query: Wavelet-based statistical signal processing using hidden Markov models

positive: – “ Mixture Densities , Maximum Likelihood and the EM Algorithm The problem of estimating the parameters which determine a mixture density has been the subject of a large, diverse body of literature spanning nearly ninety years. During the last two decades, the method of maximum likelihood has become the most widely followed approach to this problem, thanks primarily to the advent of high speed electronic computers. Here, we first offer a brief survey of the literature directed toward this problem and review maximum-likelihood estimation for it. We then turn to the subject of ultimate interest, which is a particular iterative procedure for numerically approximating maximum-likelihood estimates for mixture density problems. This procedure, known as the EM algorithm, is a specialization to the mixture density context of a general algorithm of the same name used to approximate maximum-likelihood estimates for incomplete data problems. We discuss the formulation and theoretical and practical properties of the EM algorithm for mixture densities, focussing in particular on mixtures of densities from exponential families.

positive: A multiscale random field model for Bayesian image segmentation Many approaches to Bayesian image segmentation have used maximum a posteriori (MAP) estimation in conjunction with Markov random fields (MRF). Although this approach performs well, it has a number of disadvantages. In particular, exact MAP estimates cannot be computed, approximate MAP estimates are computationally expensive to compute, and unsupervised parameter estimation of the MRF is difficult. The authors propose a new approach to Bayesian image segmentation that directly addresses these problems. The new method replaces the MRF model with a novel multiscale random field (MSRF) and replaces the MAP estimator with a sequential MAP (SMAP) estimator derived from a novel estimation criteria. Together, the proposed estimator and model result in a segmentation algorithm that is not iterative and can be computed in time proportional to MN where M is the number of classes and N is the number of pixels. The also develop a computationally efficient method for unsupervised estimation of model parameters. Simulations on synthetic images indicate that the new algorithm performs better and requires much less computation than MAP estimation using simulated annealing. The algorithm is also found to improve classification accuracy when applied to the segmentation of multispectral remotely sensed images with ground truth data.

positive: Singularity detection and processing with wavelets Most of a signal information is often carried by irregular structures and transient phenomena. The mathematical characterization of singularities with Lipschitz exponents is explained. Theorems are reviewed that estimate local Lipschitz exponents of functions from the evolution across scales of their wavelet transform. It is then proven that the local maxima of the wavelet transform modulus detect the locations of irregular structures and provide numerical procedures to compute their Lipschitz exponents. The wavelet transform of singularities with fast oscillations have a particular behavior that is studied separately. The local frequency of such oscillations are measured from the wavelet transform modulus maxima. It has been shown numerically that oneand two-dimensional signals can be reconstructed, with a good approximation, from the local maxima of their wavelet transform modulus. As an application, an algorithm is developed that removes white noises from signals by analyzing the evolution of the wavelet transform maxima across scales. In two-dimensions, the wavelet transform maxima indicate the location of edges in images. The denoising algorithm is extended for image enhancement.

positive: Embedded image coding using zerotrees of wavelet coefficients The embedded zerotree wavelet algorithm (EZW) is a simple, yet remarkably effective, image compression algorithm, having the property that the bits in the bit stream are generated in order of importance, yielding a fully embedded code. The embedded code represents a sequence of binary decisions that distinguish a n image from the “null” image. Using a n embedded coding algorithm, a n encoder can terminate the encoding a t any point thereby allowing a target rate or target distortion metric to be met exactly. Also, given a bit stream, the decoder can cease decoding a t any point in the bit stream and still produce exactly the same image that would have been encoded a t the bit rate corresponding to the truncated bit stream. In addition to producing a fully embedded bit stream, EZW consistently produces compression results that a re competitive with virtually all known compression algorithms on standard test images. Yet this performance is achieved with a technique that requires absolutely no training, no pre-stored tables or codebooks, and requires no prior knowledge of the image source. The EZW algorithm is based on four key concepts: 1) a discrete wavelet transform or hierarchical subband decomposition, 2) prediction of the absence of significant information across scales by exploiting the self-similarity inherent in images, 3) entropy-coded successive-approximation quantization, and 4) universal lossless data compression which is achieved via adaptive arithmetic coding.

positive: A tutorial on hidden Markov models and selected applications in speech recognition

negative: A 600MS/s 30mW 0.13µm CMOS ADC array achieving over 60dB SFDR with adaptive digital equalization At high conversion speed, time interleaving provides a viable way of achieving analog-to-digital conversion with low power consumption, especially when combined with the successive-approximation-register (SAR) architecture that is known to scale well in CMOS technology. In this work, we showcase a digital background-equalization technique to treat the path-mismatch problem as well as individual ADC nonlinearities in time-interleaved SAR ADC arrays. This approach was first introduced to calibrate the linearity errors in pipelined ADCs [1], and subsequently extended to treat SAR ADCs [2]. In this prototype, we demonstrate the effectiveness of this technique in a compact SAR ADC array, which achieves 7.5 ENOB and a 65dB SFDR at 600MS/s while dissipating 23.6mW excluding the on-chip DLL, and exhibiting one of the best conversion FOMs among ADCs with similar sample rates and resolutions [3].

