title: Directional water collection on wetted spider silk

abstract: The glistening of spiders' webs on a misty morning shows that they can gather water droplets from humid air with impressive efficiency. A study of the capture silk of the spider Uloborus walckenaerius reveals that this capability depends on a structural change that occurs on wetting. 'Wet-rebuilt' fibres are characterized by periodic spindle-knots made of random nanofibrils separated by joints made of aligned nanofibrils. This structure produces a surface energy gradient between the spindle-knots and the joints, and a difference in the pressure acting on drops in contact with either the spindle-knots or the joints. This ensures that water can continuously condense around the joints and is then transported to the spindle-knots, where it can accumulate in large hanging drops. An artificial silk mimicking the structure of wetted spider silk can also gather water drops from thin mist. This work could inform the design of functional surfaces that can collect fresh water from fog, or filter liquid aerosols in industrial processes. Many plants and animals make use of biological surfaces with structural features at the micro- and nanometre-scale that control the interaction with water. The appearance of dew drops on spider webs is an illustration of how they are one such material capable of efficiently collecting water from air. The water-collecting ability of the capture silk of the Uloborus walckenaerius spider is now shown to be the result of a unique fibre structure that forms after wetting. Many biological surfaces in both the plant and animal kingdom possess unusual structural features at the micro- and nanometre-scale that control their interaction with water and hence wettability1,2,3,4,5. An intriguing example is provided by desert beetles, which use micrometre-sized patterns of hydrophobic and hydrophilic regions on their backs to capture water from humid air6. As anyone who has admired spider webs adorned with dew drops will appreciate, spider silk is also capable of efficiently collecting water from air. Here we show that the water-collecting ability of the capture silk of the cribellate spider Uloborus walckenaerius is the result of a unique fibre structure that forms after wetting, with the ‘wet-rebuilt’ fibres characterized by periodic spindle-knots made of random nanofibrils and separated by joints made of aligned nanofibrils. These structural features result in a surface energy gradient between the spindle-knots and the joints and also in a difference in Laplace pressure, with both factors acting together to achieve continuous condensation and directional collection of water drops around spindle-knots. Submillimetre-sized liquid drops have been driven by surface energy gradients7,8,9 or a difference in Laplace pressure10, but until now neither force on its own has been used to overcome the larger hysteresis effects that make the movement of micrometre-sized drops more difficult. By tapping into both driving forces, spider silk achieves this task. Inspired by this finding, we designed artificial fibres that mimic the structural features of silk and exhibit its directional water-collecting ability.


Panoply: Low-TCB Linux Applications with SGX Enclaves


title: Optimal drone placement and cost-efficient target coverage

abstract: Observing mobile or static targets in the ground using flying drones is a common task for civilian and military applications. We introduce the minimum cost drone location problem and its solutions for this task in a two-dimensional terrain. The number of drones and the total energy consumption are the two cost metrics considered. We assume that each drone has a minimum and a maximum observation altitude. Moreover, the drone's energy consumption is related to this altitude. Indeed, the higher the altitude, the larger the observed area but the higher the energy consumption. The aim is to find drone locations that minimize the cost while ensuring the surveillance of all the targets. The problem is mathematically solved by defining an integer linear and a mixed integer non-linear optimization models. We also provide some centralized and localized heuristics to approximate the solution for static and mobile targets. A computational study and extensive simulations are carried out to assess the behavior of the proposed solutions.


title: Bewell: A smartphone application to monitor, model and promote wellbeing

abstract: A key challenge for mobile health is to develop new technology that can assist individuals in maintaining a healthy lifestyle by keeping track of their everyday behaviors. Smartphones embedded with a wide variety of sensors are enabling a new generation of personal health applications that can actively monitor, model and promote wellbeing. Automated wellbeing tracking systems available so far have focused on physical fitness and sleep and often require external non-phone based sensors. In this work, we take a step towards a more comprehensive smartphone based system that can track activities that impact physical, social, and mental wellbeing namely, sleep, physical activity, and social interactions and provides intelligent feedback to promote better health. We present the design, implementation and evaluation of BeWell, an automated wellbeing app for the Android smartphones and demonstrate its feasibility in monitoring multi-dimensional wellbeing. By providing a more complete picture of health, BeWell has the potential to empower individuals to improve their overall wellbeing and identify any early signs of decline.


title: Instantaneous Reactive Power Compensators Comprising Switching Devices without Energy Storage Components

abstract: The conventional reactive power in single-phase or three- phase circuits has been defined on the basis of the average value concept for sinusoidal voltage and current waveforms in steady states. The instantaneous reactive power in three-phase circuits is defined on the basis of the instantaneous value concept for arbitrary voltage and current waveforms, including transient states. A new instantaneous reactive power compensator comprising switching devices is proposed which requires practically no energy storage components.


title: Mont’e Scan: Effective Shape and Color Digitization of Cluttered 3D Artworks

abstract: We propose an approach for improving the digitization of shape and color of 3D artworks in a cluttered environment using 3D laser scanning and flash photography. To separate clutter from acquired material, semiautomated methods are employed to generate masks used to segment the range maps and the color photographs. This approach allows the removal of unwanted 3D and color data prior to the integration of acquired data in a 3D model. Sharp shadows generated by flash acquisition are easily handled by this masking process, and color deviations introduced by the flash light are corrected at the color blending step by taking into account the geometry of the object. The approach has been evaluated in a large-scale acquisition campaign of the Mont’e Prama complex. This site contains an extraordinary collection of stone fragments from the Nuragic era, which depict small models of prehistoric nuraghe (cone-shaped stone towers), as well as larger-than-life archers, warriors, and boxers. The acquisition campaign has covered 37 statues mounted on metallic supports. Color and shape were acquired at a resolution of 0.25mm, which resulted in more than 6,200 range maps (about 1.3G valid samples) and 3,817 photographs.


title: Real time digital simulation (RTDS) of a novel battery-integrated PV system for high penetration application

abstract: In this paper, a photovoltaic (PV) system with energy storage elements (ESEs) is built in Real Time Digital Power System Simulator (RTDS) which is the partial work of future hardware-in-the-loop study of high-penetration multiple PV systems. Cascaded-multilevel inverter is applied as the power electronics interface among the PV array, batteries and grid. The H-bridges are modeled in RTDS small time-step environment while other power circuits and control system are realized in the large time-step main network. Interface transformer is used to connect the different time-step sub-networks. Difficulties of the system implementation in RTDS are discussed and solutions are provided. Finally, RTDS simulation results are given out and compared with Matlab/Simulink+PSIM results to verify the model.


Using Representation Learning and Out-of-domain Data for a Paralinguistic Speech Task


title: Don’t Evaluate, Inherit

abstract: This paper studies fitness inheritance as an efficiency enhancement technique for genetic and evolutionary algorithms. Convergence and population-sizing models are derived and compared with experimental results. These models are optimized for greatest speed-up and the optimal inheritance proportion to obtain such a speed-up is derived. Results on OneMax problems show that when the inheritance effects are considered in the population-sizing model, the number of function evaluations are reduced by 20% with the use of fitness inheritance. Results indicate that for a fixed population size, the number of function evaluations can be reduced by 70% using a simple fitness inheritance technique.


title: Miniature six-DOF inertial system for tracking HMDs

abstract: Current HMD applications are hampered by the limitations of head-tracking technologies now in use. Commercially available magnetic, optical, acoustic, and mechanical head-trackers suffer from various problems such as vulnerability to interference, line-of-sight restrictions, jitter, latency, small range, and high cost. This paper presents inertial-sensor-based hybrid tracking technology that was developed to combat all these problems. Two commercially available products, the IS-300 and the IS600, are described, both based on the same miniature triaxial inertial sensor device. The IS-300 is a sourceless 3-DOF orientation tracker, using gravimetric tilt-sensing to prevent any gyroscopic drift in pitch and roll, and optional geo-magnetic compassing to prevent any gyroscopic drift in yaw. The IS-600 is a hybrid acousto-inertial 6-DOF position and orientation tracking system. It tracks changes in orientation and position by integrating the outputs of its gyros and accelerometers, and corrects drift using a room-referenced ultrasonic time-of-flight range measuring system. This paper overviews the theory of operation of both systems, and reports bench-testing results designed to evaluate the resolution, accuracy, and latency of each system.


title: Miniaturization of array microstrip antenna using peripheral slits for wireless fidelity communication

abstract: This paper proposed a new design of array microstrip antenna using slit technique to reduce dimension of the antenna for Wireless Fidelity Communication and to increase gain. Dimension of the enclosure of the proposed antenna is 269 mm × 87 mm. The proposed antenna using single layer FR4 substrate with relative permittivity (er) of 4.3, substrate thickness (h) of 1.6 mm and loss tangent (tan δ) of 0.0265. The simulation results shown that the antenna is capable working at frequency 2448 MHz with VSWR of 1.104 and return loss of −26.14 dB. Bandwidth of the proposed microstrip antenna is 320 MHz (2253 MHz–2573 MHz). Furthermore, its dimension can be reduced up to 37.5% with its gain of the proposed antenna of 6.45 dB.


