Three-Class Mammogram Classification Based on Descriptive CNN Features

Directory of Open Access Journals (Sweden)

M. Mohsin Jadoon

2017-01-01

Full Text Available 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.

CNN-based ranking for biomedical entity normalization.

Science.gov (United States)

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.

Evaluation of CNN as anthropomorphic model observer

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Yea-Hoon Kwon

2018-04-01

Full Text Available 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.

Continuous Chinese sign language recognition with CNN-LSTM

Science.gov (United States)

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.

College Students' News Gratifications, Media Use, and Current Events Knowledge.

Science.gov (United States)

Vincent, Richard C.; Basil, Michael D.

1997-01-01

Results of testing uses and gratifications theory with college students show students' media use and surveillance needs increase college year. Demographic differences and gratifications sought drive news media use. Surveillance needs result in increased use of all news media, whereas entertainment needs result in television news and CNN viewing.…

SampleCNN: End-to-End Deep Convolutional Neural Networks Using Very Small Filters for Music Classification

Directory of Open Access Journals (Sweden)

Jongpil Lee

2018-01-01

Full Text Available Convolutional Neural Networks (CNN have been applied to diverse machine learning tasks for different modalities of raw data in an end-to-end fashion. In the audio domain, a raw waveform-based approach has been explored to directly learn hierarchical characteristics of audio. However, the majority of previous studies have limited their model capacity by taking a frame-level structure similar to short-time Fourier transforms. We previously proposed a CNN architecture which learns representations using sample-level filters beyond typical frame-level input representations. The architecture showed comparable performance to the spectrogram-based CNN model in music auto-tagging. In this paper, we extend the previous work in three ways. First, considering the sample-level model requires much longer training time, we progressively downsample the input signals and examine how it affects the performance. Second, we extend the model using multi-level and multi-scale feature aggregation technique and subsequently conduct transfer learning for several music classification tasks. Finally, we visualize filters learned by the sample-level CNN in each layer to identify hierarchically learned features and show that they are sensitive to log-scaled frequency.

Engagement with News Content in Online Social Networks

Science.gov (United States)

Oeldorf-Hirsch, Anne

2011-01-01

Reports indicate that as the Internet is displacing traditional news sources, younger users continue to be disconnected from the news. Fortunately, the Internet provides new ways of sharing and discussing news stories with others through social networking sites such as Facebook, which may be important for engaging users in the news they read…

Understanding of Object Detection Based on CNN Family and YOLO

Science.gov (United States)

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.

Global news production

DEFF Research Database (Denmark)

Clausen, Lisbeth

Events around the world are broadcast by giant media players such as CNN, BBC and NHK amongst others. Consumers of news media receive the final message without knowing the processes that the images, the text and the sound have gone through. The media players can be considered as professional gene......), and it shows how events are "domesticated" for the target audience....

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

Science.gov (United States)

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.

Generalized Synchronization in AN Array of Nonlinear Dynamic Systems with Applications to Chaotic Cnn

Science.gov (United States)

Min, Lequan; Chen, Guanrong

This paper establishes some generalized synchronization (GS) theorems for a coupled discrete array of difference systems (CDADS) and a coupled continuous array of differential systems (CCADS). These constructive theorems provide general representations of GS in CDADS and CCADS. Based on these theorems, one can design GS-driven CDADS and CCADS via appropriate (invertible) transformations. As applications, the results are applied to autonomous and nonautonomous coupled Chen cellular neural network (CNN) CDADS and CCADS, discrete bidirectional Lorenz CNN CDADS, nonautonomous bidirectional Chua CNN CCADS, and nonautonomously bidirectional Chen CNN CDADS and CCADS, respectively. Extensive numerical simulations show their complex dynamic behaviors. These theorems provide new means for understanding the GS phenomena of complex discrete and continuously differentiable networks.

Brain tumor segmentation in multi-spectral MRI using convolutional neural networks (CNN).

Science.gov (United States)

Iqbal, Sajid; Ghani, M Usman; Saba, Tanzila; Rehman, Amjad

2018-04-01

A tumor could be found in any area of the brain and could be of any size, shape, and contrast. There may exist multiple tumors of different types in a human brain at the same time. Accurate tumor area segmentation is considered primary step for treatment of brain tumors. Deep Learning is a set of promising techniques that could provide better results as compared to nondeep learning techniques for segmenting timorous part inside a brain. This article presents a deep convolutional neural network (CNN) to segment brain tumors in MRIs. The proposed network uses BRATS segmentation challenge dataset which is composed of images obtained through four different modalities. Accordingly, we present an extended version of existing network to solve segmentation problem. The network architecture consists of multiple neural network layers connected in sequential order with the feeding of Convolutional feature maps at the peer level. Experimental results on BRATS 2015 benchmark data thus show the usability of the proposed approach and its superiority over the other approaches in this area of research. © 2018 Wiley Periodicals, Inc.

Studying Fake News via Network Analysis: Detection and Mitigation

OpenAIRE

Shu, Kai; Bernard, H. Russell; Liu, Huan

2018-01-01

Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost. However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information. Fake news on social media poses significant negative societal effects, and also presents unique challenges. To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news. In essence...

SIFT Meets CNN: A Decade Survey of Instance Retrieval.

Science.gov (United States)

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.

