title: A Telecom Perspective on the Internet of Drones: From LTE-Advanced to 5G

abstract: Drones are driving numerous and evolving use cases, and creating transformative socio-economic benefits. Drone operation needs wireless connectivity for communication between drones and ground control systems, among drones, and between drones and air traffic management systems. Mobile networks are well positioned to identify, track, and control the growing fleet of drones. The wide-area, quality, and secure connectivity provided by mobile networks can enhance the efficiency and effectiveness of drone operations beyond visual line-of-sight range. In this article, we elaborate how the drone ecosystem can benefit from mobile technologies, summarize key capabilities required by drone applications, and analyze the service requirements on mobile networks. We present field trial results collected in LTE-Advanced networks to gain insights into the capabilities of the current 4G+ networks for connected drones and share our vision on how 5G networks can further support diversified drone applications.


title: Genes that mediate breast cancer metastasis to the brain

abstract: The molecular basis for breast cancer metastasis to the brain is largely unknown. Brain relapse typically occurs years after the removal of a breast tumour, suggesting that disseminated cancer cells must acquire specialized functions to take over this organ. Here we show that breast cancer metastasis to the brain involves mediators of extravasation through non-fenestrated capillaries, complemented by specific enhancers of blood-brain barrier crossing and brain colonization. We isolated cells that preferentially infiltrate the brain from patients with advanced disease. Gene expression analysis of these cells and of clinical samples, coupled with functional analysis, identified the cyclooxygenase COX2 (also known as PTGS2), the epidermal growth factor receptor (EGFR) ligand HBEGF, and the alpha2,6-sialyltransferase ST6GALNAC5 as mediators of cancer cell passage through the blood-brain barrier. EGFR ligands and COX2 were previously linked to breast cancer infiltration of the lungs, but not the bones or liver, suggesting a sharing of these mediators in cerebral and pulmonary metastases. In contrast, ST6GALNAC5 specifically mediates brain metastasis. Normally restricted to the brain, the expression of ST6GALNAC5 in breast cancer cells enhances their adhesion to brain endothelial cells and their passage through the blood-brain barrier. This co-option of a brain sialyltransferase highlights the role of cell-surface glycosylation in organ-specific metastatic interactions.


title: Prediction of Good Abandonment Behavior in Mobile Search

abstract: Good abandonment behavior means that in a query, the user can obtain the required information directly through the search results without clicking any linked page or reformulating the search query, which is common in mobile search at present. In this paper, a good abandonment prediction model in mobile search was constructed from 5 groups of features: session features, query features, SERP features, mobile touch interaction features and visual attention features. The visual attention features are introduced into good abandonment prediction for the first time and proved to be able to improve the prediction accuracy.


title: Temporal pattern discovery in longitudinal electronic patient records

abstract: Large collections of electronic patient records provide a vast but still underutilised source of information on the real world use of medicines. They are maintained primarily for the purpose of patient administration, but contain a broad range of clinical information highly relevant for data analysis. While they are a standard resource for epidemiological confirmatory studies, their use in the context of exploratory data analysis is still limited. In this paper, we present a framework for open-ended pattern discovery in large patient records repositories. At the core is a graphical statistical approach to summarising and visualising the temporal association between the prescription of a drug and the occurrence of a medical event. The graphical overview contrasts the observed and expected number of occurrences of the medical event in different time periods both before and after the prescription of interest. In order to effectively screen for important temporal relationships, we introduce a new measure of temporal association, which contrasts the observed-to-expected ratio in a time period immediately after the prescription to the observed-to-expected ratio in a control period 2 years earlier. An important feature of both the observed-to-expected graph and the measure of temporal association is a statistical shrinkage towards the null hypothesis of no association, which provides protection against highlighting spurious associations. We demonstrate the usefulness of the proposed pattern discovery methodology by a set of examples from a collection of over two million patient records in the United Kingdom. The identified patterns include temporal relationships between drug prescriptions and medical events suggestive of persistent and transient risks of adverse events, possible beneficial effects of drugs, periodic co-occurrence, and systematic tendencies of patients to switch from one medication to another.


title: Exploration of computational methods for classification of movement intention during human voluntary movement from single trial EEG

abstract: Abstract Objective To explore effective combinations of computational methods for the prediction of movement intention preceding the production of self-paced right and left hand movements from single trial scalp electroencephalogram (EEG). Methods Twelve naive subjects performed self-paced movements consisting of three key strokes with either hand. EEG was recorded from 128 channels. The exploration was performed offline on single trial EEG data. We proposed that a successful computational procedure for classification would consist of spatial filtering, temporal filtering, feature selection, and pattern classification. A systematic investigation was performed with combinations of spatial filtering using principal component analysis (PCA), independent component analysis (ICA), common spatial patterns analysis (CSP), and surface Laplacian derivation (SLD); temporal filtering using power spectral density estimation (PSD) and discrete wavelet transform (DWT); pattern classification using linear Mahalanobis distance classifier (LMD), quadratic Mahalanobis distance classifier (QMD), Bayesian classifier (BSC), multi-layer perceptron neural network (MLP), probabilistic neural network (PNN), and support vector machine (SVM). A robust multivariate feature selection strategy using a genetic algorithm was employed. Results The combinations of spatial filtering using ICA and SLD, temporal filtering using PSD and DWT, and classification methods using LMD, QMD, BSC and SVM provided higher performance than those of other combinations. Utilizing one of the better combinations of ICA, PSD and SVM, the discrimination accuracy was as high as 75%. Further feature analysis showed that beta band EEG activity of the channels over right sensorimotor cortex was most appropriate for discrimination of right and left hand movement intention. Conclusions Effective combinations of computational methods provide possible classification of human movement intention from single trial EEG. Such a method could be the basis for a potential brain–computer interface based on human natural movement, which might reduce the requirement of long-term training. Significance Effective combinations of computational methods can classify human movement intention from single trial EEG with reasonable accuracy.


Structured Generation of Technical Reading Lists.


title: Does knowledge management really matter? Linking knowledge management practices, competitiveness and economic performance

abstract: Purpose – While nowadays an extensive literature promoting knowledge management (KM) exists, there is a worrying shortage of empirical studies demonstrating an actual connection between KM activities and organizational outcomes. To bridge this gap, this paper aims to examine the link between KM practices, firm competitiveness and economic performance.Design/methodology/approach – This paper proposes a framework of KM practices consisting of human resource management (HRM) and information communication technology (ICT). These both are hypothesized to impact competitiveness and economic performance of the firm. Hypotheses are then tested with structural equation modeling by using a survey dataset of 234 companies.Findings – The results show that HRM and ICT practices for managing knowledge are quite strongly correlated and have a statistically significant influence on both financial performance and competitiveness of the firm. The findings also indicate that ICT practices improve financial performance only ...


title: The BOSS is concerned with time series classification in the presence of noise

abstract: Similarity search is one of the most important and probably best studied methods for data mining. In the context of time series analysis it reaches its limits when it comes to mining raw datasets. The raw time series data may be recorded at variable lengths, be noisy, or are composed of repetitive substructures. These build a foundation for state of the art search algorithms. However, noise has been paid surprisingly little attention to and is assumed to be filtered as part of a preprocessing step carried out by a human. Our Bag-of-SFA-Symbols (BOSS) model combines the extraction of substructures with the tolerance to extraneous and erroneous data using a noise reducing representation of the time series. We show that our BOSS ensemble classifier improves the best published classification accuracies in diverse application areas and on the official UCR classification benchmark datasets by a large margin.


Competencies of Exceptional and Non-Exceptional Software Engineers


title: A cooperative approach for handshake detection based on body sensor networks

abstract: The handshake gesture is an important part of the social etiquette in many cultures. It lies at the core of many human interactions, either in formal or informal settings: exchanging greetings, offering congratulations, and finalizing a deal are all activities that typically either start or finish with a handshake. The automated detection of a handshake can enable wide range of pervasive computing scanarios; in particular, different types of information can be exchanged and processed among the handshaking persons, depending on the physical/logical contexts where they are located and on their mutual acquaintance. This paper proposes a novel handshake detection system based on body sensor networks consisting of a resource-constrained wrist-wearable sensor node and a more capable base station. The system uses an effective collaboration technique among body sensor networks of the handshaking persons which minimizes errors associated with the application of classification algorithms and improves the overall accuracy in terms of the number of false positives and false negatives.


title: Synthetic-aperture radar processing using fast factorized back-projection

abstract: Exact synthetic aperture radar (SAR) inversion for a linear aperture may be obtained using fast transform techniques. Alternatively, back-projection integration in time domain can also be used. This technique has the benefit of handling a general aperture geometry. In the past, however, back-projection has seldom been used due to heavy computational burden. We show that the back-projection integral can be recursively partitioned and an effective algorithm constructed based on aperture factorization. By representing images in local polar coordinates it is shown that the number of operations is drastically reduced and can be made to approach that of fast transform algorithms. The algorithm is applied to data from the airborne ultra-wideband CARABAS SAR and shown to give a reduction in processing time of two to three orders of magnitude.


