Query: Flexible 16 Antenna Array for Microwave Breast Cancer Detection

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Advanced Engineering Electromagnetics (Balanis, C.A.; 2012) [Book Review] <=SEP=> Since 2012 the second edition of this book is available. This new edition has all the attractive features of the first one and in my opinion it contains some new features and additions that are very useful in the EMC playground. In particular: A section on metamaterials (Section 5.7) is helpful for electromagnetic shielding oriented studies; A section on artificial impedance surfaces (AIS, EBG, PBG, HIS, AMC, PMC) (Section 8.8), is a solid base for any issue related to EBGs; and, in general, Additional smaller inserts throughout the book make it easier and enjoyable to understand; and new figures, photos, and tables have been added. As in the first edition, this second edition is a thorough and detailed engineer-oriented book. The analytical detail, rigor, and thoroughness allow many of the topics to be traced to their origin, and they are presented in sufficient detail so that the reader will follow the analytical developments. The material is presented in a methodical, sequential, and unified manner, and each chapter is subdivided into sections or subsections whose individual headings clearly identify the topics discussed, examined, or illustrated. An exhaustive list of references is included at the end of each chapter to allow the interested reader to trace each topic. A number of appendixes of mathematical identities and special functions, some represented also in tabular and graphical forms, are included. A very useful feature is a set of fortyeight basic MATLAB® computer programs developed and included in the website that is part of the book. They are an aid, also for the skilled reader, for a conceptual fruition of the results based upon the analytical formulations included throughout the book.

Stable and Flexible Materials to Mimic the Dielectric Properties of Human Soft Tissues <=SEP=> Emerging biomedical applications require realistic phantoms for validation and testing of prototype systems. These phantoms require stable and flexible tissue-mimicking materials with realistic dielectric properties in order to properly model human tissues. To create a tissue-mimicking material to fulfill these needs, carbon powder and urethane rubber mixtures were created, and the dielectric properties were measured using a dielectric probe. Both graphite and carbon black were tested. Mixtures of graphite and urethane (0% to 50% by weight) provided relatively low permittivity and conductivity, suitable for mimicking fatty tissues. Mixtures of carbon black and urethane (0% to 15% by weight) provided a broad range of suitable properties. Samples with 15% carbon black had permittivity and conductivity similar to higher-water-content tissues, however the cured samples were not mechanically suitable for moulding into complex shapes. Finally, mixtures of graphite, carbon black, and urethane were created. These exhibited a range of dielectric properties and can be used to mimic a variety of soft tissues. The mechanical properties of these samples were tested and presented properties that exceed typical phantom requirements. This tissue-mimicking material will be useful when creating thin, flexible, and robust structures such as skin layers.

Confocal Microwave Imaging for Breast Cancer Detection: Delay-Multiply-and-Sum Image Reconstruction Algorithm <=SEP=> A new image reconstruction algorithm, termed as delay-multiply-and-sum (DMAS), for breast cancer detection using an ultra-wideband confocal microwave imaging technique is proposed. In DMAS algorithm, the backscattered signals received from numerical breast phantoms simulated using the finite-difference time-domain method are time shifted, multiplied in pair, and the products are summed to form a synthetic focal point. The effectiveness of the DMAS algorithm is shown by applying it to backscattered signals received from a variety of numerical breast phantoms. The reconstructed images illustrate improvement in identification of embedded malignant tumors over the delay-and-sum algorithm. Successful detection and localization of tumors as small as 2 mm in diameter are also demonstrated.

Microwave Breast Imaging With a Monostatic Radar-Based System: A Study of Application to Patients <=SEP=> A prototype microwave breast imaging system is used to scan a small group of patients. The prototype implements a monostatic radar-based approach to microwave imaging and utilizes ultra-wideband signals. Eight patients were successfully scanned, and several of the resulting images show responses consistent with the clinical patient histories. These encouraging results motivate further studies of microwave imaging for breast health assessment.