The Determinants of International News Flow: A Network Analysis.

Science.gov (United States)

Kim, Kyungmo; Barnett, George A.

1996-01-01

Examines the structure of international news flow and its determinants. Reveals inequality of international news flow between core and periphery, with Western industrialized countries at the center. Finds that the news flow network is structured into eight geographic-linguistic groups. Indicates flow is influenced by a country's economic…

DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Science.gov (United States)

Kalia, S.; Li, S.; Ganguly, S.; Nemani, R. R.

2017-12-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remotesensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud/shadow mask from geostationary satellite data iscritical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds, which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classifycloud/shadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoder-decoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multi-spectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

A CNN Based Approach for Garments Texture Design Classification

Directory of Open Access Journals (Sweden)

S.M. Sofiqul Islam

2017-05-01

Full Text Available Identifying garments texture design automatically for recommending the fashion trends is important nowadays because of the rapid growth of online shopping. By learning the properties of images efficiently, a machine can give better accuracy of classification. Several Hand-Engineered feature coding exists for identifying garments design classes. Recently, Deep Convolutional Neural Networks (CNNs have shown better performances for different object recognition. Deep CNN uses multiple levels of representation and abstraction that helps a machine to understand the types of data more accurately. In this paper, a CNN model for identifying garments design classes has been proposed. Experimental results on two different datasets show better results than existing two well-known CNN models (AlexNet and VGGNet and some state-of-the-art Hand-Engineered feature extraction methods.

Cellular neural networks (CNN) simulation for the TN approximation of the time dependent neutron transport equation in slab geometry

International Nuclear Information System (INIS)

Hadad, Kamal; Pirouzmand, Ahmad; Ayoobian, Navid

2008-01-01

This paper describes the application of a multilayer cellular neural network (CNN) to model and solve the time dependent one-speed neutron transport equation in slab geometry. We use a neutron angular flux in terms of the Chebyshev polynomials (T N ) of the first kind and then we attempt to implement the equations in an equivalent electrical circuit. We apply this equivalent circuit to analyze the T N moments equation in a uniform finite slab using Marshak type vacuum boundary condition. The validity of the CNN results is evaluated with numerical solution of the steady state T N moments equations by MATLAB. Steady state, as well as transient simulations, shows a very good comparison between the two methods. We used our CNN model to simulate space-time response of total flux and its moments for various c (where c is the mean number of secondary neutrons per collision). The complete algorithm could be implemented using very large-scale integrated circuit (VLSI) circuitry. The efficiency of the calculation method makes it useful for neutron transport calculations

Breaking news dissemination in the media via propagation behavior based on complex network theory

Science.gov (United States)

Liu, Nairong; An, Haizhong; Gao, Xiangyun; Li, Huajiao; Hao, Xiaoqing

2016-07-01

The diffusion of breaking news largely relies on propagation behaviors in the media. The tremendous and intricate propagation relationships in the media form a complex network. An improved understanding of breaking news diffusion characteristics can be obtained through the complex network research. Drawing on the news data of Bohai Gulf oil spill event from June 2011 to May 2014, we constructed a weighted and directed complex network in which media are set as nodes, the propagation relationships as edges and the propagation times as the weight of the edges. The primary results show (1) the propagation network presents small world feature, which means relations among media are close and breaking news originating from any node can spread rapidly; (2) traditional media and official websites are the typical sources for news propagation, while business portals are news collectors and spreaders; (3) the propagation network is assortative and the group of core media facilities the spread of breaking news faster; (4) for online media, news originality factor become less important to propagation behaviors. This study offers a new insight to explore information dissemination from the perspective of statistical physics and is beneficial for utilizing the public opinion in a positive way.

Cable News: Teens Now Can Have a Global Perspective.

Science.gov (United States)

Schlosser, Martha

1991-01-01

Examines the benefits of satellite television communications for urban and suburban schools. Suggests that satellite television is "globalizing" classrooms by exposing students to world events and cultures other than their own. Describes "CNN Newsroom," a commercial-free cable television news service intended for high school…

Pushing the Quality Level in Networked News Business: Semantic-Based Content Retrieval and Composition in International News Publishing

OpenAIRE

M.W. Schranz

2006-01-01

Electronic publishing exploits numerous possibilities to present or exchange information and to communicate via most current media like the Internet. By utilizing modern Web technologies like Web Services, loosely coupled services, and peer-to-peer networks we describe the integration of an intelligent business news presentation and distribution network. Employing semantics technologies enables the coupling of multinational and multilingual business news data on a scaleable international leve...

Automatic Amharic text news classification: Aneural networks ...

African Journals Online (AJOL)

School of Computing and Electrical Engineering, Institute of Technology, Bahir Dar University, Bahir Dar ... The study is on classification of Amharic news automatically using neural networks approach. Learning Vector ... INTRODUCTION.

New Digital Approach to CNN On-chip Implementation for Pattern Recognition

OpenAIRE

Durackova, Daniela

2008-01-01

We developed a novel simulator for the CNN using the program tool Visual Basic for Application. Its algorithm is based on the same principle as the planned designed circuit. The network can process the patterns with 400 point recognition. The created universal simulator can change various simulation parameters. We found that the rounding at multiplication is not as important as we previously expected. On the basis of the simulations we designed a novel digital CNN cell implemented on a chip. ...

