title: Recent advances in nitrogen-fixing acetic acid bacteria

abstract: Nitrogen is an essential plant nutrient, widely applied as N-fertilizer to improve yield of agriculturally important crops. An interesting alternative to avoid or reduce the use of N-fertilizers could be the exploitation of plant growth-promoting bacteria (PGPB), capable of enhancing growth and yield of many plant species, several of agronomic and ecological significance. PGPB belong to diverse genera, including Azospirillum, Azotobacter, Herbaspirillum, Bacillus, Burkholderia, Pseudomonas, Rhizobium, and Gluconacetobacter, among others. They are capable of promoting plant growth through different mechanisms including (in some cases), the biological nitrogen fixation (BNF), the enzymatic reduction of the atmospheric dinitrogen (N(2)) to ammonia, catalyzed by nitrogenase. Aerobic bacteria able to oxidize ethanol to acetic acid in neutral or acid media are candidates of belonging to the family Acetobacteraceae. At present, this family has been divided into ten genera: Acetobacter, Gluconacetobacter, Gluconobacter, Acidomonas, Asaia, Kozakia, Saccharibacter, Swaminathania, Neoasaia, and Granulibacter. Among them, only three genera include N(2)-fixing species: Gluconacetobacter, Swaminathania and Acetobacter. The first N(2)-fixing acetic acid bacterium (AAB) was described in Brazil. It was found inside tissues of the sugarcane plant, and first named as Acetobacter diazotrophicus, but then renamed as Gluconacetobacter diazotrophicus. Later, two new species within the genus Gluconacetobacter, associated to coffee plants, were described in Mexico: G. johannae and G. azotocaptans. A salt-tolerant bacterium named Swaminathania salitolerans was found associated to wild rice plants. Recently, N(2)-fixing Acetobacter peroxydans and Acetobacter nitrogenifigens, associated with rice plants and Kombucha tea, respectively, were described in India. In this paper, recent advances involving nitrogen-fixing AAB are presented. Their natural habitats, physiological and genetic aspects, as well as their association with different plants and contribution through BNF are described as an overview.


title: One-Class Training for Masquerade Detection

abstract: We extend prior research on masquerade detection using UNIX commands issued by users as the audit source. Previous studies using multi-class training requires gathering data from multiple users to train specific profiles of self and non-self for each user. Oneclass training uses data representative of only one user. We apply one-class Naive Bayes using both the multivariate Bernoulli model and the Multinomial model, and the one-class SVM algorithm. The result shows that oneclass training for this task works as well as multi-class training, with the great practical advantages of collecting much less data and more efficient training. One-class SVM using binary features performs best among the oneclass training algorithms.


title: Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage

abstract: In this letter, the efficient deployment of multiple unmanned aerial vehicles (UAVs) acting as wireless base stations that provide coverage for ground users is analyzed. First, the downlink coverage probability for UAVs as a function of the altitude and the antenna gain is derived. Next, using circle packing theory, the 3-D locations of the UAVs is determined in a way that the total coverage area is maximized while maximizing the coverage lifetime of the UAVs. Our results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements. Furthermore, the minimum number of UAVs needed to guarantee a target coverage probability for a given geographical area is determined. Numerical results evaluate various tradeoffs.


title: Localization and Tracking of Implantable Biomedical Sensors

abstract: Implantable sensor systems are effective tools for biomedical diagnosis, visualization and treatment of various health conditions, attracting the interest of researchers, as well as healthcare practitioners. These systems efficiently and conveniently provide essential data of the body part being diagnosed, such as gastrointestinal (temperature, pH, pressure) parameter values, blood glucose and pressure levels and electrocardiogram data. Such data are first transmitted from the implantable sensor units to an external receiver node or network and then to a central monitoring and control (computer) unit for analysis, diagnosis and/or treatment. Implantable sensor units are typically in the form of mobile microrobotic capsules or implanted stationary (body-fixed) units. In particular, capsule-based systems have attracted significant research interest recently, with a variety of applications, including endoscopy, microsurgery, drug delivery and biopsy. In such implantable sensor systems, one of the most challenging problems is the accurate localization and tracking of the microrobotic sensor unit (e.g., robotic capsule) inside the human body. This article presents a literature review of the existing localization and tracking techniques for robotic implantable sensor systems with their merits and limitations and possible solutions of the proposed localization methods. The article also provides a brief discussion on the connection and cooperation of such techniques with wearable biomedical sensor systems.


title: Measuring university–industry collaboration in a regional innovation system

abstract: Studies of university–industry collaboration remain subject to important limitations due to the shortage of empirical data and a lack of consistency in that obtained to date. This article puts into practice a set of universities Third Mission indicators in a regional innovation system. Selected indicators previously compiled from literature were reorganized and pre-tested. We have undertaken two face-to-face surveys of 737 firms and 765 heads of research teams, respectively. The results test the validation of indicators and provide a complex map of university–industry linkages as well as some observations on the flexibility needed to address this issue.


title: Roofline Model Toolkit: A Practical Tool for Architectural and Program Analysis

abstract: We present preliminary results of the Roofline Toolkit for multicore, manycore, and accelerated architectures. This paper focuses on the processor architecture characterization engine, a collection of portable instrumented micro benchmarks implemented with Message Passing Interface (MPI), and OpenMP used to express thread-level parallelism. These benchmarks are specialized to quantify the behavior of different architectural features. Compared to previous work on performance characterization, these microbenchmarks focus on capturing the performance of each level of the memory hierarchy, along with thread-level parallelism, instruction-level parallelism and explicit SIMD parallelism, measured in the context of the compilers and run-time environments. We also measure sustained PCIe throughput with four GPU memory managed mechanisms. By combining results from the architecture characterization with the Roofline model based solely on architectural specifications, this work offers insights for performance prediction of current and future architectures and their software systems. To that end, we instrument three applications and plot their resultant performance on the corresponding Roofline model when run on a Blue Gene/Q architecture.


title: Quantifier scope and scrambling in Russian: an experimental study

abstract: Double-quantifier English sentences such as (1) and (2) are well-known to be ambiguous between surface-scope and inverse-scope readings. In Montague’s type-driven compositional semantics, the different scope readings are obtained through the operation of Quantifier Raising (QR) (May 1977, Heim & Kratzer 1998, Fox 2000). For the surface-scope reading, the subject undergoes covert QR to a position higher than the object, and for the inverse-scope reading, the opposite is the case.


title: Real Time Object Detection Based on FPGA with Big Data

abstract: Over recent years, various types of vision-based object detection models are proposed for avoiding traffic accidents or self-driving. However, in vehicle, the electronic control of driving part is achieved by SOC(System On Chip). Due to limitations on data capacity and compute power of SOC, the existing methods cannot be directly implemented to vehicles. In this paper, we present Simple-FBY(Simple-FPGA Based YOLO), a vehicle and pedestrian detection model based on FPGA which is a real time, high speed, low power computing platform. It can provide accuracy, speed and be capable of learning. To this end, we introduce a specifically designed training data set, which consists of pictures of other public self-driving data sets and a Beijing's streets data set collecting by ourselves. Compared with state-of-the-art model YOLO, our FPGA-based model can provide high performance per watt of power consumption. We also adjust the YOLO algorithm to make it more adaptable to SOC. In the experiment, we compare performance of Simple-FBY on GPU and Xilinx Zynq-Z1 FPGA Board. As a result, our model achieves better accuracy on vehicle and pedestrian detection and shows a good speed performance at FPGA platform.


Semantic Segmentation With Object Clique Potentials


title: Does corporate social responsibility affect the cost of capital

abstract: We examine the effect of corporate social responsibility (CSR) on the cost of equity capital for a large sample of US firms. Using several approaches to estimate firms' ex ante cost of equity, we find that firms with better CSR scores exhibit cheaper equity financing. In particular, our findings suggest that investment in improving responsible employee relations, environmental policies, and product strategies contributes substantially to reducing firms' cost of equity. Our results also show that participation in two "sin" industries, namely, tobacco and nuclear power, increases firms' cost of equity. These findings support arguments in the literature that firms with socially responsible practices have higher valuation and lower risk.


title: Prognostic significance of high hyperdiploid and triploid/tetraploid adult acute myeloid leukemia.

abstract: To ascertain the clinical implications of high hyperdiploid (HH; 49-65 chromosomes) and triploid/tetraploid (TT; >65 chromosomes) adult acute myeloid leukemia (AML), all such cases were retrieved from the Swedish AML Registry. Of the 3,654 cytogenetically informative cases diagnosed between January 1997 and May 2014, 68 (1.9%) were HH (n = 50)/TT (n = 18). Patients with HH/TT were older than those with intermediate risk (IR) AML (median 71 years vs. 67 years; P = 0.042) and less often had de novo AML (63% vs. 79%; P = 0.004); no such differences were observed between HH/TT and complex karyotype (CK) AML. The overall survival (OS) was similar between patients with HH/TT and CK AML (median 0.9 years vs. 0.6 years; P = 0.082), whereas OS was significantly longer (median 1.6 years; P = 0.028) for IR AML. The OS was shorter for cases with HH than with TT (median 0.6 years vs. 1.4 years; P = 0.032) and for HH/TT AMLs with adverse abnormalities (median 0.8 years vs. 1.1 years; P = 0.044). In conclusion, HH/TT AML is associated with a poor outcome, but chromosome numbers >65 and absence of adverse aberrations seem to translate into a more favorable prognosis. Thus, HH/TT AMLs are clinically heterogeneous and should not automatically be grouped as high risk.


