Gender Recognition from Human-Body Images Using Visible-Light and Thermal Camera Videos Based on a Convolutional Neural Network for Image Feature Extraction.

PubMed

Nguyen, Dat Tien; Kim, Ki Wan; Hong, Hyung Gil; Koo, Ja Hyung; Kim, Min Cheol; Park, Kang Ryoung

2017-03-20

Extracting powerful image features plays an important role in computer vision systems. Many methods have previously been proposed to extract image features for various computer vision applications, such as the scale-invariant feature transform (SIFT), speed-up robust feature (SURF), local binary patterns (LBP), histogram of oriented gradients (HOG), and weighted HOG. Recently, the convolutional neural network (CNN) method for image feature extraction and classification in computer vision has been used in various applications. In this research, we propose a new gender recognition method for recognizing males and females in observation scenes of surveillance systems based on feature extraction from visible-light and thermal camera videos through CNN. Experimental results confirm the superiority of our proposed method over state-of-the-art recognition methods for the gender recognition problem using human body images.

Superpixel-based and boundary-sensitive convolutional neural network for automated liver segmentation

NASA Astrophysics Data System (ADS)

Qin, Wenjian; Wu, Jia; Han, Fei; Yuan, Yixuan; Zhao, Wei; Ibragimov, Bulat; Gu, Jia; Xing, Lei

2018-05-01

Segmentation of liver in abdominal computed tomography (CT) is an important step for radiation therapy planning of hepatocellular carcinoma. Practically, a fully automatic segmentation of liver remains challenging because of low soft tissue contrast between liver and its surrounding organs, and its highly deformable shape. The purpose of this work is to develop a novel superpixel-based and boundary sensitive convolutional neural network (SBBS-CNN) pipeline for automated liver segmentation. The entire CT images were first partitioned into superpixel regions, where nearby pixels with similar CT number were aggregated. Secondly, we converted the conventional binary segmentation into a multinomial classification by labeling the superpixels into three classes: interior liver, liver boundary, and non-liver background. By doing this, the boundary region of the liver was explicitly identified and highlighted for the subsequent classification. Thirdly, we computed an entropy-based saliency map for each CT volume, and leveraged this map to guide the sampling of image patches over the superpixels. In this way, more patches were extracted from informative regions (e.g. the liver boundary with irregular changes) and fewer patches were extracted from homogeneous regions. Finally, deep CNN pipeline was built and trained to predict the probability map of the liver boundary. We tested the proposed algorithm in a cohort of 100 patients. With 10-fold cross validation, the SBBS-CNN achieved mean Dice similarity coefficients of 97.31  ±  0.36% and average symmetric surface distance of 1.77  ±  0.49 mm. Moreover, it showed superior performance in comparison with state-of-art methods, including U-Net, pixel-based CNN, active contour, level-sets and graph-cut algorithms. SBBS-CNN provides an accurate and effective tool for automated liver segmentation. It is also envisioned that the proposed framework is directly applicable in other medical image segmentation scenarios.

Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification

NASA Astrophysics Data System (ADS)

Anwer, Rao Muhammad; Khan, Fahad Shahbaz; van de Weijer, Joost; Molinier, Matthieu; Laaksonen, Jorma

2018-04-01

Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification.

Automated Analysis of ARM Binaries using the Low-Level Virtual Machine Compiler Framework

DTIC Science & Technology

2011-03-01

president to insist on keeping his smartphone [CNN09]. A self-proclaimed BlackBerry addict , President Obama fought hard to keep his mobile device after his... smartphone but renders a device non-functional on installation [FSe09][Hof07]. Complex interactions between hardware and software components both within... smartphone (which is a big assumption), the phone may still be vulnerable if the hardware or software does not correctly implement the design

A comparative study for chest radiograph image retrieval using binary texture and deep learning classification.

PubMed

Anavi, Yaron; Kogan, Ilya; Gelbart, Elad; Geva, Ofer; Greenspan, Hayit

2015-08-01

In this work various approaches are investigated for X-ray image retrieval and specifically chest pathology retrieval. Given a query image taken from a data set of 443 images, the objective is to rank images according to similarity. Different features, including binary features, texture features, and deep learning (CNN) features are examined. In addition, two approaches are investigated for the retrieval task. One approach is based on the distance of image descriptors using the above features (hereon termed the "descriptor"-based approach); the second approach ("classification"-based approach) is based on a probability descriptor, generated by a pair-wise classification of each two classes (pathologies) and their decision values using an SVM classifier. Best results are achieved using deep learning features in a classification scheme.

Spatio-temporal analysis of brain electrical activity in epilepsy based on cellular nonlinear networks

NASA Astrophysics Data System (ADS)

Gollas, Frank; Tetzlaff, Ronald

2009-05-01

Epilepsy is the most common chronic disorder of the nervous system. Generally, epileptic seizures appear without foregoing sign or warning. The problem of detecting a possible pre-seizure state in epilepsy from EEG signals has been addressed by many authors over the past decades. Different approaches of time series analysis of brain electrical activity already are providing valuable insights into the underlying complex dynamics. But the main goal the identification of an impending epileptic seizure with a sufficient specificity and reliability, has not been achieved up to now. An algorithm for a reliable, automated prediction of epileptic seizures would enable the realization of implantable seizure warning devices, which could provide valuable information to the patient and time/event specific drug delivery or possibly a direct electrical nerve stimulation. Cellular Nonlinear Networks (CNN) are promising candidates for future seizure warning devices. CNN are characterized by local couplings of comparatively simple dynamical systems. With this property these networks are well suited to be realized as highly parallel, analog computer chips. Today available CNN hardware realizations exhibit a processing speed in the range of TeraOps combined with low power consumption. In this contribution new algorithms based on the spatio-temporal dynamics of CNN are considered in order to analyze intracranial EEG signals and thus taking into account mutual dependencies between neighboring regions of the brain. In an identification procedure Reaction-Diffusion CNN (RD-CNN) are determined for short segments of brain electrical activity, by means of a supervised parameter optimization. RD-CNN are deduced from Reaction-Diffusion Systems, which usually are applied to investigate complex phenomena like nonlinear wave propagation or pattern formation. The Local Activity Theory provides a necessary condition for emergent behavior in RD-CNN. In comparison linear spatio-temporal autoregressive filter models are considered, for a prediction of EEG signal values. Thus Signal features values for successive, short, quasi stationary segments of brain electrical activity can be obtained, with the objective of detecting distinct changes prior to impending epileptic seizures. Furthermore long term recordings gained during presurgical diagnostics in temporal lobe epilepsy are analyzed and the predictive performance of the extracted features is evaluated statistically. Therefore a Receiver Operating Characteristic analysis is considered, assessing the distinguishability between distributions of supposed preictal and interictal periods.

A Method of Character Detection and Segmentation for Highway Guide Signs

NASA Astrophysics Data System (ADS)

Xu, Jiawei; Zhang, Chongyang

2018-01-01

In this paper, a method of character detection and segmentation for highway signs in China is proposed. It consists of four steps. Firstly, the highway sign area is detectedby colour and geometric features, andthe possible character region is obtained by multi-level projection strategy. Secondly, pseudo target character region is removed by local binary patterns (LBP) feature. Thirdly, convolutional neural network (CNN)is used to classify target regions. Finally, adaptive projection strategies are used to segment characters strings. Experimental results indicate that the proposed method achieves new state-of-the-art results.

SiNC: Saliency-injected neural codes for representation and efficient retrieval of medical radiographs

PubMed Central

Sajjad, Muhammad; Mehmood, Irfan; Baik, Sung Wook

2017-01-01

Medical image collections contain a wealth of information which can assist radiologists and medical experts in diagnosis and disease detection for making well-informed decisions. However, this objective can only be realized if efficient access is provided to semantically relevant cases from the ever-growing medical image repositories. In this paper, we present an efficient method for representing medical images by incorporating visual saliency and deep features obtained from a fine-tuned convolutional neural network (CNN) pre-trained on natural images. Saliency detector is employed to automatically identify regions of interest like tumors, fractures, and calcified spots in images prior to feature extraction. Neuronal activation features termed as neural codes from different CNN layers are comprehensively studied to identify most appropriate features for representing radiographs. This study revealed that neural codes from the last fully connected layer of the fine-tuned CNN are found to be the most suitable for representing medical images. The neural codes extracted from the entire image and salient part of the image are fused to obtain the saliency-injected neural codes (SiNC) descriptor which is used for indexing and retrieval. Finally, locality sensitive hashing techniques are applied on the SiNC descriptor to acquire short binary codes for allowing efficient retrieval in large scale image collections. Comprehensive experimental evaluations on the radiology images dataset reveal that the proposed framework achieves high retrieval accuracy and efficiency for scalable image retrieval applications and compares favorably with existing approaches. PMID:28771497

Coupling a Neural Network with Atmospheric Flow Simulations to Locate and Quantify CH4 Emissions at Well Pads

NASA Astrophysics Data System (ADS)

Travis, B. J.; Sauer, J.; Dubey, M. K.

2017-12-01

Methane (CH4) leaks from oil and gas production fields are a potentially significant source of atmospheric methane. US DOE's ARPA-E office is supporting research to locate methane emissions at 10 m size well pads to within 1 m. A team led by Aeris Technologies, and that includes LANL, Planetary Science Institute and Rice University has developed an autonomous leak detection system (LDS) employing a compact laser absorption methane sensor, a sonic anemometer and multiport sampling. The LDS system analyzes monitoring data using a convolutional neural network (cNN) to locate and quantify CH4 emissions. The cNN was trained using three sources: (1) ultra-high-resolution simulations of methane transport provided by LANL's coupled atmospheric transport model HIGRAD, for numerous controlled methane release scenarios and methane sampling configurations under variable atmospheric conditions, (2) Field tests at the METEC site in Ft. Collins, CO., and (3) Field data from other sites where point-source surface methane releases were monitored downwind. A cNN learning algorithm is well suited to problems in which the training and observed data are noisy, or correspond to complex sensor data as is typical of meteorological and sensor data over a well pad. Recent studies with our cNN emphasize the importance of tracking wind speeds and directions at fine resolution ( 1 second), and accounting for variations in background CH4 levels. A few cases illustrate the importance of sufficiently long monitoring; short monitoring may not provide enough information to determine accurately a leak location or strength, mainly because of short-term unfavorable wind directions and choice of sampling configuration. Length of multiport duty cycle sampling and sample line flush time as well as number and placement of monitoring sensors can significantly impact ability to locate and quantify leaks. Source location error at less than 10% requires about 30 or more training cases.

Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

2018-04-01

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

[Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].

PubMed

Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei

2017-08-01

The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

DOE PAGES

Lunga, Dalton D.; Yang, Hsiuhan Lexie; Reith, Andrew E.; ...

2018-02-06

Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. Here, this article investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address themore » negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.« less

Domain-Adapted Convolutional Networks for Satellite Image Classification: A Large-Scale Interactive Learning Workflow

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lunga, Dalton D.; Yang, Hsiuhan Lexie; Reith, Andrew E.

Satellite imagery often exhibits large spatial extent areas that encompass object classes with considerable variability. This often limits large-scale model generalization with machine learning algorithms. Notably, acquisition conditions, including dates, sensor position, lighting condition, and sensor types, often translate into class distribution shifts introducing complex nonlinear factors and hamper the potential impact of machine learning classifiers. Here, this article investigates the challenge of exploiting satellite images using convolutional neural networks (CNN) for settlement classification where the class distribution shifts are significant. We present a large-scale human settlement mapping workflow based-off multiple modules to adapt a pretrained CNN to address themore » negative impact of distribution shift on classification performance. To extend a locally trained classifier onto large spatial extents areas we introduce several submodules: First, a human-in-the-loop element for relabeling of misclassified target domain samples to generate representative examples for model adaptation; second, an efficient hashing module to minimize redundancy and noisy samples from the mass-selected examples; and third, a novel relevance ranking module to minimize the dominance of source example on the target domain. The workflow presents a novel and practical approach to achieve large-scale domain adaptation with binary classifiers that are based-off CNN features. Experimental evaluations are conducted on areas of interest that encompass various image characteristics, including multisensors, multitemporal, and multiangular conditions. Domain adaptation is assessed on source–target pairs through the transfer loss and transfer ratio metrics to illustrate the utility of the workflow.« less

An Amino-Terminal Polo Kinase Interaction Motif Acts in the Regulation of Centrosome Formation and Reveals a Novel Function for centrosomin (cnn) in Drosophila

PubMed Central

Eisman, Robert C.; Phelps, Melissa A. S.; Kaufman, Thomas

2015-01-01

The formation of the pericentriolar matrix (PCM) and a fully functional centrosome in syncytial Drosophila melanogaster embryos requires the rapid transport of Cnn during initiation of the centrosome replication cycle. We show a Cnn and Polo kinase interaction is apparently required during embryogenesis and involves the exon 1A-initiating coding exon, suggesting a subset of Cnn splice variants is regulated by Polo kinase. During PCM formation exon 1A Cnn-Long Form proteins likely bind Polo kinase before phosphorylation by Polo for Cnn transport to the centrosome. Loss of either of these interactions in a portion of the total Cnn protein pool is sufficient to remove native Cnn from the pool, thereby altering the normal localization dynamics of Cnn to the PCM. Additionally, Cnn-Short Form proteins are required for polar body formation, a process known to require Polo kinase after the completion of meiosis. Exon 1A Cnn-LF and Cnn-SF proteins, in conjunction with Polo kinase, are required at the completion of meiosis and for the formation of functional centrosomes during early embryogenesis. PMID:26447129

An Amino-Terminal Polo Kinase Interaction Motif Acts in the Regulation of Centrosome Formation and Reveals a Novel Function for centrosomin (cnn) in Drosophila.

PubMed

Eisman, Robert C; Phelps, Melissa A S; Kaufman, Thomas

2015-10-01

The formation of the pericentriolar matrix (PCM) and a fully functional centrosome in syncytial Drosophila melanogaster embryos requires the rapid transport of Cnn during initiation of the centrosome replication cycle. We show a Cnn and Polo kinase interaction is apparently required during embryogenesis and involves the exon 1A-initiating coding exon, suggesting a subset of Cnn splice variants is regulated by Polo kinase. During PCM formation exon 1A Cnn-Long Form proteins likely bind Polo kinase before phosphorylation by Polo for Cnn transport to the centrosome. Loss of either of these interactions in a portion of the total Cnn protein pool is sufficient to remove native Cnn from the pool, thereby altering the normal localization dynamics of Cnn to the PCM. Additionally, Cnn-Short Form proteins are required for polar body formation, a process known to require Polo kinase after the completion of meiosis. Exon 1A Cnn-LF and Cnn-SF proteins, in conjunction with Polo kinase, are required at the completion of meiosis and for the formation of functional centrosomes during early embryogenesis. Copyright © 2015 by the Genetics Society of America.

Only "In America"

ERIC Educational Resources Information Center

Miranda, Maria Eugenia

2011-01-01

As the daughter of an interracial couple growing up in a middle-class town on Long Island in the 1970s, Soledad O'Brien learned not to let inappropriate or racist comments throw her. Now as the anchorwoman of CNN's "In America" documentary unit, she says she asks those uncomfortable questions about race all the time. She shines spotlight…

The end of a monolith: Deconstructing the Cnn-Polo interaction.

PubMed

Eisman, Robert C; Phelps, Melissa A S; Kaufman, Thomas C

2016-04-02

In Drosophila melanogaster a functional pericentriolar matrix (PCM) at mitotic centrosomes requires Centrosomin-Long Form (Cnn-LF) proteins. Moreover, tissue culture cells have shown that the centrosomal localization of both Cnn-LF and Polo kinase are co-dependent, suggesting a direct interaction. Our recent study found Cnn potentially binds to and is phosphorylated by Polo kinase at 2 residues encoded by Exon1A, the initiating exon of a subset of Cnn isoforms. These interactions are required for the centrosomal localization of Cnn-LF in syncytial embryos and a mutation of either phosphorylation site is sufficient to block localization of both mutant and wild-type Cnn when they are co-expressed. Immunoprecipitation experiments show that Cnn-LF interacts directly with mitotically activated Polo kinase and requires the 2 phosphorylation sites in Exon1A. These IP experiments also show that Cnn-LF proteins form multimers. Depending on the stoichiometry between functional and mutant peptides, heteromultimers exhibit dominant negative or positive trans-complementation (rescue) effects on mitosis. Additionally, following the completion of meiosis, Cnn-Short Form (Cnn-SF) proteins are required for polar body formation in embryos, a process previously shown to require Polo kinase. These findings, when combined with previous work, clearly demonstrate the complexity of cnn and show that a view of cnn as encoding a single peptide is too simplistic.

The end of a monolith: Deconstructing the Cnn-Polo interaction

PubMed Central

2016-01-01

ABSTRACT In Drosophila melanogaster a functional pericentriolar matrix (PCM) at mitotic centrosomes requires Centrosomin-Long Form (Cnn-LF) proteins. Moreover, tissue culture cells have shown that the centrosomal localization of both Cnn-LF and Polo kinase are co-dependent, suggesting a direct interaction. Our recent study found Cnn potentially binds to and is phosphorylated by Polo kinase at 2 residues encoded by Exon1A, the initiating exon of a subset of Cnn isoforms. These interactions are required for the centrosomal localization of Cnn-LF in syncytial embryos and a mutation of either phosphorylation site is sufficient to block localization of both mutant and wild-type Cnn when they are co-expressed. Immunoprecipitation experiments show that Cnn-LF interacts directly with mitotically activated Polo kinase and requires the 2 phosphorylation sites in Exon1A. These IP experiments also show that Cnn-LF proteins form multimers. Depending on the stoichiometry between functional and mutant peptides, heteromultimers exhibit dominant negative or positive trans-complementation (rescue) effects on mitosis. Additionally, following the completion of meiosis, Cnn-Short Form (Cnn-SF) proteins are required for polar body formation in embryos, a process previously shown to require Polo kinase. These findings, when combined with previous work, clearly demonstrate the complexity of cnn and show that a view of cnn as encoding a single peptide is too simplistic. PMID:27096551

The Mps1 kinase modulates the recruitment and activity of Cnn1(CENP-T) at Saccharomyces cerevisiae kinetochores.

PubMed

Thapa, Kriti Shrestha; Oldani, Amanda; Pagliuca, Cinzia; De Wulf, Peter; Hazbun, Tony R

2015-05-01

Kinetochores are conserved protein complexes that bind the replicated chromosomes to the mitotic spindle and then direct their segregation. To better comprehend Saccharomyces cerevisiae kinetochore function, we dissected the phospho-regulated dynamic interaction between conserved kinetochore protein Cnn1(CENP-T), the centromere region, and the Ndc80 complex through the cell cycle. Cnn1 localizes to kinetochores at basal levels from G1 through metaphase but accumulates abruptly at anaphase onset. How Cnn1 is recruited and which activities regulate its dynamic localization are unclear. We show that Cnn1 harbors two kinetochore-localization activities: a C-terminal histone-fold domain (HFD) that associates with the centromere region and a N-terminal Spc24/Spc25 interaction sequence that mediates linkage to the microtubule-binding Ndc80 complex. We demonstrate that the established Ndc80 binding site in the N terminus of Cnn1, Cnn1(60-84), should be extended with flanking residues, Cnn1(25-91), to allow near maximal binding affinity to Ndc80. Cnn1 localization was proposed to depend on Mps1 kinase activity at Cnn1-S74, based on in vitro experiments demonstrating the Cnn1-Ndc80 complex interaction. We demonstrate that from G1 through metaphase, Cnn1 localizes via both its HFD and N-terminal Spc24/Spc25 interaction sequence, and deletion or mutation of either region results in anomalous Cnn1 kinetochore levels. At anaphase onset (when Mps1 activity decreases) Cnn1 becomes enriched mainly via the N-terminal Spc24/Spc25 interaction sequence. In sum, we provide the first in vivo evidence of Cnn1 preanaphase linkages with the kinetochore and enrichment of the linkages during anaphase. Copyright © 2015 by the Genetics Society of America.

Dimensionality-varied deep convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Qu, Haicheng; Liang, Xuejian; Liang, Shichao; Liu, Wanjun

2018-01-01

Many methods of hyperspectral image classification have been proposed recently, and the convolutional neural network (CNN) achieves outstanding performance. However, spectral-spatial classification of CNN requires an excessively large model, tremendous computations, and complex network, and CNN is generally unable to use the noisy bands caused by water-vapor absorption. A dimensionality-varied CNN (DV-CNN) is proposed to address these issues. There are four stages in DV-CNN and the dimensionalities of spectral-spatial feature maps vary with the stages. DV-CNN can reduce the computation and simplify the structure of the network. All feature maps are processed by more kernels in higher stages to extract more precise features. DV-CNN also improves the classification accuracy and enhances the robustness to water-vapor absorption bands. The experiments are performed on data sets of Indian Pines and Pavia University scene. The classification performance of DV-CNN is compared with state-of-the-art methods, which contain the variations of CNN, traditional, and other deep learning methods. The experiment of performance analysis about DV-CNN itself is also carried out. The experimental results demonstrate that DV-CNN outperforms state-of-the-art methods for spectral-spatial classification and it is also robust to water-vapor absorption bands. Moreover, reasonable parameters selection is effective to improve classification accuracy.

Compression fractures detection on CT

NASA Astrophysics Data System (ADS)

Bar, Amir; Wolf, Lior; Bergman Amitai, Orna; Toledano, Eyal; Elnekave, Eldad

2017-03-01

The presence of a vertebral compression fracture is highly indicative of osteoporosis and represents the single most robust predictor for development of a second osteoporotic fracture in the spine or elsewhere. Less than one third of vertebral compression fractures are diagnosed clinically. We present an automated method for detecting spine compression fractures in Computed Tomography (CT) scans. The algorithm is composed of three processes. First, the spinal column is segmented and sagittal patches are extracted. The patches are then binary classified using a Convolutional Neural Network (CNN). Finally a Recurrent Neural Network (RNN) is utilized to predict whether a vertebral fracture is present in the series of patches.

CNN code of conduct.

PubMed

2008-02-01

It is important that a CNN understands their employer's local policies, procedures and clinical protocols. To enable a CNN to practise, they must be appropriately trained, have clinical supervision and work in partnership with others. A CNN must maintain client confidentiality, and act accordingly with all partnership communications. A CNN has a duty of care to themselves, the clients, colleagues and the employer.

Cross-Modal Retrieval With CNN Visual Features: A New Baseline.

PubMed

Wei, Yunchao; Zhao, Yao; Lu, Canyi; Wei, Shikui; Liu, Luoqi; Zhu, Zhenfeng; Yan, Shuicheng

2017-02-01

Recently, convolutional neural network (CNN) visual features have demonstrated their powerful ability as a universal representation for various recognition tasks. In this paper, cross-modal retrieval with CNN visual features is implemented with several classic methods. Specifically, off-the-shelf CNN visual features are extracted from the CNN model, which is pretrained on ImageNet with more than one million images from 1000 object categories, as a generic image representation to tackle cross-modal retrieval. To further enhance the representational ability of CNN visual features, based on the pretrained CNN model on ImageNet, a fine-tuning step is performed by using the open source Caffe CNN library for each target data set. Besides, we propose a deep semantic matching method to address the cross-modal retrieval problem with respect to samples which are annotated with one or multiple labels. Extensive experiments on five popular publicly available data sets well demonstrate the superiority of CNN visual features for cross-modal retrieval.

Satellite Image Classification of Building Damages Using Airborne and Satellite Image Samples in a Deep Learning Approach

NASA Astrophysics Data System (ADS)

Duarte, D.; Nex, F.; Kerle, N.; Vosselman, G.

2018-05-01

The localization and detailed assessment of damaged buildings after a disastrous event is of utmost importance to guide response operations, recovery tasks or for insurance purposes. Several remote sensing platforms and sensors are currently used for the manual detection of building damages. However, there is an overall interest in the use of automated methods to perform this task, regardless of the used platform. Owing to its synoptic coverage and predictable availability, satellite imagery is currently used as input for the identification of building damages by the International Charter, as well as the Copernicus Emergency Management Service for the production of damage grading and reference maps. Recently proposed methods to perform image classification of building damages rely on convolutional neural networks (CNN). These are usually trained with only satellite image samples in a binary classification problem, however the number of samples derived from these images is often limited, affecting the quality of the classification results. The use of up/down-sampling image samples during the training of a CNN, has demonstrated to improve several image recognition tasks in remote sensing. However, it is currently unclear if this multi resolution information can also be captured from images with different spatial resolutions like satellite and airborne imagery (from both manned and unmanned platforms). In this paper, a CNN framework using residual connections and dilated convolutions is used considering both manned and unmanned aerial image samples to perform the satellite image classification of building damages. Three network configurations, trained with multi-resolution image samples are compared against two benchmark networks where only satellite image samples are used. Combining feature maps generated from airborne and satellite image samples, and refining these using only the satellite image samples, improved nearly 4 % the overall satellite image classification of building damages.

Understanding of Object Detection Based on CNN Family and YOLO

NASA Astrophysics Data System (ADS)

Du, Juan

2018-04-01

As a key use of image processing, object detection has boomed along with the unprecedented advancement of Convolutional Neural Network (CNN) and its variants since 2012. When CNN series develops to Faster Region with CNN (R-CNN), the Mean Average Precision (mAP) has reached 76.4, whereas, the Frame Per Second (FPS) of Faster R-CNN remains 5 to 18 which is far slower than the real-time effect. Thus, the most urgent requirement of object detection improvement is to accelerate the speed. Based on the general introduction to the background and the core solution CNN, this paper exhibits one of the best CNN representatives You Only Look Once (YOLO), which breaks through the CNN family’s tradition and innovates a complete new way of solving the object detection with most simple and high efficient way. Its fastest speed has achieved the exciting unparalleled result with FPS 155, and its mAP can also reach up to 78.6, both of which have surpassed the performance of Faster R-CNN greatly. Additionally, compared with the latest most advanced solution, YOLOv2 achieves an excellent tradeoff between speed and accuracy as well as an object detector with strong generalization ability to represent the whole image.

Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks

PubMed Central

Xu, Xin; Gui, Rong; Pu, Fangling

2018-01-01

Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification methods can only classify one pixel each time. Because all the pixels of a PolSAR image are classified independently, the inherent interrelation of different land covers is ignored. We use a fixed-feature-size CNN (FFS-CNN) to classify all pixels in a patch simultaneously. The proposed method has several advantages. First, FFS-CNN can classify all the pixels in a small patch simultaneously. When classifying a whole PolSAR image, it is faster than common CNNs. Second, FFS-CNN is trained to learn the interrelation of different land covers in a patch, so it can use the interrelation of land covers to improve the classification results. The experiments of FFS-CNN are evaluated on a Chinese Gaofen-3 PolSAR image and other two real PolSAR images. Experiment results show that FFS-CNN is comparable with the state-of-the-art PolSAR image classification methods. PMID:29510499

Multi-Pixel Simultaneous Classification of PolSAR Image Using Convolutional Neural Networks.

PubMed

Wang, Lei; Xu, Xin; Dong, Hao; Gui, Rong; Pu, Fangling

2018-03-03

Convolutional neural networks (CNN) have achieved great success in the optical image processing field. Because of the excellent performance of CNN, more and more methods based on CNN are applied to polarimetric synthetic aperture radar (PolSAR) image classification. Most CNN-based PolSAR image classification methods can only classify one pixel each time. Because all the pixels of a PolSAR image are classified independently, the inherent interrelation of different land covers is ignored. We use a fixed-feature-size CNN (FFS-CNN) to classify all pixels in a patch simultaneously. The proposed method has several advantages. First, FFS-CNN can classify all the pixels in a small patch simultaneously. When classifying a whole PolSAR image, it is faster than common CNNs. Second, FFS-CNN is trained to learn the interrelation of different land covers in a patch, so it can use the interrelation of land covers to improve the classification results. The experiments of FFS-CNN are evaluated on a Chinese Gaofen-3 PolSAR image and other two real PolSAR images. Experiment results show that FFS-CNN is comparable with the state-of-the-art PolSAR image classification methods.

Image processing for a tactile/vision substitution system using digital CNN.

PubMed

Lin, Chien-Nan; Yu, Sung-Nien; Hu, Jin-Cheng

2006-01-01

In view of the parallel processing and easy implementation properties of CNN, we propose to use digital CNN as the image processor of a tactile/vision substitution system (TVSS). The digital CNN processor is used to execute the wavelet down-sampling filtering and the half-toning operations, aiming to extract important features from the images. A template combination method is used to embed the two image processing functions into a single CNN processor. The digital CNN processor is implemented on an intellectual property (IP) and is implemented on a XILINX VIRTEX II 2000 FPGA board. Experiments are designated to test the capability of the CNN processor in the recognition of characters and human subjects in different environments. The experiments demonstrates impressive results, which proves the proposed digital CNN processor a powerful component in the design of efficient tactile/vision substitution systems for the visually impaired people.

World of intelligence defense object detection-machine learning (artificial intelligence)

NASA Astrophysics Data System (ADS)

Gupta, Anitya; Kumar, Akhilesh; Bhushan, Vinayak

2018-04-01

This paper proposes a Quick Locale based Convolutional System strategy (Quick R-CNN) for question recognition. Quick R-CNN expands on past work to effectively characterize ob-ject recommendations utilizing profound convolutional systems. Com-pared to past work, Quick R-CNN utilizes a few in-novations to enhance preparing and testing speed while likewise expanding identification precision. Quick R-CNN trains the profound VGG16 arrange 9 quicker than R-CNN, is 213 speedier at test-time, and accomplishes a higher Guide on PASCAL VOC 2012. Contrasted with SPPnet, Quick R-CNN trains VGG16 3 quicker, tests 10 speedier, and is more exact. Quick R-CNN is actualized in Python and C++ (utilizing Caffe) and is accessible under the open-source MIT Permit.

CNN-coupled Humanoid Panoramic Annular Lens (PAL)-Optical System for Military Applications (Feasibility Study)

DTIC Science & Technology

2002-01-08

new PAL with a total viewing angle of around 80° and suitable for foveal vision, it turned out that the optical design program ZEMAX EE we intended to...use was not capable for optimization. The reason was that ZEMAX -EE and all present optical design programs are based on see-through-window (STW

Coevolution of Binaries and Circumbinary Gaseous Disks

NASA Astrophysics Data System (ADS)

Fleming, David; Quinn, Thomas R.

2018-04-01

The recent discoveries of circumbinary planets by Kepler raise questions for contemporary planet formation models. Understanding how these planets form requires characterizing their formation environment, the circumbinary protoplanetary disk, and how the disk and binary interact. The central binary excites resonances in the surrounding protoplanetary disk that drive evolution in both the binary orbital elements and in the disk. To probe how these interactions impact both binary eccentricity and disk structure evolution, we ran N-body smooth particle hydrodynamics (SPH) simulations of gaseous protoplanetary disks surrounding binaries based on Kepler 38 for 10^4 binary orbital periods for several initial binary eccentricities. We find that nearly circular binaries weakly couple to the disk via a parametric instability and excite disk eccentricity growth. Eccentric binaries strongly couple to the disk causing eccentricity growth for both the disk and binary. Disks around sufficiently eccentric binaries strongly couple to the disk and develop an m = 1 spiral wave launched from the 1:3 eccentric outer Lindblad resonance (EOLR). This wave corresponds to an alignment of gas particle longitude of periastrons. We find that in all simulations, the binary semi-major axis decays due to dissipation from the viscous disk.

The Centrosome-Specific Phosphorylation of Cnn by Polo/Plk1 Drives Cnn Scaffold Assembly and Centrosome Maturation

PubMed Central

Conduit, Paul T.; Feng, Zhe; Richens, Jennifer H.; Baumbach, Janina; Wainman, Alan; Bakshi, Suruchi D.; Dobbelaere, Jeroen; Johnson, Steven; Lea, Susan M.; Raff, Jordan W.

2014-01-01

Summary Centrosomes are important cell organizers. They consist of a pair of centrioles surrounded by pericentriolar material (PCM) that expands dramatically during mitosis—a process termed centrosome maturation. How centrosomes mature remains mysterious. Here, we identify a domain in Drosophila Cnn that appears to be phosphorylated by Polo/Plk1 specifically at centrosomes during mitosis. The phosphorylation promotes the assembly of a Cnn scaffold around the centrioles that is in constant flux, with Cnn molecules recruited continuously around the centrioles as the scaffold spreads slowly outward. Mutations that block Cnn phosphorylation strongly inhibit scaffold assembly and centrosome maturation, whereas phosphomimicking mutations allow Cnn to multimerize in vitro and to spontaneously form cytoplasmic scaffolds in vivo that organize microtubules independently of centrosomes. We conclude that Polo/Plk1 initiates the phosphorylation-dependent assembly of a Cnn scaffold around centrioles that is essential for efficient centrosome maturation in flies. PMID:24656740

The centrosome-specific phosphorylation of Cnn by Polo/Plk1 drives Cnn scaffold assembly and centrosome maturation.

PubMed

Conduit, Paul T; Feng, Zhe; Richens, Jennifer H; Baumbach, Janina; Wainman, Alan; Bakshi, Suruchi D; Dobbelaere, Jeroen; Johnson, Steven; Lea, Susan M; Raff, Jordan W

2014-03-31

Centrosomes are important cell organizers. They consist of a pair of centrioles surrounded by pericentriolar material (PCM) that expands dramatically during mitosis-a process termed centrosome maturation. How centrosomes mature remains mysterious. Here, we identify a domain in Drosophila Cnn that appears to be phosphorylated by Polo/Plk1 specifically at centrosomes during mitosis. The phosphorylation promotes the assembly of a Cnn scaffold around the centrioles that is in constant flux, with Cnn molecules recruited continuously around the centrioles as the scaffold spreads slowly outward. Mutations that block Cnn phosphorylation strongly inhibit scaffold assembly and centrosome maturation, whereas phosphomimicking mutations allow Cnn to multimerize in vitro and to spontaneously form cytoplasmic scaffolds in vivo that organize microtubules independently of centrosomes. We conclude that Polo/Plk1 initiates the phosphorylation-dependent assembly of a Cnn scaffold around centrioles that is essential for efficient centrosome maturation in flies. Copyright © 2014 The Authors. Published by Elsevier Inc. All rights reserved.

Effects of a non-cyclodextrin cyclic carbohydrate on mouse melanoma cells: Characterization of a new type of hypopigmenting sugar

PubMed Central

Kunikata, Toshio; Matsumoto, Yohsuke; Hanaya, Toshiharu; Harashima, Akira; Nishimoto, Tomoyuki; Ushio, Shimpei

2017-01-01

Cyclic nigerosyl nigerose (CNN) is a cyclic tetrasaccharide that exhibits properties distinct from other conventional cyclodextrins. Herein, we demonstrate that treatment of B16 melanoma with CNN results in a dose-dependent decrease in melanin synthesis, even under conditions that stimulate melanin synthesis, without significant cytotoxity. The effects of CNN were prolonged for more than 27 days, and were gradually reversed following removal of CNN. Undigested CNN was found to accumulate within B16 cells at relatively high levels. Further, CNN showed a weak but significant direct inhibitory effect on the enzymatic activity of tyrosinase, suggesting one possible mechanism of hypopigmentation. While a slight reduction in tyrosinase expression was observed, tyrosinase expression was maintained at significant levels, processed into a mature form, and transported to late-stage melanosomes. Immunocytochemical analysis demonstrated that CNN treatment induced drastic morphological changes of Pmel17-positive and LAMP-1–positive organelles within B16 cells, suggesting that CNN is a potent organelle modulator. Colocalization of both tyrosinase-positive and LAMP-1–positive regions in CNN-treated cells indicated possible degradation of tyrosinase in LAMP-1–positive organelles; however, that possibility was ruled out by subsequent inhibition experiments. Taken together, this study opens a new paradigm of functional oligosaccharides, and offers CNN as a novel hypopigmenting molecule and organelle modulator. PMID:29045474

Effects of a non-cyclodextrin cyclic carbohydrate on mouse melanoma cells: Characterization of a new type of hypopigmenting sugar.

PubMed

Nakamura, Shuji; Kunikata, Toshio; Matsumoto, Yohsuke; Hanaya, Toshiharu; Harashima, Akira; Nishimoto, Tomoyuki; Ushio, Shimpei

2017-01-01

Cyclic nigerosyl nigerose (CNN) is a cyclic tetrasaccharide that exhibits properties distinct from other conventional cyclodextrins. Herein, we demonstrate that treatment of B16 melanoma with CNN results in a dose-dependent decrease in melanin synthesis, even under conditions that stimulate melanin synthesis, without significant cytotoxity. The effects of CNN were prolonged for more than 27 days, and were gradually reversed following removal of CNN. Undigested CNN was found to accumulate within B16 cells at relatively high levels. Further, CNN showed a weak but significant direct inhibitory effect on the enzymatic activity of tyrosinase, suggesting one possible mechanism of hypopigmentation. While a slight reduction in tyrosinase expression was observed, tyrosinase expression was maintained at significant levels, processed into a mature form, and transported to late-stage melanosomes. Immunocytochemical analysis demonstrated that CNN treatment induced drastic morphological changes of Pmel17-positive and LAMP-1-positive organelles within B16 cells, suggesting that CNN is a potent organelle modulator. Colocalization of both tyrosinase-positive and LAMP-1-positive regions in CNN-treated cells indicated possible degradation of tyrosinase in LAMP-1-positive organelles; however, that possibility was ruled out by subsequent inhibition experiments. Taken together, this study opens a new paradigm of functional oligosaccharides, and offers CNN as a novel hypopigmenting molecule and organelle modulator.

Genetic Interaction of Centrosomin and Bazooka in Apical Domain Regulation in Drosophila Photoreceptor

PubMed Central

Chen, Geng; Rogers, Alicia K.; League, Garrett P.; Nam, Sang-Chul

2011-01-01

Background Cell polarity genes including Crumbs (Crb) and Par complexes are essential for controlling photoreceptor morphogenesis. Among the Crb and Par complexes, Bazooka (Baz, Par-3 homolog) acts as a nodal component for other cell polarity proteins. Therefore, finding other genes interacting with Baz will help us to understand the cell polarity genes' role in photoreceptor morphogenesis. Methodology/Principal Findings Here, we have found a genetic interaction between baz and centrosomin (cnn). Cnn is a core protein for centrosome which is a major microtubule-organizing center. We analyzed the effect of the cnn mutation on developing eyes to determine its role in photoreceptor morphogenesis. We found that Cnn is dispensable for retinal differentiation in eye imaginal discs during the larval stage. However, photoreceptors deficient in Cnn display dramatic morphogenesis defects including the mislocalization of Crumbs (Crb) and Bazooka (Baz) during mid-stage pupal eye development, suggesting that Cnn is specifically required for photoreceptor morphogenesis during pupal eye development. This role of Cnn in apical domain modulation was further supported by Cnn's gain-of-function phenotype. Cnn overexpression in photoreceptors caused the expansion of the apical Crb membrane domain, Baz and adherens junctions (AJs). Conclusions/Significance These results strongly suggest that the interaction of Baz and Cnn is essential for apical domain and AJ modulation during photoreceptor morphogenesis, but not for the initial photoreceptor differentiation in the Drosophila photoreceptor. PMID:21253601

Application of Convolutional Neural Networks in the Diagnosis of Helicobacter pylori Infection Based on Endoscopic Images.

PubMed

Shichijo, Satoki; Nomura, Shuhei; Aoyama, Kazuharu; Nishikawa, Yoshitaka; Miura, Motoi; Shinagawa, Takahide; Takiyama, Hirotoshi; Tanimoto, Tetsuya; Ishihara, Soichiro; Matsuo, Keigo; Tada, Tomohiro

2017-11-01

The role of artificial intelligence in the diagnosis of Helicobacter pylori gastritis based on endoscopic images has not been evaluated. We constructed a convolutional neural network (CNN), and evaluated its ability to diagnose H. pylori infection. A 22-layer, deep CNN was pre-trained and fine-tuned on a dataset of 32,208 images either positive or negative for H. pylori (first CNN). Another CNN was trained using images classified according to 8 anatomical locations (secondary CNN). A separate test data set (11,481 images from 397 patients) was evaluated by the CNN, and 23 endoscopists, independently. The sensitivity, specificity, accuracy, and diagnostic time were 81.9%, 83.4%, 83.1%, and 198s, respectively, for the first CNN, and 88.9%, 87.4%, 87.7%, and 194s, respectively, for the secondary CNN. These values for the 23 endoscopists were 79.0%, 83.2%, 82.4%, and 230±65min (85.2%, 89.3%, 88.6%, and 253±92min by 6 board-certified endoscopists), respectively. The secondary CNN had a significantly higher accuracy than endoscopists (by 5.3%; 95% CI, 0.3-10.2). H. pylori gastritis could be diagnosed based on endoscopic images using CNN with higher accuracy and in a considerably shorter time compared to manual diagnosis by endoscopists. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

Influence of Cervical Node Necrosis of Different Grades on the Prognosis of Nasopharyngeal Carcinoma Patients Treated with Intensity-Modulated Radiotherapy.

PubMed

Zhang, Lu-Lu; Li, Jia-Xiang; Zhou, Guan-Qun; Tang, Ling-Long; Ma, Jun; Lin, Ai-Hua; Qi, Zhen-Yu; Sun, Ying

2017-01-01

Background: To analyze the prognostic value of cervical node necrosis (CNN) observed on pretreatment magnetic resonance imaging (MRI) in patients with nasopharyngeal carcinoma (NPC) treated with intensity-modulated radiotherapy (IMRT). Patients and Methods: The medical records of 1423 NPC patients with cervical node metastasis who underwent IMRT were retrospectively reviewed. Lymph nodes in the axial plane of pretreatment MRI were classified as follows: grade 0 CNN, no hypodense zones; grade 1 CNN, ≤33% areas showing hypodense zones; and grade 2, >33% areas showing hypodense zones. Results: CNN was detectable in 470/1423 (33%) patients. Of these 470 patients, 213 (15%) and 257 (18%) exhibited grade 1 and grade 2 CNN. The grade 0 and grade 1 CNN groups showed significant differences with regard to distant metastasis-free survival (DMFS), but not overall survival (OS), regional relapse-free survival (RRFS), local relapse-free survival (LRFS), and disease-free survival (DFS). Significant differences were observed among the grade 0 and grade 2 CNN groups with regard to OS, RRFS, LRFS, DMFS, and DFS. Moreover, OS, LRFS, RRFS, and DFS were significantly different between the grade 1 and grade 2 CNN groups, whereas DMFS showed no significant differences. Univariate and multivariate analyses revealed CNN on MRI as a significant negative prognostic factor for OS, LRFS, RRFS, DMFS, and DFS in NPC patients. Conclusions: NPC patients with CNN of different grades show various prognosis and failure patterns after IMRT. CNN on MRI can be adopted as a predictive factor for formulating individualized treatment plans for NPC patients.

CNN Newsroom Classroom Guides. July 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

CNN Newsroom is a daily 15-minute commercial-free news program specifically produced for classroom use and provided free to participating schools. The daily CNN Newsroom broadcast is supported by a Daily Classroom Guide, written by professional educators. These classroom guides are designed to accompany CNN Newsroom broadcasts for a given month,…

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification.

PubMed

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications.

Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification

PubMed Central

Rueckauer, Bodo; Lungu, Iulia-Alexandra; Hu, Yuhuang; Pfeiffer, Michael; Liu, Shih-Chii

2017-01-01

Spiking neural networks (SNNs) can potentially offer an efficient way of doing inference because the neurons in the networks are sparsely activated and computations are event-driven. Previous work showed that simple continuous-valued deep Convolutional Neural Networks (CNNs) can be converted into accurate spiking equivalents. These networks did not include certain common operations such as max-pooling, softmax, batch-normalization and Inception-modules. This paper presents spiking equivalents of these operations therefore allowing conversion of nearly arbitrary CNN architectures. We show conversion of popular CNN architectures, including VGG-16 and Inception-v3, into SNNs that produce the best results reported to date on MNIST, CIFAR-10 and the challenging ImageNet dataset. SNNs can trade off classification error rate against the number of available operations whereas deep continuous-valued neural networks require a fixed number of operations to achieve their classification error rate. From the examples of LeNet for MNIST and BinaryNet for CIFAR-10, we show that with an increase in error rate of a few percentage points, the SNNs can achieve more than 2x reductions in operations compared to the original CNNs. This highlights the potential of SNNs in particular when deployed on power-efficient neuromorphic spiking neuron chips, for use in embedded applications. PMID:29375284

CNN-based ranking for biomedical entity normalization.

PubMed

Li, Haodi; Chen, Qingcai; Tang, Buzhou; Wang, Xiaolong; Xu, Hua; Wang, Baohua; Huang, Dong

2017-10-03

Most state-of-the-art biomedical entity normalization systems, such as rule-based systems, merely rely on morphological information of entity mentions, but rarely consider their semantic information. In this paper, we introduce a novel convolutional neural network (CNN) architecture that regards biomedical entity normalization as a ranking problem and benefits from semantic information of biomedical entities. The CNN-based ranking method first generates candidates using handcrafted rules, and then ranks the candidates according to their semantic information modeled by CNN as well as their morphological information. Experiments on two benchmark datasets for biomedical entity normalization show that our proposed CNN-based ranking method outperforms traditional rule-based method with state-of-the-art performance. We propose a CNN architecture that regards biomedical entity normalization as a ranking problem. Comparison results show that semantic information is beneficial to biomedical entity normalization and can be well combined with morphological information in our CNN architecture for further improvement.

Future of Autonomous Ground Logistics: Convoys in the Department of Defense

DTIC Science & Technology

2011-02-13

Driverless van crosses from Europe to Asia, CNN, October 27, 2010, http://edition.cnn.com/2010/TECH/innovation/10/27/driverless.car/ (accessed November 13...accessed March 13, 2011). Kent, Jo Ling. “ Driverless Van Crosses from Europe to Asia,” CNN, October 27, 2010. http://edition.cnn.com/2010/TECH

Recognizing pedestrian's unsafe behaviors in far-infrared imagery at night

NASA Astrophysics Data System (ADS)

Lee, Eun Ju; Ko, Byoung Chul; Nam, Jae-Yeal

2016-05-01

Pedestrian behavior recognition is important work for early accident prevention in advanced driver assistance system (ADAS). In particular, because most pedestrian-vehicle crashes are occurred from late of night to early of dawn, our study focus on recognizing unsafe behavior of pedestrians using thermal image captured from moving vehicle at night. For recognizing unsafe behavior, this study uses convolutional neural network (CNN) which shows high quality of recognition performance. However, because traditional CNN requires the very expensive training time and memory, we design the light CNN consisted of two convolutional layers and two subsampling layers for real-time processing of vehicle applications. In addition, we combine light CNN with boosted random forest (Boosted RF) classifier so that the output of CNN is not fully connected with the classifier but randomly connected with Boosted random forest. We named this CNN as randomly connected CNN (RC-CNN). The proposed method was successfully applied to the pedestrian unsafe behavior (PUB) dataset captured from far-infrared camera at night and its behavior recognition accuracy is confirmed to be higher than that of some algorithms related to CNNs, with a shorter processing time.

Combined prognostic value of pretreatment anemia and cervical node necrosis in patients with nasopharyngeal carcinoma receiving intensity-modulated radiotherapy: A large-scale retrospective study.

PubMed

Zhang, Lu-Lu; Zhou, Guan-Qun; Li, Yi-Yang; Tang, Ling-Long; Mao, Yan-Ping; Lin, Ai-Hua; Ma, Jun; Qi, Zhen-Yu; Sun, Ying

2017-12-01

This study investigated the combined prognostic value of pretreatment anemia and cervical node necrosis (CNN) in patients with nasopharyngeal carcinoma (NPC). Retrospective review of 1302 patients with newly diagnosed nonmetastatic NPC treated with intensity-modulated radiotherapy (IMRT) ± chemotherapy. Patients were classified into four groups according to anemia and CNN status. Survival was compared using the log-rank test. Independent prognostic factors were identified using the Cox proportional hazards model. The primary end-point was overall survival (OS); secondary end-points were disease-free survival (DFS), locoregional relapse-free survival (LRRFS), and distant metastasis-free survival (DMFS). Pretreatment anemia was an independent, adverse prognostic factor for DMFS; pretreatment CNN was an independent adverse prognostic factor for all end-points. Five-year survival for non-anemia and non-CNN, anemia, CNN, and anemia and CNN groups were: OS (93.1%, 87.2%, 82.9%, 76.3%, P < 0.001), DFS (87.0%, 84.0%, 73.9%, 64.6%, P < 0.001), DMFS (94.1%, 92.1%, 82.4%, 72.5%, P < 0.001), and LRRFS (92.8%, 92.4%, 88.7%, 84.0%, P = 0.012). The non-anemia and non-CNN group had best survival outcomes; anemia and CNN group, the poorest. Multivariate analysis demonstrated combined anemia and CNN was an independent prognostic factor for OS, DFS, DMFS, and LRRFS (P < 0.05). The combination of anemia and CNN is an independent adverse prognostic factor in patients with NPC treated using IMRT ± chemotherapy. Assessment of pretreatment anemia and CNN improved risk stratification, especially for patients with anemia and CNN who have poorest prognosis. This study may aid the design of individualized treatment plans to improve treatment outcomes. © 2017 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.

Prediction of Tissue Outcome and Assessment of Treatment Effect in Acute Ischemic Stroke Using Deep Learning.

PubMed

Nielsen, Anne; Hansen, Mikkel Bo; Tietze, Anna; Mouridsen, Kim

2018-06-01

Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume. Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNN deep ) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNN deep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNN Tmax ), a shallow CNN based on a combination of 9 different biomarkers (CNN shallow ), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600×10 -6 mm 2 /s (ADC thres ). To assess whether CNN deep is capable of differentiating outcomes of ±intravenous rtPA, patients not receiving intravenous rtPA were included to train CNN deep, -rtpa to access a treatment effect. The networks' performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast. CNN deep yields significantly better performance in predicting final outcome (AUC=0.88±0.12) than generalized linear model (AUC=0.78±0.12; P =0.005), CNN Tmax (AUC=0.72±0.14; P <0.003), and ADC thres (AUC=0.66±0.13; P <0.0001) and a substantially better performance than CNN shallow (AUC=0.85±0.11; P =0.063). Measured by contrast, CNN deep improves the predictions significantly, showing superiority to all other methods ( P ≤0.003). CNN deep also seems to be able to differentiate outcomes based on treatment strategy with the volume of final infarct being significantly different ( P =0.048). The considerable prediction improvement accuracy over current state of the art increases the potential for automated decision support in providing recommendations for personalized treatment plans. © 2018 American Heart Association, Inc.

HCP: A Flexible CNN Framework for Multi-label Image Classification.

PubMed

Wei, Yunchao; Xia, Wei; Lin, Min; Huang, Junshi; Ni, Bingbing; Dong, Jian; Zhao, Yao; Yan, Shuicheng

2015-10-26

Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [44] based on hand-crafted features on the VOC 2012 dataset.

A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification

NASA Astrophysics Data System (ADS)

Zhang, Ce; Pan, Xin; Li, Huapeng; Gardiner, Andy; Sargent, Isabel; Hare, Jonathon; Atkinson, Peter M.

2018-06-01

The contextual-based convolutional neural network (CNN) with deep architecture and pixel-based multilayer perceptron (MLP) with shallow structure are well-recognized neural network algorithms, representing the state-of-the-art deep learning method and the classical non-parametric machine learning approach, respectively. The two algorithms, which have very different behaviours, were integrated in a concise and effective way using a rule-based decision fusion approach for the classification of very fine spatial resolution (VFSR) remotely sensed imagery. The decision fusion rules, designed primarily based on the classification confidence of the CNN, reflect the generally complementary patterns of the individual classifiers. In consequence, the proposed ensemble classifier MLP-CNN harvests the complementary results acquired from the CNN based on deep spatial feature representation and from the MLP based on spectral discrimination. Meanwhile, limitations of the CNN due to the adoption of convolutional filters such as the uncertainty in object boundary partition and loss of useful fine spatial resolution detail were compensated. The effectiveness of the ensemble MLP-CNN classifier was tested in both urban and rural areas using aerial photography together with an additional satellite sensor dataset. The MLP-CNN classifier achieved promising performance, consistently outperforming the pixel-based MLP, spectral and textural-based MLP, and the contextual-based CNN in terms of classification accuracy. This research paves the way to effectively address the complicated problem of VFSR image classification.

S-CNN: Subcategory-aware convolutional networks for object detection.

PubMed

Chen, Tao; Lu, Shijian; Fan, Jiayuan

2017-09-26

The marriage between the deep convolutional neural network (CNN) and region proposals has made breakthroughs for object detection in recent years. While the discriminative object features are learned via a deep CNN for classification, the large intra-class variation and deformation still limit the performance of the CNN based object detection. We propose a subcategory-aware CNN (S-CNN) to solve the object intra-class variation problem. In the proposed technique, the training samples are first grouped into multiple subcategories automatically through a novel instance sharing maximum margin clustering process. A multi-component Aggregated Channel Feature (ACF) detector is then trained to produce more latent training samples, where each ACF component corresponds to one clustered subcategory. The produced latent samples together with their subcategory labels are further fed into a CNN classifier to filter out false proposals for object detection. An iterative learning algorithm is designed for the joint optimization of image subcategorization, multi-component ACF detector, and subcategory-aware CNN classifier. Experiments on INRIA Person dataset, Pascal VOC 2007 dataset and MS COCO dataset show that the proposed technique clearly outperforms the state-of-the-art methods for generic object detection.

Fast and robust segmentation of the striatum using deep convolutional neural networks.

PubMed

Choi, Hongyoon; Jin, Kyong Hwan

2016-12-01

Automated segmentation of brain structures is an important task in structural and functional image analysis. We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN). T1 magnetic resonance (MR) images were used for our CNN-based segmentation, which require neither image feature extraction nor nonlinear transformation. We employed two serial CNN, Global and Local CNN: The Global CNN determined approximate locations of the striatum. It performed a regression of input MR images fitted to smoothed segmentation maps of the striatum. From the output volume of Global CNN, cropped MR volumes which included the striatum were extracted. The cropped MR volumes and the output volumes of Global CNN were used for inputs of Local CNN. Local CNN predicted the accurate label of all voxels. Segmentation results were compared with a widely used segmentation method, FreeSurfer. Our method showed higher Dice Similarity Coefficient (DSC) (0.893±0.017 vs. 0.786±0.015) and precision score (0.905±0.018 vs. 0.690±0.022) than FreeSurfer-based striatum segmentation (p=0.06). Our approach was also tested using another independent dataset, which showed high DSC (0.826±0.038) comparable with that of FreeSurfer. Comparison with existing method Segmentation performance of our proposed method was comparable with that of FreeSurfer. The running time of our approach was approximately three seconds. We suggested a fast and accurate deep CNN-based segmentation for small brain structures which can be widely applied to brain image analysis. Copyright © 2016 Elsevier B.V. All rights reserved.

BrainNetCNN: Convolutional neural networks for brain networks; towards predicting neurodevelopment.

PubMed

Kawahara, Jeremy; Brown, Colin J; Miller, Steven P; Booth, Brian G; Chau, Vann; Grunau, Ruth E; Zwicker, Jill G; Hamarneh, Ghassan

2017-02-01

We propose BrainNetCNN, a convolutional neural network (CNN) framework to predict clinical neurodevelopmental outcomes from brain networks. In contrast to the spatially local convolutions done in traditional image-based CNNs, our BrainNetCNN is composed of novel edge-to-edge, edge-to-node and node-to-graph convolutional filters that leverage the topological locality of structural brain networks. We apply the BrainNetCNN framework to predict cognitive and motor developmental outcome scores from structural brain networks of infants born preterm. Diffusion tensor images (DTI) of preterm infants, acquired between 27 and 46 weeks gestational age, were used to construct a dataset of structural brain connectivity networks. We first demonstrate the predictive capabilities of BrainNetCNN on synthetic phantom networks with simulated injury patterns and added noise. BrainNetCNN outperforms a fully connected neural-network with the same number of model parameters on both phantoms with focal and diffuse injury patterns. We then apply our method to the task of joint prediction of Bayley-III cognitive and motor scores, assessed at 18 months of age, adjusted for prematurity. We show that our BrainNetCNN framework outperforms a variety of other methods on the same data. Furthermore, BrainNetCNN is able to identify an infant's postmenstrual age to within about 2 weeks. Finally, we explore the high-level features learned by BrainNetCNN by visualizing the importance of each connection in the brain with respect to predicting the outcome scores. These findings are then discussed in the context of the anatomy and function of the developing preterm infant brain. Copyright © 2016 Elsevier Inc. All rights reserved.

