title: A New Calibration Method for Commercial RGB-D Sensors

abstract: Commercial RGB-D sensors such as Kinect and Structure Sensors have been widely used in the game industry, where geometric fidelity is not of utmost importance. For applications in which high quality 3D is required, i.e., 3D building models of centimeter‑level accuracy, accurate and reliable calibrations of these sensors are required. This paper presents a new model for calibrating the depth measurements of RGB-D sensors based on the structured light concept. Additionally, a new automatic method is proposed for the calibration of all RGB-D parameters, including internal calibration parameters for all cameras, the baseline between the infrared and RGB cameras, and the depth error model. When compared with traditional calibration methods, this new model shows a significant improvement in depth precision for both near and far ranges.


title: A Smart and Balanced Energy-Efficient Multihop Clustering Algorithm (Smart-BEEM) for MIMO IoT Systems in Future Networks †

abstract: Wireless Sensor Networks (WSNs) are typically composed of thousands of sensors powered by limited energy resources. Clustering techniques were introduced to prolong network longevity offering the promise of green computing. However, most existing work fails to consider the network coverage when evaluating the lifetime of a network. We believe that balancing the energy consumption in per unit area rather than on each single sensor can provide better-balanced power usage throughout the network. Our former work—Balanced Energy-Efficiency (BEE) and its Multihop version BEEM can not only extend the network longevity, but also maintain the network coverage. Following WSNs, Internet of Things (IoT) technology has been proposed with higher degree of diversities in terms of communication abilities and user scenarios, supporting a large range of real world applications. The IoT devices are embedded with multiple communication interfaces, normally referred as Multiple-In and Multiple-Out (MIMO) in 5G networks. The applications running on those devices can generate various types of data. Every interface has its own characteristics, which may be preferred and beneficial in some specific user scenarios. With MIMO becoming more available on the IoT devices, an advanced clustering solution for highly dynamic IoT systems is missing and also pressingly demanded in order to cater for differing user applications. In this paper, we present a smart clustering algorithm (Smart-BEEM) based on our former work BEE(M) to accomplish energy efficient and Quality of user Experience (QoE) supported communication in cluster based IoT networks. It is a user behaviour and context aware approach, aiming to facilitate IoT devices to choose beneficial communication interfaces and cluster headers for data transmission. Experimental results have proved that Smart-BEEM can further improve the performance of BEE and BEEM for coverage sensitive longevity.


title: High-performance integrated fan-out wafer level packaging (InFO-WLP): Technology and system integration

abstract: Integrated fan-out wafer-level packaging (InFO-WLP) technology with state-of-the-art inductors (quality factor of 42 and self-resonance frequency of 16 GHz) has been demonstrated for heterogeneous integration of digital and radio frequency (RF) systems. InFO-WLP promises superior form factor, pin count, and thermal performance to existing flip-chip ball grid array (FC-BGA) packages. In addition, InFO-WLP's high Q inductors can enhance electrical performance and lower power consumption in RF circuit applications.


title: Character-level Chinese Writer Identification using Path Signature Feature, DropStroke and Deep CNN

abstract: Most existing online writer-identification systems require that the text content is supplied in advance and rely on separately designed features and classifiers. The identifications are based on lines of text, entire paragraphs, or entire documents; however, these materials are not always available. In this paper, we introduce a path-signature feature to an end-to-end text-independent writer-identification system with a deep convolutional neural network (DCNN). Because deep models require a considerable amount of data to achieve good performance, we propose a data-augmentation method named DropStroke to enrich personal handwriting. Experiments were conducted on online handwritten Chinese characters from the CASIA-OLHWDB1.0 dataset, which consists of 3,866 classes from 420 writers. For each writer, we only used 200 samples for training and the remaining 3,666. The results reveal that the path-signature feature is useful for writer identification, and the proposed DropStroke technique enhances the generalization and significantly improves performance.


title: Combining lexicon and learning based approaches for concept-level sentiment analysis

abstract: In this paper, we present the anatomy of pSenti --- a concept-level sentiment analysis system that seamlessly integrates into opinion mining lexicon-based and learning-based approaches. Compared with pure lexicon-based systems, it achieves significantly higher accuracy in sentiment polarity classification as well as sentiment strength detection. Compared with pure learning-based systems, it offers more structured and readable results with aspect-oriented explanation and justification, while being less sensitive to the writing style of text. Our extensive experiments on two real-world datasets (CNET software reviews and IMDB movie reviews) confirm the superiority of the proposed hybrid approach over state-of-the-art systems like SentiStrength.


title: Stuff I've Seen: A System for Personal Information Retrieval and Re-Use

abstract: Most information retrieval technologies are designed to facilitate information discovery. However, much knowledge work involves finding and re-using previously seen information. We describe the design and evaluation of a system, called Stuff I've Seen (SIS), that facilitates information re-use. This is accomplished in two ways. First, the system provides a unified index of information that a person has seen, whether it was seen as email, web page, document, appointment, etc. Second, because the information has been seen before, rich contextual cues can be used in the search interface. The system has been used internally by more than 230 employees. We report on both qualitative and quantitative aspects of system use. Initial findings show that time and people are important retrieval cues. Users find information more easily using SIS, and use other search tools less frequently after installation.


title: ASF: AMD64 Extension for Lock-Free Data Structures and Transactional Memory

abstract: Advanced Synchronization Facility (ASF) is an AMD64 hardware extension for lock-free data structures and transactional memory. It provides a speculative region that atomically executes speculative accesses in the region. Five new instructions are added to demarcate the region, use speculative accesses selectively, and control the speculative hardware context. Programmers can use speculative regions to build flexible multi-word atomic primitives with no additional software support by relying on the minimum guarantee of available ASF hardware resources for lock-free programming. Transactional programs with high-level TM language constructs can either be compiled directly to the ASF code or be linked to software TM systems that use ASF to accelerate transactional execution. In this paper we develop an out-of-order hardware design to implement ASF on a future AMD processor and evaluate it with an in-house simulator. The experimental results show that the combined use of the L1 cache and the LS unit is very helpful for the performance robustness of ASF-based lock free data structures, and that the selective use of speculative accesses enables transactional programs to scale with limited ASF hardware resources.


title: TweeTime: A Minimally Supervised Method for Recognizing and Normalizing Time Expressions in Twitter

abstract: We describe TweeTIME, a temporal tagger for recognizing and normalizing time expressions in Twitter. Most previous work in social media analysis has to rely on temporal resolvers that are designed for well-edited text, and therefore suffer from the reduced performance due to domain mismatch. We present a minimally supervised method that learns from large quantities of unlabeled data and requires no hand-engineered rules or hand-annotated training corpora. TweeTIME achieves 0.68 F1 score on the end-to-end task of resolving date expressions, outperforming a broad range of state-of-the-art systems.


title: Deep Poincare Map For Robust Medical Image Segmentation

abstract: Precise segmentation is a prerequisite for an accurate quantification of the imaged objects. It is a very challenging task in many medical imaging applications due to relatively poor image quality and data scarcity. In this work, we present an innovative segmentation paradigm, named Deep Poincare Map (DPM), by coupling the dynamical system theory with a novel deep learning based approach. Firstly, we model the image segmentation process as a dynamical system, in which limit cycle models the boundary of the region of interest (ROI). Secondly, instead of segmenting the ROI directly, convolutional neural network is employed to predict the vector field of the dynamical system. Finally, the boundary of the ROI is identified using the Poincare map and the flow integration. We demonstrate that our segmentation model can be built using a very limited number of train- ing data. By cross-validation, we can achieve a mean Dice score of 94% compared to the manual delineation (ground truth) of the left ventricle ROI defined by clinical experts on a cardiac MRI dataset. Compared with other state-of-the-art methods, we can conclude that the proposed DPM method is adaptive, accurate and robust. It is straightforward to apply this method for other medical imaging applications.


title: Aspects of service-dominant logic and its implications for tourism management: Examples from the hotel industry

abstract: This paper introduces the concept of service-dominant logic as a research paradigm in marketing management. It does so in the context of tourism management’s need to engage with wider debates within the mainstream management literature. Moreover it demonstrates the importance of service-dominant logic in uncovering the role played by co-production and co-creation in the tourism industry. These ideas are developed in detail through a case study of the UK hotel industry that draws on new empirical research undertaken by the authors.


title: CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning

abstract: We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists. Our algorithm, CheXNet, is a 121-layer convolutional neural network trained on ChestX-ray14, currently the largest publicly available chest X-ray dataset, containing over 100,000 frontal-view X-ray images with 14 diseases. Four practicing academic radiologists annotate a test set, on which we compare the performance of CheXNet to that of radiologists. We find that CheXNet exceeds average radiologist performance on the F1 metric. We extend CheXNet to detect all 14 diseases in ChestX-ray14 and achieve state of the art results on all 14 diseases.