negative: LateralPaD: A surface-haptic device that produces lateral forces on a bare finger The LateralPaD is a surface haptic device that generates lateral (shear) force on a bare finger, by vibrating the touch surface simultaneously in both out-of-plane (normal) and in-plane (lateral) directions. A prototype LateralPaD has been developed in which the touch surface is glass, and piezoelectric actuators drive normal and lateral resonances at the same ultrasonic frequency (~22.3 KHz). The force that develops on the finger can be controlled by modulating the relative phase of the two resonances. A 2DOF load cell setup is used to characterize the dependence of induced lateral force on vibration amplitude, relative phase, and applied normal force. A Laser Doppler Vibrometer (LDV) is used to measure the motion of glass surface as well as of the fingertip. Together, these measurements yield insight into the mechanism of lateral force generation. We show evidence for a mechanism dependent on tilted impacts between the LateralPaD and fingertip.

negative: A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution We address the problem of image upscaling in the form of single image super-resolution based on a dictionary of lowand highresolution exemplars. Two recently proposed methods, Anchored Neighborhood Regression (ANR) and Simple Functions (SF), provide state-ofthe-art quality performance. Moreover, ANR is among the fastest known super-resolution methods. ANR learns sparse dictionaries and regressors anchored to the dictionary atoms. SF relies on clusters and corresponding learned functions. We propose A+, an improved variant of ANR, which combines the best qualities of ANR and SF. A+ builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF. We validate our method on standard images and compare with state-of-the-art methods. We obtain improved quality (i.e. 0.2-0.7dB PSNR better than ANR) and excellent time complexity, rendering A+ the most efficient dictionary-based super-resolution method to date.

negative: Experience Prototyping In this paper, we describe "Experience Prototyping" as a form of prototyping that enables design team members, users and clients to gain first-hand appreciation of existing or future conditions through active engagement with prototypes. We use examples from commercial design projects to illustrate the value of such prototypes in three critical design activities: understanding existing experiences, exploring design ideas and in communicating design concepts.

negative:
Supernumerary nostril: a case report BACKGROUND
Supernumerary nostril is a congenital anomaly that contains additional nostril with or without accessory cartilage. These rare congenital nasal deformities result from embryological defects. Since 1906, Lindsay (Trans Pathol Soc Lond. 57:329-330, 1906) has published the first research of bilateral supernumerary nostrils, and only 34 cases have been reported so far in the English literature.

CASE PRESENTATION
A 1-year-old female baby was brought to our department group for the treatment of an accessory opening above the left nostril which had been presented since her birth. Medical history was non-specific and her birth was normal. The size of a supernumerary nostril was about 0.2 cm diameter and connected to the left nostril. The right one was normal. Minimal procedure was operated for the anomaly. After 1 year, rhinoplasty was performed for the nostril asymmetry.

CONCLUSIONS
At 1 year follow-up, the functional and cosmetic result was satisfactory. In this case, it is important that we have early preoperative diagnosis. Also, it is desirable that we should perform a corrective surgery as soon as possible for the patient's psychosocial growth.

negative:
ADvanced IMage Algebra (ADIMA): a novel method for depicting multiple
sclerosis lesion heterogeneity, as demonstrated by quantitative MRI BACKGROUND
There are modest correlations between multiple sclerosis (MS) disability and white matter lesion (WML) volumes, as measured by T2-weighted (T2w) magnetic resonance imaging (MRI) scans (T2-WML). This may partly reflect pathological heterogeneity in WMLs, which is not apparent on T2w scans.

OBJECTIVE
To determine if ADvanced IMage Algebra (ADIMA), a novel MRI post-processing method, can reveal WML heterogeneity from proton-density weighted (PDw) and T2w images.

METHODS
We obtained conventional PDw and T2w images from 10 patients with relapsing-remitting MS (RRMS) and ADIMA images were calculated from these. We classified all WML into bright (ADIMA-b) and dark (ADIMA-d) sub-regions, which were segmented. We obtained conventional T2-WML and T1-WML volumes for comparison, as well as the following quantitative magnetic resonance parameters: magnetisation transfer ratio (MTR), T1 and T2. Also, we assessed the reproducibility of the segmentation for ADIMA-b, ADIMA-d and T2-WML.

RESULTS
Our study's ADIMA-derived volumes correlated with conventional lesion volumes (p < 0.05). ADIMA-b exhibited higher T1 and T2, and lower MTR than the T2-WML (p < 0.001). Despite the similarity in T1 values between ADIMA-b and T1-WML, these regions were only partly overlapping with each other. ADIMA-d exhibited quantitative characteristics similar to T2-WML; however, they were only partly overlapping. Mean intra- and inter-observer coefficients of variation for ADIMA-b, ADIMA-d and T2-WML volumes were all < 6 % and < 10 %, respectively.

