title: Toward Welding Robot With Human Knowledge: A Remotely-Controlled Approach

abstract: This paper presents a remotely controlled welding scheme that enables transformation of human welder knowledge into a welding robot. In particular, a 6-DOF UR-5 industrial robot arm is equipped with sensors to observe the welding process, including a compact 3D weld pool surface sensing system and an additional camera to provide direct view of the work-piece. Human welder operates a virtual welding torch, whose motion is tracked by a Leap sensor. To remotely operate the robot based on the motion information from the Leap sensor, a predictive control approach is proposed to accurately track the human motion by controlling the speed of the robot arm movement. Tracking experiments are conducted to track both simulated movement with varying speed and actual human hand movement. It is found that the proposed predictive controller is able to track human hand movement with satisfactory accuracy. A welding experiment has also been conducted to verify the effectiveness of the proposed remotely-controlled welding system. A foundation is thus established to realize teleoperation and help transfer human knowledge to the welding robot.


title: Comparison study of low-cost obstacle sensing solutions for Unmanned Aerial Vehicles in wildlife scenery

abstract: The use of Unmanned Aerial Vehicles (UAVs) for a wide variety of applications has been increasing during the latest years. For safe flying of UAVs, different types of obstacle sensing solutions towards collision avoidance have been presented. These solutions combine various types of on-board perception sensors to realize obstacle perception. Such sensors include optical cameras, infrared sensors, laser scanners, and ultrasonic sensors. The sensing capacity of these sensors determines the overall performance of the obstacle sensing solution. As a result, the selection of perception sensors becomes critical to achieving safe UAVs flights. This paper addresses a comparison overview study on the available low-cost obstacle sensing solutions for UAVs in wildlife environments. The cost, latency, precision, reliability, and performance across obstacles of different shapes, size, surfaces, and state (static or dynamic) in wildlife flying environments are of major concern. The main contribution of the paper includes the comparison on these critical aspects, followed by valuable recommendations on determining obstacle sensing solutions towards collision avoidance for wildlife UAVs.


title: Distributed Power-Generation Systems and Protection

abstract: Continuously expanding deployments of distributed power-generation systems (DPGSs) are transforming the conventional centralized power grid into a mixed distributed electrical network. The modern power grid requires flexible energy utilization but presents challenges in the case of a high penetration degree of renewable energy, among which wind and solar photovoltaics are typical sources. The integration level of the DPGS into the grid plays a critical role in developing sustainable and resilient power systems, especially with highly intermittent renewable energy resources. To address the challenging issues and, more importantly, to leverage the energy generation, stringent demands from both utility operators and consumers have been imposed on the DPGS. Furthermore, as the core of energy conversion, numerous power electronic converters employing advanced control techniques have been developed for the DPGS to consolidate the integration. In light of the above, this paper reviews the power-conversion and control technologies used for DPGSs. The impacts of the DPGS on the distributed grid are also examined, and more importantly, strategies for enhancing the connection and protection of the DPGS are discussed.


title: From Orthogonal to Non-orthogonal Multiple Access : Energy- and Spectrum-Efficient Resource Allocation

abstract: The rapid pace of innovations in information and communication technology (ICT) industry over the past decade has greatly improved people’s mobile communication experience. This, in turn, has escal ...


title: A Survey and a Layered Taxonomy of Software-Defined Networking

abstract: Software-defined networking (SDN) has recently gained unprecedented attention from industry and research communities, and it seems unlikely that this will be attenuated in the near future. The ideas brought by SDN, although often described as a “revolutionary paradigm shift” in networking, are not completely new since they have their foundations in programmable networks and control-data plane separation projects. SDN promises simplified network management by enabling network automation, fostering innovation through programmability, and decreasing CAPEX and OPEX by reducing costs and power consumption. In this paper, we aim at analyzing and categorizing a number of relevant research works toward realizing SDN promises. We first provide an overview on SDN roots and then describe the architecture underlying SDN and its main components. Thereafter, we present existing SDN-related taxonomies and propose a taxonomy that classifies the reviewed research works and brings relevant research directions into focus. We dedicate the second part of this paper to studying and comparing the current SDN-related research initiatives and describe the main issues that may arise due to the adoption of SDN. Furthermore, we review several domains where the use of SDN shows promising results. We also summarize some foreseeable future research challenges.


title: [Opinions on psychiatric diseases of the citizens of Novosibirsk: results of a representative study].

abstract: BACKGROUND ::: Citizens of Novosibirsk were questioned regarding their ideas about schizophrenia and depressive disorders, with one of the main concerns being a comparison with the ideas and attitudes of the German population. ::: ::: ::: METHOD ::: In 2002, a representative survey (N = 745) was conducted among the adult population of Novosibirsk, using a fully structured interview that has been used in previous surveys in Germany. ::: ::: ::: RESULTS ::: Symptoms of schizophrenia were more frequently identified as an indication of mental disorder than were depressive symptoms. Psychosocial stress and biological influences were similarly frequently seen as the cause of schizophrenia, with the former prevailing among the respondents in the case of depressive symptoms. Psychotherapy was most frequently recommended as a treatment measure, also for schizophrenia. Supposing that no treatment measures will be taken, respondents assessed the prognosis for both mental disorders negatively. Conversely, interviewees gave a positive treatment prognosis. ::: ::: ::: DISCUSSION ::: Respondents from Germany and Novosibirsk showed similar beliefs, especially as regards the importance of biological influences and psychosocial stress and the assessment of psychotherapy as compared to psychopharmacology. However, German and Russian respondents also held differing views: a comparatively strong tendency of the Russian population to consider mental disorders as self-inflicted - an important finding that could shape future anti-stigma programs.


title: Transfer Function Estimation in System Identification Toolbox via Vector Fitting

abstract: This paper considers black- and grey-box continuous-time transfer function estimation from frequency response measurements. The first contribution is a bilinear mapping of the original problem from the imaginary axis onto the unitdisk. This improves the numerics of the underlying Sanathanan-Koerner iterations and the more recent instrumental-variable iterations. Orthonormal rational basis functions on the unit disk are utilized. Each iteration step necessitates a minimal state-space realization with these basis functions. One such derivation is the second contribution. System identification with these basis functions yield zero-pole-gain models. The third contribution is an efficient method to express transfer function coefficient constraints in terms of the orthonormal rational basis functions. This allows for estimating transfer function models with arbitrary relative degrees (including improper models), along with other fixed and bounded parameter values. The algorithm is implemented in the tfest function in System Identification Toolbox (Release 2016b, for use with MATLAB) for frequency domain data. Two examples are presented to demonstrate the algorithm performance.


title: Are Call Light Use and Response Time Correlated With Inpatient Falls and Inpatient Dissatisfaction

abstract: Inpatients use call lights to seek nurses' assistance. Although implied in patient safety, no studies have analyzed data related to the use of or response time to call lights collected by existing tracking mechanisms monitoring nursing practice. This study used archived hospital data to determine th


title: 3D hand posture recognition from small unlabeled point sets

abstract: This paper is concerned with the evaluation and comparison of several methods for the classification and recognition of static hand postures from small unlabeled point sets corresponding to physical landmarks, e.g. reflective marker positions in a motion capture environment. We compare various classification algorithms based upon multiple interpretations and feature transformations of the point sets, including those based upon aggregate features (e.g. mean) and a pseudo-rasterization of the space. We find aggregate feature classifiers to be balanced across multiple users but relatively limited in maximum achievable accuracy. Certain classifiers based upon the pseudo-rasterization performed best among tested classification algorithms. The inherent difficulty in classifying certain users leads us to conclude that online learning may be necessary for the recognition of natural gestures.


title: Mechanisms of action in youth psychotherapy

abstract: Background: In this review, we address a basic, but unanswered, question about psychosocial interventions for youth: How does psychotherapy work? Methods: We propose a framework for using mediation analysis to answer this question, and we review the youth therapy outcome literature for evidence on mediating mechanisms. We focus our review on clinical trials of empirically supported treatments for youth anxiety, depression, and disruptive behavior (N a 67). Results: Contrary to previous reports indicating that potential mediators are rarely assessed, 63% of the studies included measures of potential mediating mechanisms in their designs. Across treatment domains, percentages ranged from 22% of the studies of learning-based interventions for anxiety (i.e., systematic desensitization, modeling, and reinforced practice) to 91% of parent training investigations. Despite the rather extensive assessment of potential mediators, only six studies included any attempt to use the measures in a formal mediation test. Thus, despite the positive effects of treatments and surprisingly ample assessment of mediators, we still know remarkably little about how youth psychotherapies work. Conclusions: We note common problems that hampered mediation testing (e.g., the design of many trials made it difficult to determine the temporal order of change in the mechanism and outcome), and we offer recommendations for improving study design to better assess mechanisms of therapeutic action. We also note the need to test mediation among referred youth treated in representative practice settings to complement the laboratory-based evidence on therapy mechanisms that prevails to date.


title: MEMS Mirror Controlling System with Holed-PSD

abstract: In this study, we developed a precise 2D MEMS (Micro Electro Mechanical Systems) mirror control system. The system comprises a two-sided mirror and rectangular shaped springs that fabricated on a silicon-on-insulator (SOI) wafer. The mirror movement is detected by a specialized holed position sensitive device (H-PSD), and is proportional-integral derivative (PID) controlled by a microcontroller (MCU). We also discuss an application of an optical laser projector that can depict an arbitral figures on the screen.