Multi-stream CNN: Learning representations based on human-related regions for action recognition

NARCIS (Netherlands)

Tu, Zhigang; Xie, Wei; Qin, Qianqing; Poppe, R.W.; Veltkamp, R.C.; Li, Baoxin; Yuan, Junsong

2018-01-01

The most successful video-based human action recognition methods rely on feature representations extracted using Convolutional Neural Networks (CNNs). Inspired by the two-stream network (TS-Net), we propose a multi-stream Convolutional Neural Network (CNN) architecture to recognize human actions. We

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Chinese Sentence Classification Based on Convolutional Neural Network

Science.gov (United States)

Gu, Chengwei; Wu, Ming; Zhang, Chuang

2017-10-01

Sentence classification is one of the significant issues in Natural Language Processing (NLP). Feature extraction is often regarded as the key point for natural language processing. Traditional ways based on machine learning can not take high level features into consideration, such as Naive Bayesian Model. The neural network for sentence classification can make use of contextual information to achieve greater results in sentence classification tasks. In this paper, we focus on classifying Chinese sentences. And the most important is that we post a novel architecture of Convolutional Neural Network (CNN) to apply on Chinese sentence classification. In particular, most of the previous methods often use softmax classifier for prediction, we embed a linear support vector machine to substitute softmax in the deep neural network model, minimizing a margin-based loss to get a better result. And we use tanh as an activation function, instead of ReLU. The CNN model improve the result of Chinese sentence classification tasks. Experimental results on the Chinese news title database validate the effectiveness of our model.

Evaluation of CNN architectures for gait recognition based on optical flow maps

OpenAIRE

Castro, F. M.; Marín-Jiménez, M.J.; Guil, N.; López-Tapia, S.; Pérez de la Blanca, N.

2017-01-01

This work targets people identification in video based on the way they walk (\\ie gait) by using deep learning architectures. We explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (\\ie optical flow components). The low number of training samples for each subject and the use of a test set containing subjects different from the training ones makes the search of a good CNN architecture a challenging task. Universidad de Mál...

NetWorking News

DEFF Research Database (Denmark)

Fritsch, Jonas; Iversen, Ole Sejer; Dindler, Christian

For many years cooperative design was primarily concerned with the development of IT supported systems for professional users. However, the cooperative design approach can embrace other social practices such as children’s everyday life. At a methodological level there is no difference in designing...... the Networking News workshop, offers an opportunity to make first hand studies of children’s IT supported social activities in an informal classroom setting....... with adults or children. However there is a need for new methods to support communication and collaboration between designers and children. This article proposes a new method for understandings children’s appropriation of new technology in an interactive workshop setting. The method, which we call...

H31G-1596: DeepSAT's CloudCNN: A Deep Neural Network for Rapid Cloud Detection from Geostationary Satellites

Science.gov (United States)

Kalia, Subodh; Ganguly, Sangram; Li, Shuang; Nemani, Ramakrishna R.

2017-01-01

Cloud and cloud shadow detection has important applications in weather and climate studies. It is even more crucial when we introduce geostationary satellites into the field of terrestrial remote sensing. With the challenges associated with data acquired in very high frequency (10-15 mins per scan), the ability to derive an accurate cloud shadow mask from geostationary satellite data is critical. The key to the success for most of the existing algorithms depends on spatially and temporally varying thresholds,which better capture local atmospheric and surface effects.However, the selection of proper threshold is difficult and may lead to erroneous results. In this work, we propose a deep neural network based approach called CloudCNN to classify cloudshadow from Himawari-8 AHI and GOES-16 ABI multispectral data. DeepSAT's CloudCNN consists of an encoderdecoder based architecture for binary-class pixel wise segmentation. We train CloudCNN on multi-GPU Nvidia Devbox cluster, and deploy the prediction pipeline on NASA Earth Exchange (NEX) Pleiades supercomputer. We achieved an overall accuracy of 93.29% on test samples. Since, the predictions take only a few seconds to segment a full multispectral GOES-16 or Himawari-8 Full Disk image, the developed framework can be used for real-time cloud detection, cyclone detection, or extreme weather event predictions.

CNN - en succes i krise

DEFF Research Database (Denmark)

Böss, Michael

1994-01-01

Om CNN og den krise, netværket hele tiden må bekæmpe og dække. Udgivelsesdato: 26. september......Om CNN og den krise, netværket hele tiden må bekæmpe og dække. Udgivelsesdato: 26. september...

Science versus News: On the Cutting Edge

Science.gov (United States)

Kinney, A. L.; French, V.; Villard, R.; Maran, S. P.