Self-Training for Jointly Learning to Ask and Answer Questions


title: Fully Statistical Neural Belief Tracking

abstract: This paper proposes an improvement to the existing data-driven Neural Belief Tracking (NBT) framework for Dialogue State Tracking (DST). The existing NBT model uses a hand-crafted belief state update mechanism which involves an expensive manual retuning step whenever the model is deployed to a new dialogue domain. We show that this update mechanism can be learned jointly with the semantic decoding and context modelling parts of the NBT model, eliminating the last rule-based module from this DST framework. We propose two different statistical update mechanisms and show that dialogue dynamics can be modelled with a very small number of additional model parameters. In our DST evaluation over three languages, we show that this model achieves competitive performance and provides a robust framework for building resource-light DST models.


title: Secure multidimensional range queries over outsourced data

abstract: In this paper, we study the problem of supporting multidimensional range queries on encrypted data. The problem is motivated by secure data outsourcing applications where a client may store his/her data on a remote server in encrypted form and want to execute queries using server's computational capabilities. The solution approach is to compute a secure indexing tag of the data by applying bucketization (a generic form of data partitioning) which prevents the server from learning exact values but still allows it to check if a record satisfies the query predicate. Queries are evaluated in an approximate manner where the returned set of records may contain some false positives. These records then need to be weeded out by the client which comprises the computational overhead of our scheme. We develop a bucketization procedure for answering multidimensional range queries on multidimensional data. For a given bucketization scheme, we derive cost and disclosure-risk metrics that estimate client's computational overhead and disclosure risk respectively. Given a multidimensional dataset, its bucketization is posed as an optimization problem where the goal is to minimize the risk of disclosure while keeping query cost (client's computational overhead) below a certain user-specified threshold value. We provide a tunable data bucketization algorithm that allows the data owner to control the trade-off between disclosure risk and cost. We also study the trade-off characteristics through an extensive set of experiments on real and synthetic data.


title: Reassessing Melanonychia Striata in Phototypes IV, V, and VI Patients.

abstract: BACKGROUND ::: Racial melanonychia striata is a nail plate dyschromia described as a gray coloration of the background in dermatoscopy and hypermelanosis in histology. However, many cases with brown or black coloration of the background in the dermatoscopic examination have been followed. ::: ::: ::: OBJECTIVE ::: Evaluate racial melanonychia striata dermatoscopical patterns and histological diagnosis in Fitzpatrick skin Types IV, V, or VI population. ::: ::: ::: MATERIALS AND METHODS ::: 482 Fitzpatrick skin Types IV, V, or VI patients, between 19 and 72 years of age, were clinically evaluated and underwent nail plate dermatoscopic examination. Fifty-eight patients presenting melanonychia striata and dermatoscopic patterns with brown or black coloration of the background were submitted to a nail matrix biopsy. When occurring in more than 1 digit, biopsy was performed in the lesion with the darkest coloration presented in dermatoscopy. ::: ::: ::: RESULTS ::: All skin Types IV, V, or VI patients who had melanonychia striata, with brown or black coloration of the background in the dermatoscopic examination, revealed histopathological analysis compatible with hypermelanosis. ::: ::: ::: CONCLUSION ::: Racial melanonychia in skin Types IV, V, or VI patients, resulting from hypermelanosis, may present brown or black coloration of the background in the dermatoscopic examination.


title: Methodological frameworks of digital forensics

abstract: Digital forensics is essential for the successful opposition of computer crime. It is associated with many challenges, including rapid changes in computer and digital devices, and more sophisticated attacks on computer systems and networks and the rapid increase in abuse of ICT systems. For a forensic investigation to be performed successfully there are a number of important steps that have to be considered and taken. Since digital forensics is a relatively new field compared to other forensic disciplines, there are ongoing efforts to develop examination standards and to provide structure to digital forensic examinations. This paper attempts to address the diversity of methodologies applied in digital forensic investigations.


title: Adjustable Speed Drive of Asynchronous Machine Using Volt/Hz & PI Technique

abstract: Induction Motors are generally utilized as a part of the modern application because of a high reliability, high power and minimum cost. The simulation study of speed control of IM using SPWM techniques are carried out. The execution of both the torque and speed is advanced by an altered PI controller and Volt/Hz control. A scalar control is a straightforward technique and it's worked to control the size of the control amounts in consistent speed application. Volt/Hz control has been simulated using MATLAB Simulink and compared with the PI controller.


title: Digitally controlled active clamp interleaved flyback converters for improving efficiency in photovoltaic grid-connected micro-inverter

abstract: Flyback converters show the characteristics of current source when operating in discontinuous conduction mode (DCM) and boundary conduction mode (BCM), which makes it widely used in photovoltaic grid-connected micro-inverter. In this paper, an active clamp interleaved flyback converter operating with combination of DCM and BCM is proposed in micro-inverter to achieve zero voltage switching (ZVS) for both of primary switches and fully recycle the energy in the leakage inductance. The proposed control method makes active-clamping part include only one clamp capacitor. In DCM area, only one flyback converter operates and turn-off of its auxiliary switch is suggested here to reduce resonant conduction losses, which improve the efficiency at light loads. Performance of the proposed circuit is validated by the simulation results and experimental results.


title: Query optimization in microsoft SQL server PDW

abstract: In recent years, Massively Parallel Processors have increasingly been used to manage and query vast amounts of data. Dramatic performance improvements are achieved through distributed execution of queries across many nodes. Query optimization for such system is a challenging and important problem. In this paper we describe the Query Optimizer inside the SQL Server Parallel Data Warehouse product (PDW QO). We leverage existing QO technology in Microsoft SQL Server to implement a cost-based optimizer for distributed query execution. By properly abstracting metadata we can readily reuse existing logic for query simplification, space exploration and cardinality estimation. Unlike earlier approaches that simply parallelize the best serial plan, our optimizer considers a rich space of execution alternatives, and picks one based on a cost-model for the distributed execution environment. The result is a high-quality, effective query optimizer for distributed query processing in an MPP.


title: ARTTag: aesthetic fiducial markers based on circle pairs

abstract: In this paper, we present ARTTag, an aesthetic fiducial marker system, of which the design development can be performed with any color, texture, shape, or other features as long as circle pairs are integrated. By utilizing the projective properties of circular features, ARTTag is appropriate for detection, identification, and camera-based registration in augmented reality (AR) applications.


title: Tutorial: Bayesian data analysis.

abstract: BAYESIAN DATA ANALYSIS is superseding traditional methods in sciences from anthropology to zoology. Bayesian methods solve many problems inherent in p values and confidence intervals. More importantly, Bayesian methods are more richly and intuitively informative. Bayesian analysis applies flexibly and seamlessly to simple situations or complex hierarchical models and realistic data structures, including small samples, large samples, unbalanced designs, missing data, censored data, outliers, etc. Bayesian analysis software is flexible and can be used for a wide variety of data-analytic and psychometric models. This full-day tutorial presents a ground-level, hands-on introduction to doing Bayesian data analysis. The presenter is an award-winning teacher who has honed new materials from many previous courses. He has written an acclaimed textbook on the topic, now greatly expanded in its second edition. The tutorial materials include free software and numerous programs that can be used for real data analysis.


title: An Array Antenna for Both Long- and Medium-Range 77 GHz Automotive Radar Applications

abstract: A novel array antenna with a flat-shoulder shaped radiation pattern is proposed as the transmitting antenna for 77 GHz automotive radar application. When it is used in an automotive radar comprising one transmitter and multiple identical receivers, it can meet the demands of both long-range and medium-range detections without switching the operation mode back and forth between the long-range radar scenario and the medium-range radar scenario. The proposed antenna concept is fully introduced to explain its mechanism. An unconventional array was synthesized to achieve the desired flat-shoulder shaped radiation pattern. Prototype of the proposed array antenna was also designed according to the array synthesis result, which consists of several linear series-fed patch arrays and a substrate-integrated waveguide power and phase distributing network. Fabrication and measurement of the prototype array antenna were completed, showing a good agreement between the measured data and the synthesized as well as the simulation results, thereby validating the design.


title: The pollution of the marine environment by plastic debris: a review.

abstract: The deleterious effects of plastic debris on the marine environment were reviewed by bringing together most of the literature published so far on the topic. A large number of marine species is known to be harmed and/or killed by plastic debris, which could jeopardize their survival, especially since many are already endangered by other forms of anthropogenic activities. Marine animals are mostly affected through entanglement in and ingestion of plastic litter. Other less known threats include the use of plastic debris by "invader" species and the absorption of polychlorinated biphenyls from ingested plastics. Less conspicuous forms, such as plastic pellets and "scrubbers" are also hazardous. To address the problem of plastic debris in the oceans is a difficult task, and a variety of approaches are urgently required. Some of the ways to mitigate the problem are discussed.


title: Delving into Salient Object Subitizing and Detection

abstract: Subitizing (i.e., instant judgement on the number) and detection of salient objects are human inborn abilities. These two tasks influence each other in the human visual system. In this paper, we delve into the complementarity of these two tasks. We propose a multi-task deep neural network with weight prediction for salient object detection, where the parameters of an adaptive weight layer are dynamically determined by an auxiliary subitizing network. The numerical representation of salient objects is therefore embedded into the spatial representation. The proposed joint network can be trained end-to-end using backpropagation. Experiments show the proposed multi-task network outperforms existing multi-task architectures, and the auxiliary subitizing network provides strong guidance to salient object detection by reducing false positives and producing coherent saliency maps. Moreover, the proposed method is an unconstrained method able to handle images with/without salient objects. Finally, we show state-of-theart performance on different salient object datasets.


