title: Improve regression accuracy by using an attribute weighted KNN approach

abstract: KNN (K nearest neighbor) algorithm is a widely used regression method, with a very simple principle about neighborhood. Though it achieves success in many application areas, the method has a shortcoming of weighting equal contributions to each attribute when computing distance between instances. In this paper, we applied a weighted KNN approach by using weights obtained from optimization and feature selection methods and compared the performance and efficiency of these two types of algorithms in regression problems. Experiments on two UCI datasets show that optimization algorithms like particle swarm optimization can obtain more valuable weights than feature selection algorithms, such as information gain and RelefF, with the tradeoff of running time cost. Both of them canimprove the performance of traditional KNN with equal feature weights.


Spotlight: Optimizing Device Placement for Training Deep Neural Networks


title: Multiple Fan-Beam Antenna Array for Massive MIMO Applications

abstract: In this paper, a multiple fan-beam antenna array is proposed for massive multiple-input multipleoutput (MIMO) applications. The proposed array is based on vertical spatial filtering to reduce radio frequency complexity in a massive MIMO system. A microstrip line feeding network is utilized to achieve a specific phase distribution for multiple fan beams. A 64-element antenna array is designed and fabricated to validate the design strategy. The proposed antenna array uses 16 ports to excite 64 antenna elements, which is more cost effective than traditional massive MIMO systems. The measured results demonstrate that the proposed antenna array can achieve two fan beams at 7 in the vertical dimension, and the measured gain of every port exceeds 10 dBi at 2.6 GHz.


title: Underwater Acoustic Modems

abstract: Due to the growing interest using underwater acoustic networks, there are more and more research papers about underwater communications. These papers are mainly focused on deployments and studies about the constraints of the underwater medium. The underwater acoustic channel is highly variable and the signal transmission can change according to environmental factors, such as the temperature, pressure, or salinity of the water. For this reason, it is important to know how these devices are developed and the maximum distance and data transfer rates they can achieve. To this end, this paper presents an exhaustive study of existing underwater acoustic modems where their main features are highlighted. We also review the main features of their hardware. All presented proposals in the research literature are compared with commercial underwater acoustic modems. Finally, we analyze different programs and improvements of existing network simulators that are often used to simulate and estimate the behavior of underwater networks.


title: Current and Developing Technologies for Monitoring Agents of Bioterrorism and Biowarfare

abstract: Recent events have made public health officials acutely aware of the importance of rapidly and accurately detecting acts of bioterrorism. Because bioterrorism is difficult to predict or prevent, reliable platforms to rapidly detect and identify biothreat agents are important to minimize the spread of these agents and to protect the public health. These platforms must not only be sensitive and specific, but must also be able to accurately detect a variety of pathogens, including modified or previously uncharacterized agents, directly from complex sample matrices. Various commercial tests utilizing biochemical, immunological, nucleic acid, and bioluminescence procedures are currently available to identify biological threat agents. Newer tests have also been developed to identify such agents using aptamers, biochips, evanescent wave biosensors, cantilevers, living cells, and other innovative technologies. This review describes these current and developing technologies and considers challenges to rapid, accurate detection of biothreat agents. Although there is no ideal platform, many of these technologies have proved invaluable for the detection and identification of biothreat agents.


title: The evolutionary neuroscience of musical beat perception: the Action Simulation for Auditory Prediction (ASAP) hypothesis

abstract: Every human culture has some form of music with a beat: a perceived periodic pulse that structures the perception of musical rhythm and which serves as a framework for synchronized movement to music. What are the neural mechanisms of musical beat perception, and how did they evolve? One view, which dates back to Darwin and implicitly informs some current models of beat perception, is that the relevant neural mechanisms are relatively general and are widespread among animal species. On the basis of recent neural and cross-species data on musical beat processing, this paper argues for a different view. Here we argue that beat perception is a complex brain function involving temporally-precise communication between auditory regions and motor planning regions of the cortex (even in the absence of overt movement). More specifically, we propose that simulation of periodic movement in motor planning regions provides a neural signal that helps the auditory system predict the timing of upcoming beats. This “action simulation for auditory prediction” (ASAP) hypothesis leads to testable predictions. We further suggest that ASAP relies on dorsal auditory pathway connections between auditory regions and motor planning regions via the parietal cortex, and suggest that these connections may be stronger in humans than in nonhuman primates due to the evolution of vocal learning in our lineage. This suggestion motivates cross-species research to determine which species are capable of human-like beat perception, i.e., beat perception that involves accurate temporal prediction of beat times across a fairly broad range of tempi.


Micro Expression Classification Accuracy Assessment


title: Extreme learning machine and its applications

abstract: Recently, a novel learning algorithm for single-hidden-layer feedforward neural networks (SLFNs) named extreme learning machine (ELM) was proposed by Huang et al. The essence of ELM is that the learning parameters of hidden nodes, including input weights and biases, are randomly assigned and need not be tuned while the output weights can be analytically determined by the simple generalized inverse operation. The only parameter needed to be defined is the number of hidden nodes. Compared with other traditional learning algorithms for SLFNs, ELM provides extremely faster learning speed, better generalization performance and with least human intervention. This paper firstly introduces a brief review of ELM, describing the principle and algorithm of ELM. Then, we put emphasis on the improved methods or the typical variants of ELM, especially on incremental ELM, pruning ELM, error-minimized ELM, two-stage ELM, online sequential ELM, evolutionary ELM, voting-based ELM, ordinal ELM, fully complex ELM, and symmetric ELM. Next, the paper summarized the applications of ELM on classification, regression, function approximation, pattern recognition, forecasting and diagnosis, and so on. In the last, the paper discussed several open issues of ELM, which may be worthy of exploring in the future.


title: Theoretical and Empirical Analysis of ReliefF and RReliefF

abstract: Relief algorithms are general and successful attribute estimators. They are able to detect conditional dependencies between attributes and provide a unified view on the attribute estimation in regression and classification. In addition, their quality estimates have a natural interpretation. While they have commonly been viewed as feature subset selection methods that are applied in prepossessing step before a model is learned, they have actually been used successfully in a variety of settings, e.g., to select splits or to guide constructive induction in the building phase of decision or regression tree learning, as the attribute weighting method and also in the inductive logic programming. ::: ::: A broad spectrum of successful uses calls for especially careful investigation of various features Relief algorithms have. In this paper we theoretically and empirically investigate and discuss how and why they work, their theoretical and practical properties, their parameters, what kind of dependencies they detect, how do they scale up to large number of examples and features, how to sample data for them, how robust are they regarding the noise, how irrelevant and redundant attributes influence their output and how different metrics influences them.


title: The Evolution of Android Malware and Android Analysis Techniques

abstract: With the integration of mobile devices into daily life, smartphones are privy to increasing amounts of sensitive information. Sophisticated mobile malware, particularly Android malware, acquire or utilize such data without user consent. It is therefore essential to devise effective techniques to analyze and detect these threats. This article presents a comprehensive survey on leading Android malware analysis and detection techniques, and their effectiveness against evolving malware. This article categorizes systems by methodology and date to evaluate progression and weaknesses. This article also discusses evaluations of industry solutions, malware statistics, and malware evasion techniques and concludes by supporting future research paths.


title: Improving Content-Based Similarity Measures by Training a Collaborative Model

abstract: We observed that for multimedia data – especially music collaborative similarity measures perform much better than similarity measures derived from content-based sound features. Our observation is based on a large scale evaluation with >250,000,000 collaborative data points crawled from the web and >190,000 songs annotated with content-based sound feature sets. A song mentioned in a playlist is regarded as one collaborative data point. In this paper we present a novel approach to bridging the performance gap between collaborative and contentbased similarity measures. In the initial training phase a model vector for each song is computed, based on collaborative data. Each vector consists of 200 overlapping unlabelled 'genres' or song clusters. Instead of using explicit numerical voting, we use implicit user profile data as collaborative data source, which is, for example, available as purchase histories in many large scale ecommerce applications. After the training phase, we used support vector machines based on content-based sound features to predict the collaborative model vectors. These predicted model vectors are finally used to compute the similarity between songs. We show that combining collaborative and content-based similarity measures can help to overcome the new item problem in e-commerce applications that offer a collaborative similarity recommender as service to their customers.


title: A simulation study of OBDD-based proper splitting strategies for power systems under consideration of transient stability

abstract: System splitting, also called controlled system islanding, can effectively prevent blackouts. Following the OBDD-based three-phase method given in for proper splitting strategies, which satisfy necessary steady-state constraints, This work studies the feasibility of the proper splitting strategies by means of power system transient simulations on the IEEE 118-bus system. Simulation results show that a considerable proportion of proper splitting strategies can successfully split the power system into stable islands. Furthermore, considering a general knowledge that a controlled power system can easily maintain its stability after a small disturbance, This work presents "threshold value constraint" to restrict the degrees of the disturbances caused by proper splitting strategies. An approach is proposed to select threshold values for the constraint. Further simulation results show that by checking the constraint for proper splitting strategies, feasible splitting strategies can be found, which can successfully split the system into islands satisfying transient stability constraints.


title: Arthroscopy in acute traumatic hemarthrosis of the knee. Incidence of anterior cruciate tears and other injuries.