1998-12-01

This session is to aid communication between scientists and journalists, to motivate astronomers to be active in communicating their science to the public via the press, and to help both astronomers and journalists to understand the constraints under which the other group is operating. The session consists of two talks of about 20 minutes, followed by a panel discussion. The first talk is "What Makes a Topic News?" This segment, by Miles O'Brien of CNN News, takes the AAS audiences behind the scenes in the world of producing science news stories. --- What drives selection of assignments? How does the science reporter convince their editor to cover a story? What factors about television producing help and also hurt getting science subjects across to the public? The second talk is "Public Knowledge on Science: The Growing Gap Between Scientists and the Taxpayer." This presentation by Jon Miller, a public opinion expert will emphasize the problems scientists and society, face in communicating to the public. --- What does the public know about science and scientific method? How much translation is required to communicate with the public to engage their interest without unacceptable compromise of scientific accuracy? The final segment is a panel of both science journalists and astronomers moderated by Steve Maran. Together they will tackle a question that gets to the heart of the Science-Vs-News controversies, "When Should Results Go Public?" Published too soon, science is called "hype"; Published too late, it is no longer "news." Should all results be peer reviewed first, and is that a satisfactory prerequisite? Do scientists take self-serving advantage of the public interest by making announcements before results appear in journals? How do we address the public desire to experience science unfolding and to see real-time data such as planetary science missions? The panelists are Dr. David Helfand, from Columbia University, Dr. Bruce Margon, from the University of

Social Network Facebook in News: Comparisiion of Space Dedicated to Social Network Facebook ads its Content in Czech Media in the Years 2009 and 2011

OpenAIRE

Bezdíčková, Andrea

2012-01-01

Diploma thesis "Social Network Facebook in News: Comparison of Space Dedicated to Social Network Facebook and its Content in Czech Media in the Years 2009 and 2011", is dedicated to the way of use and citation of social network Facebook in the selected news media. The paper summarizes the establishment and strengthening of online media, particularly the phenomenon of social networks on the example of social network Facebook, and their influence on the transformation of the media sector, news ...

Words analysis of online Chinese news headlines about trending events: a complex network perspective.

Science.gov (United States)

Li, Huajiao; Fang, Wei; An, Haizhong; Huang, Xuan

2015-01-01

Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines' keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words' networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly.

Words analysis of online Chinese news headlines about trending events: a complex network perspective.

Directory of Open Access Journals (Sweden)

Huajiao Li

Full Text Available Because the volume of information available online is growing at breakneck speed, keeping up with meaning and information communicated by the media and netizens is a new challenge both for scholars and for companies who must address public relations crises. Most current theories and tools are directed at identifying one website or one piece of online news and do not attempt to develop a rapid understanding of all websites and all news covering one topic. This paper represents an effort to integrate statistics, word segmentation, complex networks and visualization to analyze headlines' keywords and words relationships in online Chinese news using two samples: the 2011 Bohai Bay oil spill and the 2010 Gulf of Mexico oil spill. We gathered all the news headlines concerning the two trending events in the search results from Baidu, the most popular Chinese search engine. We used Simple Chinese Word Segmentation to segment all the headlines into words and then took words as nodes and considered adjacent relations as edges to construct word networks both using the whole sample and at the monthly level. Finally, we develop an integrated mechanism to analyze the features of words' networks based on news headlines that can account for all the keywords in the news about a particular event and therefore track the evolution of news deeply and rapidly.

IReport for CNN Transmedia Storytelling On The Brazilian Protests in 2013

Directory of Open Access Journals (Sweden)

Geane Alzamora

2015-12-01

Full Text Available This study discusses the limits and potentials of the concept of transmedia storytelling to describe citizen coverage of the 2013 protests in Brazil in the collaborative section iReport for CNN on CNN.com. The section is characteristically intermedia because it connects to online social networks and doubles as a monthly television program with the same name. But to what extent could it also be characterized as transmedia? Systematic observation of the citizen coverage between June and July 2013 revealed a restructuring of certain editorial spaces on the site aimed at user-proposed perspectives as well as communicational activity across online social networks; both important aspects for its transmedia characterization. Furthermore, the visible hierarchical differentiation of journalistic reporting puts the transmediatic potential of the collaborative experiment into perspective by reducing the importance of expanding the narrative horizontally despite the study showing regular social scheduling for journalistic coverage as evidence of the dynamics of transmedia.

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening.

Science.gov (United States)

Cho, Heeryon; Yoon, Sang Min

2018-04-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

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

Directory of Open Access Journals (Sweden)

Zeju Li

2017-01-01

Full Text Available 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.

How Transferable are CNN-based Features for Age and Gender Classification?

OpenAIRE

Özbulak, Gökhan; Aytar, Yusuf; Ekenel, Hazım Kemal

2016-01-01

Age and gender are complementary soft biometric traits for face recognition. Successful estimation of age and gender from facial images taken under real-world conditions can contribute improving the identification results in the wild. In this study, in order to achieve robust age and gender classification in the wild, we have benefited from Deep Convolutional Neural Networks based representation. We have explored transferability of existing deep convolutional neural network (CNN) models for a...

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening †

Science.gov (United States)

Yoon, Sang Min

2018-01-01

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches. PMID:29614767

Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening

Directory of Open Access Journals (Sweden)

Heeryon Cho

2018-04-01

Full Text Available Human Activity Recognition (HAR aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.

BreakingNews: Article Annotation by Image and Text Processing.