Metaheuristic Techniques


title: Business Intelligence in the Cloud

abstract: Business Intelligence (BI) deals with integrated approaches to management support. Currently, there are constraints to BI adoption and a new era of analytic data management for business intelligence these constraints are the integrated infrastructures that are subject to BI have become complex, costly, and inflexible, the effort required consolidating and cleansing enterprise data and Performance impact on existing infrastructure / inadequate IT infrastructure. So, in this paper Cloud computing will be used as a possible remedy for these issues. We will represent a new environment atmosphere for the business intelligence to make the ability to shorten BI implementation windows, reduced cost for BI programs compared with traditional on-premise BI software, Ability to add environments for testing, proof-of-concepts and upgrades, offer users the potential for faster deployments and increased flexibility. Also, Cloud computing enables organizations to analyze terabytes of data faster and more economically than ever before. Business intelligence (BI) in the cloud can be like a big puzzle. Users can jump in and put together small pieces of the puzzle but until the whole thing is complete the user will lack an overall view of the big picture. In this paper reading each section will fill in a piece of the puzzle.


Daubechies Complex Wavelet-Based Computer Vision Applications


title: Morphology of the Mechanosensory Lateral Line System in Elasmobranch Fishes: Ecological and Behavioral Considerations

abstract: The relationship between morphology of the mechanosensory lateral line system and behavior is essentially unknown in elasmobranch fishes. Gross anatomy and spatial distribution of different peripheral lateral line components were examined in several batoids (Raja eglanteria, Narcine brasiliensis, Gymnura micrura, and Dasyatis sabina) and a bonnethead shark, Sphyrna tiburo, and are interpreted to infer possible behavioral functions for superficial neuromasts, canals, and vesicles of Savi in these species. Narcine brasiliensis has canals on the dorsal surface with 1 pore per tubule branch, lacks a ventral canal system, and has 8–10 vesicles of Savi in bilateral rows on the dorsal rostrum and numerous vesicles (\(\overline {\text{x}} \) = 65 ± 6 SD per side) on the ventral rostrum. Raja eglanteria has superficial neuromasts in bilateral rows along the dorsal body midline and tail, a pair anterior to each endolymphatic pore, and a row of 5–6 between the infraorbital canal and eye. Raja eglanteria also has dorsal canals with 1 pore per tubule branch, pored and non-pored canals on the ventral surface, and lacks a ventral subpleural loop. Gymnura micrura has a pored dorsal canal system with extensive branch patterns, a pored ventral hyomandibular canal, and non-pored canal sections around the mouth. Dasyatis sabina has more canal pores on the dorsal body surface, but more canal neuromasts and greater diameter canals on the ventral surface. Sphyrna tiburo has primarily pored canals on both the dorsal and ventral surfaces of the head, as well as the posterior lateral line canal along the lateral body surface. Based upon these morphological data, pored canals on the dorsal body and tail of elasmobranchs are best positioned to detect water movements across the body surface generated by currents, predators, conspecifics, or distortions in the animal's flow field while swimming. In addition, pored canals on the ventral surface likely also detect water movements generated by prey. Superficial neuromasts are protected from stimulation caused by forward swimming motion by their position at the base of papillar grooves, and may detect water flow produced by currents, prey, predators, or conspecifics. Ventral non-pored canals and vesicles of Savi, which are found in benthic batoids, likely function as tactile or vibration receptors that encode displacements of the skin surface caused by prey, the substrate, or conspecifics. This mechanotactile mechanism is supported by the presence of compliant canal walls, neuromasts that are enclosed in wide diameter canals, and the presence of hair cells in neuromasts that are polarized both parallel to and nearly perpendicular to the canal axis in D. sabina. The mechanotactile, schooling, and mechanosensory parallel processing hypotheses are proposed as future directions to address the relationships between morphology and physiology of the mechanosensory lateral line system and behavior in elasmobranch fishes.


title: A comparison of volume-based and surface-based multi-voxel pattern analysis

abstract: Abstract For functional magnetic resonance imaging (fMRI), multi-voxel pattern analysis (MVPA) has been shown to be a sensitive method to detect areas that encode certain stimulus dimensions. By moving a searchlight through the volume of the brain, one can continuously map the information content about the experimental conditions of interest to the brain. Traditionally, the searchlight is defined as a volume sphere that does not take into account the anatomy of the cortical surface. Here we present a method that uses a cortical surface reconstruction to guide voxel selection for information mapping. This approach differs in two important aspects from a volume-based searchlight definition. First, it uses only voxels that are classified as grey matter based on an anatomical scan. Second, it uses a surface-based geodesic distance metric to define neighbourhoods of voxels, and does not select voxels across a sulcus. We study here the influence of these two factors onto classification accuracy and onto the spatial specificity of the resulting information map. In our example data set, participants pressed one of four fingers while undergoing fMRI. We used MVPA to identify regions in which local fMRI patterns can successfully discriminate which finger was moved. We show that surface-based information mapping is a more sensitive measure of local information content, and provides better spatial selectivity. This makes surface-based information mapping a useful technique for a data-driven analysis of information representation in the cerebral cortex.


title: Reusable Ontologies, Knowledge-Acquisition Tools, and Performance Systems: PROTÉGÉ-II Solutions to Sisyphus-2

abstract: Abstract This paper describes how we applied the PROTEGE-II architecture to build a knowledge-based system that configures elevators. The elevator-configuration task was solved originally with a system that employed the propose-and-revise problem-solving method (VT). A variant of this task, here named the Sisyphus-2 problem, is used by the knowledge-acquisition community for comparative studies. PROTEGE-II is a knowledge-engineering environment that focuses on the use of reusable ontologies and problem-solving methods to generate task-specific knowledge-acquisition tools and executable problem solvers. The main goal of this paper is to describe in detail how we used PROTEGE-II to model the elevator-configuration task. This description provides a starting point for comparison with other frameworks that use abstract problem-solving methods. Beginning with the textual description of the elevator-configuration task, we analysed the domain knowledge with respect to PROTEGE-II’s main goal: to build domain-specific knowledge-acquisition tools. We used PROTEGE-II’s suite of tools to construct a knowledge-based system, called ELVIS, that includes a reusable domain ontology, a knowledge-acquisition tool, and a propose-and-revise problem-solving method that is optimized to solve the elevator-configuration task. We entered domain-specific knowledge about elevator configuration into the knowledge base with the help of a task-specific knowledge-acquisition tool that PROTEGE-II generated from the ontologies. After we constructed mapping relations to connect the knowledge base with the method’s code, the final executable problem solver solved the test case provided with the Sisyphus-2 material. We have found that the development of ELVIS has afforded a valuable test case for evaluating PROTEGE-II’s suite of system-building tools. Only projects based on reasonably large problems, such as the Sisyphus-2 task, will allow us to improve the design of PROTEGE-II and its ability to produce reusable components.


title: High-$Q$ Tunable Microwave Cavity Resonators and Filters Using SOI-Based RF MEMS Tuners

abstract: This paper presents the modeling, design, fabrication, and measurement of microelectromechanical systems-enabled continuously tunable evanescent-mode electromagnetic cavity resonators and filters with very high unloaded quality factors (Qu). Integrated electrostatically actuated thin diaphragms are used, for the first time, for tuning the frequency of the resonators/filters. An example tunable resonator with 2.6:1 (5.0-1.9 GHz) tuning ratio and Qu of 300-650 is presented. A continuously tunable two-pole filter from 3.04 to 4.71 GHz with 0.7% bandwidth and insertion loss of 3.55-2.38 dB is also shown as a technology demonstrator. Mechanical stability measurements show that the tunable resonators/filters exhibit very low frequency drift (less than 0.5% for 3 h) under constant bias voltage. This paper significantly expands upon previously reported tunable resonators.


title: Evaluation of recommender systems: A new approach

abstract: It is difficult to deny that comparison between recommender systems requires a common way for evaluating them. Nevertheless, at present, they have been evaluated in many, often incompatible, ways. We affirm this problem is mainly due to the lack of a common framework for recommender systems, a framework general enough so that we may include the whole range of recommender systems to date, but specific enough so that we can obtain solid results. In this paper, we propose such a framework, attempting to extract the essential features of recommender systems. In this framework, the most essential feature is the objective of the recommender system. What is more, in this paper, recommender systems are viewed as applications with the following essential objective. Recommender systems must: (i) choose which (of the items) should be shown to the user, (ii) decide when and how the recommendations must be shown. Next, we will show that a new metric emerges naturally from this framework. Finally, we will conclude by comparing the properties of this new metric with the traditional ones. Among other things, we will show that we may evaluate the whole range of recommender systems with this single metric.


title: IONet: Learning to Cure the Curse of Drift in Inertial Odometry

abstract: Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications. However, low-cost inertial sensors, as commonly found in smartphones, are plagued by bias and noise, which leads to unbounded growth in error when accelerations are double integrated to obtain displacement. Small errors in state estimation propagate to make odometry virtually unusable in a matter of seconds. We propose to break the cycle of continuous integration, and instead segment inertial data into independent windows. The challenge becomes estimating the latent states of each window, such as velocity and orientation, as these are not directly observable from sensor data. We demonstrate how to formulate this as an optimization problem, and show how deep recurrent neural networks can yield highly accurate trajectories, outperforming state-of-the-art shallow techniques, on a wide range of tests and attachments. In particular, we demonstrate that IONet can generalize to estimate odometry for non-periodic motion, such as a shopping trolley or baby-stroller, an extremely challenging task for existing techniques.


title: Utilize OCR text to extract receipt data and classify receipts with common Machine Learning algorithms

abstract: This study investigated if it was feasible to use machine learning tools on OCR extracted text data to classify receipts and extract specific data points. Two OCR tools were evaluated, the first wa ...