CONCLUSION
ADIMA enabled the simple classification of WML into two groups having different quantitative magnetic resonance properties, which can be reproducibly distinguished.

negative: Hybrid speech recognition with Deep Bidirectional LSTM Deep Bidirectional LSTM (DBLSTM) recurrent neural networks have recently been shown to give state-of-the-art performance on the TIMIT speech database. However, the results in that work relied on recurrent-neural-network-specific objective functions, which are difficult to integrate with existing large vocabulary speech recognition systems. This paper investigates the use of DBLSTM as an acoustic model in a standard neural network-HMM hybrid system. We find that a DBLSTM-HMM hybrid gives equally good results on TIMIT as the previous work. It also outperforms both GMM and deep network benchmarks on a subset of the Wall Street Journal corpus. However the improvement in word error rate over the deep network is modest, despite a great increase in framelevel accuracy. We conclude that the hybrid approach with DBLSTM appears to be well suited for tasks where acoustic modelling predominates. Further investigation needs to be conducted to understand how to better leverage the improvements in frame-level accuracy towards better word error rates.

negative: A Survey of Logical Models for OLAP Databases In this paper, we present different proposals for multidimensional data cubes, which are the basic logical model for OLAP applications. We have grouped the work in the field in two categories: commercial tools (presented along with terminology and standards) and academic efforts. We further divide the academic efforts in two subcategories: the relational model extensions and the cube-oriented approaches. Finally, we attempt a comparative analysis of the various efforts.

negative: Design of Multiple-Level Hybrid Classifier for Intrusion Detection System As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. However, constructing and maintaining a misuse detection system is very labor-intensive since attack scenarios and patterns need to be analyzed and categorized, and the corresponding rules and patterns need to be carefully hand-coded. Thus, data mining can be used to ease this inconvenience. This paper proposes a multiple-level hybrid classifier, an intrusion detection system that uses a combination of tree classifiers and clustering algorithms to detect intrusions. Performance of this new algorithm is compared to other popular approaches such as MADAM ID and 3-level tree classifiers, and significant improvement has been achieved from the viewpoint of both high intrusion detection rate and reasonably low false alarm rate

negative: Project Lachesis: Parsing and Modeling Location Histories A datatype with increasing importance in GIS is what we call the location history–a record of an entity’s location in geographical space over an interval of time. This paper proposes a number of rigorously defined data structures and algorithms for analyzing and generating location histories. Stays are instances where a subject has spent some time at a single location, and destinations are clusters of stays. Using stays and destinations, we then propose two methods for modeling location histories probabilistically. Experiments show the value of these data structures, as well as the possible applications of probabilistic models of location histories.

negative: Extending feature usage: a study of the post-adoption of electronic medical records In order to study the factors that influence how professionals use complex systems, we create a tentative research model that builds on PAM and TAM. Specifically we include PEOU and the construct ‘Professional Association Guidance’. We postulate that feature usage is enhanced when professional associations influence PU by highlighting additional benefits. We explore the theory in the context of post-adoption use of Electronic Medical Records (EMRs) by primary care physicians in Ontario. The methodology can be extended to other professional environments and we suggest directions for future research.

negative: Information theory-based shot cut/fade detection and video summarization New methods for detecting shot boundaries in video sequences and for extracting key frames using metrics based on information theory are proposed. The method for shot boundary detection relies on the mutual information (MI) and the joint entropy (JE) between the frames. It can detect cuts, fade-ins and fade-outs. The detection technique was tested on the TRECVID2003 video test set having different types of shots and containing significant object and camera motion inside the shots. It is demonstrated that the method detects both fades and abrupt cuts with high accuracy. The information theory measure provides us with better results because it exploits the inter-frame information in a more compact way than frame subtraction. It was also successfully compared to other methods published in literature. The method for key frame extraction uses MI as well. We show that it captures satisfactorily the visual content of the shot.

negative: Small Discrete Fourier Transforms on GPUs Efficient implementations of the Discrete Fourier Transform (DFT) for GPUs provide good performance with large data sizes, but are not competitive with CPU code for small data sizes. On the other hand, several applications perform multiple DFTs on small data sizes. In fact, even algorithms for large data sizes use a divide-and-conquer approach, where eventually small DFTs need to be performed. We discuss our DFT implementation, which is efficient for multiple small DFTs. One feature of our implementation is the use of the asymptotically slow matrix multiplication approach for small data sizes, which improves performance on the GPU due to its regular memory access and computational patterns. We combine this algorithm with the mixed radix algorithm for 1-D, 2-D, and 3-D complex DFTs. We also demonstrate the effect of different optimization techniques. When GPUs are used to accelerate a component of an application running on the host, it is important that decisions taken to optimize the GPU performance not affect the performance of the rest of the application on the host. One feature of our implementation is that we use a data layout that is not optimal for the GPU so that the overall effect on the application is better. Our implementation performs up to two orders of magnitude faster than cuFFT on an NVIDIA GeForce 9800 GTX GPU and up to one to two orders of magnitude faster than FFTW on a CPU for multiple small DFTs. Furthermore, we show that our implementation can accelerate the performance of a Quantum Monte Carlo application for which cuFFT is not effective. The primary contributions of this work lie in demonstrating the utility of the matrix multiplication approach and also in providing an implementation that is efficient for small DFTs when a GPU is used to accelerate an application running on the host.

