure of the TAM may be robust across cultures and that the factor loading pattern and factor loadings appeared to be equivalent across the cultures examined. While all the paths in the structural model were significant, the variance accounted for in the dependent variable (ITU) was much larger in the Malaysian sample relative to the Singaporean sample.


title: Probabilistic inference in hybrid domains by weighted model integration

abstract: Weighted model counting (WMC) on a propositional knowledge base is an effective and general approach to probabilistic inference in a variety of formalisms, including Bayesian and Markov Networks. However, an inherent limitation of WMC is that it only admits the inference of discrete probability distributions. In this paper, we introduce a strict generalization of WMC called weighted model integration that is based on annotating Boolean and arithmetic constraints, and combinations thereof. This methodology is shown to capture discrete, continuous and hybrid Markov networks. We then consider the task of parameter learning for a fragment of the language. An empirical evaluation demonstrates the applicability and promise of the proposal.


title: IoT Healthcare Analytics: The Importance of Anomaly Detection

abstract: Healthcare data is quite rich and often contains human survival related information. Analyzing healthcare data is of prime importance particularly considering the immense potential of saving human life and improving quality of life. Furthermore, IoT revolution has redefined modern health care systems and management. IoT offers its greatest promise to deliver excellent progress in healthcare domain. In this talk, proactive healthcare analytics specifically for cardiac disease prevention will be discussed. Anomaly detection plays a prominent role in healthcare analytics. In fact, the anomalous events are to be accurately detected with low false negative alarms often under high noise (low SNR) condition. An exemplary case of smartphone based cardiac anomaly detection will be presented.


title: Fingerprint recognition by combining global structure and local cues

abstract: As an important feature, orientation field describes the global structure of fingerprints. It provides robust discriminatory information other than traditional widely-used minutiae points. However, there are few works explicitly incorporating this information into fingerprint matching stage, partly due to the difficulty of saving the orientation field in the feature template. In this paper, we propose a novel representation for fingerprints which includes both minutiae and model-based orientation field. Then, fingerprint matching can be done by combining the decisions of the matchers based on the global structure (orientation field) and the local cue (minutiae). We have conducted a set of experiments on large-scale databases and made thorough comparisons with the state-of-the-arts. Extensive experimental results show that combining these local and global discriminative information can largely improve the performance. The proposed system is more robust and accurate than conventional minutiae-based methods, and also better than the previous works which implicitly incorporate the orientation information. In this system, the feature template takes less than 420 bytes, and the feature extraction and matching procedures can be done in about 0.30 s. We also show that the global orientation field is beneficial to the alignment of the fingerprints which are either incomplete or poor-qualitied.


title: MojiTalk: Generating Emotional Responses at Scale

abstract: Generating emotional language is a key step towards building empathetic natural language processing agents. However, a major challenge for this line of research is the lack of large-scale labeled training data, and previous studies are limited to only small sets of human annotated sentiment labels. Additionally, explicitly controlling the emotion and sentiment of generated text is also difficult. In this paper, we take a more radical approach: we exploit the idea of leveraging Twitter data that are naturally labeled with emojis. More specifically, we collect a large corpus of Twitter conversations that include emojis in the response, and assume the emojis convey the underlying emotions of the sentence. We then introduce a reinforced conditional variational encoder approach to train a deep generative model on these conversations, which allows us to use emojis to control the emotion of the generated text. Experimentally, we show in our quantitative and qualitative analyses that the proposed models can successfully generate high-quality abstractive conversation responses in accordance with designated emotions.


title: The Central Nervous System and the Gut Microbiome

abstract: Neurodevelopment is a complex process governed by both intrinsic and extrinsic signals. While historically studied by researching the brain, inputs from the periphery impact many neurological conditions. Indeed, emerging data suggest communication between the gut and the brain in anxiety, depression, cognition, and autism spectrum disorder (ASD). The development of a healthy, functional brain depends on key pre- and post-natal events that integrate environmental cues, such as molecular signals from the gut. These cues largely originate from the microbiome, the consortium of symbiotic bacteria that reside within all animals. Research over the past few years reveals that the gut microbiome plays a role in basic neurogenerative processes such as the formation of the blood-brain barrier, myelination, neurogenesis, and microglia maturation and also modulates many aspects of animal behavior. Herein, we discuss the biological intersection of neurodevelopment and the microbiome and explore the hypothesis that gut bacteria are integral contributors to development and function of the nervous system and to the balance between mental health and disease.


title: EEG-Based Classification of Fast and Slow Hand Movements Using Wavelet-CSP Algorithm

abstract: A brain-computer interface (BCI) acquires brain signals, extracts informative features, and translates these features to commands to control an external device. This paper investigates the application of a noninvasive electroencephalography (EEG)based BCI to identify brain signal features in regard to actual hand movement speed. This provides a more refined control for a BCI system in terms of movement parameters. An experiment was performed to collect EEG data from subjects while they performed right-hand movement at two different speeds, namely fast and slow, in four different directions. The informative features from the data were obtained using the Wavelet-Common Spatial Pattern (W-CSP) algorithm that provided high-temporal-spatial-spectral resolution. The applicability of these features to classify the two speeds and to reconstruct the speed profile was studied. The results for classifying speed across seven subjects yielded a mean accuracy of 83.71% using a Fisher Linear Discriminant (FLD) classifier. The speed components were reconstructed using multiple linear regression and significant correlation of 0.52 (Pearson's linear correlation coefficient) was obtained between recorded and reconstructed velocities on an average. The spatial patterns of the W-CSP features obtained showed activations in parietal and motor areas of the brain. The results achieved promises to provide a more refined control in BCI by including control of movement speed.