title: Object detection algorithm for segregating similar coloured objects and database formation

abstract: Recent advancement in the processing power of onboard computers has encouraged engineers to impart visual feedbacks into various systems like mechatronics and internet of things. Applications ranging from CCTV surveillance to target detection and tracking using UAVs, there is a wide variety of demand on image processing techniques in terms of computational time and quality. In this scenario, developing generalised algorithms which gives a freedom to user in choosing the trade-off between quality and quick response is a challenging task. In this paper a novel boundary detection algorithm for segregating similar coloured objects in an image is presented, which accommodates a degree of freedom in choosing resolution of object detection to the detection time. This method uses colour based segmentation as preprocessing technique to reduce overall computational complexity. It is independent of the shape (convex or non-convex) and size of the object. Algorithm is developed using Open-CV libraries and implemented for separating similar coloured vehicles from an image of different vehicles on road. Implementation results showing different choices of boundary tightness and computation times are showcased.


title: An Ideal-Security Protocol for Order-Preserving Encoding

abstract: Order-preserving encryption - an encryption scheme where the sort order of ciphertexts matches the sort order of the corresponding plaintexts - allows databases and other applications to process queries involving order over encrypted data efficiently. The ideal security guarantee for order-preserving encryption put forth in the literature is for the ciphertexts to reveal no information about the plaintexts besides order. Even though more than a dozen schemes were proposed, all these schemes leak more information than order. This paper presents the first order-preserving scheme that achieves ideal security. Our main technique is mutable ciphertexts, meaning that over time, the ciphertexts for a small number of plaintext values change, and we prove that mutable ciphertexts are needed for ideal security. Our resulting protocol is interactive, with a small number of interactions. We implemented our scheme and evaluated it on microbenchmarks and in the context of an encrypted MySQL database application. We show that in addition to providing ideal security, our scheme achieves 1 - 2 orders of magnitude higher performance than the state-of-the-art order-preserving encryption scheme, which is less secure than our scheme.


title: Reducing the population's sodium intake: the UK Food Standards Agency's salt reduction programme

abstract: Objective To describe the UK Food Standards Agency's (FSA) salt reduction programme undertaken between 2003 and 2010 and to discuss its effectiveness. Design Relevant scientific papers, campaign materials and evaluations and consultation responses to the FSA's salt reduction programme were used. Setting Adult salt intakes, monitored using urinary Na data collected from UK-wide surveys, indicate a statistically significant reduction in the population's average salt intake from 9·5 g/d in 2000–2001 to 8·6 g/d in 2008, which is likely to have health benefits. Subjects Reducing salt intake will have an impact on blood pressure; an estimated 6 % of deaths from CHD in the UK can be avoided if the number of people with high blood pressure is reduced by 50 %. Results Salt levels in food, monitored using commercial label data and information collected through an industry self-reporting framework, indicated that substantial reductions of up to 70 % in some foods had been achieved. The FSA's consumer campaign evaluation showed increased awareness of the benefits of reducing salt intake on health, with 43 % of adults in 2009 claiming to have made a special effort to reduce salt in their diet compared with 34 % of adults in 2004, before the campaign commenced. Conclusions The UK's salt reduction programme successfully reduced the average salt intake of the population and increased consumers’ awareness. Significant challenges remain in achieving the population average salt intake of 6 g/d recommended by the UK's Scientific Advisory Committee on Nutrition. However, the UK has demonstrated the success of its programme and this approach is now being implemented elsewhere in the world.


title: How not to get scar(r)ed: pointers to the correct diagnosis in patients with suspected primary cicatricial alopecia

abstract: Primary cicatricial alopecias (PCAs) are a rare, but important, group of disorders that cause irreversible damage to hair follicles resulting in scarring and permanent hair loss. They may also signify an underlying systemic disease. Thus, it is of paramount importance that clinicians who manage patients with hair loss are able to diagnose these disorders accurately. Unfortunately, PCAs are notoriously difficult conditions to diagnose and treat. The aim of this review is to present a rational and pragmatic guide to help clinicians in the professional assessment, investigation and diagnosis of patients with PCA. Illustrating typical clinical and histopathological presentations of key PCA entities we show how dermatoscopy can be profitably used for clinical diagnosis. Further, we advocate the search for loss of follicular ostia as a clinical hallmark of PCA, and suggest pragmatic strategies that allow rapid formulation of a working diagnosis.


title: Building fast Bayesian computing machines out of intentionally stochastic, digital parts

abstract: The brain interprets ambiguous sensory information faster and more reliably than modern computers, using neurons that are slower and less reliable than logic gates. But Bayesian inference, which underpins many computational models of perception and cognition, appears computationally challenging even given modern transistor speeds and energy budgets. The computational principles and structures needed to narrow this gap are unknown. Here we show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude. We find that by connecting stochastic digital components according to simple mathematical rules, one can build massively parallel, low precision circuits that solve Bayesian inference problems and are compatible with the Poisson firing statistics of cortical neurons. We evaluate circuits for depth and motion perception, perceptual learning and causal reasoning, each performing inference over 10,000+ latent variables in real time - a 1,000x speed advantage over commodity microprocessors. These results suggest a new role for randomness in the engineering and reverse-engineering of intelligent computation.


title: Untrusted User Detection in Microblogs

abstract: As Micro blogs such as Sina Weibo and Twitter are becoming increasingly popular, a lot of users are benefiting from the convenience of social communication and information sharing. However, untrusted users can take advantage of these sites as gateways to make money, spread rumors and so on. This paper proposes a label propagation algorithm based on the trust model to detect the untrusted users on social networking sites. The algorithm consists of two main steps. Firstly, we conduct a hybrid graph with three types of nodes (users, tweets, topics) to model the trust relationship between each user pair. Secondly, we utilize a new method inspired by the Label Propagation Algorithm to detect more untrusted users based on a small known seed set. We conduct experiments with a real data set, and the evaluation demonstrates the effectiveness, efficiency, and scalability of the proposed solution.


title: Spec2Fab: a reducer-tuner model for translating specifications to 3D prints

abstract: Multi-material 3D printing allows objects to be composed of complex, heterogenous arrangements of materials. It is often more natural to define a functional goal than to define the material composition of an object. Translating these functional requirements to fabri-cable 3D prints is still an open research problem. Recently, several specific instances of this problem have been explored (e.g., appearance or elastic deformation), but they exist as isolated, monolithic algorithms. In this paper, we propose an abstraction mechanism that simplifies the design, development, implementation, and reuse of these algorithms. Our solution relies on two new data structures: a reducer tree that efficiently parameterizes the space of material assignments and a tuner network that describes the optimization process used to compute material arrangement. We provide an application programming interface for specifying the desired object and for defining parameters for the reducer tree and tuner network. We illustrate the utility of our framework by implementing several fabrication algorithms as well as demonstrating the manufactured results.


title: From Repeatability to Reproducibility and Corroboration

abstract: Being able to repeat experiments is considered a hallmark of the scientific method, used to confirm or refute hypotheses and previously obtained results. But this can take many forms, from precise repetition using the original experimental artifacts, to conceptual reproduction of the main experimental idea using new artifacts. Furthermore, the conclusions from previous work can also be corroborated using a different experimental methodology altogether. In order to promote a better understanding and use of such methodologies we propose precise definitions for different terms, and suggest when and why each should be used.


title: Effect of Hurdle Technology in Food Preservation: A Review

abstract: Hurdle technology is used in industrialized as well as in developing countries for the gentle but effective preservation of foods. Hurdle technology was developed several years ago as a new concept for the production of safe, stable, nutritious, tasty, and economical foods. Previously hurdle technology, i.e., a combination of preservation methods, was used empirically without much knowledge of the governing principles. The intelligent application of hurdle technology has become more prevalent now, because the principles of major preservative factors for foods (e.g., temperature, pH, aw, Eh, competitive flora), and their interactions, became better known. Recently, the influence of food preservation methods on the physiology and behavior of microorganisms in foods, i.e. their homeostasis, metabolic exhaustion, stress reactions, are taken into account, and the novel concept of multi-target food preservation emerged. The present contribution reviews the concept of the potential hurdles for foods, the hurdle effect, and the hurdle technology for the prospects of the future goal of a multi-target preservation of foods.


title: Scene Text Detection and Recognition: The Deep Learning Era

abstract: With the rise and development of deep learning, computer vision has been tremendously transformed and reshaped. As an important research area in computer vision, scene text detection and recognition has been inescapably influenced by this wave of revolution, consequentially entering the era of deep learning. In recent years, the community has witnessed substantial advancements in mindset, approach and performance. This survey is aimed at summarizing and analyzing the major changes and significant progresses of scene text detection and recognition in the deep learning era. Through this article, we devote to: (1) introduce new insights and ideas; (2) highlight recent techniques and benchmarks; (3) look ahead into future trends. Specifically, we will emphasize the dramatic differences brought by deep learning and the grand challenges still remained. We expect that this review paper would serve as a reference book for researchers in this field. Related resources are also collected and compiled in our Github repository: https://github.com/Jyouhou/SceneTextPapers.