Centrioles regulate centrosome size by controlling the rate of Cnn incorporation into the PCM.

PubMed

Conduit, Paul T; Brunk, Kathrin; Dobbelaere, Jeroen; Dix, Carly I; Lucas, Eliana P; Raff, Jordan W

2010-12-21

centrosomes are major microtubule organizing centers in animal cells, and they comprise a pair of centrioles surrounded by an amorphous pericentriolar material (PCM). Centrosome size is tightly regulated during the cell cycle, and it has recently been shown that the two centrosomes in certain stem cells are often asymmetric in size. There is compelling evidence that centrioles influence centrosome size, but how centrosome size is set remains mysterious. we show that the conserved Drosophila PCM protein Cnn exhibits an unusual dynamic behavior, because Cnn molecules only incorporate into the PCM closest to the centrioles and then spread outward through the rest of the PCM. Cnn incorporation into the PCM is driven by an interaction with the conserved centriolar proteins Asl (Cep152 in humans) and DSpd-2 (Cep192 in humans). The rate of Cnn incorporation into the PCM is tightly regulated during the cell cycle, and this rate influences the amount of Cnn in the PCM, which in turn is an important determinant of overall centrosome size. Intriguingly, daughter centrioles in syncytial embryos only start to incorporate Cnn as they disengage from their mothers; this generates a centrosome size asymmetry, with mother centrioles always initially organizing more Cnn than their daughters. centrioles can control the amount of PCM they organize by regulating the rate of Cnn incorporation into the PCM. This mechanism can explain how centrosome size is regulated during the cell cycle and also allows mother and daughter centrioles to set centrosome size independently of one another.

Classification of mass and normal breast tissue: A convolution neural network classifier with spatial domain and texture images

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sahiner, B.; Chan, H.P.; Petrick, N.

1996-10-01

The authors investigated the classification of regions of interest (ROI`s) on mammograms as either mass or normal tissue using a convolution neural network (CNN). A CNN is a back-propagation neural network with two-dimensional (2-D) weight kernels that operate on images. A generalized, fast and stable implementation of the CNN was developed. The input images to the CNN were obtained form the ROI`s using two techniques. The first technique employed averaging and subsampling. The second technique employed texture feature extraction methods applied to small subregions inside the ROI. Features computed over different subregions were arranged as texture images, which were subsequentlymore » used as CNN inputs. The effects of CNN architecture and texture feature parameters on classification accuracy were studied. Receiver operating characteristic (ROC) methodology was used to evaluate the classification accuracy. A data set consisting of 168 ROI`s containing biopsy-proven masses and 504 ROI`s containing normal breast tissue was extracted from 168 mammograms by radiologists experienced in mammography. This data set was used for training and testing the CNN. With the best combination of CNN architecture and texture feature parameters, the area under the test ROC curve reached 0.87, which corresponded to a true-positive fraction of 90% at a false positive fraction of 31%. The results demonstrate the feasibility of using a CNN for classification of masses and normal tissue on mammograms.« less

A method for medulloblastoma tumor differentiation based on convolutional neural networks and transfer learning

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Arévalo, John; Judkins, Alexander; Madabhushi, Anant; González, Fabio

2015-12-01

Convolutional neural networks (CNN) have been very successful at addressing different computer vision tasks thanks to their ability to learn image representations directly from large amounts of labeled data. Features learned from a dataset can be used to represent images from a different dataset via an approach called transfer learning. In this paper we apply transfer learning to the challenging task of medulloblastoma tumor differentiation. We compare two different CNN models which were previously trained in two different domains (natural and histopathology images). The first CNN is a state-of-the-art approach in computer vision, a large and deep CNN with 16-layers, Visual Geometry Group (VGG) CNN. The second (IBCa-CNN) is a 2-layer CNN trained for invasive breast cancer tumor classification. Both CNNs are used as visual feature extractors of histopathology image regions of anaplastic and non-anaplastic medulloblastoma tumor from digitized whole-slide images. The features from the two models are used, separately, to train a softmax classifier to discriminate between anaplastic and non-anaplastic medulloblastoma image regions. Experimental results show that the transfer learning approach produce competitive results in comparison with the state of the art approaches for IBCa detection. Results also show that features extracted from the IBCa-CNN have better performance in comparison with features extracted from the VGG-CNN. The former obtains 89.8% while the latter obtains 76.6% in terms of average accuracy.

Learning Oriented Region-based Convolutional Neural Networks for Building Detection in Satellite Remote Sensing Images

NASA Astrophysics Data System (ADS)

Chen, C.; Gong, W.; Hu, Y.; Chen, Y.; Ding, Y.

2017-05-01

The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement

PubMed Central

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-01-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment. PMID:29494524

Region Based CNN for Foreign Object Debris Detection on Airfield Pavement.

PubMed

Cao, Xiaoguang; Wang, Peng; Meng, Cai; Bai, Xiangzhi; Gong, Guoping; Liu, Miaoming; Qi, Jun

2018-03-01

In this paper, a novel algorithm based on convolutional neural network (CNN) is proposed to detect foreign object debris (FOD) based on optical imaging sensors. It contains two modules, the improved region proposal network (RPN) and spatial transformer network (STN) based CNN classifier. In the improved RPN, some extra select rules are designed and deployed to generate high quality candidates with fewer numbers. Moreover, the efficiency of CNN detector is significantly improved by introducing STN layer. Compared to faster R-CNN and single shot multiBox detector (SSD), the proposed algorithm achieves better result for FOD detection on airfield pavement in the experiment.

Deep Learning the Universe

NASA Astrophysics Data System (ADS)

Singh, Shiwangi; Bard, Deborah

2017-01-01

Weak gravitational lensing is an effective tool to map the structure of matter in the universe, and has been used for more than ten years as a probe of the nature of dark energy. Beyond the well-established two-point summary statistics, attention is now turning to methods that use the full statistical information available in the lensing observables, through analysis of the reconstructed shear field. This offers an opportunity to take advantage of powerful deep learning methods for image analysis. We present two early studies that demonstrate that deep learning can be used to characterise features in weak lensing convergence maps, and to identify the underlying cosmological model that produced them.We developed an unsupervised Denoising Convolutional Autoencoder model in order to learn an abstract representation directly from our data. This model uses a convolution-deconvolution architecture, which is fed with input data (corrupted with binomial noise to prevent over-fitting). Our model effectively trains itself to minimize the mean-squared error between the input and the output using gradient descent, resulting in a model which, theoretically, is broad enough to tackle other similarly structured problems. Using this model we were able to successfully reconstruct simulated convergence maps and identify the structures in them. We also determined which structures had the highest “importance” - i.e. which structures were most typical of the data. We note that the structures that had the highest importance in our reconstruction were around high mass concentrations, but were highly non-Gaussian.We also developed a supervised Convolutional Neural Network (CNN) for classification of weak lensing convergence maps from two different simulated theoretical models. The CNN uses a softmax classifier which minimizes a binary cross-entropy loss between the estimated distribution and true distribution. In other words, given an unseen convergence map the trained CNN determines probabilistically which theoretical model fits the data best. This preliminary work demonstrates that we can classify the cosmological model that produced the convergence maps with 80% accuracy.

Experimental analysis of chaotic neural network models for combinatorial optimization under a unifying framework.

PubMed

Kwok, T; Smith, K A

2000-09-01

The aim of this paper is to study both the theoretical and experimental properties of chaotic neural network (CNN) models for solving combinatorial optimization problems. Previously we have proposed a unifying framework which encompasses the three main model types, namely, Chen and Aihara's chaotic simulated annealing (CSA) with decaying self-coupling, Wang and Smith's CSA with decaying timestep, and the Hopfield network with chaotic noise. Each of these models can be represented as a special case under the framework for certain conditions. This paper combines the framework with experimental results to provide new insights into the effect of the chaotic neurodynamics of each model. By solving the N-queen problem of various sizes with computer simulations, the CNN models are compared in different parameter spaces, with optimization performance measured in terms of feasibility, efficiency, robustness and scalability. Furthermore, characteristic chaotic neurodynamics crucial to effective optimization are identified, together with a guide to choosing the corresponding model parameters.

Gravitational waveforms from unequal-mass binaries with arbitrary spins under leading order spin-orbit coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tessmer, Manuel

This paper generalizes the structure of gravitational waves from orbiting spinning binaries under leading order spin-orbit coupling, as given in the work by Koenigsdoerffer and Gopakumar [Phys. Rev. D 71, 024039 (2005)] for single-spin and equal-mass binaries, to unequal-mass binaries and arbitrary spin configurations. The orbital motion is taken to be quasicircular and the fractional mass difference is assumed to be small against one. The emitted gravitational waveforms are given in analytic form.

Evaluation of CNN as anthropomorphic model observer

NASA Astrophysics Data System (ADS)

Massanes, Francesc; Brankov, Jovan G.

2017-03-01

Model observers (MO) are widely used in medical imaging to act as surrogates of human observers in task-based image quality evaluation, frequently towards optimization of reconstruction algorithms. In this paper, we explore the use of convolutional neural networks (CNN) to be used as MO. We will compare CNN MO to alternative MO currently being proposed and used such as the relevance vector machine based MO and channelized Hotelling observer (CHO). As the success of the CNN, and other deep learning approaches, is rooted in large data sets availability, which is rarely the case in medical imaging systems task-performance evaluation, we will evaluate CNN performance on both large and small training data sets.

Three-Class Mammogram Classification Based on Descriptive CNN Features

PubMed Central

Zhang, Qianni; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques. PMID:28191461

Three-Class Mammogram Classification Based on Descriptive CNN Features.

PubMed

Jadoon, M Mohsin; Zhang, Qianni; Haq, Ihsan Ul; Butt, Sharjeel; Jadoon, Adeel

2017-01-01

In this paper, a novel classification technique for large data set of mammograms using a deep learning method is proposed. The proposed model targets a three-class classification study (normal, malignant, and benign cases). In our model we have presented two methods, namely, convolutional neural network-discrete wavelet (CNN-DW) and convolutional neural network-curvelet transform (CNN-CT). An augmented data set is generated by using mammogram patches. To enhance the contrast of mammogram images, the data set is filtered by contrast limited adaptive histogram equalization (CLAHE). In the CNN-DW method, enhanced mammogram images are decomposed as its four subbands by means of two-dimensional discrete wavelet transform (2D-DWT), while in the second method discrete curvelet transform (DCT) is used. In both methods, dense scale invariant feature (DSIFT) for all subbands is extracted. Input data matrix containing these subband features of all the mammogram patches is created that is processed as input to convolutional neural network (CNN). Softmax layer and support vector machine (SVM) layer are used to train CNN for classification. Proposed methods have been compared with existing methods in terms of accuracy rate, error rate, and various validation assessment measures. CNN-DW and CNN-CT have achieved accuracy rate of 81.83% and 83.74%, respectively. Simulation results clearly validate the significance and impact of our proposed model as compared to other well-known existing techniques.

Deformable Image Registration based on Similarity-Steered CNN Regression.

PubMed

Cao, Xiaohuan; Yang, Jianhua; Zhang, Jun; Nie, Dong; Kim, Min-Jeong; Wang, Qian; Shen, Dinggang

2017-09-01

Existing deformable registration methods require exhaustively iterative optimization, along with careful parameter tuning, to estimate the deformation field between images. Although some learning-based methods have been proposed for initiating deformation estimation, they are often template-specific and not flexible in practical use. In this paper, we propose a convolutional neural network (CNN) based regression model to directly learn the complex mapping from the input image pair (i.e., a pair of template and subject) to their corresponding deformation field. Specifically, our CNN architecture is designed in a patch-based manner to learn the complex mapping from the input patch pairs to their respective deformation field. First, the equalized active-points guided sampling strategy is introduced to facilitate accurate CNN model learning upon a limited image dataset. Then, the similarity-steered CNN architecture is designed, where we propose to add the auxiliary contextual cue, i.e., the similarity between input patches, to more directly guide the learning process. Experiments on different brain image datasets demonstrate promising registration performance based on our CNN model. Furthermore, it is found that the trained CNN model from one dataset can be successfully transferred to another dataset, although brain appearances across datasets are quite variable.

Automated Detection of Obstructive Sleep Apnea Events from a Single-Lead Electrocardiogram Using a Convolutional Neural Network.

PubMed

Urtnasan, Erdenebayar; Park, Jong-Uk; Joo, Eun-Yeon; Lee, Kyoung-Joung

2018-04-23

In this study, we propose a method for the automated detection of obstructive sleep apnea (OSA) from a single-lead electrocardiogram (ECG) using a convolutional neural network (CNN). A CNN model was designed with six optimized convolution layers including activation, pooling, and dropout layers. One-dimensional (1D) convolution, rectified linear units (ReLU), and max pooling were applied to the convolution, activation, and pooling layers, respectively. For training and evaluation of the CNN model, a single-lead ECG dataset was collected from 82 subjects with OSA and was divided into training (including data from 63 patients with 34,281 events) and testing (including data from 19 patients with 8571 events) datasets. Using this CNN model, a precision of 0.99%, a recall of 0.99%, and an F 1 -score of 0.99% were attained with the training dataset; these values were all 0.96% when the CNN was applied to the testing dataset. These results show that the proposed CNN model can be used to detect OSA accurately on the basis of a single-lead ECG. Ultimately, this CNN model may be used as a screening tool for those suspected to suffer from OSA.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text.

PubMed

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-05-01

Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. andyli@ece.ufl.edu or aconesa@ufl.edu. Supplementary data are available at Bioinformatics online.

GRAM-CNN: a deep learning approach with local context for named entity recognition in biomedical text

PubMed Central

Zhu, Qile; Li, Xiaolin; Conesa, Ana; Pereira, Cécile

2018-01-01

Abstract Motivation Best performing named entity recognition (NER) methods for biomedical literature are based on hand-crafted features or task-specific rules, which are costly to produce and difficult to generalize to other corpora. End-to-end neural networks achieve state-of-the-art performance without hand-crafted features and task-specific knowledge in non-biomedical NER tasks. However, in the biomedical domain, using the same architecture does not yield competitive performance compared with conventional machine learning models. Results We propose a novel end-to-end deep learning approach for biomedical NER tasks that leverages the local contexts based on n-gram character and word embeddings via Convolutional Neural Network (CNN). We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not require any specific knowledge or feature engineering and can be theoretically applied to a wide range of existing NER problems. The GRAM-CNN approach was evaluated on three well-known biomedical datasets containing different BioNER entities. It obtained an F1-score of 87.26% on the Biocreative II dataset, 87.26% on the NCBI dataset and 72.57% on the JNLPBA dataset. Those results put GRAM-CNN in the lead of the biological NER methods. To the best of our knowledge, we are the first to apply CNN based structures to BioNER problems. Availability and implementation The GRAM-CNN source code, datasets and pre-trained model are available online at: https://github.com/valdersoul/GRAM-CNN. Contact andyli@ece.ufl.edu or aconesa@ufl.edu Supplementary information Supplementary data are available at Bioinformatics online. PMID:29272325

Agile convolutional neural network for pulmonary nodule classification using CT images.

PubMed

Zhao, Xinzhuo; Liu, Liyao; Qi, Shouliang; Teng, Yueyang; Li, Jianhua; Qian, Wei

2018-04-01

To distinguish benign from malignant pulmonary nodules using CT images is critical for their precise diagnosis and treatment. A new Agile convolutional neural network (CNN) framework is proposed to conquer the challenges of a small-scale medical image database and the small size of the nodules, and it improves the performance of pulmonary nodule classification using CT images. A hybrid CNN of LeNet and AlexNet is constructed through combining the layer settings of LeNet and the parameter settings of AlexNet. A dataset with 743 CT image nodule samples is built up based on the 1018 CT scans of LIDC to train and evaluate the Agile CNN model. Through adjusting the parameters of the kernel size, learning rate, and other factors, the effect of these parameters on the performance of the CNN model is investigated, and an optimized setting of the CNN is obtained finally. After finely optimizing the settings of the CNN, the estimation accuracy and the area under the curve can reach 0.822 and 0.877, respectively. The accuracy of the CNN is significantly dependent on the kernel size, learning rate, training batch size, dropout, and weight initializations. The best performance is achieved when the kernel size is set to [Formula: see text], the learning rate is 0.005, the batch size is 32, and dropout and Gaussian initialization are used. This competitive performance demonstrates that our proposed CNN framework and the optimization strategy of the CNN parameters are suitable for pulmonary nodule classification characterized by small medical datasets and small targets. The classification model might help diagnose and treat pulmonary nodules effectively.

Evaluation of CROES Nephrolithometry Nomogram as a Preoperative Predictive System for Percutaneous Nephrolithotomy Outcomes.

PubMed

Kumar, Sumit; Sreenivas, Jayaram; Karthikeyan, Vilvapathy Senguttuvan; Mallya, Ashwin; Keshavamurthy, Ramaiah

2016-10-01

Scoring systems have been devised to predict outcomes of percutaneous nephrolithotomy (PCNL). CROES nephrolithometry nomogram (CNN) is the latest tool devised to predict stone-free rate (SFR). We aim to compare predictive accuracy of CNN against Guy stone score (GSS) for SFR and postoperative outcomes. Between January 2013 and December 2015, 313 patients undergoing PCNL were analyzed for predictive accuracy of GSS, CNN, and stone burden (SB) for SFR, complications, operation time (OT), and length of hospitalization (LOH). We further stratified patients into risk groups based on CNN and GSS. Mean ± standard deviation (SD) SB was 298.8 ± 235.75 mm 2 . SB, GSS, and CNN (area under curve [AUC]: 0.662, 0.660, 0.673) were found to be predictors of SFR. However, predictability for complications was not as good (AUC: SB 0.583, GSS 0.554, CNN 0.580). Single implicated calix (Adj. OR 3.644; p = 0.027), absence of staghorn calculus (Adj. OR 3.091; p = 0.044), single stone (Adj. OR 3.855; p = 0.002), and single puncture (Adj. OR 2.309; p = 0.048) significantly predicted SFR on multivariate analysis. Charlson comorbidity index (CCI; p = 0.020) and staghorn calculus (p = 0.002) were independent predictors for complications on linear regression. SB and GSS independently predicted OT on multivariate analysis. SB and complications significantly predicted LOH, while GSS and CNN did not predict LOH. CNN offered better risk stratification for residual stones than GSS. CNN and GSS have good preoperative predictive accuracy for SFR. Number of implicated calices may affect SFR, and CCI affects complications. Studies should incorporate these factors in scoring systems and assess if predictability of PCNL outcomes improves.

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search

PubMed Central

Fang, Leyuan; Cunefare, David; Wang, Chong; Guymer, Robyn H.; Li, Shutao; Farsiu, Sina

2017-01-01

We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique. PMID:28663902

Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search.

PubMed

Fang, Leyuan; Cunefare, David; Wang, Chong; Guymer, Robyn H; Li, Shutao; Farsiu, Sina

2017-05-01

We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.

Video-based convolutional neural networks for activity recognition from robot-centric videos

NASA Astrophysics Data System (ADS)

Ryoo, M. S.; Matthies, Larry

2016-05-01

In this evaluation paper, we discuss convolutional neural network (CNN)-based approaches for human activity recognition. In particular, we investigate CNN architectures designed to capture temporal information in videos and their applications to the human activity recognition problem. There have been multiple previous works to use CNN-features for videos. These include CNNs using 3-D XYT convolutional filters, CNNs using pooling operations on top of per-frame image-based CNN descriptors, and recurrent neural networks to learn temporal changes in per-frame CNN descriptors. We experimentally compare some of these different representatives CNNs while using first-person human activity videos. We especially focus on videos from a robots viewpoint, captured during its operations and human-robot interactions.

A novel cloning template designing method by using an artificial bee colony algorithm for edge detection of CNN based imaging sensors.

PubMed

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods.

A Novel Cloning Template Designing Method by Using an Artificial Bee Colony Algorithm for Edge Detection of CNN Based Imaging Sensors

PubMed Central

Parmaksızoğlu, Selami; Alçı, Mustafa

2011-01-01

Cellular Neural Networks (CNNs) have been widely used recently in applications such as edge detection, noise reduction and object detection, which are among the main computer imaging processes. They can also be realized as hardware based imaging sensors. The fact that hardware CNN models produce robust and effective results has attracted the attention of researchers using these structures within image sensors. Realization of desired CNN behavior such as edge detection can be achieved by correctly setting a cloning template without changing the structure of the CNN. To achieve different behaviors effectively, designing a cloning template is one of the most important research topics in this field. In this study, the edge detecting process that is used as a preliminary process for segmentation, identification and coding applications is conducted by using CNN structures. In order to design the cloning template of goal-oriented CNN architecture, an Artificial Bee Colony (ABC) algorithm which is inspired from the foraging behavior of honeybees is used and the performance analysis of ABC for this application is examined with multiple runs. The CNN template generated by the ABC algorithm is tested by using artificial and real test images. The results are subjectively and quantitatively compared with well-known classical edge detection methods, and other CNN based edge detector cloning templates available in the imaging literature. The results show that the proposed method is more successful than other methods. PMID:22163903

CNN Newsroom Classroom Guides. September 1997.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

CNN Newsroom is a daily 15-minute news program specifically produced for classroom use and provided free to participating schools. These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for September 2-29, 1997, provide program rundowns, suggestions for class activities and discussion, student…

Utilization of CNN Newsroom in School Classrooms.

ERIC Educational Resources Information Center

Jordan, Sandra S.

This study was an educational assessment of the CNN (Cable News Network) Newsroom by enrolled users throughout the state of Georgia. CNN Newsroom is a 15-minute commercial-free newscast aimed at students in public school classrooms. Supplementing the newscasts are daily curriculum guides (available electronically) that outline questions and…

CNN Newsroom Classroom Guides. January 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

CNN Newsroom is a daily 15-minute commercial-free news program specifically produced for classroom use and provided free to participating schools. These Daily Classroom Guides support broadcasts of CNN Newsroom for January 1999. Each guide contains program rundowns for that day's broadcast, discussion activities, and links to external Web sites.…

a Rough Set Decision Tree Based Mlp-Cnn for Very High Resolution Remotely Sensed Image Classification

NASA Astrophysics Data System (ADS)

Zhang, C.; Pan, X.; Zhang, S. Q.; Li, H. P.; Atkinson, P. M.

2017-09-01

Recent advances in remote sensing have witnessed a great amount of very high resolution (VHR) images acquired at sub-metre spatial resolution. These VHR remotely sensed data has post enormous challenges in processing, analysing and classifying them effectively due to the high spatial complexity and heterogeneity. Although many computer-aid classification methods that based on machine learning approaches have been developed over the past decades, most of them are developed toward pixel level spectral differentiation, e.g. Multi-Layer Perceptron (MLP), which are unable to exploit abundant spatial details within VHR images. This paper introduced a rough set model as a general framework to objectively characterize the uncertainty in CNN classification results, and further partition them into correctness and incorrectness on the map. The correct classification regions of CNN were trusted and maintained, whereas the misclassification areas were reclassified using a decision tree with both CNN and MLP. The effectiveness of the proposed rough set decision tree based MLP-CNN was tested using an urban area at Bournemouth, United Kingdom. The MLP-CNN, well capturing the complementarity between CNN and MLP through the rough set based decision tree, achieved the best classification performance both visually and numerically. Therefore, this research paves the way to achieve fully automatic and effective VHR image classification.

UFCN: a fully convolutional neural network for road extraction in RGB imagery acquired by remote sensing from an unmanned aerial vehicle

NASA Astrophysics Data System (ADS)

Kestur, Ramesh; Farooq, Shariq; Abdal, Rameen; Mehraj, Emad; Narasipura, Omkar; Mudigere, Meenavathi

2018-01-01

Road extraction in imagery acquired by low altitude remote sensing (LARS) carried out using an unmanned aerial vehicle (UAV) is presented. LARS is carried out using a fixed wing UAV with a high spatial resolution vision spectrum (RGB) camera as the payload. Deep learning techniques, particularly fully convolutional network (FCN), are adopted to extract roads by dense semantic segmentation. The proposed model, UFCN (U-shaped FCN) is an FCN architecture, which is comprised of a stack of convolutions followed by corresponding stack of mirrored deconvolutions with the usage of skip connections in between for preserving the local information. The limited dataset (76 images and their ground truths) is subjected to real-time data augmentation during training phase to increase the size effectively. Classification performance is evaluated using precision, recall, accuracy, F1 score, and brier score parameters. The performance is compared with support vector machine (SVM) classifier, a one-dimensional convolutional neural network (1D-CNN) model, and a standard two-dimensional CNN (2D-CNN). The UFCN model outperforms the SVM, 1D-CNN, and 2D-CNN models across all the performance parameters. Further, the prediction time of the proposed UFCN model is comparable with SVM, 1D-CNN, and 2D-CNN models.

Memristor-based cellular nonlinear/neural network: design, analysis, and applications.

PubMed

Duan, Shukai; Hu, Xiaofang; Dong, Zhekang; Wang, Lidan; Mazumder, Pinaki

2015-06-01

Cellular nonlinear/neural network (CNN) has been recognized as a powerful massively parallel architecture capable of solving complex engineering problems by performing trillions of analog operations per second. The memristor was theoretically predicted in the late seventies, but it garnered nascent research interest due to the recent much-acclaimed discovery of nanocrossbar memories by engineers at the Hewlett-Packard Laboratory. The memristor is expected to be co-integrated with nanoscale CMOS technology to revolutionize conventional von Neumann as well as neuromorphic computing. In this paper, a compact CNN model based on memristors is presented along with its performance analysis and applications. In the new CNN design, the memristor bridge circuit acts as the synaptic circuit element and substitutes the complex multiplication circuit used in traditional CNN architectures. In addition, the negative differential resistance and nonlinear current-voltage characteristics of the memristor have been leveraged to replace the linear resistor in conventional CNNs. The proposed CNN design has several merits, for example, high density, nonvolatility, and programmability of synaptic weights. The proposed memristor-based CNN design operations for implementing several image processing functions are illustrated through simulation and contrasted with conventional CNNs. Monte-Carlo simulation has been used to demonstrate the behavior of the proposed CNN due to the variations in memristor synaptic weights.

Reduction of metal artifacts in x-ray CT images using a convolutional neural network

NASA Astrophysics Data System (ADS)

Zhang, Yanbo; Chu, Ying; Yu, Hengyong

2017-09-01

Patients usually contain various metallic implants (e.g. dental fillings, prostheses), causing severe artifacts in the x-ray CT images. Although a large number of metal artifact reduction (MAR) methods have been proposed in the past four decades, MAR is still one of the major problems in clinical x-ray CT. In this work, we develop a convolutional neural network (CNN) based MAR framework, which combines the information from the original and corrected images to suppress artifacts. Before the MAR, we generate a group of data and train a CNN. First, we numerically simulate various metal artifacts cases and build a dataset, which includes metal-free images (used as references), metal-inserted images and various MAR methods corrected images. Then, ten thousands patches are extracted from the databased to train the metal artifact reduction CNN. In the MAR stage, the original image and two corrected images are stacked as a three-channel input image for CNN, and a CNN image is generated with less artifacts. The water equivalent regions in the CNN image are set to a uniform value to yield a CNN prior, whose forward projections are used to replace the metal affected projections, followed by the FBP reconstruction. Experimental results demonstrate the superior metal artifact reduction capability of the proposed method to its competitors.

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists.

PubMed

Haenssle, H A; Fink, C; Schneiderbauer, R; Toberer, F; Buhl, T; Blum, A; Kalloo, A; Hassen, A Ben Hadj; Thomas, L; Enk, A; Uhlmann, L

2018-05-28

Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking. Google's Inception v4 CNN architecture was trained and validated using dermoscopic images and corresponding diagnoses. In a comparative cross-sectional reader study a 100-image test-set was used (level-I: dermoscopy only; level-II: dermoscopy plus clinical information and images). Main outcome measures were sensitivity, specificity and area under the curve (AUC) of receiver operating characteristics (ROC) for diagnostic classification (dichotomous) of lesions by the CNN versus an international group of 58 dermatologists during level-I or -II of the reader study. Secondary end points included the dermatologists' diagnostic performance in their management decisions and differences in the diagnostic performance of dermatologists during level-I and -II of the reader study. Additionally, the CNN's performance was compared with the top-five algorithms of the 2016 International Symposium on Biomedical Imaging (ISBI) challenge. In level-I dermatologists achieved a mean (±standard deviation) sensitivity and specificity for lesion classification of 86.6% (±9.3%) and 71.3% (±11.2%), respectively. More clinical information (level-II) improved the sensitivity to 88.9% (±9.6%, P = 0.19) and specificity to 75.7% (±11.7%, P < 0.05). The CNN ROC curve revealed a higher specificity of 82.5% when compared with dermatologists in level-I (71.3%, P < 0.01) and level-II (75.7%, P < 0.01) at their sensitivities of 86.6% and 88.9%, respectively. The CNN ROC AUC was greater than the mean ROC area of dermatologists (0.86 versus 0.79, P < 0.01). The CNN scored results close to the top three algorithms of the ISBI 2016 challenge. For the first time we compared a CNN's diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians' experience, they may benefit from assistance by a CNN's image classification. This study was registered at the German Clinical Trial Register (DRKS-Study-ID: DRKS00013570; https://www.drks.de/drks_web/).

Deep Learning Localizes and Identifies Polyps in Real Time with 96% Accuracy in Screening Colonoscopy.

PubMed

Urban, Gregor; Tripathi, Priyam; Alkayali, Talal; Mittal, Mohit; Jalali, Farid; Karnes, William; Baldi, Pierre

2018-06-18

The benefit of colonoscopy for colorectal cancer prevention depends on the adenoma detection rate (ADR). The ADR should reflect adenoma prevalence rate, estimated to be greater than 50% among the screening-age population. Yet the rate of adenoma detection by colonoscopists varies from 7% to 53%. It is estimated that every 1% increase in ADR reduces the risk of interval colorectal cancers by 3-6%. New strategies are needed to increase the ADR during colonoscopy. We tested the ability of computer-assisted image analysis, with convolutional neural networks (a deep learning model for image analysis), to improve polyp detection, a surrogate of ADR. We designed and trained deep convolutional neural networks (CNN) to detect polyps using a diverse and representative set of 8641 hand labeled images from screening colonoscopies collected from over 2000 patients. We tested the models on 20 colonoscopy videos with a total duration of 5 hours. Expert colonoscopists were asked to identify all polyps in 9 de-identified colonoscopy videos, selected from archived video studies, either with or without benefit of the CNN overlay. Their findings were compared with those of the CNN, using CNN-assisted expert review as the reference. When tested on manually labeled images, the CNN identified polyps with an area under the receiver operating characteristic curve (ROC-AUC) of 0.991 and an accuracy of 96.4%. In the analysis of colonoscopy videos in which 28 polyps were removed, 4 expert reviewers identified 8 additional polyps without CNN assistance that had not been removed and identified an additional 17 polyps with CNN assistance (45 in total). All polyps removed and identified by expert review were detected by the CNN. The CNN had a false-positive rate of 7%. In a set of 8641 colonoscopy images containing 4088 unique polyps the CNN identified polyps with a cross-validation accuracy of 96.4% and ROC-AUC value of 0.991. The CNN system can detect and localize polyps well within real-time constraints using an ordinary desktop machine with a contemporary graphics processing unit. This system could increase ADR and reduce interval colorectal cancers but requires validation in large multicenter trials. Copyright © 2018 AGA Institute. Published by Elsevier Inc. All rights reserved.

Respiratory motion correction for free-breathing 3D abdominal MRI using CNN-based image registration: a feasibility study.

PubMed

Lv, Jun; Yang, Ming; Zhang, Jue; Wang, Xiaoying

2018-02-01

Free-breathing abdomen imaging requires non-rigid motion registration of unavoidable respiratory motion in three-dimensional undersampled data sets. In this work, we introduce an image registration method based on the convolutional neural network (CNN) to obtain motion-free abdominal images throughout the respiratory cycle. Abdominal data were acquired from 10 volunteers using a 1.5 T MRI system. The respiratory signal was extracted from the central-space spokes, and the acquired data were reordered in three bins according to the corresponding breathing signal. Retrospective image reconstruction of the three near-motion free respiratory phases was performed using non-Cartesian iterative SENSE reconstruction. Then, we trained a CNN to analyse the spatial transform among the different bins. This network could generate the displacement vector field and be applied to perform registration on unseen image pairs. To demonstrate the feasibility of this registration method, we compared the performance of three different registration approaches for accurate image fusion of three bins: non-motion corrected (NMC), local affine registration method (LREG) and CNN. Visualization of coronal images indicated that LREG had caused broken blood vessels, while the vessels of the CNN were sharper and more consecutive. As shown in the sagittal view, compared to NMC and CNN, distorted and blurred liver contours were caused by LREG. At the same time, zoom-in axial images presented that the vessels were delineated more clearly by CNN than LREG. The statistical results of the signal-to-noise ratio, visual score, vessel sharpness and registration time over all volunteers were compared among the NMC, LREG and CNN approaches. The SNR indicated that the CNN acquired the best image quality (207.42 ± 96.73), which was better than NMC (116.67 ± 44.70) and LREG (187.93 ± 96.68). The image visual score agreed with SNR, marking CNN (3.85 ± 0.12) as the best, followed by LREG (3.43 ± 0.13) and NMC (2.55 ± 0.09). A vessel sharpness assessment yielded similar values between the CNN (0.81 ± 0.03) and LREG (0.80 ± 0.04), differentiating them from the NMC (0.78 ± 0.06). When compared with the LREG-based reconstruction, the CNN-based reconstruction reduces the registration time from 1 h to 1 min. Our preliminary results demonstrate the feasibility of the CNN-based approach, and this scheme outperforms the NMC- and LREG-based methods. Advances in knowledge: This method reduces the registration time from ~1 h to ~1 min, which has promising prospects for clinical use. To the best of our knowledge, this study shows the first convolutional neural network-based registration method to be applied in abdominal images.

Using CNN Newsroom in Advanced Listening Classes.

ERIC Educational Resources Information Center

Vann, Samuel

A university teacher of English as a Second Language describes the use of CNN Newsroom materials to teach listening skills. The basic news broadcast materials, including video and audio tapes, are provided by CNN, and have been developed by the teacher into instructional units. A classroom guide is available on the Internet. The instruction is…

Channel One and CNN Newsroom: A Comparative Study of Seven Districts.

ERIC Educational Resources Information Center

Nasstrom, Roy; Gierok, Anne

Many American schools use the televised news programs Channel One and CNN Newsroom. Channel One has received considerable scrutiny, some of it highly unfavorable, while attention to CNN Newsroom has been less extensive and mostly benign. This study compares the two programs within seven school districts in Wisconsin. The study addresses three…

CNN-SVM for Microvascular Morphological Type Recognition with Data Augmentation.

PubMed

Xue, Di-Xiu; Zhang, Rong; Feng, Hui; Wang, Ya-Lei

2016-01-01

This paper focuses on the problem of feature extraction and the classification of microvascular morphological types to aid esophageal cancer detection. We present a patch-based system with a hybrid SVM model with data augmentation for intraepithelial papillary capillary loop recognition. A greedy patch-generating algorithm and a specialized CNN named NBI-Net are designed to extract hierarchical features from patches. We investigate a series of data augmentation techniques to progressively improve the prediction invariance of image scaling and rotation. For classifier boosting, SVM is used as an alternative to softmax to enhance generalization ability. The effectiveness of CNN feature representation ability is discussed for a set of widely used CNN models, including AlexNet, VGG-16, and GoogLeNet. Experiments are conducted on the NBI-ME dataset. The recognition rate is up to 92.74% on the patch level with data augmentation and classifier boosting. The results show that the combined CNN-SVM model beats models of traditional features with SVM as well as the original CNN with softmax. The synthesis results indicate that our system is able to assist clinical diagnosis to a certain extent.

Classification of crystal structure using a convolutional neural network

PubMed Central

Park, Woon Bae; Chung, Jiyong; Sohn, Keemin; Pyo, Myoungho

2017-01-01

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds. PMID:28875035

Classification of crystal structure using a convolutional neural network.

PubMed

Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee-Sun

2017-07-01

A deep machine-learning technique based on a convolutional neural network (CNN) is introduced. It has been used for the classification of powder X-ray diffraction (XRD) patterns in terms of crystal system, extinction group and space group. About 150 000 powder XRD patterns were collected and used as input for the CNN with no handcrafted engineering involved, and thereby an appropriate CNN architecture was obtained that allowed determination of the crystal system, extinction group and space group. In sharp contrast with the traditional use of powder XRD pattern analysis, the CNN never treats powder XRD patterns as a deconvoluted and discrete peak position or as intensity data, but instead the XRD patterns are regarded as nothing but a pattern similar to a picture. The CNN interprets features that humans cannot recognize in a powder XRD pattern. As a result, accuracy levels of 81.14, 83.83 and 94.99% were achieved for the space-group, extinction-group and crystal-system classifications, respectively. The well trained CNN was then used for symmetry identification of unknown novel inorganic compounds.

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

PubMed

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

2018-04-11

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

Image-based corrosion recognition for ship steel structures

NASA Astrophysics Data System (ADS)

Ma, Yucong; Yang, Yang; Yao, Yuan; Li, Shengyuan; Zhao, Xuefeng

2018-03-01

Ship structures are subjected to corrosion inevitably in service. Existed image-based methods are influenced by the noises in images because they recognize corrosion by extracting features. In this paper, a novel method of image-based corrosion recognition for ship steel structures is proposed. The method utilizes convolutional neural networks (CNN) and will not be affected by noises in images. A CNN used to recognize corrosion was designed through fine-turning an existing CNN architecture and trained by datasets built using lots of images. Combining the trained CNN classifier with a sliding window technique, the corrosion zone in an image can be recognized.

Ternary and coupled binary zinc tin oxide nanopowders: Synthesis, characterization, and potential application in photocatalytic processes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ivetić, T.B., E-mail: tamara.ivetic@df.uns.ac.rs; Finčur, N.L.; Đačanin, Lj. R.

2015-02-15

Highlights: • Mechanochemically synthesized nanocrystalline zinc tin oxide (ZTO) powders. • Photocatalytic degradation of alprazolam in the presence of ZTO water suspensions. • Coupled binary ZTO exhibits enhanced photocatalytic activity compared to ternary ZTO. - Abstract: In this paper, ternary and coupled binary zinc tin oxide nanocrystalline powders were prepared via simple solid-state mechanochemical method. X-ray diffraction, scanning electron microscopy, Raman and reflectance spectroscopy were used to study the structure and optical properties of the obtained powder samples. The thermal behavior of zinc tin oxide system was examined through simultaneous thermogravimetric-differential scanning calorimetric analysis. The efficiencies of ternary (Zn{sub 2}SnO{submore » 4} and ZnSnO{sub 3}) and coupled binary (ZnO/SnO{sub 2}) zinc tin oxide water suspensions in the photocatalytic degradation of alprazolam, short-acting anxiolytic of the benzodiazepine class of psychoactive drugs, under UV irradiation were determined and compared with the efficiency of pure ZnO and SnO{sub 2}.« less

Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture.

PubMed

Esses, Steven J; Lu, Xiaoguang; Zhao, Tiejun; Shanbhogue, Krishna; Dane, Bari; Bruno, Mary; Chandarana, Hersh

2018-03-01

To develop and test a deep learning approach named Convolutional Neural Network (CNN) for automated screening of T 2 -weighted (T 2 WI) liver acquisitions for nondiagnostic images, and compare this automated approach to evaluation by two radiologists. We evaluated 522 liver magnetic resonance imaging (MRI) exams performed at 1.5T and 3T at our institution between November 2014 and May 2016 for CNN training and validation. The CNN consisted of an input layer, convolutional layer, fully connected layer, and output layer. 351 T 2 WI were anonymized for training. Each case was annotated with a label of being diagnostic or nondiagnostic for detecting lesions and assessing liver morphology. Another independently collected 171 cases were sequestered for a blind test. These 171 T 2 WI were assessed independently by two radiologists and annotated as being diagnostic or nondiagnostic. These 171 T 2 WI were presented to the CNN algorithm and image quality (IQ) output of the algorithm was compared to that of two radiologists. There was concordance in IQ label between Reader 1 and CNN in 79% of cases and between Reader 2 and CNN in 73%. The sensitivity and the specificity of the CNN algorithm in identifying nondiagnostic IQ was 67% and 81% with respect to Reader 1 and 47% and 80% with respect to Reader 2. The negative predictive value of the algorithm for identifying nondiagnostic IQ was 94% and 86% (relative to Readers 1 and 2). We demonstrate a CNN algorithm that yields a high negative predictive value when screening for nondiagnostic T 2 WI of the liver. 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:723-728. © 2017 International Society for Magnetic Resonance in Medicine.

Research on High Accuracy Detection of Red Tide Hyperspecrral Based on Deep Learning Cnn

NASA Astrophysics Data System (ADS)

Hu, Y.; Ma, Y.; An, J.

2018-04-01

Increasing frequency in red tide outbreaks has been reported around the world. It is of great concern due to not only their adverse effects on human health and marine organisms, but also their impacts on the economy of the affected areas. this paper put forward a high accuracy detection method based on a fully-connected deep CNN detection model with 8-layers to monitor red tide in hyperspectral remote sensing images, then make a discussion of the glint suppression method for improving the accuracy of red tide detection. The results show that the proposed CNN hyperspectral detection model can detect red tide accurately and effectively. The red tide detection accuracy of the proposed CNN model based on original image and filter-image is 95.58 % and 97.45 %, respectively, and compared with the SVM method, the CNN detection accuracy is increased by 7.52 % and 2.25 %. Compared with SVM method base on original image, the red tide CNN detection accuracy based on filter-image increased by 8.62 % and 6.37 %. It also indicates that the image glint affects the accuracy of red tide detection seriously.

Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

PubMed

Li, Siqi; Jiang, Huiyan; Pang, Wenbo

2017-05-01

Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies.

PubMed

Vivanti, Refael; Joskowicz, Leo; Lev-Cohain, Naama; Ephrat, Ariel; Sosna, Jacob

2018-03-10

Radiological longitudinal follow-up of tumors in CT scans is essential for disease assessment and liver tumor therapy. Currently, most tumor size measurements follow the RECIST guidelines, which can be off by as much as 50%. True volumetric measurements are more accurate but require manual delineation, which is time-consuming and user-dependent. We present a convolutional neural networks (CNN) based method for robust automatic liver tumor delineation in longitudinal CT studies that uses both global and patient specific CNNs trained on a small database of delineated images. The inputs are the baseline scan and the tumor delineation, a follow-up scan, and a liver tumor global CNN voxel classifier built from radiologist-validated liver tumor delineations. The outputs are the tumor delineations in the follow-up CT scan. The baseline scan tumor delineation serves as a high-quality prior for the tumor characterization in the follow-up scans. It is used to evaluate the global CNN performance on the new case and to reliably predict failures of the global CNN on the follow-up scan. High-scoring cases are segmented with a global CNN; low-scoring cases, which are predicted to be failures of the global CNN, are segmented with a patient-specific CNN built from the baseline scan. Our experimental results on 222 tumors from 31 patients yield an average overlap error of 17% (std = 11.2) and surface distance of 2.1 mm (std = 1.8), far better than stand-alone segmentation. Importantly, the robustness of our method improved from 67% for stand-alone global CNN segmentation to 100%. Unlike other medical imaging deep learning approaches, which require large annotated training datasets, our method exploits the follow-up framework to yield accurate tumor tracking and failure detection and correction with a small training dataset. Graphical abstract Flow diagram of the proposed method. In the offline mode (orange), a global CNN is trained as a voxel classifier to segment liver tumor as in [31]. The online mode (blue) is used for each new case. The input is baseline scan with delineation and the follow-up CT scan to be segmented. The main novelty is the ability to predict failures by trying the system on the baseline scan and the ability to correct them using the patient-specific CNN.

Deep Learning to Classify Radiology Free-Text Reports.

PubMed

Chen, Matthew C; Ball, Robyn L; Yang, Lingyao; Moradzadeh, Nathaniel; Chapman, Brian E; Larson, David B; Langlotz, Curtis P; Amrhein, Timothy J; Lungren, Matthew P

2018-03-01

Purpose To evaluate the performance of a deep learning convolutional neural network (CNN) model compared with a traditional natural language processing (NLP) model in extracting pulmonary embolism (PE) findings from thoracic computed tomography (CT) reports from two institutions. Materials and Methods Contrast material-enhanced CT examinations of the chest performed between January 1, 1998, and January 1, 2016, were selected. Annotations by two human radiologists were made for three categories: the presence, chronicity, and location of PE. Classification of performance of a CNN model with an unsupervised learning algorithm for obtaining vector representations of words was compared with the open-source application PeFinder. Sensitivity, specificity, accuracy, and F1 scores for both the CNN model and PeFinder in the internal and external validation sets were determined. Results The CNN model demonstrated an accuracy of 99% and an area under the curve value of 0.97. For internal validation report data, the CNN model had a statistically significant larger F1 score (0.938) than did PeFinder (0.867) when classifying findings as either PE positive or PE negative, but no significant difference in sensitivity, specificity, or accuracy was found. For external validation report data, no statistical difference between the performance of the CNN model and PeFinder was found. Conclusion A deep learning CNN model can classify radiology free-text reports with accuracy equivalent to or beyond that of an existing traditional NLP model. © RSNA, 2017 Online supplemental material is available for this article.

Optical analogue of relativistic Dirac solitons in binary waveguide arrays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tran, Truong X., E-mail: truong.tran@mpl.mpg.de; Max Planck Institute for the Science of Light, Günther-Scharowsky str. 1, 91058 Erlangen; Longhi, Stefano

2014-01-15

We study analytically and numerically an optical analogue of Dirac solitons in binary waveguide arrays in the presence of Kerr nonlinearity. Pseudo-relativistic soliton solutions of the coupled-mode equations describing dynamics in the array are analytically derived. We demonstrate that with the found soliton solutions, the coupled mode equations can be converted into the nonlinear relativistic 1D Dirac equation. This paves the way for using binary waveguide arrays as a classical simulator of quantum nonlinear effects arising from the Dirac equation, something that is thought to be impossible to achieve in conventional (i.e. linear) quantum field theory. -- Highlights: •An opticalmore » analogue of Dirac solitons in nonlinear binary waveguide arrays is suggested. •Analytical solutions to pseudo-relativistic solitons are presented. •A correspondence of optical coupled-mode equations with the nonlinear relativistic Dirac equation is established.« less

Analyzing the Appropriateness of Internet-Based School News Programs for Social Studies Classrooms: "CNN Student News" as a Case Study

ERIC Educational Resources Information Center

Journell, Wayne

2014-01-01

This article describes a research study on the appropriateness for social studies classrooms of "CNN Student News," a free online news program specifically aimed at middle and high school students. The author conducted a content analysis of "CNN Student News" during October 2012 and evaluated the program's content for…

Deep convolutional neural network for prostate MR segmentation

NASA Astrophysics Data System (ADS)

Tian, Zhiqiang; Liu, Lizhi; Fei, Baowei

2017-03-01

Automatic segmentation of the prostate in magnetic resonance imaging (MRI) has many applications in prostate cancer diagnosis and therapy. We propose a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage based on prostate MR images and the corresponding ground truths, and learns to make inference for pixel-wise segmentation. Experiments were performed on our in-house data set, which contains prostate MR images of 20 patients. The proposed CNN model obtained a mean Dice similarity coefficient of 85.3%+/-3.2% as compared to the manual segmentation. Experimental results show that our deep CNN model could yield satisfactory segmentation of the prostate.

Small-size pedestrian detection in large scene based on fast R-CNN

NASA Astrophysics Data System (ADS)

Wang, Shengke; Yang, Na; Duan, Lianghua; Liu, Lu; Dong, Junyu

2018-04-01

Pedestrian detection is a canonical sub-problem of object detection with high demand during recent years. Although recent deep learning object detectors such as Fast/Faster R-CNN have shown excellent performance for general object detection, they have limited success for small size pedestrian detection in large-view scene. We study that the insufficient resolution of feature maps lead to the unsatisfactory accuracy when handling small instances. In this paper, we investigate issues involving Fast R-CNN for pedestrian detection. Driven by the observations, we propose a very simple but effective baseline for pedestrian detection based on Fast R-CNN, employing the DPM detector to generate proposals for accuracy, and training a fast R-CNN style network to jointly optimize small size pedestrian detection with skip connection concatenating feature from different layers to solving coarseness of feature maps. And the accuracy is improved in our research for small size pedestrian detection in the real large scene.

Interphase centrosome organization by the PLP-Cnn scaffold is required for centrosome function

PubMed Central

Lerit, Dorothy A.; Jordan, Holly A.; Poulton, John S.; Fagerstrom, Carey J.; Galletta, Brian J.; Peifer, Mark

2015-01-01

Pericentriolar material (PCM) mediates the microtubule (MT) nucleation and anchoring activity of centrosomes. A scaffold organized by Centrosomin (Cnn) serves to ensure proper PCM architecture and functional changes in centrosome activity with each cell cycle. Here, we investigate the mechanisms that spatially restrict and temporally coordinate centrosome scaffold formation. Focusing on the mitotic-to-interphase transition in Drosophila melanogaster embryos, we show that the elaboration of the interphase Cnn scaffold defines a major structural rearrangement of the centrosome. We identify an unprecedented role for Pericentrin-like protein (PLP), which localizes to the tips of extended Cnn flares, to maintain robust interphase centrosome activity and promote the formation of interphase MT asters required for normal nuclear spacing, centrosome segregation, and compartmentalization of the syncytial embryo. Our data reveal that Cnn and PLP directly interact at two defined sites to coordinate the cell cycle–dependent rearrangement and scaffolding activity of the centrosome to permit normal centrosome organization, cell division, and embryonic viability. PMID:26150390

Interphase centrosome organization by the PLP-Cnn scaffold is required for centrosome function.

PubMed

Lerit, Dorothy A; Jordan, Holly A; Poulton, John S; Fagerstrom, Carey J; Galletta, Brian J; Peifer, Mark; Rusan, Nasser M

2015-07-06

Pericentriolar material (PCM) mediates the microtubule (MT) nucleation and anchoring activity of centrosomes. A scaffold organized by Centrosomin (Cnn) serves to ensure proper PCM architecture and functional changes in centrosome activity with each cell cycle. Here, we investigate the mechanisms that spatially restrict and temporally coordinate centrosome scaffold formation. Focusing on the mitotic-to-interphase transition in Drosophila melanogaster embryos, we show that the elaboration of the interphase Cnn scaffold defines a major structural rearrangement of the centrosome. We identify an unprecedented role for Pericentrin-like protein (PLP), which localizes to the tips of extended Cnn flares, to maintain robust interphase centrosome activity and promote the formation of interphase MT asters required for normal nuclear spacing, centrosome segregation, and compartmentalization of the syncytial embryo. Our data reveal that Cnn and PLP directly interact at two defined sites to coordinate the cell cycle-dependent rearrangement and scaffolding activity of the centrosome to permit normal centrosome organization, cell division, and embryonic viability.

Wavelet-enhanced convolutional neural network: a new idea in a deep learning paradigm.

PubMed

Savareh, Behrouz Alizadeh; Emami, Hassan; Hajiabadi, Mohamadreza; Azimi, Seyed Majid; Ghafoori, Mahyar

2018-05-29

Manual brain tumor segmentation is a challenging task that requires the use of machine learning techniques. One of the machine learning techniques that has been given much attention is the convolutional neural network (CNN). The performance of the CNN can be enhanced by combining other data analysis tools such as wavelet transform. In this study, one of the famous implementations of CNN, a fully convolutional network (FCN), was used in brain tumor segmentation and its architecture was enhanced by wavelet transform. In this combination, a wavelet transform was used as a complementary and enhancing tool for CNN in brain tumor segmentation. Comparing the performance of basic FCN architecture against the wavelet-enhanced form revealed a remarkable superiority of enhanced architecture in brain tumor segmentation tasks. Using mathematical functions and enhancing tools such as wavelet transform and other mathematical functions can improve the performance of CNN in any image processing task such as segmentation and classification.

Exploring the effects of transducer models when training convolutional neural networks to eliminate reflection artifacts in experimental photoacoustic images

NASA Astrophysics Data System (ADS)

Allman, Derek; Reiter, Austin; Bell, Muyinatu

2018-02-01

We previously proposed a method of removing reflection artifacts in photoacoustic images that uses deep learning. Our approach generally relies on using simulated photoacoustic channel data to train a convolutional neural network (CNN) that is capable of distinguishing sources from artifacts based on unique differences in their spatial impulse responses (manifested as depth-based differences in wavefront shapes). In this paper, we directly compare a CNN trained with our previous continuous transducer model to a CNN trained with an updated discrete acoustic receiver model that more closely matches an experimental ultrasound transducer. These two CNNs were trained with simulated data and tested on experimental data. The CNN trained using the continuous receiver model correctly classified 100% of sources and 70.3% of artifacts in the experimental data. In contrast, the CNN trained using the discrete receiver model correctly classified 100% of sources and 89.7% of artifacts in the experimental images. The 19.4% increase in artifact classification accuracy indicates that an acoustic receiver model that closely mimics the experimental transducer plays an important role in improving the classification of artifacts in experimental photoacoustic data. Results are promising for developing a method to display CNN-based images that remove artifacts in addition to only displaying network-identified sources as previously proposed.

Bio-inspired nano-sensor-enhanced CNN visual computer.

PubMed

Porod, Wolfgang; Werblin, Frank; Chua, Leon O; Roska, Tamas; Rodriguez-Vazquez, Angel; Roska, Botond; Fay, Patrick; Bernstein, Gary H; Huang, Yih-Fang; Csurgay, Arpad I

2004-05-01

Nanotechnology opens new ways to utilize recent discoveries in biological image processing by translating the underlying functional concepts into the design of CNN (cellular neural/nonlinear network)-based systems incorporating nanoelectronic devices. There is a natural intersection joining studies of retinal processing, spatio-temporal nonlinear dynamics embodied in CNN, and the possibility of miniaturizing the technology through nanotechnology. This intersection serves as the springboard for our multidisciplinary project. Biological feature and motion detectors map directly into the spatio-temporal dynamics of CNN for target recognition, image stabilization, and tracking. The neural interactions underlying color processing will drive the development of nanoscale multispectral sensor arrays for image fusion. Implementing such nanoscale sensors on a CNN platform will allow the implementation of device feedback control, a hallmark of biological sensory systems. These biologically inspired CNN subroutines are incorporated into the new world of analog-and-logic algorithms and software, containing also many other active-wave computing mechanisms, including nature-inspired (physics and chemistry) as well as PDE-based sophisticated spatio-temporal algorithms. Our goal is to design and develop several miniature prototype devices for target detection, navigation, tracking, and robotics. This paper presents an example illustrating the synergies emerging from the convergence of nanotechnology, biotechnology, and information and cognitive science.

Classification of Land Cover and Land Use Based on Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Yang, Chun; Rottensteiner, Franz; Heipke, Christian

2018-04-01

Land cover describes the physical material of the earth's surface, whereas land use describes the socio-economic function of a piece of land. Land use information is typically collected in geospatial databases. As such databases become outdated quickly, an automatic update process is required. This paper presents a new approach to determine land cover and to classify land use objects based on convolutional neural networks (CNN). The input data are aerial images and derived data such as digital surface models. Firstly, we apply a CNN to determine the land cover for each pixel of the input image. We compare different CNN structures, all of them based on an encoder-decoder structure for obtaining dense class predictions. Secondly, we propose a new CNN-based methodology for the prediction of the land use label of objects from a geospatial database. In this context, we present a strategy for generating image patches of identical size from the input data, which are classified by a CNN. Again, we compare different CNN architectures. Our experiments show that an overall accuracy of up to 85.7 % and 77.4 % can be achieved for land cover and land use, respectively. The classification of land cover has a positive contribution to the classification of the land use classification.