title: Interleaved Triangular Current Mode (TCM) resonant transition, single phase PFC rectifier with high efficiency and high power density

abstract: This paper presents a new topology for a highly compact and highly efficient single-phase Power Factor Corrected (PFC) rectifier system. The new topology consists of several interleaved boost stages which operate in a mode called Triangular Current Mode (TCM) in order to simultaneously achieve a high power density as well as a high efficiency. Applying TCM, ZVS is achieved over the full mains period by proper control of the power MOSFETs. The high TCM inductor current ripple is not transferred to the mains as the superposition of all boost cell input currents results in a smooth mains current waveform. Furthermore, one bridge leg operates synchronously to the mains frequency and connects the mains directly to an output rail. This results in very low common mode noise and only a small common mode filter has to be considered. The excellent behavior of this topology also applies to inverter operation.


title: Detecting object affordances with Convolutional Neural Networks

abstract: We present a novel and real-time method to detect object affordances from RGB-D images. Our method trains a deep Convolutional Neural Network (CNN) to learn deep features from the input data in an end-to-end manner. The CNN has an encoder-decoder architecture in order to obtain smooth label predictions. The input data are represented as multiple modalities to let the network learn the features more effectively. Our method sets a new benchmark on detecting object affordances, improving the accuracy by 20% in comparison with the state-of-the-art methods that use hand-designed geometric features. Furthermore, we apply our detection method on a full-size humanoid robot (WALK-MAN) to demonstrate that the robot is able to perform grasps after efficiently detecting the object affordances.


title: Infrared Dissimilar Joining of Ti50Ni50 and 316L Stainless Steel with Copper Barrier Layer in between Two Silver-Based Fillers

abstract: Infrared dissimilar joining Ti50Ni50 and 316L stainless steel using Cu foil in between Cusil-ABA and BAg-8 filler metals has been studied. The Cu foil serves as a barrier layer with thicknesses of 70 μm and 50 μm, and it successfully isolates the interfacial reaction between Ti and Fe at the 316L SS (stainless steel) substrate side. In contrast, the Cu foil with 25 μm in thickness is completely dissolved into the braze melt during brazing and fails to be a barrier layer. A layer of (CuxNi1−x)2Ti intermetallic is formed at the Ti50Ni50 substrate side, and the Cu interlayer is dissolved into the Cusil-ABA melt to from a few proeutectic Cu particles for all specimens. For the 316L SS substrate side, no interfacial layer is observed and (Ag, Cu) eutectic dominates the brazed joint for 70 μm/50 μm Cu foil. The average shear strength of the bond with Cu barrier layer is greatly increased compared with that without Cu. The brazed joints with a 50 μm Cu layer demonstrate the highest average shear strengths of 354 MPa and 349 MPa for samples joined at 820 °C and 850 °C, respectively. Cracks are initiated/propagated in (Ag, Cu) eutectic next to the 316L substrate side featured with ductile dimple fracture. It shows great potential for industrial application.


title: Physiological responses to interval endurance exercise at different levels of blood flow restriction

abstract: PURPOSE ::: We aimed to identify a blood flow restriction (BFR) endurance exercise protocol that would both maximize cardiopulmonary and metabolic strain, and minimize the perception of effort. ::: ::: ::: METHODS ::: Twelve healthy males (23 ± 2 years, 75 ± 7 kg) performed five different exercise protocols in randomized order: HI, high-intensity exercise starting at 105% of the incremental peak power (P peak); I-BFR30, intermittent BFR at 30% P peak; C-BFR30, continuous BFR at 30% P peak; CON30, control exercise without BFR at 30% P peak; I-BFR0, intermittent BFR during unloaded exercise. Cardiopulmonary, gastrocnemius oxygenation (StO2), capillary lactate ([La]), and perceived exertion (RPE) were measured. ::: ::: ::: RESULTS ::: V̇O2, ventilation (V̇ E), heart rate (HR), [La] and RPE were greater in HI than all other protocols. However, muscle StO2 was not different between HI (set1-57.8 ± 5.8; set2-58.1 ± 7.2%) and I-BRF30 (set1-59.4 ± 4.1; set2-60.5 ± 6.6%, p < 0.05). While physiologic responses were mostly similar between I-BFR30 and C-BFR30, [La] was greater in I-BFR30 (4.2 ± 1.1 vs. 2.6 ± 1.1 mmol L-1, p = 0.014) and RPE was less (5.6 ± 2.1 and 7.4 ± 2.6; p = 0.014). I-BFR30 showed similar reduced muscle StO2 compared with HI, and increased blood lactate compared to C-BFR30 exercise. ::: ::: ::: CONCLUSION ::: Therefore, this study demonstrate that endurance cycling with intermittent BFR promotes muscle deoxygenation and metabolic strain, which may translate into increased endurance training adaptations while minimizing power output and RPE.


title: LCNN: Lookup-Based Convolutional Neural Network

abstract: Porting state of the art deep learning algorithms to resource constrained compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose a fast, compact, and accurate model for convolutional neural networks that enables efficient learning and inference. We introduce LCNN, a lookup-based convolutional neural network that encodes convolutions by few lookups to a dictionary that is trained to cover the space of weights in CNNs. Training LCNN involves jointly learning a dictionary and a small set of linear combinations. The size of the dictionary naturally traces a spectrum of trade-offs between efficiency and accuracy. Our experimental results on ImageNet challenge show that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at inference, but it also enables efficient training. In this paper, we show the benefits of LCNN in few-shot learning and few-iteration learning, two crucial aspects of on-device training of deep learning models.