title: End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion

abstract: Knowledge graph embedding has been an active research topic for knowledge base completion, with progressive improvement from the initial TransE, TransH, DistMult et al to the current state-of-the-art ConvE. ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure. In this work, we propose a novel end-to-end StructureAware Convolutional Network (SACN) that takes the benefit of GCN and ConvE together. SACN consists of an encoder of a weighted graph convolutional network (WGCN), and a decoder of a convolutional network called Conv-TransE. WGCN utilizes knowledge graph node structure, node attributes and edge relation types. It has learnable weights that adapt the amount of information from neighbors used in local aggregation, leading to more accurate embeddings of graph nodes. Node attributes in the graph are represented as additional nodes in the WGCN. The decoder Conv-TransE enables the state-of-the-art ConvE to be translational between entities and relations while keeps the same link prediction performance as ConvE. We demonstrate the effectiveness of the proposed SACN on standard FB15k-237 and WN18RR datasets, and it gives about 10% relative improvement over the state-of-theart ConvE in terms of HITS@1, HITS@3 and HITS@10.


title: Review: Software fault prediction: A literature review and current trends

abstract: Software engineering discipline contains several prediction approaches such as test effort prediction, correction cost prediction, fault prediction, reusability prediction, security prediction, effort prediction, and quality prediction. However, most of these prediction approaches are still in preliminary phase and more research should be conducted to reach robust models. Software fault prediction is the most popular research area in these prediction approaches and recently several research centers started new projects on this area. In this study, we investigated 90 software fault prediction papers published between year 1990 and year 2009 and then we categorized these papers according to the publication year. This paper surveys the software engineering literature on software fault prediction and both machine learning based and statistical based approaches are included in this survey. Papers explained in this article reflect the outline of what was published so far, but naturally this is not a complete review of all the papers published so far. This paper will help researchers to investigate the previous studies from metrics, methods, datasets, performance evaluation metrics, and experimental results perspectives in an easy and effective manner. Furthermore, current trends are introduced and discussed.


title: Moderation of distress-induced eating by emotional eating scores

abstract: Earlier studies assessing the possible moderator effect of self-reported emotional eating on the relation between stress and actual food intake have obtained mixed results. The null findings in some of these studies might be attributed to misclassification of participants due to the use of the median splits and/or insufficient participants with extreme scores. The objective of the two current studies was to test whether it is possible to predict distress-induced eating with a self-report emotional eating scale by using extreme scorers. In study 1 (n = 45) we used a between-subjects design and emotional eating was assessed after food intake during a negative or a neutral mood (induced by a movie). In study 2 (n = 47) we used a within-subjects design and emotional eating was assessed well before food intake, which occurred after a control or stress task (Trier Social Stress Task). The main outcome measure was actual food intake. In both studies self-reported emotional eating significantly moderated the relation between distress and food intake. As expected, low emotional eaters ate less during the sad movie or after stress than during the neutral movie or after the control task, whereas high emotional eaters ate more. No such moderator effect was found for emotional eating in the entire sample (n = 124) of study 1 using the median-split procedure or the full range of emotional eating scores. We conclude that it is possible to predict distress-induced food intake using self-reports of emotional eating provided that the participants have sufficiently extreme emotional eating scores.


Phase-space rotators and their applications in optics


title: Accounting Information System (AIS) Alignment and Non-financial Performance in Small Firm: A Contingency Perspective

abstract: Accounting Information System (AIS) is very important for Small and Medium Enterprises (SMEs) because it can provide information for the owner in decision making. In line with the result of previous study, which find that AIS alignment is needed because it will affect company performance. This study aims to test the direct relationship and mediating relationship among three contingency variables of AIS alignment on non-financial performance. This study employs questionnaire with 87 SMEs owner in Yogyakarta region as samples. The results show that AIS sophisticated, owner commitment affect non-financial performance, either directly or mediated by AIS alignment. This study implies that AIS is very essential for SMEs to survive in a very competitive environme470686nt.


title: SonarSnoop: active acoustic side-channel attacks

abstract: We report the first active acoustic side-channel attack. Speakers are used to emit human inaudible acoustic signals, and the echo is recorded via microphones, turning the acoustic system of a smart phone into a sonar system. The echo signal can be used to profile user interaction with the device. For example, a victim’s finger movements can be inferred to steal Android unlock patterns. In our empirical study, the number of candidate unlock patterns that an attacker must try to authenticate herself to a Samsung S4 phone can be reduced by up to 70% using this novel acoustic side channel. The attack is entirely unnoticeable to victims. Our approach can be easily applied to other application scenarios and device types. Overall, our work highlights a new family of security threats.


title: Social Network Analysis of Elders' Health Literacy and their Use of Online Health Information

abstract: Objectives: Utilizing social network analysis, this study aimed to analyze the main keywords in the literature regarding the health literacy of and the use of online health information by aged persons over 65. Methods: Medical Subject Heading keywords were extracted from articles on the PubMed database of the National Library of Medicine. For health literacy, 110 articles out of 361 were initially extracted. Seventy-one keywords out of 1,021 were finally selected after removing repeated keywords and applying pruning. Regarding the use of online health information, 19 articles out of 26 were selected. One hundred fortyfour keywords were initially extracted. After removing the repeated keywords, 74 keywords were finally selected. Results: Health literacy was found to be strongly connected with ‘Health knowledge, attitudes, practices’ and ‘Patient education as topic.’ ‘Com puter literacy’ had strong connections with ‘Internet’ and ‘Attitude towards computers.’ ‘Computer literacy’ was connected to ‘Health literacy,’ and was studied according to the parameters ‘towards health’ and ‘Patient education as topic.’ The use of online health information was strongly connected with ‘Health knowledge, attitudes, practices,’ ‘Consumer health information,’ ‘Patient education as topic,’ etc. In the network, ‘Computer literacy’ was connected with ‘Health education,’ ‘Patient satisfaction,’ ‘Self-efficacy,’ ‘Attitude to computer,’ etc. Conclusions: Research on older citizens’ health literacy and their use of online health information was conducted together with study of computer literacy, patient education, attitude towards health, health educa tion, patient satisfaction, etc. In particular, self-efficacy was noted as an important keyword. Further research should be con ducted to identify the effective outcomes of self-efficacy in the area of interest.


title: A nested PID steering control for lane keeping in vision based autonomous vehicles

abstract: In this paper a nested PID steering control for lane keeping in vision based autonomous vehicles is designed to perform path following in the case of roads with an uncertain curvature. The control input is the steering wheel angle: it is designed on the basis of the yaw rate, measured by a gyroscope, and the lateral offset, measured by the vision system as the distance between the road centerline and a virtual point at a fixed distance from the vehicle. No lateral acceleration and no lateral speed measurements are required. A PI active front steering control on the yaw rate tracking error is used to reject constant disturbances and the overall effect of parameter variations while improving vehicle steering dynamics. The yaw rate reference is viewed as the control input in an external control loop: it is designed using a PID control on the lateral offset to reject the disturbances on the curvature which increase linearly with respect to time. The robustness is investigated with respect to speed variations and uncertain vehicle physical parameters: it is shown that the controlled system is asymptotically stable for all perturbations in the range of interest. Several simulations are carried out on a standard big sedan CarSim vehicle model to explore the robustness with respect to unmodelled effects such as combined lateral and longitudinal tire forces, pitch and roll. The simulations show reduced lateral offset and new stable μ-split braking manoeuvres in comparison with the CarSim model predictive steering controller implemented by CarSim.


title: An Efficient Structure of Marx Generator Using Buck–Boost Converter

abstract: In this paper, a new structure of Marx generator (MG) based on buck–boost converter is proposed to generate high-voltage pulses. In this structure, a single-phase inverter is employed to supply parallel diode–capacitor units by positive and negative values of the input dc source ( $\pm V_{\mathrm {in}}$ ). The main contribution of this paper is proposing a new switching strategy, by which a group of capacitors are charged properly. Finally, the charged capacitors are connected in series such that the output voltage is equal to summation of the capacitors’ voltages. Considering specified value of the output voltage, the number of circuit elements in the proposed structure is reduced in comparison with other topologies of unipolar MG. Furthermore, voltage rating of switches and diodes in the proposed topology is lower than that of other unipolar MG structures. Design of the structure ensures that there is no need to connect the switches in series, when the number of stages is increased. To verify the performance of the proposed MG structure, simulation has been carried out in MATLAB/Simulink. Furthermore, a prototype of the proposed structure has been implemented in the lab. The simulation and experimental results confirm the capability of the structure for generating high-voltage pulses.


title: A View Of The Em Algorithm That Justifies Incremental, Sparse, And Other Variants

abstract: The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect to the distribution over the unobserved variables. From this perspective, it is easy to justify an incremental variant of the EM algorithm in which the distribution for only one of the unobserved variables is recalculated in each E step. This variant is shown empirically to give faster convergence in a mixture estimation problem. A variant of the algorithm that exploits sparse conditional distributions is also described, and a wide range of other variant algorithms are also seen to be possible.