negative: Groundnut leaf disease detection and classification by using back probagation algorithm Many studies shows that quality of agricultural products may be reduced from many causes. One of the most important factors contributing to low yield is disease attack. The plant disease such as fungi, bacteria and viruses. The leaf disease completely destroys the quality of the leaf. Common ground nut disease is cercospora. It is one of the type of disease in early stage of ground nut leaf. The upgraded processing pattern comprises of four leading steps. Initially a color renovation constitute intended for the input RGB image is formed, This RGB is converted into HSV because RGB is for Color generation and color descriptor. The next step is plane separation. Next performed the color features. Then using back propagation algorithm detection of leaf disease is done.

negative: Development of Robotic Gloves for Hand Rehabilitation Post-Stroke This paper presents the hardware and software developments that have been achieved and implemented in the robotic gloves for hand rehabilitation post-stroke. The practical results are shown that prove the functionalities of the robotic gloves in common operating conditions. This work focused on development of a lightweight and low-cost robotic glove that patients can wear and use to recover hand functionality. We developed some structures for the robotic glove, as exoskeleton and as soft robotic glove. The paper presents a comparison of these structures.

negative: Customised Visualisations of Linked Open Data This paper aims to tackle on Linked Open Data (LOD) customised visualisations. The work is part of an ongoing research on interfaces and tools for helping non-programmers to easily present, analyse and support sense making over large semantic dataset. The customisation is a key aspect of our work. Producing effective customised visualisations is still difficult due to the complexity of the existing tools and the limited set of options they offer, especially to those with little knowledge in LOD and semantic data models. How can we give users full control on the primitives of the visualisation and their properties, without requiring them to master Semantic Web technologies or programming languages? The paper presents a conceptual model that can be used as a reference to build tools for generating customisable infoviews. We used it to conduct a survey on existing tools in terms of customisation capabilities. Starting from the feedback collected in this phase, we will implement these customisation features into some prototypes we are currently experimenting on.

negative: Enhancing representation learning with tensor decompositions for knowledge graphs and high dimensional sequence modeling The capability of processing and digesting raw data is one of the key features of a humanlike artificial intelligence system. For instance, real-time machine translation should be able to process and understand spoken natural language, and autonomous driving relies on the comprehension of visual inputs. Representation learning is a class of machine learning techniques that autonomously learn to derive latent features from raw data. These new features are expected to represent the data instances in a vector space that facilitates the machine learning task. This thesis studies two specific data situations that require efficient representation learning: knowledge graph data and high dimensional sequences. In the first part of this thesis, we first review multiple relational learning models based on tensor decomposition for knowledge graphs. We point out that relational learning is in fact a means of learning representations through one-hot mapping of entities. Furthermore, we generalize this mapping function to consume a feature vector that encodes all known facts about each entity. It enables the relational model to derive the latent representation instantly for a new entity, without having to re-train the tensor decomposition. In the second part, we focus on learning representations from high dimensional sequential data. Sequential data often pose the challenge that they are of variable lengths. Electronic health records, for instance, could consist of clinical event data that have been collected at subsequent time steps. But each patient may have a medical history of variable length. We apply recurrent neural networks to produce fixed-size latent representations from the raw feature sequences of various lengths. By exposing a prediction model to these learned representations instead of the raw features, we can predict the therapy prescriptions more accurately as a means of clinical decision support. We further propose Tensor-Train recurrent neural networks. We give a detailed introduction to the technique of tensorizing and decomposing large weight matrices into a few smaller tensors. We demonstrate the specific algorithms to perform the forward-pass and the back-propagation in this setting. Then we apply this approach to the input-to-hidden weight matrix in recurrent neural networks. This novel architecture can process extremely high dimensional sequential features such as video data. The model also provides a promising solution to processing sequential features with high sparsity. This is, for instance, the case with electronic health records, since they are often of categorical nature and have to be binary-coded. We incorporate a statistical survival model with this representation learning model, which shows superior prediction quality.

negative: Analyzing and Managing Role-Based Access Control Policies Today more and more security-relevant data is stored on computer systems; security-critical business processes are mapped to their digital counterparts. This situation applies to various domains such as health care industry, digital government, and financial service institutes requiring that different security requirements must be fulfilled. Authorisation constraints can help the policy architect design and express higher-level organisational rules. Although the importance of authorisation constraints has been addressed in the literature, there does not exist a systematic way to verify and validate authorisation constraints. In this paper, we specify both non-temporal and history-based authorisation constraints in the Object Constraint Language (OCL) and first-order linear temporal logic (LTL). Based upon these specifications, we attempt to formally verify role-based access control policies with the help of a theorem prover and to validate policies with the USE system, a validation tool for OCL constraints. We also describe an authorisation engine, which supports the enforcement of authorisation constraints.