title: Training Neural Machine Translation using Word Embedding-based Loss

abstract: In neural machine translation (NMT), the computational cost at the output layer increases with the size of the target-side vocabulary. Using a limited-size vocabulary instead may cause a significant decrease in translation quality. This trade-off is derived from a softmax-based loss function that handles in-dictionary words independently, in which word similarity is not considered. In this paper, we propose a novel NMT loss function that includes word similarity in forms of distances in a word embedding space. The proposed loss function encourages an NMT decoder to generate words close to their references in the embedding space; this helps the decoder to choose similar acceptable words when the actual best candidates are not included in the vocabulary due to its size limitation. In experiments using ASPEC Japanese-to-English and IWSLT17 English-to-French data sets, the proposed method showed improvements against a standard NMT baseline in both datasets; especially with IWSLT17 En-Fr, it achieved up to +1.72 in BLEU and +1.99 in METEOR. When the target-side vocabulary was very limited to 1,000 words, the proposed method demonstrated a substantial gain, +1.72 in METEOR with ASPEC Ja-En.


title: Natural medicines from plant source used for therapy of diabetes mellitus: An overview of its pharmacological aspects

abstract: Medicinal plants play an important role in the treatment of diabetes mellitus, especially in the developing countries due to their cost effectiveness. Diabetes mellitus, a metabolic disorder, is becoming a serious threat to mankind health. The prevalence of diabetes mellitus is expected to reach up to 4.4% in the world by 2030. Among all type of diabetes, type 2 diabetes is main complication. Currently available treatment options in modern medicine have several adverse effects. Therefore, there is a need to develop safe and effective treatment modalities for diabetes. In this regard, plants provide the best option for search of desired safe and effective medications. Since ancient times, plants have been an exemplary source of medicine. Various plants have been found to possess significant anti-diabetic property after their preclinical and clinical evaluation. This present review presents the profiles of plants with hypoglycaemic properties reported in the literature from 2009 to 2011. Use of these plants may delay the development of diabetic complications and can correct the metabolic abnormalities through variety of mechanisms. Moreover, during the past few years many phytoconstituents responsible for anti-diabetic effects have been isolated from plants. Since this review has been presented in a very interactive manner showing geographical region of availability, parts of plant used, mechanism of action and phytoconstituents responsible for particular action, it will be of great importance to intrested readers to easily identify and go for further research on the plant of their interest.


title: Saliency Map Generation by the Convolutional Neural Network for Real-Time Traffic Light Detection Using Template Matching

abstract: A critical issue in autonomous vehicle navigation and advanced driver assistance systems (ADAS) is the accurate real-time detection of traffic lights. Typically, vision-based sensors are used to detect the traffic light. However, the detection of traffic lights using computer vision, image processing, and learning algorithms is not trivial. The challenges include appearance variations, illumination variations, and reduced appearance information in low illumination conditions. To address these challenges, we present a visual camera-based real-time traffic light detection algorithm, where we identify the spatially constrained region-of-interest in the image containing the traffic light. Given, the identified region-of-interest, we achieve high traffic light detection accuracy with few false positives, even in adverse environments. To perform robust traffic light detection in varying conditions with few false positives, the proposed algorithm consists of two steps, an offline saliency map generation and a real-time traffic light detection. In the offline step, a convolutional neural network, i.e., a deep learning framework, detects and recognizes the traffic lights in the image using region-of-interest information provided by an onboard GPS sensor. The detected traffic light information is then used to generate the saliency maps with a modified multidimensional density-based spatial clustering of applications with noise (M-DBSCAN) algorithm. The generated saliency maps are indexed using the vehicle GPS information. In the real-time step, traffic lights are detected by retrieving relevant saliency maps and performing template matching by using colour information. The proposed algorithm is validated with the datasets acquired in varying conditions and different countries, e.g., USA, Japan, and France. The experimental results report a high detection accuracy with negligible false positives under varied illumination conditions. More importantly, an average computational time of 10 ms/frame is achieved. A detailed parameter analysis is conducted and the observations are summarized and reported in this paper.


title: Miniaturized Circular Polarized TE $_{10}$-Mode Substrate-Integrated-Waveguide Antenna

abstract: We propose a miniaturized circularly polarized (CP) antenna based on a substrate integrated waveguide (SIW) working in the dominant mode. To reduce the overall size, only 1/8 of the SIW cavity is used as the radiation aperture. The two cavities are orthogonally placed to allow CP operation. Because of the via walls on the SIW, the two cavities can be closely placed, which reduces the overall size as well. The performance of the devices is demonstrated using the simulated and measurement results. The 10-dB impedance bandwidth is 1.63-1.66 GHz, and the 3-dB axial-ratio bandwidth is 1.64-1.65 GHz. The measured peak gain and axial ratio are 0.7 dBi and 2.3 dB at 1.645 GHz, respectively.


title: DenseBox: Unifying Landmark Localization with End to End Object Detection

abstract: How can a single fully convolutional neural network (FCN) perform on object detection? We introduce DenseBox, a unified end-to-end FCN framework that directly predicts bounding boxes and object class confidences through all locations and scales of an image. Our contribution is two-fold. First, we show that a single FCN, if designed and optimized carefully, can detect multiple different objects extremely accurately and efficiently. Second, we show that when incorporating with landmark localization during multi-task learning, DenseBox further improves object detection accuray. We present experimental results on public benchmark datasets including MALF face detection and KITTI car detection, that indicate our DenseBox is the state-of-the-art system for detecting challenging objects such as faces and cars.


title: A Foray into Conficker’s Logic and Rendezvous Points

abstract: We present an in depth static analysis of the Conficker worm, primarily through the exploration of the client-side binary logic. In this paper, we summarize various aspects of the inner workings of binary variants A and B, which were the first in a chain of recent revisions aimed to keep this epidemic resistant to ongoing eradication attempts. These first two variants have combined to produce a multi-million node population of infected hosts, whose true main purpose has yet to be fully understood. We further validate aspects of our analysis through in-situ network analyses, and discuss some attribution links about its origins.