abstract: In a prospective study, all injured knees that had traumatic hemarthrosis and absent or negligible instability on clinical examination underwent arthroscopy and examination under anesthesia. Eighty-five knees (eighty-three patients) were examined over a 125-week period. Some degree of disruption of the anterior cruciate ligament was found in sixty-one (72 per cent) of the knees (a partial tear in 28 per cent and a complete tear in 44 per cent), frequently associated with an injury of varying severity to other joint structures. These included minor ligament sprains without laxity in 41 per cent, a major associated ligament injury in 21 per cent, meniscal tears in 62 per cent (partial in 30 per cent and complete in 70 per cent), and a femoral chondral fracture or surface defect in 20 per cent. A popping sensation at injury occurred in 33 per cent of knees with a normal anterior cruciate ligament and in 36 per cent of those with a disruption. One-third of the knees had no to slight pain at the time of injury. The anterior drawer test without anesthesia was positive in only 24 per cent of the knees with a torn anterior cruciate ligament. We concluded that: (1) a traumatic hemarthrosis indicates a significant knee injury; (2) examination under anesthesia plus arthroscopy allows a more accurate diagnosis of injury to joint structures; and (3) such data are required for a rational treatment program to be outlined.


title: The restorative benefits of nature: Toward an integrative framework

abstract: Directed attention plays an important role in human information processing; its fatigue, in turn, has farreaching consequences. Attention Restoration Theory provides an analysis of the kinds of experiences that lead to recovery from such fatigue. Natural environments turn out to be particularly rich in the characteristics necessary for restorative experiences. An integrative framework is proposed that places both directed attention and stress in the larger context of human-environment relationships.


Frame Instance Extraction and Clustering for Default Knowledge Building


title: Soil Resistivity Data Computations; Single and Two - Layer Soil Resistivity Structure and Its Implication on Earthing Design

abstract: Performing High Voltage (HV) tasks with a multi craft work force create a special set of safety circumstances. This paper aims to present vital information relating to when it is acceptable to use a single or a two-layer soil structure. Also it discusses the implication of the high voltage infrastructure on the earth grid and the safety of this implication under a single or a two-layer soil structure. A multiple case study is investigated to show the importance of using the right soil resistivity structure during the earthing system design. Keywords—Earth Grid, EPR, High Voltage, Soil Resistivity Structure, Step Voltage, Touch Voltage.


Search and Coherence-Building in Intuition and Insight Problem Solving


title: Problems in the interplay of development and IT operations in system development projects

abstract: ContextThe assumption of the presented work is that the ability of system developers and IT operations personnel to cooperate effectively in system development projects has great impact on the quality of the final system solution, as well as on the service level of its subsequent operation. ObjectiveThe present research explores the interplay of system development and IT operations and the challenges they are meeting. We are especially interested in identifying problems encountered between these two parties in system development projects. MethodWe identify and rank problems by using a ranking-type Delphi study. We involved 42 Norwegian IT experts and split them into three expert panels: system developers, IT operations personnel and system owners. We then guided them through the three phases of the Delphi method - brainstorming, reduction and ranking. ResultsA comprehensive list of 66 problems, organized into seven groups, is compiled. Through a selection and ranking procedure, the panels found the following to be the six most serious problems in the interplay of system development and IT operations: (1) IT operations not being involved in the requirements specification; (2) poor communication and information flow; (3) unsatisfactory test environments; (4) lack of knowledge transfer; (5) systems being put into production before they are complete; and (6) operational routines not being established prior to deployment. ConclusionThe sheer amount and variety of problems mentioned and the respondents' explanations confirm that this interplay needs attention; the parties agree that they do not cooperate effectively in development projects. The results imply that IT operations should be regarded as an important stakeholder throughout several systems development activities, especially requirements analysis, testing and deployment. Moreover, such involvement should be facilitated by an increased focus on enhancing cooperation and communication.


title: A simple strategy for calibrating the geometry of light sources

abstract: We present a methodology for calibrating multiple light source locations in 3D from images. The procedure involves the use of a novel calibration object that consists of three spheres at known relative positions. The process uses intensity images to find the positions of the light sources. We conducted experiments to locate light sources in 51 different positions in a laboratory setting. Our data shows that the vector from a point in the scene to a light source can be measured to within 2.7/spl plusmn/4/spl deg/ at /spl alpha/=.05 (6 percent relative) of its true direction and within 0.13/spl plusmn/.02 m at /spl alpha/=.05 (9 percent relative) of its true magnitude compared to empirically measured ground truth. Finally, we demonstrate how light source information is used for color correction.


title: Distributed Publish/Subscribe Query Processing on the Spatio-Textual Data Stream

abstract: Huge amount of data with both space and text information, e.g., geo-tagged tweets, is flooding on the Internet. Such spatio-textual data stream contains valuable information for millions of users with various interests on different keywords and locations. Publish/subscribe systems enable efficient and effective information distribution by allowing users to register continuous queries with both spatial and textual constraints. However, the explosive growth of data scale and user base has posed challenges to the existing centralized publish/subscribe systems for spatio-textual data streams. ::: In this paper, we propose our distributed publish/subscribe system, called PS2Stream, which digests a massive spatio-textual data stream and directs the stream to target users with registered interests. Compared with existing systems, PS2Stream achieves a better workload distribution in terms of both minimizing the total amount of workload and balancing the load of workers. To achieve this, we propose a new workload distribution algorithm considering both space and text properties of the data. Additionally, PS2Stream supports dynamic load adjustments to adapt to the change of the workload, which makes PS2Stream adaptive. Extensive empirical evaluation, on commercial cloud computing platform with real data, validates the superiority of our system design and advantages of our techniques on system performance improvement.


title: Efficient Skin Temperature Sensor and Stable Gel‐Less Sticky ECG Sensor for a Wearable Flexible Healthcare Patch

abstract: Wearable, flexible healthcare devices, which can monitor health data to predict and diagnose disease in advance, benefit society. Toward this future, various flexible and stretchable sensors as well as other components are demonstrated by arranging materials, structures, and processes. Although there are many sensor demonstrations, the fundamental characteristics such as the dependence of a temperature sensor on film thickness and the impact of adhesive for an electrocardiogram (ECG) sensor are yet to be explored in detail. In this study, the effect of film thickness for skin temperature measurements, adhesive force, and reliability of gel-less ECG sensors as well as an integrated real-time demonstration is reported. Depending on the ambient conditions, film thickness strongly affects the precision of skin temperature measurements, resulting in a thin flexible film suitable for a temperature sensor in wearable device applications. Furthermore, by arranging the material composition, stable gel-less sticky ECG electrodes are realized. Finally, real-time simultaneous skin temperature and ECG signal recordings are demonstrated by attaching an optimized device onto a volunteer's chest.


title: A cross-sectional study on attitudes toward gender equality, sexual behavior, positive sexual experiences, and communication about sex among sexually active and non-sexually active adolescents in Bolivia and Ecuador

abstract: Background : It is widely agreed upon that gender is a key aspect of sexuality however, questions remain on how gender exactly influences adolescents’ sexual health. Objective : The aim of this research was to study correlations between gender equality attitudes and sexual behavior, sexual experiences and communication about sex among sexually active and non-sexually active adolescents in 2 Latin American countries. Design : In 2011, a cross-sectional study was carried out among 5,913 adolescents aged 14–18 in 20 secondary schools in Cochabamba (Bolivia) and 6 secondary schools in Cuenca (Ecuador). Models were built using logistic regressions to assess the predictive value of attitudes toward gender equality on adolescents’ sexual behavior, on experiences and on communication. Results : The analysis shows that sexually active adolescents who consider gender equality as important report higher current use of contraceptives within the couple. They are more likely to describe their last sexual intercourse as a positive experience and consider it easier to talk with their partner about sexuality than sexually experienced adolescents who are less positively inclined toward gender equality. These correlations remained consistent whether the respondent was a boy or a girl. Non-sexually active adolescents, who consider gender equality to be important, are more likely to think that sexual intercourse is a positive experience. They consider it less necessary to have sexual intercourse to maintain a relationship and find it easier to communicate with their girlfriend or boyfriend than sexually non-active adolescents who consider gender equality to be less important. Comparable results were found for boys and girls. Conclusions : Our results suggest that gender equality attitudes have a positive impact on adolescents’ sexual and reproductive health (SRH) and wellbeing. Further research is necessary to better understand the relationship between gender attitudes and specific SRH outcomes such as unwanted teenage pregnancies and sexual pleasure among adolescents worldwide. Keywords : adolescents; gender attitudes; Latin America; sexual behavior; positive sexual experiences (Published: 11 July 2014) Citation : Glob Health Action 2014, 7 : 24089 - http://dx.doi.org/10.3402/gha.v7.24089


title: Improving Survey Methods Using a New Objective Metric for Measuring Driving Time Variability in Survey and GPS Data

abstract: Accurate estimates of day-to-day variability in driving times are of critical importance for many travel-related issues: predicting traffic congestion from past traffic patterns, estimating emissions, and estimating relative crash risk compared to baseline driving exposure. Driving times based on survey data necessarily rely upon subjective recall as obtained in daily diaries, interviews or phone surveys. Within the last few decades, objective GPS data have become available in travel studies carried out in many major cities in the U.S. and other countries. Few studies to date have compared GPS and survey data for the same subjects on the same day of travel, and even fewer studies have collected both GPS and survey data for more than one day of travel. This paper proposes a new objective metric for comparing GPS and diary data either on the same day or across multiple days that does not require trip matching, thereby improving accuracy of both GPS and survey methods for estimating day-to-day variability. The proposed metric allows an objective measure for within- and between-day comparisons between survey and GPS methods, as well as estimating day-to-day variability in driving times within survey and GPS methods. This new metric may improve reliability and accuracy of both survey and GPS estimates of day-to-day driving consistency.