Precision open-ended coaxial probes for in vivo and ex vivo dielectric spectroscopy of biological tissues at microwave frequencies <=SEP=> Hermetic stainless-steel open-ended coaxial probes have been designed for precision dielectric spectroscopy of biological tissue, such as breast tissue, over the 0.5-20-GHz frequency range. Robust data-processing techniques have also been developed for extracting the unknown permittivity of the tissue under test from the reflection coefficient measured with the precision probe and a vector network analyzer. The first technique, referred to as a reflection-coefficient deembedding method, converts the reflection coefficient measured at the probe's calibration plane to the desired aperture-plane reflection coefficient. The second technique uses a rational function model to solve the inverse problem, i.e., to convert the aperture-plane reflection coefficient to the tissue permittivity. The results of the characterization and validation studies demonstrate that these precision probes, used with the prescribed measurement protocols and data-processing techniques, provide highly accurate and reliable in vivo and ex vivo biological tissue measurements, including breast tissue spectroscopy.

Painting Outside the Box: Image Outpainting with GANs <=SEP=> The challenging task of image outpainting (extrapolation) has received comparatively little attention in relation to its cousin, image inpainting (completion). Accordingly, we present a deep learning approach based on [4] for adversarially training a network to hallucinate past image boundaries. We use a three-phase training schedule to stably train a DCGAN architecture on a subset of the Places365 dataset. In line with [4], we also use local discriminators to enhance the quality of our output. Once trained, our model is able to outpaint 128 × 128 color images relatively realistically, thus allowing for recursive outpainting. Our results show that deep learning approaches to image outpainting are both feasible and promising.

CSLIM: contextual SLIM recommendation algorithms <=SEP=> Context-aware recommender systems (CARS) take contextual conditions into account when providing item recommendations. In recent years, context-aware matrix factorization (CAMF) has emerged as an extension of the matrix factorization technique that also incorporates contextual conditions. In this paper, we introduce another matrix factorization approach for contextual recommendations, the contextual SLIM (CSLIM) recommendation approach. It is derived from the sparse linear method (SLIM) which was designed for Top-N recommendations in traditional recommender systems. Based on the experimental evaluations over several context-aware data sets, we demonstrate that CLSIM can be an effective approach for context-aware recommendations, in many cases outperforming state-of-the-art CARS algorithms in the Top-N recommendation task.

Primal-Dual Group Convolutions for Deep Neural Networks <=SEP=> In this paper, we present a simple and modularized neural network architecture, named primal-dual group convolutional neural networks (PDGCNets). The main point lies in a novel building block, a pair of two successive group convolutions: primal group convolution and dual group convolution. The two group convolutions are complementary: (i) the convolution on each primal partition in primal group convolution is a spatial convolution, while on each dual partition in dual group convolution, the convolution is a point-wise convolution; (ii) the channels in the same dual partition come from different primal partitions. We discuss one representative advantage: Wider than a regular convolution with the number of parameters and the computation complexity preserved. We also show that regular convolutions, group convolution with summation fusion (as used in ResNeXt), and the Xception block are special cases of primal-dual group convolutions. Empirical results over standard benchmarks, CIFAR-10, CIFAR-100, SVHN and ImageNet demonstrate that our networks are more efficient in using parameters and computation complexity with similar or higher accuracy.

DC-DC converters for electric vehicle applications <=SEP=> An integral part of any modern day electric vehicle is power electronic circuits (PECs) comprising of DC-AC inverters and DC-DC converters. A DC-AC inverter supplies the high power electric motor and utility loads such as air-conditioning system, whereas a DC-DC converter supplies conventional low-power, low-voltage loads. However, the need for high power bidirectional DC- DC converters in future electric vehicles has led to the development of many new topologies of DC-DC converters. This paper presents an overview of state-of-the-art DC-DC converters used in battery electric vehicles (BEVs), hybrid electric vehicles (HEVs), and fuel cell vehicles (FCVs). Several DC-DC converters such as isolated, nonisolated, half-bridge, full-bridge, unidirectional and bidirectional topologies, and their applications in electric vehicles are presented.

Deep Learning and Wavelets for High-Frequency Price Forecasting <=SEP=> This paper presents improvements in financial time series prediction using a Deep Neural Network (DNN) in conjunction with a Discrete Wavelet Transform (DWT). When comparing our model to other three alternatives, including ARIMA and other deep learning topologies, ours has a better performance. All of the experiments were conducted on High-Frequency Data (HFD). Given the fact that DWT decomposes signals in terms of frequency and time, we expect this transformation will make a better representation of the sequential behavior of high-frequency data. The input data for every experiment consists of 27 variables: The last 3 one-minute pseudo-log-returns and last 3 oneminute compressed tick-by-tick wavelet vectors, each vector is a product of compressing the tick-by-tick transactions inside a particular minute using a DWT with length 8. Furthermore, the DNN predicts the next one-minute pseudo-log-return that can be transformed into the next predicted one-minute average price. For testing purposes, we use tick-by-tick data of 19 companies in the Dow Jones Industrial Average Index (DJIA), from January 2015 to July 2017. The proposed DNN’s Directional Accuracy (DA) presents a remarkable forecasting performance ranging from 64% to 72%.