title: Dynablock: Dynamic 3D Printing for Instant and Reconstructable Shape Formation

abstract: This paper introduces Dynamic 3D Printing, a fast and reconstructable shape formation system. Dynamic 3D Printing can assemble an arbitrary three-dimensional shape from a large number of small physical elements. Also, it can disassemble the shape back to elements and reconstruct a new shape. Dynamic 3D Printing combines the capabilities of 3D printers and shape displays: Like conventional 3D printing, it can generate arbitrary and graspable three-dimensional shapes, while allowing shapes to be rapidly formed and reformed as in a shape display. To demonstrate the idea, we describe the design and implementation of Dynablock, a working prototype of a dynamic 3D printer. Dynablock can form a three-dimensional shape in seconds by assembling 3,000 9 mm blocks, leveraging a 24 x 16 pin-based shape display as a parallel assembler. Dynamic 3D printing is a step toward achieving our long-term vision in which 3D printing becomes an interactive medium, rather than the means for fabrication that it is today. In this paper, we explore possibilities for this vision by illustrating application scenarios that are difficult to achieve with conventional 3D printing or shape display systems.


title: A Two Stage Data Compression and Decompression Technique for Point Cloud Data

abstract: The domain of Augmented Reality (AR) and Mixed Reality (MR) that involves capturing and transmitting real time point cloud data, are areas of research for over a decade. However, there are lot of challenges in transmitting huge volume of data over the network because of network bandwidth constraint. This paper proposes a compression strategy for 3D positional data (derived from depth frame) to reduce large volume of captured point cloud data before sending over network to enhance data transmission efficiency, and a decompression technique to be applied at the receiving end to recover the original data. The proposed strategy is well suited for AR/MR interactive applications such as teleconferencing, tele-health monitoring, remote machine monitoring etc. where human subject(s) are the primary data for transmission. In the proposed two stage strategy (hybrid approach), the 3D positional data is first optimized in an in-memory buffer (Buffer Differential technique) without loss of quality. In the second stage, a standard compression algorithm is applied to the output of the first stage for further improving the compression ratio. For this work, we have chosen the Zstandard compression algorithm. The original data is then recovered by applying decompression techniques in the reverse order. A significant improvement has been achieved for this particular class of applications.


title: A datacube approach to agro-geoinformatics

abstract: Agricultural maps produced from earth observation data are useful at several stages in the agricultural value chain and allow farmers to make rational and comprehensive decisions when planning, planting and growing new crops. Towards this direction, a platform for the online, real-time analysis of big earth observation data for precision agriculture applications has been designed and implemented. This platform currently provides services like vegetation detection, calculation of standard vegetation indices, canopy greenness estimation, land surface temperature estimation and time-series analytics. The service interface utilizes the OGC Web Coverage Processing Service (WCPS) standard which establishes analytics on spatio-temporal datacubes. Implementation of the service is based on the open-source scalable array engine, rasdaman.


title: Bedside Ultrasound for Tracheal Tube Verification in Pediatric Emergency Department and ICU Patients: A Systematic Review.

abstract: Objectives: Improper placement of the tracheal tube during intubation can lead to dangerous complications, and bedside ultrasound has been proposed as a method of quickly and accurately identifying tube placement. Recent studies in adults have found it to be accurate, but its applicability in pediatric patients is unclear. This systematic review aims to describe the current available data on the accuracy and feasibility of bedside ultrasound for tracheal tube placement in children. Data Sources: OVID MEDLINE and EMBASE. Study Selection: Available articles on bedside neck or lung/diaphragm ultrasound for confirmation of tracheal tube placement in children through December 2015. Data Extraction: Two reviewers screened studies for eligibility and abstracted data independently. The quality of selected articles was evaluated using Quality Assessment of Diagnostic Accuracy Studies statement. Data Synthesis: A total of nine articles were identified: one study using neck ultrasound, two using lung/diaphragmatic ultrasound, one with both, and five studies looking at direct visualization of the tracheal tube tip met our inclusion criteria. There were 81 intubations evaluated using neck ultrasound, 214 intubations evaluated using diaphragmatic or pleural sliding, and 165 intubations evaluated for feasibility of bedside ultrasound in visualizing tracheal tube tip placement. The sensitivities of transtracheal ultrasound for intubation were overall high ranging from 0.92 to 1.00 with excellent specificities at 1.00. For lung ultrasound, the sensitivities for tracheal placement versus esophageal placement were high at 1.00, but only one study reported esophageal intubations and had a specificity of 1.00. When assessing the appropriate tracheal tube depth for tracheal intubations using lung ultrasound, the sensitivities ranged from 0.91 to 1.00 with specificities ranging from 0.5 to 1.0. Regarding feasibility of direct visualization of tracheal tube tip, visualization ranged from 83% to 100%. Conclusion: Bedside ultrasound has been described to be feasible in determining tracheal tube placement in several small single center studies and could be a useful adjunct tool in confirming tracheal tube placement in critically ill pediatric patients, but further studies are needed to assess its accuracy in a randomized multicenter setting.


title: IVQAD 2017: An immersive video quality assessment database

abstract: This paper presents a new database, Immersive Video Quality Assessment Database 2017 (IVQAD 2017), intended for immersive video quality assessment in virtual reality environment. Video quality assessment (VQA) plays an important role in video research fields. Nowadays virtual reality technology have been widely used and playing videos in virtual reality visual system is becoming more and more popular. However, existing research in VQA fields mainly focus on traditional videos. In this paper, we build the IVQAD which contains 10 raw videos and 150 distorted videos. Bit rate, frame rate and resolution were considered as quality degradation factors. All the videos were encoded with MPEG-4. Subjects were asked to assess the video under virtual reality environment and mean opinion score (MOS) was derived by computing. Using IVQAD 2017, researchers can explore the influence of resolution, video compression and video packet loss on immersive videos' quality.


How Risky Do I Invest : The Role of Risk Attitudes , Risk Perceptions and Overconfidence March 6 , 2010


title: Reservoir Computing as a Model for In-Materio Computing

abstract: Research in substrate-based computing has shown that materials contain rich properties that can be exploited to solve computational problems. One such technique known as Evolution-in-materio uses evolutionary algorithms to manipulate material substrates for computation. However, in general, modelling the computational processes occurring in such systems is a difficult task and understanding what part of the embodied system is doing the computation is still fairly ill-defined. This chapter discusses the prospects of using Reservoir Computing as a model for in-materio computing, introducing new training techniques (taken from Reservoir Computing) that could overcome training difficulties found in the current Evolution-in-Materio technique.


title: Sentence Level Discourse Parsing Using Syntactic And Lexical Information

abstract: We introduce two probabilistic models that can be used to identify elementary discourse units and build sentence-level discourse parse trees. The models use syntactic and lexical features. A discourse parsing algorithm that implements these models derives discourse parse trees with an error reduction of 18.8% over a state-of-the-art decision-based discourse parser. A set of empirical evaluations shows that our discourse parsing model is sophisticated enough to yield discourse trees at an accuracy level that matches near-human levels of performance.


title: Super Class-AB OTAs With Adaptive Biasing and Dynamic Output Current Scaling

abstract: A new family of single-stage super Class-AB operational transconductance amplifiers (OTAs) suitable for low-voltage operation and low power consumption is presented. Three novel topologies are proposed featuring simplicity and compactness. They are based on the combination of adaptive biasing techniques for the differential input stage and nonlinear current mirrors for the active load that provide additional dynamic current boosting. The OTAs have been fabricated in a standard 0.5-mum CMOS process. Experimental results show a greatly improved slew rate by factors 30-60 and gain-bandwidth product by factors 11.5-17 when compared to a classical Class-A OTA. The circuits are operated at plusmn1-V supply voltage with only 10 muA of bias current


title: Deep Learning in Label-free Cell Classification

abstract: Label-free cell analysis is essential to personalized genomics, cancer diagnostics, and drug development as it avoids adverse effects of staining reagents on cellular viability and cell signaling. However, currently available label-free cell assays mostly rely only on a single feature and lack sufficient differentiation. Also, the sample size analyzed by these assays is limited due to their low throughput. Here, we integrate feature extraction and deep learning with high-throughput quantitative imaging enabled by photonic time stretch, achieving record high accuracy in label-free cell classification. Our system captures quantitative optical phase and intensity images and extracts multiple biophysical features of individual cells. These biophysical measurements form a hyperdimensional feature space in which supervised learning is performed for cell classification. We compare various learning algorithms including artificial neural network, support vector machine, logistic regression, and a novel deep learning pipeline, which adopts global optimization of receiver operating characteristics. As a validation of the enhanced sensitivity and specificity of our system, we show classification of white blood T-cells against colon cancer cells, as well as lipid accumulating algal strains for biofuel production. This system opens up a new path to data-driven phenotypic diagnosis and better understanding of the heterogeneous gene expressions in cells.


title: A High-Efficiency Ultra-Low-Power CMOS Rectifier for RF Energy Harvesting Applications

abstract: This paper presents a novel ultra-low power rectifier for RF energy harvester, designed and implemented in standard 130 nm CMOS technology. The proposed 915 MHz ISM band RF energy harvester is designed for wearable medical devices and internet of things (IoT) applications. An off-chip differential matching network passively boosts the low-level incoming AC signal generated by the antenna. Then, a novel self-compensated cross-coupled rectifier is designed to convert the AC signal into a DC output voltage. The rectifier is comprised of 10 stages and it uses both dynamic and static bias compensation to decrease the transistors forward voltage drop. The post-layout simulation results demonstrate a sensitivity of −30.5 dBm for 1 V output at a capacitive load which is lower than the current state-of-the-art. The peak end-to-end efficiency is 42.8 % at −16 dBm input power, delivering 2.32 V at 0.5 MΩ resistor load.