Identifikationsverfahren zur Analyse von EEG-Signalen bei Epilepsie mit Reaktions-Diffusions Netzwerken

NASA Astrophysics Data System (ADS)

Gollas, F.; Tetzlaff, R.

2007-06-01

Partielle Differentialgleichungen des Reaktions-Diffusions-Typs beschreiben Phänomene wie Musterbildung, nichtlineare Wellenausbreitung und deterministisches Chaos und werden oft zur Untersuchung komplexer Vorgänge auf den Gebieten der Biologie, Chemie und Physik herangezogen. Zellulare Nichtlineare Netzwerke (CNN) sind eine räumliche Anordnung vergleichsweise einfacher dynamischer Systeme, die eine lokale Kopplung untereinander aufweisen. Durch eine Diskretisierung der Ortsvariablen können Reaktions-Diffusions-Gleichungen häufig auf CNN mit nichtlinearen Gewichtsfunktionen abgebildet werden. Die resultierenden Reaktions-Diffusions-CNN (RD-CNN) weisen dann in ihrer Dynamik näherungsweise gleiches Verhalten wie die zugrunde gelegten Reaktions-Diffusions-Systeme auf. Werden RD-CNN zur Identifikation neuronaler Strukturen anhand von EEG-Signalen herangezogen, so besteht die Möglichkeit festzustellen, ob das gefundene Netzwerk lokale Aktivität aufweist. Die von Chua eingeführte Theorie der lokalen Aktivität Chua (1998); Dogaru und Chua (1998) liefert eine notwendige Bedingung für das Auftreten von emergentem Verhalten in zellularen Netzwerken. Änderungen in den Parametern bestimmter RD-CNN könnten auf bevorstehende epileptische Anfälle hinweisen. In diesem Beitrag steht die Identifikation neuronaler Strukturen anhand von EEG-Signalen durch Reaktions-Diffusions-Netzwerke im Vordergrund der dargestellten Untersuchungen. In der Ergebnisdiskussion wird insbesondere auch die Frage nach einer geeigneten Netzwerkstruktur mit minimaler Komplexität behandelt.

Dataset variability leverages white-matter lesion segmentation performance with convolutional neural network

NASA Astrophysics Data System (ADS)

Ravnik, Domen; Jerman, Tim; Pernuš, Franjo; Likar, Boštjan; Å piclin, Žiga

2018-03-01

Performance of a convolutional neural network (CNN) based white-matter lesion segmentation in magnetic resonance (MR) brain images was evaluated under various conditions involving different levels of image preprocessing and augmentation applied and different compositions of the training dataset. On images of sixty multiple sclerosis patients, half acquired on one and half on another scanner of different vendor, we first created a highly accurate multi-rater consensus based lesion segmentations, which were used in several experiments to evaluate the CNN segmentation result. First, the CNN was trained and tested without preprocessing the images and by using various combinations of preprocessing techniques, namely histogram-based intensity standardization, normalization by whitening, and train dataset augmentation by flipping the images across the midsagittal plane. Then, the CNN was trained and tested on images of the same, different or interleaved scanner datasets using a cross-validation approach. The results indicate that image preprocessing has little impact on performance in a same-scanner situation, while between-scanner performance benefits most from intensity standardization and normalization, but also further by incorporating heterogeneous multi-scanner datasets in the training phase. Under such conditions the between-scanner performance of the CNN approaches that of the ideal situation, when the CNN is trained and tested on the same scanner dataset.

A parametric texture model based on deep convolutional features closely matches texture appearance for humans.

PubMed

Wallis, Thomas S A; Funke, Christina M; Ecker, Alexander S; Gatys, Leon A; Wichmann, Felix A; Bethge, Matthias

2017-10-01

Our visual environment is full of texture-"stuff" like cloth, bark, or gravel as distinct from "things" like dresses, trees, or paths-and humans are adept at perceiving subtle variations in material properties. To investigate image features important for texture perception, we psychophysically compare a recent parametric model of texture appearance (convolutional neural network [CNN] model) that uses the features encoded by a deep CNN (VGG-19) with two other models: the venerable Portilla and Simoncelli model and an extension of the CNN model in which the power spectrum is additionally matched. Observers discriminated model-generated textures from original natural textures in a spatial three-alternative oddity paradigm under two viewing conditions: when test patches were briefly presented to the near-periphery ("parafoveal") and when observers were able to make eye movements to all three patches ("inspection"). Under parafoveal viewing, observers were unable to discriminate 10 of 12 original images from CNN model images, and remarkably, the simpler Portilla and Simoncelli model performed slightly better than the CNN model (11 textures). Under foveal inspection, matching CNN features captured appearance substantially better than the Portilla and Simoncelli model (nine compared to four textures), and including the power spectrum improved appearance matching for two of the three remaining textures. None of the models we test here could produce indiscriminable images for one of the 12 textures under the inspection condition. While deep CNN (VGG-19) features can often be used to synthesize textures that humans cannot discriminate from natural textures, there is currently no uniformly best model for all textures and viewing conditions.

Processing the Bouguer anomaly map of Biga and the surrounding area by the cellular neural network: application to the southwestern Marmara region

NASA Astrophysics Data System (ADS)

Aydogan, D.

2007-04-01

An image processing technique called the cellular neural network (CNN) approach is used in this study to locate geological features giving rise to gravity anomalies such as faults or the boundary of two geologic zones. CNN is a stochastic image processing technique based on template optimization using the neighborhood relationships of cells. These cells can be characterized by a functional block diagram that is typical of neural network theory. The functionality of CNN is described in its entirety by a number of small matrices (A, B and I) called the cloning template. CNN can also be considered to be a nonlinear convolution of these matrices. This template describes the strength of the nearest neighbor interconnections in the network. The recurrent perceptron learning algorithm (RPLA) is used in optimization of cloning template. The CNN and standard Canny algorithms were first tested on two sets of synthetic gravity data with the aim of checking the reliability of the proposed approach. The CNN method was compared with classical derivative techniques by applying the cross-correlation method (CC) to the same anomaly map as this latter approach can detect some features that are difficult to identify on the Bouguer anomaly maps. This approach was then applied to the Bouguer anomaly map of Biga and its surrounding area, in Turkey. Structural features in the area between Bandirma, Biga, Yenice and Gonen in the southwest Marmara region are investigated by applying the CNN and CC to the Bouguer anomaly map. Faults identified by these algorithms are generally in accordance with previously mapped surface faults. These examples show that the geologic boundaries can be detected from Bouguer anomaly maps using the cloning template approach. A visual evaluation of the outputs of the CNN and CC approaches is carried out, and the results are compared with each other. This approach provides quantitative solutions based on just a few assumptions, which makes the method more powerful than the classical methods.

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

PubMed Central

Hoo-Chang, Shin; Roth, Holger R.; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel

2016-01-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets (i.e. ImageNet) and the revival of deep convolutional neural networks (CNN). CNNs enable learning data-driven, highly representative, layered hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models (supervised) pre-trained from natural image dataset to medical image tasks (although domain transfer between two medical image datasets is also possible). In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computeraided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, with 85% sensitivity at 3 false positive per patient, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks. PMID:26886976

CNN-BLPred: a Convolutional neural network based predictor for β-Lactamases (BL) and their classes.

PubMed

White, Clarence; Ismail, Hamid D; Saigo, Hiroto; Kc, Dukka B

2017-12-28

The β-Lactamase (BL) enzyme family is an important class of enzymes that plays a key role in bacterial resistance to antibiotics. As the newly identified number of BL enzymes is increasing daily, it is imperative to develop a computational tool to classify the newly identified BL enzymes into one of its classes. There are two types of classification of BL enzymes: Molecular Classification and Functional Classification. Existing computational methods only address Molecular Classification and the performance of these existing methods is unsatisfactory. We addressed the unsatisfactory performance of the existing methods by implementing a Deep Learning approach called Convolutional Neural Network (CNN). We developed CNN-BLPred, an approach for the classification of BL proteins. The CNN-BLPred uses Gradient Boosted Feature Selection (GBFS) in order to select the ideal feature set for each BL classification. Based on the rigorous benchmarking of CCN-BLPred using both leave-one-out cross-validation and independent test sets, CCN-BLPred performed better than the other existing algorithms. Compared with other architectures of CNN, Recurrent Neural Network, and Random Forest, the simple CNN architecture with only one convolutional layer performs the best. After feature extraction, we were able to remove ~95% of the 10,912 features using Gradient Boosted Trees. During 10-fold cross validation, we increased the accuracy of the classic BL predictions by 7%. We also increased the accuracy of Class A, Class B, Class C, and Class D performance by an average of 25.64%. The independent test results followed a similar trend. We implemented a deep learning algorithm known as Convolutional Neural Network (CNN) to develop a classifier for BL classification. Combined with feature selection on an exhaustive feature set and using balancing method such as Random Oversampling (ROS), Random Undersampling (RUS) and Synthetic Minority Oversampling Technique (SMOTE), CNN-BLPred performs significantly better than existing algorithms for BL classification.

Cu-Zn binary phase diagram and diffusion couples

NASA Technical Reports Server (NTRS)

Mccoy, Robert A.

1992-01-01

The objectives of this paper are to learn: (1) what information a binary phase diagram can yield; (2) how to construct and heat treat a simple diffusion couple; (3) how to prepare a metallographic sample; (4) how to operate a metallograph; (5) how to correlate phases found in the diffusion couple with phases predicted by the phase diagram; (6) how diffusion couples held at various temperatures could be used to construct a phase diagram; (7) the relation between the thickness of an intermetallic phase layer and the diffusion time; and (8) the effect of one species of atoms diffusing faster than another species in a diffusion couple.

Role of the NH2 functionality and solvent in terdentate CNN alkoxide ruthenium complexes for the fast transfer hydrogenation of ketones in 2-propanol.

PubMed

Baratta, Walter; Ballico, Maurizio; Esposito, Gennaro; Rigo, Pierluigi

2008-01-01

The reaction of [RuCl(CNN)(dppb)] (1; HCNN=6-(4-methylphenyl)-2-pyridylmethylamine) with NaOiPr in 2-propanol/C6D6 affords the alcohol adduct alkoxide [Ru(OiPr)(CNN)(dppb)].n iPrOH (5), containing the Ru-NH2 linkage. The alkoxide [Ru(OiPr)(CNN)(dppb)] (4) is formed by treatment of the hydride [Ru(H)(CNN)(dppb)] (2) with acetone in C6D6. Complex 5 in 2-propanol/C6D6 equilibrates quickly with hydride 2 and acetone with an exchange rate of (5.4+/-0.2) s(-1) at 25 degrees C, higher than that found between 4 and 2 ((2.9+/-0.4) s(-1)). This fast process, involving a beta-hydrogen elimination versus ketone insertion into the Ru-H bond, occurs within a hydrogen-bonding network favored by the Ru-NH2 motif. The cationic alcohol complex [Ru(CNN)(dppb)(iPrOH)](BAr(f)4) (6; Ar(f)=3,5-C6H3(CF3)2), obtained from 1, Na[BAr(f)4], and 2-propanol, reacts with NaOiPr to afford 5. Complex 5 reacts with either 4,4'-difluorobenzophenone through hydride 2 or with 4,4'-difluorobenzhydrol through protonation, affording the alkoxide [Ru(OCH(4-C6H4F)2)(CNN)(dppb)] (7) in 90 and 85 % yield of the isolated product. The chiral CNN-ruthenium compound [RuCl(CNN)((S,S)-Skewphos)] (8), obtained by the reaction of [RuCl2(PPh3)3] with (S,S)-Skewphos and orthometalation of HCNN in the presence of NEt3, is a highly active catalyst for the enantioselective transfer hydrogenation of methylaryl ketones (turnover frequencies (TOFs) of up to 1.4 x 10(6) h(-1) at reflux were obtained) with up to 89% ee. Also the ketone CF3CO(4-C6H4F), containing the strong electron-withdrawing CF3 group, is reduced to the R alcohol with 64% ee and a TOF of 1.5 x 10(4) h(-1). The chiral alkoxide [Ru(OiPr)(CNN)((S,S)-Skewphos)]n iPrOH (9), obtained from 8 and NaOiPr in the presence of 2-propanol, reacts with CF3CO(4-C6H4F) to afford a mixture of the diastereomer alkoxides [Ru(OCH(CF3)(4-C6H4F))(CNN)((S,S)-Skewphos)] (10/11; 74% yield) with 67% de. This value is very close to the enantiomeric excess of the alcohol (R)-CF3CH(OH)(4-C6H4F) formed in catalysis, thus suggesting that diastereoisomeric alkoxides with the Ru-NH2 linkage are key species in the catalytic asymmetric transfer hydrogenation reaction.

Two Novel Glycoside Hydrolases Responsible for the Catabolism of Cyclobis-(1→6)-α-nigerosyl*

PubMed Central

Tagami, Takayoshi; Miyano, Eri; Sadahiro, Juri; Okuyama, Masayuki; Iwasaki, Tomohito; Kimura, Atsuo

2016-01-01

The actinobacterium Kribbella flavida NBRC 14399T produces cyclobis-(1→6)-α-nigerosyl (CNN), a cyclic glucotetraose with alternate α-(1→6)- and α-(1→3)-glucosidic linkages, from starch in the culture medium. We identified gene clusters associated with the production and intracellular catabolism of CNN in the K. flavida genome. One cluster encodes 6-α-glucosyltransferase and 3-α-isomaltosyltransferase, which are known to coproduce CNN from starch. The other cluster contains four genes annotated as a transcriptional regulator, sugar transporter, glycoside hydrolase family (GH) 31 protein (Kfla1895), and GH15 protein (Kfla1896). Kfla1895 hydrolyzed the α-(1→3)-glucosidic linkages of CNN and produced isomaltose via a possible linear tetrasaccharide. The initial rate of hydrolysis of CNN (11.6 s−1) was much higher than that of panose (0.242 s−1), and hydrolysis of isomaltotriose and nigerose was extremely low. Because Kfla1895 has a strong preference for the α-(1→3)-isomaltosyl moiety and effectively hydrolyzes the α-(1→3)-glucosidic linkage, it should be termed 1,3-α-isomaltosidase. Kfla1896 effectively hydrolyzed isomaltose with liberation of β-glucose, but displayed low or no activity toward CNN and the general GH15 enzyme substrates such as maltose, soluble starch, or dextran. The kcat/Km for isomaltose (4.81 ± 0.18 s−1 mm−1) was 6.9- and 19-fold higher than those for panose and isomaltotriose, respectively. These results indicate that Kfla1896 is a new GH15 enzyme with high substrate specificity for isomaltose, suggesting the enzyme should be designated an isomaltose glucohydrolase. This is the first report to identify a starch-utilization pathway that proceeds via CNN. PMID:27302067

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

PubMed

Shin, Hoo-Chang; Roth, Holger R; Gao, Mingchen; Lu, Le; Xu, Ziyue; Nogues, Isabella; Yao, Jianhua; Mollura, Daniel; Summers, Ronald M

2016-05-01

Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer learning, i.e., fine-tuning CNN models pre-trained from natural image dataset to medical image tasks. In this paper, we exploit three important, but previously understudied factors of employing deep convolutional neural networks to computer-aided detection problems. We first explore and evaluate different CNN architectures. The studied models contain 5 thousand to 160 million parameters, and vary in numbers of layers. We then evaluate the influence of dataset scale and spatial image context on performance. Finally, we examine when and why transfer learning from pre-trained ImageNet (via fine-tuning) can be useful. We study two specific computer-aided detection (CADe) problems, namely thoraco-abdominal lymph node (LN) detection and interstitial lung disease (ILD) classification. We achieve the state-of-the-art performance on the mediastinal LN detection, and report the first five-fold cross-validation classification results on predicting axial CT slices with ILD categories. Our extensive empirical evaluation, CNN model analysis and valuable insights can be extended to the design of high performance CAD systems for other medical imaging tasks.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update.

PubMed

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-04-15

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the "good" models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm.

Enhancement of ELDA Tracker Based on CNN Features and Adaptive Model Update

PubMed Central

Gao, Changxin; Shi, Huizhang; Yu, Jin-Gang; Sang, Nong

2016-01-01

Appearance representation and the observation model are the most important components in designing a robust visual tracking algorithm for video-based sensors. Additionally, the exemplar-based linear discriminant analysis (ELDA) model has shown good performance in object tracking. Based on that, we improve the ELDA tracking algorithm by deep convolutional neural network (CNN) features and adaptive model update. Deep CNN features have been successfully used in various computer vision tasks. Extracting CNN features on all of the candidate windows is time consuming. To address this problem, a two-step CNN feature extraction method is proposed by separately computing convolutional layers and fully-connected layers. Due to the strong discriminative ability of CNN features and the exemplar-based model, we update both object and background models to improve their adaptivity and to deal with the tradeoff between discriminative ability and adaptivity. An object updating method is proposed to select the “good” models (detectors), which are quite discriminative and uncorrelated to other selected models. Meanwhile, we build the background model as a Gaussian mixture model (GMM) to adapt to complex scenes, which is initialized offline and updated online. The proposed tracker is evaluated on a benchmark dataset of 50 video sequences with various challenges. It achieves the best overall performance among the compared state-of-the-art trackers, which demonstrates the effectiveness and robustness of our tracking algorithm. PMID:27092505

Feature learning and change feature classification based on deep learning for ternary change detection in SAR images

NASA Astrophysics Data System (ADS)

Gong, Maoguo; Yang, Hailun; Zhang, Puzhao

2017-07-01

Ternary change detection aims to detect changes and group the changes into positive change and negative change. It is of great significance in the joint interpretation of spatial-temporal synthetic aperture radar images. In this study, sparse autoencoder, convolutional neural networks (CNN) and unsupervised clustering are combined to solve ternary change detection problem without any supervison. Firstly, sparse autoencoder is used to transform log-ratio difference image into a suitable feature space for extracting key changes and suppressing outliers and noise. And then the learned features are clustered into three classes, which are taken as the pseudo labels for training a CNN model as change feature classifier. The reliable training samples for CNN are selected from the feature maps learned by sparse autoencoder with certain selection rules. Having training samples and the corresponding pseudo labels, the CNN model can be trained by using back propagation with stochastic gradient descent. During its training procedure, CNN is driven to learn the concept of change, and more powerful model is established to distinguish different types of changes. Unlike the traditional methods, the proposed framework integrates the merits of sparse autoencoder and CNN to learn more robust difference representations and the concept of change for ternary change detection. Experimental results on real datasets validate the effectiveness and superiority of the proposed framework.

Determination of ionization energies of CnN (n=4-12): Vacuum-ultraviolet (VUV) photoionization experiments and theoretical calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kostko, Oleg; Zhou, Jia; Sun, Bian Jian

2010-06-10

Results from single photon vacuum ultraviolet photoionization of astrophysically relevant CnN clusters, n = 4 - 12, in the photon energy range of 8.0 eV to 12.8 eV are presented. The experimental photoionization efficiency curves, combined with electronic structure calculations, provide improved ionization energies of the CnN species. A search through numerous nitrogen-terminated CnN isomers for n=4-9 indicates that the linear isomer has the lowest energy, and therefore should be the most abundant isomer in the molecular beam. Comparison with calculated results also shed light on the energetics of the linear CnN clusters, particularly in the trends of the even-carbonmore » and the odd-carbon series. These results can help guide the search of potential astronomical observations of these neutral molecules together with their cations in highly ionized regions or regions with a high UV/VUV photon flux (ranging from the visible to VUV with flux maxima in the Lyman- region) in the interstellar medium.« less

Determination of ionization energies of CnN (n=4-12): Vacuum-ultraviolet (VUV) photoionization experiments and theoretical calculations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kostko, Oleg; Zhou, Jia; Sun, Bian Jian

2010-03-02

Results from single photon vacuum ultraviolet photoionization of astrophysically relevant CnN clusters, n = 4 - 12, in the photon energy range of 8.0 eV to 12.8 eV are presented. The experimental photoionization efficiency curves, combined with electronic structure calculations, provide improved ionization energies of the CnN species. A search through numerous nitrogen-terminated CnN isomers for n=4-9 indicates that the linear isomer has the lowest energy, and therefore should be the most abundant isomer in the molecular beam. Comparison with calculated results also shed light on the energetics of the linear CnN clusters, particularly in the trends of the even-carbonmore » and the odd-carbon series. These results can help guide the search of potential astronomical observations of these neutral molecules together with their cations in highly ionized regions or regions with a high UV/VUV photon flux (ranging from the visible to VUV with flux maxima in the Lyman-a region) in the interstellar medium.« less

Improving CNN Performance Accuracies With Min-Max Objective.

PubMed

Shi, Weiwei; Gong, Yihong; Tao, Xiaoyu; Wang, Jinjun; Zheng, Nanning

2017-06-09

We propose a novel method for improving performance accuracies of convolutional neural network (CNN) without the need to increase the network complexity. We accomplish the goal by applying the proposed Min-Max objective to a layer below the output layer of a CNN model in the course of training. The Min-Max objective explicitly ensures that the feature maps learned by a CNN model have the minimum within-manifold distance for each object manifold and the maximum between-manifold distances among different object manifolds. The Min-Max objective is general and able to be applied to different CNNs with insignificant increases in computation cost. Moreover, an incremental minibatch training procedure is also proposed in conjunction with the Min-Max objective to enable the handling of large-scale training data. Comprehensive experimental evaluations on several benchmark data sets with both the image classification and face verification tasks reveal that employing the proposed Min-Max objective in the training process can remarkably improve performance accuracies of a CNN model in comparison with the same model trained without using this objective.

Continuous Chinese sign language recognition with CNN-LSTM

NASA Astrophysics Data System (ADS)

Yang, Su; Zhu, Qing

2017-07-01

The goal of sign language recognition (SLR) is to translate the sign language into text, and provide a convenient tool for the communication between the deaf-mute and the ordinary. In this paper, we formulate an appropriate model based on convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) network, in order to accomplish the continuous recognition work. With the strong ability of CNN, the information of pictures captured from Chinese sign language (CSL) videos can be learned and transformed into vector. Since the video can be regarded as an ordered sequence of frames, LSTM model is employed to connect with the fully-connected layer of CNN. As a recurrent neural network (RNN), it is suitable for sequence learning tasks with the capability of recognizing patterns defined by temporal distance. Compared with traditional RNN, LSTM has performed better on storing and accessing information. We evaluate this method on our self-built dataset including 40 daily vocabularies. The experimental results show that the recognition method with CNN-LSTM can achieve a high recognition rate with small training sets, which will meet the needs of real-time SLR system.

Component Pin Recognition Using Algorithms Based on Machine Learning

NASA Astrophysics Data System (ADS)

Xiao, Yang; Hu, Hong; Liu, Ze; Xu, Jiangchang

2018-04-01

The purpose of machine vision for a plug-in machine is to improve the machine’s stability and accuracy, and recognition of the component pin is an important part of the vision. This paper focuses on component pin recognition using three different techniques. The first technique involves traditional image processing using the core algorithm for binary large object (BLOB) analysis. The second technique uses the histogram of oriented gradients (HOG), to experimentally compare the effect of the support vector machine (SVM) and the adaptive boosting machine (AdaBoost) learning meta-algorithm classifiers. The third technique is the use of an in-depth learning method known as convolution neural network (CNN), which involves identifying the pin by comparing a sample to its training. The main purpose of the research presented in this paper is to increase the knowledge of learning methods used in the plug-in machine industry in order to achieve better results.

PSNet: prostate segmentation on MRI based on a convolutional neural network.

PubMed

Tian, Zhiqiang; Liu, Lizhi; Zhang, Zhenfeng; Fei, Baowei

2018-04-01

Automatic segmentation of the prostate on magnetic resonance images (MRI) has many applications in prostate cancer diagnosis and therapy. We proposed a deep fully convolutional neural network (CNN) to segment the prostate automatically. Our deep CNN model is trained end-to-end in a single learning stage, which uses prostate MRI and the corresponding ground truths as inputs. The learned CNN model can be used to make an inference for pixel-wise segmentation. Experiments were performed on three data sets, which contain prostate MRI of 140 patients. The proposed CNN model of prostate segmentation (PSNet) obtained a mean Dice similarity coefficient of [Formula: see text] as compared to the manually labeled ground truth. Experimental results show that the proposed model could yield satisfactory segmentation of the prostate on MRI.

Orbital synchronization capture of two binaries emitting gravitational waves

NASA Astrophysics Data System (ADS)

Seto, Naoki

2018-03-01

We study the possibility of orbital synchronization capture for a hierarchical quadrupole stellar system composed by two binaries emitting gravitational waves. Based on a simple model including the mass transfer for white dwarf binaries, we find that the capture might be realized for inter-binary distances less than their gravitational wavelength. We also discuss related intriguing phenomena such as a parasitic relation between the coupled white dwarf binaries and significant reductions of gravitational and electromagnetic radiations.

Gastrointestinal bleeding detection in wireless capsule endoscopy images using handcrafted and CNN features.

PubMed

Xiao Jia; Meng, Max Q-H

2017-07-01

Gastrointestinal (GI) bleeding detection plays an essential role in wireless capsule endoscopy (WCE) examination. In this paper, we present a new approach for WCE bleeding detection that combines handcrafted (HC) features and convolutional neural network (CNN) features. Compared with our previous work, a smaller-scale CNN architecture is constructed to lower the computational cost. In experiments, we show that the proposed strategy is highly capable when training data is limited, and yields comparable or better results than the latest methods.

Law Enforcement’s Dilemma: Fighting 21st Century Encrypted Communications with 20th Century Legislation

DTIC Science & Technology

2018-03-01

Update. 187 Rishi Iyengar, “Apple Is Removing VPN Apps that Allow Users to Skirt China’s Great Firewall,” CNN Money , July 29, 2017, http...On iCloud,” CNN Money , February 22, 2016, http://money.cnn.com/2016/02/22/technology/apple-privacy-icloud/index.html. 213 District Attorney, New...it now stands, targets of investigation can communicate surreptitiously on various platforms free from detection.236 James Comey, the former

Estimating Mass Parameters of Doubly Synchronous Binary Asteroids

NASA Astrophysics Data System (ADS)

Davis, Alex; Scheeres, Daniel J.

2017-10-01

The non-spherical mass distributions of binary asteroid systems lead to coupled mutual gravitational forces and torques. Observations of the coupled attitude and orbital dynamics can be leveraged to provide information about the mass parameters of the binary system. The full 3-dimensional motion has 9 degrees of freedom, and coupled dynamics require the use of numerical investigation only. In the current study we simplify the system to a planar ellipsoid-ellipsoid binary system in a doubly synchronous orbit. Three modes are identified for the system, which has 4 degrees of freedom, with one degree of freedom corresponding to an ignorable coordinate. The three modes correspond to the three major librational modes of the system when it is in a doubly synchronous orbit. The linearized periods of each mode are a function of the mass parameters of the two asteroids, enabling measurement of these parameters based on observations of the librational motion. Here we implement estimation techniques to evaluate the capabilities of this mass measurement method. We apply this methodology to the Trojan binary asteroid system 617 Patroclus and Menoetius (1906 VY), the final flyby target of the recently announced LUCY Discovery mission. This system is of interest because a stellar occultation campaign of the Patroclus and Menoetius system has suggested that the asteroids are similarly sized oblate ellipsoids moving in a doubly-synchronous orbit, making the system an ideal test for this investigation. A number of missed observations during the campaign also suggested the possibility of a crater on the southern limb of Menoetius, the presence of which could be evaluated by our mass estimation method. This presentation will review the methodology and potential accuracy of our approach in addition to evaluating how the dynamical coupling can be used to help understand light curve and stellar occultation observations for librating binary systems.

Electroencephalography Based Fusion Two-Dimensional (2D)-Convolution Neural Networks (CNN) Model for Emotion Recognition System.

PubMed

Kwon, Yea-Hoon; Shin, Sae-Byuk; Kim, Shin-Dug

2018-04-30

The purpose of this study is to improve human emotional classification accuracy using a convolution neural networks (CNN) model and to suggest an overall method to classify emotion based on multimodal data. We improved classification performance by combining electroencephalogram (EEG) and galvanic skin response (GSR) signals. GSR signals are preprocessed using by the zero-crossing rate. Sufficient EEG feature extraction can be obtained through CNN. Therefore, we propose a suitable CNN model for feature extraction by tuning hyper parameters in convolution filters. The EEG signal is preprocessed prior to convolution by a wavelet transform while considering time and frequency simultaneously. We use a database for emotion analysis using the physiological signals open dataset to verify the proposed process, achieving 73.4% accuracy, showing significant performance improvement over the current best practice models.

Quaternion-based adaptive output feedback attitude control of spacecraft using Chebyshev neural networks.

PubMed

Zou, An-Min; Dev Kumar, Krishna; Hou, Zeng-Guang

2010-09-01

This paper investigates the problem of output feedback attitude control of an uncertain spacecraft. Two robust adaptive output feedback controllers based on Chebyshev neural networks (CNN) termed adaptive neural networks (NN) controller-I and adaptive NN controller-II are proposed for the attitude tracking control of spacecraft. The four-parameter representations (quaternion) are employed to describe the spacecraft attitude for global representation without singularities. The nonlinear reduced-order observer is used to estimate the derivative of the spacecraft output, and the CNN is introduced to further improve the control performance through approximating the spacecraft attitude motion. The implementation of the basis functions of the CNN used in the proposed controllers depends only on the desired signals, and the smooth robust compensator using the hyperbolic tangent function is employed to counteract the CNN approximation errors and external disturbances. The adaptive NN controller-II can efficiently avoid the over-estimation problem (i.e., the bound of the CNNs output is much larger than that of the approximated unknown function, and hence, the control input may be very large) existing in the adaptive NN controller-I. Both adaptive output feedback controllers using CNN can guarantee that all signals in the resulting closed-loop system are uniformly ultimately bounded. For performance comparisons, the standard adaptive controller using the linear parameterization of spacecraft attitude motion is also developed. Simulation studies are presented to show the advantages of the proposed CNN-based output feedback approach over the standard adaptive output feedback approach.

Probing the Boundaries of Orthology: The Unanticipated Rapid Evolution of Drosophila centrosomin

PubMed Central

Eisman, Robert C.; Kaufman, Thomas C.

2013-01-01

The rapid evolution of essential developmental genes and their protein products is both intriguing and problematic. The rapid evolution of gene products with simple protein folds and a lack of well-characterized functional domains typically result in a low discovery rate of orthologous genes. Additionally, in the absence of orthologs it is difficult to study the processes and mechanisms underlying rapid evolution. In this study, we have investigated the rapid evolution of centrosomin (cnn), an essential gene encoding centrosomal protein isoforms required during syncytial development in Drosophila melanogaster. Until recently the rapid divergence of cnn made identification of orthologs difficult and questionable because Cnn violates many of the assumptions underlying models for protein evolution. To overcome these limitations, we have identified a group of insect orthologs and present conserved features likely to be required for the functions attributed to cnn in D. melanogaster. We also show that the rapid divergence of Cnn isoforms is apparently due to frequent coding sequence indels and an accelerated rate of intronic additions and eliminations. These changes appear to be buffered by multi-exon and multi-reading frame maximum potential ORFs, simple protein folds, and the splicing machinery. These buffering features also occur in other genes in Drosophila and may help prevent potentially deleterious mutations due to indels in genes with large coding exons and exon-dense regions separated by small introns. This work promises to be useful for future investigations of cnn and potentially other rapidly evolving genes and proteins. PMID:23749319

Applying a deep learning based CAD scheme to segment and quantify visceral and subcutaneous fat areas from CT images

NASA Astrophysics Data System (ADS)

Wang, Yunzhi; Qiu, Yuchen; Thai, Theresa; Moore, Kathleen; Liu, Hong; Zheng, Bin

2017-03-01

Abdominal obesity is strongly associated with a number of diseases and accurately assessment of subtypes of adipose tissue volume plays a significant role in predicting disease risk, diagnosis and prognosis. The objective of this study is to develop and evaluate a new computer-aided detection (CAD) scheme based on deep learning models to automatically segment subcutaneous fat areas (SFA) and visceral (VFA) fat areas depicting on CT images. A dataset involving CT images from 40 patients were retrospectively collected and equally divided into two independent groups (i.e. training and testing group). The new CAD scheme consisted of two sequential convolutional neural networks (CNNs) namely, Selection-CNN and Segmentation-CNN. Selection-CNN was trained using 2,240 CT slices to automatically select CT slices belonging to abdomen areas and SegmentationCNN was trained using 84,000 fat-pixel patches to classify fat-pixels as belonging to SFA or VFA. Then, data from the testing group was used to evaluate the performance of the optimized CAD scheme. Comparing to manually labelled results, the classification accuracy of CT slices selection generated by Selection-CNN yielded 95.8%, while the accuracy of fat pixel segmentation using Segmentation-CNN yielded 96.8%. Therefore, this study demonstrated the feasibility of using deep learning based CAD scheme to recognize human abdominal section from CT scans and segment SFA and VFA from CT slices with high agreement compared with subjective segmentation results.

A novel deep learning-based approach to high accuracy breast density estimation in digital mammography

NASA Astrophysics Data System (ADS)

Ahn, Chul Kyun; Heo, Changyong; Jin, Heongmin; Kim, Jong Hyo

2017-03-01

Mammographic breast density is a well-established marker for breast cancer risk. However, accurate measurement of dense tissue is a difficult task due to faint contrast and significant variations in background fatty tissue. This study presents a novel method for automated mammographic density estimation based on Convolutional Neural Network (CNN). A total of 397 full-field digital mammograms were selected from Seoul National University Hospital. Among them, 297 mammograms were randomly selected as a training set and the rest 100 mammograms were used for a test set. We designed a CNN architecture suitable to learn the imaging characteristic from a multitudes of sub-images and classify them into dense and fatty tissues. To train the CNN, not only local statistics but also global statistics extracted from an image set were used. The image set was composed of original mammogram and eigen-image which was able to capture the X-ray characteristics in despite of the fact that CNN is well known to effectively extract features on original image. The 100 test images which was not used in training the CNN was used to validate the performance. The correlation coefficient between the breast estimates by the CNN and those by the expert's manual measurement was 0.96. Our study demonstrated the feasibility of incorporating the deep learning technology into radiology practice, especially for breast density estimation. The proposed method has a potential to be used as an automated and quantitative assessment tool for mammographic breast density in routine practice.

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

PubMed

Jiang, Jiewei; Liu, Xiyang; Zhang, Kai; Long, Erping; Wang, Liming; Li, Wangting; Liu, Lin; Wang, Shuai; Zhu, Mingmin; Cui, Jiangtao; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Wang, Jinghui; Lin, Haotian

2017-11-21

Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

Convolutional neural network architectures for predicting DNA–protein binding

PubMed Central

Zeng, Haoyang; Edwards, Matthew D.; Liu, Ge; Gifford, David K.

2016-01-01

Motivation: Convolutional neural networks (CNN) have outperformed conventional methods in modeling the sequence specificity of DNA–protein binding. Yet inappropriate CNN architectures can yield poorer performance than simpler models. Thus an in-depth understanding of how to match CNN architecture to a given task is needed to fully harness the power of CNNs for computational biology applications. Results: We present a systematic exploration of CNN architectures for predicting DNA sequence binding using a large compendium of transcription factor datasets. We identify the best-performing architectures by varying CNN width, depth and pooling designs. We find that adding convolutional kernels to a network is important for motif-based tasks. We show the benefits of CNNs in learning rich higher-order sequence features, such as secondary motifs and local sequence context, by comparing network performance on multiple modeling tasks ranging in difficulty. We also demonstrate how careful construction of sequence benchmark datasets, using approaches that control potentially confounding effects like positional or motif strength bias, is critical in making fair comparisons between competing methods. We explore how to establish the sufficiency of training data for these learning tasks, and we have created a flexible cloud-based framework that permits the rapid exploration of alternative neural network architectures for problems in computational biology. Availability and Implementation: All the models analyzed are available at http://cnn.csail.mit.edu. Contact: gifford@mit.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:27307608

Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images.

PubMed

Hirasawa, Toshiaki; Aoyama, Kazuharu; Tanimoto, Tetsuya; Ishihara, Soichiro; Shichijo, Satoki; Ozawa, Tsuyoshi; Ohnishi, Tatsuya; Fujishiro, Mitsuhiro; Matsuo, Keigo; Fujisaki, Junko; Tada, Tomohiro

2018-07-01

Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images. A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN. The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface. The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.

Simultaneous Local Binary Feature Learning and Encoding for Homogeneous and Heterogeneous Face Recognition.

PubMed

Lu, Jiwen; Erin Liong, Venice; Zhou, Jie

2017-08-09

In this paper, we propose a simultaneous local binary feature learning and encoding (SLBFLE) approach for both homogeneous and heterogeneous face recognition. Unlike existing hand-crafted face descriptors such as local binary pattern (LBP) and Gabor features which usually require strong prior knowledge, our SLBFLE is an unsupervised feature learning approach which automatically learns face representation from raw pixels. Unlike existing binary face descriptors such as the LBP, discriminant face descriptor (DFD), and compact binary face descriptor (CBFD) which use a two-stage feature extraction procedure, our SLBFLE jointly learns binary codes and the codebook for local face patches so that discriminative information from raw pixels from face images of different identities can be obtained by using a one-stage feature learning and encoding procedure. Moreover, we propose a coupled simultaneous local binary feature learning and encoding (C-SLBFLE) method to make the proposed approach suitable for heterogeneous face matching. Unlike most existing coupled feature learning methods which learn a pair of transformation matrices for each modality, we exploit both the common and specific information from heterogeneous face samples to characterize their underlying correlations. Experimental results on six widely used face datasets are presented to demonstrate the effectiveness of the proposed method.

Going Deeper With Contextual CNN for Hyperspectral Image Classification.

PubMed

Lee, Hyungtae; Kwon, Heesung

2017-10-01

In this paper, we describe a novel deep convolutional neural network (CNN) that is deeper and wider than other existing deep networks for hyperspectral image classification. Unlike current state-of-the-art approaches in CNN-based hyperspectral image classification, the proposed network, called contextual deep CNN, can optimally explore local contextual interactions by jointly exploiting local spatio-spectral relationships of neighboring individual pixel vectors. The joint exploitation of the spatio-spectral information is achieved by a multi-scale convolutional filter bank used as an initial component of the proposed CNN pipeline. The initial spatial and spectral feature maps obtained from the multi-scale filter bank are then combined together to form a joint spatio-spectral feature map. The joint feature map representing rich spectral and spatial properties of the hyperspectral image is then fed through a fully convolutional network that eventually predicts the corresponding label of each pixel vector. The proposed approach is tested on three benchmark data sets: the Indian Pines data set, the Salinas data set, and the University of Pavia data set. Performance comparison shows enhanced classification performance of the proposed approach over the current state-of-the-art on the three data sets.

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising.

PubMed

Zhang, Kai; Zuo, Wangmeng; Chen, Yunjin; Meng, Deyu; Zhang, Lei

2017-07-01

The discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance. In this paper, we take one step forward by investigating the construction of feed-forward denoising convolutional neural networks (DnCNNs) to embrace the progress in very deep architecture, learning algorithm, and regularization method into image denoising. Specifically, residual learning and batch normalization are utilized to speed up the training process as well as boost the denoising performance. Different from the existing discriminative denoising models which usually train a specific model for additive white Gaussian noise at a certain noise level, our DnCNN model is able to handle Gaussian denoising with unknown noise level (i.e., blind Gaussian denoising). With the residual learning strategy, DnCNN implicitly removes the latent clean image in the hidden layers. This property motivates us to train a single DnCNN model to tackle with several general image denoising tasks, such as Gaussian denoising, single image super-resolution, and JPEG image deblocking. Our extensive experiments demonstrate that our DnCNN model can not only exhibit high effectiveness in several general image denoising tasks, but also be efficiently implemented by benefiting from GPU computing.

A CNN Regression Approach for Real-Time 2D/3D Registration.

PubMed

Shun Miao; Wang, Z Jane; Rui Liao

2016-05-01

In this paper, we present a Convolutional Neural Network (CNN) regression approach to address the two major limitations of existing intensity-based 2-D/3-D registration technology: 1) slow computation and 2) small capture range. Different from optimization-based methods, which iteratively optimize the transformation parameters over a scalar-valued metric function representing the quality of the registration, the proposed method exploits the information embedded in the appearances of the digitally reconstructed radiograph and X-ray images, and employs CNN regressors to directly estimate the transformation parameters. An automatic feature extraction step is introduced to calculate 3-D pose-indexed features that are sensitive to the variables to be regressed while robust to other factors. The CNN regressors are then trained for local zones and applied in a hierarchical manner to break down the complex regression task into multiple simpler sub-tasks that can be learned separately. Weight sharing is furthermore employed in the CNN regression model to reduce the memory footprint. The proposed approach has been quantitatively evaluated on 3 potential clinical applications, demonstrating its significant advantage in providing highly accurate real-time 2-D/3-D registration with a significantly enlarged capture range when compared to intensity-based methods.

3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT.

PubMed

Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2017-01-01

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

3D convolutional neural network for automatic detection of lung nodules in chest CT

NASA Astrophysics Data System (ADS)

Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2017-03-01

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

Infrared variation reduction by simultaneous background suppression and target contrast enhancement for deep convolutional neural network-based automatic target recognition

NASA Astrophysics Data System (ADS)

Kim, Sungho

2017-06-01

Automatic target recognition (ATR) is a traditionally challenging problem in military applications because of the wide range of infrared (IR) image variations and the limited number of training images. IR variations are caused by various three-dimensional target poses, noncooperative weather conditions (fog and rain), and difficult target acquisition environments. Recently, deep convolutional neural network-based approaches for RGB images (RGB-CNN) showed breakthrough performance in computer vision problems, such as object detection and classification. The direct use of RGB-CNN to the IR ATR problem fails to work because of the IR database problems (limited database size and IR image variations). An IR variation-reduced deep CNN (IVR-CNN) to cope with the problems is presented. The problem of limited IR database size is solved by a commercial thermal simulator (OKTAL-SE). The second problem of IR variations is mitigated by the proposed shifted ramp function-based intensity transformation. This can suppress the background and enhance the target contrast simultaneously. The experimental results on the synthesized IR images generated by the thermal simulator (OKTAL-SE) validated the feasibility of IVR-CNN for military ATR applications.

Statistical studies in stellar rotation 2: A method of analyzing rotational coupling in double stars and an introduction to its applications

NASA Technical Reports Server (NTRS)

Bernacca, P. L.

1971-01-01

The correlation between the equatorial velocities of the components of double stars is studied from a statistical standpoint. A theory of rotational correlation is developed and discussed with regard to its applicability to existing observations. The theory is then applied to a sample of visual binaries which are the least studied for rotational coupling. Consideration of eclipsing systems and spectroscopic binaries is limited to show how the degrees of freedom in the spin parallelism problem can be reduced. The analysis lends support to the existence of synchronism in closely spaced binaries.

Computer-Aided Diagnosis Based on Convolutional Neural Network System for Colorectal Polyp Classification: Preliminary Experience.

PubMed

Komeda, Yoriaki; Handa, Hisashi; Watanabe, Tomohiro; Nomura, Takanobu; Kitahashi, Misaki; Sakurai, Toshiharu; Okamoto, Ayana; Minami, Tomohiro; Kono, Masashi; Arizumi, Tadaaki; Takenaka, Mamoru; Hagiwara, Satoru; Matsui, Shigenaga; Nishida, Naoshi; Kashida, Hiroshi; Kudo, Masatoshi

2017-01-01

Computer-aided diagnosis (CAD) is becoming a next-generation tool for the diagnosis of human disease. CAD for colon polyps has been suggested as a particularly useful tool for trainee colonoscopists, as the use of a CAD system avoids the complications associated with endoscopic resections. In addition to conventional CAD, a convolutional neural network (CNN) system utilizing artificial intelligence (AI) has been developing rapidly over the past 5 years. We attempted to generate a unique CNN-CAD system with an AI function that studied endoscopic images extracted from movies obtained with colonoscopes used in routine examinations. Here, we report our preliminary results of this novel CNN-CAD system for the diagnosis of colon polyps. A total of 1,200 images from cases of colonoscopy performed between January 2010 and December 2016 at Kindai University Hospital were used. These images were extracted from the video of actual endoscopic examinations. Additional video images from 10 cases of unlearned processes were retrospectively assessed in a pilot study. They were simply diagnosed as either an adenomatous or nonadenomatous polyp. The number of images used by AI to learn to distinguish adenomatous from nonadenomatous was 1,200:600. These images were extracted from the videos of actual endoscopic examinations. The size of each image was adjusted to 256 × 256 pixels. A 10-hold cross-validation was carried out. The accuracy of the 10-hold cross-validation is 0.751, where the accuracy is the ratio of the number of correct answers over the number of all the answers produced by the CNN. The decisions by the CNN were correct in 7 of 10 cases. A CNN-CAD system using routine colonoscopy might be useful for the rapid diagnosis of colorectal polyp classification. Further prospective studies in an in vivo setting are required to confirm the effectiveness of a CNN-CAD system in routine colonoscopy. © 2017 S. Karger AG, Basel.

Diverse Region-Based CNN for Hyperspectral Image Classification.

PubMed

Zhang, Mengmeng; Li, Wei; Du, Qian

2018-06-01

Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.

Comparison of hand-craft feature based SVM and CNN based deep learning framework for automatic polyp classification.

PubMed

Younghak Shin; Balasingham, Ilangko

2017-07-01

Colonoscopy is a standard method for screening polyps by highly trained physicians. Miss-detected polyps in colonoscopy are potential risk factor for colorectal cancer. In this study, we investigate an automatic polyp classification framework. We aim to compare two different approaches named hand-craft feature method and convolutional neural network (CNN) based deep learning method. Combined shape and color features are used for hand craft feature extraction and support vector machine (SVM) method is adopted for classification. For CNN approach, three convolution and pooling based deep learning framework is used for classification purpose. The proposed framework is evaluated using three public polyp databases. From the experimental results, we have shown that the CNN based deep learning framework shows better classification performance than the hand-craft feature based methods. It achieves over 90% of classification accuracy, sensitivity, specificity and precision.

Enhancing deep convolutional neural network scheme for breast cancer diagnosis with unlabeled data.

PubMed

Sun, Wenqing; Tseng, Tzu-Liang Bill; Zhang, Jianying; Qian, Wei

2017-04-01

In this study we developed a graph based semi-supervised learning (SSL) scheme using deep convolutional neural network (CNN) for breast cancer diagnosis. CNN usually needs a large amount of labeled data for training and fine tuning the parameters, and our proposed scheme only requires a small portion of labeled data in training set. Four modules were included in the diagnosis system: data weighing, feature selection, dividing co-training data labeling, and CNN. 3158 region of interests (ROIs) with each containing a mass extracted from 1874 pairs of mammogram images were used for this study. Among them 100 ROIs were treated as labeled data while the rest were treated as unlabeled. The area under the curve (AUC) observed in our study was 0.8818, and the accuracy of CNN is 0.8243 using the mixed labeled and unlabeled data. Copyright © 2016. Published by Elsevier Ltd.

Multiscale deep features learning for land-use scene recognition

NASA Astrophysics Data System (ADS)

Yuan, Baohua; Li, Shijin; Li, Ning

2018-01-01

The features extracted from deep convolutional neural networks (CNNs) have shown their promise as generic descriptors for land-use scene recognition. However, most of the work directly adopts the deep features for the classification of remote sensing images, and does not encode the deep features for improving their discriminative power, which can affect the performance of deep feature representations. To address this issue, we propose an effective framework, LASC-CNN, obtained by locality-constrained affine subspace coding (LASC) pooling of a CNN filter bank. LASC-CNN obtains more discriminative deep features than directly extracted from CNNs. Furthermore, LASC-CNN builds on the top convolutional layers of CNNs, which can incorporate multiscale information and regions of arbitrary resolution and sizes. Our experiments have been conducted using two widely used remote sensing image databases, and the results show that the proposed method significantly improves the performance when compared to other state-of-the-art methods.

Using convolutional neural networks to explore the microbiome.

PubMed

Reiman, Derek; Metwally, Ahmed; Yang Dai

2017-07-01

The microbiome has been shown to have an impact on the development of various diseases in the host. Being able to make an accurate prediction of the phenotype of a genomic sample based on its microbial taxonomic abundance profile is an important problem for personalized medicine. In this paper, we examine the potential of using a deep learning framework, a convolutional neural network (CNN), for such a prediction. To facilitate the CNN learning, we explore the structure of abundance profiles by creating the phylogenetic tree and by designing a scheme to embed the tree to a matrix that retains the spatial relationship of nodes in the tree and their quantitative characteristics. The proposed CNN framework is highly accurate, achieving a 99.47% of accuracy based on the evaluation on a dataset 1967 samples of three phenotypes. Our result demonstrated the feasibility and promising aspect of CNN in the classification of sample phenotype.

A comparison study between MLP and convolutional neural network models for character recognition

NASA Astrophysics Data System (ADS)

Ben Driss, S.; Soua, M.; Kachouri, R.; Akil, M.

2017-05-01

Optical Character Recognition (OCR) systems have been designed to operate on text contained in scanned documents and images. They include text detection and character recognition in which characters are described then classified. In the classification step, characters are identified according to their features or template descriptions. Then, a given classifier is employed to identify characters. In this context, we have proposed the unified character descriptor (UCD) to represent characters based on their features. Then, matching was employed to ensure the classification. This recognition scheme performs a good OCR Accuracy on homogeneous scanned documents, however it cannot discriminate characters with high font variation and distortion.3 To improve recognition, classifiers based on neural networks can be used. The multilayer perceptron (MLP) ensures high recognition accuracy when performing a robust training. Moreover, the convolutional neural network (CNN), is gaining nowadays a lot of popularity for its high performance. Furthermore, both CNN and MLP may suffer from the large amount of computation in the training phase. In this paper, we establish a comparison between MLP and CNN. We provide MLP with the UCD descriptor and the appropriate network configuration. For CNN, we employ the convolutional network designed for handwritten and machine-printed character recognition (Lenet-5) and we adapt it to support 62 classes, including both digits and characters. In addition, GPU parallelization is studied to speed up both of MLP and CNN classifiers. Based on our experimentations, we demonstrate that the used real-time CNN is 2x more relevant than MLP when classifying characters.

Segmentation of white matter hyperintensities using convolutional neural networks with global spatial information in routine clinical brain MRI with none or mild vascular pathology.

PubMed

Rachmadi, Muhammad Febrian; Valdés-Hernández, Maria Del C; Agan, Maria Leonora Fatimah; Di Perri, Carol; Komura, Taku

2018-06-01

We propose an adaptation of a convolutional neural network (CNN) scheme proposed for segmenting brain lesions with considerable mass-effect, to segment white matter hyperintensities (WMH) characteristic of brains with none or mild vascular pathology in routine clinical brain magnetic resonance images (MRI). This is a rather difficult segmentation problem because of the small area (i.e., volume) of the WMH and their similarity to non-pathological brain tissue. We investigate the effectiveness of the 2D CNN scheme by comparing its performance against those obtained from another deep learning approach: Deep Boltzmann Machine (DBM), two conventional machine learning approaches: Support Vector Machine (SVM) and Random Forest (RF), and a public toolbox: Lesion Segmentation Tool (LST), all reported to be useful for segmenting WMH in MRI. We also introduce a way to incorporate spatial information in convolution level of CNN for WMH segmentation named global spatial information (GSI). Analysis of covariance corroborated known associations between WMH progression, as assessed by all methods evaluated, and demographic and clinical data. Deep learning algorithms outperform conventional machine learning algorithms by excluding MRI artefacts and pathologies that appear similar to WMH. Our proposed approach of incorporating GSI also successfully helped CNN to achieve better automatic WMH segmentation regardless of network's settings tested. The mean Dice Similarity Coefficient (DSC) values for LST-LGA, SVM, RF, DBM, CNN and CNN-GSI were 0.2963, 0.1194, 0.1633, 0.3264, 0.5359 and 5389 respectively. Crown Copyright © 2018. Published by Elsevier Ltd. All rights reserved.

Deep learning classifier with optical coherence tomography images for early dental caries detection

NASA Astrophysics Data System (ADS)

Karimian, Nima; Salehi, Hassan S.; Mahdian, Mina; Alnajjar, Hisham; Tadinada, Aditya

2018-02-01

Dental caries is a microbial disease that results in localized dissolution of the mineral content of dental tissue. Despite considerable decline in the incidence of dental caries, it remains a major health problem in many societies. Early detection of incipient lesions at initial stages of demineralization can result in the implementation of non-surgical preventive approaches to reverse the demineralization process. In this paper, we present a novel approach combining deep convolutional neural networks (CNN) and optical coherence tomography (OCT) imaging modality for classification of human oral tissues to detect early dental caries. OCT images of oral tissues with various densities were input to a CNN classifier to determine variations in tissue densities resembling the demineralization process. The CNN automatically learns a hierarchy of increasingly complex features and a related classifier directly from training data sets. The initial CNN layer parameters were randomly selected. The training set is split into minibatches, with 10 OCT images per batch. Given a batch of training patches, the CNN employs two convolutional and pooling layers to extract features and then classify each patch based on the probabilities from the SoftMax classification layer (output-layer). Afterward, the CNN calculates the error between the classification result and the reference label, and then utilizes the backpropagation process to fine-tune all the layer parameters to minimize this error using batch gradient descent algorithm. We validated our proposed technique on ex-vivo OCT images of human oral tissues (enamel, cortical-bone, trabecular-bone, muscular-tissue, and fatty-tissue), which attested to effectiveness of our proposed method.

Deep learning analyzes Helicobacter pylori infection by upper gastrointestinal endoscopy images.

PubMed

Itoh, Takumi; Kawahira, Hiroshi; Nakashima, Hirotaka; Yata, Noriko

2018-02-01

Helicobacter pylori (HP)-associated chronic gastritis can cause mucosal atrophy and intestinal metaplasia, both of which increase the risk of gastric cancer. The accurate diagnosis of HP infection during routine medical checks is important. We aimed to develop a convolutional neural network (CNN), which is a machine-learning algorithm similar to deep learning, capable of recognizing specific features of gastric endoscopy images. The goal behind developing such a system was to detect HP infection early, thus preventing gastric cancer.  For the development of the CNN, we used 179 upper gastrointestinal endoscopy images obtained from 139 patients (65 were HP-positive: ≥ 10 U/mL and 74 were HP-negative: < 3 U/mL on HP IgG antibody assessment). Of the 179 images, 149 were used as training images, and the remaining 30 (15 from HP-negative patients and 15 from HP-positive patients) were set aside to be used as test images. The 149 training images were subjected to data augmentation, which yielded 596 images. We used the CNN to create a learning tool that would recognize HP infection and assessed the decision accuracy of the CNN with the 30 test images by calculating the sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve (AUC).  The sensitivity and specificity of the CNN for the detection of HP infection were 86.7 % and 86.7 %, respectively, and the AUC was 0.956.  CNN-aided diagnosis of HP infection seems feasible and is expected to facilitate and improve diagnosis during health check-ups.

Classification of CT brain images based on deep learning networks.

PubMed

Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

2017-01-01

While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information rooted in both 2D slices and 3D blocks of CT images and an elaborated hand-crated approach of 3D KAZE. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images.