title: Industry experience with the IBM Active Middleware Technology (AMiT) Complex Event Processing engine

abstract: Complex event processing (CEP) is the analysis of event data in real-time to generate immediate insight and enable instant response to changing conditions. In this paper we share the experience we accumulated over the last decade in designing and deploying CEP applications using the IBM Active Middleware Technology (AMiT) CEP engine, a research asset developed at IBM Research - Haifa. Over the years we applied the technology in solutions for various industries, such as banking, insurance, healthcare, chemical and petroleum and more. In each scenario, the CEP engine was used to address different functional requirements including event-based routing, observation, monitoring and correlation. It was also often required to meet nonfunctional requirements such as scalability and transactional support. We describe several solutions from different domains in which AMiT played at least one of these functional or nonfunctional roles. Our experience shows that across the different industries and applications, using the CEP technology, independent of a specific engine implementation, has been consistently proven to be highly successful. It has played a vital role in designing the applications by providing a means to expressively and flexibly define and maintain the event processing logic of the application, and in runtime by being able to meet all the functional and non-functional requirements without taking a toll on the application performance.


title: Stereo reconstruction and contrast restoration in daytime fog

abstract: Stereo reconstruction serves many outdoor applications, and thus sometimes faces foggy weather. The quality of the reconstruction by state of the art algorithms is then degraded as contrast is reduced with the distance because of scattering. However, as shown by defogging algorithms from a single image, fog provides an extra depth cue in the gray level of far away objects. Our idea is thus to take advantage of both stereo and atmospheric veil depth cues to achieve better stereo reconstructions in foggy weather. To our knowledge, this subject has never been investigated earlier by the computer vision community. We thus propose a Markov Random Field model of the stereo reconstruction and defogging problem which can be optimized iteratively using the α-expansion algorithm. Outputs are a dense disparity map and an image where contrast is restored. The proposed model is evaluated on synthetic images. This evaluation shows that the proposed method achieves very good results on both stereo reconstruction and defogging compared to standard stereo reconstruction and single image defogging.


title: Traffic generation of IEC 61850 sampled values

abstract: The work presented in this paper is targeted at the first phase of the test and measurements product life cycle, namely standardisation. During this initial phase of any product, the emphasis is on the development of standards that support new technologies while leaving the scope of implementations as open as possible. To allow the engineer to freely create and invent tools that can quickly help him simulate or emulate his ideas are paramount. Within this scope, a traffic generation system has been developed for IEC 61850 Sampled Values which will help in the evaluation of the data models, data acquisition, data fusion, data integration and data distribution between the various devices and components that use this complex set of evolving standards in Smart Grid systems.


title: Exploring convolutional networks for end-to-end visual servoing

abstract: Present image based visual servoing approaches rely on extracting hand crafted visual features from an image. Choosing the right set of features is important as it directly affects the performance of any approach. Motivated by recent breakthroughs in performance of data driven methods on recognition and localization tasks, we aim to learn visual feature representations suitable for servoing tasks in unstructured and unknown environments. In this paper, we present an end-to-end learning based approach for visual servoing in diverse scenes where the knowledge of camera parameters and scene geometry is not available a priori. This is achieved by training a convolutional neural network over color images with synchronised camera poses. Through experiments performed in simulation and on a quadrotor, we demonstrate the efficacy and robustness of our approach for a wide range of camera poses in both indoor as well as outdoor environments.


title: A Cubic 3-Axis Magnetic Sensor Array for Wirelessly Tracking Magnet Position and Orientation

abstract: In medical diagnoses and treatments, e.g., endoscopy, dosage transition monitoring, it is often desirable to wirelessly track an object that moves through the human GI tract. In this paper, we propose a magnetic localization and orientation system for such applications. This system uses a small magnet enclosed in the object to serve as excitation source, so it does not require the connection wire and power supply for the excitation signal. When the magnet moves, it establishes a static magnetic field around, whose intensity is related to the magnet's position and orientation. With the magnetic sensors, the magnetic intensities in some predetermined spatial positions can be detected, and the magnet's position and orientation parameters can be computed based on an appropriate algorithm. Here, we propose a real-time tracking system developed by a cubic magnetic sensor array made of Honeywell 3-axis magnetic sensors, HMC1043. Using some efficient software modules and calibration methods, the system can achieve satisfactory tracking accuracy if the cubic sensor array has enough number of 3-axis magnetic sensors. The experimental results show that the average localization error is 1.8 mm.


Expand CRF to Model Long Distance Dependencies in Prosodic Break Prediction.


title: Inferring Chord Sequence Meanings via Lyrics: Process and Evaluation

abstract: We improve upon our simple approach for learning the “associational meaning” of chord sequences from lyrics based on contingency statistics induced over a set of lyrics with chord annotations. Specifically, we refine this process by using word alignment tools developed for statistical machine translation, and we also use a much larger set of chord annotations. In addition, objective evaluation measures are included. Thus, this work validates a novel application of lexicon induction techniques over parallel corpora to a domain outside of natural language learning. To confirm the associations commonly attributed to major versus minor chords (i.e., happy and sad, respectively), we compare the inferred word associations against synonyms reflecting this dichotomy. To evaluate meanings associated with chord sequences, we check how often tagged chords occur in songs labeled with the same overall meaning.


title: Monolingual and Cross-lingual Zero-shot Style Transfer

abstract: We introduce the task of zero-shot style transfer between different languages. Our training data includes multilingual parallel corpora, but does not contain any parallel sentences between styles, similarly to the recent previous work. We propose a unified multilingual multi-style machine translation system design, that allows to perform zero-shot style conversions during inference; moreover, it does so both monolingually and cross-lingually. Our model allows to increase the presence of dissimilar styles in corpus by up to 3 times, easily learns to operate with various contractions, and provides reasonable lexicon swaps as we see from manual evaluation.


title: Mindfulness and craving: effects and mechanisms

abstract: Abstract Mindfulness meditation has a long tradition of being used to manage cravings. This paper reviews 30 experimental studies that have examined the effects of different types of mindfulness practice on cravings for food, cigarettes and alcohol. The findings are interpreted in light of relevant theories of craving. The studies show most support for the elaborated intrusion theory of desire and conditioning models. They suggest that whilst mindfulness strategies may bring about immediate reductions in craving, such effects are likely to stem from working memory load, and will not necessarily be superior to alternative strategies that also load working memory. Likewise, reductions in craving over the medium term may occur due to extinction processes that result from the individual inhibiting craving-related responses. Again, alternative strategies that promote response suppression may be equally effective. Nevertheless, a smaller number of studies show promising results where mindfulness exercises have been repeatedly practiced over a longer period of time. The results of these studies provide tentative support for Buddhist models of craving that suggest mindfulness practice may confer unique benefits in terms of both craving reduction and reducing the extent to which craving leads to consumption. Further research would be needed to confirm this.


title: External-Rotor $6-10$ Switched Reluctance Motor for an Electric Bicycle

abstract: As a cost-effective, healthy, and environmentally friendly personal mode of transportation, electric bicycles (E-bikes) are gaining an increasing market share from conventional bicycles and automobiles in urban communities. At the same time, the simple structure, high torque and power density, as well as low cost of switched reluctance machines (SRM) make them a strong candidate for E-bikes. This paper presents a 3-phase, external-rotor SRM with 6 stator poles and 10 rotor poles (6-10), designed for an E-bike application. The SRM design and simulation is initiated by evaluation of the bicycle kinematics. Analytic estimates are used to determine machine main dimensions and output power equation, followed by comprehensive finite element analysis (FEA). A torque ripple reduction control is presented and detailed thermal analysis conducted to improve the design.


title: Transcriptome analysis reveals global regulation in response to CO2 supplementation in oleaginous microalga Coccomyxa subellipsoidea C-169

abstract: BACKGROUND ::: Microalgae are emerging as suitable feedstock for renewable biofuel production and providing a promising way to alleviate green house gas CO2. Characterizing the metabolic pathways involved in the biosynthesis of energy-rich compounds and their global regulation upon elevated CO2 is necessary to explore the mechanism underlying rapid growth and lipid accumulation, so as to realize the full potential of these organisms as energy resources. ::: ::: ::: RESULTS ::: In the present study, 2 and 5 % CO2 increased growth rate and lipid accumulation in autotrophically cultured green alga Coccomyxa subellipsoidea C-169. Overall biomass productivity as 222 mg L(-1) day(-1) and fatty acid content as 48.5 % dry cell weight were attained in 2 % CO2, suggesting C-169 as a great candidate for lipid production via CO2 supplementation. Transcriptomic analysis of 2 % against 0.04 % CO2-cultured C-169 unveiled the global regulation of important metabolic processes. Other than enhancing gene expression in the Calvin cycle, C-169 upregulated the expression of phosphoenolpyruvate carboxylase, pyruvate carboxylase and carbamoyl-phosphate synthetase II to enhance the anaplerotic carbon assimilation reactions upon elevated CO2. Upregulation of ferredoxin and ferredoxin-NADP(+) reductase implied that plentiful energy captured through photosynthesis was transferred through ferredoxin to sustain rapid growth and lipid accumulation. Genes involved in the glycolysis, TCA cycle and oxidative phosphorylation were predominantly upregulated presumably to provide abundant intermediates and metabolic energy for anabolism. Coordinated upregulation of nitrogen acquisition and assimilation genes, together with activation of specific carbamoyl-phosphate synthetase and ornithine pathway genes, might help C-169 to maintain carbon/nitrogen balance upon elevated CO2. Significant downregulation of fatty acid degradation genes, as well as the upregulation of fatty acid synthesis genes at the later stage might contribute to the tremendous lipid accumulation. ::: ::: ::: CONCLUSION ::: Global and collaborative regulation was employed by C-169 to assimilate more carbon and maintain carbon/nitrogen balance upon elevated CO2, which provide abundant carbon skeleton and affluent metabolic energy to sustain rapid growth and lipid accumulation. Data here for the first time bring significant insights into the regulatory profile of metabolism and acclimation to elevated CO2 in C-169, which provide important information for future metabolic engineering in the development of sustainable microalgae-based biofuels.