Multipath TCP path schedulers for streaming video <=SEP=> Modern internet traffic is heavily composed of some form of video communication. In addition, video streaming is slowly replacing regular phone connections. Given that most video transport utilizes Video over Hypertext Transfer Protocol/Transmission Control Protocol (HTTP/TCP), it is important to understand TCP performance in transporting video streams. Recently, multipath transport protocols have allowed video streaming over multiple paths to become a reality. In this paper, we investigate packet scheduling disciplines for injecting video stream packets into multiple paths at the video server. We study video streaming performance when subjected to these schedulers in conjunction with various TCP variants. We utilize network performance measures, as well as video quality metrics, to characterize the performance and interactions between network and application layers of video streams for various network scenarios.

Time-Dependent Trajectory Regression on Road Networks via Multi-Task Learning <=SEP=> Road travel costs are important knowledge hidden in largescale GPS trajectory data sets, the discovery of which can benefit many applications such as intelligent route planning and automatic driving navigation. While there are previous studies which tackled this task by modeling it as a regression problem with spatial smoothness taken into account, they unreasonably assumed that the latent cost of each road remains unchanged over time. Other works on route planning and recommendation that have considered temporal factors simply assumed that the temporal dynamics be known in advance as a parametric function over time, which is not faithful to reality. To overcome these limitations, in this paper, we propose an extension to a previous static trajectory regression framework by learning the temporal dynamics of road travel costs in an innovative non-parametric manner which can effectively overcome the temporal sparsity problem. In particular, we unify multiple different trajectory regression problems in a multi-task framework by introducing a novel crosstask regularization which encourages temporal smoothness on the change of road travel costs. We then propose an efficient block coordinate descent method to solve the resulting problem by exploiting its separable structures and prove its convergence to global optimum. Experiments conducted on both synthetic and real data sets demonstrate the effectiveness of our method and its improved accuracy on travel time

Robust detection of non-motorized road users using deep learning on optical and LIDAR data <=SEP=> Detection of non-motorized road users, such as cyclists and pedestrians, is a challenging problem in collision warning/collision avoidance (CW/CA) systems as direct information (e.g. location, speed, and class) cannot be obtained from such users. In this paper, we propose a fusion of LIDAR data and a deep learning-based computer vision algorithm, to substantially improve the detection of regions of interest (ROIs) and subsequent identification of road users. Experimental results on the KITTI object detection benchmark quantify the effectiveness of incorporating LIDAR data with region-based deep convolutional networks. Thus our work provides another step towards the goal of designing safe and smart transportation systems of the future.

Production and structural elucidation of exopolysaccharide from endophytic Pestalotiopsis sp. BC55. <=SEP=> There is a little information on exopolysaccharide production by endophytic fungi. In this investigation endophytic Pestalotiopsis sp. BC55 was used for optimization of exopolysaccharide production. One variable at a time method and response surface methodology were adopted to find out the best culture conditions and medium compositions for maximum exopolysaccharide production. The organism produced maximum exopolysaccharide (4.320 ± 0.022 g/l EPS) in 250 ml Erlenmeyer flask containing 75 ml potato dextrose broth supplemented with (g%/l) glucose, 7.66; urea, 0.29; CaCl2, 0.05 with medium pH 6.93; after 3.76 days of incubation at 24°C. Exopolysaccharide [EPS (EP-I)] produced by this organism have Mw ∼2×10(5)Da with a melting point range of 122-124°C. Structural elucidation of the EPS (PS-I) was carried out after a series of experiments. Result indicated the presence of only (1→3)-linked β-d-glucopyranosyl moiety. The structure of the repeating unit was established as - →3)-β-d-Glcp-(1→.

Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems <=SEP=> Numerical solution of ill-posed problems is often accomplished by discretization (projection onto a finite dimensional subspace) followed by regularization. If the discrete problem has high dimension, though, typically we compute an approximate solution by projecting the discrete problem onto an even smaller dimensional space, via iterative methods based on Krylov subspaces. In this work we present a common framework for efficient algorithms that regularize after this second projection rather than before it. We show that determining regularization parameters based on the final projected problem rather than on the original discretization has firmer justification and often involves less computational expense. We prove some results on the approximate equivalence of this approach to other forms of regularization, and we present numerical examples.

Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries <=SEP=> Query expansion is an important and commonly used technique for improving Web search results. Existing methods for query expansion have mostly relied on global or local analysis of document collection, click-through data, or simple ontologies such as WordNet. In this paper, we present the results of a systematic study of the methods leveraging the ConceptNet knowledge base, an emerging new Web resource, for query expansion. Specifically, we focus on the methods leveraging ConceptNet to improve the search results for poorly performing (or difficult) queries. Unlike other lexico-semantic resources, such as WordNet and Wikipedia, which have been extensively studied in the past, ConceptNet features a graph-based representation model of commonsense knowledge, in which the terms are conceptually related through rich relational ontology. Such representation structure enables complex, multi-step inferences between the concepts, which can be applied to query expansion. We first demonstrate through simulation experiments that expanding queries with the related concepts from ConceptNet has great potential for improving the search results for difficult queries. We then propose and study several supervised and unsupervised methods for selecting the concepts from ConceptNet for automatic query expansion. The experimental results on multiple data sets indicate that the proposed methods can effectively leverage ConceptNet to improve the retrieval performance of difficult queries both when used in isolation as well as in combination with pseudo-relevance feedback.

Measuring pictorial balance perception at first glance using Japanese calligraphy <=SEP=> According to art theory, pictorial balance acts to unify picture elements into a cohesive composition. For asymmetrical compositions, balancing elements is thought to be similar to balancing mechanical weights in a framework of symmetry axes. Assessment of preference for balance (APB), based on the symmetry-axes framework suggested in Arnheim R, 1974 Art and Visual Perception: A Psychology of the Creative Eye (Berkeley, CA: University of California Press), successfully matched subject balance ratings of images of geometrical shapes over unlimited viewing time. We now examine pictorial balance perception of Japanese calligraphy during first fixation, isolated from later cognitive processes, comparing APB measures with results from balance-rating and comparison tasks. Results show high between-task correlation, but low correlation with APB. We repeated the rating task, expanding the image set to include five rotations of each image, comparing balance perception of artist and novice participant groups. Rotation has no effect on APB balance computation but dramatically affects balance rating, especially for art experts. We analyze the variety of rotation effects and suggest that, rather than depending on element size and position relative to symmetry axes, first fixation balance processing derives from global processes such as grouping of lines and shapes, object recognition, preference for horizontal and vertical elements, closure, and completion, enhanced by vertical symmetry.

Development of an Electromagnetic Actuated Microdisplacement Module <=SEP=> This paper presents the mechanical design and control system design of an electromagnetic actuator-based microdisplacement module. The microdisplacement module composed of a symmetrical leaf-spring parallelogram mechanism and an electromagnetic actuator. The characteristics of the mechanism in terms of stiffness and natural frequencies are derived and verified. Both leakage flux and core inductance are taken into consideration during modeling the mathematic model of the electromagnetic actuator, and based on which, the system dynamic model is established. Due to the nonlinearity characteristics of the system, a dynamic sliding-mode controller is designed without linearizing the system dynamics. A prototype of the microdisplacement module is fabricated, and the parameters of the system are identified and calibrated. Finally, the designed dynamic sliding-mode controller is applied; step response and tracking performance are studied. Experimental results demonstrate that a submicrometer accuracy can be achieved by the module, which validate the effectiveness of the proposed mechanism and controller design as well. The research results show that the electromagnetic actuator-based module can be extended to wide applications in the field of micro/nanopositioning and manipulation.