Effects of Transverse Side Walls on Aerodynamic Characteristics of a Micro Quad-Rotor in Hovering


title: An Electronic Travel Aid to Assist Blind and Visually Impaired People to Avoid Obstacles

abstract: When devices and applications provide assistance to people they become part of assistive technology. If the assistance is given to impaired people, then it is possible to refer those technologies as adaptive technologies. The main aims of these systems are substitution of physical assistants and the improvement of typical tools already available for impaired people. In this paper some benefits and examples of adaptive technology applications will be discussed. Moreover we present an adaptive technology framework to avoid obstacles to be exploited by visually impaired and blind people. The proposed assistive technology has been designed to perform vision substitution; specifically it provides Electronic Travel Aid ETA capabilities through the processing of information acquired with a depth sensor such that the user can avoid obstacles during the environment exploration. In the proposed system we require to know just the height of the sensor with respect to the ground floor to calibrate the ETA system. Experiments are performed to asses the proposed system.


title: Screening for bioactive natural products from a 67-compound library of Glycyrrhiza inflata.

abstract: Licorice shows a variety of pharmacological activities. This work aims to discover bioactive natural products from one botanical source of licorice, Glycyrrhiza inflata. A total of 67 free phenolics were isolated to form a compound library. Based on the bioactivities of licorice, these compounds were screened using cell- or enzyme-based bioassay methods. A total of 11 compounds exhibited potent cytotoxic activities against three human cancer cell lines (HepG2, SW480 and MCF7), while showed little toxicity on human normal cell lines LO2 and HEK293T. A number of chalcones showed remarkable anti-inflammatory activities. Among them, 2 (licochalcone B, IC50 8.78μM), 10 (licoagrochalcone C, IC50 9.35μM) and 13 (licochalcone E, IC50 9.09μM) exhibited the most potent inhibitory activities on LPS-induced NO production, whereas 1, 8, 10, 12 and 13 (IC50 13.9, 7.27, 2.44, 6.67 and 3.83μM) showed potent inhibitory activities on NF-κB transcription. Nine prenylated phenolics were found to be PTP1B inhibitors. Particularly, licoagrochalcone A (4), kanzonol C (7), 2'-hydroxyisolupalbigenin (35), gancaonin Q (45), glisoflavanone (50) and glabrol (53) showed IC50 values of 0.31-0.97μM. Compounds 24 (semilicoisoflavone B, IC50 0.25μM), 26 (allolicoisoflavone B, IC50 0.80μM) and 64 (glabridin, IC50 0.10μM) showed noticeable tyrosinase inhibitory activities. Most of the above bioactive compounds were reported for the first time.


title: An 11 $\mu$ w, two-electrode transimpedance biosignal amplifier with active current feedback stabilization

abstract: A novel two-electrode biosignal amplifier circuit is demonstrated by using a composite transimpedance amplifier input stage with active current feedback. Micropower, low gain-bandwidth product operational amplifiers can be used, leading to the lowest reported overall power consumption in the literature for a design implemented with off-the-shelf commercial integrated circuits (11 μW). Active current feedback forces the common-mode input voltage to stay within the supply rails, reducing baseline drift and amplifier saturation problems that can be present in two-electrode systems. The bandwidth of the amplifier extends from 0.05-200 Hz and the midband voltage gain (assuming an electrode-to-skin resistance of 100 kΩ) is 48 dB. The measured output noise level is 1.2 mV pp, corresponding to a voltage signal-to-noise ratio approaching 50 dB for a typical electrocardiogram (ECG) level input of 1 mVpp. Recordings were taken from a subject by using the proposed two-electrode circuit and, simultaneously, a three-electrode standard ECG circuit. The residual of the normalized ensemble averages for both measurements was computed, and the power of this residual was 0.54% of the power of the standard ECG measurement output. While this paper primarily focuses on ECG applications, the circuit can also be used for amplifying other biosignals, such as the electroencephalogram.


title: Work-Life Balance and Employees' Performance: The Mediating Role of Affective Commitment

abstract: Introduction Individuals experience more conflict between work and personal life as they continue to pursue the quality of life that they need (Casper et al., 2011). Thus, successfully balancing work and family life is one of the major challenges facing current individual workers (Halpern, 2005). Historically, work-life balance issues have been considered personal issues (Emslie & Hunt, 2009), and employers have just responded to their employees' needs by providing additional benefits such as on-site childcare service and paid maternity leave in the workplace. However, with environmental shifts and value changes of employees, employees' desire for work-life balance has increased and employers have begun to offer more active support of their employees' work-life balance (Thornthwaite, 2004). In its list of the 100 best companies to work for, Fortune magazine identifies organizations that make an effort to assist employees in managing the duties of work and family (Muse et al., 2008). Thus, organizational efforts for ensuring employees' work-life balance are needed and valued more than ever. Many researchers have generally agreed on the important role of work-life balance as it is related with an individual's psychological well-being and overall sense of harmony in life, which is an indicator of balance between the workplace role and the role in family (Clark, 2000; Marks and MacDermid, 1996). Recent research shows that both employees and organizations benefit from successfully balanced work and family life (e.g., Greenhaus and Powell, 2006; Hammer et al., 2005). In family domains, when people experience a lack of work-life balance, this experience threatens key domains of their personal lives (Lachman and Boone-James, 1997); on the other hand, work-life balance enhances their well-being and family satisfaction (Grzywacz, 2000). In work domains, the absence of work-life balance causes poor performance and more absenteeism of employees (Frone et al., 1997), but balanced work and family life is associated with increased job satisfaction and organizational commitment (Cegarra-Leiva et al., 2012; Wayne et al., 2004). In other words, employees' work-life balance experiences deepen their role-related engagement, which is related to organizational performance improvement (Carlson et al., 2008). Work-life balance in the workplace has become a more important issue as it tends to exhibit positive results such as low turnover, work engagement, organizational citizenship behavior, in-role performance, increased firm productivity, job satisfaction, and organizational commitment (Konrad and Mangel, 2000; Lambert, 2000; Shepard et al., 1996; Wang and Walumbwa, 2007). As emphasized by several researchers, managing work-life balance has become one of the most critical managerial strategies for ensuring employees' performance and organizational performance improvement. Research Purpose and Problems As was previously noted, the interest in and importance of work-life balance is increasing. Indeed, it is widely accepted by researchers that work-life balance is associated with desirable outcomes in both the workplace area and family area (e.g., Harrington and Ladge, 2009; Parkes and Langford, 2008). Despite this increased interest and these favorable outcomes of work-life balance, few studies have directly linked it with outcomes (Carlson et al., 2009; Frone, 2003; Grzywacz and Butler, 2005). Also, several researchers have pointed out that the effect of work-life balance on employees' attitudes and behaviors is still unclear and have called for more in-depth research studies to identify what types of performance are related with work-life balance (Casper and Buffardi, 2004; Kossek and Ozeki, 1998). The situation is no different in the Asian context and for the case of South Korea. Workers are experiencing an increase in their average income, resulting in a rise in their living standards, which consequently as caused a growth in the interest of work-life balance issues (Lim et al. …


title: The complex network of global cargo ship movements

abstract: Transportation networks play a crucial role in human mobility, the exchange of goods, and the spread of invasive species. With 90% of world trade carried by sea, the global network of merchant ships provides one of the most important modes of transportation. Here we use information about the itineraries of 16,363 cargo ships during the year 2007 to construct a network of links between ports. We show that the network has several features which set it apart from other transportation networks. In particular, most ships can be classified in three categories: bulk dry carriers, container ships and oil tankers. These three categories do not only differ in the ships' physical characteristics, but also in their mobility patterns and networks. Container ships follow regularly repeating paths whereas bulk dry carriers and oil tankers move less predictably between ports. The network of all ship movements possesses a heavy-tailed distribution for the connectivity of ports and for the loads transported on the links with systematic differences between ship types. The data analyzed in this paper improve current assumptions based on gravity models of ship movements, an important step towards understanding patterns of global trade and bioinvasion.


title: Fast All-Season Soil STrength (FASST)

abstract: Abstract : The ability to predict the state of the ground is essential to manned and unmanned vehicle mobility and personnel movement, as well as determining sensor performance for both military and civilian activities. As part of the Army's Battlespace Terrain Reasoning and Awareness research program, the 1-D dynamic state of the ground model FASST (Fast All-season Soil STrength) was developed. It calculates the ground's moisture content, ice content, temperature, and freeze/thaw profiles, as well as soil strength and surface ice and snow accumulation/depletion. The fundamental operations of FASST are the calculation of an energy and water budget that quantifies both the flow of heat and moisture within the soil and also the exchange of heat and moisture at all interfaces (ground/air or ground/snow; snow/air) using both meteorological and terrain data. FASST is designed to accommodate a range of users, from those who have intricate knowledge of their site to those who know only the site location. It allows for 22 different terrain materials, including asphalt, concrete, bedrock, permanent snow, and the USCS soil types. At a minimum, the only weather information required is air temperature.


title: Smart power management system for home appliances and wellness based on wireless sensors network and mobile technology

abstract: This work is focused on the development of a new management system for building and home automation that provides a fully real time monitor of household appliances and home environmental parameters. The developed system consists of a smart sensing unit, wireless sensors and actuators and a web-based interface for remote and mobile applications. The main advantages of the proposed solution rely on the reliability of the developed algorithmics, on modularity and open-system characteristics, on low power consumption and system cost efficiency.