PubMed

Wang, Hongkai; Zhou, Zongwei; Li, Yingci; Chen, Zhonghua; Lu, Peiou; Wang, Wenzhi; Liu, Wanyu; Yu, Lijuan

2017-12-01

This study aimed to compare one state-of-the-art deep learning method and four classical machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer (NSCLC) from 18 F-FDG PET/CT images. Another objective was to compare the discriminative power of the recently popular PET/CT texture features with the widely used diagnostic features such as tumor size, CT value, SUV, image contrast, and intensity standard deviation. The four classical machine learning methods included random forests, support vector machines, adaptive boosting, and artificial neural network. The deep learning method was the convolutional neural networks (CNN). The five methods were evaluated using 1397 lymph nodes collected from PET/CT images of 168 patients, with corresponding pathology analysis results as gold standard. The comparison was conducted using 10 times 10-fold cross-validation based on the criterion of sensitivity, specificity, accuracy (ACC), and area under the ROC curve (AUC). For each classical method, different input features were compared to select the optimal feature set. Based on the optimal feature set, the classical methods were compared with CNN, as well as with human doctors from our institute. For the classical methods, the diagnostic features resulted in 81~85% ACC and 0.87~0.92 AUC, which were significantly higher than the results of texture features. CNN's sensitivity, specificity, ACC, and AUC were 84, 88, 86, and 0.91, respectively. There was no significant difference between the results of CNN and the best classical method. The sensitivity, specificity, and ACC of human doctors were 73, 90, and 82, respectively. All the five machine learning methods had higher sensitivities but lower specificities than human doctors. The present study shows that the performance of CNN is not significantly different from the best classical methods and human doctors for classifying mediastinal lymph node metastasis of NSCLC from PET/CT images. Because CNN does not need tumor segmentation or feature calculation, it is more convenient and more objective than the classical methods. However, CNN does not make use of the import diagnostic features, which have been proved more discriminative than the texture features for classifying small-sized lymph nodes. Therefore, incorporating the diagnostic features into CNN is a promising direction for future research.

Semi-supervised Machine Learning for Analysis of Hydrogeochemical Data and Models

NASA Astrophysics Data System (ADS)

Vesselinov, Velimir; O'Malley, Daniel; Alexandrov, Boian; Moore, Bryan

2017-04-01

Data- and model-based analyses such as uncertainty quantification, sensitivity analysis, and decision support using complex physics models with numerous model parameters and typically require a huge number of model evaluations (on order of 10^6). Furthermore, model simulations of complex physics may require substantial computational time. For example, accounting for simultaneously occurring physical processes such as fluid flow and biogeochemical reactions in heterogeneous porous medium may require several hours of wall-clock computational time. To address these issues, we have developed a novel methodology for semi-supervised machine learning based on Non-negative Matrix Factorization (NMF) coupled with customized k-means clustering. The algorithm allows for automated, robust Blind Source Separation (BSS) of groundwater types (contamination sources) based on model-free analyses of observed hydrogeochemical data. We have also developed reduced order modeling tools, which coupling support vector regression (SVR), genetic algorithms (GA) and artificial and convolutional neural network (ANN/CNN). SVR is applied to predict the model behavior within prior uncertainty ranges associated with the model parameters. ANN and CNN procedures are applied to upscale heterogeneity of the porous medium. In the upscaling process, fine-scale high-resolution models of heterogeneity are applied to inform coarse-resolution models which have improved computational efficiency while capturing the impact of fine-scale effects at the course scale of interest. These techniques are tested independently on a series of synthetic problems. We also present a decision analysis related to contaminant remediation where the developed reduced order models are applied to reproduce groundwater flow and contaminant transport in a synthetic heterogeneous aquifer. The tools are coded in Julia and are a part of the MADS high-performance computational framework (https://github.com/madsjulia/Mads.jl).

Deep Learning for Automated Extraction of Primary Sites From Cancer Pathology Reports.

PubMed

Qiu, John X; Yoon, Hong-Jun; Fearn, Paul A; Tourassi, Georgia D

2018-01-01

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. In this study, we investigated deep learning and a convolutional neural network (CNN), for extracting ICD-O-3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations as the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro- and macro-F score increases of up to 0.132 and 0.226, respectively, when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on the CNN method and cancer site. These encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.

SIFT Meets CNN: A Decade Survey of Instance Retrieval.

PubMed

Zheng, Liang; Yang, Yi; Tian, Qi

2018-05-01

In the early days, content-based image retrieval (CBIR) was studied with global features. Since 2003, image retrieval based on local descriptors (de facto SIFT) has been extensively studied for over a decade due to the advantage of SIFT in dealing with image transformations. Recently, image representations based on the convolutional neural network (CNN) have attracted increasing interest in the community and demonstrated impressive performance. Given this time of rapid evolution, this article provides a comprehensive survey of instance retrieval over the last decade. Two broad categories, SIFT-based and CNN-based methods, are presented. For the former, according to the codebook size, we organize the literature into using large/medium-sized/small codebooks. For the latter, we discuss three lines of methods, i.e., using pre-trained or fine-tuned CNN models, and hybrid methods. The first two perform a single-pass of an image to the network, while the last category employs a patch-based feature extraction scheme. This survey presents milestones in modern instance retrieval, reviews a broad selection of previous works in different categories, and provides insights on the connection between SIFT and CNN-based methods. After analyzing and comparing retrieval performance of different categories on several datasets, we discuss promising directions towards generic and specialized instance retrieval.

Building Extraction from Remote Sensing Data Using Fully Convolutional Networks

NASA Astrophysics Data System (ADS)

Bittner, K.; Cui, S.; Reinartz, P.

2017-05-01

Building detection and footprint extraction are highly demanded for many remote sensing applications. Though most previous works have shown promising results, the automatic extraction of building footprints still remains a nontrivial topic, especially in complex urban areas. Recently developed extensions of the CNN framework made it possible to perform dense pixel-wise classification of input images. Based on these abilities we propose a methodology, which automatically generates a full resolution binary building mask out of a Digital Surface Model (DSM) using a Fully Convolution Network (FCN) architecture. The advantage of using the depth information is that it provides geometrical silhouettes and allows a better separation of buildings from background as well as through its invariance to illumination and color variations. The proposed framework has mainly two steps. Firstly, the FCN is trained on a large set of patches consisting of normalized DSM (nDSM) as inputs and available ground truth building mask as target outputs. Secondly, the generated predictions from FCN are viewed as unary terms for a Fully connected Conditional Random Fields (FCRF), which enables us to create a final binary building mask. A series of experiments demonstrate that our methodology is able to extract accurate building footprints which are close to the buildings original shapes to a high degree. The quantitative and qualitative analysis show the significant improvements of the results in contrast to the multy-layer fully connected network from our previous work.

Breaking Binaries? Biomedicine and Serostatus Borderlands among Couples with Mixed HIV Status.

PubMed

Persson, Asha; Newman, Christy E; Ellard, Jeanne

2017-01-01

With recent breakthroughs in HIV treatment and prevention, the meanings of HIV-positivity and HIV-negativity are changing at biomedical and community levels. We explore how binary constructions of HIV serostatus identities are giving way to something more complex that brings both welcome possibilities and potential concerns. We draw on research with couples with mixed HIV status to argue that, in the context of lived experiences, serostatus identities have always been more ambiguous than allowed for in HIV discourse. However, their supposed dichotomous quality seems even more dubious now in view of contemporary biomedical technologies. Invoking the anthropological concept of "borderlands," we consider how biomedicine is generating more diverse serostatus identities, widening the options for how to live with HIV, and eroding the stigmatizing serostatus binary that has haunted the epidemic. But we also ask whether this emerging borderland, and its "normalizing" tendencies, is concomitantly giving rise to new and troubling binaries.

Automated embolic signal detection using Deep Convolutional Neural Network.

PubMed

Sombune, Praotasna; Phienphanich, Phongphan; Phuechpanpaisal, Sutanya; Muengtaweepongsa, Sombat; Ruamthanthong, Anuchit; Tantibundhit, Charturong

2017-07-01

This work investigated the potential of Deep Neural Network in detection of cerebral embolic signal (ES) from transcranial Doppler ultrasound (TCD). The resulting system is aimed to couple with TCD devices in diagnosing a risk of stroke in real-time with high accuracy. The Adaptive Gain Control (AGC) approach developed in our previous study is employed to capture suspected ESs in real-time. By using spectrograms of the same TCD signal dataset as that of our previous work as inputs and the same experimental setup, Deep Convolutional Neural Network (CNN), which can learn features while training, was investigated for its ability to bypass the traditional handcrafted feature extraction and selection process. Extracted feature vectors from the suspected ESs are later determined whether they are of an ES, artifact (AF) or normal (NR) interval. The effectiveness of the developed system was evaluated over 19 subjects going under procedures generating emboli. The CNN-based system could achieve in average of 83.0% sensitivity, 80.1% specificity, and 81.4% accuracy, with considerably much less time consumption in development. The certainly growing set of training samples and computational resources will contribute to high performance. Besides having potential use in various clinical ES monitoring settings, continuation of this promising study will benefit developments of wearable applications by leveraging learnable features to serve demographic differentials.

A nonlinear circuit architecture for magnetoencephalographic signal analysis.

PubMed

Bucolo, M; Fortuna, L; Frasca, M; La Rosa, M; Virzì, M C; Shannahoff-Khalsa, D

2004-01-01

The objective of this paper was to face the complex spatio-temporal dynamics shown by Magnetoencephalography (MEG) data by applying a nonlinear distributed approach for the Blind Sources Separation. The effort was to characterize and differ-entiate the phases of a yogic respiratory exercise used in the treatment of obsessive compulsive disorders. The patient performed a precise respiratory protocol, at one breath per minute for 31 minutes, with 10 minutes resting phase before and after. The two steps of classical Independent Component Approach have been performed by using a Cellular Neural Network with two sets of templates. The choice of the couple of suitable templates has been carried out using genetic algorithm optimization techniques. Performing BSS with a nonlinear distributed approach, the outputs of the CNN have been compared to the ICA ones. In all the protocol phases, the main components founded with CNN have similar trends compared with that ones obtained with ICA. Moreover, using this distributed approach, a spatial location has been associated to each component. To underline the spatio-temporal and the nonlinearly of the neural process a distributed nonlinear architecture has been proposed. This strategy has been designed in order to overcome the hypothesis of linear combination among the sources signals, that is characteristic of the ICA approach, taking advantage of the spatial information.

CNNEDGEPOT: CNN based edge detection of 2D near surface potential field data

NASA Astrophysics Data System (ADS)

Aydogan, D.

2012-09-01

All anomalies are important in the interpretation of gravity and magnetic data because they indicate some important structural features. One of the advantages of using gravity or magnetic data for searching contacts is to be detected buried structures whose signs could not be seen on the surface. In this paper, a general view of the cellular neural network (CNN) method with a large scale nonlinear circuit is presented focusing on its image processing applications. The proposed CNN model is used consecutively in order to extract body and body edges. The algorithm is a stochastic image processing method based on close neighborhood relationship of the cells and optimization of A, B and I matrices entitled as cloning template operators. Setting up a CNN (continues time cellular neural network (CTCNN) or discrete time cellular neural network (DTCNN)) for a particular task needs a proper selection of cloning templates which determine the dynamics of the method. The proposed algorithm is used for image enhancement and edge detection. The proposed method is applied on synthetic and field data generated for edge detection of near-surface geological bodies that mask each other in various depths and dimensions. The program named as CNNEDGEPOT is a set of functions written in MATLAB software. The GUI helps the user to easily change all the required CNN model parameters. A visual evaluation of the outputs due to DTCNN and CTCNN are carried out and the results are compared with each other. These examples demonstrate that in detecting the geological features the CNN model can be used for visual interpretation of near surface gravity or magnetic anomaly maps.

CNN pincer ruthenium catalysts for hydrogenation and transfer hydrogenation of ketones: experimental and computational studies.

PubMed

Baratta, Walter; Baldino, Salvatore; Calhorda, Maria José; Costa, Paulo J; Esposito, Gennaro; Herdtweck, Eberhardt; Magnolia, Santo; Mealli, Carlo; Messaoudi, Abdelatif; Mason, Sax A; Veiros, Luis F

2014-10-13

Reaction of [RuCl(CNN)(dppb)] (1-Cl) (HCNN=2-aminomethyl-6-(4-methylphenyl)pyridine; dppb=Ph2 P(CH2 )4 PPh2 ) with NaOCH2 CF3 leads to the amine-alkoxide [Ru(CNN)(OCH2 CF3 )(dppb)] (1-OCH2 CF3 ), whose neutron diffraction study reveals a short RuO⋅⋅⋅HN bond length. Treatment of 1-Cl with NaOEt and EtOH affords the alkoxide [Ru(CNN)(OEt)(dppb)]⋅(EtOH)n (1-OEt⋅n EtOH), which equilibrates with the hydride [RuH(CNN)(dppb)] (1-H) and acetaldehyde. Compound 1-OEt⋅n EtOH reacts reversibly with H2 leading to 1-H and EtOH through dihydrogen splitting. NMR spectroscopic studies on 1-OEt⋅n EtOH and 1-H reveal hydrogen bond interactions and exchange processes. The chloride 1-Cl catalyzes the hydrogenation (5 atm of H2 ) of ketones to alcohols (turnover frequency (TOF) up to 6.5×10(4) h(-1) , 40 °C). DFT calculations were performed on the reaction of [RuH(CNN')(dmpb)] (2-H) (HCNN'=2-aminomethyl-6-(phenyl)pyridine; dmpb=Me2 P(CH2 )4 PMe2 ) with acetone and with one molecule of 2-propanol, in alcohol, with the alkoxide complex being the most stable species. In the first step, the Ru-hydride transfers one hydrogen atom to the carbon of the ketone, whereas the second hydrogen transfer from NH2 is mediated by the alcohol and leads to the key "amide" intermediate. Regeneration of the hydride complex may occur by reaction with 2-propanol or with H2 ; both pathways have low barriers and are alcohol assisted. © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Deep learning for tissue microarray image-based outcome prediction in patients with colorectal cancer

NASA Astrophysics Data System (ADS)

Bychkov, Dmitrii; Turkki, Riku; Haglund, Caj; Linder, Nina; Lundin, Johan

2016-03-01

Recent advances in computer vision enable increasingly accurate automated pattern classification. In the current study we evaluate whether a convolutional neural network (CNN) can be trained to predict disease outcome in patients with colorectal cancer based on images of tumor tissue microarray samples. We compare the prognostic accuracy of CNN features extracted from the whole, unsegmented tissue microarray spot image, with that of CNN features extracted from the epithelial and non-epithelial compartments, respectively. The prognostic accuracy of visually assessed histologic grade is used as a reference. The image data set consists of digitized hematoxylin-eosin (H and E) stained tissue microarray samples obtained from 180 patients with colorectal cancer. The patient samples represent a variety of histological grades, have data available on a series of clinicopathological variables including long-term outcome and ground truth annotations performed by experts. The CNN features extracted from images of the epithelial tissue compartment significantly predicted outcome (hazard ratio (HR) 2.08; CI95% 1.04-4.16; area under the curve (AUC) 0.66) in a test set of 60 patients, as compared to the CNN features extracted from unsegmented images (HR 1.67; CI95% 0.84-3.31, AUC 0.57) and visually assessed histologic grade (HR 1.96; CI95% 0.99-3.88, AUC 0.61). As a conclusion, a deep-learning classifier can be trained to predict outcome of colorectal cancer based on images of H and E stained tissue microarray samples and the CNN features extracted from the epithelial compartment only resulted in a prognostic discrimination comparable to that of visually determined histologic grade.

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

PubMed

Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

2017-11-01

Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Maintaining the proper connection between the centrioles and the pericentriolar matrix requires Drosophila centrosomin.

PubMed

Lucas, Eliana P; Raff, Jordan W

2007-08-27

Centrosomes consist of two centrioles surrounded by an amorphous pericentriolar matrix (PCM), but it is unknown how centrioles and PCM are connected. We show that the centrioles in Drosophila embryos that lack the centrosomal protein Centrosomin (Cnn) can recruit PCM components but cannot maintain a proper attachment to the PCM. As a result, the centrioles "rocket" around in the embryo and often lose their connection to the nucleus in interphase and to the spindle poles in mitosis. This leads to severe mitotic defects in embryos and to errors in centriole segregation in somatic cells. The Cnn-related protein CDK5RAP2 is linked to microcephaly in humans, but cnn mutant brains are of normal size, and we observe only subtle defects in the asymmetric divisions of mutant neuroblasts. We conclude that Cnn maintains the proper connection between the centrioles and the PCM; this connection is required for accurate centriole segregation in somatic cells but is not essential for the asymmetric division of neuroblasts.

Metaheuristic Algorithms for Convolution Neural Network

PubMed Central

Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent). PMID:27375738

Metaheuristic Algorithms for Convolution Neural Network.

PubMed

Rere, L M Rasdi; Fanany, Mohamad Ivan; Arymurthy, Aniati Murni

2016-01-01

A typical modern optimization technique is usually either heuristic or metaheuristic. This technique has managed to solve some optimization problems in the research area of science, engineering, and industry. However, implementation strategy of metaheuristic for accuracy improvement on convolution neural networks (CNN), a famous deep learning method, is still rarely investigated. Deep learning relates to a type of machine learning technique, where its aim is to move closer to the goal of artificial intelligence of creating a machine that could successfully perform any intellectual tasks that can be carried out by a human. In this paper, we propose the implementation strategy of three popular metaheuristic approaches, that is, simulated annealing, differential evolution, and harmony search, to optimize CNN. The performances of these metaheuristic methods in optimizing CNN on classifying MNIST and CIFAR dataset were evaluated and compared. Furthermore, the proposed methods are also compared with the original CNN. Although the proposed methods show an increase in the computation time, their accuracy has also been improved (up to 7.14 percent).

A molecular mechanism of mitotic centrosome assembly in Drosophila

PubMed Central

Conduit, Paul T; Richens, Jennifer H; Wainman, Alan; Holder, James; Vicente, Catarina C; Pratt, Metta B; Dix, Carly I; Novak, Zsofia A; Dobbie, Ian M; Schermelleh, Lothar; Raff, Jordan W

2014-01-01

Centrosomes comprise a pair of centrioles surrounded by pericentriolar material (PCM). The PCM expands dramatically as cells enter mitosis, but it is unclear how this occurs. In this study, we show that the centriole protein Asl initiates the recruitment of DSpd-2 and Cnn to mother centrioles; both proteins then assemble into co-dependent scaffold-like structures that spread outwards from the mother centriole and recruit most, if not all, other PCM components. In the absence of either DSpd-2 or Cnn, mitotic PCM assembly is diminished; in the absence of both proteins, it appears to be abolished. We show that DSpd-2 helps incorporate Cnn into the PCM and that Cnn then helps maintain DSpd-2 within the PCM, creating a positive feedback loop that promotes robust PCM expansion around the mother centriole during mitosis. These observations suggest a surprisingly simple mechanism of mitotic PCM assembly in flies. DOI: http://dx.doi.org/10.7554/eLife.03399.001 PMID:25149451

Classification of time-series images using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Hatami, Nima; Gavet, Yann; Debayle, Johan

2018-04-01

Convolutional Neural Networks (CNN) has achieved a great success in image recognition task by automatically learning a hierarchical feature representation from raw data. While the majority of Time-Series Classification (TSC) literature is focused on 1D signals, this paper uses Recurrence Plots (RP) to transform time-series into 2D texture images and then take advantage of the deep CNN classifier. Image representation of time-series introduces different feature types that are not available for 1D signals, and therefore TSC can be treated as texture image recognition task. CNN model also allows learning different levels of representations together with a classifier, jointly and automatically. Therefore, using RP and CNN in a unified framework is expected to boost the recognition rate of TSC. Experimental results on the UCR time-series classification archive demonstrate competitive accuracy of the proposed approach, compared not only to the existing deep architectures, but also to the state-of-the art TSC algorithms.

Vision-based posture recognition using an ensemble classifier and a vote filter

NASA Astrophysics Data System (ADS)

Ji, Peng; Wu, Changcheng; Xu, Xiaonong; Song, Aiguo; Li, Huijun

2016-10-01

Posture recognition is a very important Human-Robot Interaction (HRI) way. To segment effective posture from an image, we propose an improved region grow algorithm which combining with the Single Gauss Color Model. The experiment shows that the improved region grow algorithm can get the complete and accurate posture than traditional Single Gauss Model and region grow algorithm, and it can eliminate the similar region from the background at the same time. In the posture recognition part, and in order to improve the recognition rate, we propose a CNN ensemble classifier, and in order to reduce the misjudgments during a continuous gesture control, a vote filter is proposed and applied to the sequence of recognition results. Comparing with CNN classifier, the CNN ensemble classifier we proposed can yield a 96.27% recognition rate, which is better than that of CNN classifier, and the proposed vote filter can improve the recognition result and reduce the misjudgments during the consecutive gesture switch.

Deep learning approach for classifying, detecting and predicting photometric redshifts of quasars in the Sloan Digital Sky Survey stripe 82

NASA Astrophysics Data System (ADS)

Pasquet-Itam, J.; Pasquet, J.

2018-04-01

We have applied a convolutional neural network (CNN) to classify and detect quasars in the Sloan Digital Sky Survey Stripe 82 and also to predict the photometric redshifts of quasars. The network takes the variability of objects into account by converting light curves into images. The width of the images, noted w, corresponds to the five magnitudes ugriz and the height of the images, noted h, represents the date of the observation. The CNN provides good results since its precision is 0.988 for a recall of 0.90, compared to a precision of 0.985 for the same recall with a random forest classifier. Moreover 175 new quasar candidates are found with the CNN considering a fixed recall of 0.97. The combination of probabilities given by the CNN and the random forest makes good performance even better with a precision of 0.99 for a recall of 0.90. For the redshift predictions, the CNN presents excellent results which are higher than those obtained with a feature extraction step and different classifiers (a K-nearest-neighbors, a support vector machine, a random forest and a Gaussian process classifier). Indeed, the accuracy of the CNN within |Δz| < 0.1 can reach 78.09%, within |Δz| < 0.2 reaches 86.15%, within |Δz| < 0.3 reaches 91.2% and the value of root mean square (rms) is 0.359. The performance of the KNN decreases for the three |Δz| regions, since within the accuracy of |Δz| < 0.1, |Δz| < 0.2, and |Δz| < 0.3 is 73.72%, 82.46%, and 90.09% respectively, and the value of rms amounts to 0.395. So the CNN successfully reduces the dispersion and the catastrophic redshifts of quasars. This new method is very promising for the future of big databases such as the Large Synoptic Survey Telescope. A table of the candidates is only available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/611/A97

Convolutional neural networks and face recognition task

NASA Astrophysics Data System (ADS)

Sochenkova, A.; Sochenkov, I.; Makovetskii, A.; Vokhmintsev, A.; Melnikov, A.

2017-09-01

Computer vision tasks are remaining very important for the last couple of years. One of the most complicated problems in computer vision is face recognition that could be used in security systems to provide safety and to identify person among the others. There is a variety of different approaches to solve this task, but there is still no universal solution that would give adequate results in some cases. Current paper presents following approach. Firstly, we extract an area containing face, then we use Canny edge detector. On the next stage we use convolutional neural networks (CNN) to finally solve face recognition and person identification task.

Learning Deep Representations for Ground to Aerial Geolocalization (Open Access)

DTIC Science & Technology

2015-10-15

proposed approach, Where-CNN, is inspired by deep learning success in face verification and achieves significant improvements over tra- ditional hand...crafted features and existing deep features learned from other large-scale databases. We show the ef- fectiveness of Where-CNN in finding matches

Implementing transmission eigenchannels of disordered media by a binary-control digital micromirror device

NASA Astrophysics Data System (ADS)

Kim, Donggyu; Choi, Wonjun; Kim, Moonseok; Moon, Jungho; Seo, Keumyoung; Ju, Sanghyun; Choi, Wonshik

2014-11-01

We report a method for measuring the transmission matrix of a disordered medium using a binary-control of a digital micromirror device (DMD). With knowledge of the measured transmission matrix, we identified the transmission eigenchannels of the medium. We then used binary control of the DMD to shape the wavefront of incident waves and to experimentally couple light to individual eigenchannels. When the wave was coupled to the eigenchannel with the largest eigenvalue, in particular, we were able to achieve about two times more energy transmission than the mean transmittance of the medium. Our study provides an elaborated use of the DMD as a high-speed wavefront shaping device for controlling the multiple scattering of waves in highly scattering media.

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

DOE PAGES

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.; ...

2017-05-03

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

Three-dimensional fingerprint recognition by using convolution neural network

NASA Astrophysics Data System (ADS)

Tian, Qianyu; Gao, Nan; Zhang, Zonghua

2018-01-01

With the development of science and technology and the improvement of social information, fingerprint recognition technology has become a hot research direction and been widely applied in many actual fields because of its feasibility and reliability. The traditional two-dimensional (2D) fingerprint recognition method relies on matching feature points. This method is not only time-consuming, but also lost three-dimensional (3D) information of fingerprint, with the fingerprint rotation, scaling, damage and other issues, a serious decline in robustness. To solve these problems, 3D fingerprint has been used to recognize human being. Because it is a new research field, there are still lots of challenging problems in 3D fingerprint recognition. This paper presents a new 3D fingerprint recognition method by using a convolution neural network (CNN). By combining 2D fingerprint and fingerprint depth map into CNN, and then through another CNN feature fusion, the characteristics of the fusion complete 3D fingerprint recognition after classification. This method not only can preserve 3D information of fingerprints, but also solves the problem of CNN input. Moreover, the recognition process is simpler than traditional feature point matching algorithm. 3D fingerprint recognition rate by using CNN is compared with other fingerprint recognition algorithms. The experimental results show that the proposed 3D fingerprint recognition method has good recognition rate and robustness.

Visual Cortex Inspired CNN Model for Feature Construction in Text Analysis

PubMed Central

Fu, Hongping; Niu, Zhendong; Zhang, Chunxia; Ma, Jing; Chen, Jie

2016-01-01

Recently, biologically inspired models are gradually proposed to solve the problem in text analysis. Convolutional neural networks (CNN) are hierarchical artificial neural networks, which include a various of multilayer perceptrons. According to biological research, CNN can be improved by bringing in the attention modulation and memory processing of primate visual cortex. In this paper, we employ the above properties of primate visual cortex to improve CNN and propose a biological-mechanism-driven-feature-construction based answer recommendation method (BMFC-ARM), which is used to recommend the best answer for the corresponding given questions in community question answering. BMFC-ARM is an improved CNN with four channels respectively representing questions, answers, asker information and answerer information, and mainly contains two stages: biological mechanism driven feature construction (BMFC) and answer ranking. BMFC imitates the attention modulation property by introducing the asker information and answerer information of given questions and the similarity between them, and imitates the memory processing property through bringing in the user reputation information for answerers. Then the feature vector for answer ranking is constructed by fusing the asker-answerer similarities, answerer's reputation and the corresponding vectors of question, answer, asker, and answerer. Finally, the Softmax is used at the stage of answer ranking to get best answers by the feature vector. The experimental results of answer recommendation on the Stackexchange dataset show that BMFC-ARM exhibits better performance. PMID:27471460

Visual Cortex Inspired CNN Model for Feature Construction in Text Analysis.

PubMed

Fu, Hongping; Niu, Zhendong; Zhang, Chunxia; Ma, Jing; Chen, Jie

2016-01-01

Recently, biologically inspired models are gradually proposed to solve the problem in text analysis. Convolutional neural networks (CNN) are hierarchical artificial neural networks, which include a various of multilayer perceptrons. According to biological research, CNN can be improved by bringing in the attention modulation and memory processing of primate visual cortex. In this paper, we employ the above properties of primate visual cortex to improve CNN and propose a biological-mechanism-driven-feature-construction based answer recommendation method (BMFC-ARM), which is used to recommend the best answer for the corresponding given questions in community question answering. BMFC-ARM is an improved CNN with four channels respectively representing questions, answers, asker information and answerer information, and mainly contains two stages: biological mechanism driven feature construction (BMFC) and answer ranking. BMFC imitates the attention modulation property by introducing the asker information and answerer information of given questions and the similarity between them, and imitates the memory processing property through bringing in the user reputation information for answerers. Then the feature vector for answer ranking is constructed by fusing the asker-answerer similarities, answerer's reputation and the corresponding vectors of question, answer, asker, and answerer. Finally, the Softmax is used at the stage of answer ranking to get best answers by the feature vector. The experimental results of answer recommendation on the Stackexchange dataset show that BMFC-ARM exhibits better performance.

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.

PubMed

Ren, Shaoqing; He, Kaiming; Girshick, Ross; Sun, Jian

2017-06-01

State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet [1] and Fast R-CNN [2] have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features-using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model [3] , our detection system has a frame rate of 5 fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks. Code has been made publicly available.

Deep Learning for Automated Extraction of Primary Sites from Cancer Pathology Reports

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qiu, John; Yoon, Hong-Jun; Fearn, Paul A.

Pathology reports are a primary source of information for cancer registries which process high volumes of free-text reports annually. Information extraction and coding is a manual, labor-intensive process. Here in this study we investigated deep learning and a convolutional neural network (CNN), for extracting ICDO- 3 topographic codes from a corpus of breast and lung cancer pathology reports. We performed two experiments, using a CNN and a more conventional term frequency vector approach, to assess the effects of class prevalence and inter-class transfer learning. The experiments were based on a set of 942 pathology reports with human expert annotations asmore » the gold standard. CNN performance was compared against a more conventional term frequency vector space approach. We observed that the deep learning models consistently outperformed the conventional approaches in the class prevalence experiment, resulting in micro and macro-F score increases of up to 0.132 and 0.226 respectively when class labels were well populated. Specifically, the best performing CNN achieved a micro-F score of 0.722 over 12 ICD-O-3 topography codes. Transfer learning provided a consistent but modest performance boost for the deep learning methods but trends were contingent on CNN method and cancer site. Finally, these encouraging results demonstrate the potential of deep learning for automated abstraction of pathology reports.« less

A hybrid CNN feature model for pulmonary nodule malignancy risk differentiation.

PubMed

Wang, Huafeng; Zhao, Tingting; Li, Lihong Connie; Pan, Haixia; Liu, Wanquan; Gao, Haoqi; Han, Fangfang; Wang, Yuehai; Qi, Yifan; Liang, Zhengrong

2018-01-01

The malignancy risk differentiation of pulmonary nodule is one of the most challenge tasks of computer-aided diagnosis (CADx). Most recently reported CADx methods or schemes based on texture and shape estimation have shown relatively satisfactory on differentiating the risk level of malignancy among the nodules detected in lung cancer screening. However, the existing CADx schemes tend to detect and analyze characteristics of pulmonary nodules from a statistical perspective according to local features only. Enlightened by the currently prevailing learning ability of convolutional neural network (CNN), which simulates human neural network for target recognition and our previously research on texture features, we present a hybrid model that takes into consideration of both global and local features for pulmonary nodule differentiation using the largest public database founded by the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). By comparing three types of CNN models in which two of them were newly proposed by us, we observed that the multi-channel CNN model yielded the best discrimination in capacity of differentiating malignancy risk of the nodules based on the projection of distributions of extracted features. Moreover, CADx scheme using the new multi-channel CNN model outperformed our previously developed CADx scheme using the 3D texture feature analysis method, which increased the computed area under a receiver operating characteristic curve (AUC) from 0.9441 to 0.9702.

Tiled architecture of a CNN-mostly IP system

NASA Astrophysics Data System (ADS)

Spaanenburg, Lambert; Malki, Suleyman

2009-05-01

Multi-core architectures have been popularized with the advent of the IBM CELL. On a finer grain the problems in scheduling multi-cores have already existed in the tiled architectures, such as the EPIC and Da Vinci. It is not easy to evaluate the performance of a schedule on such architecture as historical data are not available. One solution is to compile algorithms for which an optimal schedule is known by analysis. A typical example is an algorithm that is already defined in terms of many collaborating simple nodes, such as a Cellular Neural Network (CNN). A simple node with a local register stack together with a 'rotating wheel' internal communication mechanism has been proposed. Though the basic CNN allows for a tiled implementation of a tiled algorithm on a tiled structure, a practical CNN system will have to disturb this regularity by the additional need for arithmetical and logical operations. Arithmetic operations are needed for instance to accommodate for low-level image processing, while logical operations are needed to fork and merge different data streams without use of the external memory. It is found that the 'rotating wheel' internal communication mechanism still handles such mechanisms without the need for global control. Overall the CNN system provides for a practical network size as implemented on a FPGA, can be easily used as embedded IP and provides a clear benchmark for a multi-core compiler.

Contextual convolutional neural networks for lung nodule classification using Gaussian-weighted average image patches

NASA Astrophysics Data System (ADS)

Lee, Haeil; Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

Lung cancer is the most common cause of cancer-related death. To diagnose lung cancers in early stages, numerous studies and approaches have been developed for cancer screening with computed tomography (CT) imaging. In recent years, convolutional neural networks (CNN) have become one of the most common and reliable techniques in computer aided detection (CADe) and diagnosis (CADx) by achieving state-of-the-art-level performances for various tasks. In this study, we propose a CNN classification system for false positive reduction of initially detected lung nodule candidates. First, image patches of lung nodule candidates are extracted from CT scans to train a CNN classifier. To reflect the volumetric contextual information of lung nodules to 2D image patch, we propose a weighted average image patch (WAIP) generation by averaging multiple slice images of lung nodule candidates. Moreover, to emphasize central slices of lung nodules, slice images are locally weighted according to Gaussian distribution and averaged to generate the 2D WAIP. With these extracted patches, 2D CNN is trained to achieve the classification of WAIPs of lung nodule candidates into positive and negative labels. We used LUNA 2016 public challenge database to validate the performance of our approach for false positive reduction in lung CT nodule classification. Experiments show our approach improves the classification accuracy of lung nodules compared to the baseline 2D CNN with patches from single slice image.

Cephalometric landmark detection in dental x-ray images using convolutional neural networks

NASA Astrophysics Data System (ADS)

Lee, Hansang; Park, Minseok; Kim, Junmo

2017-03-01

In dental X-ray images, an accurate detection of cephalometric landmarks plays an important role in clinical diagnosis, treatment and surgical decisions for dental problems. In this work, we propose an end-to-end deep learning system for cephalometric landmark detection in dental X-ray images, using convolutional neural networks (CNN). For detecting 19 cephalometric landmarks in dental X-ray images, we develop a detection system using CNN-based coordinate-wise regression systems. By viewing x- and y-coordinates of all landmarks as 38 independent variables, multiple CNN-based regression systems are constructed to predict the coordinate variables from input X-ray images. First, each coordinate variable is normalized by the length of either height or width of an image. For each normalized coordinate variable, a CNN-based regression system is trained on training images and corresponding coordinate variable, which is a variable to be regressed. We train 38 regression systems with the same CNN structure on coordinate variables, respectively. Finally, we compute 38 coordinate variables with these trained systems from unseen images and extract 19 landmarks by pairing the regressed coordinates. In experiments, the public database from the Grand Challenges in Dental X-ray Image Analysis in ISBI 2015 was used and the proposed system showed promising performance by successfully locating the cephalometric landmarks within considerable margins from the ground truths.

Context-Aware Local Binary Feature Learning for Face Recognition.

PubMed

Duan, Yueqi; Lu, Jiwen; Feng, Jianjiang; Zhou, Jie

2018-05-01

In this paper, we propose a context-aware local binary feature learning (CA-LBFL) method for face recognition. Unlike existing learning-based local face descriptors such as discriminant face descriptor (DFD) and compact binary face descriptor (CBFD) which learn each feature code individually, our CA-LBFL exploits the contextual information of adjacent bits by constraining the number of shifts from different binary bits, so that more robust information can be exploited for face representation. Given a face image, we first extract pixel difference vectors (PDV) in local patches, and learn a discriminative mapping in an unsupervised manner to project each pixel difference vector into a context-aware binary vector. Then, we perform clustering on the learned binary codes to construct a codebook, and extract a histogram feature for each face image with the learned codebook as the final representation. In order to exploit local information from different scales, we propose a context-aware local binary multi-scale feature learning (CA-LBMFL) method to jointly learn multiple projection matrices for face representation. To make the proposed methods applicable for heterogeneous face recognition, we present a coupled CA-LBFL (C-CA-LBFL) method and a coupled CA-LBMFL (C-CA-LBMFL) method to reduce the modality gap of corresponding heterogeneous faces in the feature level, respectively. Extensive experimental results on four widely used face datasets clearly show that our methods outperform most state-of-the-art face descriptors.

CNN Newsroom Classroom Guides, November 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of November 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: economic stimulus and U.S. steps up the bombing campaign in…

CNN Newsroom Classroom Guides, September 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of September 2001 provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: shark attacks ignite controversy in some Florida communities,…

CNN Newsroom Classroom Guides, August 2000.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of August 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: the GOP opens its 37th national convention in Philadelphia, outraged…

CNN Newsroom Classroom Guides. June 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

CNN Newsroom is a daily 15-minute commercial-free news program specifically produced for classroom use and provided free to participating schools. These daily classroom guides present top stories, headlines, environmental news, and other current events, along with suggested class discussion topics and activities to accompany the broadcasts for one…

CNN Newsroom Classroom Guides, March 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Seattle earthquake and U.S. economy working class communities fear a…

CNN Newsroom Classroom Guides. March 1-31, 1996.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: negative campaign ads, the end of the Sarajevo siege, alternative medicine in…

CNN Newsroom Classroom Guides, April 2000.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of April 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: failure of settlement talks between Microsoft and the U.S. government,…

CNN Newsroom Classroom Guides. May 2-31, 1994.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of May provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) the Palestinian Liberation Organization (PLO) and Palestine, Hawaiian…

CNN Newsroom Guides: April 3-28, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily Cable News Network (CNN) Newsroom broadcasts for the month of April provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guide include: (1) reckless driving, hearing impairment, ancient to modern cities,…

CNN Newsroom Classroom Guides, June 2001.

ERIC Educational Resources Information Center

Turner Learning, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of June 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Indonesian President Wahid faces impeachment (June 1); suicide bombing…

CNN Newsroom Classroom Guides. January 1-31, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of January, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: U.S. House of Representatives prepares for ethics battle, diplomatic immunity,…

CNN Newsroom Classroom Guides, April 2001.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of April 2001 provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: former Yugoslav President Slobodan Milosevic is arrested, a Chinese…

CNN Newsroom Guides. March 1-31, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily Cable News Network (CNN) Newsroom broadcasts for the month of March provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guide include: (1) investment terminology, Republican presidential nominations, the shuttle…

CNN Newsroom Classroom Guides. March 1-31, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: monkeys cloned in Oregon, Iran suffers massive earthquake, tornados affect…

CNN Newsroom Classroom Guides. September 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for September 1-30, 1999, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: Venezuela constitutional crisis, Panama's first female…

CNN Newsroom Classroom Guides, November 2000.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of November 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: independent U.S. oil companies struggle to survive, U.S.…

Perceptions and Use of News Media by College Students.

ERIC Educational Resources Information Center

Henke, Lucy L.

1985-01-01

This study investigated college students' use of and attitudes toward traditional and nontraditional news media, and the role of cable news network (CNN) and its integration into evolving news consumption patterns. Results indicate later college years are associated with heavier consumption. CNN viewers are heavier users of traditional media.…

CNN Newsroom Classroom Guides, August 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of August 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: special series on the teenage brain, and MTV celebrates its 20th…

CNN Newsroom Classroom Guides. December 1-31, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of December, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: Japan hosts the Climate Change Conference, space shuttle is unable to deploy…

CNN Newsroom Classroom Guides. June, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of June, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: France gets a new government and Prime Minister as the Socialist Party defeats the…

CNN Newsroom Classroom Guides. August 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for August 2-31, 1999, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: the drought and heatwave in the northeastern United…

CNN Newsroom Classroom Guides, April 2002.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of April 2002, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Israeli soldiers attack Yasser Arafat's headquarters in Ramallah,…

CNN Newsroom Classroom Guides, January 2001.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of January 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: George W. Bush nominates the last three vacant Cabinet posts,…

CNN Newsroom Classroom Guides, May 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of May 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: President Bush will announce his plans for a missile defense system,…

CNN Newsroom Classroom Guides, November 1-30, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of November, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: the Bosnia peace talks, hot-air balloons, salt…

CNN Newsroom Classroom Guides. October, 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of October, 1998, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: scientists find trace fossil evidence of billion-year old worms, the…

CNN Newsroom Classroom Guides, June 2002.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of June 2002, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Major topics covered include: the Kashmir conflict; the Pakistan and the Kazahkstan Summit;…

CNN Newsroom Classroom Guides, May 1-31, 1996.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for the month of May, provide program rundowns, suggestions for class activities and discussion, student handouts, and lists of related news terms. Topics covered include: United States-Israel anti-terrorism accord, the comeback of baseball…

CNN Newsroom Classroom Guides. September 1998.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of September, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: the reaction of world markets to Russia's Duma rejection of Viktor…

CNN Newsroom Classroom Guides. May 1-31, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of May, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: Chelsea Clinton decides to attend Stanford University, Zaire's president and rebel…

CNN Newsroom Classroom Guides. August 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These Classroom Guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of August, provide program rundowns, suggestions for class activities and discussion, links to pertinent World Wide Web sites, and lists of related news terms. Topics include: meetings over weapons inspections in Iraq could either…

CNN Newsroom Classroom Guides. August 1-31, 1994.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of August provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) Haiti, exploration of Mars, Rwandan refugees, Goodwill Games, Paris…

CNN Newsroom Classroom Guides, October 2000.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of October 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Chinese authorities detain Falun Gong protesters on Tiananmen Square…

CNN Newsroom Classroom Guides, January 2002.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of January 2002, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: tensions escalate between Pakistan and India, (January 3-4); the…

CNN Newsroom Classroom Guides, October 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of October 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Stories include: Taliban update/tribal troubles, U.S. officials report progress in the…

CNN Newsroom Classroom Guides. November, 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for the month of November, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: Iraq refuses to cooperate with United Nations weapons inspectors, expansion of…

CNN Newsroom Classroom Guides. March 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

CNN Newsroom is a daily 15-minute commercial-free news program specifically produced for classroom use and provided free to participating schools. These daily classroom guides present top stories, headlines, environmental news, and other current events, along with suggested class discussion topics and activities to accompany the broadcasts for one…

CNN Newsroom Classroom Guides. February 1-28, 1998.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of February, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: United States lobbies for support for possible air strike against Iraq,…

CNN Newsroom Classroom Guides. March 14-31, 1994.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of March provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) Bophuthatswana, Best Quest, language immersion, Bosnia diaries, Nepal,…

CNN Newsroom Classroom Guides, May 2000.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of May 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: U.S. Government files a proposal to split up Microsoft, terrorism source…

CNN Newsroom Classroom Guides, February 2001.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of February 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Libyan intelligence agent is convicted of the Lockerbie bombing, and…

CNN Newsroom Classroom Guides. May 1999.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of May, provide program rundowns, suggestions for class activities and discussion, links to related World Wide Web sites, and lists of related news terms. Top stories include: Reverend Jesse Jackson secures release of U.S. soldiers…

CNN Newsroom Classroom Guides. September 1-30, 1994.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of August provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) truce in Northern Ireland, school censorship, scientific method, burial…

CNN Newsroom Classroom Guides. February 1-29, 1996.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) broadcasts for the month of February, 1996 provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Each daily guide includes a Black History Month biographical profile. Other topics covered…

CNN Newsroom Classroom Guides. April 1-30, 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

CNN Newsroom is a daily 15-minute commercial-free news program specifically produced for classroom use and provided free to participating schools. These daily Classroom Guides are designed to accompany the broadcast, and contain activities for discussing top stories, headlines, and other current events topics; each guide also includes World Wide…

CNN Newsroom Classroom Guides, June 2000.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of June 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: President Clinton prepares to visit Germany, and federal court of…

CNN Newsroom Classroom Guides, February 2002.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of February 2002, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Afghanistan's interim leader is making a global impression (February…

CNN Newsroom Classroom Guides. October 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of October, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: bedroom community business, freedom of expression and…

CNN Newsroom Classroom Guides. April 1-30, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of April, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Headlines include: Arab League boycott, Zaire peace talks, Russia and Belarus sign agreement,…

CNN Newsroom Classroom Guides, December 1-31, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the first half of the month of December, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered include: President Clinton's visit to Northern Ireland,…

CNN Newsroom Classroom Guides, December 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of December 2001, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: President Bush responds to the recent acts of terrorism in Israel,…

CNN Newsroom Classroom Guides, October 1-31, 1996.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of October, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: the Middle East peace summit in Washington, DC, Israel's Netanyahu and…

CNN Newsroom Classroom Guides. November 1-30, 1996.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of November, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: presidential candidates travel the United States searching for votes, FBI…

CNN Newsroom Classroom Guides. February 1999.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of February, provide program rundowns, suggestions for class activities and discussion, links to related World Wide Web sites, and lists of related news terms. Topics include: Monica Lewinsky scheduled to be deposed for the Senate,…

CNN Newsroom Classroom Guides, December 2000.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of December 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: the United States Supreme Court hears the presidential candidates'…

CNN Newsroom Classroom Guides. December 1-31, 1996.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for December 1-20, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: eighth annual World AIDS Day, protests in Belgrade, Mother Theresa's condition…

DOE Office of Scientific and Technical Information (OSTI.GOV)

Schäfer, Gerhard

The current knowledge in the post-Newtonian (PN) dynamics and motion of non-spinning and spinning compact binaries will be presented based on the Arnowitt-Deser-Misner Hamiltonian approach to general relativity. The presentation will cover the binary dynamics with non-spinning components up to the 4PN order and for spinning binaries up to the next-to-next-to-leading order in the spin-orbit and spin-spin couplings. Radiation reaction will be treated for both non-spinning and spinning binaries. Explicit analytic expressions for the motion will be given, innermost stable circular orbits will be discussed.

Accurate lithography simulation model based on convolutional neural networks

NASA Astrophysics Data System (ADS)

Watanabe, Yuki; Kimura, Taiki; Matsunawa, Tetsuaki; Nojima, Shigeki

2017-07-01

Lithography simulation is an essential technique for today's semiconductor manufacturing process. In order to calculate an entire chip in realistic time, compact resist model is commonly used. The model is established for faster calculation. To have accurate compact resist model, it is necessary to fix a complicated non-linear model function. However, it is difficult to decide an appropriate function manually because there are many options. This paper proposes a new compact resist model using CNN (Convolutional Neural Networks) which is one of deep learning techniques. CNN model makes it possible to determine an appropriate model function and achieve accurate simulation. Experimental results show CNN model can reduce CD prediction errors by 70% compared with the conventional model.

Static facial expression recognition with convolution neural networks

NASA Astrophysics Data System (ADS)

Zhang, Feng; Chen, Zhong; Ouyang, Chao; Zhang, Yifei

2018-03-01

Facial expression recognition is a currently active research topic in the fields of computer vision, pattern recognition and artificial intelligence. In this paper, we have developed a convolutional neural networks (CNN) for classifying human emotions from static facial expression into one of the seven facial emotion categories. We pre-train our CNN model on the combined FER2013 dataset formed by train, validation and test set and fine-tune on the extended Cohn-Kanade database. In order to reduce the overfitting of the models, we utilized different techniques including dropout and batch normalization in addition to data augmentation. According to the experimental result, our CNN model has excellent classification performance and robustness for facial expression recognition.

Formation of Circumbinary Planets in a Dead Zone

NASA Astrophysics Data System (ADS)

Martin, Rebecca G.; Armitage, Philip J.; Alexander, Richard D.

2013-08-01

Circumbinary planets have been observed at orbital radii where binary perturbations may have significant effects on the gas disk structure, on planetesimal velocity dispersion, and on the coupling between turbulence and planetesimals. Here, we note that the impact of all of these effects on planet formation is qualitatively altered if the circumbinary disk structure is layered, with a non-turbulent midplane layer (dead zone) and strongly turbulent surface layers. For close binaries, we find that the dead zone typically extends from a radius close to the inner disk edge up to a radius of around 10-20 AU from the center of mass of the binary. The peak in the surface density occurs within the dead zone, far from the inner disk edge, close to the snow line, and may act as a trap for aerodynamically coupled solids. We suggest that circumbinary planet formation may be easier near this preferential location than for disks around single stars. However, dead zones around wide binaries are less likely, and hence planet formation may be more difficult there.

Near-Earth asteroid satellite spins under spin-orbit coupling

DOE Office of Scientific and Technical Information (OSTI.GOV)

Naidu, Shantanu P.; Margot, Jean-Luc

We develop a fourth-order numerical integrator to simulate the coupled spin and orbital motions of two rigid bodies having arbitrary mass distributions under the influence of their mutual gravitational potential. We simulate the dynamics of components in well-characterized binary and triple near-Earth asteroid systems and use surface of section plots to map the possible spin configurations of the satellites. For asynchronous satellites, the analysis reveals large regions of phase space where the spin state of the satellite is chaotic. For synchronous satellites, we show that libration amplitudes can reach detectable values even for moderately elongated shapes. The presence of chaoticmore » regions in the phase space has important consequences for the evolution of binary asteroids. It may substantially increase spin synchronization timescales, explain the observed fraction of asychronous binaries, delay BYORP-type evolution, and extend the lifetime of binaries. The variations in spin rate due to large librations also affect the analysis and interpretation of light curve and radar observations.« less

CNN Newsroom Classroom Guides. October 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for October 1-29, 1999, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: nuclear accident in Japan (October 1); debate over the nuclear…

CNN Newsroom Classroom Guides, July 2001.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of July 2001 provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Slobodan Milosevic prepares to go before the U.N. war crimes tribunal,…

CNN Newsroom Classroom Guides. January 1-31, 1996.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of January, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: teen obesity, the Yangtze River Dam and its hydroelectric…

CNN Newsroom Classroom Guides. June 1-30, 1994.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of June provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) Congressman Dan Rostenkowski, D-Day, cars and Singapore, Rodney King civil…

CNN Newsroom Classroom Guides. December 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for December 1-17, 1999, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: World AIDS Day, World Trade Organization protests in Seattle,…

CNN Newsroom Classroom Guides. November 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for November 1-30, 1999, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: EgyptAir Flight 990 crash, Oslo summit, India cyclone,…

National Television News in Seven Rural Districts. Report 96-2.

ERIC Educational Resources Information Center

Nasstrom, Roy; Gierok, Anne

The implementation, delivery, and impact on students of news programs delivered to schools by Channel One and CNN-Newsroom were examined in seven rural districts in Wisconsin. Investigation covered three districts using CNN and four districts using Channel One within a three-county area. Involved administrators, teachers, and students responded to…

CNN Newsroom Classroom Guides. January 2000.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for January 3-28, 2000, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: issues of the Millennium, 100 hours of the Millennium, Mideast…

CNN Newsroom Classroom Guides. July 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for July 1-30, 1999, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related new terms. Top stories include: Kosovo after the strikes, and saving the Everglades (July 1-2);…

CNN Newsroom Classroom Guides. April 1-29, 1994.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of April provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) peace in the Middle East, Tom Bradley, and minority superheroes (April 1);…

CNN Newsroom Classroom Guides. August, 1997.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These guides are designed to accompany CNN Newsroom, a daily 15-minute news program produced for classroom use and provided free to participating schools. Top stories include: peace talks stalled due to a suicide bombing in a Jerusalem market; inauguration of Iran's new president; UPS strike; budget agreement signed into law; news on teenage drug…

CNN Newsroom Classroom Guides. September 1-30, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of September, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: the women's conference in China, "No Man Is an…

Human Factors Evaluation of the Hidalgo Equivital EQ-02 Physiological Status Monitoring System

DTIC Science & Technology

2013-10-11

Destruction – Civil Support Team (WMD-CST) responding (11), and ricin letters that were intercepted en route to a member of Congress and the President...positive for ricin at Washington mail facility. CNN U.S., April 17, 2013. (http://www.cnn.com/2013/04/16/us/tainted-letter- intercepted accessed

CNN Newsroom Classroom Guides. February 2000.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for February 1-29, 2000, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: significance of the New Hampshire Primary, victors in the New…

CNN Newsroom Classroom Guides, March 2000.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March, provide program rundowns, suggestions for class activities and discussion, Web links, and a list of related news terms. Top stories include: primary victories in the Bush campaign and preparations by Gore and Bradley for the…

CNN Newsroom Classroom Guides. June 1999.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These guides, designed to accompany the daily Cable News Network (CNN) Newsroom broadcasts for June 1-30, 1999, provide program rundowns, suggestions for class activities and discussion, links to relevant World Wide Web sites, and a list of related news terms. Top stories include: NATO bombings in Belgrade amid peace negotiations, people of South…

CNN Newsroom Classroom Guides. April 1999.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Newtown, PA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of April, provide program rundowns, suggestions for class activities and discussion, links to related World Wide Web sites, and lists of related news terms. Top stories include: NATO includes Belgrade in its targets, three U.S.…

CNN Newsroom Classroom Guides. March 1-31, 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: United Nations (UN) and Iraq interpret their recent deal in different ways,…

CNN Newsroom Classroom Guides. May 1-31, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of May provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guide include: (1) security systems and security at the Olympics, drawing to scale, civil war in…

TV 101: Good Broadcast Journalism for the Classroom?

ERIC Educational Resources Information Center

Haney, James M.

A study was conducted to assess the arguments that have been made in favor of and opposed to the electronic curricular supplements, Channel One and CNN Newsroom. Four types of information were analyzed: (1) corporate news releases and research results were reviewed; (2) Channel One and CNN Newsroom programs were studied for format, style, and…

CNN Newsroom Classroom Guides, July 2002.

ERIC Educational Resources Information Center

Turner Learning, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of July 2002, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Lead stories include: authorities arrest a man accused of starting the Rodeo fire in Arizona,…

CNN Newsroom Classroom Guides. October 1-31, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of October, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: immigrants illegally in the United States try to gain legal status before being…

CNN Newsroom Classroom Guides, March 2002.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of March 2002, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Lead stories include: the U.S. expands the War on Terrorism into the Republic of Georgia and…

CNN Newsroom Classroom Guides. July, 1997.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

CNN Newsroom is a daily 15-minute commercial-free news program specifically produced for classroom use and provided free of charge to participating schools. This guide is designed to accompany the program for July 1997. Top stories include the following: Britain's hand over of Hong Kong to the People's Republic of China; regulating business on the…

CNN Newsroom Classroom Guides. February 1-28, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of February, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: elections in Pakistan for a new prime minister, U.S. President Clinton unveils…

CNN Newsroom Classroom Guides, November 1-30, 1997.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of November 1997, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics include: U.S. leaders call for the use of force as Iraq refuses to permit access…

CNN Newsroom Classroom Guides, September 2000.

ERIC Educational Resources Information Center

Turner Educational Services, Inc., Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of September 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: FBI arrests a suspect in the Emulex hoax case (September 1); U.S.…

CNN Newsroom Classroom Guides. June 1-30, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of June provide program rundowns, suggestions for class activities and discussions, student handouts, and a list of related news terms. Topics covered by the guides include: (1) amusement park physics, media resources and literacy, and the war in Bosnia…

CNN Newsroom Classroom Guides, July 2000.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides, designed to accompany the daily CNN (Cable News Network) Newsroom broadcasts for the month of July 2000, provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Top stories include: Mexican voters go to polls in a landmark election (July 3); Mexico's…

CNN Newsroom Classroom Guides. July 1-31, 1995.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of July provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) British Prime Minister John Major, trade and Tijuana, sports physics, and…

CNN Newsroom Classroom Guides. July 1-29, 1994.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

These classroom guides for the daily CNN (Cable News Network) Newsroom broadcasts for the month of July provide program rundowns, suggestions for class activities and discussion, student handouts, and a list of related news terms. Topics covered by the guides include: (1) Yasser Arafat and online projects (July 1); (2) Yasser Arafat, athletes as…

Knowledge-guided golf course detection using a convolutional neural network fine-tuned on temporally augmented data

NASA Astrophysics Data System (ADS)

Chen, Jingbo; Wang, Chengyi; Yue, Anzhi; Chen, Jiansheng; He, Dongxu; Zhang, Xiuyan

2017-10-01

The tremendous success of deep learning models such as convolutional neural networks (CNNs) in computer vision provides a method for similar problems in the field of remote sensing. Although research on repurposing pretrained CNN to remote sensing tasks is emerging, the scarcity of labeled samples and the complexity of remote sensing imagery still pose challenges. We developed a knowledge-guided golf course detection approach using a CNN fine-tuned on temporally augmented data. The proposed approach is a combination of knowledge-driven region proposal, data-driven detection based on CNN, and knowledge-driven postprocessing. To confront data complexity, knowledge-derived cooccurrence, composition, and area-based rules are applied sequentially to propose candidate golf regions. To confront sample scarcity, we employed data augmentation in the temporal domain, which extracts samples from multitemporal images. The augmented samples were then used to fine-tune a pretrained CNN for golf detection. Finally, commission error was further suppressed by postprocessing. Experiments conducted on GF-1 imagery prove the effectiveness of the proposed approach.