title: An Industry-Based Survey of Reliability in Power Electronic Converters

abstract: A questionnaire survey was carried out to determine the industrial requirements and expectations of reliability in power electronic converters. The survey was subjective and conducted with a number of high-profile semiconductor manufacturers, integrators, and users in the aerospace, automation, motor drive, utility power, and other industry sectors. According to the survey, power semiconductor devices ranked the most fragile components. It was concluded that main stresses were from the environment, transients, and heavy loads, which should be considered during power electronic system design and normal operation. This paper has also highlighted that there is a significant need identified by the responders for better reliability-monitoring methods and indicators.


title: Gamification for Engaging Computer Science Students in Learning Activities: A Case Study

abstract: Gamification is the use of game design elements in non-game settings to engage participants and encourage desired behaviors. It has been identified as a promising technique to improve students' engagement which could have a positive impact on learning. This study evaluated the learning effectiveness and engagement appeal of a gamified learning activity targeted at the learning of C-programming language. Furthermore, the study inquired into which gamified learning activities were more appealing to students. The study was conducted using the mixed-method sequential explanatory protocol. The data collected and analysed included logs, questionnaires, and pre- and post-tests. The results of the evaluation show positive effects on the engagement of students toward the gamified learning activities and a moderate improvement in learning outcomes. Students reported different motivations for continuing and stopping activities once they completed the mandatory assignment. The preferences for different gamified activities were also conditioned by academic milestones.


title: PAWdio: Hand Input for Mobile VR using Acoustic Sensing

abstract: Hand input offers a natural, efficient and immersive form of input for virtual reality (VR), but it has been difficult to implement on mobile VR platforms. Accurate hand-tracking requires a depth sensor and performing computer vision on a smartphone is computationally intensive, which may degrade the frame rate of a VR simulation and drain battery life. PAWdio is a novel 1 degree of freedom (DOF) hand input technique that uses acoustic sensing to track the relative position of an earbud from a headset that the user holds in their hand. PAWdio requires no instrumentation and its low computational overhead assures a high frame rate. A user study with 18 subjects evaluates PAWdio with button input that is commonly available on VR adapters. Results with a 3D target selection task found a similar accuracy and usability, a significantly slower performance, but higher immersion for PAWdio. We discuss limitations and game applications of PAWdio.


title: Review of Near-Field Wireless Power and Communication for Biomedical Applications

abstract: Near-field magnetic wireless systems have distinct advantages over their conventional far-field counterparts in water-rich environments, such as underwater, underground, and in biological tissues, due to lower power absorption. This paper presents a comprehensive review of near-field magnetic wireless power transfer (WPT) and communication technologies in a variety of applications from general free-space systems, to implantable biomedical devices we find of particular interest. To implement a fully wirelessly-powered implantable system, both high-efficiency power transfer and high-rate data communication are essential. This paper first presents the history and the fundamentals of near-field WPT and communication in free-space systems, followed by technical details for their specific use in implantable biomedical devices. Finally, this paper reviews recent advances in simultaneous wireless information and power transfer and highlights their applications in implantable biomedical systems. The knowledge reviewed in the paper could provide intuition in the design of various wireless and mobile systems such as wireless body area networks, small-cell 5G cellular, as well as in-body biomedical applications, especially for efficient power and data management and higher security.


title: Ischemic Stroke Lesion Segmentation in CT Perfusion Scans using Pyramid Pooling and Focal Loss

abstract: We present a fully convolutional neural network for segmenting ischemic stroke lesions in CT perfusion images for the ISLES 2018 challenge. Treatment of stroke is time sensitive and current standards for lesion identification require manual segmentation, a time consuming and challenging process. Automatic segmentation methods present the possibility of accurately identifying lesions and improving treatment planning. Our model is based on the PSPNet, a network architecture that makes use of pyramid pooling to provide global and local contextual information. To learn the varying shapes of the lesions, we train our network using focal loss, a loss function designed for the network to focus on learning the more difficult samples. We compare our model to networks trained using the U-Net and V-Net architectures. Our approach demonstrates effective performance in lesion segmentation and ranked among the top performers at the challenge conclusion.


title: Conditioning Prices on Purchase History

abstract: The rapid advance in information technology now makes it feasible for sellers to condition their price offers on consumers' prior purchase behavior. In this paper we examine when it is profitable to engage in this form of price discrimination when consumers can adopt strategies to protect their privacy. ::: ::: Our baseline model involves rational consumers with constant valuations for the goods being sold and a monopoly merchant who can commit to a pricing policy. Applying results from the prior literature, we show that although it is feasible to price so as to distinguish high-value and low-value consumers, the merchant will never find it optimal to do so. ::: ::: We then consider various generalizations of this model, such as allowing the seller to offer enhanced services to previous customers, making the merchant unable to commit to a pricing policy, and allowing competition in the marketplace. In these cases we show that sellers will, in general, find it profitable to condition prices on purchase history.


title: On the Suitability of BPMN for Business Process Modelling

abstract: In this paper we examine the suitability of the Business Process Modelling Notation (BPMN) for business process modelling, using the Workflow Patterns as an evaluation framework. The Workflow Patterns are a collection of patterns developed for assessing control-flow, data and resource capabilities in the area of Process Aware Information Systems (PAISs). In doing so, we provide a comprehensive evaluation of the capabilities of BPMN, and its strengths and weaknesses when utilised for business process modelling. The analysis provided for BPMN is part of a larger effort aiming at an unbiased and vendor-independent survey of the suitability and the expressive power of some mainstream process modelling languages. It is a sequel to previous work in which languages including BPEL and UML Activity Diagrams were evaluated.


title: Prenatal Choline Supplementation Improves Child Color-location Memory Task Performance at 7 Y of Age (FS05-01-19)

abstract: Objectives ::: To assess the effect of 3rd trimester maternal choline supplementation on child memory at 7 y of age. Animal studies have provided strong evidence that maternal choline supplementation improves offspring cognition, including memory, but few experimental studies have evaluated this intervention in humans. ::: ::: ::: Methods ::: Pregnant women (n = 26) were randomized to consume 480 mg (approx. the Adequate Intake [AI]) or 930 mg choline/d from gestational wk 27 until delivery as part of a controlled-feeding study. All food (providing 380 mg choline/d), choline supplements (100 mg or 550 mg/d), and prenatal vitamins were provided by the study. An ancillary follow-up was conducted to assess offspring cognitive functioning, including memory, at age 7 y (n = 20). Children performed a computer-based color-location memory task in which the location of dots on a cartoon figure were recalled after a retention interval (RI) of 1 or 8 s. The task increased in difficulty level (number of dots to be recalled; 1 to 5 dots) every 4 trials. Data were analyzed using mixed models; pairwise comparisons were corrected for multiplicity. ::: ::: ::: Results ::: Children in the 930 mg/d group passed more levels than those in the 480 mg/d group (P = .048; see Figure), indicative of superior memory span. While the choline group by RI interaction was non-significant (P = .31), contrasts revealed that the 930 (v. 480) mg/d group tended to pass more levels at the 1 s RI (P = .052), but not at the 8 s RI (P = .58). Similarly, the analysis of total dots correct revealed a choline group by RI interaction (P = .08) such that children in the 930 (v. 480) mg/d group recalled more dots correctly at the 1 s RI (P = .02), but not at the 8 s RI (P > .99). ::: ::: ::: Conclusions ::: Children whose mothers consumed 930 (v. 480) mg choline/d performed significantly better on a task of color-location memory at age 7 y, suggesting a long-term beneficial effect of prenatal choline supplementation on offspring cognition in humans. These findings indicate that the choline AI for pregnant women may not be sufficient for optimal offspring cognitive functioning and raise concerns about choline intake during pregnancy in North America, which on average is only approx. 70% of the AI. ::: ::: ::: Funding Sources ::: Funded by NIFA/USDA and the Balchem Corp. JEHN and CLB were supported by NIH Traineeships. CLB was supported by an Egg Nutrition Center Young Investigator Research Award for Early Exploration. ::: ::: ::: Supporting Tables Images and/or Graphs


Pest Detection and Extraction Using Image Processing Techniques


title: Wait-free hash maps in the entity-component-system pattern for realtime interactive systems

abstract: In the past, the Entity-Component-System (ECS) pattern has become a major design pattern used in modern architectures for Realtime Interactive Systems (RIS). In this paper we introduce high performance wait-free hash maps for the System access of Components within the ECS pattern. This allows non-locking read and write operations, leading to a highly responsive low-latency data access while maintaining a consistent data state. Furthermore, we present centralized as well as decentralized approaches for reducing the memory demand of these memory-intensive wait-free hash maps for diverse RIS applications. Our approaches gain their efficiency by Component-wise queues which use atomic markup operations for fast memory deletion. We have implemented our new method in a current RIS and the results show that our approach is able to efficiently reduce the memory usage of wait-free hash maps very effectively by more than a factor of ten while still maintaining their high performance. Furthermore, we derive best practices from our numerical results for different use cases of wait-free hash map memory management in diverse RIS applications.