title: The Cognitive Benefits of Interacting With Nature

abstract: We compare the restorative effects on cognitive functioning of interactions with natural versus urban environments. Attention restoration theory (ART) provides an analysis of the kinds of environments that lead to improvements in directed-attention abilities. Nature, which is filled with intriguing stimuli, modestly grabs attention in a bottom-up fashion, allowing top-down directed-attention abilities a chance to replenish. Unlike natural environments, urban environments are filled with stimulation that captures attention dramatically and additionally requires directed attention (e.g., to avoid being hit by a car), making them less restorative. We present two experiments that show that walking in nature or viewing pictures of nature can improve directed-attention abilities as measured with a backwards digit-span task and the Attention Network Task, thus validating attention restoration theory.


title: Art-Bots: Toward Chat-Based Conversational Experiences in Museums

abstract: In this work we explore how a widely used conversational interface can be employed to offer engaging experiences in museums. “Art-bots” interact with visitors through chat and convey information about the museum artifacts in the form of short stories. The wide adoption of chat platforms such as Facebook messenger offers new ground to revisit approaches on avatars and virtual guides and build interactive dialogues to engage visitors. We take a practical approach based on a set of responses that are triggered by certain keywords and a curated story that guides visitors to a gamified information hunt.


title: Reconstruction of Alar Defects

abstract: ANATOMY Successful reconstruction of the nose depends on a thorough understanding of the anatomic and functional components of the ala. The crescentic alar groove serves as a topographic landmark that frames the ala and separates this convex structure from the surrounding cosmetic subunits. The ala abuts the nasal tip anteriorly and the nasal sidewall superiorly. The alar groove deepens as it extends posteriorly. This posterior portion of the alar groove is often called the alar-facial sulcus and separates the ala from the cheek and hairless apical triangle of the lip. The ala is a critical cosmetic and functional landmark. The distal free margin of the alar lobule and the transition from the shadows of the alar groove to the reflection of the convex surface of the ala are important visual landmarks. The ala also frames the lateral aspect of the external nasal valve, a critical path for airflow during inspiration. Altered position of the ala during reconstructive surgery can compromise function of the external nasal valve. Relative to the nasal tip and sidewall, the alar tissue is more compliant, because it does not contain cartilage. 1 The alar lobule consists of skeletal muscle and fat enveloped by dermis and epithelium on both the vestibular and external aspects. 2 The lower lateral cartilage does not cross the alar groove and it is not part of the alar lobule. 2 The lack of an intrinsic osseouscartilaginous skeleton and the complete absence of support at its distal free margin make this delicate structure particularly susceptible to distortion during reconstructive surgery. Although the alar lobule does not contain cartilage, the ala gains dynamic and static support from the close relationship of its muscles with the osseous-cartilaginous framework of the nose. 1 A brief description of the osseouscartilaginous framework is helpful. The nasal bone and maxilla frame the pyriform aperture. The paired upper lateral cartilages are firmly stabilized as they flare laterally from cartilaginous septum and fix to the deep aspect of the nasal bone. The intercartilaginous ligament stabilizes the cephalic margin of the lateral crura of the lower lateral cartilages to the caudal aspect of the upper lateral cartilages. 3 The lower lateral cartilages gain additional stability from loose connective tissue that links the domes of the lower lateral cartilages and possibly from direct connection of the medial crura with the caudal septum. 3 A fascial system, called the pyriform ligament, stabilizes the entire cartilaginous framework by connecting the lateral cartilages with the pyriform rim. 4


title: Biomechanical considerations for rehabilitation of the knee.

abstract: Knowledge of the anatomy and biomechanics of the knee is critical for successful rehabilitation following knee injury and/or surgery. Biomechanics of both the tibiofemoral and patellofemoral joints must be considered. The purpose of this paper is to provide a framework for rehabilitation of the knee by reviewing the biomechanics of the tibiofemoral and patellofemoral joints. This will include discussion of the relevant arthrokinematics as well as the effects of open and closed chain exercises. The implications for rehabilitation of the knee will be highlighted.


title: Design and fabrication of a 3DoF upper limb exoskeleton

abstract: In this paper, design and fabrication of a 3Dof upper limb exoskeleton robot have been discussed. The main application of this robot is for rehabilitation purposes. Therefore, the necessary components for studying the interaction between robot and human body has been implemented on the robot. Moreover the ability to adjust link's length based on patient's body and controlling structure have been considered for the robot. In order to measure the force between human body and robot, strain gage sensors have been used at each arm connector. Due to high level of controllability of dynamixel servomotors, these motors are being used as robot actuators. At the end, robot and human interaction is discussed based on sensor results.


title: Gender differences in aggression as a function of provocation: a meta-analysis.

abstract: In this article, we meta-analytically examine experimental studies to assess the moderating effect of provocation on gender differences in aggression. Convergent evidence shows that, whereas unprovoked men are more aggressive than women, provocation markedly attenuates this gender difference. Gender differences in appraisals of provocation intensity and fear of danger from retaliation (but not negative affect) partially mediate the attenuating effect of provocation. However, they do not entirely account for its manipulated effect. Type of provocation and other contextual variables also affect the magnitude of gender differences in aggression. The results support a social role analysis of gender differences in aggression and counter A. H. Eagly and V. Steffen's (1986) meta-analytic inability to confirm an attenuating effect of provocation on gender differences in aggression.


title: Hybrid Garbage Collection for Multi-Version Concurrency Control in SAP HANA

abstract: While multi-version concurrency control (MVCC) supports fast and robust performance in in-memory, relational databases, it has the potential problem of a growing number of versions over time due to obsolete versions. Although a few TB of main memory is available for enterprise machines, the memory resource should be used carefully for economic and practical reasons. Thus, in order to maintain the necessary number of versions in MVCC, versions which will no longer be used need to be deleted. This process is called garbage collection. MVCC uses the concept of visibility to define garbage. A set of versions for each record is first identified as candidate if their version timestamps are lower than the minimum value of snapshot timestamps of active snapshots in the system. All such candidates, except the one which has the maximum version timestamp, are safely reclaimed as garbage versions. In mixed OLTP and OLAP workloads, the typical garbage collector may not effectively reclaim record versions. In these workloads, OLTP applications generate a high volume of new versions, while long-lived queries or transactions in OLAP applications often block garbage collection, since we need to compare the version timestamp of each record version with the snapshot timestamp of the oldest, long-lived snapshot. Thus, these workloads typically cause the in-memory version space to grow. Additionally, the increasing version chains of records over time may also increase the traversal cost for them. In this paper, we present an efficient and effective garbage collector called HybridGC in SAP HANA. HybridGC integrates three novel concepts of garbage collection: timestamp-based group garbage collection, table garbage collection, and interval garbage collection. Through experiments using mixed OLTP and OLAP workloads, we show that HybridGC effectively and efficiently collects garbage versions with negligible overhead.


title: Developing and Testing Top-N Recommendation Algorithms for 0-1 Data using recommenderlab ∗

abstract: The problem of creating recommendations given a large data base from directly elicited ratings (e.g., ratings of 1 through 5 stars) is a popular research area which was lately boosted by the Netflix Prize competition. While computing recommendations using these type of data has direct application for example for large on-line retailers, there are many potential applications for recommender systems where such data is not available. However, in many cases there might be 0-1 rating data available or can be derived from other data sources (e.g., purchase records, Web click data) which can be utilized. Although this type of data differs significantly from directly elicited ratings, only very limited research is available for 0-1 data. This paper describes recommenderlab which provides the infrastructure to test and develop recommender algorithms. Currently the focus is on recommender systems for 0-1 data, however in the future it can be extended to also the more thoroughly researched case of directly elicited, real-valued rating data.


title: Wind Turbine Structural Health Monitoring: A Short Investigation Based on SCADA Data

abstract: The use of offshore wind farms has been growing in recent years, as steadier and higher wind speeds can be generally found over water compared to land. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore their reliability is crucial if they ever need to be fully integrated into the energy arena. As wind turbines have become more complex, efficient, and expensive structures, they require more sophisticated monitoring systems, especially in offshore sites where the financial losses due to failure could be substantial. This paper presents the preliminary analysis of supervisor control and data acquisition (SCADA) extracts from the Lillgrund wind farm for the purposes of structural health monitoring. A machine learning approach is applied in order to produce individual power curves, and then predict measurements of the power produced of each wind turbine from the measurements of the other wind turbines in the farm. A comparison between neural network and Gaussian process regression is also made.


title: Improving Workforce Scheduling of Aircraft Line Maintenance at Sabena Technics

abstract: This paper presents our application of a visualization tool and optimization model based on mixed-integer linear programming to solve a workforce staffing and scheduling problem at Sabena Technics, a major aircraft maintenance company in Belgium. We used the software to generate many alternative, cost-efficient schedules and to analyze multiple scenarios. In several management meetings, stakeholders evaluated the schedules and raised concerns. We subsequently changed the model to successfully address their concerns. The model has resulted in considerable savings and a more efficient use of human resources.


title: Do Local Characteristics Matter? Secondary School Track Choice in Poland

abstract: This paper examines to which extent the school track choice in Poland between vocational and general education can be attributed to variation of personal characteristics and to which extent to variation of the local conditions. We assume that not only a family background, but also local characteristics are important determinants of school track decisions. As we make distinction between three different types of secondary schools: basic vocational, secondary vocational, and general secondary, and we want to examine the county specific characteristics we apply a multilevel method for multinomial logistics regression to address the issue. Our results indicate that both child’s characteristics such as sex and school performance and characteristics of household are related to school track decisions. We also found that there is statistically significant variation in school track choice on the county level. Local unemployment rate is statistically significant determinant of school track decision and it also explains a great variation between counties. This finding is important in the light of EU regional cohesion policy aiming at diminishing regional disparities.