PCANet: A Simple Deep Learning Baseline for Image Classification? <=SEP=> In this paper, we propose a very simple deep learning network for image classification that is based on very basic data processing components: 1) cascaded principal component analysis (PCA); 2) binary hashing; and 3) blockwise histograms. In the proposed architecture, the PCA is employed to learn multistage filter banks. This is followed by simple binary hashing and block histograms for indexing and pooling. This architecture is thus called the PCA network (PCANet) and can be extremely easily and efficiently designed and learned. For comparison and to provide a better understanding, we also introduce and study two simple variations of PCANet: 1) RandNet and 2) LDANet. They share the same topology as PCANet, but their cascaded filters are either randomly selected or learned from linear discriminant analysis. We have extensively tested these basic networks on many benchmark visual data sets for different tasks, including Labeled Faces in the Wild (LFW) for face verification; the MultiPIE, Extended Yale B, AR, Facial Recognition Technology (FERET) data sets for face recognition; and MNIST for hand-written digit recognition. Surprisingly, for all tasks, such a seemingly naive PCANet model is on par with the state-of-the-art features either prefixed, highly hand-crafted, or carefully learned [by deep neural networks (DNNs)]. Even more surprisingly, the model sets new records for many classification tasks on the Extended Yale B, AR, and FERET data sets and on MNIST variations. Additional experiments on other public data sets also demonstrate the potential of PCANet to serve as a simple but highly competitive baseline for texture classification and object recognition.

A SiC-Based High-Efficiency Isolated Onboard PEV Charger With Ultrawide DC-Link Voltage Range <=SEP=> In LLC-based onboard battery charging architectures used in plug-in electric vehicles (PEV), the dc link voltage can be actively regulated to follow the battery pack voltage so that the LLC converter can operate in proximity of resonant frequency and achieve high efficiencies over the wide range of battery pack voltage. However, conventional boost-type power factor correction (PFC) converters are unable to provide ultrawide dc link voltages since their output voltages should always be larger than their input voltages. This paper proposes a Silicon Carbide (SiC)-based onboard PEV charger using single-ended primary-inductor converter (SEPIC) PFC converter followed by an isolated LLC resonant converter. With the proposed charger architecture, the SEPIC PFC converter is able to provide an ultrawide range for dc link voltage, and consequently enhance the efficiency of the LLC stage by ensuring operation in proximity of resonant frequency. A 1-kW SiC-based prototype is designed to validate the proposed idea. The experimental result shows that the SEPIC PFC converter achieves unity power factor, 2.72% total harmonic distortion, and 95.3% peak conversion efficiency. The LLC  converter achieves 97.1% peak efficiency and always demonstrates a very high efficiency across the ultrawide dc-link voltage range. The overall efficiency of the charger is 88.5% to 93.5% from 20% of the rated load to full load.

Development of Computer-Aided Diagnostic (CADx) System for Distinguishing Neoplastic from Nonneoplastic Lesions in CT Colonography (CTC): Toward CTC beyond Detection <=SEP=> Half of the polyps surgically removed during conventional colonoscopy are benign with no malignant potential. Our purpose was to develop a CADx system for distinction between neoplastic and non-neoplastic lesions in CTC to reduce "unnecessary" colonoscopic polypectomy. Although computer aided detection (CADe) systems have been developed, less attention was given to the development of CADx systems. Our CADx system consists of shape-index-based coarse segmentation of lesions, 3D volume growing and sub-voxel refinement for fine segmentation of lesions, morphologic and texture feature analysis, Wilks' lambda-based stepwise feature selection, linear discriminant analysis for providing an integrated imaging biomarker for diagnosis of neoplastic lesions. Our database contained biopsy-confirmed 54 neoplastic lesions in 29 patients and 14 non-neoplastic lesions in 10 patients. Our CADx system integrating the selected features was able to determine an accurate likelihood of being a neoplasm and distinguish 87% (47/54) neoplastic lesions from 57% (8/14) non-neoplastic lesions correctly only using computed tomography (CT) images, achieving an area under the receiver operating characteristic curve (AUC) of 0.82. This study showed the potential of the use of CTC as a diagnostic tool beyond already accepted detection, thus, CTC with CADx would be potentially useful for reducing "unnecessary" polypectomy.