title: Holographic Photolysis for Multiple Cell Stimulation in Mouse Hippocampal Slices

abstract: BACKGROUND: Advanced light microscopy offers sensitive and non-invasive means to image neural activity and to control signaling with photolysable molecules and, recently, light-gated channels. These approaches require precise and yet flexible light excitation patterns. For synchronous stimulation of subsets of cells, they also require large excitation areas with millisecond and micrometric resolution. We have recently developed a new method for such optical control using a phase holographic modulation of optical wave-fronts, which minimizes power loss, enables rapid switching between excitation patterns, and allows a true 3D sculpting of the excitation volumes. In previous studies we have used holographic photololysis to control glutamate uncaging on single neuronal cells. Here, we extend the use of holographic photolysis for the excitation of multiple neurons and of glial cells. METHODS/PRINCIPAL FINDINGS: The system combines a liquid crystal device for holographic patterned photostimulation, high-resolution optical imaging, the HiLo microscopy, to define the stimulated regions and a conventional Ca(2+) imaging system to detect neural activity. By means of electrophysiological recordings and calcium imaging in acute hippocampal slices, we show that the use of excitation patterns precisely tailored to the shape of multiple neuronal somata represents a very efficient way for the simultaneous excitation of a group of neurons. In addition, we demonstrate that fast shaped illumination patterns also induce reliable responses in single glial cells. CONCLUSIONS/SIGNIFICANCE: We show that the main advantage of holographic illumination is that it allows for an efficient excitation of multiple cells with a spatiotemporal resolution unachievable with other existing approaches. Although this paper focuses on the photoactivation of caged molecules, our approach will surely prove very efficient for other probes, such as light-gated channels, genetically encoded photoactivatable proteins, photoactivatable fluorescent proteins, and voltage-sensitive dyes.


title: Persistence of coagulase negative staphylococcal intramammary infections in dairy goats

abstract: The objectives of the research described here were to describe the persistence of intramammary infections (IMI) caused by coagulase negative staphylococci (CNS) in goats using strain-typing, and to evaluate the relationship between species-specific CNS IMI and somatic cell score (SCS) at the udder-half level. Udder-half milk samples were collected from all 909 lactating goats (1817 halves; 1 blind half) in a single herd. Milk samples were cultured on Columbia blood agar, and 220 goats with at least one half yielding a single colony type CNS were enrolled for two additional half-level samplings at approximately 1-month intervals. Isolates were identified to the species level by matrix-assisted laser desorption-ionisation time-of-flight mass spectrometry or PCR amplification and partial sequencing of tuf or rpoB. An IMI was defined as persistent when ≥1 follow-up sample yielded the same species and strain as on Day 0 based on pulsed-field gel electrophoresis. A generalised mixed linear model was used to evaluate the odds of persistence as a function of CNS species. A mixed linear model was used to evaluate the relationship between IMI status on a given day and SCS. Among 192 IMI, 69.8% were persistent based on species and strain-type. Staphylococcus simulans IMI had higher odds of persistence than Staphylococcus arlettae IMI. In primiparous goats, Staphylococcus epidermidis IMI was associated with higher SCS than S. arlettae, Staphylococcus xylosus and ‘other CNS’ IMI. The differences detected in the present study between CNS species, with regard to persistence of IMI and association with SCS, highlight the need to study CNS at the species and strain level to understand the pathogenicity and epidemiology of CNS in goats.


title: Learning Detailed Face Reconstruction from a Single Image

abstract: Reconstructing the detailed geometric structure of a face from a given image is a key to many computer vision and graphics applications, such as motion capture and reenactment. The reconstruction task is challenging as human faces vary extensively when considering expressions, poses, textures, and intrinsic geometries. While many approaches tackle this complexity by using additional data to reconstruct the face of a single subject, extracting facial surface from a single image remains a difficult problem. As a result, single-image based methods can usually provide only a rough estimate of the facial geometry. In contrast, we propose to leverage the power of convolutional neural networks to produce a highly detailed face reconstruction from a single image. For this purpose, we introduce an end-to-end CNN framework which derives the shape in a coarse-to-fine fashion. The proposed architecture is composed of two main blocks, a network that recovers the coarse facial geometry (CoarseNet), followed by a CNN that refines the facial features of that geometry (FineNet). The proposed networks are connected by a novel layer which renders a depth image given a mesh in 3D. Unlike object recognition and detection problems, there are no suitable datasets for training CNNs to perform face geometry reconstruction. Therefore, our training regime begins with a supervised phase, based on synthetic images, followed by an unsupervised phase that uses only unconstrained facial images. The accuracy and robustness of the proposed model is demonstrated by both qualitative and quantitative evaluation tests.


title: What do people ask their social networks, and why?: a survey study of status message q&a behavior

abstract: People often turn to their friends, families, and colleagues when they have questions. The recent, rapid rise of online social networking tools has made doing this on a large scale easy and efficient. In this paper we explore the phenomenon of using social network status messages to ask questions. We conducted a survey of 624 people, asking them to share the questions they have asked and answered of their online social networks. We present detailed data on the frequency of this type of question asking, the types of questions asked, and respondents' motivations for asking their social networks rather than using more traditional search tools like Web search engines. We report on the perceived speed and quality of the answers received, as well as what motivates people to respond to questions seen in their friends' status messages. We then discuss the implications of our findings for the design of next-generation search tools.


title: Reliability Analysis of a Wearable Sensor Patch (WSP) to Monitor ECG Signals

abstract: Abstract Flexible Hybrid Electronic (FHE) devices interface flexible sensors and circuits with conventional rigid electronic components. This work reports preliminary results for the reliability aspects of a project aimed at fabricating a Wearable Sensor Patch (WSP) to monitor ECG signals. The device was fabricated by interfacing flexible electroplated Cu circuit lines and an ECG sensor on a Kapton® polyimide (PI) substrate with rigid electronics connected using SnPb solder (reflow temperature: 204 °C), making it a FHE device. Phase I of this project faced reliability issues as Cu circuit lines were susceptible to failure due to cracking near the front-end signal conditioning chip. This issue needed to be resolved in Phase II of the project to produce a robust device fit to be used in real world applications. The effect of changes in Cu trace thickness (2 and 6 μm) and Kapton® PI thickness (2 and 5 mil) on device robustness was tested. Effect of the use of low reflow temperature SnBi solder (reflow temper...


title: Schemaless and Structureless Graph Querying

abstract: Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users. Due to their complex schemas and variational information descriptions, it becomes very hard for users to formulate a query that can be properly processed by the existing systems. We argue that for a user-friendly graph query engine, it must support various kinds of transformations such as synonym, abbreviation, and ontology. Furthermore, the derived query results must be ranked in a principled manner. ::: ::: In this paper, we introduce a novel framework enabling s chema l ess and s tructure l ess graph q uerying (SLQ), where a user need not describe queries precisely as required by most databases. The query engine is built on a set of transformation functions that automatically map keywords and linkages from a query to their matches in a graph. It automatically learns an effective ranking model, without assuming manually labeled training examples, and can efficiently return top ranked matches using graph sketch and belief propagation. The architecture of SLQ is elastic for "plug-in" new transformation functions and query logs. Our experimental results show that this new graph querying paradigm is promising: It identifies high-quality matches for both keyword and graph queries over real-life knowledge graphs, and outperforms existing methods significantly in terms of effectiveness and efficiency.


title: Gaussian filters for nonlinear filtering problems

abstract: We develop and analyze real-time and accurate filters for nonlinear filtering problems based on the Gaussian distributions. We present the systematic formulation of Gaussian filters and develop efficient and accurate numerical integration of the optimal filter. We also discuss the mixed Gaussian filters in which the conditional probability density is approximated by the sum of Gaussian distributions. A new update rule of weights for Gaussian sum filters is proposed. Our numerical tests demonstrate that new filters significantly improve the extended Kalman filter with no additional cost, and the new Gaussian sum filter has a nearly optimal performance.


title: HIV-1 gp120 and morphine induced oxidative stress: role in cell cycle regulation

abstract: HIV infection and illicit drugs are known to induce oxidative stress and linked with severity of viral replication, disease progression, impaired cell cycle regulation and neurodegeneration. Studies have shown that morphine accelerates HIV infection and disease progression mediated by Reactive oxygen species (ROS). Oxidative stress impact redox balance and ROS production affect cell cycle regulation. However, the role of morphine in HIV associated acceleration of oxidative stress and its link to cell cycle regulation and neurodegeneration has not been elucidated. The aim of present study is to elucidate the mechanism of oxidative stress induced glutathione synthases (GSS), super oxide dismutase (SOD), and glutathione peroxidase (GPx) impact cell cycle regulated protein cyclin-dependent kinase 1, cell division cycle 2 (CDK-1/CDC-2), cyclin B, and cell division cycle 25C (CDC-25C) influencing neuronal dysfunction by morphine co-morbidity with HIV-1 gp120. It was observed that redox imbalance inhibited the GSS, GPx and increased SOD which, subsequently inhibited CDK-1/CDC-2 whereas cyclin B and CDC-25C significantly up regulated in HIV-1 gp120 with morphine compared to either HIV-1 gp120 or morphine treated alone in human microglial cell line. These results suggest that HIV positive morphine users have increased levels of oxidative stress and effect of cell cycle machinery, which may cause the HIV infection and disease progression.


title: PAIGE: PAirwise Image Geometry Encoding for improved efficiency in Structure-from-Motion

abstract: Large-scale Structure-from-Motion systems typically spend major computational effort on pairwise image matching and geometric verification in order to discover connected components in large-scale, unordered image collections. In recent years, the research community has spent significant effort on improving the efficiency of this stage. In this paper, we present a comprehensive overview of various state-of-the-art methods, evaluating and analyzing their performance. Based on the insights of this evaluation, we propose a learning-based approach, the PAirwise Image Geometry Encoding (PAIGE), to efficiently identify image pairs with scene overlap without the need to perform exhaustive putative matching and geometric verification. PAIGE achieves state-of-the-art performance and integrates well into existing Structure-from-Motion pipelines.