title: The Digital Economy: Promise and Peril in the Age of Networked Intelligence

abstract: In this eagerly awaited follow-up to his best-selling Paradigm Shift, global IT expert Don Tapscott answers the one question that burns on the mind of every forward-looking executive and manager: What does the new technology mean to me and my business? In clear, jargon-free English, using actual examples of leading-edge organizations who are successfully riding the new IT wave, Don Tapscott reveals how the new technology and business strategies are transforming not only business processes but also the way products and services are created and marketed, the structure and goals of the enterprise, the dynamics of competition, and all the rules for business success. But the remarkable journey doesn't end there. The Digital Economy also takes you to the epicenter of a new convergence of computing, telecommunications, and entertainment. From Wal-Mart's electronically linked purchasing systems to Sun Microsystem's desktop university to Chase Manhattan Bank's consumer video kiosks, and beyond, it discloses how results-hungry organizations are moving past simple reengineering to the complete IT-enabled transformation of the corporation. The Digital Economy also tackles the dark side of the information highway - the first frank, balanced, and comprehensive look at the perils of the revolution underway for every business, society, and individual. With this book Tapscott opens an international debate on the role of business in the transition to the new economy and a new society based on fairness, justice, and democracy.


title: Factors affecting dielectric loss and d.c. conductivity measurements on oils

abstract: Both the loss tangent and the d.c. conductivity of insulating oils decrease with time after the oil is introduced into the test cell, in the absence of electric stress and at room temperature. When the cell is heated immediately after filling, the loss tangent and d.c. conductivity rise to a maximum, but decrease to a much lower value with time at constant temperature. The maximum value, the rate of rise to that value and the subsequent decrease with time are functions of the thermal capacity and shape of the cell, the technique used to clean the cell, the temperature and the oxygen content of the oil. The subsequent decrease in dielectric loss is thought to be due to catalytic oxidation of some component in the oil at the metal-electrode surface, although it is possible that ions may be merely trapped at this surface. The effects have been observed with a low-viscosity cable impregnant, both unused and severely contaminated, a transformer oil and a synthetic hydrocarbon. The implications of the effect are discussed in relation to routine testing of oils, to investigations of the effects of contamination in oils and to work on oil-impregnated paper. It is possible that the effect may be partially or wholly eliminated by the use of plastic-coated electrodes.


title: Developmental dyslexia: genetic dissection of a complex cognitive trait

abstract: Developmental dyslexia, a specific impairment of reading ability despite adequate intelligence and educational opportunity, is one of the most frequent childhood disorders. Since the first documented cases at the beginning of the last century, it has become increasingly apparent that the reading problems of people with dyslexia form part of a heritable neurobiological syndrome. As for most cognitive and behavioural traits, phenotypic definition is fraught with difficulties and the genetic basis is complex, making the isolation of genetic risk factors a formidable challenge. Against such a background, it is notable that several recent studies have reported the localization of genes that influence dyslexia and other language-related traits. These investigations exploit novel research approaches that are relevant to many areas of human neurogenetics.


title: In-vitro antimicrobial activity and synergistic/antagonistic effect of interactions between antibiotics and some spice essential oils.

abstract: Spices and herbs have been used for many years by different cultures. The aim of the present study is (1) to investigate in-vitro antimicrobial effects of different spices and herbs (5 species: Rosmarinus officinalis (Rosemary), Coriandrum sativum (coriander), Micromeria fruticosa (L.) Druce subsp. Brachycalyx P.H. Davis (White micromeria), Cumium cyminum (cumin), Mentha piperita (Peppermint) against different bacteria and fungi species, and (2) to discuss the in-vitro possible effects between the plants and antibiotics. The microorganisms used were Micrococcus luteus LA 2971, Bacillus megaterium NRS, Bacillus brevis FMC 3, Enterococcus faecalis ATCC 15753, Pseudomonas pyocyaneus DC 127, Mycobacterium smegmatis CCM 2067, Escherichia coil DM, Aeromonas hydrophila ATCC 7966, Yersinia enterocolitica AU 19, Staphylococcus aureus Cowan 1, Streptococcus faecalis DC 74 bacteria, and Saccharomyces cerevisiae WET 136, Kluvyeromyces fragilis DC 98 fungi in this study. The results indicated that essential oils of Rosmarinus officinalis, Coriandrum sativum L., Micromeria fruticosa (L.) Druce subsp. brachycalyx P.H. Davis, Cumium cyminum L., Mentha piperita L. were shown antimicrobial activity in the range of 7-60 mm 2 microl(-1) inhibition zone to the microorganisms tested, using disc diffusion method. Standard antibiotic such as Gentamicin (10 microg), Cephalothin (30 microg), Ceftriaxone (10 microg), Nystatin (10 U) discs were used for comparison with the antimicrobial activities of essential oils of these plants. In addition, antibacterial activity of essential oils of these plants was researched by effects when it was used together with these standard antibiotics in vitro. However, antibacterial activity changed also by in vitro interactions between these standard antibiotics and essential oils of these plants. Synergic, additive or antagonist effects were observed in antibacterial activity.


title: MATLAB/Simulink Based Modelling of Solar Photovoltaic Cell

abstract: This paper focuses on a Matlab/SIMULINK model of a photovoltaic cell. This model is based on mathematical equations and is described through an equivalent circuit including a photocurrent source, a diode, a series resistor and a shunt resistor. The developed model allows the prediction of PV cell behaviour under different physical and environmental parameters. The model can also be used to extract the physical parameters for a given solar PV cell as a function of temperature and solar radiation. In addition, this study outlines the working principle of PV module as well as PV array. In order to validate the developed model, an experimental test bench was built and the obtained results exhibited a good agreement with the simulation ones. Keywords-Matlab, SIMULINK, PV, solar cell model, solar array model, solar radiation, maximum power point


title: Economic Determinants and Female Labour Force Participation: An Empirical Analysis of Pakistan

abstract: Females labour force plays a crucial role in economic development by enhancing the development potential of a country . This paper investigates the effects of economic determinants on female labour force participation of Pakistan by employing ARDL, ECM approach and Granger Causality over the period of 1980-2010.The outcome of the study shows that favourable economic policies encourage female labour supply to participate in the economic and productive activities. The results support this phenomenon for both long and short run periods. This study suggested that the government should devise policies that promote females contribution in the formal sector of the economy. Moreover, cottage industries need to be promoted and informal sector needs restructuring by encouraging females. Key Words: Female labour Force Participation, Economic determinants, trade liberalization.


title: Heuristic evaluation: Comparing ways of finding and reporting usability problems

abstract: Research on heuristic evaluation in recent years has focused on improving its effectiveness and efficiency with respect to user testing. The aim of this paper is to refine a research agenda for comparing and contrasting evaluation methods. To reach this goal, a framework is presented to evaluate the effectiveness of different types of support for structured usability problem reporting. This paper reports on an empirical study of this framework that compares two sets of heuristics, Nielsen's heuristics and the cognitive principles of Gerhardt-Powals, and two media of reporting a usability problem, i.e. either using a web tool or paper. The study found that there were no significant differences between any of the four groups in effectiveness, efficiency and inter-evaluator reliability. A more significant contribution of this research is that the framework used for the experiments proved successful and should be reusable by other researchers because of its thorough structure.


title: WiStep: Device-free Step Counting with WiFi Signals

abstract: Inspired by the emerging WiFi-based applications, in this paper, we leverage ubiquitous WiFi signals and propose a device-free step counting system, called WiStep. Based on the multipath propagation model, when a person is walking, her torso and limbs move at different speeds, which modulates wireless signals to the propagation paths with different lengths and thus introduces different frequency components into the received Channel State Information (CSI). To count walking steps, we first utilize time-frequency analysis techniques to segment and recognize the walking movement, and then dynamically select the sensitive subcarriers with largest amplitude variances from multiple CSI streams. Wavelet decomposition is applied to extract the detail coefficients corresponding to the frequencies induced by feet or legs, and compress the data so as to improve computing speed. Short-time energy of the coefficients is then calculated as the metric for step counting. Finally, we combine the results derived from the selected subcarriers to produce a reliable step count estimation. In contrast to counting steps based on the torso frequency analysis, WiStep can count the steps of in-place walking even when the person's torso speed is null. We implement WiStep on commodity WiFi devices in two different indoor scenarios, and various influence factors are taken into consideration when evaluating the performance of WiStep. The experimental results demonstrate that WiStep can realize overall step counting accuracies of 90.2% and 87.59% respectively in these two scenarios, and it is resilient to the change of scenarios.


title: Learning Robotic Assembly from CAD

abstract: In this work, motivated by recent manufacturing trends, we investigate autonomous robotic assembly. Industrial assembly tasks require contact-rich manipulation skills, which are challenging to acquire using classical control and motion planning approaches. Consequently, robot controllers for assembly domains are presently engineered to solve a particular task, and cannot easily handle variations in the product or environment. Reinforcement learning (RL) is a promising approach for autonomously acquiring robot skills that involve contact-rich dynamics. However, RL relies on random exploration for learning a control policy, which requires many robot executions, and often gets trapped in locally suboptimal solutions. Instead, we posit that prior knowledge, when available, can improve RL performance. We exploit the fact that in modern assembly domains, geometric information about the task is readily available via the CAD design files. We propose to leverage this prior knowledge by guiding RL along a geometric motion plan, calculated using the CAD data. We show that our approach effectively improves over traditional control approaches for tracking the motion plan, and can solve assembly tasks that require high precision, even without accurate state estimation. In addition, we propose a neural network architecture that can learn to track the motion plan, and generalize the assembly controller to changes in the object positions.