Exercise with blood flow restriction: an updated evidence-based approach for enhanced muscular development. <=SEP=> A growing body of evidence supports the use of moderate blood flow restriction (BFR) combined with low-load resistance exercise to enhance hypertrophic and strength responses in skeletal muscle. Research also suggests that BFR during low-workload aerobic exercise can result in small but significant morphological and strength gains, and BFR alone may attenuate atrophy during periods of unloading. While BFR appears to be beneficial for both clinical and athletic cohorts, there is currently no common consensus amongst scientists and practitioners regarding the best practice for implementing BFR methods. If BFR is not employed appropriately, there is a risk of injury to the participant. It is also important to understand how variations in the cuff application can affect the physiological responses and subsequent adaptation to BFR training. The optimal way to manipulate acute exercise variables, such as exercise type, load, volume, inter-set rest periods and training frequency, must also be considered prior to designing a BFR training programme. The purpose of this review is to provide an evidence-based approach to implementing BFR exercise. These guidelines could be useful for practitioners using BFR training in either clinical or athletic settings, or for researchers in the design of future studies investigating BFR exercise.

Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clustering <=SEP=> This paper proposes a new dimensionality reduction algorithm named branching embedding (BE). It converts a dendrogram to a two-dimensional scatter plot, and visualizes the inherent structures of the original high-dimensional data. Since the conversion part is not computationally demanding, the BE algorithm would be beneficial for the case where hierarchical clustering is already performed. Numerical experiments revealed that the outputs of the algorithm moderately preserve the original hierarchical structures.

POINTWISE: Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data <=SEP=> Basketball is a game of decisions; at any moment, a player can change the character of a possession by choosing to pass, dribble, or shoot. The current state of basketball analytics, however, provides no way to quantitatively evaluate the vast majority of decisions that players make, as most metrics are driven by events that occur at or near the end of a possession, such as points, turnovers, and assists. We propose a framework for using player-tracking data to assign a point value to each moment of a possession by computing how many points the offense is expected to score by the end of the possession, a quantity we call expected possession value (EPV). EPV allows analysts to evaluate every decision made during a basketball game – whether it is to pass, dribble, or shoot – opening the door for a multitude of new metrics and analyses of basketball that quantify value in terms of points. In this paper, we propose a modeling framework for estimating EPV, present results of EPV computations performed using playertracking data from the 2012-13 season, and provide several examples of EPV-derived metrics that answer real basketball questions. A new microeconomics for the NBA Basketball players, coaches, and fans often compare a possession to a high-speed chess match, where teams employ tactics that do not necessarily generate points immediately, but can yield higher-value opportunities several “moves” down the line. Watching the game in this way can reveal that the decisive moment in a given possession may not have been the open shot at the end, but the pass that led to the open shot, or even the preceding drive that collapsed the defense. These ideas lie at the heart of offensive strategies and the decisions that players make over the course of a possession. Unfortunately, contemporary basketball analytics fail to account for this core idea. Despite many recent innovations, most advanced metrics (PER [1] and +/variations [2], for example) remain based on simple tallies relating to the terminal states of possessions like points, rebounds, and turnovers. While these have shed light on the game, they are akin to analyzing a chess match based only on the move that resulted in checkmate, leaving unexplored the possibility that the key move occurred several turns before. This leaves a major gap to be filled, as an understanding of how players contribute to the whole possession – not just the events that end it – can be critical in evaluating players, assessing the quality of their decision-making, and predicting the success of particular in-game tactics. The major obstacle to closing this gap is the current inability to evaluate the individual tactical decisions that form the substructure of every possession of every basketball game. For example, there is no current method to estimate the value of a dribble penetration or to compare the option of taking a contested shot to the option of passing to an open teammate. In this paper, we propose and implement a framework that removes this obstacle. Using player-tracking data, we develop a coherent, quantitative representation of a whole possession that summarizes each moment of the possession in terms of the number of points the offense is expected to score – a quantity we call expected possession value, or EPV (see Figure 1 for an illustration of EPV). We accomplish this by specifying and fitting a probabilistic model that encodes how ball handlers make decisions based on the spatial configuration of the players on the court.

Mobile Phishing Attacks and Mitigation Techniques <=SEP=> Health Information Technology (HIT) professionals are in increasing demand as healthcare providers need help in the adoption and meaningful use of Electronic Health Record (EHR) systems while the HIT industry needs workforce skilled in HIT and EHR development. To respond to this increasing demand, the School of Computing and Software Engineering at Southern Polytechnic State University designed and implemented a series of HIT educational programs. This paper summarizes our experience in the HIT curriculum development and provides an overview of HIT workforce development initiatives and major HIT and health information management (HIM) educational resources. It also provides instructional implications and experiences for positioning HIT programs and enhancing curriculum.