title: On Detection of Emerging Anomalous Traffic Patterns Using GPS Data

abstract: The increasing availability of large-scale trajectory data provides us great opportunity to explore them for knowledge discovery in transportation systems using advanced data mining techniques. Nowadays, large number of taxicabs in major metropolitan cities are equipped with a GPS device. Since taxis are on the road nearly 24h a day (with drivers changing shifts), they can now act as reliable sensors to monitor the behavior of traffic. In this article, we use GPS data from taxis to monitor the emergence of unexpected behavior in the Beijing metropolitan area, which has the potential to estimate and improve traffic conditions in advance. We adapt likelihood ratio test statistic (LRT) which have previously been mostly used in epidemiological studies to describe traffic patterns. To the best of our knowledge the use of LRT in traffic domain is not only novel but results in accurate and rapid detection of anomalous behavior.


title: LSB: A Lightweight Scalable BlockChain for IoT Security and Privacy

abstract: BlockChain (BC) has attracted tremendous attention due to its immutable nature and the associated security and privacy benefits. BC has the potential to overcome security and privacy challenges of Internet of Things (IoT). However, BC is computationally expensive, has limited scalability and incurs significant bandwidth overheads and delays which are not suited to the IoT context. We propose a tiered Lightweight Scalable BC (LSB) that is optimized for IoT requirements. We explore LSB in a smart home setting as a representative example for broader IoT applications. Low resource devices in a smart home benefit from a centralized manager that establishes shared keys for communication and processes all incoming and outgoing requests. LSB achieves decentralization by forming an overlay network where high resource devices jointly manage a public BC that ensures end-to-end privacy and security. The overlay is organized as distinct clusters to reduce overheads and the cluster heads are responsible for managing the public BC. LSB incorporates several optimizations which include algorithms for lightweight consensus, distributed trust and throughput management. Qualitative arguments demonstrate that LSB is resilient to several security attacks. Extensive simulations show that LSB decreases packet overhead and delay and increases BC scalability compared to relevant baselines.


title: Low cost DC lines PLC based photovoltaic plants parameters smart monitoring communications and control module

abstract: Nowadays Photovoltaic (PV) plants have become significant investment projects with long term Return Of Investment (ROI). This is making investors together with operations managers to deal with reliable information on photovoltaic plants performances. Most of the information is gathered through data monitoring systems and also supplied by proper inverters in case of grid connected plants. It usually relates to series/parallel combinations of PV panels strings, in most cases, but rarely to individual PV panels. Furthermore, in case of huge dimensions PV plants, with different ground profiles, etc., should any adverse circumstances happen (panel failure, sudden shadowing, clouds, strong wind), it is difficult to identify the exact problem location. The use of distributed wired or wireless sensors can be a solution. Nevertheless, no one is problems free and all are significant cost. In this article is proposed a low cost DC Power Lines Communications (DC PLC) based PV plant parameters smart monitoring communications and control module. The aim is the development of a micro controller (uC) based sensor module with corresponding modem for communications through already existing DC plant power wiring as data transmission lines. This will reduce drastically both hardware and transmission lines costs.


title: Asylum‐seekers and refugees: a structuration theory analysis of their experiences in the UK

abstract: This is the peer reviewed version of the following article: Healey, R. L. (2006). Asylum-seekers and refugees: A structuration theory analysis of their experiences in the UK 2006. Population, Space and Place, 12(4), pp. 257-271, which has been published in final form at http://dx.doi.org/10.1002/psp.412 . This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.


title: Understanding Out-of-Home Food Environment, Family Restaurant Choices, and Childhood Obesity with an Agent-Based Huff Model

abstract: Out-of-home eating plays an increasingly important role in the American diet and weight. This research studied out-of-home food environment and restaurant choices in one rural county of eastern Alabama, United States, and examined the impact on African American children’s weight status. A mixed methods approach was used in this study. Questionnaires were collected for 613 African American children at all four public elementary schools in the county. The healthfulness of restaurants was assessed with the Nutrition Environment Measures Survey-Restaurant (NEMS-R). An agent-based model integrated with Huff’s model was developed in order to examine family dining patterns with consideration for individual and community socio-demographics; restaurant location, size, and healthfulness; and the spatial dynamics between consumers and food retailers. We found that this model performed well, as evidenced by validation with the 2013–2014 National Health and Nutrition Examination Survey (p, 0.54–0.96), and by comparison with the original Huff model. Frequency of dining at low-quality full-service restaurants (r = 0.084; p <0.05) was associated with rural children’s body mass index (BMI) percentile. These findings may increase public awareness of the importance of family restaurant choices as well as the potential unhealthiness of full-service restaurants.


Enhancing the AI2 Diagrams dataset using Rhetorical Structure Theory


title: 3D large scale integration technology using Wafer-on-Wafer (WOW) stacking

abstract: Wafer-scale three-dimensional (3D) technologies, so-called Wafer-on-Wafer (WOW), beyond post-scaling for high-density integration are discussed. WOW module technologies consisting of wafer thinning, stacking, TSV (Through-Silicon-Via) interconnects, and packaging are described. No degradation for advanced 35-nm SRAM logic and FRAM devices was observed with ultra-thinning below 10-μm for 300-mm and 200-mm wafers. Production-worthy die yield due to wafer stacking and a comparison of critical scaling for future manufacturability are presented.


title: Long short term memory neural network for keyboard gesture decoding

abstract: Gesture typing is an efficient input method for phones and tablets using continuous traces created by a pointed object (e.g., finger or stylus). Translating such continuous gestures into textual input is a challenging task as gesture inputs exhibit many features found in speech and handwriting such as high variability, co-articulation and elision. In this work, we address these challenges with a hybrid approach, combining a variant of recurrent networks, namely Long Short Term Memories [1] with conventional Finite State Transducer decoding [2]. Results using our approach show considerable improvement relative to a baseline shape-matching-based system, amounting to 4% and 22% absolute improvement respectively for small and large lexicon decoding on real datasets and 2% on a synthetic large scale dataset.


title: Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks

abstract: Humans can understand and produce new utterances effortlessly, thanks to their compositional skills. Once a person learns the meaning of a new verb "dax," he or she can immediately understand the meaning of "dax twice" or "sing and dax." In this paper, we introduce the SCAN domain, consisting of a set of simple compositional navigation commands paired with the corresponding action sequences. We then test the zero-shot generalization capabilities of a variety of recurrent neural networks (RNNs) trained on SCAN with sequence-to-sequence methods. We find that RNNs can make successful zero-shot generalizations when the differences between training and test commands are small, so that they can apply "mix-and-match" strategies to solve the task. However, when generalization requires systematic compositional skills (as in the "dax" example above), RNNs fail spectacularly. We conclude with a proof-of-concept experiment in neural machine translation, suggesting that lack of systematicity might be partially responsible for neural networks' notorious training data thirst.


title: A signal detection theory approach for camera tamper detection

abstract: Camera tamper detection is the ability to detect faults and operational failures in video surveillance cameras by analyzing the video. Researchers have increasingly focused on such techniques attributing to the ubiquitous deployment of large scale surveillance systems. In this paper, a signal detection theory approach is proposed to quantitatively analyze the information being captured by the camera and to detect tampers. Signal activity is used as a feature to measure the amount of information in the image. The distribution of features representing the normal operation of a camera are modeled as a Gaussian mixture model (GMM). The GMM is trained using synthetic data. To reduce the effects of noise, a Kalman filter is used to model changes in signal activity in the video. Experimental results show that the proposed approach out performed the state-of-the-art [13] in detecting tampered images with higher accuracy while generating lower false alarms.


title: A taxonomy for user-healthcare robot interaction

abstract: This paper evaluates existing taxonomies aimed at characterizing the interaction between robots and their users and modifies them for health care applications. The modifications are based on existing robot technologies and user acceptance of robotics. Characterization of the user, or in this case the patient, is a primary focus of the paper, as they present a unique new role as robot users. While therapeutic and monitoring-related applications for robots are still relatively uncommon, we believe they will begin to grow and thus it is important that the spurring relationship between robot and patient is well understood.