Using WordNet as a Knowledge Base for Measuring Semantic Similarity Between Words


Training Structured Efficient Convolutional Layers


title: Half-DRAM: a high-bandwidth and low-power DRAM architecture from the rethinking of fine-grained activation

abstract: DRAM memory is a major contributor for the total power consumption in modern computing systems. Consequently, power reduction for DRAM memory is critical to improve system-level power efficiency. Fine-grained DRAM architecture [1, 2] has been proposed to reduce the activation/ precharge power. However, those prior work either incurs significant performance degradation or introduces large area overhead. In this paper, we propose a novel memory architecture Half-DRAM, in which the DRAM array is reorganized to enable only half of a row being activated. The half-row activation can effectively reduce activation power and meanwhile sustain the full bandwidth one bank can provide. In addition, the half-row activation in Half-DRAM relaxes the power constraint in DRAM, and opens up opportunities for further performance gain. Furthermore, two half-row accesses can be issued in parallel by integrating the sub-array level parallelism to improve the memory level parallelism. The experimental results show that Half-DRAM can achieve both significant performance improvement and power reduction, with negligible design overhead


title: Review on IT adoption: insights from recent technologies

abstract: Purpose – The purpose of this paper is to review the literature on information technology adoption in organizations to understand the need of integrated models for technology adoption. It further makes an attempt to identify key parameters to integrate technology acceptance model (TAM) and technology-organization-environment (TOE) framework for firm level technology adoption. This integration is intended to improve predictive power of resulting model. Design/methodology/approach – The research papers are accessed from the popular databases from 2000 to 2012. The selected papers have addressed technology adoption in context of recent technologies such as e-commerce, ERP, RFID, EDI and knowledge management, etc. The paper attempts to review the studies based on TAM model and TOE framework to identify relevant set of variables for the adoption of these technologies in organizations. Findings – TAM and its extended versions have high capability to explain the technology adoption while the significance of TOE ...


title: Performance of a hierarchical temporal memory network in noisy sequence learning

abstract: As neurobiological evidence points to the neocortex as the brain region mainly involved in high-level cognitive functions, an innovative model of neocortical information processing has been recently proposed. Based on a simplified model of a neocortical neuron, and inspired by experimental evidence of neocortical organisation, the Hierarchical Temporal Memory (HTM) model attempts at understanding intelligence, but also at building learning machines. This paper focuses on analysing HTM's ability for online, adaptive learning of sequences. In particular, we seek to determine whether the approach is robust to noise in its inputs, and to compare and contrast its performance and attributes to an alternative Hidden Markov Model (HMM) approach. We reproduce a version of a HTM network and apply it to a visual pattern recognition task under various learning conditions. Our first set of experiments explore the HTM network's capability to learn repetitive patterns and sequences of patterns within random data streams. Further experimentation involves assessing the network's learning performance in terms of inference and prediction under different noise conditions. HTM results are compared with those of a HMM trained at the same tasks. Online learning performance results demonstrate the HTM's capacity to make use of context in order to generate stronger predictions, whereas results on robustness to noise reveal an ability to deal with noisy environments. Our comparisons also, however, emphasise a manner in which HTM differs significantly from HMM, which is that HTM generates predicted observations rather than hidden states, and each observation is a sparse distributed representation.


title: Adaptive 360 VR Video Streaming Based on MPEG-DASH SRD

abstract: We demonstrate an adaptive bandwidth-efficient 360 VR video streaming system based on MPEG-DASH SRD. We extend MPEG-DASH SRD to the 3D space of 360 VR videos, and showcase a dynamic view-aware adaptation technique to tackle the high bandwidth demands of streaming 360 VR videos to wireless VR headsets. We spatially partition the underlying 3D mesh into multiple 3D sub-meshes, and construct an efficient 3D geometry mesh called hexaface sphere to optimally represent tiled 360 VR videos in the 3D space. We then spatially divide the 360 videos into multiple tiles while encoding and packaging, use MPEG-DASH SRD to describe the spatial relationship of tiles in the 3D space, and prioritize the tiles in the Field of View (FoV) for view-aware adaptation. Our initial evaluation results show that we can save up to 72% of the required bandwidth on 360 VR video streaming with minor negative quality impacts compared to the baseline scenario when no adaptations is applied.


title: Ligra: a lightweight graph processing framework for shared memory

abstract: There has been significant recent interest in parallel frameworks for processing graphs due to their applicability in studying social networks, the Web graph, networks in biology, and unstructured meshes in scientific simulation. Due to the desire to process large graphs, these systems have emphasized the ability to run on distributed memory machines. Today, however, a single multicore server can support more than a terabyte of memory, which can fit graphs with tens or even hundreds of billions of edges. Furthermore, for graph algorithms, shared-memory multicores are generally significantly more efficient on a per core, per dollar, and per joule basis than distributed memory systems, and shared-memory algorithms tend to be simpler than their distributed counterparts. In this paper, we present a lightweight graph processing framework that is specific for shared-memory parallel/multicore machines, which makes graph traversal algorithms easy to write. The framework has two very simple routines, one for mapping over edges and one for mapping over vertices. Our routines can be applied to any subset of the vertices, which makes the framework useful for many graph traversal algorithms that operate on subsets of the vertices. Based on recent ideas used in a very fast algorithm for breadth-first search (BFS), our routines automatically adapt to the density of vertex sets. We implement several algorithms in this framework, including BFS, graph radii estimation, graph connectivity, betweenness centrality, PageRank and single-source shortest paths. Our algorithms expressed using this framework are very simple and concise, and perform almost as well as highly optimized code. Furthermore, they get good speedups on a 40-core machine and are significantly more efficient than previously reported results using graph frameworks on machines with many more cores.