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF

PubMed Central

Li, Zeju; Shi, Zhifeng; Guo, Yi; Chen, Liang; Mao, Ying

2017-01-01

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas. PMID:29065666

Low-Grade Glioma Segmentation Based on CNN with Fully Connected CRF.

PubMed

Li, Zeju; Wang, Yuanyuan; Yu, Jinhua; Shi, Zhifeng; Guo, Yi; Chen, Liang; Mao, Ying

2017-01-01

This work proposed a novel automatic three-dimensional (3D) magnetic resonance imaging (MRI) segmentation method which would be widely used in the clinical diagnosis of the most common and aggressive brain tumor, namely, glioma. The method combined a multipathway convolutional neural network (CNN) and fully connected conditional random field (CRF). Firstly, 3D information was introduced into the CNN which makes more accurate recognition of glioma with low contrast. Then, fully connected CRF was added as a postprocessing step which purposed more delicate delineation of glioma boundary. The method was applied to T2flair MRI images of 160 low-grade glioma patients. With 59 cases of data training and manual segmentation as the ground truth, the Dice similarity coefficient (DSC) of our method was 0.85 for the test set of 101 MRI images. The results of our method were better than those of another state-of-the-art CNN method, which gained the DSC of 0.76 for the same dataset. It proved that our method could produce better results for the segmentation of low-grade gliomas.

NetVLAD: CNN Architecture for Weakly Supervised Place Recognition.

PubMed

Arandjelovic, Relja; Gronat, Petr; Torii, Akihiko; Pajdla, Tomas; Sivic, Josef

2018-06-01

We tackle the problem of large scale visual place recognition, where the task is to quickly and accurately recognize the location of a given query photograph. We present the following four principal contributions. First, we develop a convolutional neural network (CNN) architecture that is trainable in an end-to-end manner directly for the place recognition task. The main component of this architecture, NetVLAD, is a new generalized VLAD layer, inspired by the "Vector of Locally Aggregated Descriptors" image representation commonly used in image retrieval. The layer is readily pluggable into any CNN architecture and amenable to training via backpropagation. Second, we create a new weakly supervised ranking loss, which enables end-to-end learning of the architecture's parameters from images depicting the same places over time downloaded from Google Street View Time Machine. Third, we develop an efficient training procedure which can be applied on very large-scale weakly labelled tasks. Finally, we show that the proposed architecture and training procedure significantly outperform non-learnt image representations and off-the-shelf CNN descriptors on challenging place recognition and image retrieval benchmarks.

Research on improving image recognition robustness by combining multiple features with associative memory

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Wang, Zhe

2018-05-01

Convolutional neural networks (CNN) achieve great success in computer vision, it can learn hierarchical representation from raw pixels and has outstanding performance in various image recognition tasks [1]. However, CNN is easy to be fraudulent in terms of it is possible to produce images totally unrecognizable to human eyes that CNNs believe with near certainty are familiar objects. [2]. In this paper, an associative memory model based on multiple features is proposed. Within this model, feature extraction and classification are carried out by CNN, T-SNE and exponential bidirectional associative memory neural network (EBAM). The geometric features extracted from CNN and the digital features extracted from T-SNE are associated by EBAM. Thus we ensure the recognition of robustness by a comprehensive assessment of the two features. In our model, we can get only 8% error rate with fraudulent data. In systems that require a high safety factor or some key areas, strong robustness is extremely important, if we can ensure the image recognition robustness, network security will be greatly improved and the social production efficiency will be extremely enhanced.

Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition

NASA Astrophysics Data System (ADS)

Yan, Yue

2018-03-01

A synthetic aperture radar (SAR) automatic target recognition (ATR) method based on the convolutional neural networks (CNN) trained by augmented training samples is proposed. To enhance the robustness of CNN to various extended operating conditions (EOCs), the original training images are used to generate the noisy samples at different signal-to-noise ratios (SNRs), multiresolution representations, and partially occluded images. Then, the generated images together with the original ones are used to train a designed CNN for target recognition. The augmented training samples can contrapuntally improve the robustness of the trained CNN to the covered EOCs, i.e., the noise corruption, resolution variance, and partial occlusion. Moreover, the significantly larger training set effectively enhances the representation capability for other conditions, e.g., the standard operating condition (SOC), as well as the stability of the network. Therefore, better performance can be achieved by the proposed method for SAR ATR. For experimental evaluation, extensive experiments are conducted on the Moving and Stationary Target Acquisition and Recognition dataset under SOC and several typical EOCs.

Multi-focus image fusion with the all convolutional neural network

NASA Astrophysics Data System (ADS)

Du, Chao-ben; Gao, She-sheng

2018-01-01

A decision map contains complete and clear information about the image to be fused, which is crucial to various image fusion issues, especially multi-focus image fusion. However, in order to get a satisfactory image fusion effect, getting a decision map is very necessary and usually difficult to finish. In this letter, we address this problem with convolutional neural network (CNN), aiming to get a state-of-the-art decision map. The main idea is that the max-pooling of CNN is replaced by a convolution layer, the residuals are propagated backwards by gradient descent, and the training parameters of the individual layers of the CNN are updated layer by layer. Based on this, we propose a new all CNN (ACNN)-based multi-focus image fusion method in spatial domain. We demonstrate that the decision map obtained from the ACNN is reliable and can lead to high-quality fusion results. Experimental results clearly validate that the proposed algorithm can obtain state-of-the-art fusion performance in terms of both qualitative and quantitative evaluations.

Classification of C2C12 cells at differentiation by convolutional neural network of deep learning using phase contrast images.

PubMed

Niioka, Hirohiko; Asatani, Satoshi; Yoshimura, Aina; Ohigashi, Hironori; Tagawa, Seiichi; Miyake, Jun

2018-01-01

In the field of regenerative medicine, tremendous numbers of cells are necessary for tissue/organ regeneration. Today automatic cell-culturing system has been developed. The next step is constructing a non-invasive method to monitor the conditions of cells automatically. As an image analysis method, convolutional neural network (CNN), one of the deep learning method, is approaching human recognition level. We constructed and applied the CNN algorithm for automatic cellular differentiation recognition of myogenic C2C12 cell line. Phase-contrast images of cultured C2C12 are prepared as input dataset. In differentiation process from myoblasts to myotubes, cellular morphology changes from round shape to elongated tubular shape due to fusion of the cells. CNN abstract the features of the shape of the cells and classify the cells depending on the culturing days from when differentiation is induced. Changes in cellular shape depending on the number of days of culture (Day 0, Day 3, Day 6) are classified with 91.3% accuracy. Image analysis with CNN has a potential to realize regenerative medicine industry.

A transient radio jet in an erupting dwarf nova.

PubMed

Körding, Elmar; Rupen, Michael; Knigge, Christian; Fender, Rob; Dhawan, Vivek; Templeton, Matthew; Muxlow, Tom

2008-06-06

Astrophysical jets seem to occur in nearly all types of accreting objects, from supermassive black holes to young stellar objects. On the basis of x-ray binaries, a unified scenario describing the disc/jet coupling has evolved and been extended to many accreting objects. The only major exceptions are thought to be cataclysmic variables: Dwarf novae, weakly accreting white dwarfs, show similar outburst behavior to x-ray binaries, but no jet has yet been detected. Here we present radio observations of a dwarf nova in outburst showing variable flat-spectrum radio emission that is best explained as synchrotron emission originating in a transient jet. Both the inferred jet power and the relation to the outburst cycle are analogous to those seen in x-ray binaries, suggesting that the disc/jet coupling mechanism is ubiquitous.

Catalytic transfer hydrogenation with terdentate CNN ruthenium complexes: the influence of the base.

PubMed

Baratta, Walter; Siega, Katia; Rigo, Pierluigi

2007-01-01

The catalytic activity of the terdentate complex [RuCl(CNN)(dppb)] (A) [dppb=Ph(2)P(CH(2))(4)PPh(2); HCNN=6-(4'-methylphenyl)-2-pyridylmethylamine] in the transfer hydrogenation of acetophenone (S) with 2-propanol has been found to be dependent on the base concentration. The limit rate has been observed when NaOiPr is used in high excess (A/base molar ratio > 10). The amino-isopropoxide species [Ru(OiPr)(CNN)(dppb)] (B), which forms by reaction of A with sodium isopropoxide via displacement of the chloride, is catalytically active. The rate of conversion of acetophenone obeys second-order kinetics v=k[S][B] with the rate constants in the range 218+/-8 (40 degrees C) to 3000+/-70 M(-1) s(-1) (80 degrees C). The activation parameters, evaluated from the Eyring equation are DeltaH(++)=14.0+/-0.2 kcal mol(-1) and DeltaS(++)=-3.2 +/-0.5 eu. In a pre-equilibrium reaction with 2-propanol complex B gives the cationic species [Ru(CNN)(dppb)(HOiPr)](+)[OiPr](-) (C) with K approximately 2x10(-5) M. The hydride species [RuH(CNN)(dppb)] (H), which forms from B via beta-hydrogen elimination process, catalyzes the reduction of S and, importantly, its activity increases by addition of base. The catalytic behavior of the hydride H has been compared to that of the system A/NaOiPr (1:1 molar ratio) and indicates that the two systems are equivalent.

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

PubMed

Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

2018-04-01

The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

Deletion of calponin 2 in macrophages attenuates the severity of inflammatory arthritis in mice.

PubMed

Huang, Qi-Quan; Hossain, M Moazzem; Sun, Wen; Xing, Lianping; Pope, Richard M; Jin, J-P

2016-10-01

Calponin is an actin cytoskeleton-associated protein that regulates motility-based cellular functions. Three isoforms of calponin are present in vertebrates, among which calponin 2 encoded by the Cnn2 gene is expressed in multiple types of cells, including blood cells from the myeloid lineage. Our previous studies demonstrated that macrophages from Cnn2 knockout (KO) mice exhibit increased migration and phagocytosis. Intrigued by an observation that monocytes and macrophages from patients with rheumatoid arthritis had increased calponin 2, we investigated anti-glucose-6-phosphate isomerase serum-induced arthritis in Cnn2-KO mice for the effect of calponin 2 deletion on the pathogenesis and pathology of inflammatory arthritis. The results showed that the development of arthritis was attenuated in systemic Cnn2-KO mice with significantly reduced inflammation and bone erosion than that in age- and stain background-matched C57BL/6 wild-type mice. In vitro differentiation of calponin 2-null mouse bone marrow cells produced fewer osteoclasts with decreased bone resorption. The attenuation of inflammatory arthritis was confirmed in conditional myeloid cell-specific Cnn2-KO mice. The increased phagocytotic activity of calponin 2-null macrophages may facilitate the clearance of autoimmune complexes and the resolution of inflammation, whereas the decreased substrate adhesion may reduce osteoclastogenesis and bone resorption. The data suggest that calponin 2 regulation of cytoskeleton function plays a novel role in the pathogenesis of inflammatory arthritis, implicating a potentially therapeutic target. Copyright © 2016 the American Physiological Society.

Overexpressed Calponin3 by Subsonic Vibration Induces Neural Differentiation of hUC-MSCs by Regulating the Ionotropic Glutamate Receptor.

PubMed

Kim, Hyun-Jung; Kim, Jin-Hee; Song, Yeo-Ju; Seo, Young-Kwon; Park, Jung-Keug; Kim, Chan-Wha

2015-09-01

In this study, we used proteomics to investigate the effects of sonic vibration (SV) on mesenchymal stem cells derived from human umbilical cords (hUC-MSCs) during neural differentiation to understand how SV enhances neural differentiation of hUC-MSCs. We investigated the levels of gene and protein related to neural differentiation after 3 or 5 days in a group treated with 40-Hz SV. In addition, protein expression patterns were compared between the control and the 40-Hz SV-treated hUC-MSC groups via a proteomic approach. Among these proteins, calponin3 (CNN3) was confirmed to have 299 % higher expression in the 40-Hz SV stimulated hUC-MSCs group than that in the control by Western blotting. Notably, overexpression of CNN3-GFP in Chinese hamster ovary (CHO)-K1 cells had positive effects on the stability and reorganization of F-actin compared with that in GFP-transfected cells. Moreover, CNN3 changed the morphology of the cells by making a neurite-like form. After being subjected to SV, messenger RNA (mRNA) levels of glutamate receptors such as PSD95, GluR1, and NR1 as well as intracellular calcium levels were upregulated. These results suggest that the activity of glutamate receptors increased because of CNN3 characteristics. Taken together, these results demonstrate that overexpressed CNN3 during SV increases expression of glutamate receptors and promotes functional neural differentiation of hUC-MSCs.

On the Lack of Circumbinary Planets Orbiting Isolated Binary Stars

NASA Astrophysics Data System (ADS)

Fleming, David P.; Barnes, Rory; Graham, David E.; Luger, Rodrigo; Quinn, Thomas R.

2018-05-01

We outline a mechanism that explains the observed lack of circumbinary planets (CBPs) via coupled stellar–tidal evolution of isolated binary stars. Tidal forces between low-mass, short-period binary stars on the pre-main sequence slow the stellar rotations transferring rotational angular momentum to the orbit as the stars approach the tidally locked state. This transfer increases the binary orbital period, expanding the region of dynamical instability around the binary, and destabilizing CBPs that tend to preferentially orbit just beyond the initial dynamical stability limit. After the stars tidally lock, we find that angular momentum loss due to magnetic braking can significantly shrink the binary orbit, and hence the region of dynamical stability, over time, impacting where surviving CBPs are observed relative to the boundary. We perform simulations over a wide range of parameter space and find that the expansion of the instability region occurs for most plausible initial conditions and that, in some cases, the stability semimajor axis doubles from its initial value. We examine the dynamical and observable consequences of a CBP falling within the dynamical instability limit by running N-body simulations of circumbinary planetary systems and find that, typically, at least one planet is ejected from the system. We apply our theory to the shortest-period Kepler binary that possesses a CBP, Kepler-47, and find that its existence is consistent with our model. Under conservative assumptions, we find that coupled stellar–tidal evolution of pre-main sequence binary stars removes at least one close-in CBP in 87% of multi-planet circumbinary systems.

Atypical epigenetic mark in an atypical location: cytosine methylation at asymmetric (CNN) sites within the body of a non-repetitive tomato gene.

PubMed

González, Rodrigo M; Ricardi, Martiniano M; Iusem, Norberto D

2011-05-20

Eukaryotic DNA methylation is one of the most studied epigenetic processes, as it results in a direct and heritable covalent modification triggered by external stimuli. In contrast to mammals, plant DNA methylation, which is stimulated by external cues exemplified by various abiotic types of stress, is often found not only at CG sites but also at CNG (N denoting A, C or T) and CNN (asymmetric) sites. A genome-wide analysis of DNA methylation in Arabidopsis has shown that CNN methylation is preferentially concentrated in transposon genes and non-coding repetitive elements. We are particularly interested in investigating the epigenetics of plant species with larger and more complex genomes than Arabidopsis, particularly with regards to the associated alterations elicited by abiotic stress. We describe the existence of CNN-methylated epialleles that span Asr1, a non-transposon, protein-coding gene from tomato plants that lacks an orthologous counterpart in Arabidopsis. In addition, to test the hypothesis of a link between epigenetics modifications and the adaptation of crop plants to abiotic stress, we exhaustively explored the cytosine methylation status in leaf Asr1 DNA, a model gene in our system, resulting from water-deficit stress conditions imposed on tomato plants. We found that drought conditions brought about removal of methyl marks at approximately 75 of the 110 asymmetric (CNN) sites analysed, concomitantly with a decrease of the repressive H3K27me3 epigenetic mark and a large induction of expression at the RNA level. When pinpointing those sites, we observed that demethylation occurred mostly in the intronic region. These results demonstrate a novel genomic distribution of CNN methylation, namely in the transcribed region of a protein-coding, non-repetitive gene, and the changes in those epigenetic marks that are caused by water stress. These findings may represent a general mechanism for the acquisition of new epialleles in somatic cells, which are pivotal for regulating gene expression in plants.

Recognition of prokaryotic and eukaryotic promoters using convolutional deep learning neural networks.

PubMed

Umarov, Ramzan Kh; Solovyev, Victor V

2017-01-01

Accurate computational identification of promoters remains a challenge as these key DNA regulatory regions have variable structures composed of functional motifs that provide gene-specific initiation of transcription. In this paper we utilize Convolutional Neural Networks (CNN) to analyze sequence characteristics of prokaryotic and eukaryotic promoters and build their predictive models. We trained a similar CNN architecture on promoters of five distant organisms: human, mouse, plant (Arabidopsis), and two bacteria (Escherichia coli and Bacillus subtilis). We found that CNN trained on sigma70 subclass of Escherichia coli promoter gives an excellent classification of promoters and non-promoter sequences (Sn = 0.90, Sp = 0.96, CC = 0.84). The Bacillus subtilis promoters identification CNN model achieves Sn = 0.91, Sp = 0.95, and CC = 0.86. For human, mouse and Arabidopsis promoters we employed CNNs for identification of two well-known promoter classes (TATA and non-TATA promoters). CNN models nicely recognize these complex functional regions. For human promoters Sn/Sp/CC accuracy of prediction reached 0.95/0.98/0,90 on TATA and 0.90/0.98/0.89 for non-TATA promoter sequences, respectively. For Arabidopsis we observed Sn/Sp/CC 0.95/0.97/0.91 (TATA) and 0.94/0.94/0.86 (non-TATA) promoters. Thus, the developed CNN models, implemented in CNNProm program, demonstrated the ability of deep learning approach to grasp complex promoter sequence characteristics and achieve significantly higher accuracy compared to the previously developed promoter prediction programs. We also propose random substitution procedure to discover positionally conserved promoter functional elements. As the suggested approach does not require knowledge of any specific promoter features, it can be easily extended to identify promoters and other complex functional regions in sequences of many other and especially newly sequenced genomes. The CNNProm program is available to run at web server http://www.softberry.com.

Taxonomy of multi-focal nematode image stacks by a CNN based image fusion approach.

PubMed

Liu, Min; Wang, Xueping; Zhang, Hongzhong

2018-03-01

In the biomedical field, digital multi-focal images are very important for documentation and communication of specimen data, because the morphological information for a transparent specimen can be captured in form of a stack of high-quality images. Given biomedical image stacks containing multi-focal images, how to efficiently extract effective features from all layers to classify the image stacks is still an open question. We present to use a deep convolutional neural network (CNN) image fusion based multilinear approach for the taxonomy of multi-focal image stacks. A deep CNN based image fusion technique is used to combine relevant information of multi-focal images within a given image stack into a single image, which is more informative and complete than any single image in the given stack. Besides, multi-focal images within a stack are fused along 3 orthogonal directions, and multiple features extracted from the fused images along different directions are combined by canonical correlation analysis (CCA). Because multi-focal image stacks represent the effect of different factors - texture, shape, different instances within the same class and different classes of objects, we embed the deep CNN based image fusion method within a multilinear framework to propose an image fusion based multilinear classifier. The experimental results on nematode multi-focal image stacks demonstrated that the deep CNN image fusion based multilinear classifier can reach a higher classification rate (95.7%) than that by the previous multilinear based approach (88.7%), even we only use the texture feature instead of the combination of texture and shape features as in the previous work. The proposed deep CNN image fusion based multilinear approach shows great potential in building an automated nematode taxonomy system for nematologists. It is effective to classify multi-focal image stacks. Copyright © 2018 Elsevier B.V. All rights reserved.

Bladder cancer treatment response assessment using deep learning in CT with transfer learning

NASA Astrophysics Data System (ADS)

Cha, Kenny H.; Hadjiiski, Lubomir M.; Chan, Heang-Ping; Samala, Ravi K.; Cohan, Richard H.; Caoili, Elaine M.; Paramagul, Chintana; Alva, Ajjai; Weizer, Alon Z.

2017-03-01

We are developing a CAD system for bladder cancer treatment response assessment in CT. We compared the performance of the deep-learning convolution neural network (DL-CNN) using different network sizes, and with and without transfer learning using natural scene images or regions of interest (ROIs) inside and outside the bladder. The DL-CNN was trained to identify responders (T0 disease) and non-responders to chemotherapy. ROIs were extracted from segmented lesions in pre- and post-treatment scans of a patient and paired to generate hybrid pre-post-treatment paired ROIs. The 87 lesions from 82 patients generated 104 temporal lesion pairs and 6,700 pre-post-treatment paired ROIs. Two-fold cross-validation and receiver operating characteristic analysis were performed and the area under the curve (AUC) was calculated for the DL-CNN estimates. The AUCs for prediction of T0 disease after treatment were 0.77+/-0.08 and 0.75+/-0.08, respectively, for the two partitions using DL-CNN without transfer learning and a small network, and were 0.74+/-0.07 and 0.74+/-0.08 with a large network. The AUCs were 0.73+/-0.08 and 0.62+/-0.08 with transfer learning using a small network pre-trained with bladder ROIs. The AUC values were 0.77+/-0.08 and 0.73+/-0.07 using the large network pre-trained with the same bladder ROIs. With transfer learning using the large network pretrained with the Canadian Institute for Advanced Research (CIFAR-10) data set, the AUCs were 0.72+/-0.06 and 0.64+/-0.09, respectively, for the two partitions. None of the differences in the methods reached statistical significance. Our study demonstrated the feasibility of using DL-CNN for the estimation of treatment response in CT. Transfer learning did not improve the treatment response estimation. The DL-CNN performed better when transfer learning with bladder images was used instead of natural scene images.

Axillary Lymph Node Evaluation Utilizing Convolutional Neural Networks Using MRI Dataset.

PubMed

Ha, Richard; Chang, Peter; Karcich, Jenika; Mutasa, Simukayi; Fardanesh, Reza; Wynn, Ralph T; Liu, Michael Z; Jambawalikar, Sachin

2018-04-25

The aim of this study is to evaluate the role of convolutional neural network (CNN) in predicting axillary lymph node metastasis, using a breast MRI dataset. An institutional review board (IRB)-approved retrospective review of our database from 1/2013 to 6/2016 identified 275 axillary lymph nodes for this study. Biopsy-proven 133 metastatic axillary lymph nodes and 142 negative control lymph nodes were identified based on benign biopsies (100) and from healthy MRI screening patients (42) with at least 3 years of negative follow-up. For each breast MRI, axillary lymph node was identified on first T1 post contrast dynamic images and underwent 3D segmentation using an open source software platform 3D Slicer. A 32 × 32 patch was then extracted from the center slice of the segmented tumor data. A CNN was designed for lymph node prediction based on each of these cropped images. The CNN consisted of seven convolutional layers and max-pooling layers with 50% dropout applied in the linear layer. In addition, data augmentation and L2 regularization were performed to limit overfitting. Training was implemented using the Adam optimizer, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments. Code for this study was written in Python using the TensorFlow module (1.0.0). Experiments and CNN training were done on a Linux workstation with NVIDIA GTX 1070 Pascal GPU. Two class axillary lymph node metastasis prediction models were evaluated. For each lymph node, a final softmax score threshold of 0.5 was used for classification. Based on this, CNN achieved a mean five-fold cross-validation accuracy of 84.3%. It is feasible for current deep CNN architectures to be trained to predict likelihood of axillary lymph node metastasis. Larger dataset will likely improve our prediction model and can potentially be a non-invasive alternative to core needle biopsy and even sentinel lymph node evaluation.

Automated Detection of Fronts using a Deep Learning Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Biard, J. C.; Kunkel, K.; Racah, E.

2017-12-01

A deeper understanding of climate model simulations and the future effects of global warming on extreme weather can be attained through direct analyses of the phenomena that produce weather. Such analyses require these phenomena to be identified in automatic, unbiased, and comprehensive ways. Atmospheric fronts are centrally important weather phenomena because of the variety of significant weather events, such as thunderstorms, directly associated with them. In current operational meteorology, fronts are identified and drawn visually based on the approximate spatial coincidence of a number of quasi-linear localized features - a trough (relative minimum) in air pressure in combination with gradients in air temperature and/or humidity and a shift in wind, and are categorized as cold, warm, stationary, or occluded, with each type exhibiting somewhat different characteristics. Fronts are extended in space with one dimension much larger than the other (often represented by complex curved lines), which poses a significant challenge for automated approaches. We addressed this challenge by using a Deep Learning Convolutional Neural Network (CNN) to automatically identify and classify fronts. The CNN was trained using a "truth" dataset of front locations identified by National Weather Service meteorologists as part of operational 3-hourly surface analyses. The input to the CNN is a set of 5 gridded fields of surface atmospheric variables, including 2m temperature, 2m specific humidity, surface pressure, and the two components of the 10m horizontal wind velocity vector at 3-hr resolution. The output is a set of feature maps containing the per - grid cell probabilities for the presence of the 4 front types. The CNN was trained on a subset of the data and then used to produce front probabilities for each 3-hr time snapshot over a 14-year period covering the continental United States and some adjacent areas. The total frequencies of fronts derived from the CNN outputs matches very well with the truth dataset. There is a slight underestimate in total numbers in the CNN results but the spatial pattern is a close match. The categorization of front types by CNN is best for cold and occluded and worst for warm. These initial results from our ongoing development highlight the great promise of this technology.

Cascaded ensemble of convolutional neural networks and handcrafted features for mitosis detection

NASA Astrophysics Data System (ADS)

Wang, Haibo; Cruz-Roa, Angel; Basavanhally, Ajay; Gilmore, Hannah; Shih, Natalie; Feldman, Mike; Tomaszewski, John; Gonzalez, Fabio; Madabhushi, Anant

2014-03-01

Breast cancer (BCa) grading plays an important role in predicting disease aggressiveness and patient outcome. A key component of BCa grade is mitotic count, which involves quantifying the number of cells in the process of dividing (i.e. undergoing mitosis) at a specific point in time. Currently mitosis counting is done manually by a pathologist looking at multiple high power fields on a glass slide under a microscope, an extremely laborious and time consuming process. The development of computerized systems for automated detection of mitotic nuclei, while highly desirable, is confounded by the highly variable shape and appearance of mitoses. Existing methods use either handcrafted features that capture certain morphological, statistical or textural attributes of mitoses or features learned with convolutional neural networks (CNN). While handcrafted features are inspired by the domain and the particular application, the data-driven CNN models tend to be domain agnostic and attempt to learn additional feature bases that cannot be represented through any of the handcrafted features. On the other hand, CNN is computationally more complex and needs a large number of labeled training instances. Since handcrafted features attempt to model domain pertinent attributes and CNN approaches are largely unsupervised feature generation methods, there is an appeal to attempting to combine these two distinct classes of feature generation strategies to create an integrated set of attributes that can potentially outperform either class of feature extraction strategies individually. In this paper, we present a cascaded approach for mitosis detection that intelligently combines a CNN model and handcrafted features (morphology, color and texture features). By employing a light CNN model, the proposed approach is far less demanding computationally, and the cascaded strategy of combining handcrafted features and CNN-derived features enables the possibility of maximizing performance by leveraging the disconnected feature sets. Evaluation on the public ICPR12 mitosis dataset that has 226 mitoses annotated on 35 High Power Fields (HPF, x400 magnification) by several pathologists and 15 testing HPFs yielded an F-measure of 0.7345. Apart from this being the second best performance ever recorded for this MITOS dataset, our approach is faster and requires fewer computing resources compared to extant methods, making this feasible for clinical use.

A CNN based Hybrid approach towards automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal V.; Katiyar, Sunil K.

2013-06-01

Image registration is a key component of various image processing operations which involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however inability to properly model object shape as well as contextual information had limited the attainable accuracy. In this paper, we propose a framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as Vector Machines, Cellular Neural Network (CNN), SIFT, coreset, and Cellular Automata. CNN has found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using corset optimization The salient features of this work are cellular neural network approach based SIFT feature point optimisation, adaptive resampling and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. System has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. Methodology also illustrated to be effective in providing intelligent interpretation and adaptive resampling. Rejestracja obrazu jest kluczowym składnikiem różnych operacji jego przetwarzania. W ostatnich latach do automatycznej rejestracji obrazu wykorzystuje się metody sztucznej inteligencji, których największą wadą, obniżającą dokładność uzyskanych wyników jest brak możliwości dobrego wymodelowania kształtu i informacji kontekstowych. W niniejszej pracy zaproponowano zasady dokładnego modelowania kształtu oraz adaptacyjnego resamplingu z wykorzystaniem zaawansowanych technik, takich jak Vector Machines (VM), komórkowa sieć neuronowa (CNN), przesiewanie (SIFT), Coreset i automaty komórkowe. Stwierdzono, że za pomocą CNN można skutecznie poprawiać dopasowanie obiektów obrazowych oraz resampling kolejnych kroków rejestracji, zaś zastosowanie optymalizacji metodą Coreset znacznie redukuje złożoność podejścia. Zasadniczym przedmiotem pracy są: optymalizacja punktów metodą SIFT oparta na podejściu CNN, adaptacyjny resampling oraz inteligentne modelowanie obiektów. Opracowana metoda została porównana ze współcześnie stosowanymi metodami wykorzystującymi różne miary statystyczne. Badania nad różnymi obrazami satelitarnymi wykazały, że stosując opracowane podejście osiągnięto bardzo dobre wyniki. System stosując podejście CNN-prolog dynamicznie wykorzystuje informacje spektralne i przestrzenne dla reprezentacji wiedzy kontekstowej. Metoda okazała się również skuteczna w dostarczaniu inteligentnych interpretacji i w adaptacyjnym resamplingu.

Eighth Grade Reading Improvement with CNN Newsroom and "USA Today."

ERIC Educational Resources Information Center

Zamorano, Wanda Jean

A practicum was designed to improve the reading growth and achievement of 60 eighth-grade students who were one or more years behind grade level by utilizing CNN Newsroom and the "USA Today" newspaper as an integral part of the reading program. Pre- and posttests were administered to measure outcomes. The six areas measured were: (1)…

CNN Newsroom Classroom Guides. May 1-29, 1998.

ERIC Educational Resources Information Center

Cable News Network, Atlanta, GA.

CNN Newsroom is a daily 15-minute commercial-free news program specifically produced for classroom use and provided free to participating schools. These guides are designed to accompany the program broadcasts for May 1-29, 1998. Top stories include: effects of a labor strike on Denmark's economy (May 1); the new currency of the European Union, the…

On the Lack of Circumbinary Planets Orbiting Isolated Binary Stars

NASA Astrophysics Data System (ADS)

Fleming, David; Barnes, Rory; Graham, David E.; Luger, Rodrigo; Quinn, Thomas R.

2018-04-01

To date, no binary star system with an orbital period less than 7.5 days has been observed to host a circumbinary planet (CBP), a puzzling observation given the thousands of binary stars with orbital periods < 10 days discovered by the Kepler mission (Kirk et al., 2016) and the observational biases that favor their detection (Munoz & Lai, 2015). We outline a mechanism that explains the observed lack of CBPs via coupled stellar-tidal evolution of isolated binary stars. Tidal forces between low-mass, short-period binary stars on the pre-main sequence slow the stellar rotations, transferring rotational angular momentum to the orbit as the stars approach the tidally locked state. This transfer increases the binary orbital period, expanding the region of dynamical instability around the binary, and destabilizing CBPs that tend to preferentially orbit just beyond the initial dynamical stability limit. After the stars tidally lock, we find that angular momentum loss due to magnetic braking can significantly shrink the binary orbit, and hence the region of dynamical stability, over time impacting where surviving CBPs are observed relative to the boundary. We perform simulations over a wide range of parameter space and find that the expansion of the instability region occurs for most plausible initial conditions and that in some cases, the stability semi-major axis doubles from its initial value. We examine the dynamical and observable consequences of a CBP falling within the dynamical instability limit by running N-body simulations of circumbinary planetary systems and find that typically, at least one planet is ejected from the system. We apply our theory to the shortest period Kepler binary that possesses a CBP, Kepler-47, and find that its existence is consistent with our model. Under conservative assumptions, we find that coupled stellar-tidal evolution of pre-main sequence binary stars removes at least one close-in CBP in 87% of multi-planet circumbinary systems.

Using CNN Features to Better Understand What Makes Visual Artworks Special.

PubMed

Brachmann, Anselm; Barth, Erhardt; Redies, Christoph

2017-01-01

One of the goal of computational aesthetics is to understand what is special about visual artworks. By analyzing image statistics, contemporary methods in computer vision enable researchers to identify properties that distinguish artworks from other (non-art) types of images. Such knowledge will eventually allow inferences with regard to the possible neural mechanisms that underlie aesthetic perception in the human visual system. In the present study, we define measures that capture variances of features of a well-established Convolutional Neural Network (CNN), which was trained on millions of images to recognize objects. Using an image dataset that represents traditional Western, Islamic and Chinese art, as well as various types of non-art images, we show that we need only two variance measures to distinguish between the artworks and non-art images with a high classification accuracy of 93.0%. Results for the first variance measure imply that, in the artworks, the subregions of an image tend to be filled with pictorial elements, to which many diverse CNN features respond ( richness of feature responses). Results for the second measure imply that this diversity is tied to a relatively large variability of the responses of individual CNN feature across the subregions of an image. We hypothesize that this combination of richness and variability of CNN feature responses is one of properties that makes traditional visual artworks special. We discuss the possible neural underpinnings of this perceptual quality of artworks and propose to study the same quality also in other types of aesthetic stimuli, such as music and literature.

Balance the nodule shape and surroundings: a new multichannel image based convolutional neural network scheme on lung nodule diagnosis

NASA Astrophysics Data System (ADS)

Sun, Wenqing; Zheng, Bin; Huang, Xia; Qian, Wei

2017-03-01

Deep learning is a trending promising method in medical image analysis area, but how to efficiently prepare the input image for the deep learning algorithms remains a challenge. In this paper, we introduced a novel artificial multichannel region of interest (ROI) generation procedure for convolutional neural networks (CNN). From LIDC database, we collected 54880 benign nodule samples and 59848 malignant nodule samples based on the radiologists' annotations. The proposed CNN consists of three pairs of convolutional layers and two fully connected layers. For each original ROI, two new ROIs were generated: one contains the segmented nodule which highlighted the nodule shape, and the other one contains the gradient of the original ROI which highlighted the textures. By combining the three channel images into a pseudo color ROI, the CNN was trained and tested on the new multichannel ROIs (multichannel ROI II). For the comparison, we generated another type of multichannel image by replacing the gradient image channel with a ROI contains whitened background region (multichannel ROI I). With the 5-fold cross validation evaluation method, the CNN using multichannel ROI II achieved the ROI based area under the curve (AUC) of 0.8823+/-0.0177, compared to the AUC of 0.8484+/-0.0204 generated by the original ROI. By calculating the average of ROI scores from one nodule, the lesion based AUC using multichannel ROI was 0.8793+/-0.0210. By comparing the convolved features maps from CNN using different types of ROIs, it can be noted that multichannel ROI II contains more accurate nodule shapes and surrounding textures.

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

PubMed

Tajbakhsh, Nima; Shin, Jae Y; Gurudu, Suryakanth R; Hurst, R Todd; Kendall, Christopher B; Gotway, Michael B; Jianming Liang

2016-05-01

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

A deep convolutional neural network model to classify heartbeats.

PubMed

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adam, Muhammad; Gertych, Arkadiusz; Tan, Ru San

2017-10-01

The electrocardiogram (ECG) is a standard test used to monitor the activity of the heart. Many cardiac abnormalities will be manifested in the ECG including arrhythmia which is a general term that refers to an abnormal heart rhythm. The basis of arrhythmia diagnosis is the identification of normal versus abnormal individual heart beats, and their correct classification into different diagnoses, based on ECG morphology. Heartbeats can be sub-divided into five categories namely non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is challenging and time-consuming to distinguish these heartbeats on ECG as these signals are typically corrupted by noise. We developed a 9-layer deep convolutional neural network (CNN) to automatically identify 5 different categories of heartbeats in ECG signals. Our experiment was conducted in original and noise attenuated sets of ECG signals derived from a publicly available database. This set was artificially augmented to even out the number of instances the 5 classes of heartbeats and filtered to remove high-frequency noise. The CNN was trained using the augmented data and achieved an accuracy of 94.03% and 93.47% in the diagnostic classification of heartbeats in original and noise free ECGs, respectively. When the CNN was trained with highly imbalanced data (original dataset), the accuracy of the CNN reduced to 89.07%% and 89.3% in noisy and noise-free ECGs. When properly trained, the proposed CNN model can serve as a tool for screening of ECG to quickly identify different types and frequency of arrhythmic heartbeats. Copyright © 2017 Elsevier Ltd. All rights reserved.

Generative Adversarial Networks for Noise Reduction in Low-Dose CT.

PubMed

Wolterink, Jelmer M; Leiner, Tim; Viergever, Max A; Isgum, Ivana

2017-12-01

Noise is inherent to low-dose CT acquisition. We propose to train a convolutional neural network (CNN) jointly with an adversarial CNN to estimate routine-dose CT images from low-dose CT images and hence reduce noise. A generator CNN was trained to transform low-dose CT images into routine-dose CT images using voxelwise loss minimization. An adversarial discriminator CNN was simultaneously trained to distinguish the output of the generator from routine-dose CT images. The performance of this discriminator was used as an adversarial loss for the generator. Experiments were performed using CT images of an anthropomorphic phantom containing calcium inserts, as well as patient non-contrast-enhanced cardiac CT images. The phantom and patients were scanned at 20% and 100% routine clinical dose. Three training strategies were compared: the first used only voxelwise loss, the second combined voxelwise loss and adversarial loss, and the third used only adversarial loss. The results showed that training with only voxelwise loss resulted in the highest peak signal-to-noise ratio with respect to reference routine-dose images. However, CNNs trained with adversarial loss captured image statistics of routine-dose images better. Noise reduction improved quantification of low-density calcified inserts in phantom CT images and allowed coronary calcium scoring in low-dose patient CT images with high noise levels. Testing took less than 10 s per CT volume. CNN-based low-dose CT noise reduction in the image domain is feasible. Training with an adversarial network improves the CNNs ability to generate images with an appearance similar to that of reference routine-dose CT images.

Real-Time Human Detection for Aerial Captured Video Sequences via Deep Models.

PubMed

AlDahoul, Nouar; Md Sabri, Aznul Qalid; Mansoor, Ali Mohammed

2018-01-01

Human detection in videos plays an important role in various real life applications. Most of traditional approaches depend on utilizing handcrafted features which are problem-dependent and optimal for specific tasks. Moreover, they are highly susceptible to dynamical events such as illumination changes, camera jitter, and variations in object sizes. On the other hand, the proposed feature learning approaches are cheaper and easier because highly abstract and discriminative features can be produced automatically without the need of expert knowledge. In this paper, we utilize automatic feature learning methods which combine optical flow and three different deep models (i.e., supervised convolutional neural network (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine) for human detection in videos captured using a nonstatic camera on an aerial platform with varying altitudes. The models are trained and tested on the publicly available and highly challenging UCF-ARG aerial dataset. The comparison between these models in terms of training, testing accuracy, and learning speed is analyzed. The performance evaluation considers five human actions (digging, waving, throwing, walking, and running). Experimental results demonstrated that the proposed methods are successful for human detection task. Pretrained CNN produces an average accuracy of 98.09%. S-CNN produces an average accuracy of 95.6% with soft-max and 91.7% with Support Vector Machines (SVM). H-ELM has an average accuracy of 95.9%. Using a normal Central Processing Unit (CPU), H-ELM's training time takes 445 seconds. Learning in S-CNN takes 770 seconds with a high performance Graphical Processing Unit (GPU).

Brain tumor segmentation with Deep Neural Networks.

PubMed

Havaei, Mohammad; Davy, Axel; Warde-Farley, David; Biard, Antoine; Courville, Aaron; Bengio, Yoshua; Pal, Chris; Jodoin, Pierre-Marc; Larochelle, Hugo

2017-01-01

In this paper, we present a fully automatic brain tumor segmentation method based on Deep Neural Networks (DNNs). The proposed networks are tailored to glioblastomas (both low and high grade) pictured in MR images. By their very nature, these tumors can appear anywhere in the brain and have almost any kind of shape, size, and contrast. These reasons motivate our exploration of a machine learning solution that exploits a flexible, high capacity DNN while being extremely efficient. Here, we give a description of different model choices that we've found to be necessary for obtaining competitive performance. We explore in particular different architectures based on Convolutional Neural Networks (CNN), i.e. DNNs specifically adapted to image data. We present a novel CNN architecture which differs from those traditionally used in computer vision. Our CNN exploits both local features as well as more global contextual features simultaneously. Also, different from most traditional uses of CNNs, our networks use a final layer that is a convolutional implementation of a fully connected layer which allows a 40 fold speed up. We also describe a 2-phase training procedure that allows us to tackle difficulties related to the imbalance of tumor labels. Finally, we explore a cascade architecture in which the output of a basic CNN is treated as an additional source of information for a subsequent CNN. Results reported on the 2013 BRATS test data-set reveal that our architecture improves over the currently published state-of-the-art while being over 30 times faster. Copyright © 2016 Elsevier B.V. All rights reserved.

Using CNN Features to Better Understand What Makes Visual Artworks Special

PubMed Central

Brachmann, Anselm; Barth, Erhardt; Redies, Christoph

2017-01-01

One of the goal of computational aesthetics is to understand what is special about visual artworks. By analyzing image statistics, contemporary methods in computer vision enable researchers to identify properties that distinguish artworks from other (non-art) types of images. Such knowledge will eventually allow inferences with regard to the possible neural mechanisms that underlie aesthetic perception in the human visual system. In the present study, we define measures that capture variances of features of a well-established Convolutional Neural Network (CNN), which was trained on millions of images to recognize objects. Using an image dataset that represents traditional Western, Islamic and Chinese art, as well as various types of non-art images, we show that we need only two variance measures to distinguish between the artworks and non-art images with a high classification accuracy of 93.0%. Results for the first variance measure imply that, in the artworks, the subregions of an image tend to be filled with pictorial elements, to which many diverse CNN features respond (richness of feature responses). Results for the second measure imply that this diversity is tied to a relatively large variability of the responses of individual CNN feature across the subregions of an image. We hypothesize that this combination of richness and variability of CNN feature responses is one of properties that makes traditional visual artworks special. We discuss the possible neural underpinnings of this perceptual quality of artworks and propose to study the same quality also in other types of aesthetic stimuli, such as music and literature. PMID:28588537

Diffusion of Magnetized Binary Ionic Mixtures at Ultracold Plasma Conditions

NASA Astrophysics Data System (ADS)

Vidal, Keith R.; Baalrud, Scott D.

2017-10-01

Ultracold plasma experiments offer an accessible means to test transport theories for strongly coupled systems. Application of an external magnetic field might further increase their utility by inhibiting heating mechanisms of ions and electrons and increasing the temperature at which strong coupling effects are observed. We present results focused on developing and validating a transport theory to describe binary ionic mixtures across a wide range of coupling and magnetization strengths relevant to ultracold plasma experiments. The transport theory is an extension of the Effective Potential Theory (EPT), which has been shown to accurately model correlation effects at these conditions, to include magnetization. We focus on diffusion as it can be measured in ultracold plasma experiments. Using EPT within the framework of the Chapman-Enskog expansion, the parallel and perpendicular self and interdiffusion coefficients for binary ionic mixtures with varying mass ratios are calculated and are compared to molecular dynamics simulations. The theory is found to accurately extend Braginskii-like transport to stronger coupling, but to break down when the magnetization strength becomes large enough that the typical gyroradius is smaller than the interaction scale length. This material is based upon work supported by the Air Force Office of Scientific Research under Award Number FA9550-16-1-0221.

Differences in Television Sports Reporting of Men's and Women's Athletics: ESPN SportsCenter and CNN Sports Tonight.

ERIC Educational Resources Information Center

Tuggle, C. A.

1997-01-01

Examines the amount of coverage given to women's athletics by ESPN SportsCenter and CNN Sports Tonight. Results indicated: both programs devoted only about 5% of their air time to women's sports; story placement and on-camera comments indicated an emphasis on men's athletics; and stories about women involved individual competition, with almost no…

"The CNN Effect:" TV & Foreign Policy. Study Guide. Episode #834. America's Defense Monitor, Educational TV for the Classroom.

ERIC Educational Resources Information Center

Edwards, B. T.

This program examines the current acceleration of the decision-making cycle in the conduct of foreign policy due to the instantaneous reporting of events, called "The CNN Effect." The sometimes paradoxical consequences of global media coverage are noted, along with the examination of the medium of television itself, and its shortcomings…

Constructing three-dimensional porous graphene-carbon quantum dots/g-C3N4 nanosheet aerogel metal-free photocatalyst with enhanced photocatalytic activity

NASA Astrophysics Data System (ADS)

He, Huijuan; Huang, Langhuan; Zhong, Zijun; Tan, Shaozao

2018-05-01

Photocatalysis has been widely considered to be an effective way for solving the worldwide environmental pollution issues. Herein, a new type of three-dimensional (3D) ternary graphene-carbon quantum dots/g-C3N4 nanosheet (GA-CQDs/CNN) aerogel visible-light-driven photocatalyst was synthesized via a two-step hydrothermal method. In this unique ternary photocatalyst, both carbon quantum dots (CQDs) and reduced graphene oxide (rGO) could improve the visible light absorption and promote the charge separation. Furthermore, reduced graphene oxide (rGO) could act as a supportor for the 3D framework. Such a ternary system overcame the drawbacks of bulk g-C3N4 (BCN) and achieved the enhanced photocatalytic activity and long-term stability. As a result, the methyl orange (MO) removal ratio of GA-CQDs/CNN-24% was up to 91.1%, which was about 7.6 times higher than that of bulk g-C3N4 (BCN) under the identical conditions. Moreover that GA-CQDs/CNN-24% exhibited inappreciable loss of photocatalytic activity after four-cycle degradation processes. Finally, the photocatalytic mechanism of GA-CQDs/CNN-24% was interpreted both theoretically and experimentally.

MKID digital readout tuning with deep learning

NASA Astrophysics Data System (ADS)

Dodkins, R.; Mahashabde, S.; O'Brien, K.; Thatte, N.; Fruitwala, N.; Walter, A. B.; Meeker, S. R.; Szypryt, P.; Mazin, B. A.

2018-04-01

Microwave Kinetic Inductance Detector (MKID) devices offer inherent spectral resolution, simultaneous read out of thousands of pixels, and photon-limited sensitivity at optical wavelengths. Before taking observations the readout power and frequency of each pixel must be individually tuned, and if the equilibrium state of the pixels change, then the readout must be retuned. This process has previously been performed through manual inspection, and typically takes one hour per 500 resonators (20 h for a ten-kilo-pixel array). We present an algorithm based on a deep convolution neural network (CNN) architecture to determine the optimal bias power for each resonator. The bias point classifications from this CNN model, and those from alternative automated methods, are compared to those from human decisions, and the accuracy of each method is assessed. On a test feed-line dataset, the CNN achieves an accuracy of 90% within 1 dB of the designated optimal value, which is equivalent accuracy to a randomly selected human operator, and superior to the highest scoring alternative automated method by 10%. On a full ten-kilopixel array, the CNN performs the characterization in a matter of minutes - paving the way for future mega-pixel MKID arrays.

Low-dose x-ray tomography through a deep convolutional neural network

DOE PAGES

Yang, Xiaogang; De Andrade, Vincent; Scullin, William; ...

2018-02-07

Synchrotron-based X-ray tomography offers the potential of rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short exposure time projections enhanced with CNN show similar signal to noise ratios as compared with long exposure time projections and muchmore » lower noise and more structural information than low-dose fats acquisition without CNN. We optimized this approach using simulated samples and further validated on experimental nano-computed tomography data of radiation sensitive mouse brains acquired with a transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in datasets collected with low dose-CNN. As a result, this method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.« less

Low-dose x-ray tomography through a deep convolutional neural network

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiaogang; De Andrade, Vincent; Scullin, William

Synchrotron-based X-ray tomography offers the potential of rapid large-scale reconstructions of the interiors of materials and biological tissue at fine resolution. However, for radiation sensitive samples, there remain fundamental trade-offs between damaging samples during longer acquisition times and reducing signals with shorter acquisition times. We present a deep convolutional neural network (CNN) method that increases the acquired X-ray tomographic signal by at least a factor of 10 during low-dose fast acquisition by improving the quality of recorded projections. Short exposure time projections enhanced with CNN show similar signal to noise ratios as compared with long exposure time projections and muchmore » lower noise and more structural information than low-dose fats acquisition without CNN. We optimized this approach using simulated samples and further validated on experimental nano-computed tomography data of radiation sensitive mouse brains acquired with a transmission X-ray microscopy. We demonstrate that automated algorithms can reliably trace brain structures in datasets collected with low dose-CNN. As a result, this method can be applied to other tomographic or scanning based X-ray imaging techniques and has great potential for studying faster dynamics in specimens.« less

Multiclass classification of obstructive sleep apnea/hypopnea based on a convolutional neural network from a single-lead electrocardiogram.

PubMed

Urtnasan, Erdenebayar; Park, Jong-Uk; Lee, Kyoung-Joung

2018-05-24

In this paper, we propose a convolutional neural network (CNN)-based deep learning architecture for multiclass classification of obstructive sleep apnea and hypopnea (OSAH) using single-lead electrocardiogram (ECG) recordings. OSAH is the most common sleep-related breathing disorder. Many subjects who suffer from OSAH remain undiagnosed; thus, early detection of OSAH is important. In this study, automatic classification of three classes-normal, hypopnea, and apnea-based on a CNN is performed. An optimal six-layer CNN model is trained on a training dataset (45,096 events) and evaluated on a test dataset (11,274 events). The training set (69 subjects) and test set (17 subjects) were collected from 86 subjects with length of approximately 6 h and segmented into 10 s durations. The proposed CNN model reaches a mean -score of 93.0 for the training dataset and 87.0 for the test dataset. Thus, proposed deep learning architecture achieved a high performance for multiclass classification of OSAH using single-lead ECG recordings. The proposed method can be employed in screening of patients suspected of having OSAH. © 2018 Institute of Physics and Engineering in Medicine.

Classification of volcanic ash particles using a convolutional neural network and probability.

PubMed

Shoji, Daigo; Noguchi, Rina; Otsuki, Shizuka; Hino, Hideitsu

2018-05-25

Analyses of volcanic ash are typically performed either by qualitatively classifying ash particles by eye or by quantitatively parameterizing its shape and texture. While complex shapes can be classified through qualitative analyses, the results are subjective due to the difficulty of categorizing complex shapes into a single class. Although quantitative analyses are objective, selection of shape parameters is required. Here, we applied a convolutional neural network (CNN) for the classification of volcanic ash. First, we defined four basal particle shapes (blocky, vesicular, elongated, rounded) generated by different eruption mechanisms (e.g., brittle fragmentation), and then trained the CNN using particles composed of only one basal shape. The CNN could recognize the basal shapes with over 90% accuracy. Using the trained network, we classified ash particles composed of multiple basal shapes based on the output of the network, which can be interpreted as a mixing ratio of the four basal shapes. Clustering of samples by the averaged probabilities and the intensity is consistent with the eruption type. The mixing ratio output by the CNN can be used to quantitatively classify complex shapes in nature without categorizing forcibly and without the need for shape parameters, which may lead to a new taxonomy.

Hazardous gas detection for FTIR-based hyperspectral imaging system using DNN and CNN

NASA Astrophysics Data System (ADS)

Kim, Yong Chan; Yu, Hyeong-Geun; Lee, Jae-Hoon; Park, Dong-Jo; Nam, Hyun-Woo

2017-10-01

Recently, a hyperspectral imaging system (HIS) with a Fourier Transform InfraRed (FTIR) spectrometer has been widely used due to its strengths in detecting gaseous fumes. Even though numerous algorithms for detecting gaseous fumes have already been studied, it is still difficult to detect target gases properly because of atmospheric interference substances and unclear characteristics of low concentration gases. In this paper, we propose detection algorithms for classifying hazardous gases using a deep neural network (DNN) and a convolutional neural network (CNN). In both the DNN and CNN, spectral signal preprocessing, e.g., offset, noise, and baseline removal, are carried out. In the DNN algorithm, the preprocessed spectral signals are used as feature maps of the DNN with five layers, and it is trained by a stochastic gradient descent (SGD) algorithm (50 batch size) and dropout regularization (0.7 ratio). In the CNN algorithm, preprocessed spectral signals are trained with 1 × 3 convolution layers and 1 × 2 max-pooling layers. As a result, the proposed algorithms improve the classification accuracy rate by 1.5% over the existing support vector machine (SVM) algorithm for detecting and classifying hazardous gases.

Objects Classification by Learning-Based Visual Saliency Model and Convolutional Neural Network.

PubMed

Li, Na; Zhao, Xinbo; Yang, Yongjia; Zou, Xiaochun

2016-01-01

Humans can easily classify different kinds of objects whereas it is quite difficult for computers. As a hot and difficult problem, objects classification has been receiving extensive interests with broad prospects. Inspired by neuroscience, deep learning concept is proposed. Convolutional neural network (CNN) as one of the methods of deep learning can be used to solve classification problem. But most of deep learning methods, including CNN, all ignore the human visual information processing mechanism when a person is classifying objects. Therefore, in this paper, inspiring the completed processing that humans classify different kinds of objects, we bring forth a new classification method which combines visual attention model and CNN. Firstly, we use the visual attention model to simulate the processing of human visual selection mechanism. Secondly, we use CNN to simulate the processing of how humans select features and extract the local features of those selected areas. Finally, not only does our classification method depend on those local features, but also it adds the human semantic features to classify objects. Our classification method has apparently advantages in biology. Experimental results demonstrated that our method made the efficiency of classification improve significantly.

Deep recurrent neural network reveals a hierarchy of process memory during dynamic natural vision.

PubMed

Shi, Junxing; Wen, Haiguang; Zhang, Yizhen; Han, Kuan; Liu, Zhongming

2018-05-01

The human visual cortex extracts both spatial and temporal visual features to support perception and guide behavior. Deep convolutional neural networks (CNNs) provide a computational framework to model cortical representation and organization for spatial visual processing, but unable to explain how the brain processes temporal information. To overcome this limitation, we extended a CNN by adding recurrent connections to different layers of the CNN to allow spatial representations to be remembered and accumulated over time. The extended model, or the recurrent neural network (RNN), embodied a hierarchical and distributed model of process memory as an integral part of visual processing. Unlike the CNN, the RNN learned spatiotemporal features from videos to enable action recognition. The RNN better predicted cortical responses to natural movie stimuli than the CNN, at all visual areas, especially those along the dorsal stream. As a fully observable model of visual processing, the RNN also revealed a cortical hierarchy of temporal receptive window, dynamics of process memory, and spatiotemporal representations. These results support the hypothesis of process memory, and demonstrate the potential of using the RNN for in-depth computational understanding of dynamic natural vision. © 2018 Wiley Periodicals, Inc.

Hand pose estimation in depth image using CNN and random forest

NASA Astrophysics Data System (ADS)

Chen, Xi; Cao, Zhiguo; Xiao, Yang; Fang, Zhiwen

2018-03-01

Thanks to the availability of low cost depth cameras, like Microsoft Kinect, 3D hand pose estimation attracted special research attention in these years. Due to the large variations in hand`s viewpoint and the high dimension of hand motion, 3D hand pose estimation is still challenging. In this paper we propose a two-stage framework which joint with CNN and Random Forest to boost the performance of hand pose estimation. First, we use a standard Convolutional Neural Network (CNN) to regress the hand joints` locations. Second, using a Random Forest to refine the joints from the first stage. In the second stage, we propose a pyramid feature which merges the information flow of the CNN. Specifically, we get the rough joints` location from first stage, then rotate the convolutional feature maps (and image). After this, for each joint, we map its location to each feature map (and image) firstly, then crop features at each feature map (and image) around its location, put extracted features to Random Forest to refine at last. Experimentally, we evaluate our proposed method on ICVL dataset and get the mean error about 11mm, our method is also real-time on a desktop.