title: Pediatric Intestinal Failure: Nutrition, Pharmacologic, and Surgical Approaches

abstract: Intestinal failure (IF) is a condition where there is insufficient functional bowel to allow for adequate nutrient and fluid absorption to sustain adequate growth in children. Several etiologies can predispose to IF, including necrotizing enterocolitis, gastroschisis, and intestinal atresias. Intestinal rehabilitation can be seen as a 3-pronged strategy merging nutrition, pharmacologic, and surgical approaches to achieve the ultimate goal of enteral nutrition. Nutrition approaches should seek to facilitate transition from parenteral nutrition (PN) to enteral nutrition because prolonged use of PN is associated with severe morbidity and mortality. Enteral nutrition, on the other hand, promotes and enhances an adaptive response in the intestine. Medications used in the treatment of IF may help alleviate symptoms of diarrhea, bacterial overgrowth, and gastrointestinal dysmotility. Surgical procedures, such as longitudinal intestinal lengthening and tapering (LILT) or serial transverse enteroplasty (STEP), can...


title: GT-CFS: A Game Theoretic Coalition Formulation Strategy for Reducing Power Loss in Micro Grids

abstract: In this chapter, we move away from the smart-grid demand-side management topic to another important one of micro grids. Even though a significant progress has been achieved in the development of the micro grids, the power loss minimization between the micro grids and also between the macro station and an individual micro grid is still receiving much attention. Niyato and Wang [1] considered an algorithm aimed to optimize the transmission strategy in order to minimize the total cost including the power loss. The power losses minimization in the energy distribution networks has conventionally been investigated using a single and deterministic demand level. A novel algorithm to overcome this problem was designed in [2]. The “cost-aware smart micro grid network design” in [3] allows economic power transactions within the smart grid with manageable power losses. Meliopoulos et al. [4] also discussed power loss minimization issues by proposing a coordinated control scheme at real-time with the inclusion of distributed generation resources (micro grids) with the existing grid. A novel load management solution to coordinate the charging of multiple PHEVs in a smart grid system was considered in [5] which also considered the power loss problem. An efficient optimal reconfiguration algorithm for power loss minimization was studied in [6]. Costabeber et al. [7] demonstrated that the power loss reduction is viable without central controllers by exploiting local measurement, communication, and control capability in the micro grids. Saad et al. [8] used cooperative game theory to formulate novel cooperative strategies between the micro grids of an energy distribution network aimed to reduce the power loss. Also, Costabeber et al. [9] and Tenti et al. [10] discussed cooperative operation of neighboring power processing units to reduce distribution losses. Corso et al. [11] addressed the daily schedule of distributed generators to minimize power loss in a micro grid connected to the main grid. A more detailed methodology to develop an autonomous micro grid for coping with power loss can be found in [12]. Also, a heuristic-based greedy algorithm was proposed in [13] to reduce the power loss. A review of technologies, methodologies, and operational approaches for improving the efficiency of power distribution systems like micro grids can be accessed in [14]. The “autonomous regional active network management system” project aimed to design a distribution network power loss management algorithm in [15]. The tie-set graph theory and its application to smart grid networks was introduced to minimize the power loss in [16]. Power loss of the energy distribution networks was also considered in [17–21].


title: Implementation of Back Propagation Algorithm ( of neural networks ) in VHDL

abstract: Department Of Electronics and Communication Engineering, Thapar Institute of Engineering & Technology, Patiala.


title: IoT-aided robotics applications : technological implications , target domains and open issues

abstract: The ongoing revolution of Internet of Things (IoT), together with the growing diffusion of robots in many activities of every day life, makes IoT-aided robotics applications a tangible reality of our upcoming future. Accordingly, new advanced services, based on the interplay between robots and "things", are being conceived in assisting humans. Nevertheless, the path to a mature development of IoT-aided robotics applications requires several pivotal issues to be solved, design methodologies to be consolidated, and strong architectural choices to be discussed. This paper discusses technological implications, open issues, and target applications in the IoT-aided robotics domain. In particular, the present contribution is four-folded. First, it provides a solid state of the art on the main topics related to IoT-aided robotics services: communication networks, robotics applications in distributed and pervasive environments, semantic-oriented approaches to consensus, and network security. Second, it highlights the most important research challenges to be faced. Third, it describes the technological tools available nowadays. Fourth, it summarizes lessons learned to foster a joint scientific investigation among research teams with complementary skills.


title: A Survey on Mobile Edge Computing: The Communication Perspective

abstract: Driven by the visions of Internet of Things and 5G communications, recent years have seen a paradigm shift in mobile computing, from the centralized Mobile Cloud Computing towards Mobile Edge Computing (MEC). The main feature of MEC is to push mobile computing, network control and storage to the network edges (e.g., base stations and access points) so as to enable computation-intensive and latency-critical applications at the resource-limited mobile devices. MEC promises dramatic reduction in latency and mobile energy consumption, tackling the key challenges for materializing 5G vision. The promised gains of MEC have motivated extensive efforts in both academia and industry on developing the technology. A main thrust of MEC research is to seamlessly merge the two disciplines of wireless communications and mobile computing, resulting in a wide-range of new designs ranging from techniques for computation offloading to network architectures. This paper provides a comprehensive survey of the state-of-the-art MEC research with a focus on joint radio-and-computational resource management. We also present a research outlook consisting of a set of promising directions for MEC research, including MEC system deployment, cache-enabled MEC, mobility management for MEC, green MEC, as well as privacy-aware MEC. Advancements in these directions will facilitate the transformation of MEC from theory to practice. Finally, we introduce recent standardization efforts on MEC as well as some typical MEC application scenarios.


title: Communication Optimization of Iterative Sparse Matrix-Vector Multiply on GPUs and FPGAs

abstract: Trading communication with redundant computation can increase the silicon efficiency of FPGAs and GPUs in accelerating communication-bound sparse iterative solvers. While $k$ iterations of the iterative solver can be unrolled to provide $O(k)$ reduction in communication cost, the extent of this unrolling depends on the underlying architecture, its memory model, and the growth in redundant computation. This paper presents a systematic procedure to select this algorithmic parameter $k$ , which provides communication-computation tradeoff on hardware accelerators like FPGA and GPU. We provide predictive models to understand this tradeoff and show how careful selection of $k$ can lead to performance improvement that otherwise demands significant increase in memory bandwidth. On an Nvidia C2050 GPU, we demonstrate a 1.9 $\times$ -42.6 $\times$ speedup over standard iterative solvers for a range of benchmarks and that this speedup is limited by the growth in redundant computation. In contrast, for FPGAs, we present an architecture-aware algorithm that limits off-chip communication but allows communication between the processing cores. This reduces redundant computation and allows large $k$ and hence higher speedups. Our approach for FPGA provides a 0.3 $\times$ -4.4 $\times$ speedup over same-generation GPU devices where $k$ is picked carefully for both architectures for a range of benchmarks.


title: The Importance of Skip Connections in Biomedical Image Segmentation

abstract: In this paper, we study the influence of both long and short skip connections on Fully Convolutional Networks (FCN) for biomedical image segmentation. In standard FCNs, only long skip connections are used to skip features from the contracting path to the expanding path in order to recover spatial information lost during downsampling. We extend FCNs by adding short skip connections, that are similar to the ones introduced in residual networks, in order to build very deep FCNs (of hundreds of layers). A review of the gradient flow confirms that for a very deep FCN it is beneficial to have both long and short skip connections. Finally, we show that a very deep FCN can achieve near-to-state-of-the-art results on the EM dataset without any further post-processing.


title: Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space.

abstract: Objective: In both diagnostic and research applications, the interpretation of MR images of the human brain is facilitated when different data sets can be compared by visual inspection of equivalent anatomical planes. Quantitative analysis with predefined atlas templates often requires the initial alignment of atlas and image planes. Unfortunately, the axial planes acquired during separate scanning sessions are often different in their relative position and orientation, and these slices are not coplanar with those in the atlas. We have developed a completely automatic method to register a given volumetric data set with Talairach stereotaxic coordinate system. Materials and Methods: The registration method is based on multiscale, three-dimensional (3D) cross-correlation with the Talairach stereotaxic space


title: A Survey of Commonsense Knowledge Acquisition

abstract: Collecting massive commonsense knowledge (CSK) for commonsense reasoning has been a long time standing challenge within artificial intelligence research. Numerous methods and systems for acquiring CSK have been developed to overcome the knowledge acquisition bottleneck. Although some specific commonsense reasoning tasks have been presented to allow researchers to measure and compare the performance of their CSK systems, we compare them at a higher level from the following aspects: CSK acquisition task (what CSK is acquired from where), technique used (how can CSK be acquired), and CSK evaluation methods (how to evaluate the acquired CSK). In this survey, we first present a categorization of CSK acquisition systems and the great challenges in the field. Then, we review and compare the CSK acquisition systems in detail. Finally, we conclude the current progress in this field and explore some promising future research issues.


title: Distributed inference for latent dirichlet allocation

abstract: We investigate the problem of learning a widely-used latent-variable model - the Latent Dirichlet Allocation (LDA) or "topic" model - using distributed computation, where each of P processors only sees 1/P of the total data set. We propose two distributed inference schemes that are motivated from different perspectives. The first scheme uses local Gibbs sampling on each processor with periodic updates—it is simple to implement and can be viewed as an approximation to a single processor implementation of Gibbs sampling. The second scheme relies on a hierarchical Bayesian extension of the standard LDA model to directly account for the fact that data are distributed across P processors—it has a theoretical guarantee of convergence but is more complex to implement than the approximate method. Using five real-world text corpora we show that distributed learning works very well for LDA models, i.e., perplexity and precision-recall scores for distributed learning are indistinguishable from those obtained with single-processor learning. Our extensive experimental results include large-scale distributed computation on 1000 virtual processors; and speedup experiments of learning topics in a 100-million word corpus using 16 processors.