Multi-Agent Programming: Languages, Platforms and Applications


title: Walking on Water: Biolocomotion at the Interface

abstract: We consider the hydrodynamics of creatures capable of sustaining themselves on the water surface by means other than flotation. Particular attention is given to classifying water walkers according to their principal means of weight support and lateral propulsion. The various propulsion mechanisms are rationalized through consideration of energetics, hydrodynamic forces applied, or momentum transferred by the driving stroke. We review previous research in this area and suggest directions for future work. Special attention is given to introductory discussions of problems not previously treated in the fluid mechanics literature, with hopes of attracting physicists, applied mathematicians, and engineers to this relatively unexplored area of fluid mechanics.


title: Unsupervised Learning of Local Features for Music Classification

abstract: In this work we investigate the applicability of unsupervised feature learning methods to the task of automatic genre prediction of music pieces. More specifically we evaluate a framework that recently has been successfully used to recognize objects in images. We first extract local patches from the time-frequency transformed audio signal, which are then pre-processed and used for unsupervised learning of an overcomplete dictionary of local features. For learning we either use a bootstrapped k-means clustering approach or select features randomly. We further extract feature responses in a convolutional manner and train a linear SVM for classification. We extensively evaluate the approach on the GTZAN dataset, emphasizing the influence of important design choices such as dimensionality reduction, pooling and patch dimension on the classification accuracy. We show that convolutional extraction of local feature responses is crucial to reach high performance. Furthermore we find that using this approach, simple and fast learning techniques such as k-means or randomly selected features are competitive with previously published results which also learn features from audio signals.


title: A RISC-V vector processor with tightly-integrated switched-capacitor DC-DC converters in 28nm FDSOI

abstract: This work demonstrates a RISC-V vector microprocessor implemented in 28nm FDSOI with fully-integrated non-interleaved switched-capacitor DCDC (SC-DCDC) converters and adaptive clocking that generates four on-chip voltages between 0.5V and 1V using only 1.0V core and 1.8V IO voltage inputs. The design pushes the capabilities of dynamic voltage scaling by enabling fast transitions (20ns), simple packaging (no off-chip passives), low area overhead (16%), high conversion efficiency (80–86%), and high energy efficiency (26.2 DP GFLOPS/W) for mobile devices.


Robust and Efficient Shape Indexing through Curvature Scale Space


title: Ultra-Low-Voltage Operation of CMOS Analog Circuits: Amplifiers, Oscillators, and Rectifiers

abstract: This letter presents MOS analog circuits that can operate with very low supply voltages. Operation of the MOS transistor in the triode region is emphasized owing to the limited voltages available. Special attention has been given to the properties of the zero-VT transistor due to its high drive capability at low voltages. Simulation and measurement results for a Colpitts oscillator prototype set up around a zero-VT MOSFET demonstrated that the oscillator operates with supply voltages lower than the thermal voltage kT/q. Finally, ultra-low-voltage rectifiers using diodes or diode-connected MOSFETs operating in weak inversion are analyzed.


title: Leveraging Unlabeled Whole-Slide-Images for Mitosis Detection

abstract: Mitosis count is an important biomarker for prognosis of various cancers. At present, pathologists typically perform manual counting on a few selected regions of interest in breast whole-slide-images (WSIs) of patient biopsies. This task is very time-consuming, tedious and subjective. Automated mitosis detection methods have made great advances in recent years. However, these methods require exhaustive labeling of a large number of selected regions of interest. This task is very expensive because expert pathologists are needed for reliable and accurate annotations. In this paper, we present a semi-supervised mitosis detection method which is designed to leverage a large number of unlabeled breast cancer WSIs. As a result, our method capitalizes on the growing number of digitized histology images, without relying on exhaustive annotations, subsequently improving mitosis detection. Our method first learns a mitosis detector from labeled data, uses this detector to mine additional mitosis samples from unlabeled WSIs, and then trains the final model using this larger and diverse set of mitosis samples. The use of unlabeled data improves F1-score by $\sim$5\% compared to our best performing fully-supervised model on the TUPAC validation set. Our submission (single model) to TUPAC challenge ranks highly on the leaderboard with an F1-score of 0.64.


title: Recent Patient Health Monitoring Platforms Incorporating Internet of Things-Enabled Smart Devices

abstract: Synergistic integration of the Internet of Things (IoT), cloud computing, and big data technologies in healthcare have led to the notion of "smart health." Smart health is an emerging concept that refers to the provision of healthcare services for prevention, diagnosis, treatment, and follow-up management at any time or any place by connecting information technologies and healthcare. As a significant breakthrough in smart healthcare development, IoT-enabled smart devices allow medical centers to carry out preventive care, diagnosis, and treatment more competently. This review focuses on recently developed patient health monitoring platforms based on IoT-enabled smart devices that can collect real-time patient data and transfer information for assessment by healthcare providers, including doctors, hospitals, and clinics, or for self-management. We aimed to summarize the available information about recently approved devices and state-of-the-art developments through a comprehensive, systematic literature review. In this review, we also discuss possible future directions for the integration of cloud computing and blockchain, which may offer unprecedented breakthroughs in on-demand medical services. The combination of IoT with real-time, remote patient monitoring empowers patients to assert more control over their care, thereby allowing them to actively monitor their particular health conditions.


title: The histogenetic potential of neural plate cells of early-somite-stage mouse embryos.

abstract: The mesencephalic neural plate of early-somite-stage mouse embryos differentiated underneath the renal capsule to form mostly neural tissues together with other tissues some of which were probably of neural crest cell origin. The capacity to form non-neural tissues such as skeletal tissues and melanocytes was lost at about the 5-somite stage. The lateral areas of the plate tended to form non-neural tissues more than the medial areas. The cephalic neural plate of presomite head-fold-stage embryos differentiated extensively to form both ectodermal and mesodermal tissues. However, upon completion of neurulation, the mesencephalic neuroepithelium of forelimb-bud-stage embryos gave rise to neural tissues only. Therefore there is a progressive restriction in the histogenetic capacity of the mesencephalic neural plate during neurulation and this could be attributed to the cellular commitment for neural differentiation and the loss of the neural crest cells.


title: The Body Uneasiness Test (BUT): Development and validation of a new body image assessment scale

abstract: Objective: To investigate the psychometric properties of the Body Uneasiness Test (BUT), a 71-item self-report questionnaire that consists of two parts: BUTA which mea- sures weight phobia, body image concerns, avoidance, compulsive self-monitoring, detach- ment and estrangement feelings towards one's own body (depersonalization); and BUTB which looks at specific worries about particular body parts or functions. Methods: We recruited a clinical sample of 531 subjects (491 females) suffering from eating disorders and a general population sample of 3273 subjects (2016 females) with BMI <25 and Eating Attitudes Test-26 scores under the cut-off 20. Results: The exploratory and confirmatory analyses confirmed a structural five-factor model for BUTA and an eight-factor model for BUTB. Internal consistency was satisfactory. The test-retest correlation coefficients were highly sig- nificant. Concurrent validity with other tests (Eating Disorder Inventory, EDI-2; Eating Attitudes Test, EAT-26; Symptom Check List, SCL-90R and Coopersmith Self-Esteem Inventory, SEI) was evaluated. Normative values for BUT scores in non-clinical samples of normal-weight non eating disordered subjects, from adolescence to old age, males and females, were calculated. The differences between males and females were highly significant, above all in the 18-39-age range. As for the comparison between women with eating disor- ders and controls, the results demonstrated a good predictive validity for anorexia nervosa and bulimia nervosa. Conclusions: The BUT is psychometrically sound. It can be a valuable tool for the screening and the clinical assessment of abnormal body image attitudes and eat- ing disorders.


title: Discriminative Multi-View Interactive Image Re-Ranking

abstract: Given an unreliable visual patterns and insufficient query information, content-based image retrieval is often suboptimal and requires image re-ranking using auxiliary information. In this paper, we propose a discriminative multi-view interactive image re-ranking (DMINTIR), which integrates user relevance feedback capturing users’ intentions and multiple features that sufficiently describe the images. In DMINTIR, heterogeneous property features are incorporated in the multi-view learning scheme to exploit their complementarities. In addition, a discriminatively learned weight vector is obtained to reassign updated scores and target images for re-ranking. Compared with other multi-view learning techniques, our scheme not only generates a compact representation in the latent space from the redundant multi-view features but also maximally preserves the discriminative information in feature encoding by the large-margin principle. Furthermore, the generalization error bound of the proposed algorithm is theoretically analyzed and shown to be improved by the interactions between the latent space and discriminant function learning. Experimental results on two benchmark data sets demonstrate that our approach boosts baseline retrieval quality and is competitive with the other state-of-the-art re-ranking strategies.


title: Learning to sketch human facial portraits using personal styles by case-based reasoning

abstract: This paper employs case-based reasoning (CBR) to capture the personal styles of individual artists and generate the human facial portraits from photos accordingly. For each human artist to be mimicked, a series of cases are firstly built-up from her/his exemplars of source facial photo and hand-drawn sketch, and then its stylization for facial photo is transformed as a style-transferring process of iterative refinement by looking-for and applying best-fit cases in a sense of style optimization. Two models, fitness evaluation model and parameter estimation model, are learned for case retrieval and adaptation respectively from these cases. The fitness evaluation model is to decide which case is best-fitted to the sketching of current interest, and the parameter estimation model is to automate case adaptation. The resultant sketch is synthesized progressively with an iterative loop of retrieval and adaptation of candidate cases until the desired aesthetic style is achieved. To explore the effectiveness and advantages of the novel approach, we experimentally compare the sketch portraits generated by the proposed method with that of a state-of-the-art example-based facial sketch generation algorithm as well as a couple commercial software packages. The comparisons reveal that our CBR based synthesis method for facial portraits is superior both in capturing and reproducing artists’ personal illustration styles to the peer methods.