Three-port beam splitter of a binary fused-silica grating.

PubMed

Feng, Jijun; Zhou, Changhe; Wang, Bo; Zheng, Jiangjun; Jia, Wei; Cao, Hongchao; Lv, Peng

2008-12-10

A deep-etched polarization-independent binary fused-silica phase grating as a three-port beam splitter is designed and manufactured. The grating profile is optimized by use of the rigorous coupled-wave analysis around the 785 nm wavelength. The physical explanation of the grating is illustrated by the modal method. Simple analytical expressions of the diffraction efficiencies and modal guidelines for the three-port beam splitter grating design are given. Holographic recording technology and inductively coupled plasma etching are used to manufacture the fused-silica grating. Experimental results are in good agreement with the theoretical values.

Solidification of a binary mixture

NASA Technical Reports Server (NTRS)

Antar, B. N.

1982-01-01

The time dependent concentration and temperature profiles of a finite layer of a binary mixture are investigated during solidification. The coupled time dependent Stefan problem is solved numerically using an implicit finite differencing algorithm with the method of lines. Specifically, the temporal operator is approximated via an implicit finite difference operator resulting in a coupled set of ordinary differential equations for the spatial distribution of the temperature and concentration for each time. Since the resulting differential equations set form a boundary value problem with matching conditions at an unknown spatial point, the method of invariant imbedding is used for its solution.

FORMATION OF CIRCUMBINARY PLANETS IN A DEAD ZONE

DOE Office of Scientific and Technical Information (OSTI.GOV)

Martin, Rebecca G.; Armitage, Philip J.; Alexander, Richard D.

Circumbinary planets have been observed at orbital radii where binary perturbations may have significant effects on the gas disk structure, on planetesimal velocity dispersion, and on the coupling between turbulence and planetesimals. Here, we note that the impact of all of these effects on planet formation is qualitatively altered if the circumbinary disk structure is layered, with a non-turbulent midplane layer (dead zone) and strongly turbulent surface layers. For close binaries, we find that the dead zone typically extends from a radius close to the inner disk edge up to a radius of around 10-20 AU from the center ofmore » mass of the binary. The peak in the surface density occurs within the dead zone, far from the inner disk edge, close to the snow line, and may act as a trap for aerodynamically coupled solids. We suggest that circumbinary planet formation may be easier near this preferential location than for disks around single stars. However, dead zones around wide binaries are less likely, and hence planet formation may be more difficult there.« less

Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.

Coupled binary embedding for large-scale image retrieval.

PubMed

Zheng, Liang; Wang, Shengjin; Tian, Qi

2014-08-01

Visual matching is a crucial step in image retrieval based on the bag-of-words (BoW) model. In the baseline method, two keypoints are considered as a matching pair if their SIFT descriptors are quantized to the same visual word. However, the SIFT visual word has two limitations. First, it loses most of its discriminative power during quantization. Second, SIFT only describes the local texture feature. Both drawbacks impair the discriminative power of the BoW model and lead to false positive matches. To tackle this problem, this paper proposes to embed multiple binary features at indexing level. To model correlation between features, a multi-IDF scheme is introduced, through which different binary features are coupled into the inverted file. We show that matching verification methods based on binary features, such as Hamming embedding, can be effectively incorporated in our framework. As an extension, we explore the fusion of binary color feature into image retrieval. The joint integration of the SIFT visual word and binary features greatly enhances the precision of visual matching, reducing the impact of false positive matches. Our method is evaluated through extensive experiments on four benchmark datasets (Ukbench, Holidays, DupImage, and MIR Flickr 1M). We show that our method significantly improves the baseline approach. In addition, large-scale experiments indicate that the proposed method requires acceptable memory usage and query time compared with other approaches. Further, when global color feature is integrated, our method yields competitive performance with the state-of-the-arts.

Deep convolutional neural network and 3D deformable approach for tissue segmentation in musculoskeletal magnetic resonance imaging.

PubMed

Liu, Fang; Zhou, Zhaoye; Jang, Hyungseok; Samsonov, Alexey; Zhao, Gengyan; Kijowski, Richard

2018-04-01

To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. Magn Reson Med 79:2379-2391, 2018. © 2017 International Society for Magnetic Resonance in Medicine. © 2017 International Society for Magnetic Resonance in Medicine.

Relative location prediction in CT scan images using convolutional neural networks.

PubMed

Guo, Jiajia; Du, Hongwei; Zhu, Jianyue; Yan, Ting; Qiu, Bensheng

2018-07-01

Relative location prediction in computed tomography (CT) scan images is a challenging problem. Many traditional machine learning methods have been applied in attempts to alleviate this problem. However, the accuracy and speed of these methods cannot meet the requirement of medical scenario. In this paper, we propose a regression model based on one-dimensional convolutional neural networks (CNN) to determine the relative location of a CT scan image both quickly and precisely. In contrast to other common CNN models that use a two-dimensional image as an input, the input of this CNN model is a feature vector extracted by a shape context algorithm with spatial correlation. Normalization via z-score is first applied as a pre-processing step. Then, in order to prevent overfitting and improve model's performance, 20% of the elements of the feature vectors are randomly set to zero. This CNN model consists primarily of three one-dimensional convolutional layers, three dropout layers and two fully-connected layers with appropriate loss functions. A public dataset is employed to validate the performance of the proposed model using a 5-fold cross validation. Experimental results demonstrate an excellent performance of the proposed model when compared with contemporary techniques, achieving a median absolute error of 1.04 cm and mean absolute error of 1.69 cm. The time taken for each relative location prediction is approximately 2 ms. Results indicate that the proposed CNN method can contribute to a quick and accurate relative location prediction in CT scan images, which can improve efficiency of the medical picture archiving and communication system in the future. Copyright © 2018 Elsevier B.V. All rights reserved.

Pedestrian Detection Based on Adaptive Selection of Visible Light or Far-Infrared Light Camera Image by Fuzzy Inference System and Convolutional Neural Network-Based Verification.

PubMed

Kang, Jin Kyu; Hong, Hyung Gil; Park, Kang Ryoung

2017-07-08

A number of studies have been conducted to enhance the pedestrian detection accuracy of intelligent surveillance systems. However, detecting pedestrians under outdoor conditions is a challenging problem due to the varying lighting, shadows, and occlusions. In recent times, a growing number of studies have been performed on visible light camera-based pedestrian detection systems using a convolutional neural network (CNN) in order to make the pedestrian detection process more resilient to such conditions. However, visible light cameras still cannot detect pedestrians during nighttime, and are easily affected by shadows and lighting. There are many studies on CNN-based pedestrian detection through the use of far-infrared (FIR) light cameras (i.e., thermal cameras) to address such difficulties. However, when the solar radiation increases and the background temperature reaches the same level as the body temperature, it remains difficult for the FIR light camera to detect pedestrians due to the insignificant difference between the pedestrian and non-pedestrian features within the images. Researchers have been trying to solve this issue by inputting both the visible light and the FIR camera images into the CNN as the input. This, however, takes a longer time to process, and makes the system structure more complex as the CNN needs to process both camera images. This research adaptively selects a more appropriate candidate between two pedestrian images from visible light and FIR cameras based on a fuzzy inference system (FIS), and the selected candidate is verified with a CNN. Three types of databases were tested, taking into account various environmental factors using visible light and FIR cameras. The results showed that the proposed method performs better than the previously reported methods.

Diffusion tensor imaging for anatomical localization of cranial nerves and cranial nerve nuclei in pontine lesions: initial experiences with 3T-MRI.

PubMed

Ulrich, Nils H; Ahmadli, Uzeyir; Woernle, Christoph M; Alzarhani, Yahea A; Bertalanffy, Helmut; Kollias, Spyros S

2014-11-01

With continuous refinement of neurosurgical techniques and higher resolution in neuroimaging, the management of pontine lesions is constantly improving. Among pontine structures with vital functions that are at risk of being damaged by surgical manipulation, cranial nerves (CN) and cranial nerve nuclei (CNN) such as CN V, VI, and VII are critical. Pre-operative localization of the intrapontine course of CN and CNN should be beneficial for surgical outcomes. Our objective was to accurately localize CN and CNN in patients with intra-axial lesions in the pons using diffusion tensor imaging (DTI) and estimate its input in surgical planning for avoiding unintended loss of their function during surgery. DTI of the pons obtained pre-operatively on a 3Tesla MR scanner was analyzed prospectively for the accurate localization of CN and CNN V, VI and VII in seven patients with intra-axial lesions in the pons. Anatomical sections in the pons were used to estimate abnormalities on color-coded fractional anisotropy maps. Imaging abnormalities were correlated with CN symptoms before and after surgery. The course of CN and the area of CNN were identified using DTI pre- and post-operatively. Clinical associations between post-operative improvements and the corresponding CN area of the pons were demonstrated. Our results suggest that pre- and post-operative DTI allows identification of key anatomical structures in the pons and enables estimation of their involvement by pathology. It may predict clinical outcome and help us to better understand the involvement of the intrinsic anatomy by pathological processes. Copyright © 2014 Elsevier Ltd. All rights reserved.

Efficiency Improvement in a Busy Radiology Practice: Determination of Musculoskeletal Magnetic Resonance Imaging Protocol Using Deep-Learning Convolutional Neural Networks.

PubMed

Lee, Young Han

2018-04-04

The purposes of this study are to evaluate the feasibility of protocol determination with a convolutional neural networks (CNN) classifier based on short-text classification and to evaluate the agreements by comparing protocols determined by CNN with those determined by musculoskeletal radiologists. Following institutional review board approval, the database of a hospital information system (HIS) was queried for lists of MRI examinations, referring department, patient age, and patient gender. These were exported to a local workstation for analyses: 5258 and 1018 consecutive musculoskeletal MRI examinations were used for the training and test datasets, respectively. The subjects for pre-processing were routine or tumor protocols and the contents were word combinations of the referring department, region, contrast media (or not), gender, and age. The CNN Embedded vector classifier was used with Word2Vec Google news vectors. The test set was tested with each classification model and results were output as routine or tumor protocols. The CNN determinations were evaluated using the receiver operating characteristic (ROC) curves. The accuracies were evaluated by a radiologist-confirmed protocol as the reference protocols. The optimal cut-off values for protocol determination between routine protocols and tumor protocols was 0.5067 with a sensitivity of 92.10%, a specificity of 95.76%, and an area under curve (AUC) of 0.977. The overall accuracy was 94.2% for the ConvNet model. All MRI protocols were correct in the pelvic bone, upper arm, wrist, and lower leg MRIs. Deep-learning-based convolutional neural networks were clinically utilized to determine musculoskeletal MRI protocols. CNN-based text learning and applications could be extended to other radiologic tasks besides image interpretations, improving the work performance of the radiologist.

Does the Fast Patrol Boat Have a Future in the Navy?

DTIC Science & Technology

2002-05-31

Admiral Dennis Blair (Commander and Chief, United States Pacific Command) testified to Congress “countering terrorism, weapons proliferation...United States Navy. Blair, Dennis C., Admiral, USN. 2001a. Interview by Maria Ressa, CNN Jakarta Bureau, December 1. Interview transcript on-line...Available from http://www. pacom.mil/speeches/sst2001/011201blairCNN.htm. Internet accessed 3 March 2002. Blair, Dennis C., Admiral, USN. 2001b

Deep learning with non-medical training used for chest pathology identification

NASA Astrophysics Data System (ADS)

Bar, Yaniv; Diamant, Idit; Wolf, Lior; Greenspan, Hayit

2015-03-01

In this work, we examine the strength of deep learning approaches for pathology detection in chest radiograph data. Convolutional neural networks (CNN) deep architecture classification approaches have gained popularity due to their ability to learn mid and high level image representations. We explore the ability of a CNN to identify different types of pathologies in chest x-ray images. Moreover, since very large training sets are generally not available in the medical domain, we explore the feasibility of using a deep learning approach based on non-medical learning. We tested our algorithm on a dataset of 93 images. We use a CNN that was trained with ImageNet, a well-known large scale nonmedical image database. The best performance was achieved using a combination of features extracted from the CNN and a set of low-level features. We obtained an area under curve (AUC) of 0.93 for Right Pleural Effusion detection, 0.89 for Enlarged heart detection and 0.79 for classification between healthy and abnormal chest x-ray, where all pathologies are combined into one large class. This is a first-of-its-kind experiment that shows that deep learning with large scale non-medical image databases may be sufficient for general medical image recognition tasks.

Highly productive CNN pincer ruthenium catalysts for the asymmetric reduction of alkyl aryl ketones.

PubMed

Baratta, Walter; Chelucci, Giorgio; Magnolia, Santo; Siega, Katia; Rigo, Pierluigi

2009-01-01

Chiral pincer ruthenium complexes of formula [RuCl(CNN)(Josiphos)] (2-7; Josiphos = 1-[1-(dicyclohexylphosphano)ethyl]-2-(diarylphosphano)ferrocene) have been prepared by treating [RuCl(2)(PPh(3))(3)] with (S,R)-Josiphos diphosphanes and 1-substituted-1-(6-arylpyridin-2-yl)methanamines (HCNN; substituent = H (1 a), Me (1 b), and tBu (1 c)) with NEt(3). By using 1 b and 1 c as a racemic mixture, complexes 4-7 were obtained through a diastereoselective synthesis promoted by acetic acid. These pincer complexes, which display correctly matched chiral PP and CNN ligands, are remarkably active catalysts for the asymmetric reduction of alkyl aryl ketones in basic alcohol media by both transfer hydrogenation (TH) and hydrogenation (HY), achieving enantioselectivities of up to 99 %. In 2-propanol, the enantioselective TH of ketones was accomplished by using a catalyst loading as low as 0.002 mol % and afforded a turnover frequency (TOF) of 10(5)-10(6) h(-1) (60 and 82 degrees C). In methanol/ethanol mixtures, the CNN pincer complexes catalyzed the asymmetric HY of ketones with H(2) (5 atm) at 0.01 mol % relative to the complex with a TOF of approximately 10(4) h(-1) at 40 degrees C.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Single-image-based Rain Detection and Removal via CNN

NASA Astrophysics Data System (ADS)

Chen, Tianyi; Fu, Chengzhou

2018-04-01

The quality of the image is degraded by rain streaks, which have negative impact when we extract image features for many visual tasks, such as feature extraction for classification and recognition, tracking, surveillance and autonomous navigation. Hence, it is necessary to detect and remove rain streaks from single images, which is a challenging problem since we have no spatial-temporal information of rain streaks compared to the dynamic video stream. Inspired by the priori that the rain streaks have almost the same feature, such as the direction or the thickness, although they are in different types of real-world images. The paper aims at proposing an effective convolutional neural network (CNN) to detect and remove rain streaks from single image. Two models of synthesized rainy image, linear additive composite model (LACM model) and screen blend model (SCM model), are considered in this paper. The main idea is that it is easier for our CNN network to find the mapping between the rainy image and rain streaks than between the rainy image and clean image. The reason is that rain streaks have fixed features, but clean images have various features. The experiments show that the designed CNN network outperforms state-of-the-art approaches on both synthesized and real-world images, which indicates the effectiveness of our proposed framework.

Lung nodule detection from CT scans using 3D convolutional neural networks without candidate selection

NASA Astrophysics Data System (ADS)

Jenuwine, Natalia M.; Mahesh, Sunny N.; Furst, Jacob D.; Raicu, Daniela S.

2018-02-01

Early detection of lung nodules from CT scans is key to improving lung cancer treatment, but poses a significant challenge for radiologists due to the high throughput required of them. Computer-Aided Detection (CADe) systems aim to automatically detect these nodules with computer algorithms, thus improving diagnosis. These systems typically use a candidate selection step, which identifies all objects that resemble nodules, followed by a machine learning classifier which separates true nodules from false positives. We create a CADe system that uses a 3D convolutional neural network (CNN) to detect nodules in CT scans without a candidate selection step. Using data from the LIDC database, we train a 3D CNN to analyze subvolumes from anywhere within a CT scan and output the probability that each subvolume contains a nodule. Once trained, we apply our CNN to detect nodules from entire scans, by systematically dividing the scan into overlapping subvolumes which we input into the CNN to obtain the corresponding probabilities. By enabling our network to process an entire scan, we expect to streamline the detection process while maintaining its effectiveness. Our results imply that with continued training using an iterative training scheme, the one-step approach has the potential to be highly effective.

Remote pedestrians detection at night time in FIR Image using contrast filtering and locally projected region based CNN

NASA Astrophysics Data System (ADS)

Kim, Taehwan; Kim, Sungho

2017-02-01

This paper presents a novel method to detect the remote pedestrians. After producing the human temperature based brightness enhancement image using the temperature data input, we generates the regions of interest (ROIs) by the multiscale contrast filtering based approach including the biased hysteresis threshold and clustering, remote pedestrian's height, pixel area and central position information. Afterwards, we conduct local vertical and horizontal projection based ROI refinement and weak aspect ratio based ROI limitation to solve the problem of region expansion in the contrast filtering stage. Finally, we detect the remote pedestrians by validating the final ROIs using transfer learning with convolutional neural network (CNN) feature, following non-maximal suppression (NMS) with strong aspect ratio limitation to improve the detection performance. In the experimental results, we confirmed that the proposed contrast filtering and locally projected region based CNN (CFLP-CNN) outperforms the baseline method by 8% in term of logaveraged miss rate. Also, the proposed method is more effective than the baseline approach and the proposed method provides the better regions that are suitably adjusted to the shape and appearance of remote pedestrians, which makes it detect the pedestrian that didn't find in the baseline approach and are able to help detect pedestrians by splitting the people group into a person.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Parameterization of MARVELS Spectra Using Deep Learning

NASA Astrophysics Data System (ADS)

Gilda, Sankalp; Ge, Jian; MARVELS

2018-01-01

Like many large-scale surveys, the Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS) was designed to operate at a moderate spectral resolution ($\\sim$12,000) for efficiency in observing large samples, which makes the stellar parameterization difficult due to the high degree of blending of spectral features. Two extant solutions to deal with this issue are to utilize spectral synthesis, and to utilize spectral indices [Ghezzi et al. 2014]. While the former is a powerful and tested technique, it can often yield strongly coupled atmospheric parameters, and often requires high spectral resolution (Valenti & Piskunov 1996). The latter, though a promising technique utilizing measurements of equivalent widths of spectral indices, has only been employed with respect to FKG dwarfs and sub-giants and not red-giant branch stars, which constitute ~30% of MARVELS targets. In this work, we tackle this problem using a convolution neural network (CNN). In particular, we train a one-dimensional CNN on appropriately processed PHOENIX synthetic spectra using supervised training to automatically distinguish the features relevant for the determination of each of the three atmospheric parameters – T_eff, log(g), [Fe/H] – and use the knowledge thus gained by the network to parameterize 849 MARVELS giants. When tested on the synthetic spectra themselves, our estimates of the parameters were consistent to within 11 K, .02 dex, and .02 dex (in terms of mean absolute errors), respectively. For MARVELS dwarfs, the accuracies are 80K, .16 dex and .10 dex, respectively.

Effect of gold nanoparticles on structure and dynamics of binary Lennard-Jones liquid: direct space analysis.

PubMed

Separdar, L; Davatolhagh, S

2013-02-01

We investigate the static structure and diffusive dynamics of binary Lennard-Jones mixture upon supercooling in the presence of gold nanoparticle within the framework of the mode-coupling theory of the dynamic glass transition in the direct space by means of constant-NVT molecular dynamics simulations. It is found that the presence of gold nanoparticle causes the energy per particle and the pressure of this system to decrease with respect to the bulk binary Lennard-Jones mixture. Furthermore, the presence of nanoparticle has a direct effect on the liquid structure and causes the peaks of the radial distribution functions to become shorter with respect to the bulk binary Lennard-Jones liquid. The dynamics of the liquid at a given density is found to be consistent with the mode-coupling theory (MCT) predictions in a certain range at low temperatures. In accordance with the idealized MCT, the diffusion constants D(T) show a power-law behavior at low temperatures for both types of binary Lennard-Jones (BLJ) particles as well as the gold atoms comprising the nanoparticle. The mode-coupling crossover temperature T(c) is the same for all particle types; however, T(c)=0.4 is reduced with respect to that of the bulk BLJ liquid, and the γ exponent is found to depend on the particle type. The existence of the nanoparticle causes the short-time β-relaxation regime to shorten and the range of validity of the MCT shrinks with respect to the bulk BLJ. It is also found that at intermediate and low temperatures the curves of the mean-squared displacements (MSDs) versus tD(T) fall onto a master curve. The MSDs follow the master curve in an identical time range with the long-time α-relaxation regime of the mode-coupling theory. By obtaining the viscosity, it is observed that the Stokes-Einstein relation remains valid at high and intermediate temperatures but breaks down as the temperatures approach T(c) as a result of the cooperative motion or activated processes.

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography.

PubMed

Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta; Gürsoy, Dogˇa

2017-03-01

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc. An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

Smart-Pixel Array Processors Based on Optimal Cellular Neural Networks for Space Sensor Applications

NASA Technical Reports Server (NTRS)

Fang, Wai-Chi; Sheu, Bing J.; Venus, Holger; Sandau, Rainer

1997-01-01

A smart-pixel cellular neural network (CNN) with hardware annealing capability, digitally programmable synaptic weights, and multisensor parallel interface has been under development for advanced space sensor applications. The smart-pixel CNN architecture is a programmable multi-dimensional array of optoelectronic neurons which are locally connected with their local neurons and associated active-pixel sensors. Integration of the neuroprocessor in each processor node of a scalable multiprocessor system offers orders-of-magnitude computing performance enhancements for on-board real-time intelligent multisensor processing and control tasks of advanced small satellites. The smart-pixel CNN operation theory, architecture, design and implementation, and system applications are investigated in detail. The VLSI (Very Large Scale Integration) implementation feasibility was illustrated by a prototype smart-pixel 5x5 neuroprocessor array chip of active dimensions 1380 micron x 746 micron in a 2-micron CMOS technology.

Combining convolutional neural networks and Hough Transform for classification of images containing lines

NASA Astrophysics Data System (ADS)

Sheshkus, Alexander; Limonova, Elena; Nikolaev, Dmitry; Krivtsov, Valeriy

2017-03-01

In this paper, we propose an expansion of convolutional neural network (CNN) input features based on Hough Transform. We perform morphological contrasting of source image followed by Hough Transform, and then use it as input for some convolutional filters. Thus, CNNs computational complexity and the number of units are not affected. Morphological contrasting and Hough Transform are the only additional computational expenses of introduced CNN input features expansion. Proposed approach was demonstrated on the example of CNN with very simple structure. We considered two image recognition problems, that were object classification on CIFAR-10 and printed character recognition on private dataset with symbols taken from Russian passports. Our approach allowed to reach noticeable accuracy improvement without taking much computational effort, which can be extremely important in industrial recognition systems or difficult problems utilising CNNs, like pressure ridge analysis and classification.

Stability Training for Convolutional Neural Nets in LArTPC

NASA Astrophysics Data System (ADS)

Lindsay, Matt; Wongjirad, Taritree

2017-01-01

Convolutional Neural Nets (CNNs) are the state of the art for many problems in computer vision and are a promising method for classifying interactions in Liquid Argon Time Projection Chambers (LArTPCs) used in neutrino oscillation experiments. Despite the good performance of CNN's, they are not without drawbacks, chief among them is vulnerability to noise and small perturbations to the input. One solution to this problem is a modification to the learning process called Stability Training developed by Zheng et al. We verify existing work and demonstrate volatility caused by simple Gaussian noise and also that the volatility can be nearly eliminated with Stability Training. We then go further and show that a traditional CNN is also vulnerable to realistic experimental noise and that a stability trained CNN remains accurate despite noise. This further adds to the optimism for CNNs for work in LArTPCs and other applications.

Convolutional neural networks with balanced batches for facial expressions recognition

NASA Astrophysics Data System (ADS)

Battini Sönmez, Elena; Cangelosi, Angelo

2017-03-01

This paper considers the issue of fully automatic emotion classification on 2D faces. In spite of the great effort done in recent years, traditional machine learning approaches based on hand-crafted feature extraction followed by the classification stage failed to develop a real-time automatic facial expression recognition system. The proposed architecture uses Convolutional Neural Networks (CNN), which are built as a collection of interconnected processing elements to simulate the brain of human beings. The basic idea of CNNs is to learn a hierarchical representation of the input data, which results in a better classification performance. In this work we present a block-based CNN algorithm, which uses noise, as data augmentation technique, and builds batches with a balanced number of samples per class. The proposed architecture is a very simple yet powerful CNN, which can yield state-of-the-art accuracy on the very competitive benchmark algorithm of the Extended Cohn Kanade database.

Boosting CNN performance for lung texture classification using connected filtering

NASA Astrophysics Data System (ADS)

Tarando, Sebastián. Roberto; Fetita, Catalin; Kim, Young-Wouk; Cho, Hyoun; Brillet, Pierre-Yves

2018-02-01

Infiltrative lung diseases describe a large group of irreversible lung disorders requiring regular follow-up with CT imaging. Quantifying the evolution of the patient status imposes the development of automated classification tools for lung texture. This paper presents an original image pre-processing framework based on locally connected filtering applied in multiresolution, which helps improving the learning process and boost the performance of CNN for lung texture classification. By removing the dense vascular network from images used by the CNN for lung classification, locally connected filters provide a better discrimination between different lung patterns and help regularizing the classification output. The approach was tested in a preliminary evaluation on a 10 patient database of various lung pathologies, showing an increase of 10% in true positive rate (on average for all the cases) with respect to the state of the art cascade of CNNs for this task.

Correlated Fluctuations in Strongly Coupled Binary Networks Beyond Equilibrium

NASA Astrophysics Data System (ADS)

Dahmen, David; Bos, Hannah; Helias, Moritz

2016-07-01

Randomly coupled Ising spins constitute the classical model of collective phenomena in disordered systems, with applications covering glassy magnetism and frustration, combinatorial optimization, protein folding, stock market dynamics, and social dynamics. The phase diagram of these systems is obtained in the thermodynamic limit by averaging over the quenched randomness of the couplings. However, many applications require the statistics of activity for a single realization of the possibly asymmetric couplings in finite-sized networks. Examples include reconstruction of couplings from the observed dynamics, representation of probability distributions for sampling-based inference, and learning in the central nervous system based on the dynamic and correlation-dependent modification of synaptic connections. The systematic cumulant expansion for kinetic binary (Ising) threshold units with strong, random, and asymmetric couplings presented here goes beyond mean-field theory and is applicable outside thermodynamic equilibrium; a system of approximate nonlinear equations predicts average activities and pairwise covariances in quantitative agreement with full simulations down to hundreds of units. The linearized theory yields an expansion of the correlation and response functions in collective eigenmodes, leads to an efficient algorithm solving the inverse problem, and shows that correlations are invariant under scaling of the interaction strengths.

Single-solute and bisolute sorption of phenol and trichloroethylene from aqueous solution onto modified montmorillonite and application of sorption models.

PubMed

Wu, C D; Wang, L; Hu, C X; He, M H

2013-01-01

The single-solute and bisolute sorption behaviour of phenol and trichloroethylene, two organic compounds with different structures, onto cetyltrimethylammonium bromide (CTAB)-montmorillonite was studied. The monolayer Langmuir model (MLM) and empirical Freundlich model (EFM) were applied to the single-solute sorption of phenol or trichloroethylene from water onto monolayer or multilayer CTAB-montmorillonite. The parameters contained in the MLM and EFM were determined for each solute by fitting to the single-solute isotherm data, and subsequently utilized in binary sorption. The extended Langmuir model (ELM) coupled with the single-solute MLM and the ideal adsorbed solution theory (IAST) coupled with the single-solute EFM were used to predict the binary sorption of phenol and trichloroethylene onto CTAB-montmorillonite. It was found that the EFM was better than the MLM at describing single-solute sorption from water onto CTAB-montmorillonite, and the IAST was better than the ELM at describing the binary sorption from water onto CTAB-montmorillonite.

Surface Inhomogeneities of the White Dwarf in the Binary EUVE J2013+400

NASA Astrophysics Data System (ADS)

Vennes, Stephane

We propose to study the white dwarf in the binary EUVE J2013+400. The object is paired with a dMe star and new extreme ultraviolet (EUV) observations will offer critical insights into the properties of the white dwarf. The binary behaves, in every other aspects, like its siblings EUVE J0720-317 and EUVE J1016-053 and new EUV observations will help establish their class properties; in particular, EUV photometric variations in 0720-317 and 1016-053 over a period of 11 hours and 57 minutes, respectively, are indicative of surface abundance inhomogeneities coupled with the white dwarfs rotation period. These variations and their large photospheric helium abundance are best explained by a diffusion-accretion model in which time-variable accretion and possible coupling to magnetic poles contribute to abundance variations across the surface and possibly as a function of depth. EUV spectroscopy will also enable a study of the helium abundance as a function of depth and a detailed comparison with theoretical diffusion profile.

Using artificial intelligence strategies for process-related automated inspection in the production environment

NASA Astrophysics Data System (ADS)

Anding, K.; Kuritcyn, P.; Garten, D.

2016-11-01

In this paper a new method for the automatic visual inspection of metallic surfaces is proposed by using Convolutional Neural Networks (CNN). The different combinations of network parameters were developed and tested. The obtained results of CNN were analysed and compared with the results of our previous investigations with color and texture features as input parameters for a Support Vector Machine. Advantages and disadvantages of the different classifying methods are explained.

Cartel Next: How Army Design Methodology Offers Holistic and Dissimilar Approaches to the Mexican Drug Problem

DTIC Science & Technology

2011-06-30

things. - Gerald M. Weinberg 1 Author‟ s Note: This paper is a theoretical exercise that attempts to deliver one possible Army...military and the overarching web of government agencies and international actors could approach Mexico‟ s current issues- however, this is a purely...interact.” 2 Raj Kumar, Why Mexico‟ s Violence is America‟ s Problem (CNN Opinion, April 11, 2011, http://www.cnn.com/2011/OPINION/04/11

Mission Driven Scene Understanding: Candidate Model Training and Validation

DTIC Science & Technology

2016-09-01

driven scene understanding. One of the candidate engines that we are evaluating is a convolutional neural network (CNN) program installed on a Windows 10...Theano-AlexNet6,7) installed on a Windows 10 notebook computer. To the best of our knowledge, an implementation of the open-source, Python-based...AlexNet CNN on a Windows notebook computer has not been previously reported. In this report, we present progress toward the proof-of-principle testing

A novel deep learning approach for classification of EEG motor imagery signals.

PubMed

Tabar, Yousef Rezaei; Halici, Ugur

2017-02-01

Signal classification is an important issue in brain computer interface (BCI) systems. Deep learning approaches have been used successfully in many recent studies to learn features and classify different types of data. However, the number of studies that employ these approaches on BCI applications is very limited. In this study we aim to use deep learning methods to improve classification performance of EEG motor imagery signals. In this study we investigate convolutional neural networks (CNN) and stacked autoencoders (SAE) to classify EEG Motor Imagery signals. A new form of input is introduced to combine time, frequency and location information extracted from EEG signal and it is used in CNN having one 1D convolutional and one max-pooling layers. We also proposed a new deep network by combining CNN and SAE. In this network, the features that are extracted in CNN are classified through the deep network SAE. The classification performance obtained by the proposed method on BCI competition IV dataset 2b in terms of kappa value is 0.547. Our approach yields 9% improvement over the winner algorithm of the competition. Our results show that deep learning methods provide better classification performance compared to other state of art approaches. These methods can be applied successfully to BCI systems where the amount of data is large due to daily recording.

Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification

PubMed Central

Hou, Le; Samaras, Dimitris; Kurc, Tahsin M.; Gao, Yi; Davis, James E.; Saltz, Joel H.

2016-01-01

Convolutional Neural Networks (CNN) are state-of-the-art models for many image classification tasks. However, to recognize cancer subtypes automatically, training a CNN on gigapixel resolution Whole Slide Tissue Images (WSI) is currently computationally impossible. The differentiation of cancer subtypes is based on cellular-level visual features observed on image patch scale. Therefore, we argue that in this situation, training a patch-level classifier on image patches will perform better than or similar to an image-level classifier. The challenge becomes how to intelligently combine patch-level classification results and model the fact that not all patches will be discriminative. We propose to train a decision fusion model to aggregate patch-level predictions given by patch-level CNNs, which to the best of our knowledge has not been shown before. Furthermore, we formulate a novel Expectation-Maximization (EM) based method that automatically locates discriminative patches robustly by utilizing the spatial relationships of patches. We apply our method to the classification of glioma and non-small-cell lung carcinoma cases into subtypes. The classification accuracy of our method is similar to the inter-observer agreement between pathologists. Although it is impossible to train CNNs on WSIs, we experimentally demonstrate using a comparable non-cancer dataset of smaller images that a patch-based CNN can outperform an image-based CNN. PMID:27795661

Vehicle Detection in Aerial Images Based on Region Convolutional Neural Networks and Hard Negative Example Mining.

PubMed

Tang, Tianyu; Zhou, Shilin; Deng, Zhipeng; Zou, Huanxin; Lei, Lin

2017-02-10

Detecting vehicles in aerial imagery plays an important role in a wide range of applications. The current vehicle detection methods are mostly based on sliding-window search and handcrafted or shallow-learning-based features, having limited description capability and heavy computational costs. Recently, due to the powerful feature representations, region convolutional neural networks (CNN) based detection methods have achieved state-of-the-art performance in computer vision, especially Faster R-CNN. However, directly using it for vehicle detection in aerial images has many limitations: (1) region proposal network (RPN) in Faster R-CNN has poor performance for accurately locating small-sized vehicles, due to the relatively coarse feature maps; and (2) the classifier after RPN cannot distinguish vehicles and complex backgrounds well. In this study, an improved detection method based on Faster R-CNN is proposed in order to accomplish the two challenges mentioned above. Firstly, to improve the recall, we employ a hyper region proposal network (HRPN) to extract vehicle-like targets with a combination of hierarchical feature maps. Then, we replace the classifier after RPN by a cascade of boosted classifiers to verify the candidate regions, aiming at reducing false detection by negative example mining. We evaluate our method on the Munich vehicle dataset and the collected vehicle dataset, with improvements in accuracy and robustness compared to existing methods.

Neural Encoding and Decoding with Deep Learning for Dynamic Natural Vision.

PubMed

Wen, Haiguang; Shi, Junxing; Zhang, Yizhen; Lu, Kun-Han; Cao, Jiayue; Liu, Zhongming

2017-10-20

Convolutional neural network (CNN) driven by image recognition has been shown to be able to explain cortical responses to static pictures at ventral-stream areas. Here, we further showed that such CNN could reliably predict and decode functional magnetic resonance imaging data from humans watching natural movies, despite its lack of any mechanism to account for temporal dynamics or feedback processing. Using separate data, encoding and decoding models were developed and evaluated for describing the bi-directional relationships between the CNN and the brain. Through the encoding models, the CNN-predicted areas covered not only the ventral stream, but also the dorsal stream, albeit to a lesser degree; single-voxel response was visualized as the specific pixel pattern that drove the response, revealing the distinct representation of individual cortical location; cortical activation was synthesized from natural images with high-throughput to map category representation, contrast, and selectivity. Through the decoding models, fMRI signals were directly decoded to estimate the feature representations in both visual and semantic spaces, for direct visual reconstruction and semantic categorization, respectively. These results corroborate, generalize, and extend previous findings, and highlight the value of using deep learning, as an all-in-one model of the visual cortex, to understand and decode natural vision. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Learning Compact Binary Face Descriptor for Face Recognition.

PubMed

Lu, Jiwen; Liong, Venice Erin; Zhou, Xiuzhuang; Zhou, Jie

2015-10-01

Binary feature descriptors such as local binary patterns (LBP) and its variations have been widely used in many face recognition systems due to their excellent robustness and strong discriminative power. However, most existing binary face descriptors are hand-crafted, which require strong prior knowledge to engineer them by hand. In this paper, we propose a compact binary face descriptor (CBFD) feature learning method for face representation and recognition. Given each face image, we first extract pixel difference vectors (PDVs) in local patches by computing the difference between each pixel and its neighboring pixels. Then, we learn a feature mapping to project these pixel difference vectors into low-dimensional binary vectors in an unsupervised manner, where 1) the variance of all binary codes in the training set is maximized, 2) the loss between the original real-valued codes and the learned binary codes is minimized, and 3) binary codes evenly distribute at each learned bin, so that the redundancy information in PDVs is removed and compact binary codes are obtained. Lastly, we cluster and pool these binary codes into a histogram feature as the final representation for each face image. Moreover, we propose a coupled CBFD (C-CBFD) method by reducing the modality gap of heterogeneous faces at the feature level to make our method applicable to heterogeneous face recognition. Extensive experimental results on five widely used face datasets show that our methods outperform state-of-the-art face descriptors.

New observations and new models of spin-orbit coupling in binary asteroids

NASA Astrophysics Data System (ADS)

Margot, Jean-Luc; Naidu, Shantanu

2015-08-01

The YORP-induced rotational fission hypothesis is the leading candidate for explaining the formation of binaries, triples, and pairs among small (<20 km) asteroids (e.g., Margot et al, Asteroids IV, subm., 2015). Various evolutionary paths following rotational fission have been suggested, but many important questions remain about the evolutionary mechanisms and timescales. We test hypotheses about the evolution of binary asteroids by obtaining precise descriptions of the orbits and components of binary systems with radar and by examining the system dynamics with detailed numerical simulations. Predictions for component spin states and orbital precession rates can then be compared to observables in our data sets or in other data sets to elucidate the states of various systems and their likely evolutionary paths.Accurate simulations require knowledge of the masses, shapes, and spin states of individual binary components. Because radar observations can provide exquisite data sets spanning days with spatial resolutions at the decameter level, we can invert for the component shapes and measure spin states. We can also solve for the mutual orbit by fitting the observed separations between components. In addition, the superb (10e-7--10e-8) fractional uncertainties in range allow us to measure the reflex motions directly, allowing masses of individual components to be determined.We use recently published observations of the binary 2000 DP107 (Naidu et al. AJ, subm., 2015) and that of other systems to simulate the dynamics of components in well-characterized binary systems (Naidu and Margot, AJ 149, 80, 2015). We model the coupled spin and orbital motions of two rigid, ellipsoidal bodies under the influence of their mutual gravitational potential. We use surface of section plots to map the possible spin configurations of the satellites. For asynchronous satellites, the analysis reveals large regions of phase space where the spin state of the satellite is chaotic. The presence of chaotic regions may substantially increase spin synchronization timescales, delay BYORP-type evolution, extend the lifetime of binaries, and explain the observed fraction of asynchronous binaries.

Towards constructing multi-bit binary adder based on Belousov-Zhabotinsky reaction

NASA Astrophysics Data System (ADS)

Zhang, Guo-Mao; Wong, Ieong; Chou, Meng-Ta; Zhao, Xin

2012-04-01

It has been proposed that the spatial excitable media can perform a wide range of computational operations, from image processing, to path planning, to logical and arithmetic computations. The realizations in the field of chemical logical and arithmetic computations are mainly concerned with single simple logical functions in experiments. In this study, based on Belousov-Zhabotinsky reaction, we performed simulations toward the realization of a more complex operation, the binary adder. Combining with some of the existing functional structures that have been verified experimentally, we designed a planar geometrical binary adder chemical device. Through numerical simulations, we first demonstrated that the device can implement the function of a single-bit full binary adder. Then we show that the binary adder units can be further extended in plane, and coupled together to realize a two-bit, or even multi-bit binary adder. The realization of chemical adders can guide the constructions of other sophisticated arithmetic functions, ultimately leading to the implementation of chemical computer and other intelligent systems.

A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control.

PubMed

Pinzon-Morales, Ruben-Dario; Hirata, Yutaka

2014-01-01

To acquire and maintain precise movement controls over a lifespan, changes in the physical and physiological characteristics of muscles must be compensated for adaptively. The cerebellum plays a crucial role in such adaptation. Changes in muscle characteristics are not always symmetrical. For example, it is unlikely that muscles that bend and straighten a joint will change to the same degree. Thus, different (i.e., asymmetrical) adaptation is required for bending and straightening motions. To date, little is known about the role of the cerebellum in asymmetrical adaptation. Here, we investigate the cerebellar mechanisms required for asymmetrical adaptation using a bi-hemispheric cerebellar neuronal network model (biCNN). The bi-hemispheric structure is inspired by the observation that lesioning one hemisphere reduces motor performance asymmetrically. The biCNN model was constructed to run in real-time and used to control an unstable two-wheeled balancing robot. The load of the robot and its environment were modified to create asymmetrical perturbations. Plasticity at parallel fiber-Purkinje cell synapses in the biCNN model was driven by error signal in the climbing fiber (cf) input. This cf input was configured to increase and decrease its firing rate from its spontaneous firing rate (approximately 1 Hz) with sensory errors in the preferred and non-preferred direction of each hemisphere, as demonstrated in the monkey cerebellum. Our results showed that asymmetrical conditions were successfully handled by the biCNN model, in contrast to a single hemisphere model or a classical non-adaptive proportional and derivative controller. Further, the spontaneous activity of the cf, while relatively small, was critical for balancing the contribution of each cerebellar hemisphere to the overall motor command sent to the robot. Eliminating the spontaneous activity compromised the asymmetrical learning capabilities of the biCNN model. Thus, we conclude that a bi-hemispheric structure and adequate spontaneous activity of cf inputs are critical for cerebellar asymmetrical motor learning.

Synthesis, structure and catalytic properties of CNN pincer palladium(II) and ruthenium(II) complexes with N-substituted-2-aminomethyl-6-phenylpyridines.

PubMed

Wang, Tao; Hao, Xin-Qi; Zhang, Xiao-Xue; Gong, Jun-Fang; Song, Mao-Ping

2011-09-21

N-substituted-2-aminomethyl-6-phenylpyridines 2a-c have been easily prepared from commercially available 6-bromo-2-picolinaldehyde in two steps. Reaction of 2a-c with PdCl(2) in toluene in the presence of triethylamine gave the CNN pincer Pd(II) complexes 3a-c in 18-28% yields. The CNN pincer Ru(II) complex 5 containing a Ru-NHR functionality could be obtained in a 71% yield by treatment of 2c with a Ru(II) precursor instead of PdCl(2). Additionally, the related CNN pincer Ru(II) complex 7 containing a Ru-NH(2) functionality has been synthesized by the reaction of 2-aminomethyl-6-phenylpyridine with the same Ru(II) precursor in a 68% yield. All the new compounds were characterized by elemental analysis (MS for ligands), (1)H, (13)C NMR, (31)P{(1)H} NMR (for Ru complexes) and IR spectra. Molecular structures of Pd complex 3c as well as Ru complexes 5 and 7 have been determined by X-ray single-crystal diffraction. The obtained Pd complexes 3a-c were effective catalysts for the allylation of aldehydes as well as for three-component allylation of aldehydes, arylamines and allyltributyltin and their activity was found to be much higher than a related NCN Pd(II) pincer in the allylation of aldehyde. On the other hand, the two new CNN pincer Ru(II) complexes 5 and 7 displayed excellent catalytic activity in the transfer hydrogenation of ketones in refluxing 2-propanol with the latter being much more active. The final TOF values were up to 4510 h(-1) with 0.01 mol% of 5 and 220,800 h(-1) with 0.005 mol% of 7, respectively. This journal is © The Royal Society of Chemistry 2011

Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN.

PubMed

Xu, Xuanang; Zhou, Fugen; Liu, Bo

2018-03-19

Automatic approach for bladder segmentation from computed tomography (CT) images is highly desirable in clinical practice. It is a challenging task since the bladder usually suffers large variations of appearance and low soft-tissue contrast in CT images. In this study, we present a deep learning-based approach which involves a convolutional neural network (CNN) and a 3D fully connected conditional random fields recurrent neural network (CRF-RNN) to perform accurate bladder segmentation. We also propose a novel preprocessing method, called dual-channel preprocessing, to further advance the segmentation performance of our approach. The presented approach works as following: first, we apply our proposed preprocessing method on the input CT image and obtain a dual-channel image which consists of the CT image and an enhanced bladder density map. Second, we exploit a CNN to predict a coarse voxel-wise bladder score map on this dual-channel image. Finally, a 3D fully connected CRF-RNN refines the coarse bladder score map and produce final fine-localized segmentation result. We compare our approach to the state-of-the-art V-net on a clinical dataset. Results show that our approach achieves superior segmentation accuracy, outperforming the V-net by a significant margin. The Dice Similarity Coefficient of our approach (92.24%) is 8.12% higher than that of the V-net. Moreover, the bladder probability maps performed by our approach present sharper boundaries and more accurate localizations compared with that of the V-net. Our approach achieves higher segmentation accuracy than the state-of-the-art method on clinical data. Both the dual-channel processing and the 3D fully connected CRF-RNN contribute to this improvement. The united deep network composed of the CNN and 3D CRF-RNN also outperforms a system where the CRF model acts as a post-processing method disconnected from the CNN.

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study.

PubMed

Yasaka, Koichiro; Akai, Hiroyuki; Abe, Osamu; Kiryu, Shigeru

2018-03-01

Purpose To investigate diagnostic performance by using a deep learning method with a convolutional neural network (CNN) for the differentiation of liver masses at dynamic contrast agent-enhanced computed tomography (CT). Materials and Methods This clinical retrospective study used CT image sets of liver masses over three phases (noncontrast-agent enhanced, arterial, and delayed). Masses were diagnosed according to five categories (category A, classic hepatocellular carcinomas [HCCs]; category B, malignant liver tumors other than classic and early HCCs; category C, indeterminate masses or mass-like lesions [including early HCCs and dysplastic nodules] and rare benign liver masses other than hemangiomas and cysts; category D, hemangiomas; and category E, cysts). Supervised training was performed by using 55 536 image sets obtained in 2013 (from 460 patients, 1068 sets were obtained and they were augmented by a factor of 52 [rotated, parallel-shifted, strongly enlarged, and noise-added images were generated from the original images]). The CNN was composed of six convolutional, three maximum pooling, and three fully connected layers. The CNN was tested with 100 liver mass image sets obtained in 2016 (74 men and 26 women; mean age, 66.4 years ± 10.6 [standard deviation]; mean mass size, 26.9 mm ± 25.9; 21, nine, 35, 20, and 15 liver masses for categories A, B, C, D, and E, respectively). Training and testing were performed five times. Accuracy for categorizing liver masses with CNN model and the area under receiver operating characteristic curve for differentiating categories A-B versus categories C-E were calculated. Results Median accuracy of differential diagnosis of liver masses for test data were 0.84. Median area under the receiver operating characteristic curve for differentiating categories A-B from C-E was 0.92. Conclusion Deep learning with CNN showed high diagnostic performance in differentiation of liver masses at dynamic CT. © RSNA, 2017 Online supplemental material is available for this article.

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

PubMed

Karimi, Davood; Samei, Golnoosh; Kesch, Claudia; Nir, Guy; Salcudean, Septimiu E

2018-05-15

Most of the existing convolutional neural network (CNN)-based medical image segmentation methods are based on methods that have originally been developed for segmentation of natural images. Therefore, they largely ignore the differences between the two domains, such as the smaller degree of variability in the shape and appearance of the target volume and the smaller amounts of training data in medical applications. We propose a CNN-based method for prostate segmentation in MRI that employs statistical shape models to address these issues. Our CNN predicts the location of the prostate center and the parameters of the shape model, which determine the position of prostate surface keypoints. To train such a large model for segmentation of 3D images using small data (1) we adopt a stage-wise training strategy by first training the network to predict the prostate center and subsequently adding modules for predicting the parameters of the shape model and prostate rotation, (2) we propose a data augmentation method whereby the training images and their prostate surface keypoints are deformed according to the displacements computed based on the shape model, and (3) we employ various regularization techniques. Our proposed method achieves a Dice score of 0.88, which is obtained by using both elastic-net and spectral dropout for regularization. Compared with a standard CNN-based method, our method shows significantly better segmentation performance on the prostate base and apex. Our experiments also show that data augmentation using the shape model significantly improves the segmentation results. Prior knowledge about the shape of the target organ can improve the performance of CNN-based segmentation methods, especially where image features are not sufficient for a precise segmentation. Statistical shape models can also be employed to synthesize additional training data that can ease the training of large CNNs.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

PubMed

Chen, Shuo; Luo, Chenggao; Wang, Hongqiang; Deng, Bin; Cheng, Yongqiang; Zhuang, Zhaowen

2018-04-26

As a promising radar imaging technique, terahertz coded-aperture imaging (TCAI) can achieve high-resolution, forward-looking, and staring imaging by producing spatiotemporal independent signals with coded apertures. However, there are still two problems in three-dimensional (3D) TCAI. Firstly, the large-scale reference-signal matrix based on meshing the 3D imaging area creates a heavy computational burden, thus leading to unsatisfactory efficiency. Secondly, it is difficult to resolve the target under low signal-to-noise ratio (SNR). In this paper, we propose a 3D imaging method based on matched filtering (MF) and convolutional neural network (CNN), which can reduce the computational burden and achieve high-resolution imaging for low SNR targets. In terms of the frequency-hopping (FH) signal, the original echo is processed with MF. By extracting the processed echo in different spike pulses separately, targets in different imaging planes are reconstructed simultaneously to decompose the global computational complexity, and then are synthesized together to reconstruct the 3D target. Based on the conventional TCAI model, we deduce and build a new TCAI model based on MF. Furthermore, the convolutional neural network (CNN) is designed to teach the MF-TCAI how to reconstruct the low SNR target better. The experimental results demonstrate that the MF-TCAI achieves impressive performance on imaging ability and efficiency under low SNR. Moreover, the MF-TCAI has learned to better resolve the low-SNR 3D target with the help of CNN. In summary, the proposed 3D TCAI can achieve: (1) low-SNR high-resolution imaging by using MF; (2) efficient 3D imaging by downsizing the large-scale reference-signal matrix; and (3) intelligent imaging with CNN. Therefore, the TCAI based on MF and CNN has great potential in applications such as security screening, nondestructive detection, medical diagnosis, etc.

Application of deep learning in determining IR precipitation occurrence: a Convolutional Neural Network model

NASA Astrophysics Data System (ADS)

Wang, C.; Hong, Y.

2017-12-01

Infrared (IR) information from Geostationary satellites can be used to retrieve precipitation at pretty high spatiotemporal resolutions. Traditional artificial intelligence (AI) methodologies, such as artificial neural networks (ANN), have been designed to build the relationship between near-surface precipitation and manually derived IR features in products including PERSIANN and PERSIANN-CCS. This study builds an automatic precipitation detection model based on IR data using Convolutional Neural Network (CNN) which is implemented by the newly developed deep learning framework, Caffe. The model judges whether there is rain or no rain at pixel level. Compared with traditional ANN methods, CNN can extract features inside the raw data automatically and thoroughly. In this study, IR data from GOES satellites and precipitation estimates from the next generation QPE (Q2) over the central United States are used as inputs and labels, respectively. The whole datasets during the study period (June to August in 2012) are randomly partitioned to three sub datasets (train, validation and test) to establish the model at the spatial resolution of 0.08°×0.08° and the temporal resolution of 1 hour. The experiments show great improvements of CNN in rain identification compared to the widely used IR-based precipitation product, i.e., PERSIANN-CCS. The overall gain in performance is about 30% for critical success index (CSI), 32% for probability of detection (POD) and 12% for false alarm ratio (FAR). Compared to other recent IR-based precipitation retrieval methods (e.g., PERSIANN-DL developed by University of California Irvine), our model is simpler with less parameters, but achieves equally or even better results. CNN has been applied in computer vision domain successfully, and our results prove the method is suitable for IR precipitation detection. Future studies can expand the application of CNN from precipitation occurrence decision to precipitation amount retrieval.

A bi-hemispheric neuronal network model of the cerebellum with spontaneous climbing fiber firing produces asymmetrical motor learning during robot control

PubMed Central

Pinzon-Morales, Ruben-Dario; Hirata, Yutaka

2014-01-01

To acquire and maintain precise movement controls over a lifespan, changes in the physical and physiological characteristics of muscles must be compensated for adaptively. The cerebellum plays a crucial role in such adaptation. Changes in muscle characteristics are not always symmetrical. For example, it is unlikely that muscles that bend and straighten a joint will change to the same degree. Thus, different (i.e., asymmetrical) adaptation is required for bending and straightening motions. To date, little is known about the role of the cerebellum in asymmetrical adaptation. Here, we investigate the cerebellar mechanisms required for asymmetrical adaptation using a bi-hemispheric cerebellar neuronal network model (biCNN). The bi-hemispheric structure is inspired by the observation that lesioning one hemisphere reduces motor performance asymmetrically. The biCNN model was constructed to run in real-time and used to control an unstable two-wheeled balancing robot. The load of the robot and its environment were modified to create asymmetrical perturbations. Plasticity at parallel fiber-Purkinje cell synapses in the biCNN model was driven by error signal in the climbing fiber (cf) input. This cf input was configured to increase and decrease its firing rate from its spontaneous firing rate (approximately 1 Hz) with sensory errors in the preferred and non-preferred direction of each hemisphere, as demonstrated in the monkey cerebellum. Our results showed that asymmetrical conditions were successfully handled by the biCNN model, in contrast to a single hemisphere model or a classical non-adaptive proportional and derivative controller. Further, the spontaneous activity of the cf, while relatively small, was critical for balancing the contribution of each cerebellar hemisphere to the overall motor command sent to the robot. Eliminating the spontaneous activity compromised the asymmetrical learning capabilities of the biCNN model. Thus, we conclude that a bi-hemispheric structure and adequate spontaneous activity of cf inputs are critical for cerebellar asymmetrical motor learning. PMID:25414644

INSPECTION MEANS FOR INDUCTION MOTORS

DOEpatents

Williams, A.W.

1959-03-10

an appartus is descripbe for inspcting electric motors and more expecially an appartus for detecting falty end rings inn suqirrel cage inductio motors while the motor is running. In its broua aspects, the mer would around ce of reference tedtor means also itons in the phase ition of the An electronic circuit for conversion of excess-3 binary coded serial decimal numbers to straight binary coded serial decimal numbers is reported. The converter of the invention in its basic form generally coded pulse words of a type having an algebraic sign digit followed serially by a plurality of decimal digits in order of decreasing significance preceding a y algebraic sign digit followed serially by a plurality of decimal digits in order of decreasing significance. A switching martix is coupled to said input circuit and is internally connected to produce serial straight binary coded pulse groups indicative of the excess-3 coded input. A stepping circuit is coupled to the switching matrix and to a synchronous counter having a plurality of x decimal digit and plurality of y decimal digit indicator terminals. The stepping circuit steps the counter in synchornism with the serial binary pulse group output from the switching matrix to successively produce pulses at corresponding ones of the x and y decimal digit indicator terminals. The combinations of straight binary coded pulse groups and corresponding decimal digit indicator signals so produced comprise a basic output suitable for application to a variety of output apparatus.

Elastic, mechanical, and thermodynamic properties of Bi-Sb binaries: Effect of spin-orbit coupling

NASA Astrophysics Data System (ADS)

Singh, Sobhit; Valencia-Jaime, Irais; Pavlic, Olivia; Romero, Aldo H.

2018-02-01

Using first-principles calculations, we systematically study the elastic stiffness constants, mechanical properties, elastic wave velocities, Debye temperature, melting temperature, and specific heat of several thermodynamically stable crystal structures of BixSb1 -x (0

A CNN based neurobiology inspired approach for retinal image quality assessment.

PubMed

Mahapatra, Dwarikanath; Roy, Pallab K; Sedai, Suman; Garnavi, Rahil

2016-08-01

Retinal image quality assessment (IQA) algorithms use different hand crafted features for training classifiers without considering the working of the human visual system (HVS) which plays an important role in IQA. We propose a convolutional neural network (CNN) based approach that determines image quality using the underlying principles behind the working of the HVS. CNNs provide a principled approach to feature learning and hence higher accuracy in decision making. Experimental results demonstrate the superior performance of our proposed algorithm over competing methods.