title: Bandwidth Analysis of RF-DC Converters Under Multisine Excitation

abstract: The use of multisine signals to improve the efficiency of wireless power transfer (WPT) for low average received power was proposed recently. Several measurement-based studies illustrated the gain that can be achieved for different circuit or waveform instances, focusing on the impact of a time-varying amplitude on the rectifying efficiency. This paper first establishes a model enabling a thorough analysis of the multisine-based WPT system focusing on the bandwidth of the signal and the rectifier. This model enables a codesign of signal and rectifier for optimal WPT. The proposed model provides insight into the output voltage and power, as a function of the input waveform for different circuit models. By including the input matching and the clamper, our model is generic and can include a wide range of rectifiers with different voltage multiplication approaches. The key insight gained from our analysis is that there is a tradeoff between the frequency spacing of the tones of the multisine signal and the cut-off frequency of the low-pass RC filter, as a main property of the rectifier circuit. Our model predicts the measured power conversion efficiency and voltage with an error below 0.1 and 0.2 V, respectively.


title: Neural network ensembles

abstract: Several means for improving the performance and training of neural networks for classification are proposed. Crossvalidation is used as a tool for optimizing network parameters and architecture. It is shown that the remaining residual generalization error can be reduced by invoking ensembles of similar networks. >


title: MATLAB Meets LEGO Mindstorms—A Freshman Introduction Course Into Practical Engineering

abstract: In today's teaching and learning approaches for first-semester students, practical courses more and more often complement traditional theoretical lectures. This practical element allows an early insight into the real world of engineering, augments student motivation, and enables students to acquire soft skills early. This paper describes a new freshman introduction course into practical engineering, which has been established within the Bachelor of Science curriculum of Electrical Engineering and Information Technology of RWTH Aachen University, Germany. The course is organized as an eight-day, full-time block laboratory for over 300 freshman students, who were supervised by more than 60 tutors from 23 institutes of the Electrical Engineering Department. Based on a threefold learning concept comprising mathematical methods, MATLAB programming, and practical engineering, the students were required to transfer mathematical basics to algorithms in MATLAB in order to control LEGO Mindstorms robots. Toward this end, a new toolbox, called the ?RWTH-Mindstorms NXT Toolbox,? was developed, which enables the robots to be controlled remotely via MATLAB from a host computer. This paper describes how the laboratory course is organized and how it induces students to think as actual engineers would in solving real-world tasks with limited resources. Evaluation results show that the project improves the students' MATLAB programming skills, enhances motivation, and enables a peer learning process.


title: Towards Collaborative Data Analysis with Diverse Crowds – A Design Science Approach

abstract: The last years have witnessed an increasing shortage of data experts capable of analyzing the omnipresent data and producing meaningful insights. Furthermore, some data scientists mention data preprocessing to take up to 80% of the whole project time. This paper proposes a method for collaborative data analysis that involves a crowd without data analysis expertise. Orchestrated by an expert, the team of novices conducts data analysis through iterative refinement of results up to its successful completion. To evaluate the proposed method, we implemented a tool that supports collaborative data analysis for teams with mixed level of expertise. Our evaluation demonstrates that with proper guidance data analysis tasks, especially preprocessing, can be distributed and successfully accomplished by non-experts. Using the design science approach, iterative development also revealed some important features for the collaboration tool, such as support for dynamic development, code deliberation, and project journal. As such we pave the way for building tools that can leverage the crowd to address the shortage of data analysts.


title: UEcho: A Model of Uncertainty Management in Human Abductive Reasoning

abstract: This paper explores the uncertainty aspects of human abdu c- tive reasoning. Echo, a model of abduction based on the Th e- ory of Explanatory Coherence (Thagard, 1992), captures many aspects of human abductive reasoning, but fails to su f- ficiently manage the uncertainty in abduction. In particular, Echo does not handle belief acquisition and dynamic belief revision, two essential components of human abductive re a- soning. We propose a modified Echo model (UEcho), in which we add a learning mechanism for belief acquisition and a dynamic processing mechanism for belief revision. To evaluate the model, we report an empirical study in which base rate learning serves as a testbed for belief acquisition and the order effect serves as a testbed for belief r evision.


title: Encoding words into Cloud models from interval-valued data via fuzzy statistics and membership function fitting

abstract: When constructing the model of a word by collecting interval-valued data from a group of individuals, both interpersonal and intrapersonal uncertainties coexist. Similar to the interval type-2 fuzzy set (IT2 FS) used in the enhanced interval approach (EIA), the Cloud model characterized by only three parameters can manage both uncertainties. Thus, based on the Cloud model, this paper proposes a new representation model for a word from interval-valued data. In our proposed method, firstly, the collected data intervals are preprocessed to remove the bad ones. Secondly, the fuzzy statistical method is used to compute the histogram of the surviving intervals. Then, the generated histogram is fitted by a Gaussian curve function. Finally, the fitted results are mapped into the parameters of a Cloud model to obtain the parametric model for a word. Compared with eight or nine parameters needed by an IT2 FS, only three parameters are needed to represent a Cloud model. Therefore, we develop a much more parsimonious parametric model for a word based on the Cloud model. Generally a simpler representation model with less parameters usually means less computations and memory requirements in applications. Moreover, the comparison experiments with the recent EIA show that, our proposed method can not only obtain much thinner footprints of uncertainty (FOUs) but also capture sufficient uncertainties of words.


title: Robust White Matter Lesion Segmentation in FLAIR MRI

abstract: This paper discusses a white matter lesion (WML) segmentation scheme for fluid attenuation inversion recovery (FLAIR) MRI. The method computes the volume of lesions with subvoxel precision by accounting for the partial volume averaging (PVA) artifact. As WMLs are related to stroke and carotid disease, accurate volume measurements are most important. Manual volume computation is laborious, subjective, time consuming, and error prone. Automated methods are a nice alternative since they quantify WML volumes in an objective, efficient, and reliable manner. PVA is initially modeled with a localized edge strength measure since PVA resides in the boundaries between tissues. This map is computed in 3-D and is transformed to a global representation to increase robustness to noise. Significant edges correspond to PVA voxels, which are used to find the PVA fraction α (amount of each tissue present in mixture voxels). Results on simulated and real FLAIR images show high WML segmentation performance compared to ground truth (98.9% and 83% overlap, respectively), which outperforms other methods. Lesion load studies are included that automatically analyze WML volumes for each brain hemisphere separately. This technique does not require any distributional assumptions/parameters or training samples and is applied on a single MR modality, which is a major advantage compared to the traditional methods.


title: Room-Temperature Quantum Hall Effect in Graphene

abstract: The quantum Hall effect (QHE), one example of a quantum phenomenon that occurs on a truly macroscopic scale, has attracted intense interest since its discovery in 1980 and has helped elucidate many important aspects of quantum physics. It has also led to the establishment of a new metrological standard, the resistance quantum. Disappointingly, however, the QHE has been observed only at liquid-helium temperatures. We show that in graphene, in a single atomic layer of carbon, the QHE can be measured reliably even at room temperature, which makes possible QHE resistance standards becoming available to a broader community, outside a few national institutions.


title: Orienteering Algorithms for Generating Travel Itineraries

abstract: We study the problem of automatically and efficiently generating itineraries for users who are on vacation. We focus on the common case, wherein the trip duration is more than a single day. Previous efficient algorithms based on greedy heuristics suffer from two problems. First, the itineraries are often unbalanced, with excellent days visiting top attractions followed by days of exclusively lower-quality alternatives. Second, the trips often re-visit neighborhoods repeatedly in order to cover increasingly low-tier points of interest. Our primary technical contribution is an algorithm that addresses both these problems by maximizing the quality of the worst day. We give theoretical results showing that this algorithm»s competitive factor is within a factor two of the guarantee of the best available algorithm for a single day, across many variations of the problem. We also give detailed empirical evaluations using two distinct datasets:(a) anonymized Google historical visit data and(b) Foursquare public check-in data. We show first that the overall utility of our itineraries is almost identical to that of algorithms specifically designed to maximize total utility, while the utility of the worst day of our itineraries is roughly twice that obtained from other approaches. We then turn to evaluation based on human raters who score our itineraries only slightly below the itineraries created by human travel experts with deep knowledge of the area.


title: Implementing Deep Learning and Inferencing on Fog and Edge Computing Systems

abstract: The case for leveraging the computing resources of smart devices at the edge of network was conceptualized almost nine years back. Since then several concepts like Cloudlets, Fog etc. were instrumental in realizing computing at network edge, in physical proximity to the data sources for building more responsive, scalable and available Cloud based services. An essential component in smartphone applications, Internet of Things(IoT), field robotics etc. is the ability to analyze large amount of data with reasonable latency. Deep Learning is fast becoming a de facto choice for performing this data analytics owing to its ability to reduce human interventions in such workflows. Major deterrent of providing Deep Learning based Cloud services are Cloud outages and relatively high latency. In the current article the role of Fog Computing in addressing these issues is discussed, current state of standardization in Fog / Edge Computing is reviewed and the importance of optimum resource provisioning for running Edge-Analytics is highlighted. A detailed design and evaluation of the distribution and parallelization aspects of an Edge based Deep Learning framework using off-the-shelf components is presented along with strategies for optimum resource provisioning in constrained edge devices based on experiments with system resource (CPU, GPU & RAM) consumptions of a Deep Convolutional Neural Network.