title: Fault detection and fault-tolerant control of interior permanent-magnet motor drive system for electric vehicle

abstract: This work presents a control strategy that provides fault tolerance to the major sensor faults which may occur in an interior-permanent-magnet-motor (IPMM)-based electric vehicle propulsion drive system. Failures of a position sensor, a dc-link voltage sensor, and current sensors are all included in the study assuming no multiple faults. For each possible sensor fault, a corresponding method of detection or diagnosis is provided. Additionally, once the fault is detected, the control scheme is automatically reconfigured to provide post-fault operational capability. A state observer is used to provide missing current information in the case of current sensor faults. Experimental results demonstrate the effectiveness of both the fault detection algorithm and the reconfigurable control scheme. The resulting IPMM drive system proves to be resilient to sensor failures while providing smooth transition to the post-fault operational mode.


title: A Joint Training Framework for Robust Automatic Speech Recognition

abstract: Robustness against noise and reverberation is critical for ASR systems deployed in real-world environments. In robust ASR, corrupted speech is normally enhanced using speech separation or enhancement algorithms before recognition. This paper presents a novel joint training framework for speech separation and recognition. The key idea is to concatenate a deep neural network (DNN) based speech separation frontend and a DNN-based acoustic model to build a larger neural network, and jointly adjust the weights in each module. This way, the separation fron-tend is able to provide enhanced speech desired by the acoustic model and the acoustic model can guide the separation frontend to produce more discriminative enhancement. In addition, we apply sequence training to the jointly trained DNN so that the linguistic information contained in the acoustic and language models can be back-propagated to influence the separation frontend at the training stage. To further improve the robustness, we add more noise- and reverberation-robust features for acoustic modeling. At the test stage, utterance-level unsupervised adaptation is performed to adapt the jointly trained network by learning a linear transformation of the input of the separation frontend. The resulting sequence-discriminative jointly-trained multistream system with run-time adaptation achieves 10.63% average word error rate (WER) on the test set of the reverberant and noisy CHiME-2 dataset (task-2), which represents the best performance on this dataset and a 22.75% error reduction over the best existing method.


title: Computational drug repositioning: from data to therapeutics.

abstract: Traditionally, most drugs have been discovered using phenotypic or target-based screens. Subsequently, their indications are often expanded on the basis of clinical observations, providing additional benefit to patients. This review highlights computational techniques for systematic analysis of transcriptomics (Connectivity Map, CMap), side effects, and genetics (genome-wide association study, GWAS) data to generate new hypotheses for additional indications. We also discuss data domains such as electronic health records (EHRs) and phenotypic screening that we consider promising for novel computational repositioning methods.


title: An innovative outcomes-based medical education program built on adult learning principles

abstract: An innovative medical curriculum at the University of New South Wales (UNSW) has been developed through a highly collaborative process aimed at building faculty ownership and ongoing sustainability. The result is a novel capability-based program that features early clinical experience and small-group teaching, which offers students considerable flexibility and achieves a high degree of alignment between graduate outcomes, learning activities and assessments. Graduate capabilities that focus student learning on generic outcomes are described (critical evaluation, reflection, communication and teamwork) along with traditional outcomes in biomedical science, social aspects, clinical performance and ethics. Each two-year phase promotes a distinctive learning process to support and develop autonomous learning across six years. The approaches emphasize important adult education themes: student autonomy; learning from experience; collaborative learning; and adult teacher-learner relationships. Teaching in each phase draws on stages of the human life cycle to provide an explicit organization for the vertical integration of knowledge and skills. A learning environment that values the social nature of learning is fostered through the program's design and assessment system, which supports interdisciplinary integration and rewards students who exhibit self-direction. Assessment incorporates criterion referencing, inter- disciplinary examinations, a balance between continuous and barrier assessments, peer feedback and performance assessments of clinical competence. A portfolio examination in each phase, in which students submit evidence of reflection and achievement for each capability, ensures overall alignment.


title: Li-substituted K0.5Na0.5NbO3-based piezoelectric ceramics: Crystal structures and the effect of atmosphere on electrical properties

abstract: Abstract Crystal structures and electrical properties of (Na 0.5 K 0.5 ) 1− x Li x NbO 3 ( x = 0, 0.055, 0.06, 0.065, 0.07; KNN- x Li) ceramics were investigated systematically. Rietveld refinements confirmed that the ceramic with x = 0.06 modification had a typical morphtropic phase boundary (MPB) structure, where the proportions of orthorhombic ( Amm 2) and tetragonal ( P 4 mm ) phases were 58.37% and 41.63%, respectively. However, tetragonal phase structure was predominant in the x = 0.07 ceramic with 75.57% P 4 mm phase. Piezoelectric ceramics KNN- x Li were post-annealed in nitrogen (N 2 ) and oxygen (O 2 ), and atmosphere had a strong impact on the electrical properties of them. O 2 treatment resulted in improvements in ferroelectric polarization, electrostriction characteristic and piezoelectric property, due to the decrease in oxygen vacancy concentration. On the contrary, a deteriorated performance was found in the N 2 -treated samples. Impedance data of KNN- x Li ceramics were largely dominated by the sample bulk, and variable-atmosphere experiments suggested that the KNN-0.06Li ceramic was mainly n -type material. The O 2 -treated KNN-0.06Li ceramic with a T c of 473 °C had a remnant polarization ( P r ) of 29.2 μC/cm 2 , a field-induced strain ( S 33 ) of 0.152%, a piezoelectric coefficient ( d 33 ) of 218 pC/N and an electromechanical coupling factor ( k p ) of 43%.


title: A highly efficient power harvester with wide dynamic input power range for 900 MHz wireless power transfer applications

abstract: This work demonstrates the design of an adaptive reconfigurable rectifier to address the issue of early breakdown voltage in a conventional rectifier and extends the rectifier's operation for wide dynamic input power range. A depletion-mode field-effect transistor has been introduced to operate as a switch and compensate at low and high input power levels for rectifier. This design accomplishes 40% of RF-DC power conversion efficiency over a wide dynamic input power range from −10 dBm to 27 dBm, while exhibiting 78% of peak power efficiency at 22 dBm. The power harvester is designed to operate in the 900 MHz ISM band and suitable for Wireless Power Transfer applications.


title: Apples-to-apples in cross-validation studies: pitfalls in classifier performance measurement

abstract: Cross-validation is a mainstay for measuring performance and progress in machine learning. There are subtle differences in how exactly to compute accuracy, F-measure and Area Under the ROC Curve (AUC) in cross-validation studies. However, these details are not discussed in the literature, and incompatible methods are used by various papers and software packages. This leads to inconsistency across the research literature. Anomalies in performance calculations for particular folds and situations go undiscovered when they are buried in aggregated results over many folds and datasets, without ever a person looking at the intermediate performance measurements. This research note clarifies and illustrates the differences, and it provides guidance for how best to measure classification performance under cross-validation. In particular, there are several divergent methods used for computing F-measure, which is often recommended as a performance measure under class imbalance, e.g., for text classification domains and in one-vs.-all reductions of datasets having many classes. We show by experiment that all but one of these computation methods leads to biased measurements, especially under high class imbalance. This paper is of particular interest to those designing machine learning software libraries and researchers focused on high class imbalance.


title: Cyber security quantification model

abstract: Security of information systems is a major concern today because the existing threats are getting new dimensions. Information Security (IS) is to protect our important information assets from accidental or deliberate damages. Cyber Security (CS) is a whole set of procedures and systems providing protection of computer systems and networks from the intentional and unintentional damages or dangers in the cyberspace through services like confidentiality, integrity, authentication, availability, non-repudiation, auditing, and digital signature. To counter the increasing cyber terrorism threats, we need predictive calculation of cyber attacks occurrences. We can do this by giving mathematical models for the elements of CS systems. Researchers have suggested some models for the quantification of CS, however, the existing models have either enforced only qualitative measures or the quantification models lack modeling features and without validation with the realistic data. There is a requirement for a unified model for the quantification of CS that considers majority of the parameters and services for it and which should be validated with realistic data. We propose a quantification model of cyber security, which considers most of the CS parameters. This is a generalized model, which is customizable enough to be used in multitude implementation environments. The proposed model is simulated and validated with an example of real life data for the SZABIST Islamabad Campus email server.


title: Linguistic Markers of Influence in Informal Interactions

abstract: There has been a long standing interest in understanding `Social Influence' both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts with indicator features. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the characteristics of the language and its potential to influence others.


title: The least mean fourth (LMF) adaptive algorithm and its family

abstract: New steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth, etc., sense. During adaptation, the weights undergo exponential relaxation toward their optimal solutions. Time constants have been derived, and surprisingly they turn out to be proportional to the time constants that would have been obtained if the steepest descent least mean square (LMS) algorithm of Widrow and Hoff had been used. The new gradient algorithms are insignificantly more complicated to program and to compute than the LMS algorithm. Their general form is W_{j+1} = W_{j} + 2 \mu K \epsilon_{j}^{2K-1}X_{j}, where W_{j} is the present weight vector, W_{j+1} is the next weight vector, \epsilon_{j} is the present error, X_{j} is the present input vector, \mu is a constant controlling stability and rate of convergence, and 2K is the exponent of the error being minimized. Conditions have been derived for weight-vector convergence of the mean and of the variance for the new gradient algorithms. The behavior of the least mean fourth (LMF) algorithm is of special interest. In comparing this algorithm to the LMS algorithm, when both are set to have exactly the same time constants for the weight relaxation process, the LMF algorithm, under some circumstances, will have a substantially lower weight noise than the LMS algorithm. It is possible, therefore, that a minimum mean fourth error algorithm can do a better job of least squares estimation than a mean square error algorithm. This intriguing concept has implications for all forms of adaptive algorithms, whether they are based on steepest descent or otherwise.