Strategy and Structure for Online News Production - Case Studies of CNN and NRK

NASA Astrophysics Data System (ADS)

Krumsvik, Arne H.

This cross-national comparative case study of online news production analyzes the strategies of Cable News Network (CNN) and the Norwegian Broadcasting Corporation (NRK), aiming at understanding of the implications of organizational strategy on the role of journalists, explains why traditional media organizations have a tendency to develop a multi-platform approach (distributing content on several platforms, such as television, online, mobile) rather than developing the cross-media (with interplay between media types) or multimedia approach anticipated by both scholars and practitioners.

Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

PubMed Central

Cha, Kenny H.; Hadjiiski, Lubomir; Samala, Ravi K.; Chan, Heang-Ping; Caoili, Elaine M.; Cohan, Richard H.

2016-01-01

Purpose: The authors are developing a computerized system for bladder segmentation in CT urography (CTU) as a critical component for computer-aided detection of bladder cancer. Methods: A deep-learning convolutional neural network (DL-CNN) was trained to distinguish between the inside and the outside of the bladder using 160 000 regions of interest (ROI) from CTU images. The trained DL-CNN was used to estimate the likelihood of an ROI being inside the bladder for ROIs centered at each voxel in a CTU case, resulting in a likelihood map. Thresholding and hole-filling were applied to the map to generate the initial contour for the bladder, which was then refined by 3D and 2D level sets. The segmentation performance was evaluated using 173 cases: 81 cases in the training set (42 lesions, 21 wall thickenings, and 18 normal bladders) and 92 cases in the test set (43 lesions, 36 wall thickenings, and 13 normal bladders). The computerized segmentation accuracy using the DL likelihood map was compared to that using a likelihood map generated by Haar features and a random forest classifier, and that using our previous conjoint level set analysis and segmentation system (CLASS) without using a likelihood map. All methods were evaluated relative to the 3D hand-segmented reference contours. Results: With DL-CNN-based likelihood map and level sets, the average volume intersection ratio, average percent volume error, average absolute volume error, average minimum distance, and the Jaccard index for the test set were 81.9% ± 12.1%, 10.2% ± 16.2%, 14.0% ± 13.0%, 3.6 ± 2.0 mm, and 76.2% ± 11.8%, respectively. With the Haar-feature-based likelihood map and level sets, the corresponding values were 74.3% ± 12.7%, 13.0% ± 22.3%, 20.5% ± 15.7%, 5.7 ± 2.6 mm, and 66.7% ± 12.6%, respectively. With our previous CLASS with local contour refinement (LCR) method, the corresponding values were 78.0% ± 14.7%, 16.5% ± 16.8%, 18.2% ± 15.0%, 3.8 ± 2.3 mm, and 73.9% ± 13.5%, respectively. Conclusions: The authors demonstrated that the DL-CNN can overcome the strong boundary between two regions that have large difference in gray levels and provides a seamless mask to guide level set segmentation, which has been a problem for many gradient-based segmentation methods. Compared to our previous CLASS with LCR method, which required two user inputs to initialize the segmentation, DL-CNN with level sets achieved better segmentation performance while using a single user input. Compared to the Haar-feature-based likelihood map, the DL-CNN-based likelihood map could guide the level sets to achieve better segmentation. The results demonstrate the feasibility of our new approach of using DL-CNN in combination with level sets for segmentation of the bladder. PMID:27036584

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tovmassian, G.; González–Buitrago, D.; Zharikov, S.

We studied two objects identified as cataclysmic variables (CVs) with periods exceeding the natural boundary for Roche-lobe-filling zero-age main sequence (ZAMS) secondary stars. We present observational results for V1082 Sgr with a 20.82 hr orbital period, an object that shows a low luminosity state when its flux is totally dominated by a chromospherically active K star with no signs of ongoing accretion. Frequent accretion shutoffs, together with characteristics of emission lines in a high state, indicate that this binary system is probably detached, and the accretion of matter on the magnetic white dwarf takes place through stellar wind from themore » active donor star via coupled magnetic fields. Its observational characteristics are surprisingly similar to V479 And, a 14.5 hr binary system. They both have early K-type stars as donor stars. We argue that, similar to the shorter-period prepolars containing M dwarfs, these are detached binaries with strong magnetic components. Their magnetic fields are coupled, allowing enhanced stellar wind from the K star to be captured and channeled through the bottleneck connecting the two stars onto the white dwarf’s magnetic pole, mimicking a magnetic CV. Hence, they become interactive binaries before they reach contact. This will help to explain an unexpected lack of systems possessing white dwarfs with strong magnetic fields among detached white+red dwarf systems.« less

Border-oriented post-processing refinement on detected vehicle bounding box for ADAS

NASA Astrophysics Data System (ADS)

Chen, Xinyuan; Zhang, Zhaoning; Li, Minne; Li, Dongsheng

2018-04-01

We investigate a new approach for improving localization accuracy of detected vehicles for object detection in advanced driver assistance systems(ADAS). Specifically, we implement a bounding box refinement as a post-processing of the state-of-the-art object detectors (Faster R-CNN, YOLOv2, etc.). The bounding box refinement is achieved by individually adjusting each border of the detected bounding box to its target location using a regression method. We use HOG features which perform well on the edge detection of vehicles to train the regressor and the regressor is independent of the CNN-based object detectors. Experiment results on the KITTI 2012 benchmark show that we can achieve up to 6% improvements over YOLOv2 and Faster R-CNN object detectors on the IoU threshold of 0.8. Also, the proposed refinement framework is computationally light, allowing for processing one bounding box within a few milliseconds on CPU. Further, this refinement method can be added to any object detectors, especially those with high speed but less accuracy.

Fusion of Deep Learning and Compressed Domain features for Content Based Image Retrieval.

PubMed

Liu, Peizhong; Guo, Jing-Ming; Wu, Chi-Yi; Cai, Danlin

2017-08-29

This paper presents an effective image retrieval method by combining high-level features from Convolutional Neural Network (CNN) model and low-level features from Dot-Diffused Block Truncation Coding (DDBTC). The low-level features, e.g., texture and color, are constructed by VQ-indexed histogram from DDBTC bitmap, maximum, and minimum quantizers. Conversely, high-level features from CNN can effectively capture human perception. With the fusion of the DDBTC and CNN features, the extended deep learning two-layer codebook features (DL-TLCF) is generated using the proposed two-layer codebook, dimension reduction, and similarity reweighting to improve the overall retrieval rate. Two metrics, average precision rate (APR) and average recall rate (ARR), are employed to examine various datasets. As documented in the experimental results, the proposed schemes can achieve superior performance compared to the state-of-the-art methods with either low- or high-level features in terms of the retrieval rate. Thus, it can be a strong candidate for various image retrieval related applications.

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

PubMed

Zhang, Jianhua; Li, Sunan; Wang, Rubin

2017-01-01

In this paper, we deal with the Mental Workload (MWL) classification problem based on the measured physiological data. First we discussed the optimal depth (i.e., the number of hidden layers) and parameter optimization algorithms for the Convolutional Neural Networks (CNN). The base CNNs designed were tested according to five classification performance indices, namely Accuracy, Precision, F-measure, G-mean, and required training time. Then we developed an Ensemble Convolutional Neural Network (ECNN) to enhance the accuracy and robustness of the individual CNN model. For the ECNN design, three model aggregation approaches (weighted averaging, majority voting and stacking) were examined and a resampling strategy was used to enhance the diversity of individual CNN models. The results of MWL classification performance comparison indicated that the proposed ECNN framework can effectively improve MWL classification performance and is featured by entirely automatic feature extraction and MWL classification, when compared with traditional machine learning methods.

a Cloud Boundary Detection Scheme Combined with Aslic and Cnn Using ZY-3, GF-1/2 Satellite Imagery

NASA Astrophysics Data System (ADS)

Guo, Z.; Li, C.; Wang, Z.; Kwok, E.; Wei, X.

2018-04-01

Remote sensing optical image cloud detection is one of the most important problems in remote sensing data processing. Aiming at the information loss caused by cloud cover, a cloud detection method based on convolution neural network (CNN) is presented in this paper. Firstly, a deep CNN network is used to extract the multi-level feature generation model of cloud from the training samples. Secondly, the adaptive simple linear iterative clustering (ASLIC) method is used to divide the detected images into superpixels. Finally, the probability of each superpixel belonging to the cloud region is predicted by the trained network model, thereby generating a cloud probability map. The typical region of GF-1/2 and ZY-3 were selected to carry out the cloud detection test, and compared with the traditional SLIC method. The experiment results show that the average accuracy of cloud detection is increased by more than 5 %, and it can detected thin-thick cloud and the whole cloud boundary well on different imaging platforms.

A deep convolutional neural network for recognizing foods

NASA Astrophysics Data System (ADS)

Jahani Heravi, Elnaz; Habibi Aghdam, Hamed; Puig, Domenec

2015-12-01

Controlling the food intake is an efficient way that each person can undertake to tackle the obesity problem in countries worldwide. This is achievable by developing a smartphone application that is able to recognize foods and compute their calories. State-of-art methods are chiefly based on hand-crafted feature extraction methods such as HOG and Gabor. Recent advances in large-scale object recognition datasets such as ImageNet have revealed that deep Convolutional Neural Networks (CNN) possess more representation power than the hand-crafted features. The main challenge with CNNs is to find the appropriate architecture for each problem. In this paper, we propose a deep CNN which consists of 769; 988 parameters. Our experiments show that the proposed CNN outperforms the state-of-art methods and improves the best result of traditional methods 17%. Moreover, using an ensemble of two CNNs that have been trained two different times, we are able to improve the classification performance 21:5%.

Seismic waveform classification using deep learning

NASA Astrophysics Data System (ADS)

Kong, Q.; Allen, R. M.

2017-12-01

MyShake is a global smartphone seismic network that harnesses the power of crowdsourcing. It has an Artificial Neural Network (ANN) algorithm running on the phone to distinguish earthquake motion from human activities recorded by the accelerometer on board. Once the ANN detects earthquake-like motion, it sends a 5-min chunk of acceleration data back to the server for further analysis. The time-series data collected contains both earthquake data and human activity data that the ANN confused. In this presentation, we will show the Convolutional Neural Network (CNN) we built under the umbrella of supervised learning to find out the earthquake waveform. The waveforms of the recorded motion could treat easily as images, and by taking the advantage of the power of CNN processing the images, we achieved very high successful rate to select the earthquake waveforms out. Since there are many non-earthquake waveforms than the earthquake waveforms, we also built an anomaly detection algorithm using the CNN. Both these two methods can be easily extended to other waveform classification problems.

Strong convective storm nowcasting using a hybrid approach of convolutional neural network and hidden Markov model

NASA Astrophysics Data System (ADS)

Zhang, Wei; Jiang, Ling; Han, Lei

2018-04-01

Convective storm nowcasting refers to the prediction of the convective weather initiation, development, and decay in a very short term (typically 0 2 h) .Despite marked progress over the past years, severe convective storm nowcasting still remains a challenge. With the boom of machine learning, it has been well applied in various fields, especially convolutional neural network (CNN). In this paper, we build a servere convective weather nowcasting system based on CNN and hidden Markov model (HMM) using reanalysis meteorological data. The goal of convective storm nowcasting is to predict if there is a convective storm in 30min. In this paper, we compress the VDRAS reanalysis data to low-dimensional data by CNN as the observation vector of HMM, then obtain the development trend of strong convective weather in the form of time series. It shows that, our method can extract robust features without any artificial selection of features, and can capture the development trend of strong convective storm.

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue

PubMed Central

Peyster, Eliot G.; Frank, Renee; Margulies, Kenneth B.; Feldman, Michael D.

2018-01-01

Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome. PMID:29614076

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

PubMed

Zhan, Huijing; Shi, Boxin; Kot, Alex C

2017-08-04

Cross-domain shoe image retrieval is a challenging problem, because the query photo from the street domain (daily life scenario) and the reference photo in the online domain (online shop images) have significant visual differences due to the viewpoint and scale variation, self-occlusion, and cluttered background. This paper proposes the Semantic Hierarchy Of attributE Convolutional Neural Network (SHOE-CNN) with a three-level feature representation for discriminative shoe feature expression and efficient retrieval. The SHOE-CNN with its newly designed loss function systematically merges semantic attributes of closer visual appearances to prevent shoe images with the obvious visual differences being confused with each other; the features extracted from image, region, and part levels effectively match the shoe images across different domains. We collect a large-scale shoe dataset composed of 14341 street domain and 12652 corresponding online domain images with fine-grained attributes to train our network and evaluate our system. The top-20 retrieval accuracy improves significantly over the solution with the pre-trained CNN features.

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Xiaogang; De Carlo, Francesco; Phatak, Charudatta

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential forreducing or removingother artifacts caused by instrument instability, detector non-linearity,etc. An open-source toolbox, which integratesmore » the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.« less

A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue.

PubMed

Nirschl, Jeffrey J; Janowczyk, Andrew; Peyster, Eliot G; Frank, Renee; Margulies, Kenneth B; Feldman, Michael D; Madabhushi, Anant

2018-01-01

Over 26 million people worldwide suffer from heart failure annually. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. However, manual EMB interpretation has high inter-rater variability. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. Importantly, the CNN outperformed two expert pathologists by nearly 20%. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.

AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images.

PubMed

Albarqouni, Shadi; Baur, Christoph; Achilles, Felix; Belagiannis, Vasileios; Demirci, Stefanie; Navab, Nassir

2016-05-01

The lack of publicly available ground-truth data has been identified as the major challenge for transferring recent developments in deep learning to the biomedical imaging domain. Though crowdsourcing has enabled annotation of large scale databases for real world images, its application for biomedical purposes requires a deeper understanding and hence, more precise definition of the actual annotation task. The fact that expert tasks are being outsourced to non-expert users may lead to noisy annotations introducing disagreement between users. Despite being a valuable resource for learning annotation models from crowdsourcing, conventional machine-learning methods may have difficulties dealing with noisy annotations during training. In this manuscript, we present a new concept for learning from crowds that handle data aggregation directly as part of the learning process of the convolutional neural network (CNN) via additional crowdsourcing layer (AggNet). Besides, we present an experimental study on learning from crowds designed to answer the following questions. 1) Can deep CNN be trained with data collected from crowdsourcing? 2) How to adapt the CNN to train on multiple types of annotation datasets (ground truth and crowd-based)? 3) How does the choice of annotation and aggregation affect the accuracy? Our experimental setup involved Annot8, a self-implemented web-platform based on Crowdflower API realizing image annotation tasks for a publicly available biomedical image database. Our results give valuable insights into the functionality of deep CNN learning from crowd annotations and prove the necessity of data aggregation integration.

Structured illumination of the interface between centriole and peri-centriolar material

PubMed Central

Fu, Jingyan; Glover, David M.

2012-01-01

The increase in centrosome size in mitosis was described over a century ago, and yet it is poorly understood how centrioles, which lie at the core of centrosomes, organize the pericentriolar material (PCM) in this process. Now, structured illumination microscopy reveals in Drosophila that, before clouds of PCM appear, its proteins are closely associated with interphase centrioles in two tube-like layers: an inner layer occupied by centriolar microtubules, Sas-4, Spd-2 and Polo kinase; and an outer layer comprising Pericentrin-like protein (Dplp), Asterless (Asl) and Plk4 kinase. Centrosomin (Cnn) and γ-tubulin associate with this outer tube in G2 cells and, upon mitotic entry, Polo activity is required to recruit them together with Spd-2 into PCM clouds. Cnn is required for Spd-2 to expand into the PCM during this maturation process but can itself contribute to PCM independently of Spd-2. By contrast, the centrioles of spermatocytes elongate from a pre-existing proximal unit during the G2 preceding meiosis. Sas-4 is restricted to the microtubule-associated, inner cylinder and Dplp and Cnn to the outer cylinder of this proximal part. γ-Tubulin and Asl associate with the outer cylinder and Spd-2 with the inner cylinder throughout the entire G2 centriole. Although they occupy different spatial compartments on the G2 centriole, Cnn, Spd-2 and γ-tubulin become diminished at the centriole upon entry into meiosis to become part of PCM clouds. PMID:22977736

Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range

NASA Astrophysics Data System (ADS)

Huertas-Company, M.; Primack, J. R.; Dekel, A.; Koo, D. C.; Lapiner, S.; Ceverino, D.; Simons, R. C.; Snyder, G. F.; Bernardi, M.; Chen, Z.; Domínguez-Sánchez, H.; Lee, C. T.; Margalef-Bentabol, B.; Tuccillo, D.

2018-05-01

We use machine learning to identify in color images of high-redshift galaxies an astrophysical phenomenon predicted by cosmological simulations. This phenomenon, called the blue nugget (BN) phase, is the compact star-forming phase in the central regions of many growing galaxies that follows an earlier phase of gas compaction and is followed by a central quenching phase. We train a convolutional neural network (CNN) with mock “observed” images of simulated galaxies at three phases of evolution— pre-BN, BN, and post-BN—and demonstrate that the CNN successfully retrieves the three phases in other simulated galaxies. We show that BNs are identified by the CNN within a time window of ∼0.15 Hubble times. When the trained CNN is applied to observed galaxies from the CANDELS survey at z = 1–3, it successfully identifies galaxies at the three phases. We find that the observed BNs are preferentially found in galaxies at a characteristic stellar mass range, 109.2–10.3 M ⊙ at all redshifts. This is consistent with the characteristic galaxy mass for BNs as detected in the simulations and is meaningful because it is revealed in the observations when the direct information concerning the total galaxy luminosity has been eliminated from the training set. This technique can be applied to the classification of other astrophysical phenomena for improved comparison of theory and observations in the era of large imaging surveys and cosmological simulations.

Deep 3D convolution neural network for CT brain hemorrhage classification

NASA Astrophysics Data System (ADS)

Jnawali, Kamal; Arbabshirani, Mohammad R.; Rao, Navalgund; Patel, Alpen A.

2018-02-01

Intracranial hemorrhage is a critical conditional with the high mortality rate that is typically diagnosed based on head computer tomography (CT) images. Deep learning algorithms, in particular, convolution neural networks (CNN), are becoming the methodology of choice in medical image analysis for a variety of applications such as computer-aided diagnosis, and segmentation. In this study, we propose a fully automated deep learning framework which learns to detect brain hemorrhage based on cross sectional CT images. The dataset for this work consists of 40,367 3D head CT studies (over 1.5 million 2D images) acquired retrospectively over a decade from multiple radiology facilities at Geisinger Health System. The proposed algorithm first extracts features using 3D CNN and then detects brain hemorrhage using the logistic function as the last layer of the network. Finally, we created an ensemble of three different 3D CNN architectures to improve the classification accuracy. The area under the curve (AUC) of the receiver operator characteristic (ROC) curve of the ensemble of three architectures was 0.87. Their results are very promising considering the fact that the head CT studies were not controlled for slice thickness, scanner type, study protocol or any other settings. Moreover, the proposed algorithm reliably detected various types of hemorrhage within the skull. This work is one of the first applications of 3D CNN trained on a large dataset of cross sectional medical images for detection of a critical radiological condition

Performance comparison of deep learning and segmentation-based radiomic methods in the task of distinguishing benign and malignant breast lesions on DCE-MRI

NASA Astrophysics Data System (ADS)

Antropova, Natasha; Huynh, Benjamin; Giger, Maryellen

2017-03-01

Intuitive segmentation-based CADx/radiomic features, calculated from the lesion segmentations of dynamic contrast-enhanced magnetic resonance images (DCE-MRIs) have been utilized in the task of distinguishing between malignant and benign lesions. Additionally, transfer learning with pre-trained deep convolutional neural networks (CNNs) allows for an alternative method of radiomics extraction, where the features are derived directly from the image data. However, the comparison of computer-extracted segmentation-based and CNN features in MRI breast lesion characterization has not yet been conducted. In our study, we used a DCE-MRI database of 640 breast cases - 191 benign and 449 malignant. Thirty-eight segmentation-based features were extracted automatically using our quantitative radiomics workstation. Also, 2D ROIs were selected around each lesion on the DCE-MRIs and directly input into a pre-trained CNN AlexNet, yielding CNN features. Each method was investigated separately and in combination in terms of performance in the task of distinguishing between benign and malignant lesions. Area under the ROC curve (AUC) served as the figure of merit. Both methods yielded promising classification performance with round-robin cross-validated AUC values of 0.88 (se =0.01) and 0.76 (se=0.02) for segmentationbased and deep learning methods, respectively. Combination of the two methods enhanced the performance in malignancy assessment resulting in an AUC value of 0.91 (se=0.01), a statistically significant improvement over the performance of the CNN method alone.

SAR image classification based on CNN in real and simulation datasets

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Ming; Liu, Xiaohua; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-04-01

Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR), state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.

A street rubbish detection algorithm based on Sift and RCNN

NASA Astrophysics Data System (ADS)

Yu, XiPeng; Chen, Zhong; Zhang, Shuo; Zhang, Ting

2018-02-01

This paper presents a street rubbish detection algorithm based on image registration with Sift feature and RCNN. Firstly, obtain the rubbish region proposal on the real-time street image and set up the CNN convolution neural network trained by the rubbish samples set consists of rubbish and non-rubbish images; Secondly, for every clean street image, obtain the Sift feature and do image registration with the real-time street image to obtain the differential image, the differential image filters a lot of background information, obtain the rubbish region proposal rect where the rubbish may appear on the differential image by the selective search algorithm. Then, the CNN model is used to detect the image pixel data in each of the region proposal on the real-time street image. According to the output vector of the CNN, it is judged whether the rubbish is in the region proposal or not. If it is rubbish, the region proposal on the real-time street image is marked. This algorithm avoids the large number of false detection caused by the detection on the whole image because the CNN is used to identify the image only in the region proposal on the real-time street image that may appear rubbish. Different from the traditional object detection algorithm based on the region proposal, the region proposal is obtained on the differential image not whole real-time street image, and the number of the invalid region proposal is greatly reduced. The algorithm has the high mean average precision (mAP).

A universal concept based on cellular neural networks for ultrafast and flexible solving of differential equations.

PubMed

Chedjou, Jean Chamberlain; Kyamakya, Kyandoghere

2015-04-01

This paper develops and validates a comprehensive and universally applicable computational concept for solving nonlinear differential equations (NDEs) through a neurocomputing concept based on cellular neural networks (CNNs). High-precision, stability, convergence, and lowest-possible memory requirements are ensured by the CNN processor architecture. A significant challenge solved in this paper is that all these cited computing features are ensured in all system-states (regular or chaotic ones) and in all bifurcation conditions that may be experienced by NDEs.One particular quintessence of this paper is to develop and demonstrate a solver concept that shows and ensures that CNN processors (realized either in hardware or in software) are universal solvers of NDE models. The solving logic or algorithm of given NDEs (possible examples are: Duffing, Mathieu, Van der Pol, Jerk, Chua, Rössler, Lorenz, Burgers, and the transport equations) through a CNN processor system is provided by a set of templates that are computed by our comprehensive templates calculation technique that we call nonlinear adaptive optimization. This paper is therefore a significant contribution and represents a cutting-edge real-time computational engineering approach, especially while considering the various scientific and engineering applications of this ultrafast, energy-and-memory-efficient, and high-precise NDE solver concept. For illustration purposes, three NDE models are demonstratively solved, and related CNN templates are derived and used: the periodically excited Duffing equation, the Mathieu equation, and the transport equation.

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors

PubMed Central

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-01-01

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases. PMID:29495417

Combining Deep and Handcrafted Image Features for Presentation Attack Detection in Face Recognition Systems Using Visible-Light Camera Sensors.

PubMed

Nguyen, Dat Tien; Pham, Tuyen Danh; Baek, Na Rae; Park, Kang Ryoung

2018-02-26

Although face recognition systems have wide application, they are vulnerable to presentation attack samples (fake samples). Therefore, a presentation attack detection (PAD) method is required to enhance the security level of face recognition systems. Most of the previously proposed PAD methods for face recognition systems have focused on using handcrafted image features, which are designed by expert knowledge of designers, such as Gabor filter, local binary pattern (LBP), local ternary pattern (LTP), and histogram of oriented gradients (HOG). As a result, the extracted features reflect limited aspects of the problem, yielding a detection accuracy that is low and varies with the characteristics of presentation attack face images. The deep learning method has been developed in the computer vision research community, which is proven to be suitable for automatically training a feature extractor that can be used to enhance the ability of handcrafted features. To overcome the limitations of previously proposed PAD methods, we propose a new PAD method that uses a combination of deep and handcrafted features extracted from the images by visible-light camera sensor. Our proposed method uses the convolutional neural network (CNN) method to extract deep image features and the multi-level local binary pattern (MLBP) method to extract skin detail features from face images to discriminate the real and presentation attack face images. By combining the two types of image features, we form a new type of image features, called hybrid features, which has stronger discrimination ability than single image features. Finally, we use the support vector machine (SVM) method to classify the image features into real or presentation attack class. Our experimental results indicate that our proposed method outperforms previous PAD methods by yielding the smallest error rates on the same image databases.

SECULAR EVOLUTION OF BINARIES NEAR MASSIVE BLACK HOLES: FORMATION OF COMPACT BINARIES, MERGER/COLLISION PRODUCTS AND G2-LIKE OBJECTS

DOE Office of Scientific and Technical Information (OSTI.GOV)

Prodan, Snezana; Antonini, Fabio; Perets, Hagai B., E-mail: sprodan@cita.utoronto.ca, E-mail: antonini@cita.utoronto.ca

2015-02-01

Here we discuss the evolution of binaries around massive black holes (MBHs) in nuclear stellar clusters. We focus on their secular evolution due to the perturbation by the MBHs, while simplistically accounting for their collisional evolution. Binaries with highly inclined orbits with respect to their orbits around MBHs are strongly affected by secular processes, which periodically change their eccentricities and inclinations (e.g., Kozai-Lidov cycles). During periapsis approach, dissipative processes such as tidal friction may become highly efficient, and may lead to shrinkage of a binary orbit and even to its merger. Binaries in this environment can therefore significantly change theirmore » orbital evolution due to the MBH third-body perturbative effects. Such orbital evolution may impinge on their later stellar evolution. Here we follow the secular dynamics of such binaries and its coupling to tidal evolution, as well as the stellar evolution of such binaries on longer timescales. We find that stellar binaries in the central parts of nuclear stellar clusters (NSCs) are highly likely to evolve into eccentric and/or short-period binaries, and become strongly interacting binaries either on the main sequence (at which point they may even merge), or through their later binary stellar evolution. The central parts of NSCs therefore catalyze the formation and evolution of strongly interacting binaries, and lead to the enhanced formation of blue stragglers, X-ray binaries, gravitational wave sources, and possible supernova progenitors. Induced mergers/collisions may also lead to the formation of G2-like cloud-like objects such as the one recently observed in the Galactic center.« less

Tidal resonances in binary star systems. II - Slowly rotating stars

NASA Astrophysics Data System (ADS)

Alexander, M. E.

1988-12-01

The potential energy of tidal interactions in a binary system with rotating components is formulated as a perturbation Hamiltonian which self-consistently couples the dynamics of the rotating stars' oscillations and orbital motion. The action-angle formalism used to discuss tidal resonances in the nonrotating case (Alexander, 1987) is extended to rotating stars. The behavior of a two-mode system and the procedure for treating an arbitrary number of modes are discussed.

Vision-based Detection of Acoustic Timed Events: a Case Study on Clarinet Note Onsets

NASA Astrophysics Data System (ADS)

Bazzica, A.; van Gemert, J. C.; Liem, C. C. S.; Hanjalic, A.

2017-05-01

Acoustic events often have a visual counterpart. Knowledge of visual information can aid the understanding of complex auditory scenes, even when only a stereo mixdown is available in the audio domain, \\eg identifying which musicians are playing in large musical ensembles. In this paper, we consider a vision-based approach to note onset detection. As a case study we focus on challenging, real-world clarinetist videos and carry out preliminary experiments on a 3D convolutional neural network based on multiple streams and purposely avoiding temporal pooling. We release an audiovisual dataset with 4.5 hours of clarinetist videos together with cleaned annotations which include about 36,000 onsets and the coordinates for a number of salient points and regions of interest. By performing several training trials on our dataset, we learned that the problem is challenging. We found that the CNN model is highly sensitive to the optimization algorithm and hyper-parameters, and that treating the problem as binary classification may prevent the joint optimization of precision and recall. To encourage further research, we publicly share our dataset, annotations and all models and detail which issues we came across during our preliminary experiments.

Ordinal convolutional neural networks for predicting RDoC positive valence psychiatric symptom severity scores.

PubMed

Rios, Anthony; Kavuluru, Ramakanth

2017-11-01

The CEGS N-GRID 2016 Shared Task in Clinical Natural Language Processing (NLP) provided a set of 1000 neuropsychiatric notes to participants as part of a competition to predict psychiatric symptom severity scores. This paper summarizes our methods, results, and experiences based on our participation in the second track of the shared task. Classical methods of text classification usually fall into one of three problem types: binary, multi-class, and multi-label classification. In this effort, we study ordinal regression problems with text data where misclassifications are penalized differently based on how far apart the ground truth and model predictions are on the ordinal scale. Specifically, we present our entries (methods and results) in the N-GRID shared task in predicting research domain criteria (RDoC) positive valence ordinal symptom severity scores (absent, mild, moderate, and severe) from psychiatric notes. We propose a novel convolutional neural network (CNN) model designed to handle ordinal regression tasks on psychiatric notes. Broadly speaking, our model combines an ordinal loss function, a CNN, and conventional feature engineering (wide features) into a single model which is learned end-to-end. Given interpretability is an important concern with nonlinear models, we apply a recent approach called locally interpretable model-agnostic explanation (LIME) to identify important words that lead to instance specific predictions. Our best model entered into the shared task placed third among 24 teams and scored a macro mean absolute error (MMAE) based normalized score (100·(1-MMAE)) of 83.86. Since the competition, we improved our score (using basic ensembling) to 85.55, comparable with the winning shared task entry. Applying LIME to model predictions, we demonstrate the feasibility of instance specific prediction interpretation by identifying words that led to a particular decision. In this paper, we present a method that successfully uses wide features and an ordinal loss function applied to convolutional neural networks for ordinal text classification specifically in predicting psychiatric symptom severity scores. Our approach leads to excellent performance on the N-GRID shared task and is also amenable to interpretability using existing model-agnostic approaches. Copyright © 2017 Elsevier Inc. All rights reserved.

Identifying QCD Transition Using Deep Learning

NASA Astrophysics Data System (ADS)

Zhou, Kai; Pang, Long-gang; Su, Nan; Petersen, Hannah; Stoecker, Horst; Wang, Xin-Nian

2018-02-01

In this proceeding we review our recent work using supervised learning with a deep convolutional neural network (CNN) to identify the QCD equation of state (EoS) employed in hydrodynamic modeling of heavy-ion collisions given only final-state particle spectra ρ(pT, V). We showed that there is a traceable encoder of the dynamical information from phase structure (EoS) that survives the evolution and exists in the final snapshot, which enables the trained CNN to act as an effective "EoS-meter" in detecting the nature of the QCD transition.

Hybrid Black-Hole Binary Initial Data

NASA Technical Reports Server (NTRS)

Mundim, Bruno C.; Kelly, Bernard J.; Nakano, Hiroyuki; Zlochower, Yosef; Campanelli, Manuela

2010-01-01

"Traditional black-hole binary puncture initial data is conformally flat. This unphysical assumption is coupled with a lack of radiation signature from the binary's past life. As a result, waveforms extracted from evolutions of this data display an abrupt jump. In Kelly et al. [Class. Quantum Grav. 27:114005 (2010)], a new binary black-hole initial data with radiation contents derived in the post-Newtonian (PN) calculations was adapted to puncture evolutions in numerical relativity. This data satisfies the constraint equations to the 2.5PN order, and contains a transverse-traceless "wavy" metric contribution, violating the standard assumption of conformal flatness. Although the evolution contained less spurious radiation, there were undesired features; the unphysical horizon mass loss and the large initial orbital eccentricity. Introducing a hybrid approach to the initial data evaluation, we significantly reduce these undesired features."

Dynamics of rotationally fissioned asteroids: Source of observed small asteroid systems

NASA Astrophysics Data System (ADS)

Jacobson, Seth A.; Scheeres, Daniel J.

2011-07-01

We present a model of near-Earth asteroid (NEA) rotational fission and ensuing dynamics that describes the creation of synchronous binaries and all other observed NEA systems including: doubly synchronous binaries, high- e binaries, ternary systems, and contact binaries. Our model only presupposes the Yarkovsky-O'Keefe-Radzievskii-Paddack (YORP) effect, "rubble pile" asteroid geophysics, and gravitational interactions. The YORP effect torques a "rubble pile" asteroid until the asteroid reaches its fission spin limit and the components enter orbit about each other (Scheeres, D.J. [2007]. Icarus 189, 370-385). Non-spherical gravitational potentials couple the spin states to the orbit state and chaotically drive the system towards the observed asteroid classes along two evolutionary tracks primarily distinguished by mass ratio. Related to this is a new binary process termed secondary fission - the secondary asteroid of the binary system is rotationally accelerated via gravitational torques until it fissions, thus creating a chaotic ternary system. The initially chaotic binary can be stabilized to create a synchronous binary by components of the fissioned secondary asteroid impacting the primary asteroid, solar gravitational perturbations, and mutual body tides. These results emphasize the importance of the initial component size distribution and configuration within the parent asteroid. NEAs may go through multiple binary cycles and many YORP-induced rotational fissions during their approximately 10 Myr lifetime in the inner Solar System. Rotational fission and the ensuing dynamics are responsible for all NEA systems including the most commonly observed synchronous binaries.

Hidden-sector Spectroscopy with Gravitational Waves from Binary Neutron Stars

NASA Astrophysics Data System (ADS)

Croon, Djuna; Nelson, Ann E.; Sun, Chen; Walker, Devin G. E.; Xianyu, Zhong-Zhi

2018-05-01

We show that neutron star (NS) binaries can be ideal laboratories to probe hidden sectors with a long-range force. In particular, it is possible for gravitational wave (GW) detectors such as LIGO and Virgo to resolve the correction of waveforms from ultralight dark gauge bosons coupled to NSs. We observe that the interaction of the hidden sector affects both the GW frequency and amplitude in a way that cannot be fitted by pure gravity.

Long-orbital-period Prepolars Containing Early K-type Donor Stars. Bottleneck Accretion Mechanism in Action

NASA Astrophysics Data System (ADS)

Tovmassian, G.; González–Buitrago, D.; Zharikov, S.; Reichart, D. E.; Haislip, J. B.; Ivarsen, K. M.; LaCluyze, A. P.; Moore, J. P.; Miroshnichenko, A. S.

2016-03-01

We studied two objects identified as cataclysmic variables (CVs) with periods exceeding the natural boundary for Roche-lobe-filling zero-age main sequence (ZAMS) secondary stars. We present observational results for V1082 Sgr with a 20.82 hr orbital period, an object that shows a low luminosity state when its flux is totally dominated by a chromospherically active K star with no signs of ongoing accretion. Frequent accretion shutoffs, together with characteristics of emission lines in a high state, indicate that this binary system is probably detached, and the accretion of matter on the magnetic white dwarf takes place through stellar wind from the active donor star via coupled magnetic fields. Its observational characteristics are surprisingly similar to V479 And, a 14.5 hr binary system. They both have early K-type stars as donor stars. We argue that, similar to the shorter-period prepolars containing M dwarfs, these are detached binaries with strong magnetic components. Their magnetic fields are coupled, allowing enhanced stellar wind from the K star to be captured and channeled through the bottleneck connecting the two stars onto the white dwarf’s magnetic pole, mimicking a magnetic CV. Hence, they become interactive binaries before they reach contact. This will help to explain an unexpected lack of systems possessing white dwarfs with strong magnetic fields among detached white+red dwarf systems.

Deep convolutional neural networks as strong gravitational lens detectors

NASA Astrophysics Data System (ADS)

Schaefer, C.; Geiger, M.; Kuntzer, T.; Kneib, J.-P.

2018-03-01

Context. Future large-scale surveys with high-resolution imaging will provide us with approximately 105 new strong galaxy-scale lenses. These strong-lensing systems will be contained in large data amounts, however, which are beyond the capacity of human experts to visually classify in an unbiased way. Aim. We present a new strong gravitational lens finder based on convolutional neural networks (CNNs). The method was applied to the strong-lensing challenge organized by the Bologna Lens Factory. It achieved first and third place, respectively, on the space-based data set and the ground-based data set. The goal was to find a fully automated lens finder for ground-based and space-based surveys that minimizes human inspection. Methods: We compared the results of our CNN architecture and three new variations ("invariant" "views" and "residual") on the simulated data of the challenge. Each method was trained separately five times on 17 000 simulated images, cross-validated using 3000 images, and then applied to a test set with 100 000 images. We used two different metrics for evaluation, the area under the receiver operating characteristic curve (AUC) score, and the recall with no false positive (Recall0FP). Results: For ground-based data, our best method achieved an AUC score of 0.977 and a Recall0FP of 0.50. For space-based data, our best method achieved an AUC score of 0.940 and a Recall0FP of 0.32. Adding dihedral invariance to the CNN architecture diminished the overall score on space-based data, but achieved a higher no-contamination recall. We found that using committees of five CNNs produced the best recall at zero contamination and consistently scored better AUC than a single CNN. Conclusions: We found that for every variation of our CNN lensfinder, we achieved AUC scores close to 1 within 6%. A deeper network did not outperform simpler CNN models either. This indicates that more complex networks are not needed to model the simulated lenses. To verify this, more realistic lens simulations with more lens-like structures (spiral galaxies or ring galaxies) are needed to compare the performance of deeper and shallower networks.

Lung nodule malignancy prediction using multi-task convolutional neural network

NASA Astrophysics Data System (ADS)

Li, Xiuli; Kao, Yueying; Shen, Wei; Li, Xiang; Xie, Guotong

2017-03-01

In this paper, we investigated the problem of diagnostic lung nodule malignancy prediction using thoracic Computed Tomography (CT) screening. Unlike most existing studies classify the nodules into two types benign and malignancy, we interpreted the nodule malignancy prediction as a regression problem to predict continuous malignancy level. We proposed a joint multi-task learning algorithm using Convolutional Neural Network (CNN) to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. We trained a CNN regression model to predict the nodule malignancy, and designed a multi-task learning mechanism to simultaneously share knowledge among 9 different nodule characteristics (Subtlety, Calcification, Sphericity, Margin, Lobulation, Spiculation, Texture, Diameter and Malignancy), and improved the final prediction result. Each CNN would generate characteristic-specific feature representations, and then we applied multi-task learning on the features to predict the corresponding likelihood for that characteristic. We evaluated the proposed method on 2620 nodules CT scans from LIDC-IDRI dataset with the 5-fold cross validation strategy. The multitask CNN regression result for regression RMSE and mapped classification ACC were 0.830 and 83.03%, while the results for single task regression RMSE 0.894 and mapped classification ACC 74.9%. Experiments show that the proposed method could predict the lung nodule malignancy likelihood effectively and outperforms the state-of-the-art methods. The learning framework could easily be applied in other anomaly likelihood prediction problem, such as skin cancer and breast cancer. It demonstrated the possibility of our method facilitating the radiologists for nodule staging assessment and individual therapeutic planning.

Dimensionality-varied convolutional neural network for spectral-spatial classification of hyperspectral data

NASA Astrophysics Data System (ADS)

Liu, Wanjun; Liang, Xuejian; Qu, Haicheng

2017-11-01

Hyperspectral image (HSI) classification is one of the most popular topics in remote sensing community. Traditional and deep learning-based classification methods were proposed constantly in recent years. In order to improve the classification accuracy and robustness, a dimensionality-varied convolutional neural network (DVCNN) was proposed in this paper. DVCNN was a novel deep architecture based on convolutional neural network (CNN). The input of DVCNN was a set of 3D patches selected from HSI which contained spectral-spatial joint information. In the following feature extraction process, each patch was transformed into some different 1D vectors by 3D convolution kernels, which were able to extract features from spectral-spatial data. The rest of DVCNN was about the same as general CNN and processed 2D matrix which was constituted by by all 1D data. So that the DVCNN could not only extract more accurate and rich features than CNN, but also fused spectral-spatial information to improve classification accuracy. Moreover, the robustness of network on water-absorption bands was enhanced in the process of spectral-spatial fusion by 3D convolution, and the calculation was simplified by dimensionality varied convolution. Experiments were performed on both Indian Pines and Pavia University scene datasets, and the results showed that the classification accuracy of DVCNN improved by 32.87% on Indian Pines and 19.63% on Pavia University scene than spectral-only CNN. The maximum accuracy improvement of DVCNN achievement was 13.72% compared with other state-of-the-art HSI classification methods, and the robustness of DVCNN on water-absorption bands noise was demonstrated.

Deep residual networks for automatic segmentation of laparoscopic videos of the liver

NASA Astrophysics Data System (ADS)

Gibson, Eli; Robu, Maria R.; Thompson, Stephen; Edwards, P. Eddie; Schneider, Crispin; Gurusamy, Kurinchi; Davidson, Brian; Hawkes, David J.; Barratt, Dean C.; Clarkson, Matthew J.

2017-03-01

Motivation: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. Method: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. Results: The CNN yielded segmentations with Dice scores >=0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. Conclusion: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field.

PubMed

Christiansen, Peter; Nielsen, Lars N; Steen, Kim A; Jørgensen, Rasmus N; Karstoft, Henrik

2016-11-11

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks" (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45-90 m) than RCNN. RCNN has a similar performance at a short range (0-30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).

Single-trial EEG RSVP classification using convolutional neural networks

NASA Astrophysics Data System (ADS)

Shamwell, Jared; Lee, Hyungtae; Kwon, Heesung; Marathe, Amar R.; Lawhern, Vernon; Nothwang, William

2016-05-01

Traditionally, Brain-Computer Interfaces (BCI) have been explored as a means to return function to paralyzed or otherwise debilitated individuals. An emerging use for BCIs is in human-autonomy sensor fusion where physiological data from healthy subjects is combined with machine-generated information to enhance the capabilities of artificial systems. While human-autonomy fusion of physiological data and computer vision have been shown to improve classification during visual search tasks, to date these approaches have relied on separately trained classification models for each modality. We aim to improve human-autonomy classification performance by developing a single framework that builds codependent models of human electroencephalograph (EEG) and image data to generate fused target estimates. As a first step, we developed a novel convolutional neural network (CNN) architecture and applied it to EEG recordings of subjects classifying target and non-target image presentations during a rapid serial visual presentation (RSVP) image triage task. The low signal-to-noise ratio (SNR) of EEG inherently limits the accuracy of single-trial classification and when combined with the high dimensionality of EEG recordings, extremely large training sets are needed to prevent overfitting and achieve accurate classification from raw EEG data. This paper explores a new deep CNN architecture for generalized multi-class, single-trial EEG classification across subjects. We compare classification performance from the generalized CNN architecture trained across all subjects to the individualized XDAWN, HDCA, and CSP neural classifiers which are trained and tested on single subjects. Preliminary results show that our CNN meets and slightly exceeds the performance of the other classifiers despite being trained across subjects.

DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles and Anomalies in an Agricultural Field

PubMed Central

Christiansen, Peter; Nielsen, Lars N.; Steen, Kim A.; Jørgensen, Rasmus N.; Karstoft, Henrik

2016-01-01

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit). PMID:27845717

Finding strong gravitational lenses in the Kilo Degree Survey with Convolutional Neural Networks

NASA Astrophysics Data System (ADS)

Petrillo, C. E.; Tortora, C.; Chatterjee, S.; Vernardos, G.; Koopmans, L. V. E.; Verdoes Kleijn, G.; Napolitano, N. R.; Covone, G.; Schneider, P.; Grado, A.; McFarland, J.

2017-11-01

The volume of data that will be produced by new-generation surveys requires automatic classification methods to select and analyse sources. Indeed, this is the case for the search for strong gravitational lenses, where the population of the detectable lensed sources is only a very small fraction of the full source population. We apply for the first time a morphological classification method based on a Convolutional Neural Network (CNN) for recognizing strong gravitational lenses in 255 deg2 of the Kilo Degree Survey (KiDS), one of the current-generation optical wide surveys. The CNN is currently optimized to recognize lenses with Einstein radii ≳1.4 arcsec, about twice the r-band seeing in KiDS. In a sample of 21 789 colour-magnitude selected luminous red galaxies (LRGs), of which three are known lenses, the CNN retrieves 761 strong-lens candidates and correctly classifies two out of three of the known lenses. The misclassified lens has an Einstein radius below the range on which the algorithm is trained. We down-select the most reliable 56 candidates by a joint visual inspection. This final sample is presented and discussed. A conservative estimate based on our results shows that with our proposed method it should be possible to find ∼100 massive LRG-galaxy lenses at z ≲ 0.4 in KiDS when completed. In the most optimistic scenario, this number can grow considerably (to maximally ∼2400 lenses), when widening the colour-magnitude selection and training the CNN to recognize smaller image-separation lens systems.

Direct electrochemistry and electrocatalysis of heme proteins immobilised in carbon-coated nickel magnetic nanoparticle-chitosan-dimethylformamide composite films in room-temperature ionic liquids.

PubMed

Wang, Ting; Wang, Lu; Tu, Jiaojiao; Xiong, Huayu; Wang, Shengfu

2013-12-01

The direct electrochemistry and electrocatalysis of heme proteins entrapped in carbon-coated nickel magnetic nanoparticle-chitosan-dimethylformamide (CNN-CS-DMF) composite films were investigated in the hydrophilic ionic liquid [bmim][BF4]. The surface morphologies of a representative set of films were characterised via scanning electron microscopy. The proteins immobilised in the composite films were shown to retain their native secondary structure using UV-vis spectroscopy. The electrochemical performance of the heme proteins-CNN-CS-DMF films was evaluated via cyclic voltammetry and chronoamperometry. A pair of stable and well-defined redox peaks was observed for the heme protein films at formal potentials of -0.151 V (HRP), -0.167 V (Hb), -0.155 V (Mb) and -0.193 V (Cyt c) in [bmim][BF4]. Moreover, several electrochemical parameters of the heme proteins were calculated by nonlinear regression analysis of the square-wave voltammetry. The addition of CNN significantly enhanced not only the electron transfer of the heme proteins but also their electrocatalytic activity toward the reduction of H2O2. Low apparent Michaelis-Menten constants were obtained for the heme protein-CNN-CS-DMF films, demonstrating that the biosensors have a high affinity for H2O2. In addition, the resulting electrodes displayed a low detection limit and improved sensitivity for detecting H2O2, which indicates that the biocomposite film can serve as a platform for constructing new non-aqueous biosensors for real detection. Copyright © 2013 Elsevier B.V. All rights reserved.

Using HMXBs to Probe Massive Binary Evolution

NASA Astrophysics Data System (ADS)

Garofali, Kristen

2017-09-01

We propose using deep archival Chandra data of M33 to characterize the distribution of physical parameters for the high-mass X-ray binary (HMXB) population from X-ray spectra, X-ray lightcurves, and identified optical counterparts coupled with ground-based spectroscopy. Our analysis will provide the largest clean sample of HMXBs in M33, including hardness, short- and long-term variability, luminosity, and ages. These measurements will be compared across M33 and to HMXB studies in other nearby galaxies to test correlations between HMXB population and host properties such as metallicity and star formation rate. Furthermore, our measurements will yield empirical constraints on prescriptions for models of the formation and evolution of massive stars in binaries.

Regulated dc-to-dc converter for voltage step-up or step-down with input-output isolation

NASA Technical Reports Server (NTRS)

Feng, S. Y.; Wilson, T. G. (Inventor)

1973-01-01

A closed loop regulated dc-to-dc converter employing an unregulated two winding inductive energy storage converter is provided by using a magnetically coupled multivibrator acting as duty cycle generator to drive the converter. The multivibrator is comprised of two transistor switches and a saturable transformer. The output of the converter is compared with a reference in a comparator which transmits a binary zero until the output exceeds the reference. When the output exceeds the reference, the binary output of the comparator drives transistor switches to turn the multivibrator off. The multivibrator is unbalanced so that a predetermined transistor will always turn on first when the binary feedback signal becomes zero.

Re-examining the role of Drosophila Sas-4 in centrosome assembly using two-colour-3D-SIM FRAP

PubMed Central

Conduit, Paul T; Wainman, Alan; Novak, Zsofia A; Weil, Timothy T; Raff, Jordan W

2015-01-01

Centrosomes have many important functions and comprise a ‘mother’ and ‘daughter’ centriole surrounded by pericentriolar material (PCM). The mother centriole recruits and organises the PCM and templates the formation of the daughter centriole. It has been reported that several important Drosophila PCM-organising proteins are recruited to centrioles from the cytosol as part of large cytoplasmic ‘S-CAP’ complexes that contain the centriole protein Sas-4. In a previous paper (Conduit et al., 2014b) we showed that one of these proteins, Cnn, and another key PCM-organising protein, Spd-2, are recruited around the mother centriole before spreading outwards to form a scaffold that supports mitotic PCM assembly; the recruitment of Cnn and Spd-2 is dependent on another S-CAP protein, Asl. We show here, however, that Cnn, Spd-2 and Asl are not recruited to the mother centriole as part of a complex with Sas-4. Thus, PCM recruitment in fly embryos does not appear to require cytosolic S-CAP complexes. DOI: http://dx.doi.org/10.7554/eLife.08483.001 PMID:26530814

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed

K, Jalal Deen; R, Ganesan; A, Merline

2017-07-27

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. Creative Commons Attribution License

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

PubMed

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-04-10

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks.

Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

PubMed Central

K, Jalal Deen; R, Ganesan; A, Merline

2017-01-01

Objective: Accurate segmentation of abnormal and healthy lungs is very crucial for a steadfast computer-aided disease diagnostics. Methods: For this purpose a stack of chest CT scans are processed. In this paper, novel methods are proposed for segmentation of the multimodal grayscale lung CT scan. In the conventional methods using Markov–Gibbs Random Field (MGRF) model the required regions of interest (ROI) are identified. Result: The results of proposed FCM and CNN based process are compared with the results obtained from the conventional method using MGRF model. The results illustrate that the proposed method can able to segment the various kinds of complex multimodal medical images precisely. Conclusion: However, in this paper, to obtain an exact boundary of the regions, every empirical dispersion of the image is computed by Fuzzy C-Means Clustering segmentation. A classification process based on the Convolutional Neural Network (CNN) classifier is accomplished to distinguish the normal tissue and the abnormal tissue. The experimental evaluation is done using the Interstitial Lung Disease (ILD) database. PMID:28749127

Classification of Alzheimer's Disease Based on Eight-Layer Convolutional Neural Network with Leaky Rectified Linear Unit and Max Pooling.

PubMed

Wang, Shui-Hua; Phillips, Preetha; Sui, Yuxiu; Liu, Bin; Yang, Ming; Cheng, Hong

2018-03-26

Alzheimer's disease (AD) is a progressive brain disease. The goal of this study is to provide a new computer-vision based technique to detect it in an efficient way. The brain-imaging data of 98 AD patients and 98 healthy controls was collected using data augmentation method. Then, convolutional neural network (CNN) was used, CNN is the most successful tool in deep learning. An 8-layer CNN was created with optimal structure obtained by experiences. Three activation functions (AFs): sigmoid, rectified linear unit (ReLU), and leaky ReLU. The three pooling-functions were also tested: average pooling, max pooling, and stochastic pooling. The numerical experiments demonstrated that leaky ReLU and max pooling gave the greatest result in terms of performance. It achieved a sensitivity of 97.96%, a specificity of 97.35%, and an accuracy of 97.65%, respectively. In addition, the proposed approach was compared with eight state-of-the-art approaches. The method increased the classification accuracy by approximately 5% compared to state-of-the-art methods.

Village Building Identification Based on Ensemble Convolutional Neural Networks

PubMed Central

Guo, Zhiling; Chen, Qi; Xu, Yongwei; Shibasaki, Ryosuke; Shao, Xiaowei

2017-01-01

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method. Finally, the generated ECNN was applied to a pixel-level classification frame to implement object identification. The proposed method can serve as a viable tool for village building identification with high accuracy and efficiency. The experimental results obtained from the test area in Savannakhet province, Laos, prove that the proposed ECNN model significantly outperforms existing methods, improving overall accuracy from 96.64% to 99.26%, and kappa from 0.57 to 0.86. PMID:29084154

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

NASA Astrophysics Data System (ADS)

Peng, Lijiang; Liu, Xiaohua; Liu, Ming; Dong, Liquan; Hui, Mei; Zhao, Yuejin

2018-01-01

Many convolution neural networks(CNN) architectures have been proposed to strengthen the performance on synthetic aperture radar automatic target recognition (SAR-ATR) and obtained state-of-art results on targets classification on MSTAR database, but few methods concern about the estimation of depression angle and azimuth angle of targets. To get better effect on learning representation of hierarchies of features on both 10-class target classification task and target posture estimation tasks, we propose a new CNN architecture with spatial pyramid pooling(SPP) which can build high hierarchy of features map by dividing the convolved feature maps from finer to coarser levels to aggregate local features of SAR images. Experimental results on MSTAR database show that the proposed architecture can get high recognition accuracy as 99.57% on 10-class target classification task as the most current state-of-art methods, and also get excellent performance on target posture estimation tasks which pays attention to depression angle variety and azimuth angle variety. What's more, the results inspire us the application of deep learning on SAR target posture description.

Application of Convolutional Neural Network in Classification of High Resolution Agricultural Remote Sensing Images

NASA Astrophysics Data System (ADS)

Yao, C.; Zhang, Y.; Zhang, Y.; Liu, H.

2017-09-01

With the rapid development of Precision Agriculture (PA) promoted by high-resolution remote sensing, it makes significant sense in management and estimation of agriculture through crop classification of high-resolution remote sensing image. Due to the complex and fragmentation of the features and the surroundings in the circumstance of high-resolution, the accuracy of the traditional classification methods has not been able to meet the standard of agricultural problems. In this case, this paper proposed a classification method for high-resolution agricultural remote sensing images based on convolution neural networks(CNN). For training, a large number of training samples were produced by panchromatic images of GF-1 high-resolution satellite of China. In the experiment, through training and testing on the CNN under the toolbox of deep learning by MATLAB, the crop classification finally got the correct rate of 99.66 % after the gradual optimization of adjusting parameter during training. Through improving the accuracy of image classification and image recognition, the applications of CNN provide a reference value for the field of remote sensing in PA.

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction

PubMed Central

Ma, Xiaolei; Dai, Zhuang; He, Zhengbing; Ma, Jihui; Wang, Yong; Wang, Yunpeng

2017-01-01

This paper proposes a convolutional neural network (CNN)-based method that learns traffic as images and predicts large-scale, network-wide traffic speed with a high accuracy. Spatiotemporal traffic dynamics are converted to images describing the time and space relations of traffic flow via a two-dimensional time-space matrix. A CNN is applied to the image following two consecutive steps: abstract traffic feature extraction and network-wide traffic speed prediction. The effectiveness of the proposed method is evaluated by taking two real-world transportation networks, the second ring road and north-east transportation network in Beijing, as examples, and comparing the method with four prevailing algorithms, namely, ordinary least squares, k-nearest neighbors, artificial neural network, and random forest, and three deep learning architectures, namely, stacked autoencoder, recurrent neural network, and long-short-term memory network. The results show that the proposed method outperforms other algorithms by an average accuracy improvement of 42.91% within an acceptable execution time. The CNN can train the model in a reasonable time and, thus, is suitable for large-scale transportation networks. PMID:28394270

The CHIC Model: A Global Model for Coupled Binary Data

ERIC Educational Resources Information Center

Wilderjans, Tom; Ceulemans, Eva; Van Mechelen, Iven

2008-01-01

Often problems result in the collection of coupled data, which consist of different N-way N-mode data blocks that have one or more modes in common. To reveal the structure underlying such data, an integrated modeling strategy, with a single set of parameters for the common mode(s), that is estimated based on the information in all data blocks, may…

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems.

PubMed

Oh, Sang-Il; Kang, Hang-Bong

2017-01-22

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226 × 370 image, whereas the original selective search method extracted approximately 10 6 × n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset.