Combining DMN and the Knowledge Base Paradigm for Flexible Decision Enactment


title: High-frequency characterization of through package vias formed by focused electrical-discharge in thin glass interposers

abstract: This paper presents the modeling, design, fabrication and characterization, up to 30 GHz, of low loss and high aspect-ratio 55 μm diameter through package vias (TPVs) in 300 μm thick glass interposers. These TPVs were fabricated using a novel, high-throughput, focused electrical discharge method and low cost panel-based double-side metallization processes. Such a glass interposer is targeted at two emerging applications, (a) large 30 mm to 60 mm body size 2.5D interposers to achieve 28.8 Gbps logic-memory bandwidth and (b) 3D interposers for mm wave applications at 28 GHz local multipoint distribution service (LMDS) for future 5G networks. Accurate measurement of the electrical performance of fine pitch metallized through vias in glass up to 30 GHz and beyond is critical for both these high performance interposer applications. In this paper, two novel characterization methods are applied: 1) the short-circuit-and-open-circuit method and 2) the dual-via-chain method. The resistance and the inductance of a single via are extracted by using a short-circuit structure along with an open-circuit structure. At 10 GHz, the values for the series resistance and inductance have average values of 0.1 Ω and 160 pH respectively. Long dual-via chains were designed to evaluate their performance in insertion loss, delay and eye diagram. The insertion loss achieved with the longest dual-via chain was found to be less than 1 dB/cm up to 30 GHz with only a 6.2 ps delay in the TPVs, and the simulations indicate a wide open eye.


title: A Genetic Algorithm for a 2D Industrial Packing Problem

abstract: Abstract Cutting and packing problems are encountered in many industries, with different industries incorporating different constraints and objectives. The wood-, glass- and paper industry are mainly concerned with the cutting of regular figures, whereas in the ship building, textile and leather industry irregular, arbitrary shaped items are to be packed. In this paper two genetic algorithms are described for a rectangular packing problem. Both GAs are hybridised with a heuristic placement algorithm, one of which is the well-known Bottom-Left routine. A second placement method has been developed which overcomes some of the disadvantages of the Bottom-Left rule. The two hybrid genetic algorithms are compared with heuristic placement algorithms. In order to show the effectiveness of the design of the two genetic algorithms, their performance is compared to random search.


title: Sentence-Level Emotion Detection Framework Using Rule-Based Classification

abstract: Emotion detection and analysis aims at developing applications that can detect and analyse emotions expressed by the users in a given text. Such applications have received considerable attention from experts in computer science, psychology, communications and health care. Emotion-based sentiment analysis can be performed using supervised and unsupervised techniques. The existing studies using supervised and unsupervised emotion-based sentiment analysis are based on Ekman’s basic emotion model; have limited coverage of emotion-words, polarity shifters and negations; and lack emoticons and slang. The problems associated with existing approaches can be overcome by the development of an effective, sentence-level emotion-detection sentiment analysis system under a rule-based classification scheme with extended lexicon support and an enhanced model of emotion signals: emotion words, polarity shifters, negations, emoticons and slang. In this work, we propose a rule-based framework for emotion-based sentiment classification at the sentence level obtained from user reviews. The main contribution of this work is to integrate cognitive-based emotion theory (e.g. Ekman’s model) with sentiment analysis-based computational techniques (e.g. detection of emotion words, emoticons and slang) to detect and classify emotions from natural language text. The main focus is to improve the performance of state-of-the-art methods by including additional emotion-related signals, such as emotion words, emoticons, slang, polarity shifters and negations, to efficiently detect and classify emotions in user reviews. The improved results in terms of accuracy, precision, recall and F-measure demonstrate the superiority of the proposed method’s classification results compared with baseline methods. The framework is generalized and capable of classifying emotions in any domain.


Physician-hospital alignment models: An evolving lexicon.


title: IGBT over voltage protection scheme in quasi resonant induction heating applications

abstract: Induction heating for domestic usage is quite popular and in some countries, it is a highly cost-sensitive product. A single IGBT (Insulated Gate Bipolar Transistor) quasi resonant power stage is the most popular topology for this application. In this paper we explain method in which the topology works reliably irrespective of widely varying input voltage, open loop operation and inconsistent utensil material. A protection scheme involving both hardware and firmware has been discussed. The protection scheme comprises of an efficient control-algorithms and the IGBT behaves as an active self-clamp. An induction cooker/heater prototype of 1.8kW rating has been developed with the proposed protection scheme. The results reveal that the scheme proposed provides an enhanced protection to the IGBT and increases the end product reliability manifold while reducing warranty costs to the manufacturer.


title: The spermiogenesis and the early spermatozoa of Armorloricus elegans (Loricifera, Nanaloricidae)

abstract: The spermiogenesis consisting of five spermatid stages and the early spermatozoon has been investigated in Armorloricus elegans (Loricifera) with the use of transmission electron microscopy. The male reproductive system consists of three parts; testes, vasa deferentia and seminal vesicles. Caudally, the two seminal vesicles merge together in a ciliated duct and the excretory/gonadal—and digestive systems continue through the recto-urogenital canal, which opens via the lateral gonopores and the temporarily closed anal system. Spermiogenesis mainly occurs in the testes, whereas further maturation of the late spermatids and early spermatozoa occurs in the vasa deferentia and seminal vesicles. A maturation gradient (from spermatocytes to spermatozoa) is found from the posterior peripheral part of the testes to the anterior periphery and then centrally. During spermiogenesis the round nucleus becomes more osmiophilic and condensation of chromatin occurs. Later the nucleus elongates until it becomes rod-shaped in the early spermatozoa. In the second spermatid stage, a large vesicle is formed by saccules developed from the Golgi complex. This vesicle develops further and consists of three different osmiophilic parts with some crystal-like structures inside and is on the outside almost entirely surrounded by thick striated filaments. In the mid-piece the flagellum has a typical 9 × 2 + 2 axoneme and the two mitochondria are fused into a single sheet surrounding the flagellum. In the early spermatozoon stage an acrosomal-like cap structure with an acrosome filament appears proximal to the protruded rod-shaped nucleus. This cap is not formed by the Golgi complex and therefore might not be a true acrosome. Comparing the early spermatozoa of A. elegans with other cycloneuralians has shown some similarities with especially Kinorhyncha and Priapulida. These similarities are thought to be plesiomorphic.


title: The Multilevel Modular DC Converter

abstract: The modular multilevel converter (M2C) has become an increasingly important topology in medium- and high-voltage applications. A limitation is that it relies on positive and negative half-cycles of the ac output voltage waveform to achieve charge balance on the submodule capacitors. To overcome this constraint a secondary power loop is introduced that exchanges power with the primary power loops at the input and output. Power is exchanged between the primary and secondary loops by using the principle of orthogonality of power flow at different frequencies. Two modular multilevel topologies are proposed to step up or step down dc in medium- and high-voltage dc applications: the tuned filter modular multilevel dc converter and the push-pull modular multilevel dc converter. An analytical simulation of the latter converter is presented to explain the operation.


title: Aqueous and non-aqueous microchip electrophoresis with on-chip electrospray ionization mass spectrometry on replica-molded thiol-ene microfluidic devices.

abstract: This work describes aqueous and non-aqueous capillary electrophoresis on thiol-ene-based microfluidic separation devices that feature fully integrated and sharp electrospray ionization (ESI) emitters. The chip fabrication is based on simple and low-cost replica-molding of thiol-ene polymers under standard laboratory conditions. The mechanical rigidity and the stability of the materials against organic solvents, acids and bases could be tuned by adjusting the respective stoichiometric ratio of the thiol and allyl ("ene") monomers, which allowed us to carry out electrophoresis separation in both aqueous and non-aqueous (methanol- and ethanol-based) background electrolytes. The stability of the ESI signal was generally ≤10% RSD for all emitters. The respective migration time repeatabilities in aqueous and non-aqueous background electrolytes were below 3 and 14% RSD (n=4-6, with internal standard). The analytical performance of the developed thiol-ene microdevices was shown in mass spectrometry (MS) based analysis of peptides, proteins, and small molecules. The theoretical plate numbers were the highest (1.2-2.4×104m-1) in ethanol-based background electrolytes. The ionization efficiency also increased under non-aqueous conditions compared to aqueous background electrolytes. The results show that replica-molding of thiol-enes is a feasible approach for producing ESI microdevices that perform in a stable manner in both aqueous and non-aqueous electrophoresis.


title: Superposition of shield currents in inverter-fed AC-motors

abstract: Shields generally solve EMC problems due to their high efficiency. When it comes to an electrical automotive drive system, the relatively small quantities of a non ideal shield are of importance due to the high level of emissions generated which face very stringent requirements for low level emissions. As a consequence, the shield and ground loop currents are decisive for various EMC conducted and radiated emissions issues. Among others the resulting current distributions directly generate emitted magnetic fields.


title: CAPTCHA-based image labeling on the Soylent Grid

abstract: We introduce an open labeling platform for Computer Vision researchers based on Captchas, creating as a byproduct labeled image data sets while supporting web security. For the two different tasks of annotation and detection, we explore usability issues. We present the interfaces used by researchers and users and experimental results obtained using the platform. Finally, we discuss system sustainability issues in the context of a broader "ecosystem" for the platform.