title: Exploring the Political Agenda of the European Parliament Using a Dynamic Topic Modeling Approach

abstract: This study analyzes political interactions in the European Parliament (EP) by considering how the political agenda of the plenary sessions has evolved over time and the manner in which Members of the European Parliament (MEPs) have reacted to external and internal stimuli when making Parliamentary speeches. It does so by considering the context in which speeches are made, and the content of those speeches. To detect latent themes in legislative speeches over time, speech content is analyzed using a new dynamic topic modeling method, based on two layers of matrix factorization. This method is applied to a new corpus of all English language legislative speeches in the EP plenary from the period 1999-2014. Our findings suggest that the political agenda of the EP has evolved significantly over time, is impacted upon by the committee structure of the Parliament, and reacts to exogenous events such as EU Treaty referenda and the emergence of the Euro-crisis have a significant impact on what is being discussed in Parliament. ∗Insight Centre for Data Analytics & School of Computer Science & Informatics, University College Dublin, Ireland. (derek.greene@ucd.ie) †School of Politics & International Relations, University College Dublin, Ireland. (james.cross@ucd.ie).


title: RTL-level modeling of an 8B/10B encoder-decoder using SystemC

abstract: This paper presents an RTL-level model of an 8B/10B encoder/decoder block in SystemC. The use of 8B/10B coding is an important technique in the construction of high performance serial interfaces. These are particularly suitable for alleviating the I/O bottleneck of state of the art systems (which are pinout, rather than bandwidth limited). SystemC has been chosen because it provides a homogeneous design flow for complex designs (i.e. SoC and IP based design), where system modeling at the early stages of the design becomes increasingly important.


title: Machine translation evaluation versus quality estimation

abstract: Most evaluation metrics for machine translation (MT) require reference translations for each sentence in order to produce a score reflecting certain aspects of its quality. The de facto metrics, BLEU and NIST, are known to have good correlation with human evaluation at the corpus level, but this is not the case at the segment level. As an attempt to overcome these two limitations, we address the problem of evaluating the quality of MT as a prediction task, where reference-independent features are extracted from the input sentences and their translation, and a quality score is obtained based on models produced from training data. We show that this approach yields better correlation with human evaluation as compared to commonly used metrics, even with models trained on different MT systems, language-pairs and text domains.


title: Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancer

abstract: Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify non-small cell lung cancer. In our study, we evaluated the utility of using Apple Create ML module to detect and sub-classify non-small cell carcinomas based on histopathological images. After module optimization, the program detected 100% of non-small cell lung cancer images and successfully subclassified the majority of the images. Trained modules, such as ours, can be utilized in diagnostic smartphone-based applications, augmenting diagnostic services in understaffed areas of the world.


title: Medicinal Properties of Cannabinoids, Terpenes, and Flavonoids in Cannabis, and Benefits in Migraine, Headache, and Pain: An Update on Current Evidence and Cannabis Science

abstract: BACKGROUND ::: Comprehensive literature reviews of historical perspectives and evidence supporting cannabis/cannabinoids in the treatment of pain, including migraine and headache, with associated neurobiological mechanisms of pain modulation have been well described. Most of the existing literature reports on the cannabinoids Δ9 -tetrahydrocannabinol (THC) and cannabidiol (CBD), or cannabis in general. There are many cannabis strains that vary widely in the composition of cannabinoids, terpenes, flavonoids, and other compounds. These components work synergistically to produce wide variations in benefits, side effects, and strain characteristics. Knowledge of the individual medicinal properties of the cannabinoids, terpenes, and flavonoids is necessary to cross-breed strains to obtain optimal standardized synergistic compositions. This will enable targeting individual symptoms and/or diseases, including migraine, headache, and pain. ::: ::: ::: OBJECTIVE ::: Review the medical literature for the use of cannabis/cannabinoids in the treatment of migraine, headache, facial pain, and other chronic pain syndromes, and for supporting evidence of a potential role in combatting the opioid epidemic. Review the medical literature involving major and minor cannabinoids, primary and secondary terpenes, and flavonoids that underlie the synergistic entourage effects of cannabis. Summarize the individual medicinal benefits of these substances, including analgesic and anti-inflammatory properties. ::: ::: ::: CONCLUSION ::: There is accumulating evidence for various therapeutic benefits of cannabis/cannabinoids, especially in the treatment of pain, which may also apply to the treatment of migraine and headache. There is also supporting evidence that cannabis may assist in opioid detoxification and weaning, thus making it a potential weapon in battling the opioid epidemic. Cannabis science is a rapidly evolving medical sector and industry with increasingly regulated production standards. Further research is anticipated to optimize breeding of strain-specific synergistic ratios of cannabinoids, terpenes, and other phytochemicals for predictable user effects, characteristics, and improved symptom and disease-targeted therapies.


title: A High-Resolution Compression Scheme for Ray Tracing Subdivision Surfaces with Displacement

abstract: Subdivision surfaces, especially with displacement, are one of the key modeling primitives used in high-quality rendering environments, such as, e.g., movie production. While their use easily maps to rasterization-based frameworks, they pose a significant challenge for ray tracing environments. This is due to the fact that incoherent access patterns require storing or caching fully tessellated and displaced meshes for efficient intersection computations. In this paper we use a two-tier hierarchy built on a scene's patches. It relies on compressed and quantized bounding volumes on the second tier to reduce the size of the BVH itself. Based on this acceleration structure, we propose a quantized, compact approximation for leaf nodes while being faithful to the underlying patch-geometry. We build on recent advances and present a system that shows competitive performance regarding run-time speed, which is close to full-resolution pre-tessellation methods as well as to previous compression approaches. Ultimately, we provide strong compression of up to a factor of 5: 1 compared to state-of-the-art methods while maintaining high geometrical fidelity surpassing similarly compact approximations and getting close to uncompressed geometry.


title: Learning a Rotation Invariant Detector with Rotatable Bounding Box

abstract: Detection of arbitrarily rotated objects is a challenging task due to the difficulties of locating the multi-angle objects and separating them effectively from the background. The existing methods are not robust to angle varies of the objects because of the use of traditional bounding box, which is a rotation variant structure for locating rotated objects. In this article, a new detection method is proposed which applies the newly defined rotatable bounding box (RBox). The proposed detector (DRBox) can effectively handle the situation where the orientation angles of the objects are arbitrary. The training of DRBox forces the detection networks to learn the correct orientation angle of the objects, so that the rotation invariant property can be achieved. DRBox is tested to detect vehicles, ships and airplanes on satellite images, compared with Faster R-CNN and SSD, which are chosen as the benchmark of the traditional bounding box based methods. The results shows that DRBox performs much better than traditional bounding box based methods do on the given tasks, and is more robust against rotation of input image and target objects. Besides, results show that DRBox correctly outputs the orientation angles of the objects, which is very useful for locating multi-angle objects efficiently. The code and models are available at this https URL


title: CyberCIEGE TM : An Information Assurance Teaching Tool for Training and Awareness

abstract: Abstract : Good security is not intrusive and can be almost invisible to typical users, who are often unaware of or take it for granted. However, good security practice by user populations is a critical element of an organization's information assurance strategy. This is reflected in government information assurance teaching mandates such as DoD Directive 8570.1, which outlines objectives and requirements for information assurance (IA) education, training and awareness. Although mundane education, training and awareness programs may temporarily raise user interest, for many, mandatory education is considered a distracting waste of time. A new approach is needed to convey IA concepts that will engage the user's imagination. CyberCIEGE(TM) is an innovative computer-based tool to teach information assurance concepts. The tool enhances information assurance education and training through the use of computer gaming techniques. In the CyberCIEGE virtual world, students spend virtual money to operate and defend their networks, and can watch the consequences of their choices while under attack. This paper describes CyberCIEGE and will present ways in which this tool can be used to achieve Federal and DoD information assurance teaching objectives.


title: Evolution of a jump in an articulated leg with series-elastic actuation

abstract: The remarkable ability of humans and animals to perform dynamic maneuvers, such as a jump, is largely attributed to series-elastic elements in skeletal muscle. Both the degree of elasticity and the coordination of muscular contractions have been shown to impact jump performance. The objective of this paper is to use a genetic algorithm (GA) to optimize the control and actuator parameters of a series-elastic actuator (SEA), which is functionally analogous to skeletal muscle, in an articulated leg to produce the highest jump. Similar to skeletal muscle, the control and stiffness of the SEA is found by the GA to affect jump performance, yielding solutions with biological properties. In particular, the jumps evolved by the GA made use of the stretch and shortening cycle of the series-elasticity, which is commonly seen in nature to increase the force of an explosive movement. The model studied in this paper is of a prototype leg with series-elastic actuation. A detailed leg and actuator model was developed to include the important electrical and mechanical properties of the DC motors as well as the characteristics of the motor amplifiers.


title: Climate Change Impacts on Global Food Security

abstract: Climate change could potentially interrupt progress toward a world without hunger. A robust and coherent global pattern is discernible of the impacts of climate change on crop productivity that could have consequences for food availability. The stability of whole food systems may be at risk under climate change because of short-term variability in supply. However, the potential impact is less clear at regional scales, but it is likely that climate variability and change will exacerbate food insecurity in areas currently vulnerable to hunger and undernutrition. Likewise, it can be anticipated that food access and utilization will be affected indirectly via collateral effects on household and individual incomes, and food utilization could be impaired by loss of access to drinking water and damage to health. The evidence supports the need for considerable investment in adaptation and mitigation actions toward a “climate-smart food system” that is more resilient to climate change influences on food security.