Object Detection and Classification by Decision-Level Fusion for Intelligent Vehicle Systems

PubMed Central

Oh, Sang-Il; Kang, Hang-Bong

2017-01-01

To understand driving environments effectively, it is important to achieve accurate detection and classification of objects detected by sensor-based intelligent vehicle systems, which are significantly important tasks. Object detection is performed for the localization of objects, whereas object classification recognizes object classes from detected object regions. For accurate object detection and classification, fusing multiple sensor information into a key component of the representation and perception processes is necessary. In this paper, we propose a new object-detection and classification method using decision-level fusion. We fuse the classification outputs from independent unary classifiers, such as 3D point clouds and image data using a convolutional neural network (CNN). The unary classifiers for the two sensors are the CNN with five layers, which use more than two pre-trained convolutional layers to consider local to global features as data representation. To represent data using convolutional layers, we apply region of interest (ROI) pooling to the outputs of each layer on the object candidate regions generated using object proposal generation to realize color flattening and semantic grouping for charge-coupled device and Light Detection And Ranging (LiDAR) sensors. We evaluate our proposed method on a KITTI benchmark dataset to detect and classify three object classes: cars, pedestrians and cyclists. The evaluation results show that the proposed method achieves better performance than the previous methods. Our proposed method extracted approximately 500 proposals on a 1226×370 image, whereas the original selective search method extracted approximately 106×n proposals. We obtained classification performance with 77.72% mean average precision over the entirety of the classes in the moderate detection level of the KITTI benchmark dataset. PMID:28117742

Monte Carlo simulation of magnetic properties of mixed spin (3/2, 1) ferromagnetic and ferrimagnetic disordered binary alloys with amorphous structure

NASA Astrophysics Data System (ADS)

Motlagh, H. Nakhaei; Rezaei, G.

2018-01-01

Monte Carlo simulation is used to study the magnetic properties of mixed spin (3/2, 1) disordered binary alloys on simple cubic, hexagonal and amorphous magnetic ultra-thin films with 18 × 18 × 2 atoms. To this end, at the first approximation, the exchange coupling interaction between the spins is considered as a constant value and at the second one, the Ruderman-Kittel-Kasuya-Yosida (RKKY) model is used. Effects of concentration, structure, exchange interaction, single ion-anisotropy and the film size on the magnetic properties of disordered ferromagnetic and ferrimagnetic binary alloys are investigated. Our results indicate that the spontaneous magnetization and critical temperatures of rare earth-3d transition binary alloys are affected by these parameters. It is also found that in the ferrimagnetic state, the compensation temperature (Tcom) and the magnetic rearrangement temperature (TR) appear for some concentrations.

Evolution of double white dwarf binaries undergoing direct-impact accretion: Implications for gravitational wave astronomy

NASA Astrophysics Data System (ADS)

Kremer, Kyle; Breivik, Katelyn; Larson, Shane L.; Kalogera, Vassiliki

2017-01-01

For close double white dwarf binaries, the mass-transfer phenomenon known as direct-impact accretion (when the mass transfer stream impacts the accretor directly rather than forming a disc) may play a pivotal role in the long-term evolution of the systems. In this analysis, we explore the long-term evolution of white dwarf binaries accreting through direct-impact and explore implications of such systems to gravitational wave astronomy. We cover a broad range of parameter space which includes initial component masses and the strength of tidal coupling, and show that these systems, which lie firmly within the LISA frequency range, show strong negative chirps which can last as long as several million years. Detections of double white dwarf systems in the direct-impact phase by detectors such as LISA would provide astronomers with unique ways of probing the physics governing close compact object binaries.

A disc corona-jet model for the radio/X-ray correlation in black hole X-ray binaries

NASA Astrophysics Data System (ADS)

Qiao, Erlin; Liu, B. F.

2015-04-01

The observed tight radio/X-ray correlation in the low spectral state of some black hole X-ray binaries implies the strong coupling of the accretion and jet. The correlation of L_R ∝ L_X^{˜ 0.5-0.7} was well explained by the coupling of a radiatively inefficient accretion flow and a jet. Recently, however, a growing number of sources show more complicated radio/X-ray correlations, e.g. L_R ∝ L_X^{˜ 1.4} for LX/LEdd ≳ 10-3, which is suggested to be explained by the coupling of a radiatively efficient accretion flow and a jet. In this work, we interpret the deviation from the initial radio/X-ray correlation for LX/LEdd ≳ 10-3 with a detailed disc corona-jet model. In this model, the disc and corona are radiatively and dynamically coupled. Assuming a fraction of the matter in the accretion flow, η ≡ dot{M}_jet/dot{M}, is ejected to form the jet, we can calculate the emergent spectrum of the disc corona-jet system. We calculate LR and LX at different dot{M}, adjusting η to fit the observed radio/X-ray correlation of the black hole X-ray transient H1743-322 for LX/LEdd > 10-3. It is found that always the X-ray emission is dominated by the disc corona and the radio emission is dominated by the jet. We noted that the value of η for the deviated radio/X-ray correlation for LX/LEdd > 10-3 is systematically less than that of the case for LX/LEdd < 10-3, which is consistent with the general idea that the jet is often relatively suppressed at the high-luminosity phase in black hole X-ray binaries.

A consensual neural network

NASA Technical Reports Server (NTRS)

Benediktsson, J. A.; Ersoy, O. K.; Swain, P. H.

1991-01-01

A neural network architecture called a consensual neural network (CNN) is proposed for the classification of data from multiple sources. Its relation to hierarchical and ensemble neural networks is discussed. CNN is based on the statistical consensus theory and uses nonlinearly transformed input data. The input data are transformed several times, and the different transformed data are applied as if they were independent inputs. The independent inputs are classified using stage neural networks and outputs from the stage networks are then weighted and combined to make a decision. Experimental results based on remote-sensing data and geographic data are given.

KSC-99pp0608

NASA Image and Video Library

1999-05-27

NASA Administrator Daniel S. Goldin hands Mrs. Dianne Holliman a plaque honoring her late husband, John Holliman, a CNN national correspondent. Standing behind Goldin is Center Director Roy Bridges. At right is Tom Johnson, news group chairman of CNN. A ceremony dedicated the KSC Press Site auditorium as the John Holliman Auditorium to honor the correspondent for his enthusiastic, dedicated coverage of America's space program. The auditorium was built in 1980 and has been the focal point for new coverage of Space Shuttle launches. The ceremony followed the 94th launch of a Space Shuttle, on mission STS-96, earlier this morning

Press Site Auditorium dedicated to John Holliman

NASA Technical Reports Server (NTRS)

1999-01-01

NASA Administrator Daniel S. Goldin hands Mrs. Dianne Holliman a plaque honoring her late husband, John Holliman, a CNN national correspondent. Standing behind Goldin is Center Director Roy Bridges. At right is Tom Johnson, news group chairman of CNN. A ceremony dedicated the KSC Press Site auditorium as the John Holliman Auditorium to honor the correspondent for his enthusiastic, dedicated coverage of America's space program. The auditorium was built in 1980 and has been the focal point for new coverage of Space Shuttle launches. The ceremony followed the 94th launch of a Space Shuttle, on mission STS-96, earlier this morning.

An exact solution for the solidification of a liquid slab of binary mixture

NASA Technical Reports Server (NTRS)

Antar, B. N.; Collins, F. G.; Aumalia, A. E.

1986-01-01

The time dependent temperature and concentration profiles of a one dimensional finite slab of a binary liquid alloy is investigated during solidification. The governing equations are reduced to a set of coupled, nonlinear initial value problems using the method outlined by Meyer. Two methods will be used to solve these equations. The first method uses a Runge-Kutta-Fehlberg integrator to solve the equations numerically. The second method comprises of finding closed form solutions of the equations.

An empirical relationship for homogenization in single-phase binary alloy systems

NASA Technical Reports Server (NTRS)

Unnam, J.; Tenney, D. R.; Stein, B. A.

1979-01-01

A semiempirical formula is developed for describing the extent of interaction between constituents in single-phase binary alloy systems with planar, cylindrical, or spherical interfaces. The formula contains two parameters that are functions of mean concentration and interface geometry of the couple. The empirical solution is simple, easy to use, and does not involve sequential calculations, thereby allowing quick estimation of the extent of interactions without lengthy calculations. Results obtained with this formula are in good agreement with those from a finite-difference analysis.

The First Simultaneous X-Ray/Radio Detection of the First Be/BH System MWC 656

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ribó, M.; Paredes, J. M.; Marcote, B.

2017-02-01

MWC 656 is the first known Be/black hole (BH) binary system. Be/BH binaries are important in the context of binary system evolution and sources of detectable gravitational waves because they are possible precursors of coalescing neutron star/BH binaries. X-ray observations conducted in 2013 revealed that MWC 656 is a quiescent high-mass X-ray binary (HMXB), opening the possibility to explore X-ray/radio correlations and the accretion/ejection coupling down to low luminosities for BH HMXBs. Here we report on a deep joint Chandra /VLA observation of MWC 656 (and contemporaneous optical data) conducted in 2015 July that has allowed us to unambiguously identifymore » the X-ray counterpart of the source. The X-ray spectrum can be fitted with a power law with Γ ∼ 2, providing a flux of ≃4 × 10{sup −15} erg cm{sup −2} s{sup −1} in the 0.5–8 keV energy range and a luminosity of L {sub X} ≃ 3 × 10{sup 30} erg s{sup −1} at a 2.6 kpc distance. For a 5 M{sub ⊙} BH this translates into ≃5 × 10{sup −9} L {sub Edd}. These results imply that MWC 656 is about 7 times fainter in X-rays than it was two years before and reaches the faintest X-ray luminosities ever detected in stellar-mass BHs. The radio data provide a detection with a peak flux density of 3.5 ± 1.1 μ Jy beam{sup −1}. The obtained X-ray/radio luminosities for this quiescent BH HMXB are fully compatible with those of the X-ray/radio correlations derived from quiescent BH low-mass X-ray binaries. These results show that the accretion/ejection coupling in stellar-mass BHs is independent of the nature of the donor star.« less

Discovery of a Detached, Eclipsing 40 Minute Period Double White Dwarf Binary and a Friend: Implications for He+CO White Dwarf Mergers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Brown, Warren R.; Kilic, Mukremin; Kosakowski, Alekzander

We report the discovery of two detached double white dwarf (WD) binaries, SDSS J082239.546+304857.19 and SDSS J104336.275+055149.90, with orbital periods of 40 and 46 minutes, respectively. The 40 minute system is eclipsing; it is composed of a 0.30 M {sub ⊙} and a 0.52 M {sub ⊙} WD. The 46 minute system is a likely LISA verification binary. The short 20 ± 2 Myr and ∼34 Myr gravitational-wave merger times of the two binaries imply that many more such systems have formed and merged over the age of the Milky Way. We update the estimated Milky Way He+CO WD binarymore » merger rate and affirm our previously published result: He+CO WD binaries merge at a rate at least 40 times greater than the formation rate of stable mass-transfer AM CVn binaries, and so the majority must have unstable mass-transfer. The implication is that spin–orbit coupling in He+CO WD mergers is weak, or perhaps nova-like outbursts drive He+CO WDs into merger, as proposed by Shen.« less

Gamma rays as probe of fission and quasi-fission dynamics in the reaction 32S + 197Au near the Coulomb barrier

NASA Astrophysics Data System (ADS)

Pulcini, A.; Vardaci, E.; Kozulin, E.; Ashaduzzaman, M.; Borcea, C.; Bracco, A.; Brambilla, S.; Calinescu, S.; Camera, F.; Ciemala, M.; de Canditiis, B.; Dorvaux, O.; Harca, I. M.; Itkis, I.; Kirakosyan, V. V.; Knyazheva, G.; Kozulina, N.; Kolesov, I. V.; La Rana, G.; Maj, A.; Matea, I.; Novikov, K.; Petrone, C.; Quero, D.; Rath, P.; Saveleva, E.; Schmitt, C.; Sposito, G.; Stezowski, O.; Trzaska, W. H.; Wilson, J.

2018-05-01

Compound nucleus fission and quasi-fission are both binary decay channels whose common properties make the experimental separation between them difficult. A way to achieve this separation could be to probe the angular momentum of the binary fragments. This can be done detecting gamma rays in coincidence with the two fragments. As a case study, the reaction 32S + 197Au near the Coulomb barrier has been performed at the Tandem ALTO facility at IPN ORSAY. ORGAM and PARIS, two different gamma detectors arrays, are coupled with the CORSET detector, a two-arm time-of-flight spectrometer. TOF-TOF data were analyzed to reconstruct the mass-energy distribution of the primary fragments coupled with gamma multiplicity and spectroscopic analysis. Preliminary results of will be shown.

Near optimal discrimination of binary coherent signals via atom–light interaction

NASA Astrophysics Data System (ADS)

Han, Rui; Bergou, János A.; Leuchs, Gerd

2018-04-01

We study the discrimination of weak coherent states of light with significant overlaps by nondestructive measurements on the light states through measuring atomic states that are entangled to the coherent states via dipole coupling. In this way, the problem of measuring and discriminating coherent light states is shifted to finding the appropriate atom–light interaction and atomic measurements. We show that this scheme allows us to attain a probability of error extremely close to the Helstrom bound, the ultimate quantum limit for discriminating binary quantum states, through the simple Jaynes–Cummings interaction between the field and ancilla with optimized light–atom coupling and projective measurements on the atomic states. Moreover, since the measurement is nondestructive on the light state, information that is not detected by one measurement can be extracted from the post-measurement light states through subsequent measurements.

The "SIMCLAS" Model: Simultaneous Analysis of Coupled Binary Data Matrices with Noise Heterogeneity between and within Data Blocks

ERIC Educational Resources Information Center

Wilderjans, Tom F.; Ceulemans, E.; Van Mechelen, I.

2012-01-01

In many research domains different pieces of information are collected regarding the same set of objects. Each piece of information constitutes a data block, and all these (coupled) blocks have the object mode in common. When analyzing such data, an important aim is to obtain an overall picture of the structure underlying the whole set of coupled…

Pre-trained D-CNN models for detecting complex events in unconstrained videos

NASA Astrophysics Data System (ADS)

Robinson, Joseph P.; Fu, Yun

2016-05-01

Rapid event detection faces an emergent need to process large videos collections; whether surveillance videos or unconstrained web videos, the ability to automatically recognize high-level, complex events is a challenging task. Motivated by pre-existing methods being complex, computationally demanding, and often non-replicable, we designed a simple system that is quick, effective and carries minimal overhead in terms of memory and storage. Our system is clearly described, modular in nature, replicable on any Desktop, and demonstrated with extensive experiments, backed by insightful analysis on different Convolutional Neural Networks (CNNs), as stand-alone and fused with others. With a large corpus of unconstrained, real-world video data, we examine the usefulness of different CNN models as features extractors for modeling high-level events, i.e., pre-trained CNNs that differ in architectures, training data, and number of outputs. For each CNN, we use 1-fps from all training exemplar to train one-vs-rest SVMs for each event. To represent videos, frame-level features were fused using a variety of techniques. The best being to max-pool between predetermined shot boundaries, then average-pool to form the final video-level descriptor. Through extensive analysis, several insights were found on using pre-trained CNNs as off-the-shelf feature extractors for the task of event detection. Fusing SVMs of different CNNs revealed some interesting facts, finding some combinations to be complimentary. It was concluded that no single CNN works best for all events, as some events are more object-driven while others are more scene-based. Our top performance resulted from learning event-dependent weights for different CNNs.

Automated assessment of breast tissue density in non-contrast 3D CT images without image segmentation based on a deep CNN

NASA Astrophysics Data System (ADS)

Zhou, Xiangrong; Kano, Takuya; Koyasu, Hiromi; Li, Shuo; Zhou, Xinxin; Hara, Takeshi; Matsuo, Masayuki; Fujita, Hiroshi

2017-03-01

This paper describes a novel approach for the automatic assessment of breast density in non-contrast three-dimensional computed tomography (3D CT) images. The proposed approach trains and uses a deep convolutional neural network (CNN) from scratch to classify breast tissue density directly from CT images without segmenting the anatomical structures, which creates a bottleneck in conventional approaches. Our scheme determines breast density in a 3D breast region by decomposing the 3D region into several radial 2D-sections from the nipple, and measuring the distribution of breast tissue densities on each 2D section from different orientations. The whole scheme is designed as a compact network without the need for post-processing and provides high robustness and computational efficiency in clinical settings. We applied this scheme to a dataset of 463 non-contrast CT scans obtained from 30- to 45-year-old-women in Japan. The density of breast tissue in each CT scan was assigned to one of four categories (glandular tissue within the breast <25%, 25%-50%, 50%-75%, and >75%) by a radiologist as ground truth. We used 405 CT scans for training a deep CNN and the remaining 58 CT scans for testing the performance. The experimental results demonstrated that the findings of the proposed approach and those of the radiologist were the same in 72% of the CT scans among the training samples and 76% among the testing samples. These results demonstrate the potential use of deep CNN for assessing breast tissue density in non-contrast 3D CT images.

Global detection approach for clustered microcalcifications in mammograms using a deep learning network.

PubMed

Wang, Juan; Nishikawa, Robert M; Yang, Yongyi

2017-04-01

In computerized detection of clustered microcalcifications (MCs) from mammograms, the traditional approach is to apply a pattern detector to locate the presence of individual MCs, which are subsequently grouped into clusters. Such an approach is often susceptible to the occurrence of false positives (FPs) caused by local image patterns that resemble MCs. We investigate the feasibility of a direct detection approach to determining whether an image region contains clustered MCs or not. Toward this goal, we develop a deep convolutional neural network (CNN) as the classifier model to which the input consists of a large image window ([Formula: see text] in size). The multiple layers in the CNN classifier are trained to automatically extract image features relevant to MCs at different spatial scales. In the experiments, we demonstrated this approach on a dataset consisting of both screen-film mammograms and full-field digital mammograms. We evaluated the detection performance both on classifying image regions of clustered MCs using a receiver operating characteristic (ROC) analysis and on detecting clustered MCs from full mammograms by a free-response receiver operating characteristic analysis. For comparison, we also considered a recently developed MC detector with FP suppression. In classifying image regions of clustered MCs, the CNN classifier achieved 0.971 in the area under the ROC curve, compared to 0.944 for the MC detector. In detecting clustered MCs from full mammograms, at 90% sensitivity, the CNN classifier obtained an FP rate of 0.69 clusters/image, compared to 1.17 clusters/image by the MC detector. These results indicate that using global image features can be more effective in discriminating clustered MCs from FPs caused by various sources, such as linear structures, thereby providing a more accurate detection of clustered MCs on mammograms.

2,3-Di(2-pyridyl)-5-phenylpyrazine: a NN-CNN-type bridging ligand for dinuclear transition-metal complexes.

PubMed

Wu, Si-Hai; Zhong, Yu-Wu; Yao, Jiannian

2013-07-01

A new bridging ligand, 2,3-di(2-pyridyl)-5-phenylpyrazine (dpppzH), has been synthesized. This ligand was designed so that it could bind two metals through a NN-CNN-type coordination mode. The reaction of dpppzH with cis-[(bpy)2RuCl2] (bpy = 2,2'-bipyridine) affords monoruthenium complex [(bpy)2Ru(dpppzH)](2+) (1(2+)) in 64 % yield, in which dpppzH behaves as a NN bidentate ligand. The asymmetric biruthenium complex [(bpy)2Ru(dpppz)Ru(Mebip)](3+) (2(3+)) was prepared from complex 1(2+) and [(Mebip)RuCl3] (Mebip = bis(N-methylbenzimidazolyl)pyridine), in which one hydrogen atom on the phenyl ring of dpppzH is lost and the bridging ligand binds to the second ruthenium atom in a CNN tridentate fashion. In addition, the RuPt heterobimetallic complex [(bpy)2Ru(dpppz)Pt(C≡CPh)](2+) (4(2+)) has been prepared from complex 1(2+), in which the bridging ligand binds to the platinum atom through a CNN binding mode. The electronic properties of these complexes have been probed by using electrochemical and spectroscopic techniques and studied by theoretical calculations. Complex 1(2+) is emissive at room temperature, with an emission λmax = 695 nm. No emission was detected for complex 2(3+) at room temperature in MeCN, whereas complex 4(2+) displayed an emission at about 750 nm. The emission properties of these complexes are compared to those of previously reported Ru and RuPt bimetallic complexes with a related ligand, 2,3-di(2-pyridyl)-5,6-diphenylpyrazine. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Deep neural network-based computer-assisted detection of cerebral aneurysms in MR angiography.

PubMed

Nakao, Takahiro; Hanaoka, Shouhei; Nomura, Yukihiro; Sato, Issei; Nemoto, Mitsutaka; Miki, Soichiro; Maeda, Eriko; Yoshikawa, Takeharu; Hayashi, Naoto; Abe, Osamu

2018-04-01

The usefulness of computer-assisted detection (CAD) for detecting cerebral aneurysms has been reported; therefore, the improved performance of CAD will help to detect cerebral aneurysms. To develop a CAD system for intracranial aneurysms on unenhanced magnetic resonance angiography (MRA) images based on a deep convolutional neural network (CNN) and a maximum intensity projection (MIP) algorithm, and to demonstrate the usefulness of the system by training and evaluating it using a large dataset. Retrospective study. There were 450 cases with intracranial aneurysms. The diagnoses of brain aneurysms were made on the basis of MRA, which was performed as part of a brain screening program. Noncontrast-enhanced 3D time-of-flight (TOF) MRA on 3T MR scanners. In our CAD, we used a CNN classifier that predicts whether each voxel is inside or outside aneurysms by inputting MIP images generated from a volume of interest (VOI) around the voxel. The CNN was trained in advance using manually inputted labels. We evaluated our method using 450 cases with intracranial aneurysms, 300 of which were used for training, 50 for parameter tuning, and 100 for the final evaluation. Free-response receiver operating characteristic (FROC) analysis. Our CAD system detected 94.2% (98/104) of aneurysms with 2.9 false positives per case (FPs/case). At a sensitivity of 70%, the number of FPs/case was 0.26. We showed that the combination of a CNN and an MIP algorithm is useful for the detection of intracranial aneurysms. 4 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:948-953. © 2017 International Society for Magnetic Resonance in Medicine.

Deep learning for segmentation of brain tumors: can we train with images from different institutions?

NASA Astrophysics Data System (ADS)

Paredes, David; Saha, Ashirbani; Mazurowski, Maciej A.

2017-03-01

Deep learning and convolutional neural networks (CNNs) in particular are increasingly popular tools for segmentation and classification of medical images. CNNs were shown to be successful for segmentation of brain tumors into multiple regions or labels. However, in the environment which fosters data-sharing and collection of multi-institutional datasets, a question arises: does training with data from another institution with potentially different imaging equipment, contrast protocol, and patient population impact the segmentation performance of the CNN? Our study presents preliminary data towards answering this question. Specifically, we used MRI data of glioblastoma (GBM) patients for two institutions present in The Cancer Imaging Archive. We performed a process of training and testing CNN multiple times such that half of the time the CNN was tested on data from the same institution that was used for training and half of the time it was tested on another institution, keeping the training and testing set size constant. We observed a decrease in performance as measured by Dice coefficient when the CNN was trained with data from a different institution as compared to training with data from the same institution. The changes in performance for the entire tumor and for four different labels within the tumor were: 0.72 to 0.65 (p=0.06), 0.61 to 0.58 (p=0.49), 0.54 to 0.51 (p=0.82), 0.31 to 0.24 (p<0.03), and 0.43 to 0.31(p<0.003) respectively. In summary, we found that while data across institutions can be used for development of CNNs, this might be associated with a decrease in performance.

Noise-enhanced convolutional neural networks.

PubMed

Audhkhasi, Kartik; Osoba, Osonde; Kosko, Bart

2016-06-01

Injecting carefully chosen noise can speed convergence in the backpropagation training of a convolutional neural network (CNN). The Noisy CNN algorithm speeds training on average because the backpropagation algorithm is a special case of the generalized expectation-maximization (EM) algorithm and because such carefully chosen noise always speeds up the EM algorithm on average. The CNN framework gives a practical way to learn and recognize images because backpropagation scales with training data. It has only linear time complexity in the number of training samples. The Noisy CNN algorithm finds a special separating hyperplane in the network's noise space. The hyperplane arises from the likelihood-based positivity condition that noise-boosts the EM algorithm. The hyperplane cuts through a uniform-noise hypercube or Gaussian ball in the noise space depending on the type of noise used. Noise chosen from above the hyperplane speeds training on average. Noise chosen from below slows it on average. The algorithm can inject noise anywhere in the multilayered network. Adding noise to the output neurons reduced the average per-iteration training-set cross entropy by 39% on a standard MNIST image test set of handwritten digits. It also reduced the average per-iteration training-set classification error by 47%. Adding noise to the hidden layers can also reduce these performance measures. The noise benefit is most pronounced for smaller data sets because the largest EM hill-climbing gains tend to occur in the first few iterations. This noise effect can assist random sampling from large data sets because it allows a smaller random sample to give the same or better performance than a noiseless sample gives. Copyright © 2015 Elsevier Ltd. All rights reserved.

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

NASA Astrophysics Data System (ADS)

Kroll, Christine; von der Werth, Monika; Leuck, Holger; Stahl, Christoph; Schertler, Klaus

2017-05-01

For Intelligence, Surveillance, Reconnaissance (ISR) missions of manned and unmanned air systems typical electrooptical payloads provide high-definition video data which has to be exploited with respect to relevant ground targets in real-time by automatic/assisted target recognition software. Airbus Defence and Space is developing required technologies for real-time sensor exploitation since years and has combined the latest advances of Deep Convolutional Neural Networks (CNN) with a proprietary high-speed Support Vector Machine (SVM) learning method into a powerful object recognition system with impressive results on relevant high-definition video scenes compared to conventional target recognition approaches. This paper describes the principal requirements for real-time target recognition in high-definition video for ISR missions and the Airbus approach of combining an invariant feature extraction using pre-trained CNNs and the high-speed training and classification ability of a novel frequency-domain SVM training method. The frequency-domain approach allows for a highly optimized implementation for General Purpose Computation on a Graphics Processing Unit (GPGPU) and also an efficient training of large training samples. The selected CNN which is pre-trained only once on domain-extrinsic data reveals a highly invariant feature extraction. This allows for a significantly reduced adaptation and training of the target recognition method for new target classes and mission scenarios. A comprehensive training and test dataset was defined and prepared using relevant high-definition airborne video sequences. The assessment concept is explained and performance results are given using the established precision-recall diagrams, average precision and runtime figures on representative test data. A comparison to legacy target recognition approaches shows the impressive performance increase by the proposed CNN+SVM machine-learning approach and the capability of real-time high-definition video exploitation.

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study

NASA Astrophysics Data System (ADS)

Zhen, Xin; Chen, Jiawei; Zhong, Zichun; Hrycushko, Brian; Zhou, Linghong; Jiang, Steve; Albuquerque, Kevin; Gu, Xuejun

2017-11-01

Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT  +  BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc, and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: a feasibility study.

PubMed

Zhen, Xin; Chen, Jiawei; Zhong, Zichun; Hrycushko, Brian; Zhou, Linghong; Jiang, Steve; Albuquerque, Kevin; Gu, Xuejun

2017-10-12

Better understanding of the dose-toxicity relationship is critical for safe dose escalation to improve local control in late-stage cervical cancer radiotherapy. In this study, we introduced a convolutional neural network (CNN) model to analyze rectum dose distribution and predict rectum toxicity. Forty-two cervical cancer patients treated with combined external beam radiotherapy (EBRT) and brachytherapy (BT) were retrospectively collected, including twelve toxicity patients and thirty non-toxicity patients. We adopted a transfer learning strategy to overcome the limited patient data issue. A 16-layers CNN developed by the visual geometry group (VGG-16) of the University of Oxford was pre-trained on a large-scale natural image database, ImageNet, and fine-tuned with patient rectum surface dose maps (RSDMs), which were accumulated EBRT  +  BT doses on the unfolded rectum surface. We used the adaptive synthetic sampling approach and the data augmentation method to address the two challenges, data imbalance and data scarcity. The gradient-weighted class activation maps (Grad-CAM) were also generated to highlight the discriminative regions on the RSDM along with the prediction model. We compare different CNN coefficients fine-tuning strategies, and compare the predictive performance using the traditional dose volume parameters, e.g. D 0.1/1/2cc , and the texture features extracted from the RSDM. Satisfactory prediction performance was achieved with the proposed scheme, and we found that the mean Grad-CAM over the toxicity patient group has geometric consistence of distribution with the statistical analysis result, which indicates possible rectum toxicity location. The evaluation results have demonstrated the feasibility of building a CNN-based rectum dose-toxicity prediction model with transfer learning for cervical cancer radiotherapy.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera.

PubMed

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-10-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods.

Spoof Detection for Finger-Vein Recognition System Using NIR Camera

PubMed Central

Nguyen, Dat Tien; Yoon, Hyo Sik; Pham, Tuyen Danh; Park, Kang Ryoung

2017-01-01

Finger-vein recognition, a new and advanced biometrics recognition method, is attracting the attention of researchers because of its advantages such as high recognition performance and lesser likelihood of theft and inaccuracies occurring on account of skin condition defects. However, as reported by previous researchers, it is possible to attack a finger-vein recognition system by using presentation attack (fake) finger-vein images. As a result, spoof detection, named as presentation attack detection (PAD), is necessary in such recognition systems. Previous attempts to establish PAD methods primarily focused on designing feature extractors by hand (handcrafted feature extractor) based on the observations of the researchers about the difference between real (live) and presentation attack finger-vein images. Therefore, the detection performance was limited. Recently, the deep learning framework has been successfully applied in computer vision and delivered superior results compared to traditional handcrafted methods on various computer vision applications such as image-based face recognition, gender recognition and image classification. In this paper, we propose a PAD method for near-infrared (NIR) camera-based finger-vein recognition system using convolutional neural network (CNN) to enhance the detection ability of previous handcrafted methods. Using the CNN method, we can derive a more suitable feature extractor for PAD than the other handcrafted methods using a training procedure. We further process the extracted image features to enhance the presentation attack finger-vein image detection ability of the CNN method using principal component analysis method (PCA) for dimensionality reduction of feature space and support vector machine (SVM) for classification. Through extensive experimental results, we confirm that our proposed method is adequate for presentation attack finger-vein image detection and it can deliver superior detection results compared to CNN-based methods and other previous handcrafted methods. PMID:28974031

Predicting RNA-protein binding sites and motifs through combining local and global deep convolutional neural networks.

PubMed

Pan, Xiaoyong; Shen, Hong-Bin

2018-05-02

RNA-binding proteins (RBPs) take over 5∼10% of the eukaryotic proteome and play key roles in many biological processes, e.g. gene regulation. Experimental detection of RBP binding sites is still time-intensive and high-costly. Instead, computational prediction of the RBP binding sites using pattern learned from existing annotation knowledge is a fast approach. From the biological point of view, the local structure context derived from local sequences will be recognized by specific RBPs. However, in computational modeling using deep learning, to our best knowledge, only global representations of entire RNA sequences are employed. So far, the local sequence information is ignored in the deep model construction process. In this study, we present a computational method iDeepE to predict RNA-protein binding sites from RNA sequences by combining global and local convolutional neural networks (CNNs). For the global CNN, we pad the RNA sequences into the same length. For the local CNN, we split a RNA sequence into multiple overlapping fixed-length subsequences, where each subsequence is a signal channel of the whole sequence. Next, we train deep CNNs for multiple subsequences and the padded sequences to learn high-level features, respectively. Finally, the outputs from local and global CNNs are combined to improve the prediction. iDeepE demonstrates a better performance over state-of-the-art methods on two large-scale datasets derived from CLIP-seq. We also find that the local CNN run 1.8 times faster than the global CNN with comparable performance when using GPUs. Our results show that iDeepE has captured experimentally verified binding motifs. https://github.com/xypan1232/iDeepE. xypan172436@gmail.com or hbshen@sjtu.edu.cn. Supplementary data are available at Bioinformatics online.

Pedestrian detection in video surveillance using fully convolutional YOLO neural network

NASA Astrophysics Data System (ADS)

Molchanov, V. V.; Vishnyakov, B. V.; Vizilter, Y. V.; Vishnyakova, O. V.; Knyaz, V. A.

2017-06-01

More than 80% of video surveillance systems are used for monitoring people. Old human detection algorithms, based on background and foreground modelling, could not even deal with a group of people, to say nothing of a crowd. Recent robust and highly effective pedestrian detection algorithms are a new milestone of video surveillance systems. Based on modern approaches in deep learning, these algorithms produce very discriminative features that can be used for getting robust inference in real visual scenes. They deal with such tasks as distinguishing different persons in a group, overcome problem with sufficient enclosures of human bodies by the foreground, detect various poses of people. In our work we use a new approach which enables to combine detection and classification tasks into one challenge using convolution neural networks. As a start point we choose YOLO CNN, whose authors propose a very efficient way of combining mentioned above tasks by learning a single neural network. This approach showed competitive results with state-of-the-art models such as FAST R-CNN, significantly overcoming them in speed, which allows us to apply it in real time video surveillance and other video monitoring systems. Despite all advantages it suffers from some known drawbacks, related to the fully-connected layers that obstruct applying the CNN to images with different resolution. Also it limits the ability to distinguish small close human figures in groups which is crucial for our tasks since we work with rather low quality images which often include dense small groups of people. In this work we gradually change network architecture to overcome mentioned above problems, train it on a complex pedestrian dataset and finally get the CNN detecting small pedestrians in real scenes.

Disease Staging and Prognosis in Smokers Using Deep Learning in Chest Computed Tomography.

PubMed

González, Germán; Ash, Samuel Y; Vegas-Sánchez-Ferrero, Gonzalo; Onieva Onieva, Jorge; Rahaghi, Farbod N; Ross, James C; Díaz, Alejandro; San José Estépar, Raúl; Washko, George R

2018-01-15

Deep learning is a powerful tool that may allow for improved outcome prediction. To determine if deep learning, specifically convolutional neural network (CNN) analysis, could detect and stage chronic obstructive pulmonary disease (COPD) and predict acute respiratory disease (ARD) events and mortality in smokers. A CNN was trained using computed tomography scans from 7,983 COPDGene participants and evaluated using 1,000 nonoverlapping COPDGene participants and 1,672 ECLIPSE participants. Logistic regression (C statistic and the Hosmer-Lemeshow test) was used to assess COPD diagnosis and ARD prediction. Cox regression (C index and the Greenwood-Nam-D'Agnostino test) was used to assess mortality. In COPDGene, the C statistic for the detection of COPD was 0.856. A total of 51.1% of participants in COPDGene were accurately staged and 74.95% were within one stage. In ECLIPSE, 29.4% were accurately staged and 74.6% were within one stage. In COPDGene and ECLIPSE, the C statistics for ARD events were 0.64 and 0.55, respectively, and the Hosmer-Lemeshow P values were 0.502 and 0.380, respectively, suggesting no evidence of poor calibration. In COPDGene and ECLIPSE, CNN predicted mortality with fair discrimination (C indices, 0.72 and 0.60, respectively), and without evidence of poor calibration (Greenwood-Nam-D'Agnostino P values, 0.307 and 0.331, respectively). A deep-learning approach that uses only computed tomography imaging data can identify those smokers who have COPD and predict who are most likely to have ARD events and those with the highest mortality. At a population level CNN analysis may be a powerful tool for risk assessment.

A deep learning classifier for prediction of pathological complete response to neoadjuvant chemotherapy from baseline breast DCE-MRI

NASA Astrophysics Data System (ADS)

Ravichandran, Kavya; Braman, Nathaniel; Janowczyk, Andrew; Madabhushi, Anant

2018-02-01

Neoadjuvant chemotherapy (NAC) is routinely used to treat breast tumors before surgery to reduce tumor size and improve outcome. However, no current clinical or imaging metrics can effectively predict before treatment which NAC recipients will achieve pathological complete response (pCR), the absence of residual invasive disease in the breast or lymph nodes following surgical resection. In this work, we developed and applied a convolu- tional neural network (CNN) to predict pCR from pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) scans on a per-voxel basis. In this study, DCE-MRI data for a total of 166 breast cancer pa- tients from the ISPY1 Clinical Trial were split into a training set of 133 patients and a testing set of 33 patients. A CNN consisting of 6 convolutional blocks was trained over 30 epochs. The pre-contrast and post-contrast DCE-MRI phases were considered in isolation and conjunction. A CNN utilizing a combination of both pre- and post-contrast images best distinguished responders, with an AUC of 0.77; 82% of the patients in the testing set were correctly classified based on their treatment response. Within the testing set, the CNN was able to produce probability heatmaps that visualized tumor regions that most strongly predicted therapeutic response. Multi- variate analysis with prognostic clinical variables (age, largest diameter, hormone receptor and HER2 status), revealed that the network was an independent predictor of response (p=0.05), and that the inclusion of HER2 status could further improve capability to predict response (AUC = 0.85, accuracy = 85%).

Classification of Urban Aerial Data Based on Pixel Labelling with Deep Convolutional Neural Networks and Logistic Regression

NASA Astrophysics Data System (ADS)

Yao, W.; Poleswki, P.; Krzystek, P.

2016-06-01

The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this paper, a semantic pixel labelling scheme for urban areas using multi-resolution CNN and hand-crafted spatial-spectral features of airborne remotely sensed data is presented. Both CNN and hand-crafted features are applied to image/DSM patches to produce per-pixel class probabilities with a L1-norm regularized logistical regression classifier. The evidence theory infers a degree of belief for pixel labelling from different sources to smooth regions by handling the conflicts present in the both classifiers while reducing the uncertainty. The aerial data used in this study were provided by ISPRS as benchmark datasets for 2D semantic labelling tasks in urban areas, which consists of two data sources from LiDAR and color infrared camera. The test sites are parts of a city in Germany which is assumed to consist of typical object classes including impervious surfaces, trees, buildings, low vegetation, vehicles and clutter. The evaluation is based on the computation of pixel-based confusion matrices by random sampling. The performance of the strategy with respect to scene characteristics and method combination strategies is analyzed and discussed. The competitive classification accuracy could be not only explained by the nature of input data sources: e.g. the above-ground height of nDSM highlight the vertical dimension of houses, trees even cars and the nearinfrared spectrum indicates vegetation, but also attributed to decision-level fusion of CNN's texture-based approach with multichannel spatial-spectral hand-crafted features based on the evidence combination theory.

Hydrodynamical processes in coalescing binary stars

NASA Astrophysics Data System (ADS)

Lai, Dong

1994-01-01

Coalescing neutron star binaries are considered to be the most promising sources of gravitational waves that could be detected by the planned laser-interferometer LIGO/VIRGO detectors. Extracting gravity wave signals from noisy data requires accurate theoretical waveforms in the frequency range 10-1000 Hz end detailed understanding of the dynamics of the binary orbits. We investigate the quasi-equilibrium and dynamical tidal interactions in coalescing binary stars, with particular focus on binary neutron stars. We develop a new formalism to study the equilibrium and dynamics of fluid stars in binary systems. The stars are modeled as compressible ellipsoids, and satisfy polytropic equation of state. The hydrodynamic equations are reduced to a set of ordinary differential equations for the evolution of the principal axes and other global quantities. The equilibrium binary structure is determined by a set of algebraic equations. We consider both synchronized and nonsynchronized systems, obtaining the generalizations to compressible fluid of the classical results for the ellipsoidal binary configurations. Our method can be applied to a wide variety of astrophysical binary systems containing neutron stars, white dwarfs, main-sequence stars and planets. We find that both secular and dynamical instabilities can develop in close binaries. The quasi-static (secular) orbital evolution, as well as the dynamical evolution of binaries driven by viscous dissipation and gravitational radiation reaction are studied. The development of the dynamical instability accelerates the binary coalescence at small separation, leading to appreciable radial infall velocity near contact. We also study resonant excitations of g-mode oscillations in coalescing binary neutron stars. A resonance occurs when the frequency of the tidal driving force equals one of the intrinsic g-mode frequencies. Using realistic microscopic nuclear equations of state, we determine the g-modes in a cold neutron atar. Resonant excitations of these g-modes during the last few minutes of the binary coalescence result in energy transfer and angular momentum transfer from the binary orbit to the neutron star. Because of the weak coupling between the g-modes and the tidal potential, the induced orbital phase errors due to resonances are small. However, resonant excitations of the g-modes play an important role in the tidal heating of binary neutron stars.

Optical/Infrared properties of Be stars in X-ray Binary systems

NASA Astrophysics Data System (ADS)

Naik, Sachindra

2018-04-01

Be/X-ray binaries, consisting of a Be star and a compact object (neutron star), form the largest subclass of High Mass X-ray Binaries. The orbit of the compact object around the Be star is wide and highly eccentric. Neutron stars in the Be/X-ray binaries are generally quiescent in X-ray emission. Transient X-ray outbursts seen in these objects are thought to be due to the interaction between the compact object and the circumstellar disk of the Be star at the periastron passage. Optical/infrared observations of the companion Be star during these outbursts show that the increase in the X-ray intensity of the neutron star is coupled with the decrease in the optical/infrared flux of the companion star. Apart from the change in optical/infrared flux, dramatic changes in the Be star emission line profiles are also seen during X-ray outbursts. Observational evidences of changes in the emission line profiles and optical/infrared continuum flux along with associated X-ray outbursts from the neutron stars in several Be/X-ray binaries are presented in this paper.

Generation of two-dimensional binary mixtures in complex plasmas

NASA Astrophysics Data System (ADS)

Wieben, Frank; Block, Dietmar

2016-10-01

Complex plasmas are an excellent model system for strong coupling phenomena. Under certain conditions the dust particles immersed into the plasma form crystals which can be analyzed in terms of structure and dynamics. Previous experiments focussed mostly on monodisperse particle systems whereas dusty plasmas in nature and technology are polydisperse. Thus, a first and important step towards experiments in polydisperse systems are binary mixtures. Recent experiments on binary mixtures under microgravity conditions observed a phase separation of particle species with different radii even for small size disparities. This contradicts several numerical studies of 2D binary mixtures. Therefore, dedicated experiments are required to gain more insight into the physics of polydisperse systems. In this contribution first ground based experiments on two-dimensional binary mixtures are presented. Particular attention is paid to the requirements for the generation of such systems which involve the consideration of the temporal evolution of the particle properties. Furthermore, the structure of these two-component crystals is analyzed and compared to simulations. This work was supported by the Deutsche Forschungsgemeinschaft DFG in the framework of the SFB TR24 Greifswald Kiel, Project A3b.

Conversion of stranded waste-stream carbon and nutrients into value-added products via metabolically coupled binary heterotroph-photoautotroph system

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bohutskyi, Pavlo; Kucek, Leo A.; Hill, Eric

Metabolic flexibility and robustness of phototroph- heterotroph co-cultures provide a flexible binary engineering platform for a variety of biotechnological and environmental applications. Here, we metabolically coupled a heterotrophic bacterium Bacillus subtilis with astaxanthin producing alga Haematococcus pluvialis and successfully applied this binary co-culture for conversion of the starch-rich waste stream into valuable astaxanthin-rich biomass. Importantly, the implemented system required less mass transfer of CO2 and O2 due to in-situ exchange between heterotroph and phototroph, which can contribute to reduction in energy consumption for wastewater treatment. In addition, the maximum reduction in chemical oxygen demand, total nitrogen and phosphorus reached 65%,more » 55% and 30%, respectively. The preliminary economic analysis indicated that realization of produced biomass with 0.8% astaxanthin content may generate annual revenues of $3.2M (baseline scenario) from treatment of wastewater (1,090 m3/day) from a potato processing plant. Moreover, the revenues may be increased up to $18.2M for optimized scenario with astaxanthin content in algae of 2%. This work demonstrates a successful proof-of-principle for conversion of waste carbon and nutrients into targeted value-added products through metabolic connection of heterotrophic and phototrophic organisms. Utilization of heterotrophic-algal binary cultures opens new perspectives for designing highly-efficient production processes for feedstock biomass production as well as allows utilization of variety of organic agricultural, chemical, or municipal wastes.« less

Primordial main equence binary stars in the globular cluster M71

NASA Technical Reports Server (NTRS)

Yan, Lin; Mateo, Mario

1994-01-01

We report the identification of five short-period variables near the center of the metal-rich globular cluster M71. Our observations consist of multiepoch VI charge coupled device (CCD) images centered on the cluster and covering a 6.3 min x 6.3 min field. Four of these variables are contact eclipsing binaries with periods between 0.35 and 0.41 days; one is a detached or semidetached eclipsing binary with a period of 0.56 days. Two of the variables were first identified as possible eclipsing binaries in an earlier survey by Hodder et al. (1992). We have used a variety of arguments to conclude that all five binary stars are probable members of M71, a result that is consistent with the low number (0.15) of short-period field binaries expected along this line of sight. Based on a simple model of how contact binaries evolve from initially detached binaries, we have determined a lower limit of 1.3% on the frequency of primordial binaries in M71 with initial orbital periods in the range 2.5 - 5 days. This implies that the overall primordial binary frequency, f, is 22(sup +26)(sub -12)% assuming df/d log P = const ( the 'flat' distribution), or f = 57(sup +15)(sub -8)% for df/d log P = 0.032 log P + const as observed for G-dwarf binaries in the solar neighborhood (the 'sloped' distribution). Both estimates of f correspond to binaries with initial periods shorter than 800 yr since any longer-period binaries would have been disrupted over the lifetime of the cluster. Our short-period binary frequency is in excellent agreement with the observed frequency of red-giant binaries observed in globulars if we adopt the flat distribution. For the sloped distribution, our results significantly overestimate the number of red-giant binaries. All of the short-period M71 binaries lie within 1 mag of the luminosity of the cluster turnoff in the color-magnitude diagram despite the fact we should have easily detected similar eclipsing binaries 2 - 2.5 mag fainter than this. We discuss the implications of this on our estimates of the binary frequency in M71 and on the formation of blue stragglers.

Conversion of stranded waste-stream carbon and nutrients into value-added products via metabolically coupled binary heterotroph-photoautotroph system.

PubMed

Bohutskyi, Pavlo; Kucek, Leo A; Hill, Eric; Pinchuk, Grigoriy E; Mundree, Sagadevan G; Beliaev, Alexander S

2018-07-01

Growth of heterotrophic bacterium Bacillus subtilis was metabolically coupled with the photosynthetic activity of an astaxanthin-producing alga Haematococcus pluvialis for conversion of starch-containing waste stream into carotenoid-enriched biomass. The H. pluvialis accounted for 63% of the produced co-culture biomass of 2.2 g/L. Importantly, the binary system requires neither exogenous supply of gaseous substrates nor application of energy-intensive mass transfer technologies due to in-situ exchange in CO 2 and O 2 . The maximum reduction in COD, total nitrogen and phosphorus reached 65%, 55% and 30%, respectively. Conducted techno-economic assessment suggested that the astaxanthin-rich biomass may potentially offset the costs of waste treatment, and, with specific productivity enhancements (induction of astaxanthin to 2% and increase H. pluvialis fraction to 80%), provide and additional revenue stream. The outcome of this study demonstrates a successful proof-of-principle for conversion of waste carbon and nutrients into value-added products through metabolic coupling of heterotrophic and phototrophic metabolisms. Copyright © 2018. Published by Elsevier Ltd.

Microstructure Formation in Dissimilar Metal Welds: Electron Beam Welding of Ti/Ni

NASA Astrophysics Data System (ADS)

Chatterjee, Subhradeep; Abinandanan, T. A.; Reddy, G. Madhusudhan; Chattopadhyay, Kamanio

2016-02-01

We present results for electron beam welding of a binary Ti/Ni dissimilar metal couple. The difference in physical properties of the base metals and metallurgical features (thermodynamics and kinetics) of the system influence both macroscopic transport and microstructure development in the weld. Microstructures near the fusion interfaces are markedly different from those inside the weld region. At the Ti side, Ti2Ni dendrites are observed to grow toward the fusion interface, while in the Ni side, layered growth of γ-Ni, Ni3Ti, and Ni3Ti + NiTi eutectic is observed. Different morphologies of the latter eutectic constitute the predominant microstructure inside the weld metal region. These results are compared and contrasted with those from laser welding of the same binary couple, and a scheme of solidification is proposed to explain the observations. This highlights notable departures from welding of similar and other dissimilar metals such as a significant asymmetry in heat transport that governs progress of solidification from each side of the couple, and a lack of unique liquidus isotherm characterizing the liquid-solid front.

Preparation of anti-adhesion surfaces on aluminium substrates of rubber plastic moulds using a coupling method of liquid plasma and electrochemical machining

NASA Astrophysics Data System (ADS)

Meng, Jianbing; Dong, Xiaojuan; Wei, Xiuting; Yin, Zhanmin

2014-03-01

Hard anti-adhesion surfaces, with low roughness and wear resistance, on aluminium substrates of rubber plastic moulds were fabricated via a new coupling method of liquid plasma and electrochemical machining. With the aid of liquid plasma thermal polishing and electrochemical anodic dissolution, micro/nano-scale binary structures were prepared as the base of the anti-adhesion surfaces. The anti-adhesion behaviours of the resulting aluminium surfaces were analysed by a surface roughness measuring instrument, a scanning electron microscope (SEM), a Fourier-transform infrared spectrophotometer (FTIR), an X-ray diffractometer (XRD), an optical contact angle meter, a digital Vickers micro-hardness (Hv) tester, and electronic universal testing. The results show that, after the liquid plasma and electrochemical machining, micro/nano-scale binary structures composed of micro-scale pits and nano-scale elongated boss structures were present on the sample surfaces. As a result, the anti-adhesion surfaces fabricated by the above coupling method have good anti-adhesion properties, better wear resistance and lower roughness.

Preparation of anti-adhesion surfaces on aluminium substrates of rubber plastic moulds using a coupling method of liquid plasma and electrochemical machining

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meng, Jianbing, E-mail: jianbingmeng@126.com; Dong, Xiaojuan; Wei, Xiuting

Hard anti-adhesion surfaces, with low roughness and wear resistance, on aluminium substrates of rubber plastic moulds were fabricated via a new coupling method of liquid plasma and electrochemical machining. With the aid of liquid plasma thermal polishing and electrochemical anodic dissolution, micro/nano-scale binary structures were prepared as the base of the anti-adhesion surfaces. The anti-adhesion behaviours of the resulting aluminium surfaces were analysed by a surface roughness measuring instrument, a scanning electron microscope (SEM), a Fourier-transform infrared spectrophotometer (FTIR), an X-ray diffractometer (XRD), an optical contact angle meter, a digital Vickers micro-hardness (Hv) tester, and electronic universal testing. The resultsmore » show that, after the liquid plasma and electrochemical machining, micro/nano-scale binary structures composed of micro-scale pits and nano-scale elongated boss structures were present on the sample surfaces. As a result, the anti-adhesion surfaces fabricated by the above coupling method have good anti-adhesion properties, better wear resistance and lower roughness.« less

A deep learning method for classifying mammographic breast density categories.

PubMed

Mohamed, Aly A; Berg, Wendie A; Peng, Hong; Luo, Yahong; Jankowitz, Rachel C; Wu, Shandong

2018-01-01

Mammographic breast density is an established risk marker for breast cancer and is visually assessed by radiologists in routine mammogram image reading, using four qualitative Breast Imaging and Reporting Data System (BI-RADS) breast density categories. It is particularly difficult for radiologists to consistently distinguish the two most common and most variably assigned BI-RADS categories, i.e., "scattered density" and "heterogeneously dense". The aim of this work was to investigate a deep learning-based breast density classifier to consistently distinguish these two categories, aiming at providing a potential computerized tool to assist radiologists in assigning a BI-RADS category in current clinical workflow. In this study, we constructed a convolutional neural network (CNN)-based model coupled with a large (i.e., 22,000 images) digital mammogram imaging dataset to evaluate the classification performance between the two aforementioned breast density categories. All images were collected from a cohort of 1,427 women who underwent standard digital mammography screening from 2005 to 2016 at our institution. The truths of the density categories were based on standard clinical assessment made by board-certified breast imaging radiologists. Effects of direct training from scratch solely using digital mammogram images and transfer learning of a pretrained model on a large nonmedical imaging dataset were evaluated for the specific task of breast density classification. In order to measure the classification performance, the CNN classifier was also tested on a refined version of the mammogram image dataset by removing some potentially inaccurately labeled images. Receiver operating characteristic (ROC) curves and the area under the curve (AUC) were used to measure the accuracy of the classifier. The AUC was 0.9421 when the CNN-model was trained from scratch on our own mammogram images, and the accuracy increased gradually along with an increased size of training samples. Using the pretrained model followed by a fine-tuning process with as few as 500 mammogram images led to an AUC of 0.9265. After removing the potentially inaccurately labeled images, AUC was increased to 0.9882 and 0.9857 for without and with the pretrained model, respectively, both significantly higher (P < 0.001) than when using the full imaging dataset. Our study demonstrated high classification accuracies between two difficult to distinguish breast density categories that are routinely assessed by radiologists. We anticipate that our approach will help enhance current clinical assessment of breast density and better support consistent density notification to patients in breast cancer screening. © 2017 American Association of Physicists in Medicine.

KSC-99pp0607

NASA Image and Video Library

1999-05-27

From left, Center Director Roy Bridges and NASA Administrator Daniel S. Goldin applaud as Jay Holliman, with the help of his mother, Mrs. Dianne Holliman, unveils a plaque honoring his father, the late John Holliman. At right is Tom Johnson, news group chairman of CNN. The occasion was the dedication of the KSC Press Site auditorium as the John Holliman Auditorium to honor the CNN national correspondent for his enthusiastic, dedicated coverage of America's space program. The auditorium was built in 1980 and has been the focal point for new coverage of Space Shuttle launches. The ceremony followed the 94th launch of a Space Shuttle, on mission STS-96, earlier this morning

Melon yield prediction using small unmanned aerial vehicles

NASA Astrophysics Data System (ADS)

Zhao, Tiebiao; Wang, Zhongdao; Yang, Qi; Chen, YangQuan

2017-05-01

Thanks to the development of camera technologies, small unmanned aerial systems (sUAS), it is possible to collect aerial images of field with more flexible visit, higher resolution and much lower cost. Furthermore, the performance of objection detection based on deeply trained convolutional neural networks (CNNs) has been improved significantly. In this study, we applied these technologies in the melon production, where high-resolution aerial images were used to count melons in the field and predict the yield. CNN-based object detection framework-Faster R-CNN is applied in the melon classification. Our results showed that sUAS plus CNNs were able to detect melons accurately in the late harvest season.

Zebrafish tracking using convolutional neural networks.

PubMed

Xu, Zhiping; Cheng, Xi En

2017-02-17

Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.

5W1H Information Extraction with CNN-Bidirectional LSTM

NASA Astrophysics Data System (ADS)

Nurdin, A.; Maulidevi, N. U.

2018-03-01

In this work, information about who, did what, when, where, why, and how on Indonesian news articles were extracted by combining Convolutional Neural Network and Bidirectional Long Short-Term Memory. Convolutional Neural Network can learn semantically meaningful representations of sentences. Bidirectional LSTM can analyze the relations among words in the sequence. We also use word embedding word2vec for word representation. By combining these algorithms, we obtained F-measure 0.808. Our experiments show that CNN-BLSTM outperforms other shallow methods, namely IBk, C4.5, and Naïve Bayes with the F-measure 0.655, 0.645, and 0.595, respectively.

Zebrafish tracking using convolutional neural networks

NASA Astrophysics Data System (ADS)

Xu, Zhiping; Cheng, Xi En

2017-02-01

Keeping identity for a long term after occlusion is still an open problem in the video tracking of zebrafish-like model animals, and accurate animal trajectories are the foundation of behaviour analysis. We utilize the highly accurate object recognition capability of a convolutional neural network (CNN) to distinguish fish of the same congener, even though these animals are indistinguishable to the human eye. We used data augmentation and an iterative CNN training method to optimize the accuracy for our classification task, achieving surprisingly accurate trajectories of zebrafish of different size and age zebrafish groups over different time spans. This work will make further behaviour analysis more reliable.