title: Deep learning meets ontologies: experiments to anchor the cardiovascular disease ontology in the biomedical literature

abstract: Background ::: Automatic identification of term variants or acceptable alternative free-text terms for gene and protein names from the millions of biomedical publications is a challenging task. Ontologies, such as the Cardiovascular Disease Ontology (CVDO), capture domain knowledge in a computational form and can provide context for gene/protein names as written in the literature. This study investigates: 1) if word embeddings from Deep Learning algorithms can provide a list of term variants for a given gene/protein of interest; and 2) if biological knowledge from the CVDO can improve such a list without modifying the word embeddings created.


title: Residential and GPS-Defined Activity Space Neighborhood Noise Complaints, Body Mass Index and Blood Pressure Among Low-Income Housing Residents in New York City

abstract: Little is known about how neighborhood noise influences cardiovascular disease (CVD) risk among low-income populations. The aim of this study was to investigate associations between neighborhood noise complaints and body mass index (BMI) and blood pressure (BP) among low-income housing residents in New York City (NYC), including the use of global positioning system (GPS) data. Data came from the NYC Low-Income Housing, Neighborhoods and Health Study in 2014, including objectively measured BMI and BP data (N = 102, Black = 69%), and 1 week of GPS data. Noise reports from "NYC 311" were used to create a noise complaints density (unit: 1000 reports/km2) around participants' home and GPS-defined activity space neighborhoods. In fully-adjusted models, we examined associations of noise complaints density with BMI (kg/m2), and systolic and diastolic BP (mmHg), controlling for individual- and neighborhood-level socio-demographics. We found inverse relationships between home noise density and BMI (B = -2.7 [kg/m2], p = 0.009), and systolic BP (B = -5.3 mmHg, p = 0.008) in the fully-adjusted models, and diastolic BP (B = -3.9 mmHg, p = 0.013) in age-adjusted models. Using GPS-defined activity space neighborhoods, we observed inverse associations between noise density and systolic BP (B = -10.3 mmHg, p = 0.019) in fully-adjusted models and diastolic BP (B = -7.5 mmHg, p = 0.016) in age-adjusted model, but not with BMI. The inverse associations between neighborhood noise and CVD risk factors were unexpected. Further investigation is needed to determine if these results are affected by unobserved confounding (e.g., variations in walkability). Examining how noise could be related to CVD risk could inform effective neighborhood intervention programs for CVD risk reduction.


title: Synchronization-Aware Scheduling for Virtual Clusters in Cloud

abstract: Due to high flexibility and cost-effectiveness, cloud computing is increasingly being explored as an alternative to local clusters by academic and commercial users. Recent research already confirmed the feasibility of running tightly-coupled parallel applications with virtual clusters. However, such types of applications suffer from significant performance degradation, especially as the overcommitment is common in cloud. That is, the number of executable Virtual CPUs (VCPUs) is often larger than that of available Physical CPUs (PCPUs) in the system. The performance degradation is mainly due to the fact that the current virtual machine monitors (VMMs) are unaware of the synchronization requirements of the VMs which are running parallel applications. In this paper, There are two key contributions. (1) We propose an autonomous synchronization-aware VM scheduling (SVS) algorithm, which can effectively mitigate the performance degradation of tightly-coupled parallel applications running atop them in overcommitted situation. (2) We integrate the SVS algorithm into Xen VMM scheduler, and rigorously implement a prototype. We evaluate our design on a real cluster environment with NPB benchmark and real-world trace. Experiments show that our solution attains better performance for tightly-coupled parallel applications than the state-of-the-art approaches like Xen’s Credit scheduler, balance scheduling, and hybrid scheduling.


title: Deep modeling of group preferences for group-based recommendation

abstract: Nowadays, most recommender systems (RSs) mainly aim to s suggest appropriate items for individuals. Due to the social nature of human beings, group activities have become an integral part of our daily life, thus motivating the study on group RS (GRS). However, most existing methods used by GRS make recommendations through aggregating individual ratings or individual predictive results rather than considering the collective features that govern user choices made within a group. As a result, such methods are heavily sensitive to data, hence they often fail to learn group preferences when the data are slightly inconsistent with predefined aggregation assumptions. To this end, we devise a novel GRS approach which accommodates both individual choices and group decisions in a joint model. More specifically, we propose a deep-architecture model built with collective deep belief networks and dual-wing restricted Boltzmann machines. With such a deep model, we can use high-level features, which are induced from lower-level features, to represent group preference so as to relieve the vulnerability of data. Finally, the experiments conducted on a real-world dataset prove the superiority of our deep model over other state-of-the-art methods.


title: N400 ERPs for actions: building meaning in context

abstract: Converging neuroscientific evidence suggests the existence of close links between language and sensorimotor cognition. Accordingly, during the comprehension of meaningful actions, our brain would recruit semantic-related operations similar to those associated with the processing of language information. Consistent with this view, electrophysiological findings show that the N400 component, traditionally linked to the semantic processing of linguistic material, can also be elicited by action-related material. This review outlines recent data from N400 studies that examine the understanding of action events. We focus on three specific domains, including everyday action comprehension, co-speech gesture integration, and the semantics involved in motor planning and execution. Based on the reviewed findings, we suggest that both negativities (the N400 and the action-N400) reflect a common neurocognitive mechanism involved in the construction of meaning through the expectancies created by previous experiences and current contextual information. To shed light on how this process is instantiated in the brain, a testable contextual fronto-temporo-parietal model is proposed.