title: Designing Large High-Resolution Display Workspaces

abstract: Large, high-resolution displays have enormous potential to aid in scenarios beyond their current usage. Their current usages are primarily limited to presentations, visualization demonstrations, or conducting experiments. In this paper, we present a new usage for such systems: an everyday workspace. We discuss how seemingly small large-display design decisions can have significant impacts on users' perceptions of these workspaces, and thus the usage of the space. We describe the effects that various physical configurations have on the overall usability and perception of the display. We present conclusions on how to broaden the usage scenarios of large, high-resolution displays to enable frequent and effective usage as everyday workspaces while still allowing transformation to collaborative or presentation spaces.


title: Real-time verification of network properties using Atomic Predicates

abstract: Network management will benefit from automated tools based upon formal methods. Several such tools have been published in the literature. We present a new formal method for a new tool, Atomic Predicates (AP) Verifier, which is much more time and space efficient than existing tools. Given a set of predicates representing packet filters, AP Verifier computes a set of atomic predicates, which is minimum and unique. The use of atomic predicates dramatically speeds up computation of network reachability. We evaluated the performance of AP Verifier using forwarding tables and ACLs from three large real networks. The atomic predicate sets of these networks were computed very quickly and their sizes are surprisingly small. Real networks are subject to dynamic state changes over time as a result of rule insertion and deletion by protocols and operators, failure and recovery of links and boxes, etc. In a software-defined network, the network state can be observed in real time and thus may be controlled in real time. AP Verifier includes algorithms to process such events and check compliance with network policies and properties in real time. We compare time and space costs of AP Verifier with NetPlumber using datasets from the real networks.


title: Analysis of Facial Aesthetics as Applied to Injectables.

abstract: Understanding the role of volume loss in the aging face has resulted in a paradigm shift in facial rejuvenation techniques. Injectable materials for volume restoration are among the most widespread cosmetic procedures performed. A new approach to the aesthetics of facial aging is necessary to allow the greatest improvement from volumetric techniques while maintaining natural appearing results. Examining the face in terms of facial frames and facial shadows provides the fundamental basis for our injectable analysis.


title: Stigma in patients with schizophrenia receiving community mental health care: a review of qualitative studies

abstract: PURPOSE ::: The aim of this review is to identify consistent themes among the qualitative literature on stigma as experienced by patients with schizophrenia receiving community mental health care. With the treatment focus of schizophrenia nowadays shifting more and more towards community-based mental health care, professionals need to be aware of the increased vulnerability of their clients in their social environment as a result of stigma towards their disease. In-depth knowledge on stigma is critical in order to offer a dignifying community mental health care. ::: ::: ::: METHODS ::: A systematic search of the qualitative literature in Web of Science, PubMed, PsycINFO and Francis was performed to review the subjective experiences and ideas on stigma in outpatients with schizophrenia. ::: ::: ::: RESULTS ::: Three major themes were identified in 18 studies and need to be taken into consideration when implementing an adequate community mental health care: (i) the continuing existence of stigma inherent in the health care setting, (ii) the importance of relational aspects of stigma encounters in daily life and (iii) the significance of the behavioural aspects related to previous stigma experiences and beliefs among patients. ::: ::: ::: CONCLUSIONS ::: Despite much effort in community treatment, patients still experience stigma and discrimination. Community mental health care professionals should not only be aware of structural problems in mental health care, but should also pay considerable attention towards the relational and behavioural aspects in their clients' life concerning stigma. Furthermore, they have the crucial role in the community to raise awareness about stigma in order to increase their clients' acceptance in society.


title: Oral Anticoagulants in Thromboembolic Disease

abstract: The acute and long-term management of venous thrombosis and thromboembolism remain controversial. Oral anticoagulant therapy and its risks, evolving indications, and various methods of laboratory monitoring are discussed. Although some studies of the efficacy of oral anticoagulants in thromboembolic disease have shown the therapy to be highly effective, others have found its efficacy questionable. This is the second article in a four-part series on hematology. Other articles focus on the antiphospholipid thrombosis syndromes, heparin therapy, and thrombolytic therapy.


The Foraging Behavior of Mountain Bluebirds: With Emphasis on Sexual Foraging Differences


title: WTF: the who to follow service at Twitter

abstract: WTF ("Who to Follow") is Twitter's user recommendation service, which is responsible for creating millions of connections daily between users based on shared interests, common connections, and other related factors. This paper provides an architectural overview and shares lessons we learned in building and running the service over the past few years. Particularly noteworthy was our design decision to process the entire Twitter graph in memory on a single server, which significantly reduced architectural complexity and allowed us to develop and deploy the service in only a few months. At the core of our architecture is Cassovary, an open-source in-memory graph processing engine we built from scratch for WTF. Besides powering Twitter's user recommendations, Cassovary is also used for search, discovery, promoted products, and other services as well. We describe and evaluate a few graph recommendation algorithms implemented in Cassovary, including a novel approach based on a combination of random walks and SALSA. Looking into the future, we revisit the design of our architecture and comment on its limitations, which are presently being addressed in a second-generation system under development.


title: Semantic attachments for domain-independent planning systems

abstract: Solving real-world problems using symbolic planning often requires a simplified formulation of the original problem, since certain subproblems cannot be represented at all or only in a way leading to inefficiency. For example, manipulation planning may appear as a subproblem in a robotic planning context or a packing problem can be part of a logistics task. In this paper we propose an extension of PDDL for specifying semantic attachments. This allows the evaluation of grounded predicates as well as the change of fluents by externally specified functions. Furthermore, we describe a general schema of integrating semantic attachments into a forward-chaining planner and report on our experience of adding this extension to the planners FF and Temporal Fast Downward. Finally, we present some preliminary experiments using semantic attachments.


title: A secure and unclonable embedded system using instruction-level PUF authentication

abstract: In this paper we present a secure and unclonable embedded system design that can target either an FPGA or an ASIC technology. The premise of the security is that the executed machine code and the executing environment (the embedded processor) will authenticate each other at a per-instruction basis using Physical Unclonable Functions (PUFs) that are built into the processor. The PUFs ensure that the execution of the binary code may only proceed if the binary is compiled with the correct intrinsic knowledge of the PUFs, and that such intrinsic knowledge is virtually unique to each processor and therefore unclonable. We will explain how to implement and integrate the PUFs into the processor's execution environment such that each instruction is authenticated and de-obfuscated on-demand and how to transform an ordinary binary executable into PUF-aware, obfuscated binaries. We will also present a prototype system on a Xilinx Spartan6-based FPGA board.


title: A methodological framework for data warehouse design

abstract: Though designing a data warehouse requires techniques completely different from those adopted for operational systems, no significant effort has been made so far to develop a complete and consistent design methodology for data warehouses. In this paper we outline a general methodological framework for data warehouse design, based on our Dimensional Fact Model (DFM). After analyzing the existing information system and collecting the user requirements, conceptual design is carried out semi-automatically starting from the operational database scheme. A workload is then characterized in terms of data volumes and expected queries, to be used as the input of the logical and physical design phases whose output is the final scheme for the data warehouse.


title: Deep Transfer in Reinforcement Learning by Language Grounding

abstract: In this paper, we explore the utilization of natural language to drive transfer for reinforcement learning (RL). Despite the wide-spread application of deep RL techniques, learning generalized policy representations that work across domains remains a challenging problem. We demonstrate that textual descriptions of environments provide a compact intermediate channel to facilitate effective policy transfer. We employ a model-based RL approach consisting of a differentiable planning module, a model-free component and a factorized representation to effectively utilize entity descriptions. Our model outperforms prior work on both transfer and multi-task scenarios in a variety of different environments.


title: Adaptive Sampling and Bias Estimation in Path Tracing

abstract: One of the major problems in Monte Carlo based methods for global illumination is noise. This paper investigates adaptive sampling as a method to alleviate the problem. We introduce a new refinement criterion, which takes human perception and limitations of display devices into account by incorporating the tone-operator. Our results indicate that this can lead to a significant reduction in the overall RMS-error, and even more important that noisy spots are eliminated. This leads to a very homogeneous image quality. As most adaptive sampling techniques our method is biased. In order to investigate the importance of this problem, a nonparametric bootstrap method is presented to estimate the actual bias. This provides a technique for bias correction and it shows that the bias is most significant in areas with indirect illumination.


title: Identification and classification of microaneurysms for early detection of diabetic retinopathy

abstract: Diabetic retinopathy is a progressive eye disease which may cause blindness if not detected and treated in time. The early detection and diagnosis of diabetic retinopathy is important to protect the patient's vision. The accurate detection of microaneurysms (MAs) is a critical step for early detection of diabetic retinopathy because they appear as the first sign of disease. In this paper, we propose a three-stage system for early detection of MAs using filter banks. In the first stage, the system extracts all possible candidate regions for MAs present in retinal image. In order to classify a candidate region as MA or non-MA, the system formulates a feature vector for each region depending upon certain properties, i.e. shape, color, intensity and statistics. We present a hybrid classifier which combines the Gaussian mixture model (GMM), support vector machine (SVM) and an extension of multimodel mediod based modeling approach in an ensemble to improve the accuracy of classification. The proposed system is evaluated using publicly available retinal image databases and achieved higher accuracy which is better than previously published methods.