title: Hypergeometric filters for optical flow and affine matching

abstract: This paper proposes new "hypergeometric" filters for the problem of image matching under the translational and affine model. This new set of filters has the following advantages: (1) High-precision registration of two images under the translational and affine model. Because the window effects are eliminated, we are able to achieve superb performance in both translational and affine matching. (2) Affine matching without exhaustive search or image warping. Due to the recursiveness of the filters in the spatial domain, we are able to analytically express the relation between filter outputs and the six affine parameters. This analytical relation enables us to directly compute these affine parameters. (3) Generality. The approach we demonstrate here can be applied to a broad class of matching problems, as long as the transformation between the two image patches can be mathematically represented in the frequency domain. >


title: Toward Global Corporate Citizenship: Reframing Foreign Direct Investment Law

abstract: This article argues that modern foreign direct investment law is a vestige of the colonial era during which early forms of transnational corporations emerged. Unlike international trade law and despite the dramatic developments of the twentieth century, foreign direct investment law remains largely unchanged. Due to a lack of political will, prior multilateral efforts to implement comprehensive foreign direct investment law reforms have been largely unsuccessful. However, in recent years, growing political will has emerged under the umbrella of Global Corporate Citizenship and related movements. This article posits that Global Corporate Citizenship is an opportunity to reframe and reform foreign direct investment law.


title: Two-legged hopping in autism spectrum disorders

abstract: Sensory processing deficits are common within autism spectrum disorders (ASD). Deficits have a heterogeneous dispersion across the spectrum and multimodal processing tasks are thought to magnify integration difficulties. Two-legged hopping in place in sync with an auditory cue (2.3, 3.0 Hz) was studied in a group of six individuals with expressive language impaired ASD (ELI-ASD) and an age-matched control group. Vertical ground reaction force data were collected and discrete Fourier transforms were utilized to determine dominant hopping cadence. Effective leg stiffness was computed through a mass-spring model representation. The ELI-ASD group were unsuccessful in matching their hopping cadence (2.21±0.30 hops•sec-1, 2.35±0.41 hops•sec-1) to either auditory cue with greater deviations at the 3.0 Hz cue. In contrast, the control group was able to match hopping cadence (2.35±0.06 hops•sec-1, 3.02±0.10 hops•sec-1) to either cue via an adjustment of effective leg stiffness. The ELI-ASD group demonstrated a varied response with an interquartile range (IQR) in excess of 0.5 hops•sec-1 as compared to the control group with an IQR < 0.03 hops•sec-1. Several sensorimotor mechanisms could explain the inability of participants with ELI-ASD to modulate motor output to match an external auditory cue. These results suggest that a multimodal gross motor task can (1) discriminate performance among a group of individuals with severe autism, and (2) could be a useful quantitative tool for evaluating motor performance in individuals with ASD individuals.


REAL-TIME ONSET DETECTION WITH RECURRENT NEURAL NETWORKS


title: Implementation of FPGA-based DVB-T2 modulator with multiple PLPs

abstract: Digital Video Broadcasting — Terrestrial for Second generation (DVB-T2) was released, for the purpose of supporting High Definition Television (HDTV) services. Although DVB-T2 leads to performance improvement by introducing state-of-the-art communication techniques, the introduction of such communication techniques has become problems in implementing the system. Multiple Physical Layer Pipe (PLP), initially introduced by DVB-T2, enable application of separate code rates and modulation orders to each PLP. However, due to non-sequentially generated Base Band (BB) frames, regardless of PLP indices, construction of T2 frames is complicated. In the present paper, we present an algorithm arranging randomly produced BB frames to build T2 frames, complying with the DVB-T2 standard. The proposed method is centered on allocating memory space to each PLP, based on configurable PLP information and storing BB frames in an external memory. In addition to such an algorithm, DVB-T2 modulator is implemented in a Field Programmable Gate Array (FPGA) device.


title: Ecological aspects and resource management of bamboo forests in

abstract: This thesis describes the extent and condition of the natural bamboo forests of Ethiopia and their actual and potential values for society and environment. It assesses in detail the growth and nutrient dynamics in a selected highland bamboo forest in Southwest Ethiopia (Masha) and suggests solutions for its sustainable management. A specific management aspect is addressed by means of the factors that affect Oxytenanthera abyssinica seedling emergence and subsequent growth. The following were the methodologies employed to obtain the information reported herein. (1) Literature review on the Ethiopian bamboo species in relation to the situation in a global context. (2) A field study on stand characteristics, biomass and nutrient distributions and their temporal variations. (3) A greenhouse experiment on lowland bamboo (O. abyssinica) seedling performance under varying seed orientation and sowing depth conditions. Ethiopia still has large natural bamboo forests in areas that until recently were remote and inaccessible. These resources are now being destroyed under population pressure mainly for agricultural land expansion. The traits of bamboos as the fastest growing multipurpose plants with an excellent capacity to convert solar energy into forest goods and services and, thus, their potential to redress the prevalent deforestation related problems of Ethiopia are highlighted. A workable strategy to harness their potential on sustainable basis is recommended. Proper management and expansion are considered the principal components of a sound strategy to develop the bamboo resource of Ethiopia. Management approaches are suggested to improve the productivity and product quality of the remnant bamboo forest of Masha, based on diagnostic information on its condition. Expansion of the lowland bamboo forest of Ethiopia is cheaper and more practical using seeds than other propagation materials. Some methods of seed sowing are prescribed to optimise seedling emergence and subsequent growth, as part of a management strategy for lowland bamboo forest recovery and expansion in Ethiopia using seeds.


title: Self-Compassion , Self-Esteem , and Well-Being

abstract: This article focuses on the construct of self-compassion and how it differs from self-esteem. First, it discusses the fact that while self-esteem is related to psychological well-being, the pursuit of high self-esteem can be problematic. Next it presents another way to feel good about oneself: selfcompassion. Self-compassion entails treating oneself with kindness, recognizing one’s shared humanity, and being mindful when considering negative aspects of oneself. Finally, this article suggests that self-compassion may offer similar mental health benefits as self-esteem, but with fewer downsides. Research is presented which shows that self-compassion provides greater emotional resilience and stability than self-esteem, but involves less self-evaluation, ego-defensiveness, and self-enhancement than self-esteem. Whereas self-esteem entails evaluating oneself positively and often involves the need to be special and above average, self-compassion does not entail selfevaluation or comparisons with others. Rather, it is a kind, connected, and clear-sighted way of relating to ourselves even in instances of failure, perceived inadequacy, and imperfection. Imagine that you’re an amateur singer-songwriter, and you invite your friends and family to see you perform at a nearby coffeehouse that showcases local talent. After the big night you ask everyone how they thought it went. ‘You were average’ is the reply. How would you feel in this scenario? Ashamed, humiliated, like you were a failure? In our incredibly competitive society, being average is unacceptable. We have to be special and above average to feel we have any worth at all. The problem, of course, is that it is impossible for everyone to be above average at the same time. This means that we tend to inflate our self-evaluations (Alicke & Sedikides, 2009) and put others down so that we can feel superior in comparison (Tesser, 1999) – all in the name of maintaining our selfesteem. For instance, research has shown that fully 90% of drivers think they’re more skilled than their road mates (Preston & Harris, 1965) – even people who’ve recently caused a car accident think they’re superior drivers! This paper will argue that striving for high self-esteem can sometimes be counterproductive, and that self-compassion may offer a healthier and more sustainable way to feel good about oneself. First, however, I will consider some of the problems with seeing self-esteem as the ultimate marker of psychological health.


title: StrokeBank: automating personalized chinese handwriting generation

abstract: Machine learning techniques have been successfully applied to Chinese character recognition; nonetheless, automatic generation of stylized Chinese handwriting remains a challenge. In this paper, we propose Stroke-Bank, a novel approach to automating personalized Chinese handwriting generation. We use a semi-supervised algorithm to construct a dictionary of component mappings from a small seeding set. Unlike previous work, our approach does not require human supervision in stroke extraction or knowledge of the structure of Chinese characters. This dictionary is used to generate handwriting that preserves stylistic variations, including cursiveness and spatial layout of strokes. We demonstrate the effectiveness of our model by a survey-based evaluation. The results show that our generated characters are nearly indistinguishable from ground truth handwritings.


title: Performance sensitivity of space-sharing processor scheduling in distributed-memory multicomputers

abstract: Processor scheduling in distributed-memory systems has received considerable attention in recent years. Several commercial distributed-memory systems use space-sharing processor scheduling. In space sharing, the set of processors in a system is partitioned and each partition is assigned for the exclusive use of a job. Space sharing policies can be divided into fixed, static, or dynamic categories. For distributed-memory systems, dynamic policies incur high overhead. Thus, static policies are considered as these policies provide better performance than the fixed policies. Several static policies have been proposed in the literature. In a previously proposed adaptive static policy, the partition size is a function of the number of queued jobs. This policy, however, tends to under-utilize the system resources. To improve the performance of this policy, we propose a new policy in which the partition size is a function of the total number of jobs in the system, as opposed to only the queued jobs. The results presented demonstrate that the new policy performs substantially better than the original policy for the various workload and system parameters. Another major contribution is the evaluation of the performance sensitivity to job structure, variances in inter-arrival times and job service times, and network topology.


title: The physiology of marriage: pathways to health

abstract: Marriage is the central relationship for most adults and has beneficial effects for health. At the same time, troubled marriages have negative health consequences. This review outlines the physiological pathways through which marital relationships influence health based on a stress/social support model. In addition, we review recent findings suggesting that unhappy marriages are associated with morbidity and mortality. We then turn to studies of marital interaction that include assessment of physiological pathways through which marital functioning influences health: the cardiovascular, endocrine, and immune systems. Across these studies, negative and hostile behaviors during marital conflict discussions are related to elevations in cardiovascular activity, alterations in hormones related to stress, and dysregulation of immune function. Using recent conceptualizations of the physiological impact of chronic stress, we illustrate how physiological changes associated with marital functioning in these studies have long-term implications for health outcomes. Finally, we discuss future implications of current research for understanding the relationships among marital functioning, physiology, and health.