Science.gov (United States)

Ramisa, Arnau; Yan, Fei; Moreno-Noguer, Francesc; Mikolajczyk, Krystian

2018-05-01

Building upon recent Deep Neural Network architectures, current approaches lying in the intersection of Computer Vision and Natural Language Processing have achieved unprecedented breakthroughs in tasks like automatic captioning or image retrieval. Most of these learning methods, though, rely on large training sets of images associated with human annotations that specifically describe the visual content. In this paper we propose to go a step further and explore the more complex cases where textual descriptions are loosely related to the images. We focus on the particular domain of news articles in which the textual content often expresses connotative and ambiguous relations that are only suggested but not directly inferred from images. We introduce an adaptive CNN architecture that shares most of the structure for multiple tasks including source detection, article illustration and geolocation of articles. Deep Canonical Correlation Analysis is deployed for article illustration, and a new loss function based on Great Circle Distance is proposed for geolocation. Furthermore, we present BreakingNews, a novel dataset with approximately 100K news articles including images, text and captions, and enriched with heterogeneous meta-data (such as GPS coordinates and user comments). We show this dataset to be appropriate to explore all aforementioned problems, for which we provide a baseline performance using various Deep Learning architectures, and different representations of the textual and visual features. We report very promising results and bring to light several limitations of current state-of-the-art in this kind of domain, which we hope will help spur progress in the field.

CNN-PROMOTER, NEW CONSENSUS PROMOTER PREDICTION PROGRAM BASED ON NEURAL NETWORKS CNN-PROMOTER, NUEVO PROGRAMA PARA LA PREDICCIÓN DE PROMOTORES BASADO EN REDES NEURONALES CNN-PROMOTER, NOVO PROGRAMA PARA A PREDIÇÃO DE PROMOTORES BASEADO EM REDES NEURONAIS

Directory of Open Access Journals (Sweden)

Óscar Bedoya

2011-06-01

Full Text Available A new promoter prediction program called CNN-Promoter is presented. CNN-Promoter allows DNA sequences to be submitted and predicts them as promoter or non-promoter. Several methods have been developed to predict the promoter regions of genomes in eukaryotic organisms including algorithms based on Markov's models, decision trees, and statistical methods. Although there are plenty of programs proposed, there is still a need to improve the sensitivity and specificity values. In this paper, a new program is proposed; it is based on the consensus strategy of using experts to make a better prediction. The consensus strategy is developed by using neural networks. During the training process, the sensitivity and specificity were 100 % and during the test process the model reaches a sensitivity of 74.5 % and a specificity of 82.7 %.En este artículo se presenta un programa nuevo para la predicción de promotores llamado CNN-Promoter, que toma como entrada secuencias de ADN y las clasifica como promotor o no promotor. Se han desarrollado diversos métodos para predecir las regiones promotoras en organismos eucariotas, muchos de los cuales se basan en modelos de Markov, árboles de decisión y métodos estadísticos. A pesar de la variedad de programas existentes para la predicción de promotores, se necesita aún mejorar los valores de sensibilidad y especificidad. Se propone un nuevo programa que se basa en la estrategia de mezcla de expertos usando redes neuronales. Los resultados obtenidos en las pruebas alcanzan valores de sensibilidad y especificidad de 100 % en el entrenamiento y de 74,5 % de sensibilidad y 82,7 % de especificidad en los conjuntos de validación y prueba.Neste artigo a presenta-se um novo programa para a predição de promotores chamado CNN-Promoter, que toma como entrada sequências de DNA e as classifica como promotor ou não promotor. Desenvolveramse diversos métodos para predizer as regiões promotoras em organismos eucariotas

News values on social media: News organizations’ Facebook use

Science.gov (United States)

Al-Rawi, Ahmed

2016-01-01

This study examines the news selection practices followed by news organizations through investigating the news posted on social networking sites and, in particular, the Facebook pages of four foreign Arabic language TV stations: The Iranian Al-Alam TV, Russia Today, Deutsche Welle, and BBC. A total of 15,589 news stories are analyzed in order to examine the prominence of references to countries and political actors. The study reveals that social significance and proximity as well as the news organizations’ ideological agenda are the most important elements that dictate the news selection process. PMID:29278253

News values on social media: News organizations' Facebook use.

Science.gov (United States)

Al-Rawi, Ahmed

2017-08-01

This study examines the news selection practices followed by news organizations through investigating the news posted on social networking sites and, in particular, the Facebook pages of four foreign Arabic language TV stations: The Iranian Al-Alam TV, Russia Today, Deutsche Welle, and BBC. A total of 15,589 news stories are analyzed in order to examine the prominence of references to countries and political actors. The study reveals that social significance and proximity as well as the news organizations' ideological agenda are the most important elements that dictate the news selection process.

Using CNN Features to Better Understand What Makes Visual Artworks Special

Directory of Open Access Journals (Sweden)

Anselm Brachmann

2017-05-01

Full Text Available 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.

News Release: USDA Joins Fair Food Network, State and Local Partners to

Science.gov (United States)

vitamin C, calcium, and iron. The Food Bank of Eastern Michigan in Flint is distributing those USDA Foods Joins Fair Food Network, State and Local Partners to Promote Nutrition Resources for Lead-Affected Flint Residents News Release: USDA Joins Fair Food Network, State and Local Partners to Promote Nutrition

Education for a Culture of Peace: The Culture of Peace News Network as a Case Study

Science.gov (United States)

Adams, David

2013-01-01

The Culture of Peace News Network, an internet news service, is analyzed in the framework of a general approach to education for a culture of peace. Its format reflects the eight program areas for a culture of peace as adopted by the UN General Assembly. Among its other operating principles are universality of news with all cultures and regions of…

Exploring Digital News Publishing Business Models

DEFF Research Database (Denmark)

Lindskow, Kasper

News publishers in the industrialized world are experiencing a fundamental challenge to their business models because of the changing modes of consumption, competition, and production of their offerings that are associated with the emergence of the networked information society. The erosion...... of the traditional business models poses an existential threat to news publishing and has given rise to a continuing struggle among news publishers to design digital business models that will be sustainable in the future. This dissertation argues that a central and underresearched aspect of digital news publishing...... business models concerns the production networks that support the co-production of digital news offerings. To fill this knowledge gap, this dissertation explores the strategic design of the digital news publishing production networks that are associated with HTML-based news offerings on the open Web...