negative: Greedy Layerwise Learning Can Scale to ImageNet Shallow supervised 1-hidden layer neural networks have a number of favorable properties that make them easier to interpret, analyze, and optimize than their deep counterparts, but lack their representational power. Here we use 1-hidden layer learning problems to sequentially build deep networks layer by layer, which can inherit properties from shallow networks. Contrary to previous approaches using shallow networks, we focus on problems where deep learning is reported as critical for success. We thus study CNNs on image classification tasks using the large-scale ImageNet dataset and the CIFAR-10 dataset. Using a simple set of ideas for architecture and training we find that solving sequential 1-hidden-layer auxiliary problems lead to a CNN that exceeds AlexNet performance on ImageNet. Extending this training methodology to construct individual layers by solving 2-and-3-hidden layer auxiliary problems, we obtain an 11-layer network that exceeds several members of the VGG model family on ImageNet, and can train a VGG-11 model to the same accuracy as end-to-end learning. To our knowledge, this is the first competitive alternative to end-to-end training of CNNs that can scale to ImageNet. We illustrate several interesting properties of these models theoretically and conduct a range of experiments to study the properties this training induces on the intermediate representations.

negative: Rethinking the Value of Network Pruning Network pruning is widely used for reducing the heavy computational cost of 1 deep models. A typical pruning algorithm is a three-stage pipeline, i.e., training (a 2 large model), pruning and fine-tuning. In this work, we make a rather surprising 3 observation: fine-tuning a pruned model only gives comparable or even worse 4 performance than training that model with randomly initialized weights. Our 5 results have several implications: 1) training a large, over-parameterized model is 6 not necessary to obtain an efficient final model, 2) learned “important” weights 7 of the large model are not necessarily useful for the small pruned model, 3) the 8 pruned architecture itself, rather than a set of inherited weights, is what leads to the 9 efficiency benefit in the final model, which suggests that some pruning algorithms 10 could be seen as performing network architecture search. 11

negative: Compact wideband directional antenna with three-dimensional structure for microwave-based head imaging systems Microwave-based head imaging systems demand wideband antennas with compact size. To ensure high scattered signals from the intended target point, the antennas need to have directional radiation patterns. Designing an antenna by addressing all these issues is challenging as the head imaging system utilizes low microwave frequencies (around 1-3 GHz) to maintain high signal penetration in the lossy human head. This paper reports a coplanar waveguide-fed design that overcomes all these limitations. The antenna is constructed in a three-dimensional manner by combining a dipole antenna with a folded parasitic structure printed on two substrate blocks. The antenna mechanism is explained with the description of its design parameters. The antenna covers 101% fractional bandwidth with a stable gain, good radiation efficiency and well directional radiation characteristics. The overall volume of the antenna is 0.24 × 0.1 × 0.05 λm where λm is the wavelength at the lowest frequency of operation.

negative: A spiral antenna over a high-impedance surface consisting of fan-shaped patch cells Radiation characteristics of a spiral antenna over a high-impedance surface (HIS) are analyzed. The HIS consists of fan-shaped patch cells. The fan-shaped patches are arranged homogeneously in the circumferential direction but are set non-homogeneously in the radial direction. The analysis is performed using method of moment. Radiation characteristics of a spiral antenna with a perfect electric conductor (PEC) reflector are analyzed. It is reaffirmed that wideband radiation characteristics, including input impedance and axial ratio are deteriorate. Subsequently, Radiation characteristics of a spiral antenna with a fan-shaped HIS reflector are analyzed. It is revealed that input impedance and axial ratio are mitigated by replacing the PEC reflector with the fan-shaped HIS reflector.

negative: Autoencoders, Unsupervised Learning, and Deep Architectures To better understand deep architectures and unsupervised learning, uncluttered by hardware details, we develop a general autoencoder framework for the comparative study of autoencoders, including Boolean autoencoders. We derive several results regarding autoencoders and autoencoder learning, including results on learning complexity, vertical and horizontal composition, and fundamental connections between critical points and clustering. Possible implications for the theory of deep architectures are discussed.

negative: Accelerating Differential Evolution Using an Adaptive Local Search We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.

negative: Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation Sponsored search advertising is largely based on bidding on individual terms. The richness of natural languages permits web searchers to express their information needs in myriad ways. Advertisers have difficulty discovering all the terms that are relevant to their products or services. We examine the performance of logistic regression and collaborative filtering models on two different data sources to predict terms relevant to a set of seed terms describing an advertiser’s product or service.