title: CROSS-VERIFICATION OF ONE- AND THREE-DIMENSIONAL MODELS FOR VVER STEAM GENERATOR

abstract: Within the framework of calculations of VVER accident scenarios several steam generator (SG) models for the SOCRAT integral code and the STAR CD code were developed at Nuclear Safety Institute of the Russian Academy of Science (IBRAE RAS). Cross-verification of the coolant flow over SG primary circuit was performed between these codes under rated operating conditions. The agreement of coolant velocities in tubes may be deemed as good, taking into account a large difference in the degree of nodalization of heat-exchanging tubes and collectors. Velocity field found by STAR CD calculations makes it possible to reduce the uncertainties of SG model built in the SOCRAT code. The developed SG model for the SOCRAT code may be considered as acceptable for calculations of rated conditions. With respect to accident scenarios, the validation of one-dimensional models requires, in the strict sense, additional three-dimensional calculations of two-phase mixture and superheated steam flow, coupled to heat exchange with secondary circuit water. Such a development of three-dimensional SG model is planned for the next phases of work.


title: The Hephaestus Smart Wheelchair system

abstract: The Hephaestus Smart Wheelchair System is envisioned as a series of components that clinicians and wheelchair manufacturers will be able to attach to standard power wheelchairs to convert them into "smart wheelchairs." A prototype of the system has been developed and mounted on an Everest and Jennings Lancer2000 wheelchair. The prototype bases its navigation assistance behavior on the behavior developed for the NavChair Assistive Wheelchair Navigation System, but the underlying hardware and software are being designed to facilitate commercialization. This paper describes our design goals for the Hephaestus system and discusses the current status of the system prototype as well as plans for future work.


title: Synthesis of a thioester linker precursor for a general preparation of peptide C-terminal thioacids

abstract: Abstract A general procedure to prepare peptide thioacids by solid-phase peptide synthesis is presented. The method involves the synthesis of 4-[α-( S -acetyl)mercaptobenzyl]phenoxyacetic acid as general precursor. This reagent once attached to a solid support is derivatized with the Boc-amino acid of choice after deprotection of the thiol.


title: The concept of alpha-synuclein as a prion-like protein: ten years after

abstract: Parkinson's disease is characterized by the loss of nigrostriatal dopaminergic signaling and the presence of alpha-synuclein aggregates (also called Lewy bodies and neurites) throughout the brain. In 2003, Braak and colleagues created a staging system for Parkinson's disease describing the connection between the alpha-synuclein pathology and disease severity. Later, they suggested that the pathology might initially be triggered by exogenous insults targeting the gut and olfactory system. In 2008, we and other groups documented Lewy pathology in grafted neurons in people with Parkinson's disease who had been transplanted over a decade prior to autopsy. We proposed that the Lewy pathology in the grafted neurons was the result of permissive templating or prion-like spread of alpha-synuclein pathology from neurons in the host to those in the grafts. During the following ten years, several studies described the transmission of alpha-synuclein pathology between neurons, both in cell culture and in experimental animals. Recent research has also begun to identify underlying molecular mechanisms. Collectively, these experimental studies tentatively support the idea that the progression from one Braak stage to the next is the consequence of prion-like propagation of Lewy pathology. However, definitive proof that intercellular propagation of alpha-synuclein pathology occurs in Parkinson's disease cases has proven difficult to secure. In this review, we highlight several open questions that currently prevent us from concluding with certainty that prion-like transfer of alpha-synuclein contributes to the progression of Parkinson's disease.


title: Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks

abstract: Five billion people in the world lack access to quality surgical care. Surgeon skill varies dramatically, and many surgical patients suffer complications and avoidable harm. Improving surgical training and feedback would help to reduce the rate of complications, half of which have been shown to be preventable. To do this, it is essential to assess operative skill, a process that currently requires experts and is manual, time consuming, and subjective. In this work, we introduce an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos, leveraging region-based convolutional neural networks. In order to study this problem, we also introduce a new dataset, m2cai16-tool-locations, which extends the m2cai16-tool dataset with spatial bounds of tools. While previous methods have addressed tool presence detection, ours is the first to not only detect presence but also spatially localize surgical tools in real-world laparoscopic surgical videos. We show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection. We further demonstrate the ability of our method to assess surgical quality through analysis of tool usage patterns, movement range, and economy of motion.


title: Analysis of passive suspension systems with flat and cylindrical structures

abstract: In this paper performances of two passive electrodynamic suspension systems with flat and cylindrical structures are investigated and compared. The first system consists of a permanent magnet block levitated over a moving flat conducting plate. The second system consists of a permanent magnet block levitated over a rotating cylindrical conducting shell. The influences of systems specifications on their performances are also studied by using FEM.


title: A Highly Linear Two-Stage Amplifier Integrated Circuit Using InGaP/GaAs HBT

abstract: This paper presents a linearized two-stage amplifier integrated circuit (IC), having an output power level of about 1 Watt, used for general purpose applications. A predistortion method, based on alignment of the nonlinear characteristics between the first- and the second-stage amplifiers, has been proposed and analyzed in order to enhance the linearization aspects. The resistors in the active bias circuits of both the stages are optimized to achieve the best cancellation conditions for the third-order intermodulation components. The two-stage amplifier IC, which is based on an InGaP/GaAs hetero-junction bipolar transistor (HBT) technology, has been designed and implemented for the 900 MHz band. An output 1 dB compression point (P1dB) of 29.6 dBm, a maximum third-order output intercept point (OIP3) of as high as 48.7 dBm at a two-tone average output power of 21 dBm have been obtained while having a quiescent current of 375 mA and a single bias supply of 5.5 V. The implemented amplifier is able to maintain its IMD3 performance below -60 dBc up to an output power of 21 dBm.


title: Applying online feature selection for fall detection

abstract: Recently, sensor based human activity recognition has attracted lots of researches, which overcomes the constraint of the traditional vision-based technology. Fall detection is an important part of monitoring elderly people. In order to fast and precisely detect the action fall from huge data, online feature selection method is proposed to improve online human activity recognition, which overcomes the limit of traditional batch learning methods. The high-dimensional real-world dataset is utilized to evaluate the proposed method. Better results have demonstrated the efficacy of the proposed online feature selection approach. It has also been demonstrated that data from belt is much more easily discriminated than from other three body parts.


title: Ontology augmentation: combining semantic web and text resources

abstract: This work investigates the process of selecting, extracting and reorganizing content from Semantic Web information sources, to produce an ontology meeting the specifications of a particular domain and/or task. The process is combined with traditional text-based ontology learning methods to achieve tolerance to knowledge incompleteness. The paper describes the approach and presents experiments in which an ontology was built for a diet evaluation task. Although the example presented concerns the specific case of building a nutritional ontology, the methods employed are domain independent and transferrable to other use cases.


title: Fall Detection Systems for Elderly Care: A Survey

abstract: With the introduction of new technology in our daily life, it is essential that this technology is used for the aid of the elderly. Falls cause a very high risk to the elderly's life. Accordingly, this paper's focus is on technology that would aid the elderly. These technologies include: Wearable- based, audio- based, and video-based fall detection systems. This paper surveys the literature regarding fall detection algorithms using those three branches and the various sensors they employ. Looking at wearable technology, the technology is cheap and accurate but inconvenient. Audio-based technology on the other hand is more convenient and is cheaper than video-based technology. However audio-based technology is hard to set up compared to video and wearable-based technologies. Video- based technology is accurate and easy to set up. At the moment, video-based technology is the most expensive compared to the other two, and it is also prone to occlusion. However as homes become smarter and prices for cameras continue to drop, it is expected that this technology will be the best of the three due to its versatility.


title: Single image 3D human pose estimation from noisy observations

abstract: Markerless 3D human pose detection from a single image is a severely underconstrained problem because different 3D poses can have similar image projections. In order to handle this ambiguity, current approaches rely on prior shape models that can only be correctly adjusted if 2D image features are accurately detected. Unfortunately, although current 2D part detector algorithms have shown promising results, they are not yet accurate enough to guarantee a complete disambiguation of the 3D inferred shape. In this paper, we introduce a novel approach for estimating 3D human pose even when observations are noisy. We propose a stochastic sampling strategy to propagate the noise from the image plane to the shape space. This provides a set of ambiguous 3D shapes, which are virtually undistinguishable from their image projections. Disambiguation is then achieved by imposing kinematic constraints that guarantee the resulting pose resembles a 3D human shape. We validate the method on a variety of situations in which state-of-the-art 2D detectors yield either inaccurate estimations or partly miss some of the body parts.


title: Unmanned vehicles come of age: The DARPA grand challenge

abstract: While the DARPA Grand Challenge has revitalized interest in intelligent highway systems, autonomous vehicles, and sensing technology, a host of other novel issues afford interesting design and computer-engineering challenges for the future.


title: Detection of Domain Specific Terminology Using Corpora Comparison

abstract: Identifying terms in specialized corpora is a central task in terminological work (compilation of domain-specific dictionaries), but is labour-intensive, especially when the corpora are voluminous which is often the case nowadays. For the past decade, terminologists and specialized lexicographers have been able to rely on term-extraction tools to assist them in the selection of terms. However, most term-extractors focus on the identification of complex terms. Although complex terms (cellular telephone) are central to terminology processing, retrieval of uniterms (telephone) is still a major challenge. This paper evaluates the usefulness of a corpora comparison approach in order to find pinpoint corpus specific words in order to identify uniterms in the field of telecommunications.