title: Brief Announcement: Dynamic Determinacy Race Detection for Task Parallelism with Futures

abstract: Existing dynamic determinacy race detectors for task-parallel programs are limited to programs with strict computation graphs, where a task can only wait for its descendant tasks to complete. In this paper, we present the first known determinacy race detector for non-strict computation graphs with futures. The space and time complexity of our algorithm are similar to those of the classical SP-bags algorithm, when using only structured parallel constructs such as spawn-sync and async-finish. In the presence of point-to-point synchronization using futures, the complexity of the algorithm increases by a factor determined by the number of future operations, which includes future task creation and future get operations. The experimental results show that the slowdown factor observed for our algorithm relative to the sequential version is in the range of 1.00x to 9.92x, which is very much in line with slowdowns experienced for fully strict computation graphs.


title: A Differential Evolution Performance Comparison: Comparing How Various Differential Evolution Algorithms Perform in Designing Microstrip Antennas and Arrays

abstract: In this article, we have undertaken a performance comparison of different variants and hybridized schemes of differential evolution (DE) algorithms with respect to several antenna and array designs. These include DE/rand/1, self-adaptive DE (SADE), DE with global and local neighborhood search (DEGL), biogeography-based optimization with DE (BBODE), modified DE (MDE), MDE with p-best crossover strategy (MDE-pBX), improved DE (IDE), harmonic search DE (HSDE), DE with an individual dependent mechanism (DE-IDP-IDM), and adaptive DE with optimization-state estimation (ADE).


title: Measurement of Pulse rate and SPo2 using Pulse Oximeter developed using LabVIEW

abstract: Abstract: The objective of this study is to develop a pulse Oximeter for the determination of pulse rate and percentage of oxygen in the blood of a patient. To determine these parameters the light is transmitted through the fingertip using a photodiode as a sensor and two LED’S (RED & IR) as a light source. The photo diode detects the light and the DAQ device outputs a voltage corresponding to the amount of light detected, and the final signal is a pulse. To determine the pulse rate, first the time that elapses between two successive peaks must be determined. Second to calculate the percentage of oxygen, AC & DC voltages must be determined. Based on voltages the modulation ratio is calculated, which is the ratio of magnitude of RED waveform to that of IR waveform.


title: UltraGesture: Fine-Grained Gesture Sensing and Recognition

abstract: With the rising of AR/VR technology and miniaturization of mobile devices, gesture is becoming an increasingly popular means of interacting with smart devices. Some pioneer ultrasound based human gesture recognition systems have been proposed. They mostly rely on low resolution Doppler Effect, and hence focus on whole hand motion and cannot deal with minor finger motions. In this paper, we present UltraGesture, a Channel Impulse Response (CIR) based ultrasonic finger motion perception and recognition system. CIR measurements can provide with 7 mm resolution, rendering it sufficient for minor finger motion recognition. UltraGesture encapsulates CIR measurements into an image, and builds a Convolutional Neural Network model to classify these images into different categories, which corresponding to distinct gestures. Our system runs on commercial speakers and microphones that already exist on most mobile devices without hardware modification. Our results show that UltraGesture achieves an average accuracy of greater than 97% for 12 gestures including finger click and rotation.


title: Circuit fingerprinting attacks: passive deanonymization of tor hidden services

abstract: This paper sheds light on crucial weaknesses in the design of hidden services that allow us to break the anonymity of hidden service clients and operators passively. In particular, we show that the circuits, paths established through the Tor network, used to communicate with hidden services exhibit a very different behavior compared to a general circuit. We propose two attacks, under two slightly different threat models, that could identify a hidden service client or operator using these weaknesses. We found that we can identify the users' involvement with hidden services with more than 98% true positive rate and less than 0.1% false positive rate with the first attack, and 99% true positive rate and 0.07% false positive rate with the second. We then revisit the threat model of previous website fingerprinting attacks, and show that previous results are directly applicable, with greater efficiency, in the realm of hidden services. Indeed, we show that we can correctly determine which of the 50 monitored pages the client is visiting with 88% true positive rate and false positive rate as low as 2.9%, and correctly deanonymize 50 monitored hidden service servers with true positive rate of 88% and false positive rate of 7.8% in an open world setting.


title: Improving CNN performance with min-max objective

abstract: In this paper, we propose a novel method to improve object recognition accuracies of convolutional neural networks (CNNs) by embedding the proposed Min-Max objective into a high layer of the models during the training process. The Min-Max objective explicitly enforces the learned object feature maps to have the minimum compactness for each object manifold and the maximum margin between different object manifolds. The Min-Max objective can be universally applied to different CNN models with negligible additional computation cost. Experiments with shallow and deep models on four benchmark datasets including CIFAR- 10, CIFAR-100, SVHN and MNIST demonstrate that CNN models trained with the Min-Max objective achieve remarkable performance improvements compared to the corresponding baseline models.


title: Caring for caregivers: designing for integrality

abstract: Health and wellness have drawn significant attention in the HCI and CSCW communities. Many prior studies have focused on designing technologies that are patient-centric, allowing caregivers to take better care of patients. Less has been done in understanding and minimizing the burden of caregiving in caregivers' own lives. We conducted a qualitative interview study to understand their experiences in caregiving. The findings reveal a great magnitude of challenges in the caregivers' day-to-day lives, ranging from the physical and social, to the personal and emotional. Caregivers have to constantly balance their personal lives with work, family, and their caregiver roles, which can be overwhelmingly stressful. We discuss how caregivers attempt maintaining this balance through two concepts: first, giving-impact, and second, visibility-invisibility. Our study's findings call for system design that focuses not only on patients but also caregivers, addressing the burdens that often impair their health and wellness.


title: Predicting the Outcome of NBA Playoffs Based on the Maximum Entropy Principle

abstract: Predicting the outcome of National Basketball Association (NBA) matches poses a challenging problem of interest to the research community as well as the general public. In this article, we formalize the problem of predicting NBA game results as a classification problem and apply the principle of Maximum Entropy to construct an NBA Maximum Entropy (NBAME) model that fits to discrete statistics for NBA games, and then predict the outcomes of NBA playoffs using the model. Our results reveal that the model is able to predict the winning team with 74.4% accuracy, outperforming other classical machine learning algorithms that could only afford a maximum prediction accuracy of 70.6% in the experiments that we performed.


title: The use of simulation for global supply network rationalization in the pharmaceutical industry

abstract: In today's volatile business environment, many companies are expanding, merging, contracting, or otherwise redesigning their supply chain networks. Supply chain network rationalization could significantly help to improve supply chain operational efficiency and reduce costs. As a generic technique for the analysis of complex and dynamic systems, simulation could play an important role in this field. This paper presents a real case showing how simulation could be the key enablement for a supply chain network rationalization project in the Over-The-Counter (OTC) pharmaceutical industry. An IBM tool -- Supply Chain Process Modeler (SCPM) has been applied in this project. Five major simulation scenarios are designed based on the pain points the client has, and numerical results show that simulation has addressed the key issues and provided the client accountable evaluation results for decision-making.


title: Dense Human Body Correspondences Using Convolutional Networks

abstract: We propose a deep learning approach for finding dense correspondences between 3D scans of people. Our method requires only partial geometric information in the form of two depth maps or partial reconstructed surfaces, works for humans in arbitrary poses and wearing any clothing, does not require the two people to be scanned from similar viewpoints, and runs in real time. We use a deep convolutional neural network to train a feature descriptor on depth map pixels, but crucially, rather than training the network to solve the shape correspondence problem directly, we train it to solve a body region classification problem, modified to increase the smoothness of the learned descriptors near region boundaries. This approach ensures that nearby points on the human body are nearby in feature space, and vice versa, rendering the feature descriptor suitable for computing dense correspondences between the scans. We validate our method on real and synthetic data for both clothed and unclothed humans, and show that our correspondences are more robust than is possible with state-of-the-art unsupervised methods, and more accurate than those found using methods that require full watertight 3D geometry.