title: Why people stay: Using job embeddedness to predict voluntary turnover

abstract: A new construct, entitled “job embeddedness,” is introduced. It includes individuals' (1) links to other people, teams, and groups, (2) perceptions of their fit with job, organization, and community, and (3) what they say they would have to sacrifice if they left their jobs. We developed a measure of job embeddedness with two samples. The results show that job embeddedness predicts the key outcomes of both intent to leave and 'voluntary turnover' and explains significant incremental variance over and above job satisfaction, organizational commitment, job alternatives, and job search.


title: 3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network

abstract: Low-dose computed tomography (LDCT) has attracted major attention in the medical imaging field, since CT-associated X-ray radiation carries health risks for patients. The reduction of the CT radiation dose, however, compromises the signal-to-noise ratio, which affects image quality and diagnostic performance. Recently, deep-learning-based algorithms have achieved promising results in LDCT denoising, especially convolutional neural network (CNN) and generative adversarial network (GAN) architectures. This paper introduces a conveying path-based convolutional encoder-decoder (CPCE) network in 2-D and 3-D configurations within the GAN framework for LDCT denoising. A novel feature of this approach is that an initial 3-D CPCE denoising model can be directly obtained by extending a trained 2-D CNN, which is then fine-tuned to incorporate 3-D spatial information from adjacent slices. Based on the transfer learning from 2-D to 3-D, the 3-D network converges faster and achieves a better denoising performance when compared with a training from scratch. By comparing the CPCE network with recently published work based on the simulated Mayo data set and the real MGH data set, we demonstrate that the 3-D CPCE denoising model has a better performance in that it suppresses image noise and preserves subtle structures.


title: Internet of Things - a study on the security challenges

abstract: The vision of Internet of Things (IoT) is to enable devices to collaborate with each other on the Internet. Multiple devices collaborating with each other have opened up various opportunities in multitude of areas. It has presented unique set of challenges in scaling the Internet, techniques for identification of the devices, power efficient algorithms and communication protocols. Always connected devices have access to private sensitive information and any breach in them is a huge security risk. The IoT environment is composed of the hardware, software and middleware components making it a complex system to manage and secure. The objective of this paper is to present the challenges in IoT related to security, its challenges and recent developments through a comprehensive review of the literature.


title: Situation Models in Language Comprehension and Memory

abstract: This article reviews research on the use of situation models in language comprehension and memory retrieval over the past 15 years. Situation models are integrated mental representations of a described state of affairs. Significant progress has been made in the scientific understanding of how situation models are involved in language comprehension and memory retrieval. Much of this research focuses on establishing the existence of situation models, often by using tasks that assess one dimension of a situation model. However, the authors argue that the time has now come for researchers to begin to take the multidimensionality of situation models seriously. The authors offer a theoretical framework and some methodological observations that may help researchers to tackle this issue.


title: Two Conceptions of the Physical

abstract: The debate over physicalism in philosophy of mind can be seen as concerning an inconsistent tetrad of theses: (1) if physicalism is true, a priori physicalism is true; (2) a priori physicalism is false; (3) if physicalism is false, epiphenomenalism is true; (4) epiphenomenalism is false. This paper argues that one may resolve the debate by distinguishing two conceptions of the physical: on the theory-based conception, it is plausible that (2) is true and (3) is false; on the object-based conception, it is plausible that (3) is true and (2) is false. the paper also defends and explores the version of physicalism that results from this strategy.


Learning Active Learning from Real and Synthetic Data


title: A Spin-Glass Model Based Local Community Detection Method in Social Networks

abstract: Mining community structures has become a general problem which exists in many fields including: Computer-Science, Mathematics, Physics, Biology, Sociology and so on. It has developed rapidly and been used widely in many applications: web data mining, social network analysis, criminal network mining, protein interaction network analysis, metabolic network analysis, genetic network analysis, customers relationship mining and user online behavior analysis, etc. Most community detection algorithms try to obtain the global information of the network, but increasing large scale of the current network makes it computationally expensive. In the meanwhile, the different influence and different behavior of nodes in the network are ignored. In fact, if we know the local information of the network or the interested node, we can easily detect the local community. This paper proposes a multi-resolution local community detection algorithm named MRCDA which uses local structural information in the network to optimize the multi-resolution modularity based on the Potts spin-glass model. A local community can be detected through continuous optimization of the function by expanding from an initial influential node computed by a modified PageRank sorting algorithm. The proposed MRCDA has been tested on both synthetic and real world networks and tested against other algorithms. The experiments demonstrate its efficiency and accuracy.


title: Improving Person Re-identification by Attribute and Identity Learning

abstract: Abstract Person re-identification (re-ID) and attribute recognition share a common target at learning pedestrian descriptions. Their difference consists in the granularity. Most existing re-ID methods only take identity labels of pedestrians into consideration. However, we find the attributes, containing detailed local descriptions, are beneficial in allowing the re-ID model to learn more discriminative feature representations. In this paper, based on the complementarity of attribute labels and ID labels, we propose an attribute-person recognition (APR) network, a multi-task network which learns a re-ID embedding and at the same time predicts pedestrian attributes. We manually annotate attribute labels for two large-scale re-ID datasets, and systematically investigate how person re-ID and attribute recognition benefit from each other. In addition, we re-weight the attribute predictions considering the dependencies and correlations among the attributes. The experimental results on two large-scale re-ID benchmarks demonstrate that by learning a more discriminative representation, APR achieves competitive re-ID performance compared with the state-of-the-art methods. We use APR to speed up the retrieval process by ten times with a minor accuracy drop of 2.92% on Market-1501. Besides, we also apply APR on the attribute recognition task and demonstrate improvement over the baselines.


title: Sequence Labeling: A Practical Approach

abstract: We take a practical approach to solving sequence labeling problem assuming unavailability of domain expertise and scarcity of informational and computational resources. To this end, we utilize a universal end-to-end Bi-LSTM-based neural sequence labeling model applicable to a wide range of NLP tasks and languages. The model combines morphological, semantic, and structural cues extracted from data to arrive at informed predictions. The model's performance is evaluated on eight benchmark datasets (covering three tasks: POS-tagging, NER, and Chunking, and four languages: English, German, Dutch, and Spanish). We observe state-of-the-art results on four of them: CoNLL-2012 (English NER), CoNLL-2002 (Dutch NER), GermEval 2014 (German NER), Tiger Corpus (German POS-tagging), and competitive performance on the rest.


title: 3D Geometry-Aware Semantic Labeling of Outdoor Street Scenes

abstract: This paper is concerned with the problem of how to better exploit 3D geometric information for dense semantic image labeling. Existing methods often treat the available 3D geometry information (e.g., 3D depth-map) simply as an additional image channel besides the R-G-B color channels, and apply the same technique for RGB image labeling. In this paper, we demonstrate that directly performing 3D convolution in the framework of a residual connected 3D voxel top-down modulation network can lead to superior results. Specifically, we propose a 3D semantic labeling method to label outdoor street scenes whenever a dense depth map is available. Experiments on the "Synthia" and "Cityscape" datasets show our method outperforms the state-of-the-art methods, suggesting such a simple 3D representation is effective in incorporating 3D geometric information.


title: Structure-Aware Mesh Decimation

abstract: We present a novel approach for the decimation of triangle surface meshes. Our algorithm takes as input a triangle surface mesh and a set of planar proxies detected in a pre-processing analysis step, and structured via an adjacency graph. It then performs greedy mesh decimation through a series of edge collapse, designed to approximate the local mesh geometry as well as the geometry and structure of proxies. Such structure-preserving approach is well suited to planar abstraction, i.e. extreme decimation approximating well the planar parts while filtering out the others. Our experiments on a variety of inputs illustrate the potential of our approach in terms of improved accuracy and preservation of structure.


title: Human violence recognition and detection in surveillance videos

abstract: In this paper, we focus on the important topic of violence recognition and detection in surveillance videos. Our goal is to determine if a violence occurs in a video (recognition) and when it happens (detection). Firstly, we propose an extension of the Improved Fisher Vectors (IFV) for videos, which allows to represent a video using both local features and their spatio-temporal positions. Then, we study the popular sliding window approach for violence detection, and we re-formulate the Improved Fisher Vectors and use the summed area table data structure to speed up the approach. We present an extensive evaluation, comparison and analysis of the proposed improvements on 4 state-of-the-art datasets. We show that the proposed improvements make the violence recognition more accurate (as compared to the standard IFV, IFV with spatio-temporal grid, and other state-of-the-art methods) and make the violence detection significantly faster.


title: The social and political construction of technological

abstract: It is widely accepted that the successful adoption of an information system depends to a great extent on users' perceptions of the information system. It follows then that an understanding of users' cognitive frames should be a key factor in managing the adoption of information systems. To reach such an understanding is not an easy task as cognitive frames are dynamic phenomena. For example, what an individual perceives as 'ease of use' and 'usefulness' may depend not only on intrinsic qualities of the information system but also on the changing contexts in which the information system is evaluated. This work argues that the management of information systems' adoption is a social and political process in which stakeholders frame and reframe their perceptions of an information system. A case study carried out in a European bank illustrates how the Bank's technical team influenced users' technological frames, including those of senior management in order to ensure a smooth implementation process. In conclusion, the paper introduces a set of theoretical propositions relating to the social and political processes that occur during information systems adoption.


title: Foreign Direct Investment and Wage Spillovers in Vietnam: Evidence from Firm Level Data

abstract: This paper examines the impact of FDI on wages paid by domestic firms in Vietnam. The existing literature has examined the impact of foreign firms on domestic firms' wage levels within an industry. We expand on the literature by examining interindustry linkages as an additional conduit for wage spillovers. There is strong evidence of horizontal wage spillovers from foreign firms to domestic firms in Vietnam, despite different labour market conditions and firms' characteristics. Vertical wage spillovers exist, but depend on the specific characteristics of firms and industries. A further finding is that training activities facilitate wage spillovers.