title: Early Detection of Learning Difficulties when Confronted with Novel Information in Preclinical Alzheimer’s Disease Stage 1

abstract: We employed a highly demanding experimental associative learning test (the AFE-T) to explore memory functioning in Preclinical Alzheimer's Disease stage 1 (PreAD-1) and stage 2 (PreAD-2). The task consisted in the learning of unknown object/name pairs and our comprehensive setup allowed the analysis of learning curves, immediate recall, long-term forgetting rates at one week, three months, and six months, and relearning curves. Forty-nine cognitively healthy subjects were included and classified according to the presence or absence of abnormal CSF biomarkers (Control, n = 31; PreAD-1, n = 14; PreAD-2, n = 4). Control and PreAD-1 performances on the experimental test were compared by controlling for age and education. These analyses showed clear learning difficulties in PreAD-1 subjects (F = 6.98; p = 0.01). Between-group differences in long-term forgetting rates were less notable, reaching statistical significance only for the three-month cued forgetting rate (F = 4.83; p = 0.03). Similarly, relearning sessions showed only statistical trends between the groups (F = 3.22; p = 0.08). In the whole sample, significant correlations between CSF Aβ42/tau ratio and the AFE-T were found, both in the total learning score (r = 0.52; p < 0.001) and in the three-month cued forgetting rate (r = -0.38; p < 0.01). Descriptive subanalyses involving PreAD-2 suggested greater learning and recall difficulties in these subjects when compared with the PreAD-1 group. The present results suggest that explicit learning difficulties when binding information could be one of the earliest signs of the future emergence of episodic memory difficulties on the Alzheimer's disease continuum. Our findings indicate that the AFE-T is a sensitive test, capable of detecting subtle memory difficulties in PreAD-1.


title: Hybrid Search of Feature Subsets

abstract: Feature selection is a search problem for an “optimal” subset of features. The class separability is normally used as one of the basic feature selection criteria. Instead of maximizing the class separability as in the literature, this work adopts a criterion aiming to maintain the discriminating power of the data. After examining the pros and cons of two existing algorithms for feature selection, we propose a hybrid algorithm of probabilistic and complete search that can take advantage of both algorithms. It begins by running LVF (probabilistic search) to reduce the number of features; then it runs “Automatic Branch & Bound (ABB)” (complete search). By imposing a limit on the amount of time this algorithm can run, we obtain an approximation algorithm. The empirical study suggests that dividing the time equally between the two phases yields nearly the best performance, and that the hybrid search algorithm substantially outperforms earlier methods in general.


title: Incidence of ‘Candidatus Liberibacter europaeus’ and phytoplasmas in Cacopsylla species (Hemiptera: Psyllidae) and their host/shelter plants

abstract: Psyllids, as vectors of phloem-restricted plant pathogens, are serious agricultural pests. Fruit tree phytoplasmas are transmitted by different Cacopsylla spp., while other psyllids are known vectors of liberibacters. Recently, the bacterium ‘Candidatus Liberibacter europaeus’ was found in pear trees and in Cacopsylla pyri (Linnaeus), the vector of ‘Ca. Phytoplasma pyri’. This new species does not cause symptoms in plants and is probably a symbiont rather than a pathogen. Based on these findings and the assumption that ‘Ca. Liberibacter europaeus’ is widespread, we studied its distribution in the genus Cacopsylla and in the respective host and shelter plants (where psyllids aestivate and overwinter), as well as its possible co-presence with ‘Ca. Phytoplasma’ spp. We tested 14 Cacopsylla species and 11 plant species from northwestern Italy, Hungary and Israel, characterized by warm oceanic, temperate continental and warm Mediterranean climatic conditions, respectively. ‘Ca. Liberibacter europaeus’ was common within the Cacopsylla genus, being present in nine of the 14 species screened as well as in most host plants, whereas none of the shelter plants tested positive for this bacterium. Altogether, these findings indicate the presence of ‘Ca. Liberibacter europaeus’ in continental zones, whereas it does not seem to be widespread in the Mediterranean region. Furthermore, lack of specific symptoms in all infected plants confirms an endophytic relationship with this bacterium, while its abundance in insects suggests a beneficial role for the host. Co-infections with phytoplasmas, observed in insects and plants, require further study to evaluate the possible interactions between them.


title: Learning Transferable Architectures for Scalable Image Recognition

abstract: Developing neural network image classification models often requires significant architecture engineering. In this paper, we study a method to learn the model architectures directly on the dataset of interest. As this approach is expensive when the dataset is large, we propose to search for an architectural building block on a small dataset and then transfer the block to a larger dataset. The key contribution of this work is the design of a new search space (the "NASNet search space") which enables transferability. In our experiments, we search for the best convolutional layer (or "cell") on the CIFAR-10 dataset and then apply this cell to the ImageNet dataset by stacking together more copies of this cell, each with their own parameters to design a convolutional architecture, named "NASNet architecture". We also introduce a new regularization technique called ScheduledDropPath that significantly improves generalization in the NASNet models. On CIFAR-10 itself, NASNet achieves 2.4% error rate, which is state-of-the-art. On ImageNet, NASNet achieves, among the published works, state-of-the-art accuracy of 82.7% top-1 and 96.2% top-5 on ImageNet. Our model is 1.2% better in top-1 accuracy than the best human-invented architectures while having 9 billion fewer FLOPS - a reduction of 28% in computational demand from the previous state-of-the-art model. When evaluated at different levels of computational cost, accuracies of NASNets exceed those of the state-of-the-art human-designed models. For instance, a small version of NASNet also achieves 74% top-1 accuracy, which is 3.1% better than equivalently-sized, state-of-the-art models for mobile platforms. Finally, the learned features by NASNet used with the Faster-RCNN framework surpass state-of-the-art by 4.0% achieving 43.1% mAP on the COCO dataset.


title: A One-Sample Test for Normality with Kernel Methods

abstract: We propose a new one-sample test for normality in a Reproducing Kernel Hilbert Space (RKHS). Namely, we test the null-hypothesis of belonging to a given family of Gaussian distributions. Hence our procedure may be applied either to test data for normality or to test parameters (mean and covariance) if data are assumed Gaussian. Our test is based on the same principle as the MMD (Maximum Mean Discrepancy) which is usually used for two-sample tests such as homogeneity or independence testing. Our method makes use of a special kind of parametric bootstrap (typical of goodness-of-fit tests) which is computationally more efficient than standard parametric bootstrap. Moreover, an upper bound for the Type-II error highlights the dependence on influential quantities. Experiments illustrate the practical improvement allowed by our test in high-dimensional settings where common normality tests are known to fail. We also consider an application to covariance rank selection through a sequential procedure.


title: STUDIES IN THE AMERICAN ARADIDAE WITH DESCRIPTIONS OF NEW SPECIES

abstract: The present paper deals largely with the abundance of material gathered during recent years in the collections of the California Academy of Sciences and the author, from the comparatively little known regions of the Western and Southwestern United States and Central Mexico. Appreciation is expressed to Dr. H. M. Parshley and Prof. W. S. Blatchley for information and material supplied by them, to Mr. E. P. Van Duzee for valuable assistance in working up the material as well as for permission to study the collection in his charge, and to several western Coleopterists, Dr. E. C. Van Dyke, Mr. R. L. Furniss, and Mr. E. G. Linsley, through whose field work so many of these interesting bugs have been collected. Dr. L. Bonar has very kindly determined the fungi.


title: An improved algorithm for computing logarithms over GF(p) and its cryptographic significance (Corresp.)

abstract: A cryptographic system is described which is secure if and only if computing logarithms over GF(p) is infeasible. Previously published algorithms for computing this function require O(p^{1/2}) complexity in both time and space. An improved algorithm is derived which requires O =(\log^{2} p) complexity if p - 1 has only small prime factors. Such values of p must be avoided in the cryptosystem. Constructive uses for the new algorithm are also described.


title: Sparse Subspace Clustering via Group Sparse Coding

abstract: We propose in this paper a novel sparse subspace clustering method that regularizes sparse subspace representation by exploiting the structural sharing between tasks and data points via group sparse coding. We derive simple, provably convergent, and computationally efficient algorithms for solving the proposed group formulations. We demonstrate the advantage of the framework on three challenging benchmark datasets ranging from medical record data to image and text clustering and show that they consistently outperforms rival methods.


title: Coordination Analysis of Human Movements With Body Sensor Networks: A Signal Processing Model to Evaluate Baseball Swings

abstract: Becoming proficient in a sport requires significant investment in training. Traditional training approaches such as training with a partner or an expert, and training with the help of videotaping can significantly increase progress. These techniques, however, do not provide fine grain detail about movements of the player, are time consuming, or are limited to specific locations. In contrast, wearable sensor devices can improve training due to the high level of mobility, ubiquity and intelligent feedback offered. In this paper, we present a wearable platform that provides baseball players with corrective feedback based on multidimensional physiological data collected from a body sensor network. We employ a swing model that specifies actions that must be performed properly, in the correct order, and with precise timing between limbs. The system evaluates a baseball swing using motion transcripts. Transcripts simplify interpretation of complex movements and can be used to reduce the size of data that need to be transmitted across the network. Using transcripts, we measure coordination among limb segments and joints of the body. The starting times of key events are found in the transcripts, and the coordination between these times is analyzed. The swing quality is then assessed by comparing the intersegment coordination of a test swing to that of a template swing.