Discovery and fusion of salient multimodal features toward news story segmentation

Science.gov (United States)

Hsu, Winston; Chang, Shih-Fu; Huang, Chih-Wei; Kennedy, Lyndon; Lin, Ching-Yung; Iyengar, Giridharan

2003-12-01

In this paper, we present our new results in news video story segmentation and classification in the context of TRECVID video retrieval benchmarking event 2003. We applied and extended the Maximum Entropy statistical model to effectively fuse diverse features from multiple levels and modalities, including visual, audio, and text. We have included various features such as motion, face, music/speech types, prosody, and high-level text segmentation information. The statistical fusion model is used to automatically discover relevant features contributing to the detection of story boundaries. One novel aspect of our method is the use of a feature wrapper to address different types of features -- asynchronous, discrete, continuous and delta ones. We also developed several novel features related to prosody. Using the large news video set from the TRECVID 2003 benchmark, we demonstrate satisfactory performance (F1 measures up to 0.76 in ABC news and 0.73 in CNN news), present how these multi-level multi-modal features construct the probabilistic framework, and more importantly observe an interesting opportunity for further improvement.

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

Science.gov (United States)

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.

Power structure in Chilean news media.

Science.gov (United States)

Bahamonde, Jorge; Bollen, Johan; Elejalde, Erick; Ferres, Leo; Poblete, Barbara

2018-01-01

Even democracies endowed with the most active free press struggle to maintain diversity of news coverage. Consolidation and market forces may cause only a few dominant players to control the news cycle. Editorial policies may be biased by corporate ownership relations, narrowing news coverage and focus. To an increasing degree this problem also applies to social media news distribution, since it is subject to the same socio-economic drivers. To study the effects of consolidation and ownership on news diversity, we model the diversity of Chilean coverage on the basis of ownership records and social media data. We create similarity networks of news outlets on the basis of their ownership and the topics they cover. We then examine the relationships between the topology of ownership networks and content similarity to characterize how ownership affects news coverage. A network analysis reveals that Chilean media is highly concentrated both in terms of ownership as well as in terms of topics covered. Our method can be used to determine which groups of outlets and ownership exert the greatest influence on news coverage.

Network television news coverage of environmental risks

International Nuclear Information System (INIS)

Greenberg, M.R.; Sandman, P.M.; Sachsman, D.V.; Salomone, K.L.

1989-01-01

Despite the criticisms that surround television coverage of environmental risk, there have been relatively few attempts to measure what and whom television shows. Most research has focused analysis on a few weeks of coverage of major stories like the gas leak at Bhopal, the Three Mile Island nuclear accident, or the Mount St. Helen's eruption. To advance the research into television coverage of environmental risk, an analysis has been made of all environmental risk coverage by the network nightly news broadcasts for a period of more than two years. Researchers have analyzed all environmental risk coverage-564 stories in 26 months-presented on ABC, CBS, and NBC's evening news broadcasts from January 1984 through February 1986. The quantitative information from the 564 stories was balanced by a more qualitative analysis of the television coverage of two case studies-the dioxin contamination in Times Beach, Missouri, and the suspected methyl isocyanate emissions from the Union Carbide plant in Institute, West Virginia. Both qualitative and quantitative data contributed to the analysis of the role played by experts and environmental advocacy sources in coverage of environmental risk and to the suggestions for increasing that role

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

Science.gov (United States)

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.

Communicating Ebola through social media and electronic news media outlets: A cross-sectional study.

Science.gov (United States)

Househ, Mowafa

2016-09-01

Social media and electronic news media activity are an important source of information for the general public. Yet, there is a dearth of research exploring the use of Twitter and electronic news outlets during significant worldly events such as the recent Ebola Virus scare. The purpose of this article is to investigate the use of Twitter and electronic news media outlets in communicating Ebola Virus information. A cross-sectional survey of Twitter data and Google News Trend data from 30 September till 29 October, 2014 was conducted. Between 30 September and 29 October, there were approximately 26 million tweets (25,925,152) that contained the word Ebola. The highest number of correlated activity for Twitter and electronic news outlets occurred on 16 October 2014. Other important peaks in Twitter data occurred on 1 October, 6 October, 8 October, and 12 October, 2014. The main influencers of the Twitter feeds were news media outlets. The study reveals a relationship between electronic news media publishing and Twitter activity around significant events such as Ebola. Healthcare organizations should take advantage of the relationship between electronic news media and trending events on social media sites such as Twitter and should work on developing social media campaigns in co-operation with leading electronic news media outlets (e.g. CNN, Yahoo, Reuters) that can have an influence on social media activity. © The Author(s) 2015.