title: MUSICOS: a fiber-fed spectrograph for multi-site observations

abstract: A fiber fed echelle spectrograph designed and constructed at the Paris-Meudon Observatory is described. This instrument is dedicated to the MUSICOS project, whose purpose is to organize multisite continuous spectroscopic observations. The optical scheme is described in some detail, as well as the data reduction software. A review of the spectrograph main performances which were obtained during laboratory tests and in real observational conditions at the 2 m diameter telescope at Pic du Midi (France) is given


title: [The significance of nerve damage in upper arm fractures].

abstract: Lesions of the nerves are a relatively frequent complication after humeral shaft fractures, the nerve most commonly affected being the radial nerve. In the case of proximal fractures of the humerus, particularly dislocation fractures and luxated fractures, lesion of the axillary nerve and irritation of the plexus, are most frequent. In distal humeral fractures the ulnar nerve is most frequently first place and shows a serious complication. The therapy of the fracture must differ in most of these cases from conventional procedure. We believe that primary paresis of the radial nerve is a relative indication for surgery and secondary paresis of the radial nerve, an absolute indication for operation. When we find a subcapital humeral shaft fracture with rare paresis of the nervus axillaris we wait with an operation, but if remission does not occur within approximately 8 weeks a revision should be done. Fracture luxations with irritation of the plexus are an acute indication for operation, because repositioning or displacement of the head fragment can afford relief.


title: Blockchain: A Graph Primer

abstract: Bitcoin and its underlying technology Blockchain have become popular in recent years. Designed to facilitate a secure distributed platform without central authorities, Blockchain is heralded as a paradigm that will be as powerful as Big Data, Cloud Computing and Machine learning. Blockchain incorporates novel ideas from various fields such as public key encryption and distributed systems. As such, a reader often comes across resources that explain the Blockchain technology from a certain perspective only, leaving the reader with more questions than before. We will offer a holistic view on Blockchain. Starting with a brief history, we will give the building blocks of Blockchain, and explain their interactions. As graph mining has become a major part its analysis, we will elaborate on graph theoretical aspects of the Blockchain technology. We also devote a section to the future of Blockchain and explain how extensions like Smart Contracts and De-centralized Autonomous Organizations will function. Without assuming any reader expertise, our aim is to provide a concise but complete description of the Blockchain technology.


title: Testing Strengths-Based Interventions: A Preliminary Study on the Effectiveness of a Program Targeting Curiosity, Gratitude, Hope, Humor, and Zest for Enhancing Life Satisfaction

abstract: The study compares the impact of character strengths-based positive interventions in a sample of 178 adults. An experimental group that trained strengths of the Values-in-Action classification that typically correlate highly with life satisfaction (curiosity, gratitude, hope, humor, and zest) was compared in its gain in life satisfaction with a group that trained strengths that usually demonstrate low correlations with life satisfaction (appreciation of beauty and excellence, creativity, kindness, love of learning, and perspective) and a wait-list control group. If pre and post measures in life satisfaction were compared, the group with the strengths most correlated with life satisfaction improved significantly (statistically) in their satisfaction in comparison to a control group. This could be interpreted as support for the idea that primarily those strengths that correlate highly with life satisfaction should be addressed in strengths-based interventions. When asked for subjective ratings of well-being after the interventions concluded, participants in both intervention groups indicated gains above that of a wait-listed control group. Further analyses underscore the special role of self-regulation in facilitating success in the interventions. Overall, the data underline the potential of strength-based interventions for improving human well-being.


title: Partial Constraint Satisfaction

abstract: A constraint satisfaction problem involves finding values for variables subject to constraints on which combinations of values are allowed. In some cases it may be impossible or impractical to solve these problems completely. We may seek to partially solve the problem, in particular by satisfying a maximal number of constraints. Standard backtracking and local consistency techniques for solving constraint satisfaction problems can be adapted to cope with, and take advantage of, the differences between partial and complete constraint satisfaction. Extensive experimentation on maximal satisfaction problems illuminates the relative and absolute effectiveness of these methods. A general model of partial constraint satisfaction is proposed.


Hand in Glove: Deep Feature Fusion Network Architectures for Answer Quality Prediction in Community Question Answering


title: Urban WiFi characterization via mobile crowdsensing

abstract: We present a mobile crowdsensing approach for urban WiFi characterization that leverages commodity smartphones and the natural mobility of people. Specifically, we report measurement results obtained for Edinburgh, a representative European city, on detecting the presence of deployedWiFi APs via the mobile crowdsensing approach. They show that few channels in 2.4GHz are heavily used; in contrast, there is hardly any activity in the 5GHz band even though relatively it has a greater number of available channels. Spatial analysis of spectrum usage reveals that mutual interference among nearby APs operating in the same channel can be a serious problem with around 10 APs contending with each other in many locations. We find that the characteristics of WiFi deployments at city-scale are similar to that of WiFi deployments in public spaces of different indoor environments. We validate our approach in comparison with wardriving, and also show that our findings generally match with previous studies based on other measurement approaches. As an application of the mobile crowdsensing based urban WiFi monitoring, we outline a cloud based WiFi router configuration service for better interference management with global awareness in urban areas.


title: ORGANIZATIONAL CITIZENSHIP BEHAVIOR: ITS NATURE AND ANTECEDENTS

abstract: Theory and research on Organizational Citizenship Behaviors (OCB) has presumed OCB as a set of desirable behaviors that contributes to the organizational effectiveness. So far OCB has been connoted as one of the antecedents of organizational performance. However, the antecedents of OCB are not thoroughly investigated. This study explores various existing definitions of OCB and then examines the dimensions of OCB. Based on the discussion on the dimensions of OCB, a number of antecedents were identified. When the antecedents are known, managers would be able to promote OCB among their employees for better performance.


title: Survey on Fairness Issues in TCP Congestion Control Mechanisms

abstract: In this paper, we survey the fairness issues in the congestion control mechanisms of TCP, which is one of most important service aspects in the current and future Internet. We first summarize the problems from a perspective of the fair service among connections. Several solution methods are next surveyed. Those are modifications of TCP congestion control mechanism and router support for achieving the fair service among TCP connections. We finally investigate the fair share of resources at endhosts. key words: TCP (Transmission Control Protocol), Fairness, Congestion Control


title: Using Scripted and Non Scripted Role Plays to Improve English Speaking Performance of Hotel Engineering Staff

abstract: The purpose of this study was to investigate the impact of using scripted and non-scripted role-play activities on EFL learners’ speaking performance. The participants in the study were twelve engineering staff members of a hotel in Phuket. They were divided into two groups. Both groups attended 15 lessons of the English for Hotel Engineering Staff course. The lessons were given twice a week; each lesson lasted two hours. At the end of the lesson, staff members in one group were asked to pair up and perform scripted role-play activities whereas those in the other group performed non-scripted ones in a separate room. An actual interaction with a native speaker of English was used as the pre-test and post-test to assess the participants’ oral English performance at the beginning and at the end of the study. The results of the study show that the twelve participants’ overall post-test scores were significantly higher than their pre-test scores at the level of 0.01, indicating that both scripted and non-scripted role-play activities helped the participants to improve their English speaking performance. However, non-scripted role-plays contributed to the improvement of the participants’ discrete oral performance to a more significant degree than scripted ones. The t-test results reveal that the six participants assigned to practice non-scripted role-plays showed significant speaking improvements especially in terms of accent and comprehension at the level of 0.01. Their post-test scores on vocabulary and fluency were also significantly higher than the pre-test scores at the level of 0.01. The other six participants, who were assigned to perform scripted role-plays, showed significant improvement at the level of 0.01only in terms of vocabulary. The study suggests that non-scripted role-play activities better contribute to the holistic improvement in oral English performance of the Thai EFL learners than scripted ones. Further studies are recommended to investigate the outcome of using the two types of role-play with different groups of learners, particularly those with higher levels of proficiency, and to find ways to make the most of each type of role-play in improving different aspects of the learners’ speaking performance.


title: Analysis of aperture coupled Multilayered Microstrip directional coupler

abstract: The present work describes the design and analysis of a four aperture and six aperture coupled multilayered microstrip directional coupler. The single layered four aperture and six aperture directional coupler consist of two microstrip layers one with ground plane. This comparison shows that the coupling of -10 dB was achieved with the four aperture directional coupler but the higher isolation has been achieved with the new six aperture microstrip directional coupler as compared to four aperture directional coupler. The proposed directional coupler has been designed and analyzed using IE3D software completely on experimental basis. Variation of the performance of the coupler with frequency has also been investigated. Analysis and design procedure of such high directivity asymmetric couplers consisting of various planar and layered structures are presented together with the experimental results for -10 dB couplers on silica.


title: Experiments on a Ground-Based Tomographic Synthetic Aperture Radar

abstract: This paper presents the development and experiment of three-dimensional image formation by using a ground-based tomographic synthetic aperture radar (GB-TomoSAR) system. GB-TomoSAR formulates two-dimensional synthetic aperture by the motion of antennae, both in azimuth and vertical directions. After range compression, three-dimensional image focusing is performed by applying Deramp-FFT (Fast Fourier Transform) algorithms, both in azimuth and vertical directions. Geometric and radiometric calibrations were applied to make an image cube, which is then projected into range-azimuth and range-vertical cross-sections for visualization. An experiment with a C-band GB-TomoSAR system with a scan length of 2.49 m and 1.86 m in azimuth and vertical-direction, respectively, shows distinctive three-dimensional radar backscattering of stable buildings and roads with resolutions similar to the theoretical values. Unstable objects such as trees and moving cars generate severe noise due to decorrelation during the eight-hour image-acquisition time.