title: CS168: The Modern Algorithmic Toolbox Lecture #2: Approximate Heavy Hitters and the Count-Min Sketch

abstract: 1.1 Finding the Majority Element Let’s begin with a problem that many of you have seen before. It’s a common question in technical interviews. You’re given as input an array A of length n, with the promise that it has a majority element | a value that is repeated in strictly more than n=2 of the array’s entries. Your task is to nd the majority element. In algorithm design, the usual \holy grail" is a linear-time algorithm. For this problem, your post-CS161 toolbox already contains a subroutine that gives a linear-time solution | just compute the median of A. (Note: it must be the majority element.) So let’s be more ambitious: can we compute the majority element with a single left-to-right pass through the array? If you haven’t seen it before, here’s the solution: Initialize counter := 0, current := NULL. [current stores the frontrunner for the majority element] For i = 1 to n: { If counter == 0:


title: Strategies for Intuitive Interaction in Public Urban Spaces

abstract: Characterised by the increased spread of technology out of workplaces and into public spaces and homes,theso-calledthird-waveofhuman-computerinteractionhasplacedgreaterfocusonscenarios that are socially situated, context dependent and not necessarily task oriented. This movement has led to studies investigating how people interact with digital applications in public spaces, but the discussion of intuitive interaction thus far has not been extended to this new realm of user interfaces. Designing for intuitive interaction in public spaces differs from traditional digital applications due to the inherently fluid social nature and often large-scale character of urban interventions. This article discusses the design of interaction for public spaces, based on applications we developed along the years, contrasted to similar endeavours elsewhere. We analyse them through the lens of frameworks for intuitive interactions and derive a series of strategies towards designing for immediate use and high crowd turnaround in urban interventions. RESEARCH HIGHLIGHTS • Analysis of different types of interactive public spaces, in terms of spatial layout, distribution of focal points and how people negotiate the space in order to engage in the interaction. • Classification of urban interfaces into three broad categories according to crowd self-organization around the digitally enhanced environments: performative, allotted and responsive ambient interfaces. • Discussion of intuitive aspects of interactive public spaces with reference to established literature on intuitive interaction. • Proposal of user feedback strategies for designing intuitive interaction in public spaces derived from the presented analysis and discussion.


title: Enhancement of liquids mixing using active pulsation in the laminar flow regime

abstract: Both the need for mixing highly viscous liquids more effectively and the advance of micro-scale applications urge the development of technologies for liquid mixing at low Reynolds numbers. However, currently engineering designs which offer effective jet mixing without structural and operational complexity are still lacking. In this project, the method of enhancing liquid mixing using active pulsation in the laminar flow regime is explored experimentally. This work started by improving the inline pulsation mechanism in an existing confined jet configuration whereby the fluid from a primary planar jet and two surrounding secondary planar jets are pulsated by active fluid injection control via solenoid valves in the out-of-phase mode. The influence of Reynolds number, pulsation modes, frequency, duty cycle on mixing is then investigated using PLIF and PIV experimental techniques. A combination of different mixing mechanisms is found to be at play, including sequential segmentation, shearing and stretching, vortex entrainment and breakup. At a given net flow Reynolds number, an optimal frequency exists which scales approximately with a Strouhal number (Str=fh/Uj) about 1. This optimal frequency reflects the compromise of the vorticity strength and segmentation length. Furthermore, a lower duty cycle is found to produce a better mixing due to a resultant higher instantaneous Reynolds number in the jet flow. Overall, the improvement of the rig has resulted in an excellent mixing being achieved at a net flow Reynolds number of 166 which is at least order of magnitude lower than in the original rig.In order to achieve fast laminar mixing at even lower Reynolds numbers, the active pulsation mechanism using lateral synthetic jet pairs is designed and tested at a net flow Reynolds number ranging from 2 to 166 at which a good mixing is achieved. The influence of actuation frequency and amplitude, and different jet configuration is evaluated using PLIF and PIV experimental techniques. At the mediate to high Reynolds numbers tested in this study, the interaction and subsequent breakup of vortices play a dominant role in provoking mixing. In contrast, at the lower end of Reynolds numbers the strength of vortex rollup is weakened significantly and as a result folding and shearing of sequential segments provide the main mechanism for mixing. Therefore it is essential to use multiple lateral synthetic jet pairs to achieve good mixing in both mixing channel and synthetic jet cavity at this Reynolds number. It is found that an increase in both the actuation magnitude and frequency improves mixing, thereby the velocity ratio represents the relative strength of the pulsation velocity to the mean flow velocity is crucial for mixing enhancement. In order to identify actuation conditions for good mixing, a regression fit is conducted for the correlation between the dimensionless parameters, net flow Reynolds number Ren, stroke length L and Strouhal number Str. Over the tested range of the net flow Reynolds number from 2 to 83, the relationship of parameters is found and the velocity ratio at least above 2.0. Suggested by the comparatively small exponent, net flow Reynolds number is less influential than stroke length and Strouhal number.The success in obtaining excellent mixing using lateral synthetic jet pairs at low Reynolds numbers in the present work has opened up a promising prospect of their applications in various scenarios, including mixing of highly viscous liquids at macro-scale and micro-mixing.


title: Non-cryptographic methods of MAC spoof detection in wireless LAN

abstract: The immense growth of wireless network makes it prone to number of security threats. Most prevalent among them are spoof threats, in which one network device changes its network identifiers to that of another network device. One of the possible reasons of spoofing in wireless network is that per-frame source authentication is not provided. Full scale authentication can be provided, but it is not always desirable as it requires reliable key management. The attacker can further launch Denial of Service attacks by spoofing deauthentication/disassociation or power saving frames, can bypass the access control list or can falsely advertise services to wireless clients. This paper discusses some of the non cryptographic techniques to detect MAC spoofing along with their pros and cons and the network scenarios in which they can be used. Analysis of these techniques is done on data captured from live test bed. The information in this paper can help the wireless equipment manufacturers to implement intrusion detection systems capable of detecting MAC address spoofing efficiently with least number of false alarms.