title: EtherNet/IP: Industrial Protocol White Paper

abstract: DeviceNet™ and ControlNet™ are two well-known industrial networks based on the CIP protocol (CIP = Control an Information Protocol). Both networks have been developed by Rockwell Automation, but are now owned and maintained by the two manufacturer's organizations ODVA (Open DeviceNet Vendors Association) and ControlNet International. ODVA and ControlNet International have recently introduced the newest member of this family - EtherNet/IP ("IP" stands for "Industrial Protocol"). This paper describes the techniques and mechanisms that are used to implement a fully consistent set of services and data objects on a TCP/UDP/IP based Ethernet® network. I. Introduction Automation architectures must provide users with three primary services. The first, control, is the most important. Control services involve the exchange of time-critical data between controlling devices such as PLCs and I/O devices such as variable speed drives, sensors and actuators. Networks that are tasked with the transmission of this data must provide some level of priority setting and/or interrupt capabilities. Second, networks must provide users configuration capabilities to set up and maintain their automation systems. This functionality typically involves the use of a personal computer (PC) or equivalent tool for programming of various devices in the system. This can be performed at commission, and also during runtime, such as recipe management in batch operations. Lastly, an automation architecture must allow for collection of data for the purposes of display in MMI stations, analysis and trending, and/or troubleshooting and maintenance. Networks that can provide all three services - control, configuration, and collection of data - deliver the greatest amount of flexibility and efficiency for better overall system performance. Networks that are based on the producer/consumer model - where data is identified, rather than tied to explicit source and destination addresses - can support control, configuration, and collection of data services. Application layers using distributed objects and producer/consumer communication services meet the requirements of automation architectures. In providing these services (Figure 1 shows a typical automation architecture), it cannot be assumed that a single network level will meet all application requirements as each network's physical and data link layers have their own attributes and benefits. Where a multi-level network structure is required, then the network architecture must provide consistency of data between disparate network segments.


Mersenne Twister – A Pseudo Random Number Generator and its Variants


title: A questionnaire to measure the user experience in immersive virtual environments

abstract: Most of the models of User eXperience (UX) in Immersive Virtual Environment (IVE) are partial due to the components and the measuring methods they suggest. We have presented in a previous work a holistic UX in IVE model, combining key components and influencing factors from the most common fields of Virtual Reality (education, entertainment and edutainment). We do not doubt that the best way to measure the UX in IVEs is to gather and compare results from the appropriate subjective methods with the appropriate objective methods. Nevertheless, in this paper, we chose to focus only on the questionnaire method. Indeed, most of components can be measured through questionnaires. The objective of this paper is to rely on the components of our UX model to select appropriate existing questionnaires and finally choose the more suitable items to create our own mixed questionnaire. First, this paper reviews the questionnaires chosen to create our own. Finally, it proposes an elaboration of the final questionnaire.


title: Action, Outcome, and Value A Dual-System Framework for Morality

abstract: Dual-system approaches to psychology explain the fundamental properties of human judgment, decision making, and behavior across diverse domains. Yet, the appropriate characterization of each system is a source of debate. For instance, a large body of research on moral psychology makes use of the contrast between “emotional” and “rational/cognitive” processes, yet even the chief proponents of this division recognize its shortcomings. Largely independently, research in the computational neurosciences has identified a broad division between two algorithms for learning and choice derived from formal models of reinforcement learning. One assigns value to actions intrinsically based on past experience, while another derives representations of value from an internally represented causal model of the world. This division between action- and outcome-based value representation provides an ideal framework for a dual-system theory in the moral domain.


Gesture Recognition Using The XWand


title: Facial Expression Analysis under Partial Occlusion: A Survey

abstract: Automatic machine-based Facial Expression Analysis (FEA) has made substantial progress in the past few decades driven by its importance for applications in psychology, security, health, entertainment, and human–computer interaction. The vast majority of completed FEA studies are based on nonoccluded faces collected in a controlled laboratory environment. Automatic expression recognition tolerant to partial occlusion remains less understood, particularly in real-world scenarios. In recent years, efforts investigating techniques to handle partial occlusion for FEA have seen an increase. The context is right for a comprehensive perspective of these developments and the state of the art from this perspective. This survey provides such a comprehensive review of recent advances in dataset creation, algorithm development, and investigations of the effects of occlusion critical for robust performance in FEA systems. It outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology. To the best of our knowledge, it is the first FEA survey dedicated to occlusion and aimed at promoting better-informed and benchmarked future work.


title: PGX.D: a fast distributed graph processing engine

abstract: Graph analysis is a powerful method in data analysis. Although several frameworks have been proposed for processing large graph instances in distributed environments, their performance is much lower than using efficient single-machine implementations provided with enough memory. In this paper, we present a fast distributed graph processing system, namely PGX.D. We show that PGX.D outperforms other distributed graph systems like GraphLab significantly (3x -- 90x). Furthermore, PGX.D on 4 to 16 machines is also faster than an implementation optimized for single-machine execution. Using a fast cooperative context-switching mechanism, we implement PGX.D as a low-overhead, bandwidth-efficient communication framework that supports remote data-pulling patterns. Moreover, PGX.D achieves large traffic reduction and good workload balance by applying selective ghost nodes, edge partitioning, and edge chunking transparently to the user. Our analysis confirms that each of these features is indeed crucial for overall performance of certain kinds of graph algorithms. Finally, we advocate the use of balanced beefy clusters where the sustained random DRAM-access bandwidth in aggregate is matched with the bandwidth of the underlying interconnection fabric.


title: Context-dependent third-party intervention in agonistic encounters of male Przewalski horses

abstract: One mechanism to resolve conflict among group members is third party intervention, for which several functions, such as kin protection, alliance formation, and the promotion of group cohesion have been proposed. Still, empirical research on the function of intervention behaviour is rare. We studied 40 cases of intervention behaviour in a field study on 13 semi-wild bachelor horses (Equus ferus przewalskii) in (a) standard social situations, and (b) when new horses joined the group (i.e. introductions). Only interventions in agonistic encounters were analysed. Eight of 13 animals directed intervention behaviour toward threatening animal in agonistic encounters of group members. One stallion was particularly active. The stallions did not intervene to support former group mates or kin and interventions were not reciprocated. In introduction situations and in standard social situations, the interveners supported animals which were lower in rank, but targeted, threatening animals of comparable social rank. After introductions, stallions received more affiliative behaviour from animals they supported and thus appeared to intervene for alliance formation. In standard social situations, interveners did not receive more affiliative behaviour from animals they supported and may primarily have intervened to promote group cohesion and to reduce social disruption within the group.


title: Local multi-resolution representation for 6D motion estimation and mapping with a continuously rotating 3D laser scanner

abstract: Micro aerial vehicles (MAV) pose a challenge in designing sensory systems and algorithms due to their size and weight constraints and limited computing power. We present an efficient 3D multi-resolution map that we use to aggregate measurements from a lightweight continuously rotating laser scanner. We estimate the robot's motion by means of visual odometry and scan registration, aligning consecutive 3D scans with an incrementally built map. By using local multi-resolution, we gain computational ef- ficiency by having a high resolution in the near vicinity of the robot and a lower resolution with increasing distance from the robot, which correlates with the sensor's characteristics in relative distance accuracy and measurement density. Compared to uniform grids, local multi-resolution leads to the use of fewer grid cells without loosing information and consequently results in lower computational costs. We efficiently and accurately register new 3D scans with the map in order to estimate the motion of the MAV and update the map in-flight. In experiments, we demonstrate superior accuracy and efficiency of our registration approach compared to state-of-the- art methods such as GICP. Our approach builds an accurate 3D obstacle map and estimates the vehicle's trajectory in real-time. I. INTRODUCTION Micro aerial vehicles (MAV) such as quadrotors have attracted attention in the field of aerial robotics. Their size and weight limitations pose a challenge in designing sensory systems. Most of today's MAVs are equipped with ultra sound sensors and camera systems due to their minimal size and weight. While these small and lightweight sensors provide valuable information, they suffer from a limited field- of-view and are sensitive to illumination conditions. Only few systems (1), (2), (3), (4) are equipped with 2D laser range finders (LRF) that are used for navigation. In contrast, we build a continuously rotating laser scanner that is minimalistic in terms of size and weight and thus particularly well suited for obstacle perception and localiza- tion on MAVs, allowing for environment perception in all directions. We use a hybrid multi-resolution map that stores occu- pancy information and the respective distance measurements. Measurements are stored in grid cells with increasing cell size from the robot's center. Thus, we gain computational efficiency by having a high resolution in the close proximity to the sensor and a lower resolution with increasing distance, which correlates with the sensor's characteristics in relative distance accuracy and measurement density. Compared to


title: Design and control of a wearable hand exoskeleton with force-controllable and compact actuator modules

abstract: In this paper, a wearable hand exoskeleton with force-controllable and compact actuator modules is proposed. In order to apply force feedback accurately while allowing natural finger motions, the exoskeleton linkage structure with three degrees of freedom (DOFs) was designed, which was inspired by the muscular skeletal structure of the finger. As an actuating system, a series elastic actuator (SEA) mechanism, which consisted of a small linear motor, a manually designed motor driver, a spring and potentiometers, was applied. The friction of the motor was identified and compensated for obtaining a linearized model of the actuating system. Using a LQ (linear quadratic) tuned PD (proportional and derivative) controller and a disturbance observer (DOB), the proposed actuator module could generate the desired force accurately with actual finger movements. By integrating together the proposed exoskeleton structure, actuator modules and control algorithms, a wearable hand exoskeleton with force-controllable and compact actuator modules was developed to deliver accurate force to the fingertips for flexion/extension motions.