negative: A Variational Principle for Graphical Models Graphical models bring together graph theory and probability theory in a powerful formalism for multivariate statistical modeling. In statistical signal processing— as well as in related fields such as communication theory, control theory and bioinformatics—statistical models have long been formulated in terms of graphs, and algorithms for computing basic statistical quantities such as likelihoods and marginal probabilities have often been expressed in terms of recursions operating on these graphs. Examples include hidden Markov models, Markov random fields, the forward-backward algorithm and Kalman filtering [ Rabiner and Juang (1993); Pearl (1988); Kailath et al. (2000)]. These ideas can be understood, unified and generalized within the formalism of graphical models. Indeed, graphical models provide a natural framework for formulating variations on these classical architectures, and for exploring entirely new families of statistical models. The recursive algorithms cited above are all instances of a general recursive algorithm known as the junction tree algorithm [ Lauritzen and Spiegelhalter, 1988]. The junction tree algorithm takes advantage of factorization properties of the joint probability distribution that are encoded by the pattern of missing edges in a graphical model. For suitably sparse graphs, the junction tree algorithm provides a systematic and practical solution to the general problem of computing likelihoods and other statistical quantities associated with a graphical model. Unfortunately, many graphical models of practical interest are not " suitably sparse, " so that the junction tree algorithm no longer provides a viable computational solution to the problem of computing marginal probabilities and other expectations. One popular source of methods for attempting to cope with such cases is the Markov chain Monte Carlo (MCMC) framework, and indeed there is a significant literature on

negative: Mental Stress Assessment of ECG Signal using Statistical Analysis of Bio-Orthogonal Wavelet Coefficients It is observed that the stress level is function of various statistical parameters like standard deviation, entropy, energy,, mean, Covariance and power of the ECG signals of two states i.e. normal state of mind and stressed state of mind. Further, it is observed that the features extracted are directly from the ECG in frequency domain using db4 wavelet. However, db4 introduces some error on account of db4 wavelet shape. This error in turn amplifies while measuring the features as mentioned above. In order to reduce this error, we propose a Bior 3.9 wavelet family to decompose the ECG signal. The decomposed ECG signal is now analyzed statistically to extract the above feature. Further, in the existing work, all the ECGs are taken by using the 12 leads method. This factor also adds some undue stress to the person under scanner. And this is time consuming too. Therefore, to reduce this complexity, we propose to analyze all above features by using a two lead ECG. Further, a back propagation neural network is trained using the above features as input neurons in order to classify the stress level.

negative: DropConnected neural network trained with diverse features for classifying heart sounds A fully-connected, two-hidden-layer neural network trained by error backpropagation, and regularized with DropConnect is used to classify heart sounds as normal or abnormal. The heart sounds are segmented using an open-source algorithm based on a hidden semi-Markov model. Features are extracted from the heart sounds using a wavelet transform, mel-frequency cepstral coefficients, inter-beat properties, and signal complexity. Features are normalized by subtracting by their means and dividing by their standard deviations across the whole training set. Any feature which is not significantly different between normal and abnormal recordings in the training data is removed, as are highly-correlated features. The dimensionality of the features vector is reduced by projecting it onto its first 70 principal components. A 10 fold cross-validation study gives a mean classification score of 84.1% with a variance of 2.9%. The final score on the test data was 85.2%.

negative: Dialog Act Modeling for Conversational Speech We describe an integrated approach for statistical modeling of discourse structure for natural conversational speech. Our model is based on 42`dialog acts' which were hand-labeled in 1155 conversations from the Switchboard corpus of spontaneous human-to-human telephone speech. We developed several models and algorithms to automatically detect dialog acts from transcribed or automatically recognized words and from prosodic properties of the speech signal, and by using a statistical discourse grammar. All of these components were probabilistic in nature and estimated from data, employing a variety of techniques (hidden Markov models, N-gram language models, maximum entropy estimation, decision tree classiiers, and neural networks). In preliminary studies, we achieved a dialog act labeling accuracy of 65% based on recognized words and prosody, and an accuracy of 72% based on word transcripts. Since humans achieve 84% on this task (with chance performance at 35%) we nd these results encouraging.

negative: Automatic Labelling of Tabla Signals Most of the recent developments in the field of music indexing and music information retrieval are focused on western music. In this paper, we present an automatic music transcription system dedicated to Tabla a North Indian percussion instrument. Our approach is based on three main steps: firstly, the audio signal is segmented in adjacent segments where each segment represents a single stroke. Secondly, rhythmic information such as relative durations are calculated using beat detection techniques. Finally, the transcription (recognition of the strokes) is performed by means of a statistical model based on Hidden Markov Model (HMM). The structure of this model is designed in order to represent the time dependencies between successives strokes and to take into account the specificities of the tabla score notation (transcription symbols may be context dependent). Realtime transcription of Tabla soli (or performances) with an error rate of 6.5% is made possible with this transcriber. The transcription system, along with some additional features such as sound synthesis or phrase correction, are integrated in a user-friendly environment called Tablascope.

negative: HMM-Based Word Alignment in Statistical Translation In this paper, we describe a new model for word alignment in statistical translation and present experimental results. The idea of the model is to make the alignment probabilities dependent on the differences in the alignment positions rather than on the absolute positions. To achieve this goal, the approach uses a first-order Hidden Markov model (HMM) for the word alignment problem as they are used successfully in speech recognition for the time alignment problem. The difference to the time alignment HMM is that there is no monotony constraint for the possible word orderings. We describe the details of the model and test the model on several bilingual corpora.