title: Comprehension of argument structure and semantic roles: Evidence from English-learning children and the forced-choice pointing paradigm

abstract: Research using the intermodal preferential looking paradigm (IPLP) has consistently shown that English-learning children aged 2 can associate transitive argument structure with causal events. However, studies using the same methodology investigating 2-year-old children’s knowledge of the conjoined agent intransitive and semantic role assignment have reported inconsistent findings. The aim of the present study was to establish at what age English-learning children have verb-general knowledge of both transitive and intransitive argument structure using a new method: the forced-choice pointing paradigm. The results suggest that young 2-year-olds can associate transitive structures with causal (or externally caused) events and can use transitive structure to assign agent and patient roles correctly. However, the children were unable to associate the conjoined agent intransitive with noncausal events until aged 3;4. The results confirm the pattern from previous IPLP studies and indicate that children may develop the ability to comprehend different aspects of argument structure at different ages. The implications for theories of language acquisition and the nature of the language acquisition mechanism are discussed.


title: Quantile regression neural networks: Implementation in R and application to precipitation downscaling

abstract: The qrnn package for R implements the quantile regression neural network, which is an artificial neural network extension of linear quantile regression. The model formulation follows from previous work on the estimation of censored regression quantiles. The result is a nonparametric, nonlinear model suitable for making probabilistic predictions of mixed discrete-continuous variables like precipitation amounts, wind speeds, or pollutant concentrations, as well as continuous variables. A differentiable approximation to the quantile regression error function is adopted so that gradient-based optimization algorithms can be used to estimate model parameters. Weight penalty and bootstrap aggregation methods are used to avoid overfitting. For convenience, functions for quantile-based probability density, cumulative distribution, and inverse cumulative distribution functions are also provided. Package functions are demonstrated on a simple precipitation downscaling task.


title: Resource Efficiency in Low-Power Wide-Area Networks for IoT Applications

abstract: In this paper, we investigate the resource efficiency of uplink transmission for low-power wide-area (LPWA) networks. LoRa is adopted as an example network of focus, however the work can be easily generalized to other radios. We first formulate resource allocation in LPWA networks as a joint optimization problem of channel assignment and power allocation, with guaranteeing throughput fairness among LoRa users with limited spectrum resources, especially for the case with a large number of connected devices in LPWA networks. Specifically, we formulate channel assignment as a many-to-one matching game by treating LoRa users and channels as two sets of selfish players aiming to maximize their own utilities. We then propose a low-complexity matching channel assignment algorithm (MCAA) through distributing the channel access decision making local to LoRa users. For LoRa users assigned to the same channel, we further develop an optimal power allocation algorithm to maximize the achieved minimal transmission rate in LPWA networks. Moreover, simulation results demonstrate that the proposed MCAA can achieve near-optimal performance with much lower computational complexity.


title: Dynamic Psychological Game for Adversarial Internet of Battlefield Things Systems

abstract: In this paper, a novel game-theoretic framework is introduced to analyze and enhance the security of adversarial Internet of Battlefield Things (IoBT) systems. In particular, a dynamic, psychological network interdiction game is formulated between a soldier and an attacker. In this game, the soldier seeks to find the optimal path to minimize the time needed to reach a destination, while maintaining a desired bit error rate (BER) performance by selectively communicating with certain IoBT devices. The attacker, on the other hand, seeks to find the optimal IoBT devices to attack, so as to maximize the BER of the soldier and hinder the soldier's progress. In this game, the soldier and attacker's first- order and second-order beliefs on each others' behavior are formulated to capture their psychological behavior. Using tools from psychological game theory, the soldier and attacker's intention to harm one another is captured in their utilities, based on their beliefs. A psychological forward induction-based solution is proposed to solve the dynamic game. This approach can find a psychological sequential equilibrium of the game, upon convergence. Simulation results show that, whenever the soldier explicitly intends to frustrate the attacker, the soldier's material payoff is increased by up to 15.6% compared to a traditional dynamic Bayesian game.


title: Metadiscourse tagging in academic lectures

abstract: This thesis presents a study into the nature and structure of academic lectures, with a special focus on metadiscourse phenomena. Metadiscourse refers to a set of linguistics expressions that signal specific discourse functions such as the Introduction: “Today we will talk about...” and Emphasising: “This is an important point”. These functions are important because they are part of lecturers’ strategies in understanding of what happens in a lecture. The knowledge of their presence and identity could serve as initial steps toward downstream applications that will require functional analysis of lecture content such as a browser for lectures archives, summarisation, or an automatic minute-taker for lectures. One challenging aspect for metadiscourse detection and classification is that the set of expressions are semi-fixed, meaning that different phrases can indicate the same function. ::: ::: To that end a four-stage approach is developed to study metadiscourse in academic lectures. Firstly, a corpus of metadiscourse for academic lectures from Physics and Economics courses is built by adapting an existing scheme that describes functional-oriented metadiscourse categories. Second, because producing reference transcripts is a time-consuming task and prone to some errors due to the manual efforts required, an automatic speech recognition (ASR) system is built specifically to produce transcripts of lectures. Since the reference transcripts lack time-stamp information, an alignment system is applied to the reference to be able to evaluate the ASR system. Then, a model is developed using Support Vector Machines (SVMs) to classify metadiscourse tags using both textual and acoustical features. The results show that n-grams are the most inductive features for the task; however, due to data sparsity the model does not generalise for unseen n-grams. This limits its ability to solve the variation issue in metadiscourse expressions. Continuous Bag-of-Words (CBOW) provide a promising solution as this can capture both the syntactic and semantic similarities between words and thus is able to solve the generalisation issue. However, CBOW ignores the word order completely, something which is very important to be retained when classifying metadiscourse tags. ::: ::: The final stage aims to address the issue of sequence modelling by developing a joint CBOW and Convolutional Neural Network (CNN) model. CNNs can work with continuous features such as word embedding in an elegant and robust fashion by producing a fixed-size feature vector that is able to identify indicative local information for the tagging task. The results show that metadiscourse tagging using CNNs outperforms the SVMs model significantly even on ASR outputs, owing to its ability to predict a sequence of words that is more representative for the task regardless of its position in the sentence. In addition, the inclusion of other features such as part-of-speech (POS) tags and prosodic cues improved the results further. These findings are consistent in both disciplines. ::: ::: The final contribution in this thesis is to investigate the suitability of using metadiscourse tags as discourse features in the lecture structure segmentation model, despite the fact that the task is approached as a classification model and most of the state-of-art models are unsupervised. In general, the obtained results show remarkable improvements over the state-of-the-art models in both disciplines.


title: Overview of Network Security and Its Related Trends

abstract: Network security is a complicated subject, historically only tackled by well-trained and experienced experts. However, as more and more people become “wired”, an increasing number of people need to understand the basics of security in a networked world. Cryptography is also one of the methods of network security; it plays a major role in securing the data from others which might be open. This paper briefly describes various security strategies that can be implemented, and the security measures that have to be taken in all the enterprises. To provide a better security environment to the users of the enterprise networks, security administrators have to follow the defence in depth policy and implement multiple redundant security measures suggested. It is emphasized that the security threats will increase with time along with increased dependency on the enterprise networks and Internet. However, a sound security policy and a defence in depth security implementation will provide the needed security confidence to continue the utilization of enterprise networks for all important applications.


title: The influence of eWOM in social media on consumers' purchase intentions: An extended approach to information adoption

abstract: Social media websites have created valuable opportunities for electronic word of mouth (eWOM) conversations. People are now able to discuss products and services of brands with their friends and acquaintances. The aim of this study is to examine the influence of these conversations in social media on consumers' purchase intentions. For this purpose, a conceptual model was developed based on the integration of Information Adoption Model (IAM) and related components of Theory of Reasoned Action (TRA). The new model, which is named as Information Acceptance Model (IACM), was validated through structural equation modelling (SEM) based on surveys of 384 university students who use social media websites. The results confirm that quality, credibility, usefulness and adoption of information, needs of information and attitude towards information are the key factors of eWOM in social media that influence consumers' purchase intentions. Theoretical and practical implications are discussed as well as recommendations for further research. We examine the influence of eWOM in social media on consumers' purchase intentions.We develop a new conceptual model through extending the IAM by considering the TRA.We validate our model, which is Information Acceptance Model (IACM), through SEM.We show the determinants of eWOM in social media that influence purchase intention.


title: Fast Single Shot Detection and Pose Estimation

abstract: For applications in navigation and robotics, estimating the 3D pose of objects is as important as detection. Many approaches to pose estimation rely on detecting or tracking parts or keypoints [11, 21]. In this paper we build on a recent state-of-the-art convolutional network for slidingwindow detection [10] to provide detection and rough pose estimation in a single shot, without intermediate stages of detecting parts or initial bounding boxes. While not the first system to treat pose estimation as a categorization problem, this is the first attempt to combine detection and pose estimation at the same level using a deep learning approach. The key to the architecture is a deep convolutional network where scores for the presence of an object category, the offset for its location, and the approximate pose are all estimated on a regular grid of locations in the image. The resulting system is as accurate as recent work on pose estimation (42.4% 8 View mAVP on Pascal 3D+ [21] ) and significantly faster (46 frames per second (FPS) on a TITAN X GPU). This approach to detection and rough pose estimation is fast and accurate enough to be widely applied as a pre-processing step for tasks including high-accuracy pose estimation, object tracking and localization, and vSLAM.


title: The Value of High-frequency 20 MHz Ultrasonography for Preoperative Measurement of Cutaneous Melanoma Thickness.