Resin composite--state of the art. <=SEP=> OBJECTIVES
The objective is to review the current state of the art of dental composite materials.


METHODS
An outline of the most important aspects of dental composites was created, and a subsequent literature search for articles related to their formulation, properties and clinical considerations was conducted using PubMed followed by hand searching citations from relevant articles.


RESULTS
The current state of the art of dental composites includes a wide variety of materials with a broad range of mechanical properties, handling characteristics, and esthetic possibilities. This highly competitive market continues to evolve, with the major emphasis in the past being to produce materials with adequate strength, and high wear resistance and polishability retention. The more recent research and development efforts have addressed the issue of polymerization shrinkage and its accompanying stress, which may have a deleterious effect on the composite/tooth interfacial bond. Current efforts are focused on the delivery of materials with potentially therapeutic benefits and self-adhesive properties, the latter leading to truly simplified placement in the mouth.


SIGNIFICANCE
There is no one ideal material available to the clinician, but the commercial materials that comprise the current armamentarium are of high quality and when used appropriately, have proven to deliver excellent clinical outcomes of adequate longevity.

Design of prototype humanoid robotics platform for HRP <=SEP=> This paper presents a prototype humanoid robotics platform developed for HRP-2. HRP-2 is a new humanoid robotics platform, which we have been developing in phase two of HRP. HRP is a humanoid robotics project, which has been lunched by Ministry of Economy, Trade and Industry (METI) of Japan from 1998FY to 2002FY for five years. The ability of the biped locomotion of HRP-2 is improved so that HRP-2 can cope with rough terrain in the open air and can prevent the possible damages to a humanoid robot’s own self in the event of tipping over. The ability of whole body motion of HRP-2 is also improved so that HRP-2 can get up by a humanoid robot’s own self even tough HRP-2 tips over. In this paper, the mechanisms and specifications of developed prototype humanoid robotics platform, and its electrical system are introduced.

Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition <=SEP=> End-to-end neural network models for named entity recognition (NER) have shown to achieve effective performances on general domain datasets (e.g. newswire), without requiring additional hand-crafted features. However, in biomedical domain, recent studies have shown that handengineered features (e.g. orthographic features) should be used to attain effective performance, due to the complexity of biomedical terminology (e.g. the use of acronyms and complex gene names). In this work, we propose a novel approach that allows a neural network model based on a long short-term memory (LSTM) to automatically learn orthographic features and incorporate them into a model for biomedical NER. Importantly, our bi-directional LSTM model learns and leverages orthographic features on an end-to-end basis. We evaluate our approach by comparing against existing neural network models for NER using three well-established biomedical datasets. Our experimental results show that the proposed approach consistently outperforms these strong baselines across all of the three datasets.

The attention system of the human brain. <=SEP=> The concept of attention as central to human performance extends back to the start of experimental psychology (James 1890), yet even a few years ago, it would not have been possible to outline in even a preliminary form a functional anatomy of the human attentional system. New developments in neuroscience (Hillyard & Picton 1987, Raichle 1983, Wurtz et al 1980) have opened the study of higher cognition to physiological analysis, and have revealed a system of anatomical areas that appear to be basic to the selection of information for focal (conscious) processing. The importance of attention is its unique role in connecting the mental level of description of processes used in cognitive science with the anatomical level common i neuroscience. Sperry (1988, p. 609) describes the central role that mental concepts play in understanding brain function as follows:

Improving Collocated Robot Teleoperation with Augmented Reality <=SEP=> Robot teleoperation can be a challenging task, often requiring a great deal of user training and expertise, especially for platforms with high degrees-of-freedom (e.g., industrial manipulators and aerial robots). Users often struggle to synthesize information robots collect (e.g., a camera stream) with contextual knowledge of how the robot is moving in the environment. We explore how advances in augmented reality (AR) technologies are creating a new design space for mediating robot teleoperation by enabling novel forms of intuitive, visual feedback. We prototype several aerial robot teleoperation interfaces using AR, which we evaluate in a 48-participant user study where participants completed an environmental inspection task. Our new interface designs provided several objective and subjective performance benefits over existing systems, which often force users into an undesirable paradigm that divides user attention between monitoring the robot and monitoring the robot»s camera feed(s).