negative: On-line cursive script recognition using time-delay neural networks and hidden Markov models We present a writer-independent system for online handwriting recognition that can handle a variety of writing styles including cursive script and handprinting. The input to our system contains the pen trajectory information, encoded as a time-ordered sequence of feature vectors. A time-delay neural network is used to estimate a posteriori probabilities for characters in a word. A hidden Markov model segments the word in a way that optimizes the global word score, using a dictionary in the process. A geometrical normalization scheme and a fast but efficient dictionary search are also presented. Trained on 20 k words from 59 writers, using a 25 k-word dictionary, our system reached recognition rates of 89% for characters and 80% for words on test data from a disjoint set of writers.


query: Flexible 16 Antenna Array for Microwave Breast Cancer Detection

positive: Advanced Engineering Electromagnetics (Balanis, C.A.; 2012) [Book Review] 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.

positive: Stable and Flexible Materials to Mimic the Dielectric Properties of Human Soft Tissues 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.

positive: Confocal Microwave Imaging for Breast Cancer Detection: Delay-Multiply-and-Sum Image Reconstruction Algorithm 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.

positive: Microwave Breast Imaging With a Monostatic Radar-Based System: A Study of Application to Patients 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.

positive: Precision open-ended coaxial probes for in vivo and ex vivo dielectric spectroscopy of biological tissues at microwave frequencies 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.

negative: Painting Outside the Box: Image Outpainting with GANs 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.

negative: CSLIM: contextual SLIM recommendation algorithms 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.

negative: Primal-Dual Group Convolutions for Deep Neural Networks 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.

negative: DC-DC converters for electric vehicle applications 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.

negative: Deep Learning and Wavelets for High-Frequency Price Forecasting 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%.

negative: Multipath TCP path schedulers for streaming video 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.

negative: Time-Dependent Trajectory Regression on Road Networks via Multi-Task Learning 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

negative: Robust detection of non-motorized road users using deep learning on optical and LIDAR data 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.

negative: Production and structural elucidation of exopolysaccharide from endophytic Pestalotiopsis sp. BC55. 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→.

negative: Choosing Regularization Parameters in Iterative Methods for Ill-Posed Problems 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.

negative: Tapping into knowledge base for concept feedback: leveraging conceptnet to improve search results for difficult queries 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.

negative: Measuring pictorial balance perception at first glance using Japanese calligraphy 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.

negative: Development of an Electromagnetic Actuated Microdisplacement Module 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.

negative: PCANet: A Simple Deep Learning Baseline for Image Classification? 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.

negative: A SiC-Based High-Efficiency Isolated Onboard PEV Charger With Ultrawide DC-Link Voltage Range 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.

negative: Development of Computer-Aided Diagnostic (CADx) System for Distinguishing Neoplastic from Nonneoplastic Lesions in CT Colonography (CTC): Toward CTC beyond Detection 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.

negative: Exercise with blood flow restriction: an updated evidence-based approach for enhanced muscular development. 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.

negative: Branching embedding: A heuristic dimensionality reduction algorithm based on hierarchical clustering 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.

negative: POINTWISE: Predicting Points and Valuing Decisions in Real Time with NBA Optical Tracking Data 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.

negative: Mobile Phishing Attacks and Mitigation Techniques 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.

negative:
Resin composite--state of the art. 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.

negative: Design of prototype humanoid robotics platform for HRP 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.

negative: Learning Orthographic Features in Bi-directional LSTM for Biomedical Named Entity Recognition 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.

negative: The attention system of the human brain. 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:

negative: Improving Collocated Robot Teleoperation with Augmented Reality 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).

negative: Early Breast Cancer Detection using Mammogram Images: A Review of Image Processing Techniques Breast cancer is one of the most common cancers worldwide among women so that one in eight women is affected by this disease during their lifetime. Mammography is the most effective imaging modality for early detection of breast cancer in early stages. Because of poor contrast and low visibility in the mammographic images, early detection of breast cancer is a significant step to efficient treatment of the disease. Different computeraided detection algorithms have been developed to help radiologists provide an accurate diagnosis. This paper reviews the most common image processing approaches developed for detection of masses and calcifications. The main focus of this review is on image segmentation methods and the variables used for early breast cancer detection. Texture analysis is the crucial step in any image segmentation techniques which are based on a local spatial variation of intensity or color. Therefore, various methods of texture analysis for mass and micro-calcification detection in mammography are discussed in details.

negative: A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer The advance of deep learning has made huge changes in computer vision and produced various off-the-shelf trained models. Particularly, Convolutional Neural Network (CNN) has been widely used to build image classification model which allow researchers transfer the pre-trained learning model for other classifications. We propose a transfer learning method to detect breast cancer using histopathology images based on Google's Inception v3 model which were initially trained for the classification of non-medical images. The pilot study shows the feasibility of transfer learning in the detection of breast cancer with AUC of 0.93.