title: Adjustment of fixation duration in visual search

abstract: Abstract To investigate whether fixation durations are adjusted to the duration of a foveal analysis task, we designed a search task in which each stimulus element yielded information about the position of the target. We asked subjects to look for the target by making eye movements in the direction indicated by each stimulus element. We explicitly asked the subjects to make the eye movements in the correct direction, but they did not always do this. They made only 65–80% of the eye movements in directions indicated by the stimulus elements. From these results we conclude that fixation durations are not solely determined by the immediate visual stimulus and that subjects encounter difficulties when trying to increase fixation durations to values that would enable them to direct saccades accurately. In a second experiment we shortened the presentation time in order to provide an incentive for the subjects to speed up search. Shortening the presentation time did not affect fixation duration. Therefore, we suggest that fixation duration is controlled by a mechanism that uses estimations of the foveal analysis time of previous fixated stimulus elements.


title: Graph Convolutional Networks for Classification with a Structured Label Space

abstract: It is a usual practice to ignore any structural information underlying classes in multi-class classification. In this paper, we propose a graph convolutional network (GCN) augmented neural network classifier to exploit a known, underlying graph structure of labels. The proposed approach resembles an (approximate) inference procedure in, for instance, a conditional random field (CRF). We evaluate the proposed approach on document classification and object recognition and report both accuracies and graph-theoretic metrics that correspond to the consistency of the model's prediction. The experiment results reveal that the proposed model outperforms a baseline method which ignores the graph structures of a label space in terms of graph-theoretic metrics.


title: Modeling polypharmacy side effects with graph convolutional networks

abstract: The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases and co-existing conditions. However, a major consequence of polypharmacy is a much higher risk of adverse side effects for the patient. Polypharmacy side effects emerge because of drug-drug interactions, in which activity of one drug may change if taken with another drug. The knowledge of drug interactions is limited because these complex relationships are rare, and are usually not observed in relatively small clinical testing. Discovering polypharmacy side effects thus remains an important challenge with significant implications for patient mortality. Here, we present Decagon, an approach for modeling polypharmacy side effects. The approach constructs a multimodal graph of protein-protein interactions, drug-protein target interactions, and the polypharmacy side effects, which are represented as drug-drug interactions, where each side effect is an edge of a different type. Decagon is developed specifically to handle such multimodal graphs with a large number of edge types. Our approach develops a new graph convolutional neural network for multirelational link prediction in multimodal networks. Decagon predicts the exact side effect, if any, through which a given drug combination manifests clinically. Decagon accurately predicts polypharmacy side effects, outperforming baselines by up to 69%. We find that it automatically learns representations of side effects indicative of co-occurrence of polypharmacy in patients. Furthermore, Decagon models particularly well side effects with a strong molecular basis, while on predominantly non-molecular side effects, it achieves good performance because of effective sharing of model parameters across edge types. Decagon creates opportunities to use large pharmacogenomic and patient data to flag and prioritize side effects for follow-up analysis.


title: A high-Q birdbath resonator gyroscope (BRG)

abstract: We report a three-dimensional (3-D) micro-scale shell gyroscope made of fused silica (FS), called the birdbath resonator gyroscope (BRG). The BRG operates at 10.45 kHz, far above most environmental noise frequencies, and offers high mechanical quality factor (Qn=2 = 249 k) and low stiffness and damping anisotropy (Δfn = 2 = 14 Hz, Δ1/τn=2 = 4.1 mHz). The birdbath (BB) resonator can be fabricated with good structural symmetry because its anchor is self-aligned to the rest of the structure. The BB resonator also has large frequency separation between the n=2 wineglass modes and the closest parasitic mode (|fparasitic - fn=2|/fn=2 = 0.3), which makes the gyro less sensitive to vibration. The BRG is fabricated using a 3-D μ-blow-torching process and assembly on an electrode substrate made with the Silicon-On-Glass (SOG) process. The BRG is operated in the force-rebalance mode at 400 deg/sec, an angle random walk (ARW) of 0.106 deg/√hr and a bias stability of 1 deg/hr.


title: Using a case study to test the role of three key social enablers in ERP implementation

abstract: The literature indicates that three key social enablers--strong and committed leadership, open and honest communication, and a balanced and empowered implementation team are necessary conditions/precursors for successful enterprise resource planning (ERP) implementation. In a longitudinal positivist case study, we find that, while all three enablers may contribute to ERP implementation success, only strong and committed leadership can be empirically established as a necessary condition. This presents a challenge to future ERP researchers for resolving apparent contradictions between the existing literature and the results of our analysis. One possible direction for future research would be to undertake an interpretive re-examination of the rationalisitic assumptions that underlie much of the existing literature on ERP systems implementation.


title: On Non-Functional Requirements

abstract: Although the term 'non-functional requirement' has been in use for more than 20 years, there is still no consensus in the requirements engineering community what non-functional requirements are and how we should elicit, document, and validate them. On the other hand, there is a unanimous consensus that non-functional requirements are important and can be critical for the success of a project. This paper surveys the existing definitions of the term, highlights and discusses the problems with the current definitions, and contributes concepts for overcoming these problems.


title: CBCD: Cloned buggy code detector

abstract: Developers often copy, or clone, code in order to reuse or modify functionality. When they do so, they also clone any bugs in the original code. Or, different developers may independently make the same mistake. As one example of a bug, multiple products in a product line may use a component in a similar wrong way. This paper makes two contributions. First, it presents an empirical study of cloned buggy code. In a large industrial product line, about 4% of the bugs are duplicated across more than one product or file. In three open source projects (the Linux kernel, the Git version control system, and the PostgreSQL database) we found 282, 33, and 33 duplicated bugs, respectively. Second, this paper presents a tool, CBCD, that searches for code that is semantically identical to given buggy code. CBCD tests graph isomorphism over the Program Dependency Graph (PDG) representation and uses four optimizations. We evaluated CBCD by searching for known clones of buggy code segments in the three projects and compared the results with text-based, token-based, and AST-based code clone detectors, namely Simian, CCFinder, Deckard, and CloneDR. The evaluation shows that CBCD is fast when searching for possible clones of the buggy code in a large system, and it is more precise for this purpose than the other code clone detectors.


title: Is chess the drosophila of artificial intelligence? A social history of an algorithm

abstract: Since the mid 1960s, researchers in computer science have famously referred to chess as the ‘drosophila’ of artificial intelligence (AI). What they seem to mean by this is that chess, like the comm...


title: Sleep in the Human Hippocampus: A Stereo-EEG Study

abstract: Background ::: There is compelling evidence indicating that sleep plays a crucial role in the consolidation of new declarative, hippocampus-dependent memories. Given the increasing interest in the spatiotemporal relationships between cortical and hippocampal activity during sleep, this study aimed to shed more light on the basic features of human sleep in the hippocampus. ::: ::: ::: Methodology/Principal Findings ::: We recorded intracerebral stereo-EEG directly from the hippocampus and neocortical sites in five epileptic patients undergoing presurgical evaluations. The time course of classical EEG frequency bands during the first three NREM-REM sleep cycles of the night was evaluated. We found that delta power shows, also in the hippocampus, the progressive decrease across sleep cycles, indicating that a form of homeostatic regulation of delta activity is present also in this subcortical structure. Hippocampal sleep was also characterized by: i) a lower relative power in the slow oscillation range during NREM sleep compared to the scalp EEG; ii) a flattening of the time course of the very low frequencies (up to 1 Hz) across sleep cycles, with relatively high levels of power even during REM sleep; iii) a decrease of power in the beta band during REM sleep, at odds with the typical increase of power in the cortical recordings. ::: ::: ::: Conclusions/Significance ::: Our data imply that cortical slow oscillation is attenuated in the hippocampal structures during NREM sleep. The most peculiar feature of hippocampal sleep is the increased synchronization of the EEG rhythms during REM periods. This state of resonance may have a supportive role for the processing/consolidation of memory.


title: Class-Based Construction of a Verb Lexicon

abstract: We present an approach to building a verb lexicon compatible with WordNet but with explicitly stated syntactic and semantic information, using Levin verb classes to systematically construct lexical entries. By using verb classes we capture generalizations about verb behavior and reduce the effort needed to construct the lexicon. The syntactic frames for the verb classes are represented by a Lexicalized Tree Adjoining Grammar augmented with semantic predicates, which allows a compositional interpretation.