title: Sentence level emotion tagging

abstract: This paper reports the mechanism of sentence level emotion identification based on emotion tagged word level constituents acquired by an automatic classifier applied on the SemEval 2007 Affect Sensing corpus. Basic set of six emotion types, namely, happy, sad, anger, disgust, fear and surprise have been selected for reliable and semi-automatic word level annotation. WordNet Affect lists have been preprocessed using SentiWordNet information for use in the semi-automatic word level emotion annotation process. The Conditional Random Field (CRF) based word level emotion classification has yielded an accuracy of 87.65% on a test set of 250 sentences. Sense based scoring mechanism has been applied for calculating scores of a sentence for each of the six emotion types. Probable sentence level emotion tags have been assigned based on the system produced ordered sense scores. Postprocessing strategies have been adopted for handling negative words in sentence level emotion tagging. The best two emotion tags, with the maximum sense scores, have been assigned to 250 test sentences and an accuracy of 67.2% has been achieved. The sentence level valence has been calculated based on the total sense score of the word level emotion tags. Accuracy, precision and recall are 60.47%, 67.95 and 65.11 respectively for valence identification on 250 test sentences.


title: Application of deep convolutional neural network for automated detection of myocardial infarction using ECG signals

abstract: The electrocardiogram (ECG) is a useful diagnostic tool to diagnose various cardiovascular diseases (CVDs) such as myocardial infarction (MI). The ECG records the heart's electrical activity and these signals are able to reflect the abnormal activity of the heart. However, it is challenging to visually interpret the ECG signals due to its small amplitude and duration. Therefore, we propose a novel approach to automatically detect the MI using ECG signals. In this study, we implemented a convolutional neural network (CNN) algorithm for the automated detection of a normal and MI ECG beats (with noise and without noise). We achieved an average accuracy of 93.53% and 95.22% using ECG beats with noise and without noise removal respectively. Further, no feature extraction or selection is performed in this work. Hence, our proposed algorithm can accurately detect the unknown ECG signals even with noise. So, this system can be introduced in clinical settings to aid the clinicians in the diagnosis of MI.


title: Frequency of False Positive Amphetamine Screens due to Bupropion Using the Syva Emit II Immunoassay

abstract: Bupropion is a commonly prescribed, monocyclic antidepressant often used as an aid for smoking cessation. Several case reports have described false positive amphetamine urine drug screens (UDS) associated with bupropion. We sought to determine whether false positive amphetamine UDS due to the use of bupropion would be a frequent occurrence. We conducted an IRB-approved, retrospective chart review of all emergency department patients who underwent UDS between 1 January 2006 and 31 July 2007. All urine samples were screened using Syva EMIT II Plus immunoassay reagents. All positive screens underwent confirmation by gas chromatography (GC). We reviewed the records of patients with positive amphetamine UDS. We documented prescription use of bupropion, other antidepressants, stimulants, antipsychotics, and anti-hypertensives. We recorded evidence of polysubstance abuse (PSA) as patients who had had a documented diagnosis or laboratory evidence of abuse of at least two substances (drugs or ethanol). Of 10,011 urine drug screens, 362 (3.6%) were positive for amphetamine. GC confirmed amphetamines in 234 (65%), but failed to confirm in 128 (35%). Among the 234 confirmed, records reflected use of bupropion in three (1.3%), other antidepressants in 38 (16%), antipsychotics in 17 (8%), and amphetamine in 50 (21%). Records indicated evidence of PSA in 55 (24%). Among the 128 which failed to confirm, records reflected prescription use of bupropion in 53 (41%). None whose drug screen failed to confirm had evidence of PSA. Therapeutic use of bupropion appears to be the most frequent cause of false positive urine drug screens for amphetamines in our population.


title: TECHNICAL SPOTLIGHT Computation of measures of effect size for neuroscience data sets

abstract: The overwhelming majority of research in the neurosciences employs P-values stemming from tests of statistical significance to decide on the presence or absence of an effect of some treatment variable. Although a continuous variable, the P-value is commonly used to reach a dichotomous decision about the presence of an effect around an arbitrary criterion of 0.05. This analysis strategy is widely used, but has been heavily criticized in the past decades. To counter frequent misinterpretations of P-values, it has been advocated to complement or replace P-values with measures of effect size (MES). Many psychological, biological and medical journals now recommend reporting appropriate MES. One hindrance to the more frequent use of MES may be their scarcity in standard statistical software packages. Also, the arguably most widespread data analysis software in neuroscience, matlab, does not provide MES beyond correlation and receiver-operating characteristic analysis. Here we review the most common criticisms of significance testing and provide several examples from neuroscience where use of MES conveys insights not amenable through the use of P-values alone. We introduce an open-access matlab toolbox providing a wide range of MES to complement the frequently used types of hypothesis tests, such as t-tests and analysis of variance. The accompanying documentation provides calculation formulae, intuitive explanations and example calculations for each measure. The toolbox described is usable without sophisticated statistical knowledge and should be useful to neuroscientists wishing to enhance their repertoire of statistical reporting.


title: Gaze vs. Mouse: A Fast and Accurate Gaze-Only Click Alternative

abstract: Eye gaze tracking is a promising input method which is gradually finding its way into the mainstream. An obvious question to arise is whether it can be used for point-and-click tasks, as an alternative for mouse or touch. Pointing with gaze is both fast and natural, although its accuracy is limited. There are still technical challenges with gaze tracking, as well as inherent physiological limitations. Furthermore, providing an alternative to clicking is challenging. We are considering use cases where input based purely on gaze is desired, and the click targets are discrete user interface (UI) elements which are too small to be reliably resolved by gaze alone, e.g., links in hypertext. We present Actigaze, a new gaze-only click alternative which is fast and accurate for this scenario. A clickable user interface element is selected by dwelling on one of a set of confirm buttons, based on two main design contributions: First, the confirm buttons stay on fixed positions with easily distinguishable visual identifiers such as colors, enabling procedural learning of the confirm button position. Secondly, UI elements are associated with confirm buttons through the visual identifiers in a way which minimizes the likelihood of inadvertent clicks. We evaluate two variants of the proposed click alternative, comparing them against the mouse and another gaze-only click alternative.


title: Tumor-associated copy number changes in the circulation of patients with prostate cancer identified through whole-genome sequencing

abstract: BackgroundPatients with prostate cancer may present with metastatic or recurrent disease despite initial curative treatment. The propensity of metastatic prostate cancer to spread to the bone has limited repeated sampling of tumor deposits. Hence, considerably less is understood about this lethal metastatic disease, as it is not commonly studied. Here we explored whole-genome sequencing of plasma DNA to scan the tumor genomes of these patients non-invasively.MethodsWe wanted to make whole-genome analysis from plasma DNA amenable to clinical routine applications and developed an approach based on a benchtop high-throughput platform, that is, Illuminas MiSeq instrument. We performed whole-genome sequencing from plasma at a shallow sequencing depth to establish a genome-wide copy number profile of the tumor at low costs within 2 days. In parallel, we sequenced a panel of 55 high-interest genes and 38 introns with frequent fusion breakpoints such as the TMPRSS2-ERG fusion with high coverage. After intensive testing of our approach with samples from 25 individuals without cancer we analyzed 13 plasma samples derived from five patients with castration resistant (CRPC) and four patients with castration sensitive prostate cancer (CSPC).ResultsThe genome-wide profiling in the plasma of our patients revealed multiple copy number aberrations including those previously reported in prostate tumors, such as losses in 8p and gains in 8q. High-level copy number gains in the AR locus were observed in patients with CRPC but not with CSPC disease. We identified the TMPRSS2-ERG rearrangement associated 3-Mbp deletion on chromosome 21 and found corresponding fusion plasma fragments in these cases. In an index case multiregional sequencing of the primary tumor identified different copy number changes in each sector, suggesting multifocal disease. Our plasma analyses of this index case, performed 13 years after resection of the primary tumor, revealed novel chromosomal rearrangements, which were stable in serial plasma analyses over a 9-month period, which is consistent with the presence of one metastatic clone.ConclusionsThe genomic landscape of prostate cancer can be established by non-invasive means from plasma DNA. Our approach provides specific genomic signatures within 2 days which may therefore serve as 'liquid biopsy'.


title: OWLMap: Fully Automatic Mapping of Ontology into Relational Database Schema

abstract: Semantic web is becoming a controversial issue in current research era. There must be an automated approach to transform ontology constructs into relational database so that it can be queried efficiently. The previous research work based on transformation of RDF/OWL concepts into relational database contains flaws in complete transformation of ontology constructs into relational database. Some researchers claim that their technique of transformation is entirely automated, however their approach of mapping is incomplete and miss essential OWL constructs. This paper presents a tool called OWLMap that is fully automatic and provides lossless approach for transformation of ontology into relational database format. Number of experiments have been performed for ontology to relational database transformation. Experiments show that proposed approach is fully automatic, effective and quick. Our OWLMap is based on an approach that is lossless as well as it does not loose data, data types and structure.