title: An Overview of Classifier Fusion Methods

abstract: A number of classifier fusion methods have been recently developed opening an alternative approach leading to a potential improvement in the classification performance. As there is little theory of information fusion itself, currently we are faced with different methods designed for different problems and producing different results. This paper gives an overview of classifier fusion methods and attempts to identify new trends that may dominate this area of research in future. A taxonomy of fusion methods trying to bring some order into the existing “pudding of diversities” is also provided. 1. INTRODUCTION The objective of all decision support systems (DSS) is to create a model, which given a minimum amount of input data/information, is able to produce correct decisions. Quite often, especially in safety critical systems, the correctness of the decisions taken is of crucial importance. In such cases the minimum information constraint is not that important as long as the derivation of the final decision is obtained in a reasonable time. According to one approach, the progress of DSS should be based on continuous development of existing methods as well as discovering new ones. Another approach suggests that as the limits of the existing individual method are approached and it is hard to develop a better one, the solution of the problem might be just to combine existing well performing methods, hoping that better results will be achieved. Such fusion of information seems to be worth applying in terms of uncertainty reduction. Each of individual methods produces some errors, not mentioning that the input information might be corrupted and incomplete. However, different methods performing on different data should produce different errors, and assuming that all individual methods perform well, combination of such multiple experts should reduce overall classification error and as a consequence emphasise correct outputs. Information fusion techniques have been intensively investigated in recent years and their applicability for classification domain has been widely tested [1]-[14]. The problem arouse naturally as a need of improvement of classification rates obtained from individual classifiers. Fusion of data/information can be carried out on three levels of abstraction closely connected with the flow of the classification process: data level fusion, feature level fusion, and classifier fusion [15]. There is little theory about the first two levels of information fusion. However, there have been successful attempts to transform the numerical, interval and linguistic data into a single space of symmetric trapezoidal fuzzy numbers [14], [15], and some heuristic methods have been successfully used for feature level fusion [15]. A number of methods have been developed for classifier fusion also referred to as decision fusion or mixture of experts. Essentially, there are two general groups of classifier fusion techniques. The methods subjectively associated with the first group generally operate on classifiers and put an emphasis on a development of the classifier structure. They do not do anything with classifiers outputs until combination process finds single best classifier or a selected group of classifiers and only then their outputs are taken as a final decision or for further processing [2], [9], [10]. Another group of methods operate mainly on classifiers outputs, and effectively the combination of classifiers outputs is calculated [1], [3]-[8], [11]-[15]. The methods operating on classifiers outputs can be further divided according to the type of the output produced by individual classifiers. A diagrammatic representation of the proposed taxonomy of classifier fusion methods is shown in Figure 1.


title: Effects of acute systemic administration of cannabidiol on sleep-wake cycle in rats

abstract: Cannabidiol (CBD) is one of the main components of Cannabis sativa and has a wide spectrum of action, including effects in the sleep-wake cycle.Objective:The objective of this paper is to assess th...


title: Multi-focus image fusion with a deep convolutional neural network

abstract: Introduces Convolutional neural networks (CNNs) into the field of image fusion.Discusses the feasibility and superiority of CNNs used for image fusion.Proposes a state-of-the-art CNN-based multi-focus image fusion method.Exhibits the potential of CNNs for other-type image fusion issues.Puts forward some suggestions on the future study of CNN-based image fusion. As is well known, activity level measurement and fusion rule are two crucial factors in image fusion. For most existing fusion methods, either in spatial domain or in a transform domain like wavelet, the activity level measurement is essentially implemented by designing local filters to extract high-frequency details, and the calculated clarity information of different source images are then compared using some elaborately designed rules to obtain a clarity/focus map. Consequently, the focus map contains the integrated clarity information, which is of great significance to various image fusion issues, such as multi-focus image fusion, multi-modal image fusion, etc. However, in order to achieve a satisfactory fusion performance, these two tasks are usually difficult to finish. In this study, we address this problem with a deep learning approach, aiming to learn a direct mapping between source images and focus map. To this end, a deep convolutional neural network (CNN) trained by high-quality image patches and their blurred versions is adopted to encode the mapping. The main novelty of this idea is that the activity level measurement and fusion rule can be jointly generated through learning a CNN model, which overcomes the difficulty faced by the existing fusion methods. Based on the above idea, a new multi-focus image fusion method is primarily proposed in this paper. Experimental results demonstrate that the proposed method can obtain state-of-the-art fusion performance in terms of both visual quality and objective assessment. The computational speed of the proposed method using parallel computing is fast enough for practical usage. The potential of the learned CNN model for some other-type image fusion issues is also briefly exhibited in the experiments.


title: Battery lifetime prediction for energy-aware computing

abstract: Predicting the time of full discharge of a finite-capacity energy source, such as a battery, is important for the design of portable electronic systems and applications. In this paper we present a novel analytical model of a battery that not only can be used to predict battery lifetime, but also can serve as a cost function for optimization of the energy usage in battery-powered systems. The model is physically justified, and involves only two parameters, which are easily estimated. The paper includes the results of extensive experimental evaluation of the model with respect to numerical simulations of the electrochemical cell, as well as measurements taken on a real battery. The model was tested using constant, interrupted, periodic and non-periodic discharge profiles, which were derived from standard applications run on a pocket computer.


title: Evidence-based review of interventions for autism used in or of relevance to occupational therapy.

abstract: Occupational therapy practitioners are among the professionals who provide services to children and adults with autism spectrum disorder (ASD), embracing both leadership and supportive roles in service delivery. The study's primary aims were as follows: (1) to identify, evaluate, and synthesize the research literature on interventions for ASD of relevance to occupational therapy and (2) to interpret and apply the research literature to occupational therapy. A total of 49 articles met the authors� criteria and were included in the review. Six categories of research topics were identified, the first 3 of which are most closely related to occupational therapy: (1) sensory integration and sensory-based interventions; (2) relationship-based, interactive interventions; (3) developmental skill-based programs; (4) social cognitive skill training; (5) parent-directed or parent-mediated approaches; and (6) intensive behavioral intervention. Under each category, themes supported by research evidence and applicable to occupational therapy were defined. The findings have implications for intervention methods, communication regarding efficacious practices to professionals and consumers, and future occupational therapy research


title: High throughput pipelined hardware implementation of the KECCAK hash function

abstract: The cryptographic hash algorithm has been developed by designers with the goal to enhance its performances in terms of frequency, throughput, power consumption and area. The cryptographic hash algorithm is implemented in many embedded systems to ensure security. It is become the default choice to ensure the information integrity in numerous applications. In this paper, we propose a pipelined architecture of the new algorithm SHA-3 (KECCAK). In addition, the proposed KECCAK architecture has been implemented on Xilinx FPGA platform (Virtex-5). Its frequency, efficiency, throughput and area have been compared and discussed. The FPGA implementation results show that the proposed architecture achieves good performance in terms of frequency and throughput.


title: Bias in research

abstract: By writing scientific articles we communicate science among colleagues and peers. By doing this, it is our responsibility to adhere to some basic principles like transparency and accuracy. Authors, journal editors and reviewers need to be concerned about the quality of the work submitted for publication and ensure that only studies which have been designed, conducted and reported in a transparent way, honestly and without any deviation from the truth get to be published. Any such trend or deviation from the truth in data collection, analysis, interpretation and publication is called bias. Bias in research can occur either intentionally or unintentionally. Bias causes false conclusions and is potentially misleading. Therefore, it is immoral and unethical to conduct biased research. Every scientist should thus be aware of all potential sources of bias and undertake all possible actions to reduce or minimize the deviation from the truth. This article describes some basic issues related to bias in research.


SURVEY OF HAND GESTURE RECOGNITION AND SIGN LANGUAGE RECOGNITION 1


title: Graphical user interface(GUI) to study DC motor dynamic characteristics

abstract: Study of transient behavior of DC motors can be very cumbersome for students due to hazardous conditions prevailing in power laboratories and also the differential equation are not linear and tricky to solve and comprehend analytically. This paper helps in developing a GUI (Graphical User Interface) on transient behavior of some DC motors i.e. separately excited, shunt excited and series wound DC motor. The GUI modeling is based on the block model approach in MATLAB/SIMULINK & can be used by undergraduate students in their day to day lectures and laboratory experiments.


title: Integrated Receiver Including Both Receiver Channel and TDC for a Pulsed Time-of-Flight Laser Rangefinder With cm-Level Accuracy

abstract: An integrated receiver that includes both the time-to-digital converter (TDC) and the receiver channel and is intended for a pulsed time-of-flight laser rangefinder with a measurement range of approximately 10 m has been designed and fabricated in a standard 0.13 mum CMOS process. The receiver operates by detecting the current pulse of an optical detector and producing a stop timing mark for the TDC by means of a leading edge timing discriminator. The TDC is used to measure the actual time interval between the start and stop pulses and the slew-rate of the stop pulse, to compensate for a walk error produced in the discriminator. The single-shot precision of the whole receiver is 250 ps for a minimum detectable signal, and its accuracy and power consumption are plusmn 37 ps with compensation within a dynamic range of at least 1:10,000 and less than 45 mW, respectively. The size of the die is 1300 mum times1300 mum including pads.


Geo-Spatial Big Data Analysis : An Overview


title: Efficient Stereo Matching Leveraging Deep Local and Context Information

abstract: Stereo matching is a challenging problem with respect to weak texture, discontinuities, illumination difference and occlusions. Therefore, a deep learning framework is presented in this paper, which focuses on the first and last stage of typical stereo methods: the matching cost computation and the disparity refinement. For matching cost computation, two patch-based network architectures are exploited to allow the trade-off between speed and accuracy, both of which leverage multi-size and multi-layer pooling unit with no strides to learn cross-scale feature representations. For disparity refinement, unlike traditional handcrafted refinement algorithms, we incorporate the initial optimal and sub-optimal disparity maps before outlier detection. Furthermore, diverse base learners are encouraged to focus on specific replacement tasks, corresponding to the smooth regions and details. Experiments on different datasets demonstrate the effectiveness of our approach, which is able to obtain sub-pixel accuracy and restore occlusions to a great extent. Specifically, our accurate framework attains near-peak accuracy both in non-occluded and occluded region and our fast framework achieves competitive performance against the fast algorithms on Middlebury benchmark.