Integrating affect sensors in an intelligent tutoring system


State of the Art Report: Music Emotion Recognition: A State of the Art Review


title: Delorme's operation plus sphincteroplasty for complete rectal prolapse associated with traumatic fecal incontinence

abstract: Rectal prolapse associated with traumatic fecal incontinence is a rare clinical combination. This study was designed to assess Delorme's operation with sphincteroplasty as a surgical management of this combination in terms of recurrence and improvement of fecal incontinence. In this prospective study, we enrolled patients suffering from short, full-thickness rectal prolapse associated with traumatic fecal incontinence who had been admitted to Alexandria Main University Hospital during the period of May 2010-January 2013. Preoperative data including cause of trauma, duration of symptoms, results of anal manometry, and degree of fecal incontinence using Wexner score were collected from all patients. Delorme's procedure with overlap sphincteroplasty was done in all patients. Recurrence of prolapse and improvement of fecal incontinence were assessed after 1, 3, 6 and 12 months. The study included 13 patients aged (32±8.7) years, 9 females and 4 males. Cause of sphincteric injury included previous anal surgery in 7 patients and normal labor in 6 patients. Duration between sphincteric injury and operation was (8.08±2.47) months. Preoperative Wexner's mean score was 16.07±3.4. Early postoperative complications included superficial wound infection (69.2%), minor wound dehiscence (61.5%), and postoperative bleeding (7.6%). Recurrence was detected in 1 patient at 6 month follow-up. Wexner's score showed significant improvement for all patients after 6 months (4.00±2.04). In conclusion, combination of Delorme's procedure and sphincteroplasty for treatment of patients with short complete rectal prolapse associated with traumatic fecal incontinence is a safe, effective surgical management with satisfactory results regarding anatomical and functional outcomes.


title: Salivary Biomarkers for Clinical Applications

abstract: For clinical applications such as monitoring health status, disease onset and progression, and treatment outcome, there are three necessary prerequisites: (i) a simple method for collecting biologic samples, ideally noninvasively; (ii) specific biomarkers associated with health or disease; and (iii) a technology platform to rapidly utilize the biomarkers. Saliva, often regarded as the ‘mirror of the body’, is a perfect surrogate medium to be applied for clinical diagnostics. Saliva is readily accessible via a totally noninvasive method. Salivary biomarkers, whether produced by healthy individuals or by individuals affected by specific diseases, are sentinel molecules that could be used to scrutinize health and disease surveillance. The visionary investment by the US National Institute of Dental and Craniofacial Research, the discovery of salivary biomarkers, and the ongoing development of salivary diagnostic technologies have addressed its diagnostic value for clinical applications. The availability of more sophisticated analytic techniques gives optimism that saliva can eventually be placed as a biomedium for clinical diagnostics. This review presents current salivary biomarker research and technology developmental efforts for clinical applications.


title: Sentiment analysis: capturing favorability using natural language processing

abstract: This paper illustrates a sentiment analysis approach to extract sentiments associated with polarities of positive or negative for specific subjects from a document, instead of classifying the whole document into positive or negative.The essential issues in sentiment analysis are to identify how sentiments are expressed in texts and whether the expressions indicate positive (favorable) or negative (unfavorable) opinions toward the subject. In order to improve the accuracy of the sentiment analysis, it is important to properly identify the semantic relationships between the sentiment expressions and the subject. By applying semantic analysis with a syntactic parser and sentiment lexicon, our prototype system achieved high precision (75-95%, depending on the data) in finding sentiments within Web pages and news articles.


title: SGPN: Similarity Group Proposal Network for 3D Point Cloud Instance Segmentation

abstract: We introduce Similarity Group Proposal Network (SGPN), a simple and intuitive deep learning framework for 3D object instance segmentation on point clouds. SGPN uses a single network to predict point grouping proposals and a corresponding semantic class for each proposal, from which we can directly extract instance segmentation results. Important to the effectiveness of SGPN is its novel representation of 3D instance segmentation results in the form of a similarity matrix that indicates the similarity between each pair of points in embedded feature space, thus producing an accurate grouping proposal for each point. Experimental results on various 3D scenes show the effectiveness of our method on 3D instance segmentation, and we also evaluate the capability of SGPN to improve 3D object detection and semantic segmentation results. We also demonstrate its flexibility by seamlessly incorporating 2D CNN features into the framework to boost performance.


title: Regulation of Digital Businesses with Natural M onopolies Or Third Party Payment Business M odels : Antitrust Lessons from the Analysis of Google

abstract: Some digital business models may be so innovative that they overwhelm existing regulatory mechanisms, both legislation and historical jurisprudence, and require extension to or modification of antitrust law. Regulatory policies that were developed in response to nineteenth-or twentieth-century antitrust concerns dealt principally with economies of scale leading to monopoly power and may not be well suited to the issues of network effects or third-party payer online business models such as sponsored search. From the perspective of information systems economics, we investigate if such third-party payer digital systems require intervention as profound as the government's innovative approach to the problems posed by AT&T in the 1913 Kingsbury Commitment, establishing the first private regulated monopoly. Google provides an example of a company whose innovative digital business model is difficult to fit into current regulatory frameworks, and may provide examples of the issues that might require an extension to regulatory policy.