negative: Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Breast cancer is the most common form of cancer among women worldwide. Early detection of breast cancer can increase treatment options and patients' survivability. Mammography is the gold standard for breast imaging and cancer detection. However, due to some limitations of this modality such as low sensitivity especially in dense breasts, other modalities like ultrasound and magnetic resonance imaging are often suggested to achieve additional information. Recently, computer-aided detection or diagnosis (CAD) systems have been developed to help radiologists in order to increase diagnosis accuracy. Generally, a CAD system consists of four stages: (a) preprocessing, (b) segmentation of regions of interest, (c) feature extraction and selection, and finally (d) classification. This paper presents the approaches which are applied to develop CAD systems on mammography and ultrasound images. The performance evaluation metrics of CAD systems are also reviewed.

negative: Detection of cancer tumors in mammography images using support vector machine and mixed gravitational search algorithm In this paper, support vector machine (SVM) and mixed gravitational search algorithm (MGSA) are utilized to detect the breast cancer tumors in mammography images. Sech template matching method is used to segment images and extract the regions of interest (ROIs). Gray-level co-occurrence matrix (GLCM) is used to extract features. The mixed GSA is used for optimization of the classifier parameters and selecting salient features. The main goal of using MGSA-SVM is to decrease the number of features and to improve the SVM classification accuracy. Finally, the selected features and the tuned SVM classifier are used for detecting tumors. The experimental results show that the proposed method is able to optimize both feature selection and the SVM parameters for the breast cancer tumor detection.

negative: Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model Automated breast cancer multi-classification from histopathological images plays a key role in computer-aided breast cancer diagnosis or prognosis. Breast cancer multi-classification is to identify subordinate classes of breast cancer (Ductal carcinoma, Fibroadenoma, Lobular carcinoma, etc.). However, breast cancer multi-classification from histopathological images faces two main challenges from: (1) the great difficulties in breast cancer multi-classification methods contrasting with the classification of binary classes (benign and malignant), and (2) the subtle differences in multiple classes due to the broad variability of high-resolution image appearances, high coherency of cancerous cells, and extensive inhomogeneity of color distribution. Therefore, automated breast cancer multi-classification from histopathological images is of great clinical significance yet has never been explored. Existing works in literature only focus on the binary classification but do not support further breast cancer quantitative assessment. In this study, we propose a breast cancer multi-classification method using a newly proposed deep learning model. The structured deep learning model has achieved remarkable performance (average 93.2% accuracy) on a large-scale dataset, which demonstrates the strength of our method in providing an efficient tool for breast cancer multi-classification in clinical settings.

negative: Breast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including breast cancer classification, segmentation, and detection. Breast cancer is one of the most common and dangerous cancers impacting women worldwide. In this paper, we have proposed a method for breast cancer classification with the Inception Recurrent Residual Convolutional Neural Network (IRRCNN) model. The IRRCNN is a powerful DCNN model that combines the strength of the Inception Network (Inception-v4), the Residual Network (ResNet), and the Recurrent Convolutional Neural Network (RCNN). The IRRCNN shows superior performance against equivalent Inception Networks, Residual Networks, and RCNNs for object recognition tasks. In this paper, the IRRCNN approach is applied for breast cancer classification on two publicly available datasets including BreakHis and Breast Cancer (BC) classification challenge 2015. The experimental results are compared against the existing machine learning and deep learning–based approaches with respect to image-based, patch-based, image-level, and patient-level classification. The IRRCNN model provides superior classification performance in terms of sensitivity, area under the curve (AUC), the ROC curve, and global accuracy compared to existing approaches for both datasets.

negative:
Analysis of circulating tumor DNA to monitor metastatic breast cancer. BACKGROUND
The management of metastatic breast cancer requires monitoring of the tumor burden to determine the response to treatment, and improved biomarkers are needed. Biomarkers such as cancer antigen 15-3 (CA 15-3) and circulating tumor cells have been widely studied. However, circulating cell-free DNA carrying tumor-specific alterations (circulating tumor DNA) has not been extensively investigated or compared with other circulating biomarkers in breast cancer.

METHODS
We compared the radiographic imaging of tumors with the assay of circulating tumor DNA, CA 15-3, and circulating tumor cells in 30 women with metastatic breast cancer who were receiving systemic therapy. We used targeted or whole-genome sequencing to identify somatic genomic alterations and designed personalized assays to quantify circulating tumor DNA in serially collected plasma specimens. CA 15-3 levels and numbers of circulating tumor cells were measured at identical time points.

RESULTS
Circulating tumor DNA was successfully detected in 29 of the 30 women (97%) in whom somatic genomic alterations were identified; CA 15-3 and circulating tumor cells were detected in 21 of 27 women (78%) and 26 of 30 women (87%), respectively. Circulating tumor DNA levels showed a greater dynamic range, and greater correlation with changes in tumor burden, than did CA 15-3 or circulating tumor cells. Among the measures tested, circulating tumor DNA provided the earliest measure of treatment response in 10 of 19 women (53%).

CONCLUSIONS
This proof-of-concept analysis showed that circulating tumor DNA is an informative, inherently specific, and highly sensitive biomarker of metastatic breast cancer. (Funded by Cancer Research UK and others.).