Robustness Design for CNN Templates with Performance of Extracting Closed Domain

International Nuclear Information System (INIS)

Li Weidong; Min Lequan

2006-01-01

The cellular neural/nonlinear network (CNN) is a powerful tool for image and video signal processing, robotic and biological visions. This paper introduces a kind of CNNs with performance of extracting closed domains in binary images, and gives a general method for designing templates of such a kind of CNNs. One theorem provides parameter inequalities for determining parameter intervals for implementing prescribed image processing functions, respectively. Examples for extracting closed domains in binary scale images are given.

SAR image classification based on CNN in real and simulation datasets

Science.gov (United States)

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.

Political campaigning 2.0: The influence of online news and social networking sites on attitudes and behavior

Directory of Open Access Journals (Sweden)

Montathar Faraon

2014-11-01

Full Text Available This study aimed to examine differences in influence between online news (e.g., New York Times and social networking sites (e.g., Facebook and Twitter on attitudes in political campaigns. In a web-based experiment, campaign, polls and election between two fictitious candidates were simulated. Participants’ explicit and implicit attitudes as well as voting behavior were assessed using self-report items and the Implicit Association Test (IAT. The results reveal that information emanating from online news had a significant influence on explicit and implicit attitudes while that of social networking sites did not. Overall, negative items had a stronger impact than positive ones, more so in online news compared to social networking sites. Negative information from either type of media was more likely to change participants’ explicit attitudes in a negative direction and as a consequence also change their vote. Practical implications of the findings and limitations of the study are discussed.

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

Science.gov (United States)

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.

Networked Intermedia Agenda Setting: The Geography of a Hyperlinked Scandinavian News Ecology

DEFF Research Database (Denmark)

Sjøvaag, Helle; Stavelin, Eirik; Karlsson, Michael

How does agenda setting work within the hyperlinked Scandinavian news ecology? This paper investigates intermedia agenda setting within and between the local, regional, national and supra-national levels in Sweden, Denmark and Norway; analyses the center/periphery dimensions of hyperlink connecti......, social geography and hyperlinked news agendas in Scandinavia, adding to the research on the political implications of the Internet on national public spheres....... March 2016, amounting to approximately 2 million hyperlinks, each geotagged with publication origin. The visualisation of the hyperlink structure is one of the main results of the analysis, illuminating a) the relative disconnect between local and national hyperlinked agendas, b) the relative disconnect...... between news agendas in the three countries, and c) the connectedness enabled by size, resources and central location in the Scandinavian hyperlinked information structure. The network analysis provides new insights into the relationship between centralized political structures, media ownership dispersal...

Image processing with a cellular nonlinear network

International Nuclear Information System (INIS)

Morfu, S.

2005-01-01

A cellular nonlinear network (CNN) based on uncoupled nonlinear oscillators is proposed for image processing purposes. It is shown theoretically and numerically that the contrast of an image loaded at the nodes of the CNN is strongly enhanced, even if this one is initially weak. An image inversion can be also obtained without reconfiguration of the network whereas a gray levels extraction can be performed with an additional threshold filtering. Lastly, an electronic implementation of this CNN is presented

[Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service].

Science.gov (United States)

Kim, Minji; Choi, Mona; Youm, Yoosik

2017-12-01

As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies. © 2017 Korean Society of Nursing Science

3D multi-view convolutional neural networks for lung nodule classification

Science.gov (United States)

Kang, Guixia; Hou, Beibei; Zhang, Ningbo

2017-01-01

The 3D convolutional neural network (CNN) is able to make full use of the spatial 3D context information of lung nodules, and the multi-view strategy has been shown to be useful for improving the performance of 2D CNN in classifying lung nodules. In this paper, we explore the classification of lung nodules using the 3D multi-view convolutional neural networks (MV-CNN) with both chain architecture and directed acyclic graph architecture, including 3D Inception and 3D Inception-ResNet. All networks employ the multi-view-one-network strategy. We conduct a binary classification (benign and malignant) and a ternary classification (benign, primary malignant and metastatic malignant) on Computed Tomography (CT) images from Lung Image Database Consortium and Image Database Resource Initiative database (LIDC-IDRI). All results are obtained via 10-fold cross validation. As regards the MV-CNN with chain architecture, results show that the performance of 3D MV-CNN surpasses that of 2D MV-CNN by a significant margin. Finally, a 3D Inception network achieved an error rate of 4.59% for the binary classification and 7.70% for the ternary classification, both of which represent superior results for the corresponding task. We compare the multi-view-one-network strategy with the one-view-one-network strategy. The results reveal that the multi-view-one-network strategy can achieve a lower error rate than the one-view-one-network strategy. PMID:29145492

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

Science.gov (United States)

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.

A CLOUD BOUNDARY DETECTION SCHEME COMBINED WITH ASLIC AND CNN USING ZY-3, GF-1/2 SATELLITE IMAGERY

Directory of Open Access Journals (Sweden)

Z. Guo

2018-04-01

Full Text Available 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.