title: Complex Preferences for Answer Set Optimization

abstract: The main contribution of this paper is the definition of the preference description language PDL. This language allows us to combine qualitative and quantitative, penalty based preferences in a flexible way. This makes it possible to express complex preferences which are needed in many realistic optimization settings. We show that several preference handling methods described in the literature are special cases of our approach. We also demonstrate that PDL expressions can be compiled to logic programs which can be used as tester programs in a generate-and-improve method for finding optimal answer sets.


title: Efficient localization based on scan matching with a continuous likelihood field

abstract: This paper presents a fast scan matching approach to mobile robot localization supported by a continuous likelihood field. The likelihood field plays a central role in the approach, as it avoids the necessity to establish direct correspondences; it is the connection link between scan matching and robotic localization, and it provides a reduced computational complexity. Scan matching is formulated as a non-linear least squares problem and solved by the Gauss-Newton and Levenberg-Marquardt methods. Furthermore, to reduce the influences of outliers during optimization, a loss function is introduced. The proposed solution was evaluated using a publicly available dataset and compared with AMCL, a state-of-the-art localization algorithm. Our proposal shows to be a fast and accurate localization algorithm suitable for any type of operation.


title: Biometric perils and patches

abstract: Abstract Biometrics authentication offers many advantages over conventional authentication systems that rely on possessions or special knowledge. With conventional technology, often the mere possession of an employee ID card is proof of ID, while a password potentially can be used by large groups of colleagues for long times without change. The fact that biometrics authentication is non-repudiable (hard to refute) and, yet, convenient, is among its most important advantages. Biometrics systems, however, suffer from some inherent biometrics-specific security threats. These threats are mainly related to the use of digital signals and the need for additional input devices, though we also discuss brute-force attacks of biometrics systems. There are also problems common to any pattern recognition system. These include “wolves” and “lambs”, and a new group we call “chameleons”. An additional issue with the use of biometrics is the invasion of privacy because the user has to enroll with an image of a body part. We discuss these issues and suggest some methods for mitigating their impact.


title: Earthquake damage detection in the Imperial County Services Building I: The data and time–frequency analysis

abstract: The former Imperial County Services Building was a six-story reinforced concrete structure in the El Centro, California, severely damaged by the 1979 Imperial Valley earthquake. It represents a rare case of an instrumented building that has been damaged, and thus can serve as a full-scale benchmark to evaluate and further develop structural health monitoring methods. This paper presents an analysis of inter-story drifts, and of changes in the first NS and EW system frequencies (estimated from the ridge of the Gabor transform of the relative roof displacement response) as indicators of the occurrence of damage. The detected initial decreases of system frequency, of about 28% for NS and 24% for EW motions, are not believed to be due to severe damage. The subsequent decreases, of about 44% for NS and 21% for EW motions, are attributed to damage. Near the end of shaking, increases of about 35% for the NS and 36% for EW motions were detected. (These percentage changes were computed with respect to the previous value for particular interval, rather than a fixed reference). During the most severe shaking, the inter-story drifts exceeded 0.5% for NS and 1.5% for EW motions.


title: Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution

abstract: Feature selection, as a preprocessing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. However, the recent increase of dimensionality of data poses a severe challenge to many existing feature selection methods with respect to efficiency and effectiveness. In this work, we introduce a novel concept, predominant correlation, and propose a fast filter method which can identify relevant features as well as redundancy among relevant features without pairwise correlation analysis. The efficiency and effectiveness of our method is demonstrated through extensive comparisons with other methods using real-world data of high dimensionality


3D Point Cloud Semantic Modelling: Integrated Framework for Indoor Spaces and Furniture


title: Parking Slot Markings Recognition for Automatic Parking Assist System

abstract: This paper describes a monocular vision based parking-slot-markings recognition algorithm, which is used to automate the target position selection of automatic parking assist system. Peak-pair detection and clustering in Hough space recognize marking lines. Specially, one-dimensional filter in Hough space is designed to utilize a priori knowledge about the characteristics of marking lines in bird's eye view edge image. Modified distance between point and line-segment is used to distinguish guideline from recognized marking line-segments. Once the guideline is successfully recognized, T-shape template matching easily recognizes dividing marking line-segments. Experiments show that proposed algorithm successfully recognizes parking slots even when adjacent vehicles occlude parking-slot-markings severely.


title: Revealing transition patterns between mono- and multimodal travel patterns over time: a mover-stayer model

abstract: Recent empirical evidence suggests that travellers are becoming increasingly multimodal. Coinciding with this trend, a growing interest can be observed in the transport literature to study the concept of multimodality. Most studies, in this regard, have focused on assessing the determinants of multimodal travel behaviour. While it is interesting to know which factors, at a certain moment in time, affect the membership of mono/multimodal travel patterns, one general omission in the current literature relates to the questions how and why travellers switch between the mono/multimodal travel patterns over time. This study aims to fill this knowledge gap. To this end, a mixture latent Markov model is specified and estimated using data from the German mobility panel. Our mixture latent Markov models consist of latent travel patterns as well as latent mobility styles. To acquire insights on changes in travel behaviour various model specifications are tested. The travel data is best explained by a model consisting of five latent travel patterns and three mobility styles. The five travel patterns are can be conceived as (1) strict car users, (2) public transport and occasional car users, (3) car passengers, (4) car and bicycle users and (5) bicycle and occasional public transport users, and the three underlying mobility styles are identified as (1) habitual travellers, who stay in their respective pattern for three consecutive years, (2) car (in)dependent choice travellers, who switch within car and non-car patterns, and (3) car users with an alternative mode preference, who switch between car and non-car patterns. Overall, it is concluded that mixture latent Markov models are effective to reveal (heterogeneity in) transition patterns.


A novel envelope extraction method for multichannel heart sounds signal detection