Sentiment-aware Analysis of Mobile Apps User Reviews Regarding Particular Updates


title: Generalisation in humans and deep neural networks

abstract: We compare the robustness of humans and current convolutional deep neural networks (DNNs) on object recognition under twelve different types of image degradations. First, using three well known DNNs (ResNet-152, VGG-19, GoogLeNet) we find the human visual system to be more robust to nearly all of the tested image manipulations, and we observe progressively diverging classification error-patterns between humans and DNNs when the signal gets weaker. Secondly, we show that DNNs trained directly on distorted images consistently surpass human performance on the exact distortion types they were trained on, yet they display extremely poor generalisation abilities when tested on other distortion types. For example, training on salt-and-pepper noise does not imply robustness on uniform white noise and vice versa. Thus, changes in the noise distribution between training and testing constitutes a crucial challenge to deep learning vision systems that can be systematically addressed in a lifelong machine learning approach. Our new dataset consisting of 83K carefully measured human psychophysical trials provide a useful reference for lifelong robustness against image degradations set by the human visual system.


title: A 0.65 THz Focal-Plane Array in a Quarter-Micron CMOS Process Technology

abstract: A focal-plane array (FPA) for room-temperature detection of 0.65-THz radiation has been fully integrated in a low-cost 0.25 mum CMOS process technology. The circuit architecture is based on the principle of distributed resistive self-mixing and facilitates broadband direct detection well beyond the cutoff frequency of the technology. The 3 timesZ 5 pixel array consists of differential on-chip patch antennas, NMOS direct detectors, and integrated 43-dB voltage amplifiers. At 0.65 THz the FPA achieves a responsivity (Rv) of 80 kV/W and a noise equivalent power (NEP) of 300 pW/ radic{Hz}. Active multi-pixel imaging of postal envelopes demonstrates the FPAs potential for future cost-effective terahertz imaging solutions.


title: A geometric approach for the alignment of liquid chromatography — mass spectrometry data

abstract: Motivation: Liquid chromatography coupled to mass spectrometry (LC-MS) and combined with tandem mass spectrometry (LC-MS/MS) have become a prominent tool for the analysis of complex proteomic samples. An important step in a typical workflow is the combination of results from multiple LC-MS experiments to improve confidence in the obtained measurements or to compare results from different samples. To do so, a suitable mapping or alignment between the data sets needs to be estimated. The alignment has to correct for variations in mass and elution time which are present in all mass spectrometry experiments. ::: ::: Results: We propose a novel algorithm to align LC-MS samples and to match corresponding ion species across samples. Our algorithm matches landmark signals between two data sets using a geometric technique based on pose clustering. Variations in mass and retention time are corrected by an affine dewarping function estimated from matched landmarks. We use the pairwise dewarping in an algorithm for aligning multiple samples. We show that our pose clustering approach is fast and reliable as compared to previous approaches. It is robust in the presence of noise and able to accurately align samples with only few common ion species. In addition, we can easily handle different kinds of LC-MS data and adopt our algorithm to new mass spectrometry technologies. ::: ::: Availability: This algorithm is implemented as part of the OpenMS software library for shotgun proteomics and available under the Lesser GNU Public License (LGPL) at www.openms.de ::: ::: Contact: lange@inf.fu-berlin.de


title: Evaluation of a 1200-V, 800-A All-SiC Dual Module

abstract: Enhanced material properties of silicon carbide (SiC) offer improved performance capabilities for power electronic devices compared to traditional silicon (Si) components. This paper reports on the experimental characterization of a 1200-V, 800-A all-SiC dual power module that incorporates twenty 80-A SiC MOSFETs and twenty 50-A SiC junction barrier Schottky diodes. Forward and reverse conduction characteristics were measured at multiple gate voltages, current sharing was examined between the MOSFETs, and switching energies were calculated for various currents. Additionally, this module has operated in a full-bridge circuit with a peak loading of 900 Adc, a 600 Vdc bus, and a junction temperatures of 153°C. From the experimental data, a model of the module was created and used in a dc-ac inverter simulation study to demonstrate the possible benefits of SiC compared to Si technology. The use of an all-SiC module was shown to reduce inverter losses by 40% or more for most operating conditions. Furthermore, for similar output current levels, the all-SiC module can operate at switching frequencies four times higher than that of the Si module. This advanced dual power module demonstrates the ability to produce a high-current high-power switch using SiC technology.


KANO’S METHODS FOR UNDERSTANDING CUSTOMER-DEFINED QUALITY


title: A precise and compact height of burst sensor for guided missiles

abstract: Height of Burst (HOB) sensor is one of the critical parts in guided missiles. While seekers control the guiding scheme of the missile, proximity sensors set the trigger for increased effectiveness of the warhead. For the well-developed guided missiles of Roketsan, a novel proximity sensor is developed. The design of the sensor is for multi-purpose use. In this presentation, the application of the sensor is explained for operation as a HOB sensor in the range of 3m–50m with ± 1m accuracy. Measurement results are also presented. The same sensor is currently being developed for proximity sensor for missile defence.


title: Multiplex Genome Engineering Using CRISPR/Cas Systems

abstract: Functional elucidation of causal genetic variants and elements requires precise genome editing technologies. The type II prokaryotic CRISPR (clustered regularly interspaced short palindromic repeats)/Cas adaptive immune system has been shown to facilitate RNA-guided site-specific DNA cleavage. We engineered two different type II CRISPR/Cas systems and demonstrate that Cas9 nucleases can be directed by short RNAs to induce precise cleavage at endogenous genomic loci in human and mouse cells. Cas9 can also be converted into a nicking enzyme to facilitate homology-directed repair with minimal mutagenic activity. Lastly, multiple guide sequences can be encoded into a single CRISPR array to enable simultaneous editing of several sites within the mammalian genome, demonstrating easy programmability and wide applicability of the RNA-guided nuclease technology.


title: Index Coding of Point Cloud-Based Road Map Data for Autonomous Driving

abstract: Information exchange in a vehicular network between autonomous vehicles and the roadside infrastructure is important for improving road safety. These autonomous vehicles, equipped with a sensor suite, are capable of obtaining road map data that can be used to inform other vehicles and update the central road map repository through roadside units. The roadside infrastructure nodes act as local databases for distributing regional 3D road map data in form of point clouds to autonomous vehicles passing by. Since the vehicles might have various side information regarding the road network and traffic condition, minimizing the required number of transmissions to satisfy the demand of participating vehicles through network coding is an interesting research problem in road map data dissemination. In this paper, we propose the Road Map Data Encoding and Dissemination System (REDS) and evaluate its performance in a four-way junction scenario. It is based on index coding for broadcasting road map data from a centrally-managed roadside node to vehicles. REDS uses the data availability and demand knowledge for encoding and transmitting 3D point cloud road map data from different road segments. The data availability information helps prevent the transmission of duplicated road map data and provides the sets of side information in the index coding problem, while the data demand information further defines the message transmission priority based on the data demand of different road segments. Simulation results indicate that REDS reduces the average number of transmissions and transmitted point cloud data size by around 30% when the data availability probability is about 0.5 under random mobility in all simulated scenarios when compared to the traditional broadcasting approach.


title: An energy efficient rendezvous node selection approach

abstract: Using a drone as a mobile sink is an interesting issue in wireless sensor networks. There are many studies that try to minimize the flight distance of the drone but their approaches causes unbalanced energy consumption of nodes. In this paper, we propose a new rendezvous node selection approach to achieve the balance of each node's energy consumption and to minimize the flight distance of the drones. Simulation results show that our approach can extend the entire network life-time and maintain the stable variation of the flight distance.


title: Context Cues in Reading: The Gap between Research and Popular Opinion.

abstract: There is a popular view among teachers that children read words better in context. This view is certainly supported by Kenneth Goodman's classic study in 1965 that showed a 60%‐80% gain in word recognition accuracy when children read words in context as against reading an isolated list. However, a recent replication study by Nicholson, Lillas, and Rzoska (1988) suggested that the popular view should be revised to take account of the differences between good and poor readers. The present article reports on another replication. These findings also suggest that the popular view needs to be revised.


title: Double-Hypergraph Based Sentence Ranking for Query-Focused Multi-document Summarizaton

abstract: Traditional graph based sentence ranking approaches modeled the documents as a text graph where vertices represent sentences and edges represent pairwise similarity relationships between two sentences. Such modeling cannot capture complex group relationships shared among multiple sentences which can be useful for sentence ranking. In this paper, we propose two different group relationships (sentence-topic relationship and document-topic relationship) shared among sentences, and construct a double-hypergraph integrating these relationships into a unified framework. Then, a double-hypergraph based sentence ranking algorithm is developed for query-focused multi-document summarization, in which Markov random walk is defined on each hypergraph and the mixture Markov chains are formed so as to perform transductive learning in the double-hypergraph. When evaluated on DUC datasets, performance of the proposed approach is remarkable.


title: Visual analysis of graphs with multiple connected components

abstract: In this paper, we present a system for the interactive visualization and exploration of graphs with many weakly connected components. The visualization of large graphs has recently received much research attention. However, specific systems for visual analysis of graph data sets consisting of many components are rare. In our approach, we rely on graph clustering using an extensive set of topology descriptors. Specifically, we use the Self-Organizing-Map algorithm in conjunction with a user-adaptable combination of graph features for clustering of graphs. It offers insight into the overall structure of the data set. The clustering output is presented in a grid containing clusters of the connected components of the input graph. Interactive feature selection and task-tailored data views allow the exploration of the whole graph space. The system provides also tools for assessment and display of cluster quality. We demonstrate the usefulness of our system by application to a shareholder network analysis problem based on a large real-world data set. While so far our approach is applied to weighted directed graphs only, it can be used for various graph types.