title: Fast Image Tagging

abstract: Automatic image annotation is a difficult and highly relevant machine learning task. Recent advances have significantly improved the state-of-the-art in retrieval accuracy with algorithms based on nearest neighbor classification in carefully learned metric spaces. But this comes at a price of increased computational complexity during training and testing. We propose FastTag, a novel algorithm that achieves comparable results with two simple linear mappings that are co-regularized in a joint convex loss function. The loss function can be efficiently optimized in closed form updates, which allows us to incorporate a large number of image descriptors cheaply. On several standard real-world benchmark data sets, we demonstrate that FastTag matches the current state-of-the-art in tagging quality, yet reduces the training and testing times by several orders of magnitude and has lower asymptotic complexity.


title: Competing for Attention in Social Media under Information Overload Conditions

abstract: Modern social media are becoming overloaded with information because of the rapidly-expanding number of information feeds. We analyze the user-generated content in Sina Weibo, and find evidence that the spread of popular messages often follow a mechanism that differs from the spread of disease, in contrast to common belief. In this mechanism, an individual with more friends needs more repeated exposures to spread further the information. Moreover, our data suggest that for certain messages the chance of an individual to share the message is proportional to the fraction of its neighbours who shared it with him/her, which is a result of competition for attention. We model this process using a fractional susceptible infected recovered (FSIR) model, where the infection probability of a node is proportional to its fraction of infected neighbors. Our findings have dramatic implications for information contagion. For example, using the FSIR model we find that real-world social networks have a finite epidemic threshold in contrast to the zero threshold in disease epidemic models. This means that when individuals are overloaded with excess information feeds, the information either reaches out the population if it is above the critical epidemic threshold, or it would never be well received.


title: Two-Class Weather Classification

abstract: Given a single outdoor image, we propose a collaborative learning approach using novel weather features to label the image as either sunny or cloudy. Though limited, this two-class classification problem is by no means trivial given the great variety of outdoor images captured by different cameras where the images may have been edited after capture. Our overall weather feature combines the data-driven convolutional neural network (CNN) feature and well-chosen weather-specific features. They work collaboratively within a unified optimization framework that is aware of the presence (or absence) of a given weather cue during learning and classification. In this paper we propose a new data augmentation scheme to substantially enrich the training data, which is used to train a latent SVM framework to make our solution insensitive to global intensity transfer. Extensive experiments are performed to verify our method. Compared with our previous work and the sole use of a CNN classifier, this paper improves the accuracy up to 7-8 percent. Our weather image dataset is available together with the executable of our classifier.


title: Gaussian Weighted Histogram Intersection for License Plate Classification

abstract: The conventional histogram intersection (HI) algorithm computes the intersected section of the corresponding color histograms in order to measure the matching rate between two color images. Since this algorithm is strictly based on the matching between bins of identical colors, the final matching rate can be easily affected by color variation caused by various environment changes. In this paper, a Gaussian weighted histogram intersection (GWHI) algorithm is proposed to facilitate the histogram matching via taking into account matching of both identical and similar colors. The weight is determined by the distance between two colors. The algorithm is applied to license plate classification. Experimental results show that the proposed algorithm produces a much lower intra-class distance and a much higher inter-class distance than previous HI algorithms for tested images which are captured under various illumination conditions.


title: Learning to Learn from Noisy Web Videos

abstract: Understanding the simultaneously very diverse and intricately fine-grained set of possible human actions is a critical open problem in computer vision. Manually labeling training videos is feasible for some action classes but doesnt scale to the full long-tailed distribution of actions. A promising way to address this is to leverage noisy data from web queries to learn new actions, using semi-supervised or webly-supervised approaches. However, these methods typically do not learn domain-specific knowledge, or rely on iterative hand-tuned data labeling policies. In this work, we instead propose a reinforcement learning-based formulation for selecting the right examples for training a classifier from noisy web search results. Our method uses Q-learning to learn a data labeling policy on a small labeled training dataset, and then uses this to automatically label noisy web data for new visual concepts. Experiments on the challenging Sports-1M action recognition benchmark as well as on additional fine-grained and newly emerging action classes demonstrate that our method is able to learn good labeling policies for noisy data and use this to learn accurate visual concept classifiers.


title: Survey over VANET Routing Protocols for Vehicle to Vehicle Communication

abstract: VANET (Vehicular Ad-hoc Network) is an emerging new technology with some unique characteristics that makes it different from other ad hoc network. Due to rapid topology changing and frequent disconnection it is also difficult to design an efficient routing protocol for routing data among vehicles, called V2V or vehicle to vehicle communication and vehicle to road side infrastructure, called V2I. Because of road accident daily occurrence VANET is one of the influencing areas for the improvement of Intelligent Transportation System (ITS) which can increase road safety and provide traffic information etc. The existing routing protocols for VANET are not efficient to meet every traffic scenarios. Suitable routing protocols are required to establish communication between vehicles in future for road safety. In this paper, we focus on the merits and demerits of routing protocols which will help to develop new routing protocols or improvement of existing routing protocol in near future.


title: Forward kinematics and workspace analysis of a 3-RPS medical parallel robot

abstract: The paper addresses the analysis of forward kinematics of a 3-DOF medical parallel robot with R-P-S (Revolute-Prismatic-Spherical) joint structure using MATLAB/Simulink. Forward kinematics is solved numerically using Newton-Kantorovich (N-K) method and then is verified with a 1:1 CAD model through SimMechanics Toolbox from MATLAB/Simulink. Also the workspace of the robot is determined through forward kinematics equations.


title: A 1.2V 10µW NPN-based temperature sensor in 65nm CMOS with an inaccuracy of ±0.2°C (3s) from −70°C to 125°C

abstract: This paper describes a temperature sensor realized in a 65nm CMOS process with a batch-calibrated inaccuracy of ±0.5°C (3s) and a trimmed inaccuracy of ±0.2°C (3s) from −70°C to 125°C. This represents a 10-fold improvement in accuracy compared to other deep-submicron temperature sensors [1,2], and is comparable with that of state-of-the-art sensors implemented in larger-feature-size processes [3,4]. The sensor draws 8.3µA from a 1.2V supply and occupies an area of 0.1mm2, which is 45 times less than that of sensors with comparable accuracy [3,4]. These advances are enabled by the use of NPN transistors as sensing elements, the use of dynamic techniques i.e. correlated double sampling (CDS) and dynamic element matching (DEM), and a single room-temperature trim.


Fungal Biosorption for Cadmium and Mercury Heavy Metal Ions Isolated from Some Polluted Localities in KSA


title: Mini-MAC: Raising the bar for vehicular security with a lightweight message authentication protocol

abstract: Abstract We propose Mini-MAC, a new message authentication protocol that works in existing automotive computer networks without delaying any message or increasing network traffic. Deployed in many vehicles, the CAN bus is a low-speed network connecting electronic control units, including those that control critical functionality such as braking and acceleration. The CAN bus is extremely vulnerable to malicious actors with bus access, including wireless access. Traditionally, Message Authentication Codes (MACs) help authenticate the sender of a message, and variants prevent message replay attacks; however, standard MACs are unsuitable for use on the CAN bus because of small payload sizes. Restrictions of the CAN bus, including the need not to delay messages or increase bus traffic, severely limit how well this network can be protected. Mini-MAC is based on a counter-seeded keyed-Hash MAC (HMAC), augmented with message history and truncated to fit available message space. It does not increase bus traffic and incurs a very small performance penalty relative to the provably secure HMAC. It is the first proposal to combine these two tenets for vehicle networks. The message history feature protects against all transient attackers, even if they know the keys. Though the CAN bus cannot be properly secured against a dedicated attacker, Mini-MAC meaningfully raises the bar of vehicular security, enhancing the safety of drivers and others.


title: Competitive Data Trading in Wireless-Powered Internet of Things (IoT) Crowdsensing Systems with Blockchain

abstract: With the explosive growth of smart IoT devices at the edge of the Internet, embedding sensors on mobile devices for massive data collection and collective environment sensing has been envisioned as a cost-effective solution for IoT applications. However, existing IoT platforms and framework rely on dedicated middleware for (semi-) centralized task dispatching, data storage and incentive provision. Consequently, they are usually expensive to deploy, have limited adaptability to diverse requirements, and face a series of data security and privacy issues. In this paper, we employ permissionless blockchains to construct a purely decentralized platform for data storage and trading in a wireless-powered IoT crowdsensing system. In the system, IoT sensors use power wirelessly transferred from RF-energy beacons for data sensing and transmission to an access point. The data is then forwarded to the blockchain for distributed ledger services, i.e., data/transaction verification, recording, and maintenance. Due to coupled interference of wireless transmission and transaction fee incurred from blockchain's distributed ledger services, rational sensors have to decide on their transmission rates to maximize individual utility. Thus, we formulate a noncooperative game model to analyze this competitive situation among the sensors. We provide the analytical condition for the existence of the Nash equilibrium as well as a series of insightful numerical results about the equilibrium strategies in the game.


title: Alpha-Prolog: A Logic Programming Language with Names, Binding, and Alpha-Equivalence

abstract: There are two well-known approaches to programming with names, binding, and equivalence up to consistent renaming: representing names and bindings as concrete identifiers in a first-order language (such as Prolog), or encoding names and bindings as variables and abstractions in a higher-order language (such as λProlog). However, both approaches have drawbacks: the former often involves stateful name-generation and requires manual definitions for α-equivalence and capture-avoiding substitution, and the latter is semantically very complicated, so reasoning about programs written using either approach can be very difficult. Gabbay and Pitts have developed a new approach to encoding abstract syntax with binding based on primitive operations of name-swapping and freshness. This paper presents αProlog, a logic programming language that uses this approach, along with several illustrative example programs and an operational semantics.


title: Breast conditions during pregnancy and lactatiion

abstract: During pregnancy and lactation the breast can be affected by a variety of specific and unique disorders. In addition to mastitis, lactational abscesses and other benign conditions unique to pregnancy and lactation, any breast problem seen in the non-pregnant woman may present during pregnancy or lactation. In this article some of the commoner and interesting problems are reviewed.