abstract: High-frequency ultrasonography has become an important diagnostic tool in dermatovenerology. It is used for assessment of the cutaneous layers in diverse benign and malignant skin lesions and diseases. Herein we present the practical value of preoperative measurement of cutaneous melanoma thickness, the explanation of the possible reasons for different sonographic and histologic Breslow thickness, and the practical importance of high-frequency 20 MHz ultrasonography in preoperative measurements of cutaneous melanoma thickness. Fifty patients (31 women aged 41.6±15.4 years, 19 males aged 54.5±11.4 year (mean ± Standard Deviation) were examined in the Skin Cancer Clinic from January 2014 to December 2015. Suspected cutaneous melanomas were investigated with skin ultrasound Dermascan C (Cortex Technology, Denmark) before surgical removal. The Breslow thickness of cutaneous melanomas was analyzed by precise pathological examination after extirpation. Statistical analysis revealed a significant positive correlation between ultrasonographic melanoma thickness and Breslow thickness (r=0.92, P<0.001). The mean thickness of invasive tumors evaluated by high frequency ultrasonography was 6.77 % higher compared with the mean Breslow thickness. The high-frequency ultrasonography provides a sensitive, noninvasive and reproducible method of skin evaluation, which enables objective visualization in vivo, providing valuable information, especially about cutaneous melanoma. The results of our study indicate that high frequency ultrasonography may be a useful adjunctive tool in the evaluation of cutaneous melanoma in daily practice.


title: Efficiently Answering Technical Questions — A Knowledge Graph Approach

abstract: More and more users prefer to ask their technical questions online. For machines, understanding a question is nontrivial. Current approaches lack explicit background knowledge.In this paper, we introduce a novel technical question understanding approach to recommending probable solutions to users. First, a knowledge graph is constructed which contains abundant technical information, and an augmented knowledge graph is built on the basis of the knowledge graph, to link the knowledge graph and documents. Then we develop a light weight question driven mechanism to select candidate documents. To improve the online performance, we propose an index-based random walk to support the online search. We use comprehensive experiments to evaluate the effectiveness of our approach on a large scale of real-world query logs. Our system outperforms main-stream search engine and the state-of-art information retrieval methods. Meanwhile, extensive experiments confirm the efficiency of our index-based online search mechanism.


How does teacher well-being affect student learning ?


title: Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas

abstract: Lifestyles are a valuable model for understanding individuals’ physical and mental lives, comparing social groups, and making recommendations for improving people's lives. In this paper, we examine and compare lifestyle behaviors of people living in cities of different sizes, utilizing freely available social media data as a large-scale, low-cost alternative to traditional survey methods. We use the Greater New York City area as a representative for large cities, and the Greater Rochester area as a representative for smaller cities in the United States. We employed matrix factor analysis as an unsupervised method to extract salient mobility and work-rest patterns for a large population of users within each metropolitan area. We discovered interesting human behavior patterns at both a larger scale and a finer granularity than is present in previous literature, some of which allow us to quantitatively compare the behaviors of individuals of living in big cities to those living in small cities. We believe that our social media-based approach to lifestyle analysis represents a powerful tool for social computing in the big data age.


title: The impact of stress on body function: A review

abstract: Any intrinsic or extrinsic stimulus that evokes a biological response is known as stress. The compensatory responses to these stresses are known as stress responses. Based on the type, timing and severity of the applied stimulus, stress can exert various actions on the body ranging from alterations in homeostasis to life-threatening effects and death. In many cases, the pathophysiological complications of disease arise from stress and the subjects exposed to stress, e.g. those that work or live in stressful environments, have a higher likelihood of many disorders. Stress can be either a triggering or aggravating factor for many diseases and pathological conditions. In this study, we have reviewed some of the major effects of stress on the primary physiological systems of humans.


title: Discovering Hierarchy in Reinforcement Learning with HEXQ

abstract: An open problem in reinforcement learning is discovering hierarchical structure. HEXQ, an algorithm which automatically attempts to decompose and solve a model-free factored MDP hierarchically is described. By searching for aliased Markov sub-space regions based on the state variables the algorithm uses temporal and state abstraction to construct a hierarchy of interlinked smaller MDPs.


title: Performance analysis of stochastic behavior trees

abstract: This paper presents a mathematical framework for performance analysis of Behavior Trees (BTs). BTs are a recent alternative to Finite State Machines (FSMs), for doing modular task switching in robot control architectures. By encoding the switching logic in a tree structure, instead of distributing it in the states of a FSM, modularity and reusability are improved.In this paper, we compute performance measures, such as success/failure probabilities and execution times, for plans encoded and executed by BTs. To do this, we first introduce Stochastic Behavior Trees (SBT), where we assume that the probabilistic performance measures of the basic action controllers are given. We then show how Discrete Time Markov Chains (DTMC) can be used to aggregate these measures from one level of the tree to the next. The recursive structure of the tree then enables us to step by step propagate such estimates from the leaves (basic action controllers) to the root (complete task execution). Finally, we verify our analytical results using massive Monte Carlo simulations, and provide an illustrative example of the results for a complex robotic task.


title: Segmentation of Total Cell Area in Brightfield Microscopy Images

abstract: Segmentation is one of the most important steps in microscopy image analysis. Unfortunately, most of the methods use fluorescence images for this task, which is not suitable for analysis that requires a knowledge of area occupied by cells and an experimental design that does not allow necessary labeling. In this protocol, we present a simple method, based on edge detection and morphological operations, that separates total area occupied by cells from the background using only brightfield channel image. The resulting segmented picture can be further used as a mask for fluorescence quantification and other analyses. The whole procedure is carried out in open source software Fiji.


title: CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank

abstract: This article presents an algorithm for translating the Penn Treebank into a corpus of Combinatory Categorial Grammar (CCG) derivations augmented with local and long-range word-word dependencies. The resulting corpus, CCGbank, includes 99.4% of the sentences in the Penn Treebank. It is available from the Linguistic Data Consortium, and has been used to train wide-coverage statistical parsers that obtain state-of-the-art rates of dependency recovery. ::: ::: In order to obtain linguistically adequate CCG analyses, and to eliminate noise and inconsistencies in the original annotation, an extensive analysis of the constructions and annotations in the Penn Treebank was called for, and a substantial number of changes to the Treebank were necessary. We discuss the implications of our findings for the extraction of other linguistically expressive grammars from the Treebank, and for the design of future treebanks.


title: Parasitic twin—a supernumerary limb associated with spinal malformations. A case report

abstract: We describe a case of rachipagus parasitic twin with spinal cord malformations (lipomyelomeningocele and tethered cord) in a 7-month-old Ethiopian infant. The parasitic mass had a well-formed foot, ankle and lower leg and a small sinus that resembled an anus. Magnetic resonance imaging scans revealed spinal malformations including a distal syringohydromyelia. The mass was successfully resected and the dural attachment was closed. Histopathological examination confirmed the diagnosis. Postoperatively, the child had unchanged, intact neurological function in both lower limbs. Almost all rachipagus parasitic twins are associated with spinal malformations. They should, therefore, be operated on by surgeons experienced in myelomeningocele surgery.


title: Parents as a resource: Communication quality affects the relationship between adolescents' internet use and loneliness

abstract: Abstract The authors examined the influence of parent–adolescent communication quality, as perceived by the adolescents, on the link between adolescents' Internet use and loneliness, controlling for perceived family support in general terms. Adolescents ( N = 216, M age = 15.80 years) provided data on Internet use, loneliness, Internet-related parent–adolescent communication, and perceived family support. Moderated regression analyses showed that Internet-related communication quality determined whether more extensive Internet use was associated with more loneliness. This moderation effect remained significant when perceived family support in general terms was controlled for. Gender and age of the participants did not influence the findings. Implications for successful Internet-related parenting strategies are discussed.


title: Brand followers' retweeting behavior on Twitter: How brand relationships influence brand electronic word-of-mouth

abstract: Twitter, the popular microblogging site, has received increasing attention as a unique communication tool that facilitates electronic word-of-mouth (eWOM). To gain greater insight into this potential, this study investigates how consumers’ relationships with brands influence their engagement in retweeting brand messages on Twitter. Data from a survey of 315 Korean consumers who currently follow brands on Twitter show that those who retweet brand messages outscore those who do not on brand identification, brand trust, community commitment, community membership intention, Twitter usage frequency, and total number of postings.


title: Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data

abstract: Objectives:Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. Methods:ICD-10 coding algorithms were developed by “translation” of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians’ assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. Results:Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. Conclusions:These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.


title: Wideband Circularly Polarized Patch Antennas on Reactive Impedance Substrates

abstract: A reduced-size wideband single-feed circularly polarized patch antenna is introduced for telemetry applications in S-band around 2300 MHz. The proposed structure consists of a slot-loaded patch antenna printed over an optimized metamaterial-inspired reactive impedance substrate (RIS). We demonstrate, step by step, the main role of each antenna element by comparing numerically and experimentally the performance of various antenna configurations: antenna over a single- or dual-layer substrate, standard patch or slot-loaded patch, antenna with or without RIS. The final optimized structure exhibits an axial-ratio bandwidth of about 15% and an impedance bandwidth better than 11%, which is much wider than the conventional printed antenna on the same materials.