Zika Virus: More Questions Than Answers


title: Theoretical and Experimental Comparison Between Phase-Shifted PWM and Level-Shifted PWM in a Modular Multilevel SDBC Inverter for Utility-Scale Photovoltaic Applications

abstract: This paper discusses and compares phase-shifted pulse-width modulation (PS-PWM) and level-shifted PWM (LS-PWM) in a modular multilevel single-delta bridge-cell (SDBC) inverter for utility-scale grid-tied photovoltaic applications. This three-phase SDBC inverter provides the capability of operating even under power-distribution imbalances not only among three clusters, but also among cascaded bridge cells in each cluster. The main focus of this research lies in analyzing the capability of operation under severe power-distribution imbalances and presenting power-balancing methods based on zero-sequence current injection, which result in an extended power-balancing capability. This paper highlights that LS-PWM is superior to PS-PWM in both power-balancing capability and harmonic performance at the ac side of the SDBC inverter when operating under power-distribution imbalances. Experimental results obtained from a three-phase 10-kW downscaled system show good agreement with those from the theoretical analysis.


A Scalable High-Performance In-Memory Key-Value Cache using a Microkernel-Based Design


title: InstaNAS: Instance-aware Neural Architecture Search

abstract: Neural Architecture Search (NAS) aims at finding one"single"architecture that achieves the best accuracy for a given task such as image recognition.In this paper, we study the instance-level variation,and demonstrate that instance-awareness is an important yet currently missing component of NAS. Based on this observation, we propose InstaNAS for searching toward instance-level architectures;the controller is trained to search and form a"distribution of architectures"instead of a single final architecture. Then during the inference phase, the controller selects an architecture from the distribution, tailored for each unseen image to achieve both high accuracy and short latency. The experimental results show that InstaNAS reduces the inference latency without compromising classification accuracy. On average, InstaNAS achieves 48.9% latency reduction on CIFAR-10 and 40.2% latency reduction on CIFAR-100 with respect to MobileNetV2 architecture.


title: A seamless, client-centric programming model for type safe web applications

abstract: We propose a new programming model for web applications which is (1) seamless; one program and one language is used to produce code for both client and server, (2) client-centric; the programmer takes the viewpoint of the client that runs code on the server rather than the other way around, (3) functional and type-safe, and (4) portable; everything is implemented as a Haskell library that implicitly takes care of all networking code. Our aim is to improve the painful and error-prone experience of today's standard development methods, in which clients and servers are coded in different languages and communicate with each other using ad-hoc protocols. We present the design of our library called Haste.App, an example web application that uses it, and discuss the implementation and the compiler technology on which it depends.


title: NextPlace: A Spatio-Temporal Prediction Framework for Pervasive Systems

abstract: Accurate and fine-grained prediction of future user location and geographical profile has interesting and promising applications including targeted content service, advertisement dissemination for mobile users, and recreational social networking tools for smart-phones. Existing techniques based on linear and probabilistic models are not able to provide accurate prediction of the location patterns from a spatio-temporal perspective, especially for long-term estimation. More specifically, they are able to only forecast the next location of a user, but not his/her arrival time and residence time, i.e., the interval of time spent in that location. Moreover, these techniques are often based on prediction models that are not able to extend predictions further in the future. ::: ::: In this paper we present NextPlace, a novel approach to location prediction based on nonlinear time series analysis of the arrival and residence times of users in relevant places. NextPlace focuses on the predictability of single users when they visit their most important places, rather than on the transitions between different locations. We report about our evaluation using four different datasets and we compare our forecasting results to those obtained by means of the prediction techniques proposed in the literature. We show how we achieve higher performance compared to other predictors and also more stability over time, with an overall prediction precision of up to 90% and a performance increment of at least 50% with respect to the state of the art.


title: Design insider threat hands-on labs

abstract: Insider threat continues to be of serious concern to governmental organizations and also to private companies. There is few hands-on labs/modules available for training current students, the future information assurance professionals. This paper will classify the different actors and vectors involved in these attacks focusing specifically on Information Technology (IT) sabotage, theft of intellectual property and insider fraud. Then, we will describe how to design virtual hands-on labs mainly to current or future technology security professionals. The training hands-on labs will enhance trainee's knowledge and practical security skills about how to mitigate insider threat attacks. In addition, the training hands-on labs will be implemented via CyberCIEGE, an innovative video game and tool to teach computer and network security concepts.


title: How Do Retailers Adjust Prices?: Evidence From StoreLevel Data.” Bureau of Economic Trade Commission, Working Paper No

abstract: Recent theoretical work on retail pricing dynamics suggests that retailers periodically hold sales periodic, temporary reductions in price, -even when their costs are unchanged. In this paper we extend existing theory to predict which items will go on sale, and use a new data set from the BLS to document the frequency of sales across a wide range of goods and geographic areas. We find a number of pricing regularities for the 20 categories of goods we examine. First, retailers seem to have a “regular” price, and most deviations from that price are downward. Second, there is considerable heterogeneity in sale behavior across goods within a category (e.g. cereal); the same items are regularly put on sale, while other items rarely are on sale. Third, items are more likely to go on sale when demand is highest. Fourth, for a limited number of items for which we know market shares, products with larger market shares go on sale more often. These final three observations are consistent with our theoretical result that popular products are most likely to be placed on sale.


title: Maximum-Likelihood Detection for Energy-Efficient Timing Acquisition in NB-IoT

abstract: Initial timing acquisition in narrow-band IoT (NB-IoT) devices is done by detecting a periodically transmitted known sequence. The detection has to be done at lowest possible latency, because the RF-transceiver, which dominates downlink power consumption of an NB-IoT modem, has to be turned on throughout this time. Auto-correlation detectors show low computational complexity from a signal processing point of view at the price of a higher detection latency. In contrast a maximum likelihood cross-correlation detector achieves low latency at a higher complexity as shown in this paper. We present a hardware implementation of the maximum likelihood cross-correlation detection. The detector achieves an average detection latency which is a factor of two below that of an auto-correlation method and is able to reduce the required energy per timing acquisition by up to 34%.


title: Spectral-Spatial Classification of Hyperspectral Imagery Based on Stacked Sparse Autoencoder and Random Forest

abstract: ABSTRACTIt is of great interest in exploiting spectral-spatial information for hyperspectral image (HSI) classification at different spatial resolutions. This paper proposes a new spectral-spatial deep learning-based classification paradigm. First, pixel-based scale transformation and class separability criteria are employed to measure appropriate spatial resolution HSI, and then we integrate the spectral and spatial information (i.e., both implicit and explicit features) together to construct a joint spectral-spatial feature set. Second, as a deep learning architecture, stacked sparse autoencoder provides strong learning performance and is expected to exploit even more abstract and high-level feature representations from both spectral and spatial domains. Specifically, random forest (RF) classifier is first introduced into stacked sparse autoencoder for HSI classification, based on the fact that it provides better tradeoff among generalization performance, prediction accuracy and operation speed compared...


title: Network Intrusion Detection based on attack pattern

abstract: Intrusion Detection system becomes the one of the favorite research topic due to its capability of detecting various network attacks. There are many works done in the field of intrusion detection up till now. In this paper we are proposing the pattern based Intrusion Detection model. This system is based on CIDF architecture. The Pattern based Intrusion Detection model consist of five modules: Capture Module, Decode Module, Detection Module, Known Attack Pattern Module and Action Module. A pattern based IDS monitor packets on the network and compares them against a database of known attack pattern of malicious threats. Our paper focuses on the packet sniffer and its working, various attacks on network as an intrusion and their detection using pattern based IDS.


title: Design of a 2.2-mW 24-Mb/s CMOS VLC Receiver SoC With Ambient Light Rejection and Post-Equalization for Li-Fi Applications

abstract: This paper presents an energy-efficient visible light communication (VLC) receiver system-on-a-chip (SoC) that employs ambient light rejection (ALR) and post-equalization techniques for emerging light fidelity (Li-Fi) applications using ordinary phosphorescent white light-emitting diodes (LEDs). A current-reuse transimpedance amplifier is proposed for energy-efficient applications. An ALR unit is implemented to eliminate the potential DC interference under strong ambient light. A two-stage continuous-time linear equalizer is utilized to compensate for the limited bandwidth of white LEDs, which is below 3 MHz. Implemented in a 0.18 μm CMOS process, the receiver occupies an area of 0.7 × 0.4 mm ::: 2 ::: and consumes 2.2 mW of power at 24 Mb/s from a 1.8 V power supply. An IEEE 802.15.7 PHY-II compliant Li-Fi link that employs the proposed receiver SoC and a custom transmitter SoC is demonstrated based on 1-W phosphorescent white LEDs without using blue filtering, achieving a bit error rate of 1 × 10 ::: -9 ::: for data rates up to 24 Mb/s over 1.6 m distance, and 22 Mb/s over 2.7 m distance, with on-off keying modulation and 2 ::: 11 ::: -1 pseudorandom binary sequence (PRBS-11) optical inputs. The receiver SoC achieves a bit efficiency six times better than that of the prior art using off-the-shelf discrete components, and can tolerate an ambient light level of 3500 lx. A figure of merit accommodating the most important system parameters is also proposed for a comprehensive comparison of different VLC systems.