Current source-density method and application in cat cerebral cortex: investigation of evoked potentials and EEG phenomena


title: Feature Model Composition Assisted by Formal Concept Analysis

abstract: In the domain of software product lines, Feature Models (FM) play a central role in variability modeling, completed by configuration collections (from concrete software product lines), logical representations, constraint programming or conceptual structures, coming from the field of Formal Concept Analysis (FCA). The development of feature models may take several forms, including their synthesis from configuration collections or their design in several steps (by several teams or with different concerns). FM composition (merge) operators are part of that design activity as they assist their iterative building. In this paper, we describe an approach, based on two main merging semantics (intersection and union), which assists designers in merging several FMs. This approach benefits from the help of FCA to represent all the FMs with the same configuration set through a canonical form. We describe the implementation of our approach and present a set of concrete examples.


title: Molecular ecological approaches to studying the Blackwell Publishing Ltd evolutionary impact of selective harvesting in wildlife

abstract: Harvesting of wildlife populations by humans is usually targeted by sex, age or phenotypic criteria, and is therefore selective. Selective harvesting has the potential to elicit a genetic response from the target populations in several ways. First, selective harvesting may affect population demographic structure (age structure, sex ratio), which in turn may have consequences for effective population size and hence genetic diversity. Second, wildlife-harvesting regimes that use selective criteria based on phenotypic characteristics (e.g. minimum body size, horn length or antler size) have the potential to impose artificial selection on harvested populations. If there is heritable genetic variation for the target characteristic and harvesting occurs before the age of maturity, then an evolutionary response over time may ensue. Molecular ecological techniques offer ways to predict and detect genetic change in harvested populations, and therefore have great utility for effective wildlife management. Molecular markers can be used to assess the genetic structure of wildlife populations, and thereby assist in the prediction of genetic impacts by delineating evolutionarily meaningful management units. Genetic markers can be used for monitoring genetic diversity and changes in effective population size and breeding systems. Tracking evolutionary change at the phenotypic level in the wild through quantitative genetic analysis can be made possible by genetically determined pedigrees. Finally, advances in genome sequencing and bioinformatics offer the opportunity to study the molecular basis of phenotypic variation through trait mapping and candidate gene approaches. With this understanding, it could be possible to monitor the selective impacts of harvesting at a molecular level in the future. Effective wildlife management practice needs to consider more than the direct impact of harvesting on population dynamics. Programs that utilize molecular genetic tools will be better positioned to assess the long-term evolutionary impact of artificial selection on the evolutionary trajectory and viability of harvested populations.


WHAT DEPRECATES OR INFLATES YOUR SELF- IMAGE?


title: A Modularization Method for Battery Equalizers Using Multiwinding Transformers

abstract: This paper proposes a modularized global architecture using multi-winding transformers for battery cell balancing. The global balancing for a series-connected battery string is achieved based on forward conversion in each battery module and based on flyback conversion among modules. The demagnetization of the multiwinding transformers is also simultaneously achieved by the flyback conversion among modules without the need of additional demagnetizing circuits. Moreover, all MOSFET switches are driven by two complementary pulse width modulation signals without the requirement of cell voltage sensors, and energy can be automatically and simultaneously delivered from any high voltage cells to any low voltage cells. Compared with existing equalizers requiring additional balancing circuits for battery modules, the proposed modularized equalizer shares one circuit for the balancing among cells and modules. The balancing performance of the proposed equalizer is perfectly verified through experimental results, and the maximum balancing efficiency is up to 91.3%. In summary, the proposed modularized equalizer has the advantages of easier modularization, simpler control, higher efficiency, smaller size, and lower cost, ensuring the battery system higher reliability and easier implementation.


title: Linking habitat specialization with species' traits in European birds

abstract: Ecological specialization provides information about adaptations of species to their environment. However, identification of traits representing the relevant dimensions of ecological space remains challenging. Here we endeavoured to explain how complex habitat specializations relate to various ecological traits of European birds. We employed phylogenetic generalized least squares and information theoretic approach statistically controlling for differences in geographic range size among species. Habitat specialists had narrower diet niche, wider climatic niche, higher wing length/tail length ratio and migrated on shorter distances than habitat generalists. Our results support an expected positive link between habitat and diet niche breadth estimates, however a negative relationship between habitat and climate niche breadths is surprising. It implies that habitat specialists occur mostly in spatially restricted environments with high climatic variability such as mountain areas. This, however, complicates our understanding of predicted impacts of climatic changes on avian geographical distributions. Our results further corroborate that habitat specialization reflects occupation of morphological space, when specialists depend more on manoeuvrability of the flight and are thus more closely associated to open habitats than habitat generalists. Finally, our results indicate that long distance movements might hamper narrow habitat preferences. In conclusion, we have shown that species’ distributions across habitats are informative about their positions along other axes of ecological space and can explain states of particular functional traits, however, our results also reveal that the links between different niche estimates cannot be always straightforwardly predicted.


Analysis and Optimization of IEEE 33 Bus Radial Distributed System Using Optimization Algorithm


title: The development of a wideband ‘rocket’ phased array feed

abstract: We describe a new phased array feed design. The array is composed of elements based on a solid of revolution fed by twin wire transmission lines. An infinite array model is used to simulate the noise performance of the array and this model is compared with a full EM simulation of the finite array and measured noise data. The modelling indicates that this array is an excellent candidate for a phased array feed (PAF) for radio-astronomy applications.


title: A FAST ITERATIVE METHOD FOR SOLVING THE EIKONAL EQUATION ON TETRAHEDRAL DOMAINS.

abstract: Generating numerical solutions to the eikonal equation and its many variations has a broad range of applications in both the natural and computational sciences. Efficient solvers on cutting-edge, parallel architectures require new algorithms that may not be theoretically optimal, but that are designed to allow asynchronous solution updates and have limited memory access patterns. This paper presents a parallel algorithm for solving the eikonal equation on fully unstructured tetrahedral meshes. The method is appropriate for the type of fine-grained parallelism found on modern massively-SIMD architectures such as graphics processors and takes into account the particular constraints and capabilities of these computing platforms. This work builds on previous work for solving these equations on triangle meshes; in this paper we adapt and extend previous two-dimensional strategies to accommodate three-dimensional, unstructured, tetrahedralized domains. These new developments include a local update strategy with...


title: A cost benefit analysis of a V2G-capable electric school bus compared to a traditional diesel school bus

abstract: Fuel expenses, diesel exhaust health externalities, and climate change are concerns that encourage the use of electric vehicles. Vehicle-to-grid (V2G) policies provide additional economic incentives. This analysis evaluates the costs and benefits associated with the use of electric vehicles and determines the cost effectiveness of using a V2G-capable electric school bus compared to a traditional diesel school bus. Several factors were analyzed, including fuel expense, electricity and battery costs, health externalities, and frequency regulation market price. The V2G-capable electric bus provides the school savings of $6070 per seat in net present value and becomes a net present benefit after five years of operation. Without externalities, the net present benefit would be $5700 per seat. If the entire school district’s fleet switched to V2G-capable electric buses, the net present savings would be upwards of $38 million. A sensitivity analysis was conducted to determine how the factors influenced the costs and benefits. In all cases, purchasing an electric school bus is consistently a net present benefit. Policies could be set into place to incentivize public school adoption of electric buses, encourage more efficient batteries, and develop V2G capabilities.


title: What's in a Relationship: An Ontological Analysis

abstract: In a series of publications, we have proposed a foundational system of ontological categories which has been successfully used to evaluate and improve the quality of conceptual modeling grammars and models. In this article, we continue this work by using this foundational ontology to provide real-world semantics and sound modeling guidelines for one of the most fundamental (and yet one of the most problematic) constructs in conceptual modeling, namely, the relationship type. In addition, we systematically compare our approach with a classical ontological treatment of this construct in the literature, provided by the BWW framework.


title: Drivers and Bottlenecks in the Adoption of Augmented Reality Applications

abstract: In the last decade, Augmented Reality (AR) has been one of the emerging technologies that have been in the centre of attention among academics and business practitioners. Despite the numerous studies which have demonstrated the multitude of benefits derived from AR applications, the technology has not reached yet its full potential due to various bottlenecks which are preventing it from becoming the mainstream technology that many have anticipated. In this paper, we first present briefly the history of AR followed by the evolution of related software algorithms and hardware devices. The main contribution of this paper is the overview of the drivers and challenges related to the adoption and diffusion of AR across five major application domains; (a) industry and military, (b) training and education, (c) travel and tourism, (d) medicine and health care and (e) retail and marketing. Such overview facilitates especially a cross-domain comparison, which here enabled us to identify a list of five drivers and five bottlenecks in the adoption of the current AR technology.


Supplementary Material : Zero-Shot Task Generalization with MultiTask Deep Reinforcement Learning