title: GeoDMA – Geographic Data Mining Analyst

abstract: Abstract Remote sensing images obtained by remote sensing are a key source of data for studying large-scale geographic areas. From 2013 onwards, a new generation of land remote sensing satellites from USA, China, Brazil, India and Europe will produce in 1 year as much data as 5 years of the Landsat-7 satellite. Thus, the research community needs new ways to analyze large data sets of remote sensing imagery. To address this need, this paper describes a toolbox for combing land remote sensing image analysis with data mining techniques. Data mining methods are being extensively used for statistical analysis, but up to now have had limited use in remote sensing image interpretation due to the lack of appropriate tools. The toolbox described in this paper is the Geographic Data Mining Analyst (GeoDMA). It has algorithms for segmentation, feature extraction, feature selection, classification, landscape metrics and multi-temporal methods for change detection and analysis. GeoDMA uses decision-tree strategies adapted for spatial data mining. It connects remotely sensed imagery with other geographic data types using access to local or remote database. GeoDMA has methods to assess the accuracy of simulation models, as well as tools for spatio-temporal analysis, including a visualization of time-series that helps users to find patterns in cyclic events. The software includes a new approach for analyzing spatio-temporal data based on polar coordinates transformation. This method creates a set of descriptive features that improves the classification accuracy of multi-temporal image databases. GeoDMA is tightly integrated with TerraView GIS, so its users have access to all traditional GIS features. To demonstrate GeoDMA, we show two case studies on land use and land cover change.


title: Relationship between total quality management (TQM) and continuous improvement of international project management (CIIPM)

abstract: The purpose of this study is to investigate the relationship between the total quality management (TQM) practice and the continuous improvement of international project management (CIIPM) practice. Based on a literature review and qualitative interviews with TQM and project management experts, four hypotheses are posed on how TQM elements affect CIIPM. A cross-sectional survey collected from over 100 mid to senior level international managers is used to validate these hypotheses. The study suggests that the relationship between ‘soft’ TQM elements and CIIPM is more significant than the relationship between ‘hard’ TQM elements and CIIPM. q 2006 Elsevier Ltd. All rights reserved.


title: Role of Semiotics in Linguistics

abstract: Semiotics is the study of signs. Signs take the form of words, images, sounds, odours, flavours, acts or objects but such things have no intrinsic meaning and become signs only when we invest them with meaning. 'Nothing is a sign unless it is interpreted as a sign,' declares Peirce (Peirce, 1931). The two dominant models of a sign are the linguist Ferdinand de Saussure and the philosopher Charles Sanders Peirce. This paper attempts to study the role of semiotics in linguistics. How signs play an important role in studying the language? Index: Semiotics- theory of signs and symbols Semantics- study of sentences Denotata- an actual object referred to by a linguistic expression Divergent- move apart in different directions Linguistics- scientific study of language


title: Methods to Provide System-Wide ADS-B Back-Up, Validation and Security

abstract: A major part of the business case for Automatic Dependent Surveillance - Broadcast (ADS-B) is attributed to the savings generated by decommissioning or reducing reliance on conventional radar systems. In order to maintain some form of redundancy, a networked, system-wide back-up and validation approach is required. During the transition to end-state surveillance architectures, there will likely be a mixture of various types of surveillance sources including primary surveillance radar, secondary surveillance radar, multilateration, and ADS-B. This paper describes an integrated approach to the surveillance architecture and one means for facilitating the application of multiple surveillance sources. Surveillance sensor performance is based on the categories defined in ADS-B standards. The relevant parameters are Navigation Accuracy Category (NAC), Navigation Integrity Category (NIC), and Surveillance Integrity Level (SIL). These standards have been adapted to create a generic surveillance performance categorization method. This method would enable air traffic service providers to develop criteria for combining information from multiple surveillance sensors. The categorization also enables a method for defining the associated aircraft separation requirements.


title: A Cloud-Based Car Parking Middleware for IoT-Based Smart Cities: Design and Implementation

abstract: This paper presents the generic concept of using cloud-based intelligent car parking services in smart cities as an important application of the Internet of Things (IoT) paradigm. This type of services will become an integral part of a generic IoT operational platform for smart cities due to its pure business-oriented features. A high-level view of the proposed middleware is outlined and the corresponding operational platform is illustrated. To demonstrate the provision of car parking services, based on the proposed middleware, a cloud-based intelligent car parking system for use within a university campus is described along with details of its design, implementation, and operation. A number of software solutions, including Kafka/Storm/Hbase clusters, OSGi web applications with distributed NoSQL, a rule engine, and mobile applications, are proposed to provide 'best' car parking service experience to mobile users, following the Always Best Connected and best Served (ABC&S) paradigm.


title: Power Cycling Reliability of Power Module: A Survey

abstract: Electronic devices using semiconductors such as insulated-gate bipolar transistors, metal–oxide–semiconductor field-effect transistors, and diodes are extensively used in electrical traction applications such as locomotive, elevators, subways, and cars. The long-term reliability of such power modules is then highly demanded, and their main reliability criterion is their power cycling capability. Thus, a power cycling test is the most important reliability test for power modules. This test consists in periodically applying a current to a device mounted onto a heat sink. This leads to power loss in the entire module and results in a rise in the semiconductor temperature. In this paper, the different kinds of semiconductors and power modules used for traction applications are described. Experimental and simulation methods employed for power cycling tests are presented. Modules' weak points and fatigue processes are pointed out. Then, a detailed statistical review of publications from 1994 to 2015 dealing with power cycling is presented. This review gives a clear overview of all studies dealing with power cycling that were carried out until now. It reveals the principal trends in power electronic devices and highlights the main reliability issues for which an important lack of knowledge remains.


title: Obesity in Saudi Arabia.

abstract: OBJECTIVE ::: Obesity and overweight are well known risk factors for coronary artery disease (CAD), and are expected to be increasing in the Kingdom of Saudi Arabia (KSA) particularly among females. Therefore, we designed this study with the objective to determine the prevalence of obesity and overweight among Saudis of both gender, between the ages of 30-70 years in rural as well as in urban communities. This work is part of a major national project called Coronary Artery Disease in Saudis Study (CADISS) that is designed to look at CAD and its risk factors in Saudi population. ::: ::: ::: METHODS ::: This study is a community-based national epidemiological health survey, conducted by examining Saudi subjects in the age group of 30-70 years of selected households over a 5-year period between 1995 and 2000 in KSA. Data were obtained from body mass index (BMI) and were analyzed to classify individuals with overweight (BMI = 25-29.9 kg/m2), obesity (BMI >/=30 kg/m2) and severe (gross) obesity (BMI >/=40 kg/m2) to provide the prevalence of overweight and obesity in KSA. ::: ::: ::: RESULTS ::: Data were obtained by examining 17,232 Saudi subjects from selected households who participated in the study. The prevalence of overweight was 36.9%. Overweight is significantly more prevalent in males (42.4%) compared to 31.8% of females (p<0.0001). The age-adjusted prevalence of obesity was 35.5% in KSA with an overall prevalence of 35.6% [95% CI: 34.9-36.3], while severe (gross) obesity was 3.2%. Females are significantly more obese with a prevalence of 44% than males 26.4% (p<0.0001). ::: ::: ::: CONCLUSION ::: Obesity and overweight are increasing in KSA with an overall obesity prevalence of 35.5%. Reduction in overweight and obesity are of considerable importance to public health. Therefore, we recommend a national obesity prevention program at community level to be implemented sooner to promote leaner and consequently healthier community.


title: A Tutorial on Mathematical Modeling of Biological Signaling Pathways

abstract: Mathematical models have been widely used in the studies of biological signaling pathways. Among these studies, two systems biology approaches have been applied: top-down and bottom-up systems biology. The former approach focuses on X-omics researches involving the measurement of experimental data in a large scale, for example proteomics, metabolomics, or fluxomics and transcriptomics. In contrast, the bottom-up approach studies the interaction of the network components and employs mathematical models to gain some insights about the mechanisms and dynamics of biological systems. This chapter introduces how to use the bottom-up approach to establish mathematical models for cell signaling studies.


Semi-Supervised Learning for Optical Flow with Generative Adversarial Networks Supplementary Material


title: Different optical absorption edges in AlN bulk crystals grown in m- and c-orientations

abstract: AlN single crystals were grown on m-plane (101¯0) and c-plane (0001¯) AlN seeds under identical growth conditions. The m-plane AlN crystals exhibited substantially lower oxygen incorporation, ∼1018cm−3, than the c-plane crystals, ∼1019cm−3. By investigating optical transmission spectra, m-plane AlN had absorption bands at 4.05 and 4.35eV, while c-plane AlN had an absorption band edge at 4.85eV. These below bandgap absorption bands strongly correlate with the reported transitions related to Al vacancy-impurity complexes, such as the complex of an Al vacancy and two oxygen atoms, (VAl–2ON)1− and the complex of an Al vacancy and one oxygen atom, (VAl–ON)2−, becoming the major cause for the poor, below bandgap optical transparency (α>200cm−1) of these crystals.


title: Krylov subspace exponential time domain solution of Maxwell’s equations in photonic crystal modeling

abstract: The exponential time integration, i.e., time integration which involves the matrix exponential, is an attractive tool for time domain modeling involving Maxwell’s equations. However, its application in practice often requires a substantial knowledge of numerical linear algebra algorithms, such as Krylov subspace methods. ::: ::: In this note we discuss exponential Krylov subspace time integration methods and provide a simple guide on how to use these methods in practice. While specifically aiming at nanophotonics applications, we intentionally keep the presentation as general as possible and consider full vector Maxwell’s equations with damping (i.e., with nonzero conductivity terms). ::: ::: Efficient techniques such as the Krylov shift-and-invert method and residual-based stopping criteria are discussed in detail. Numerical experiments are presented to demonstrate the efficiency of the discussed methods and their mesh independent convergence. Some of the algorithms described here are available as Octave/Matlab codes from www.math.utwente.nl/~botchevma/expm/.