title: FML-based feature similarity assessment agent for Japanese/Taiwanese language learning

abstract: In this paper, we propose a fuzzy markup language (FML)-based feature similarity assessment agent with machine-learning ability to evaluate easy-to-learn degree of the Japanese and Taiwanese words. The involved domain experts define knowledge base (KB) and rule base (RB) of the proposed agent. The KB and RB are stored in the constructed ontology, including features of pronunciation similarity, writing similarity, and culture similarity. Next, we calculate feature similarity in pronunciation, writing, and culture for each word pair between Japanese and Taiwanese. Finally, we infer the easy-to-learn degree for one Japanese word and its corresponding Taiwanese one. Additionally, a genetic learning is also adopted to tune the KB and RB of the intelligent agent. The experimental results show that after-learning results perform better than before-learning ones.


title: Detecting Densely Distributed Graph Patterns for Fine-Grained Image Categorization

abstract: Fine-grained image categorization is a challenging task aiming at distinguishing objects belonging to the same basic-level category, e.g., leaf or mushroom. It is a useful technique that can be applied for species recognition, face verification, and so on. Most of the existing methods either have difficulties to detect discriminative object components automatically, or suffer from the limited amount of training data in each sub-category. To solve these problems, this paper proposes a new fine-grained image categorization model. The key is a dense graph mining algorithm that hierarchically localizes discriminative object parts in each image. More specifically, to mimic the human hierarchical perception mechanism, a superpixel pyramid is generated for each image. Thereby, graphlets from each layer are constructed to seamlessly capture object components. Intuitively, graphlets representative to each super-/sub-category is densely distributed in their feature space. Thus, a dense graph mining algorithm is developed to discover graphlets representative to each super-/sub-category. Finally, the discovered graphlets from pairwise images are integrated into an image kernel for fine-grained recognition. Theoretically, the learned kernel can generalize several state-of-the-art image kernels. Experiments on nine image sets demonstrate the advantage of our method. Moreover, the discovered graphlets from each sub-category accurately capture those tiny discriminative object components, e.g., bird claws, heads, and bodies.


title: The Evolution of Protégé: An Environment for Knowledge-Based Systems Development

abstract: The Protege project has come a long way since Mark Musen first built the Protege meta-tool for knowledge-based systems in 1987. The original tool was a small application, aimed at building knowledge-acquisition tools for a few specialized programs in medical planning. From this initial tool, the Protege system has evolved into a durable, extensible platform for knowledge-based systems development and research. The current version, Protege-2000, can be run on a variety of platforms, supports customized user-interface extensions, incorporates the Open Knowledge-Base Connectivity (OKBC) knowledge model, interacts with standard storage formats such as relational databases, XML, and RDF, and has been used by hundreds of individuals and research groups. In this paper, we follow the evolution of the Protege project through three distinct re-implementations. We describe our overall methodology, our design decisions, and the lessons we have learned over the duration of the project. We believe that our success is one of infrastructure: Protege is a flexible, well-supported, and robust development environment. Using Protege, developers and domain experts can easily build effective knowledge-based systems, and researchers can explore ideas in a variety of knowledge-based domains.


title: Autoregressive Quantile Networks for Generative Modeling

abstract: We introduce autoregressive implicit quantile networks (AIQN), a fundamentally different approach to generative modeling than those commonly used, that implicitly captures the distribution using quantile regression. AIQN is able to achieve superior perceptual quality and improvements in evaluation metrics, without incurring a loss of sample diversity. The method can be applied to many existing models and architectures. In this work we extend the PixelCNN model with AIQN and demonstrate results on CIFAR-10 and ImageNet using Inception score, FID, non-cherry-picked samples, and inpainting results. We consistently observe that AIQN yields a highly stable algorithm that improves perceptual quality while maintaining a highly diverse distribution.


title: Single image depth estimation from predicted semantic labels

abstract: We consider the problem of estimating the depth of each pixel in a scene from a single monocular image. Unlike traditional approaches [18, 19], which attempt to map from appearance features to depth directly, we first perform a semantic segmentation of the scene and use the semantic labels to guide the 3D reconstruction. This approach provides several advantages: By knowing the semantic class of a pixel or region, depth and geometry constraints can be easily enforced (e.g., “sky” is far away and “ground” is horizontal). In addition, depth can be more readily predicted by measuring the difference in appearance with respect to a given semantic class. For example, a tree will have more uniform appearance in the distance than it does close up. Finally, the incorporation of semantic features allows us to achieve state-of-the-art results with a significantly simpler model than previous works.


The associative basis of the creative process


Real-time dense appearance-based SLAM for RGB-D sensors


title: SVM-based Multiple Classifier System for recognition of wheat leaf diseases

abstract: This work presents a SVM-based Multiple Classifier System (MCS) for pattern recognition of wheat leaf diseases. The proposed system uses stacked generalization structure to combine the classification decisions obtained from three kinds of support vector machines (SVMs)-based classifiers. And three different feature sets including color features, texture features and shape features are used as training sets for three corresponding classifiers. Firstly, these different feature sets are classified by the classifiers in low-level of MCS to different corresponding mid-level categories, which are partly described by the symptom of crop diseases according to the knowledge of plant pathology. Then the mid-level features are extracted from these mid-categories produced from low-level classifiers. Finally high-level SVMs will be trained and correct errors made by the color, texture and shape SVMs to improve the performance of recognition. Compared with previous classifiers for wheat leaf diseases, the proposed approach can obtains better success rate of recognition.


title: How Do Developers React to RESTful API Evolution

abstract: With the rapid adoption of REpresentational State Transfer (REST), more software organizations expose their applications as RESTful web APIs and client code developers integrate RESTful APIs into their applications. When web APIs evolve, the client code developers have to update their applications to incorporate the API changes accordingly. However client code developers often encounter challenges during the migration and API providers have little knowledge of how client code developers react to the API changes. In this paper, we investigate the changes among subsequent versions of APIs and classify the identified changes to understand how the RESTful web APIs evolve. We study the on-line discussion from developers to the API changes by analyzing the StackOverflow questions. Through an empirical study, we identify 21 change types and 7 of them are new compared with existing studies. We find that a larger portion of RESTful web API elements are changed between versions compared with Java APIs and WSDL services. Moreover, our results show that adding new methods in the new version causes more questions and views from developers. However the deleted methods draw more relevant discussions. In general, our results provide valuable insights of RESTful web API evolution and help service providers understand how their consumers react to the API changes in order to improve the practice of evolving the service APIs.


title: A Meta-language for Enterprise Architecture Analysis

abstract: Enterprise Architecture (EA) management is a commonly accepted instrument to support strategic decision making. The objective of EA management is to improve business IT alignment by making the impact of planned changes explicit. The increasing interconnectivity of applications with other applications and with business processes however makes it difficult to get a complete view on change impacts and dependency structures. This information is nevertheless required to support decision makers. Current meta-languages proposed for the context of EA management provide only limited support for modelling qualitative and quantitative dependencies.


title: Modeling sensory-motor decisions in natural behavior

abstract: Although a standard reinforcement learning model can capture many aspects of reward-seeking behaviors, it may not be practical for modeling human natural behaviors because of the richness of dynamic environments and limitations in cognitive resources. We propose a modular reinforcement learning model that addresses these factors. Based on this model, a modular inverse reinforcement learning algorithm is developed to estimate both the rewards and discount factors from human behavioral data, which allows predictions of human navigation behaviors in virtual reality with high accuracy across different subjects and with different tasks. Complex human navigation trajectories in novel environments can be reproduced by an artificial agent that is based on the modular model. This model provides a strategy for estimating the subjective value of actions and how they influence sensory-motor decisions in natural behavior.


title: Automatic detection of incomplete requirements via symbolic analysis

abstract: The usefulness of a system specification depends in part on the completeness of the requirements. However, enumerating all necessary requirements is difficult, especially when requirements interact with an unpredictable environment. A specification built with an idealized environmental view is incomplete if it does not include requirements to handle non-idealized behavior. Often incomplete requirements are not detected until implementation, testing, or worse, after deployment. Even when performed during requirements analysis, detecting incomplete requirements is typically an error prone, tedious, and manual task. This paper introduces Ares, a design-time approach for detecting incomplete requirements decomposition using symbolic analysis of hierarchical requirements models. We illustrate our approach by applying Ares to a requirements model of an industry-based automotive adaptive cruise control system. Ares is able to automatically detect specific instances of incomplete requirements decompositions at design-time, many of which are subtle and would be difficult to detect, either manually or with testing.


title: Point of gaze estimation through corneal surface reflection in an active illumination environment

abstract: Eye gaze tracking (EGT) is a common problem with many applications in various fields. While recent methods have achieved improvements in accuracy and usability, current techniques still share several limitations. A major issue is the need for external calibration between the gaze camera system and the scene, which commonly restricts to static planar surfaces and leads to parallax errors. To overcome these issues, the paper proposes a novel scheme that uses the corneal imaging technique to directly analyze reflections from a scene illuminated with structured light. This comprises two major contributions: First, an analytic solution is developed for the forward projection problem to obtain the gaze reflection point (GRP), where light from the point of gaze (PoG) in the scene reflects at the corneal surface into an eye image. We also develop a method to compensate for the individual offset between the optical axis and true visual axis. Second, introducing active coded illumination enables robust and accurate matching at the GRP to obtain the PoG in a scene image, which is the first use of this technique in EGT and corneal reflection analysis. For this purpose, we designed a special high-power IR LED-array projector. Experimental evaluation with a prototype system shows that the proposed scheme achieves considerable accuracy and successfully supports depth-varying environments.