title: Game theory meets network security and privacy

abstract: This survey provides a structured and comprehensive overview of research on security and privacy in computer and communication networks that use game-theoretic approaches. We present a selected set of works to highlight the application of game theory in addressing different forms of security and privacy problems in computer networks and mobile applications. We organize the presented works in six main categories: security of the physical and MAC layers, security of self-organizing networks, intrusion detection systems, anonymity and privacy, economics of network security, and cryptography. In each category, we identify security problems, players, and game models. We summarize the main results of selected works, such as equilibrium analysis and security mechanism designs. In addition, we provide a discussion on the advantages, drawbacks, and future direction of using game theory in this field. In this survey, our goal is to instill in the reader an enhanced understanding of different research approaches in applying game-theoretic methods to network security. This survey can also help researchers from various fields develop game-theoretic solutions to current and emerging security problems in computer networking.


title: Learning to personalize query auto-completion

abstract: Query auto-completion (QAC) is one of the most prominent features of modern search engines. The list of query candidates is generated according to the prefix entered by the user in the search box and is updated on each new key stroke. Query prefixes tend to be short and ambiguous, and existing models mostly rely on the past popularity of matching candidates for ranking. However, the popularity of certain queries may vary drastically across different demographics and users. For instance, while instagram and imdb have comparable popularities overall and are both legitimate candidates to show for prefix i, the former is noticeably more popular among young female users, and the latter is more likely to be issued by men. In this paper, we present a supervised framework for personalizing auto-completion ranking. We introduce a novel labelling strategy for generating offline training labels that can be used for learning personalized rankers. We compare the effectiveness of several user-specific and demographic-based features and show that among them, the user's long-term search history and location are the most effective for personalizing auto-completion rankers. We perform our experiments on the publicly available AOL query logs, and also on the larger-scale logs of Bing. The results suggest that supervised rankers enhanced by personalization features can significantly outperform the existing popularity-based base-lines, in terms of mean reciprocal rank (MRR) by up to 9%.


title: FISM: factored item similarity models for top-N recommender systems

abstract: The effectiveness of existing top-N recommendation methods decreases as the sparsity of the datasets increases. To alleviate this problem, we present an item-based method for generating top-N recommendations that learns the item-item similarity matrix as the product of two low dimensional latent factor matrices. These matrices are learned using a structural equation modeling approach, wherein the value being estimated is not used for its own estimation. A comprehensive set of experiments on multiple datasets at three different sparsity levels indicate that the proposed methods can handle sparse datasets effectively and outperforms other state-of-the-art top-N recommendation methods. The experimental results also show that the relative performance gains compared to competing methods increase as the data gets sparser.


title: Bulgaria's Fiscal Expansion: Navigating Through Stormy Waters

abstract: Most EU governments reacted to the global economic crisis with a dramatic increase in spending. Their reactions had two main goals in mind: first, to bailout failing financial systems and second, to substitute the decline in private demand with a boost in aggregate public spending. For Bulgaria, fiscal stimulus programs proved to present their own unique set of challenges.


title: The effect of user features on churn in social networks

abstract: Social sites and services rely on the continuing activity, good will and behaviour of the contributors to remain viable. There has been little empirical study of the mechanisms by which social sites maintain a viable user base. Such studies would provide a scientific understanding of the patterns that lead to user churn (i.e. users leaving the community) and the community dynamics that are associated with reduction of community members -- primary threats to the sustainability of any service. In this paper, we explore the relation between a user's value within a community - constituted from various user features - and the probability of a user churning.


title: Study of GenCos’ Cournot behaviors considering market auction mechanism

abstract: In restructured power markets participants are able to exercise market power through different bidding strategies. In this paper an important oligopolistic market is presented that all generation companies (GenCos) bid in a Cournot model considering behaviours of elastic load demands at all nodes for a whole standard day .Genetic algorithm (GA) is applied to obtain generation scheduling of each GenCo as well as hourly market clearing prices (MCP). In order to consider network constraints a multiperiod framework is applied to simulate market clearing process that requires the use of continuous and binary variables. Behaviours of generators' biddings and elastic loads are modeled through piecewise block curves. Finally impacts of market clearing process on participants' characteristic and final market prices are presented.


title: A Controlled Trial of Aromatherapy for Agitation in Nursing Home Patients with Dementia

abstract: Objectives: Two controlled trials of aromatherapy to decrease agitation in persons with dementia have recently produced promising results. However, both studies combined the use of essential oils with massage. Thus, it is unclear if the effect of the aromatherapy intervention was the result of smelling or the cutaneous absorption of the oils. The purpose of this study was to determine whether smelling lavender oil decreases the frequency of agitated behaviors in patients with dementia. Design: The study design was within-subjects ABCBA (A = lavender oil, B = thyme oil, C = unscented grapeseed oil): 4 weeks of baseline measurement, 2 weeks for each of the five treatment conditions (10-week total intervention time), and 2 weeks of postintervention measurement. Oil was placed every 3 hours on an absorbent fabric sachet pinned near the collarbone of each participant's shirt. Setting: A long-term care facility specifically for persons with dementia. Participants: Seven agitated nursing home residents with adva...


title: Pl@ntNet mobile app

abstract: Pl@ntNet is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.


title: Globus Online manages fire-and-forget file transfers for big-data , high-performance

abstract: Globus Online manages fire-and-forget file transfers for big-data, high-performance scientific collaborations.


title: DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer

abstract: We have witnessed rapid evolution of deep neural network architecture design in the past years. These latest progresses greatly facilitate the developments in various areas such as computer vision, natural language processing, etc. However, along with the extraordinary performance, these state-of-the-art models also bring in expensive computational cost. Directly deploying these models into applications with real-time requirement is still infeasible. Recently, Hinton etal. have shown that the dark knowledge within a powerful teacher model can significantly help the training of a smaller and faster student network. These knowledge are vastly beneficial to improve the generalization ability of the student model. Inspired by their work, we introduce a new type of knowledge -- cross sample similarities for model compression and acceleration. This knowledge can be naturally derived from deep metric learning model. To transfer them, we bring the learning to rank technique into deep metric learning formulation. We test our proposed DarkRank on the pedestrian re-identification task. The results are quite encouraging. Our DarkRank can improve over the baseline method by a large margin. Moreover, it is fully compatible with other existing methods. When combined, the performance can be further boosted.


title: An analysis of memory power consumption in database systems

abstract: The growing appetite for in-memory computing is increasing memory's share of total server power consumption. However, memory power consumption in database management systems is not well understood. This paper presents an empirical characterization of memory power consumption in database systems, for both analytical and transactional workloads. Our results indicate that memory power optimization will be effective only if it can reduce back-ground power through more aggressive use of low power memory idle states.


title: Inducing Domain-Specific Sentiment Lexicons from Unlabeled Corpora

abstract: A word's sentiment depends on the domain in which it is used. Computational social science research thus requires sentiment lexicons that are specific to the domains being studied. We combine domain-specific word embeddings with a label propagation framework to induce accurate domain-specific sentiment lexicons using small sets of seed words, achieving state-of-the-art performance competitive with approaches that rely on hand-curated resources. Using our framework we perform two large-scale empirical studies to quantify the extent to which sentiment varies across time and between communities. We induce and release historical sentiment lexicons for 150 years of English and community-specific sentiment lexicons for 250 online communities from the social media forum Reddit. The historical lexicons show that more than 5% of sentiment-bearing (non-neutral) English words completely switched polarity during the last 150 years, and the community-specific lexicons highlight how sentiment varies drastically between different communities.


title: Neural network-based reputation model in a distributed system

abstract: Current centralized trust models are inappropriate to apply in a large distributed multi-agent system, due to various evaluation models and partial observations in local level reputation management. This paper proposes a distributed reputation management structure, and develops a global reputation model. The global reputation model is a novel application of neural network techniques in distributed reputation evaluations. The experimental results showed that the model has robust performance under various estimation accuracy requirements. More important, the model is adaptive to changes in distributed system structures and in local reputation evaluations.


title: Age-dependent biomechanical properties of the skin

abstract: The skin fulfills one of its most important functions, that is protection from mechanical injuries, due to the mechanism of reversible deformation of the structure. Human skin is a complex living material but in biomechanical tests it reveals its homogeneous nature. Biomechanical skin parameters change with time. Results of thickness measurements, where the skin was subjected to pressure, revealed that the Young's modulus increased linearly with age. The process of ageing is the reason why the skin becomes thinner, stiffer, less tense and less flexible. Skin tension measured during in vivo uniaxial load and the elasticity modulus are higher in children than in elderly adults. Furthermore, mean ultimate skin deformation before bursting is 75% for newborns and 60% for the elderly. Several types of the main lines were distinguished on the skin. The static lines, described by Langer, correspond to the lines of maximum tension, the Kraissl's lines correspond to the movements of the skin during muscle work, whereas the Borges lines are the relaxed skin tension lines. Biomechanical tests of the human skin help to quantify the effectiveness of dermatological products, detect skin diseases, schedule and plan surgical and dermatological interventions and treatments. Language: en


title: Classifying the Political Leaning of News Articles and Users from User Votes

abstract: Social news aggregator services generate readers’ subjective reactions to news opinion articles. Can we use those as a resource to classify articles as liberal or conservative, even without knowing the self-identified political leaning of most users? We applied three semi-supervised learning methods that propagate classifications of political news articles and users as conservative or liberal, based on the assumption that liberal users will vote for liberal articles more often, and similarly for conservative users and articles. Starting from a few labeled articles and users, the algorithms propagate political leaning labels to the entire graph. In cross-validation, the best algorithm achieved 99.6% accuracy on held-out users and 96.3% accuracy on held-out articles. Adding social data such as users’ friendship or text features such as cosine similarity did not improve accuracy. The propagation algorithms, using the subjective liking data from users, also performed better than an SVM based text classifier, which achieved 92.0% accuracy on articles.