Improved Intrusion Detection System using C 4 . 5 Decision Tree and Support Vector Machine


title: Hadoop+Aparapi: Making heterogenous MapReduce programming easier

abstract: Lately, programmers have started to take advantage of GPU capabilities of cloud-based machines. Using the GPUs can decrease the number of nodes required to perform the computation by increasing the productivity per node. We combine Hadoop, a widely-used MapReduce framework, with Aparapi, a new Java-to-OpenCL conversion tool from AMD. We propose an easy-to-use API which allows easy implementation of MapReduce algorithms that make use of the GPU. Our API improves upon Hadoop by further hiding the complexity of GPU programming, thus allowing the programmer to concentrate on her algorithm. We also propose an accompanying refactoring that allows the programmer to specify the GPU part of their map computation by using very lightweight annotation.


title: The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

abstract: Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.


title: A retrospective analysis of the failure rate of three different orthodontic skeletal anchorage systems

abstract: Objectives: The aim of this retrospective study was to assess systematically the case distribution among three types of mini-implants and to evaluate the clinical factors that influence the failure rates of mini-implants used as an orthodontic anchorage. ::: ::: Material and methods: Data for 359 mini-implants (miniplates, miniscrews, and microscrews) in 129 patients were collected. The factors related to mini-implant failure were evaluated using univariate analysis and multivariate stepwise logistic regression analysis. ::: ::: Results: Among these three different types of skeletal anchorage, there was a significant difference between the failure rates of these mini-implants, with the miniscrews and microscrews showing much higher failure rates. There were no significant differences in failure rates among the mini-implants for the following variables: gender, type of malocclusion, local or full-arch treatment, whether on the buccal or lingual side, length of the screw, loading pattern, or the duration of the healing phase. Greater risks for failure were found in younger patients, when an implant was placed for retraction/protraction, when it was placed on the mandibular arch, when it was placed anterior to the second premolars, or when using the miniscrew/microscrew systems. After adjusting for potential confounding effects, only three factors (type of mini-implant, placement on the mandibular arch, and age) were found to be statistically significant in predicting mini-implant failures (P<0.05) with an R2 value of 85.2%. ::: ::: Conclusions: Mini-implants placed in younger patients or placed on the mandibular arch are at a greater risk of failing. The miniplate system has greater stability compared with miniscrews or microscrews. However, it requires flap surgery for insertion and removal, which usually causes swelling and discomfort. Therefore, selection of the proper type of skeletal anchorage should be based on the specific treatment needs of each individual patient.


title: Detection of blood cancer in microscopic images of human blood samples: A review

abstract: For the fast and cost effective production of patient diagnosis, various image processing techniques or software has been developed to get desired information from medical images. Acute Lymphoblastic Leukemia (ALL) is a type of leukemia which is more common in children. The term ‘Acute’ means that leukemia can progress quickly and if not treated may lead to fatal death within few months. Due to its non specific nature of the symptoms and signs of ALL leads wrong diagnosis. Even hematologist finds it difficult to classify the leukemia cells, there manual classification of blood cells is not only time consuming but also inaccurate. Therefore, early identification of leukemia yields in providing the appropriate treatment to the patient. As a solution to this problem the system propose individuates in the blood image the leucocytes from the blood cells, and then it selects the lymphocyte cells. It evaluates morphological index from those cells and finally it classifies the presence of leukemia. In this paper a literature review is been conducted on various techniques used for detecting cancer cells.


title: BFTCloud: A Byzantine Fault Tolerance Framework for Voluntary-Resource Cloud Computing

abstract: Cloud computing is becoming a popular and important solution for building highly reliable applications on distributed resources. However, it is a critical challenge to guarantee the system reliability of applications especially in voluntary-resource cloud due to the highly dynamic environment. In this paper, we present BFT Cloud (Byzantine Fault Tolerant Cloud), a Byzantine fault tolerance framework for building robust systems involuntary-resource cloud environments. BFT Cloud guarantees robustness of systems when up to f of totally 3f+1 resource providers are faulty, including crash faults, arbitrary behaviors faults, etc. BFT Cloud is evaluated in a large-scale real-world experiment which consists of 257 voluntary-resource providers located in 26 countries. The experimental results shows that BFT Cloud guarantees high reliability of systems built on the top of voluntary-resource cloud infrastructure and ensures good performance of these systems.


title: Pedestrian Attribute Recognition At Far Distance

abstract: The capability of recognizing pedestrian attributes, such as gender and clothing style, at far distance, is of practical interest in far-view surveillance scenarios where face and body close-shots are hardly available. We make two contributions in this paper. First, we release a new pedestrian attribute dataset, which is by far the largest and most diverse of its kind. We show that the large-scale dataset facilitates the learning of robust attribute detectors with good generalization performance. Second, we present the benchmark performance by SVM-based method and propose an alternative approach that exploits context of neighboring pedestrian images for improved attribute inference.


title: A Virtualization Based Monitoring System for Mini-intrusive Live Forensics

abstract: Digital evidences hold great significance for governing cybercrime. Unfortunately, previous acquisition tools were troubled by either the shortage of suspending the target system's running or the security of the acquisition tools themselves, thus the correctness and accuracy of their obtained evidences cannot be guaranteed. In this paper, we propose VAIL, a novel virtualization based monitoring system for mini-intrusive live forensics, which employs hardware assisted virtualization technique to gather integrated information from the native computer system. Meanwhile, the execution of the target system will not be interrupted and VAIL keeps immune to attacks from the target system. We have implemented a proof-of-concept prototype that has been validated with a Windows guest system. The experimental results show that VAIL can obtain comprehensive digital evidences from the target system as designed, including the CPU state, the physical memory content, and the I/O activities. And on average, VAIL only introduces 4.21 % performance overhead to the target system, which proves that VAIL is practical in real commercial environments.


title: Bayesian Network Multi-classifiers for Protein Secondary Structure Prediction

abstract: Successful secondary structure predictions provide a starting point for direct tertiary structure modelling, and also can significantly improve sequence analysis and sequence-structure threading for aiding in structure and function determination. Hence the improvement of predictive accuracy of the secondary structure prediction becomes essential for future development of the whole field of protein research. In this work we present several multi-classifiers that combine the predictions of the best current classifiers available on Internet. Our results prove that combining the predictions of a set of classifiers by creating composite classifiers is a fruitful one. We have created multi-classifiers that are more accurate than any of the component classifiers. The multi-classifiers are based on Bayesian networks. They are validated with 9 different datasets. Their predictive accuracy results outperform the best secondary structure predictors by 1.21% on average. Our main contributions are: (i) we improved the best know predictive accuracy by 1.21%, (ii) our best results have been obtained with a new semi [email protected]?ve Bayes approach named Pazzani-EDA and (iii) our multi-classifiers combine results of previously build classifiers predictions obtained through Internet, thanks to our development of a Java application.


title: An intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection

abstract: Intrusion detection system (IDS) is to monitor the attacks occurring in the computer or networks. Anomaly intrusion detection plays an important role in IDS to detect new attacks by detecting any deviation from the normal profile. In this paper, an intelligent algorithm with feature selection and decision rules applied to anomaly intrusion detection is proposed. The key idea is to take the advantage of support vector machine (SVM), decision tree (DT), and simulated annealing (SA). In the proposed algorithm, SVM and SA can find the best selected features to elevate the accuracy of anomaly intrusion detection. By analyzing the information from using KDD'99 dataset, DT and SA can obtain decision rules for new attacks and can improve accuracy of classification. In addition, the best parameter settings for the DT and SVM are automatically adjusted by SA. The proposed algorithm outperforms other existing approaches. Simulation results demonstrate that the proposed algorithm is successful in detecting anomaly intrusion detection.


title: Creating language resources for under-resourced languages: methodologies, and experiments with Arabic

abstract: Language resources are important for those working on computational methods to analyse and study languages. These resources are needed to help advancing the research in fields such as natural language processing, machine learning, information retrieval and text analysis in general. We describe the creation of useful resources for languages that currently lack them, taking resources for Arabic summarisation as a case study. We illustrate three different paradigms for creating language resources, namely: (1) using crowdsourcing to produce a small resource rapidly and relatively cheaply; (2) translating an existing gold-standard dataset, which is relatively easy but potentially of lower quality; and (3) using manual effort with appropriately skilled human participants to create a resource that is more expensive but of high quality. The last of these was used as a test collection for TAC-2011. An evaluation of the resources is also presented.


title: Bayesian Abductive Logic Programs: A Probabilistic Logic for Abductive Reasoning

abstract: In this proposal, we introduce Bayesian Abductive Logic Programs (BALP), a probabilistic logic that adapts Bayesian Logic Programs (BLPs) for abductive reasoning. Like BLPs, BALPs also combine first-order logic and Bayes nets. However, unlike BLPs, which use deduction to construct Bayes nets, BALPs employ logical abduction. As a result, BALPs are more suited for problems like plan/activity recognition that require abductive reasoning. In order to demonstrate the efficacy of BALPs, we apply it to two abductive reasoning tasks - plan recognition and natural language understanding.


title: D.: The synergy between non-blocking synchronization and operating system structure

abstract: Non-blocking synchronization has significant advantages over blocking synchronization: however, it has not been used to a significant degree in practice. We designed and implemented a multiprocessor operating system kernel and run-time library for high-performance, reliability and modularity. We used nonblocking synchronization, not because it was an objective in itself, but because it became the approach of choice. It was an attractive approach because of the synergy between other structuring techniques we used to achieve our primary goals and the benefits of non-blocking synchronization. This paper describes this synergy: the structuring techniques we used which facilitated non-blocking synchronization and our experience with this implementation.