title: Interactivity attributes: a new way of thinking and describing interactivity

abstract: We propose a new perspective, seeing interactivity that is the immaterial part of an interactive artifact as something concretely describable and perceivable as we do with physical materials. In order to examine the validity of this proposal, we extracted a set of interactivity attributes to be used as a design language for thinking and describing interactivity in a new way, and conducted an online survey with 14 Flash prototypes representing pairs of values of 7 interactivity attributes we extracted. The result showed that all the interactivity attributes were significant, and participants experienced distinctive and meaningful emotional effects for different interactivity attributes.


title: A Unified Cognitive Architecture for Physical Agents

abstract: In this paper we describe ICARUS, a cognitive architecture for physical agents that integrates ideas from a number of traditions, but that has been especially influenced by results from cognitive psychology. We review ICARUS' commitments to memories and representations, then present its basic processes for performance and learning. We illustrate the architecture's behavior on a task from in-city driving that requires interaction among its various components. In addition, we discuss ICARUS' consistency with qualitative findings about the nature of human cognition. In closing, we consider the framework's relation to other cognitive architectures that have been proposed in the literature.


title: A Survey of Augmented Reality Technologies, Applications and Limitations

abstract: We are on the verge of ubiquitously adopting Augmented Reality (AR) technologies to enhance our percep- tion and help us see, hear, and feel our environments in new and enriched ways. AR will support us in fields such as education, maintenance, design and reconnaissance, to name but a few. This paper describes the field of AR, including a brief definition and development history, the enabling technologies and their characteristics. It surveys the state of the art by reviewing some recent applications of AR technology as well as some known limitations regarding human factors in the use of AR systems that developers will need to overcome.


title: Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

abstract: Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, problem domain knowledge can be built into the constraints of the model, typically at the expense of difficulties during inference. In contrast, deterministic deep neural networks are constructed in such a way that inference is straightforward, but their architectures are generic and it is unclear how to incorporate knowledge. This work aims to obtain the advantages of both approaches. To do so, we start with a model-based approach and an associated inference algorithm, and \emph{unfold} the inference iterations as layers in a deep network. Rather than optimizing the original model, we \emph{untie} the model parameters across layers, in order to create a more powerful network. The resulting architecture can be trained discriminatively to perform accurate inference within a fixed network size. We show how this framework allows us to interpret conventional networks as mean-field inference in Markov random fields, and to obtain new architectures by instead using belief propagation as the inference algorithm. We then show its application to a non-negative matrix factorization model that incorporates the problem-domain knowledge that sound sources are additive. Deep unfolding of this model yields a new kind of non-negative deep neural network, that can be trained using a multiplicative backpropagation-style update algorithm. We present speech enhancement experiments showing that our approach is competitive with conventional neural networks despite using far fewer parameters.


End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification


title: Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge

abstract: Current state-of-the-art human activity recognition is focused on the classification of temporally trimmed videos in which only one action occurs per frame. We propose a simple, yet effective, method for the temporal detection of activities in temporally untrimmed videos with the help of untrimmed classification. Firstly, our model predicts the top k labels for each untrimmed video by analysing global video-level features. Secondly, frame-level binary classification is combined with dynamic programming to generate the temporally trimmed activity proposals. Finally, each proposal is assigned a label based on the global label, and scored with the score of the temporal activity proposal and the global score. Ultimately, we show that untrimmed video classification models can be used as stepping stone for temporal detection.


title: Transparent Development of the WHO Rapid Advice Guidelines

abstract: Emerging health problems require rapid advice. We describe the development and pilot testing of a systematic, transparent approach used by the World Health Organization (WHO) to develop rapid advice guidelines in response to requests from member states confronted with uncertainty about the pharmacological management of avian influenza A (H5N1) virus infection. We first searched for systematic reviews of randomized trials of treatment and prevention of seasonal influenza and for non-trial evidence on H5N1 infection, including case reports and animal and in vitro studies. A panel of clinical experts, clinicians with experience in treating patients with H5N1, influenza researchers, and methodologists was convened for a two-day meeting. Panel members reviewed the evidence prior to the meeting and agreed on the process. It took one month to put together a team to prepare the evidence profiles (i.e., summaries of the evidence on important clinical and policy questions), and it took the team only five weeks to prepare and revise the evidence profiles and to prepare draft guidelines prior to the panel meeting. A draft manuscript for publication was prepared within 10 days following the panel meeting. Strengths of the process include its transparency and the short amount of time used to prepare these WHO guidelines. The process could be improved by shortening the time required to commission evidence profiles. Further development is needed to facilitate stakeholder involvement, and evaluate and ensure the guideline's usefulness.


title: Long-lasting conversion disorder and hospitalization in a young girl: importance of early recognition and intervention.

abstract: Psychosomatic symptoms are frequently observed in children in response to psychosocial stressors and may be a reason for referral. However, it may be difficult to identify psychosomatic symptoms in a non-psychiatric clinical setting, and it is not unusual in pediatric practice for patients to be admitted and/or investigated for psychosomatic symptoms. Here, we present a case of a 10-year-old girl who was admitted and investigated for a suddenly developing motor loss in her legs for more than three months. However, no medical causes were detected to explain her clinical picture and she was eventually diagnosed with conversion disorder. We discuss herein the importance of early recognition and intervention and clues to the diagnosis of conversion disorder in children in a non-psychiatric clinical setting.


title: Modelling non-functional requirements of business processes

abstract: This paper presents an approach to the identification and inclusion of 'non-functional' aspects of a business process in modelling for business improvement. The notion of non-functional requirements (NFRs) is borrowed from software engineering, and a method developed in that field for linking NFRs to conceptual models is adapted and applied to business process modelling. Translated into this domain, NFRs are equated with the general or overall quality attributes of a business process, which, though essential aspects of any effective process, are not well captured in a functionally oriented process model. Using an example of a healthcare process (cancer registration in Jordan). We show how an analysis and evaluation of NFRs can be applied to a process model developed with role activity diagramming (RAD) to operationalise desirable quality features more explicitly in the model. This gives a useful extension to RAD and similar modelling methods, as well as providing a basis for business improvement.


title: Scale-hierarchical 3D object recognition in cluttered scenes

abstract: 3D object recognition in scenes with occlusion and clutter is a difficult task. In this paper, we introduce a method that exploits the geometric scale-variability to aid in this task. Our key insight is to leverage the rich discriminative information provided by the scale variation of local geometric structures to constrain the massive search space of potential correspondences between model and scene points. In particular, we exploit the geometric scale variability in the form of the intrinsic geometric scale of each computed feature, the hierarchy induced within the set of these intrinsic geometric scales, and the discriminative power of the local scale-dependent/invariant 3D shape descriptors. The method exploits the added information in a hierarchical coarse-to-fine manner that lets it cull the space of all potential correspondences effectively. We experimentally evaluate the accuracy of our method on an extensive set of real scenes with varying amounts of partial occlusion and achieve recognition rates higher than the state-of-the-art. Furthermore, for the first time we systematically demonstrate the method's ability to accurately localize objects despite changes in their global scales.


title: Internet Addiction Test (IAT): Which is the Best Factorial Solution?

abstract: Background: The Internet Addiction Test (IAT) by Kimberly Young is one of the most utilized diagnostic instruments for Internet addiction. Although many studies have documented psychometric properties of the IAT, consensus on the optimal overall structure of the instrument has yet to emerge since previous analyses yielded markedly different factor analytic results. Objective: The objective of this study was to evaluate the psychometric properties of the Italian version of the IAT, specifically testing the factor structure stability across cultures. Methods: In order to determine the dimensional structure underlying the questionnaire, both exploratory and confirmatory factor analyses were performed. The reliability of the questionnaire was computed by the Cronbach alpha coefficient. Results: Data analyses were conducted on a sample of 485 college students (32.3%, 157/485 males and 67.7%, 328/485 females) with a mean age of 24.05 years (SD 7.3, range 17-47). Results showed 176/485 (36.3%) participants with IAT score from 40 to 69, revealing excessive Internet use, and 11/485 (1.9%) participants with IAT score from 70 to 100, suggesting significant problems because of Internet use. The IAT Italian version showed good psychometric properties, in terms of internal consistency and factorial validity. Alpha values were satisfactory for both the one-factor solution (Cronbach alpha=.91), and the two-factor solution (Cronbach alpha=.88 and Cronbach alpha=.79). The one-factor solution comprised 20 items, explaining 36.18% of the variance. The two-factor solution, accounting for 42.15% of the variance, showed 11 items loading on Factor 1 (Emotional and Cognitive Preoccupation with the Internet) and 7 items on Factor 2 (Loss of Control and Interference with Daily Life). Goodness-of-fit indexes (NNFI: Non-Normed Fit Index; CFI: Comparative Fit Index; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Square Residual) from confirmatory factor analyses conducted on a random half subsample of participants (n=243) were satisfactory in both factorial solutions: two-factor model (χ 2 132 = 354.17, P 2 /df=2.68, NNFI=.99, CFI=.99, RMSEA=.02 [90% CI 0.000-0.038], and SRMR=.07), and one-factor model (χ 2 169 =483.79, P 2 /df=2.86, NNFI=.98, CFI=.99, RMSEA=.02 [90% CI 0.000-0.039], and SRMR=.07). Conclusions: Our study was aimed at determining the most parsimonious and veridical representation of the structure of Internet addiction as measured by the IAT. Based on our findings, support was provided for both single and two-factor models, with slightly strong support for the bidimensionality of the instrument. Given the inconsistency of the factor analytic literature of the IAT, researchers should exercise caution when using the instrument, dividing the scale into factors or subscales. Additional research examining the cross-cultural stability of factor solutions is still needed. [J Med Internet Res 2013;15(10):e225]