Histological and Trichoscopic Correlations in Scalp Disorders


Chapter 3 Auditory Processing Disorder : Biological Basis and Treatment


title: Needle echogenicity in ultrasound-guided lumbar spine injections: a cadaveric study.

abstract: BACKGROUND ::: Echogenicity of regional anesthesia needles has been tested on different preclinical models; however, previous studies were done in an ideal experimental setting utilizing high-frequency insonation and superficially located targets. Because steep-angle deep injections are typically required for spinal and other chronic pain procedures, and low-frequency transducers are used, further feasibility study is warranted. ::: ::: ::: OBJECTIVES ::: To determine effectiveness of steep-angle deep injections, typically required for spinal and other chronic pain procedures. ::: ::: ::: STUDY DESIGN ::: Experimental laboratory study. ::: ::: ::: SETTING ::: Willed Body Program, University of Washington. ::: ::: ::: METHODS ::: In-plane lumbar spine procedures with 50° and 70° angles were performed on a human cadaver. The images and video clips of a non-echogenic (Quincke-type) and echogenic (SonoPlex, StimuQuick, and EchoStim) needle placements were presented to 3 blinded assessors who rated the needle visibility on a 4-point scale. The data was statistically analyzed to determine the differences in visibility between the needles with and without the digital image enhancement, and to compare the video clips to captured images. ::: ::: ::: RESULTS ::: ANOVA analysis demonstrated that overall SonoPlex was significantly better (P = 0.02) than other needles. SonoPlex maintained its superiority in the subset of facet joint injections (P = 0.02), followed by Quincke-type, then the StimuQuik, and EchoStim needles. In deep procedures, EchoStim was comparable with SonoPlex (P = 0.03), and they both were better than the other 2 needles. The enhanced images received higher rates, with a 0.6 point mean improved rating (P = 0). ::: ::: ::: LIMITATIONS ::: This study is limited by choice of needles, number of experiments performed, and potential postmortem changes of echogenicity. ::: ::: ::: CONCLUSIONS ::: The SonoPlex needle appeared to have better echogenicity in this study. While non-echogenic Quincke-type needle visibility was adequate in superficial placements, it was limited in deep injections. An imaging enhancement is effective in improving needle visibility and should be used whenever possible.


Effective Domain Mixing for Neural Machine Translation


title: A mathematical analysis of the DCT coefficient distributions for images

abstract: Over the past two decades, there have been various studies on the distributions of the DCT coefficients for images. However, they have concentrated only on fitting the empirical data from some standard pictures with a variety of well-known statistical distributions, and then comparing their goodness of fit. The Laplacian distribution is the dominant choice balancing simplicity of the model and fidelity to the empirical data. Yet, to the best of our knowledge, there has been no mathematical justification as to what gives rise to this distribution. We offer a rigorous mathematical analysis using a doubly stochastic model of the images, which not only provides the theoretical explanations necessary, but also leads to insights about various other observations from the literature. This model also allows us to investigate how certain changes in the image statistics could affect the DCT coefficient distributions.


title: A modified faster R-CNN based on CFAR algorithm for SAR ship detection

abstract: SAR ship detection is essential to marine monitoring. Recently, with the development of the deep neural network and the spring of the SAR images, SAR ship detection based on deep neural network has been a trend. However, the multi-scale ships in SAR images cause the undesirable differences of features, which decrease the accuracy of ship detection based on deep learning methods. Aiming at this problem, this paper modifies the Faster R-CNN, a state-of-the-art object detection networks, by the traditional constant false alarm rate (CFAR). Taking the objects proposals generated by Faster R-CNN for the guard windows of CFAR algorithm, this method picks up the small-sized targets. By reevaluating the bounding boxes which have relative low classification scores in detection network, this method gain better performance of detection.


title: Arduino-based smart irrigation using water flow sensor, soil moisture sensor, temperature sensor and ESP8266 WiFi module

abstract: Emergence of Controlled Environment Agriculture (CEA) ranging from computer controlled water irrigation system to lightning and ventilation has changed the conventional scenario of farming. This paper proposes and demonstrate an economical and easy to use arduino based controlled irrigation system. The designed system deals with various environmental factors such as moisture, temperature and amount of water required by the crops using sensors like water flow sensor, temperature sensor and soil moisture sensor. Datas are collected and received by arduino which can be linked to an interactive website which show the real time values along with the standard values of different factor required by a crop. This allows user to control irrigation pumps and sprinklers from far distance through a website and to meet the standard values which would help the farmer to yield maximum and quality crops. Studies conducted on laboratory prototype suggested the designed system to be applicable which can be implemented.


title: A Distributed Control Strategy Based on DC Bus Signaling for Modular Photovoltaic Generation Systems With Battery Energy Storage

abstract: Modular generation system, which consists of modular power conditioning converters, is an effective solution to integrate renewable energy sources with conventional utility grid to improve reliability and efficiency, especially for photovoltaic generation. A distributed control strategy based on improved dc bus signaling is proposed for a modular photovoltaic (PV) generation system with battery energy storage elements. In this paper, the modular PV generation system is composed of three modular dc/dc converters for PV arrays, two grid-connected dc/ac converters, and one dc/dc converter for battery charging/discharging and local loads, which is available of either grid-connected operation or islanding operation. By using the proposed control strategy, the operations of a modular PV generation system are categorized into four modes: islanding with battery discharging, grid-connected rectification, grid-connected inversion, and islanding with constant voltage (CV) generation. The power balance of the system under extreme conditions such as the islanding operation with a full-charged battery is taken into account in this control strategy. The dc bus voltage level is employed as an information carrier to distinguish different modes and determine mode switching. Control methods of modular dc/dc converters, battery converter, and grid-connected converter are addressed. An autonomous control method for modular dc/dc converters is proposed to realize smooth switching between CV operation and maximum power point tracking operation, which enables the dc bus voltage regulation capability of modular dc/dc converters. Seamless switching of a battery converter between charging and discharging and that of a grid-connected converter between rectification and inversion are ensured by the proposed control methods. Experiments verify the practical feasibility and the effectiveness of the proposed control strategies.


title: An agent-based architecture for an adaptive human-robot interface

abstract: This paper describes an innovative agent-based architecture for mixed-initiative interaction between a human and a robot that interacts via a graphical user interface (GUI). Mixed-initiative interaction typically refers to a flexible interaction strategy between a human and a computer to contribute what is best-suited at the most appropriate time (Allen, 1999). In this paper, we extend this concept to human-robot interaction (HRI). When compared to pure human-computer interaction, HRIs encounter additional difficulty, as the user must assess the situation at the robot's remote location via limited sensory feedback. We propose an agent-based adaptive human-robot interface for mixed-initiative interaction to address this challenge. The proposed adaptive user interface (UI) architecture provides a platform for developing various agents that control robots and user interface components (UICs). Such components permit the human and the robot to communicate mission-relevant information.


title: Selective Database Projections Based Approach for Mining High-Utility Itemsets

abstract: High-utility itemset mining (HUIM) is an emerging area of data mining and is widely used. HUIM differs from the frequent itemset mining (FIM), as the latter considers only the frequency factor, whereas the former has been designed to address both quantity and profit factors to reveal the most profitable products. The challenges of generating the HUI include exponential complexity in both time and space. Moreover, the pruning techniques of reducing the search space, which is available in FIM because of their monotonic and anti-monotonic properties, cannot be used in HUIM. In this paper, we propose a novel selective database projection-based HUI mining algorithm (SPHUI-Miner). We introduce an efficient data format, named HUI-RTPL, which is an optimum and compact representation of data requiring low memory. We also propose two novel data structures, viz, selective database projection utility list and Tail-Count list to prune the search space for HUI mining. Selective projections of the database reduce the scanning time of the database making our proposed approach more efficient. It creates unique data instances and new projections for data having less dimensions thereby resulting in faster HUI mining. We also prove upper bounds on the amount of memory consumed by these projections. Experimental comparisons on various benchmark data sets show that the SPHUI-Miner algorithm outperforms the state-of-the-art algorithms in terms of computation time, memory usage, scalability, and candidates generation.


title: An RBF-PSO based approach for early detection of DDoS attacks in SDN

abstract: The architecture of SDN helps in early detection and mitigation of DDoS attacks, which is a challenge in the traditional network. With appropriate identification patterns and detection scheme, the SDN controller detects DDoS attacks in infant stages and suppresses their impact on the entire network. This paper proposes a feature set for classification of DDoS attacks from normal traffic. Further, a system model for detection of DDoS attacks implemented with SDN controller is proposed. The detection module in system model is based on RBF network having PSO optimized learning that is further compared with several schemes. The effectiveness of the system model has been verified by its real-time emulation with mininet emulator. The proposed system model is efficient in classifying heavy traffic load in the network from that of DDoS attacks and is capable of handling the DDoS attacks in their early stages itself.