CNN for breaking text-based CAPTCHA with noise

Science.gov (United States)

Liu, Kaixuan; Zhang, Rong; Qing, Ke

2017-07-01

A CAPTCHA ("Completely Automated Public Turing test to tell Computers and Human Apart") system is a program that most humans can pass but current computer programs could hardly pass. As the most common type of CAPTCHAs , text-based CAPTCHA has been widely used in different websites to defense network bots. In order to breaking textbased CAPTCHA, in this paper, two trained CNN models are connected for the segmentation and classification of CAPTCHA images. Then base on these two models, we apply sliding window segmentation and voting classification methods realize an end-to-end CAPTCHA breaking system with high success rate. The experiment results show that our method is robust and effective in breaking text-based CAPTCHA with noise.

ACCURACY AND RELIABILITY AS CRITERIA OF INFORMATIVENESS IN THE NEWS STORY

Directory of Open Access Journals (Sweden)

Melnikova Ekaterina Aleksandrovna

2014-12-01

Full Text Available The article clarifies the meaning of the terms accuracy and reliability of the news story, offers a researcher's approach to obtaining objective data that helps to verify linguistic means of accuracy and reliability presence in the informative structure of the text. The accuracy of the news story is defined as a high relevance degree of event reflection through language representation of its constituents; the reliability is viewed as news story originality that is proved by introducing citations and sources of information considered being trustworthy into the text content. Having based the research on an event nominative density identification method, the author composed nominative charts of 115 news story texts, collected at web-sites of BBC and CNN media corporations; distinguished qualitative and quantitative markers of accuracy and reliability in the news story text; confirmed that the accuracy of the news story is achieved with terminological clearness in nominating event constituents in the text, thematic bind between words, presence of onyms that help deeply identify characteristics of the referent event. The reliability of the text is discovered in eyewitness accounts, quotations, and references to the sources being considered as trustworthy. Accurate revision of associations between accuracy and reliability and informing strategies in digital news nets allowed the author to set two variants of information delivery, that differ in their communicative and pragmatic functions: developing (that informs about major and minor details of an event and truncated (which gives some details thus raising the interest to the event and urging a reader to open a full story.

Histopathological Breast-Image Classification Using Local and Frequency Domains by Convolutional Neural Network

Directory of Open Access Journals (Sweden)

Abdullah-Al Nahid

2018-01-01

Full Text Available Identification of the malignancy of tissues from Histopathological images has always been an issue of concern to doctors and radiologists. This task is time-consuming, tedious and moreover very challenging. Success in finding malignancy from Histopathological images primarily depends on long-term experience, though sometimes experts disagree on their decisions. However, Computer Aided Diagnosis (CAD techniques help the radiologist to give a second opinion that can increase the reliability of the radiologist’s decision. Among the different image analysis techniques, classification of the images has always been a challenging task. Due to the intense complexity of biomedical images, it is always very challenging to provide a reliable decision about an image. The state-of-the-art Convolutional Neural Network (CNN technique has had great success in natural image classification. Utilizing advanced engineering techniques along with the CNN, in this paper, we have classified a set of Histopathological Breast-Cancer (BC images utilizing a state-of-the-art CNN model containing a residual block. Conventional CNN operation takes raw images as input and extracts the global features; however, the object oriented local features also contain significant information—for example, the Local Binary Pattern (LBP represents the effective textural information, Histogram represent the pixel strength distribution, Contourlet Transform (CT gives much detailed information about the smoothness about the edges, and Discrete Fourier Transform (DFT derives frequency-domain information from the image. Utilizing these advantages, along with our proposed novel CNN model, we have examined the performance of the novel CNN model as Histopathological image classifier. To do so, we have introduced five cases: (a Convolutional Neural Network Raw Image (CNN-I; (b Convolutional Neural Network CT Histogram (CNN-CH; (c Convolutional Neural Network CT LBP (CNN-CL; (d Convolutional

Learning Traffic as Images: A Deep Convolutional Neural Network for Large-Scale Transportation Network Speed Prediction.

Science.gov (United States)

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.

Nonlinear Circuits and Neural Networks: Chip Implementation and Applications of the TeraOPS CNN Dynamic Array Supercomputer

National Research Council Canada - National Science Library

Chua, L

1998-01-01

.... Advances in research have been made in the following areas: (1) The design and implementation of the first-ever ARAM in the CNN Chip Set Architecture was successfully competed, and the samples were successfully tested; (2...

SAR target recognition and posture estimation using spatial pyramid pooling within CNN

Science.gov (United States)

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.

Decoding the codes: A content analysis of the news coverage of genetic cloning by three online news sites and three national daily newspapers, 1996 through 1998

Science.gov (United States)

Hyde, Jon E.

This study compared news coverage of genetic cloning research in three online news sites (CNN.com, ABC.com, and MSNBC.com) and three national daily newspapers (The New York Times, The Washington Post, and USA Today). The study involved the analysis of 230 online and print news articles concerning genetic cloning published from 1996 through 1998. Articles were examined with respect to formats, sources, focus, tone, and assessments about the impact of cloning research. Findings indicated that while print news formats remained relatively constant for the duration of this study, online news formats changed significantly with respect to the kinds of media used to represent the news, the layouts used to represent cloning news, and the emphasis placed on audio-visual content. Online stories were as much as 20 to 70% shorter than print stories. More than 50% of the articles appearing online were composed by outside sources (wire services, guest columnists, etc.). By comparison, nearly 90% of the articles published by print newspapers were written "in-house" by science reporters. Online news sites cited fewer sources and cited a smaller variety of sources than the newspapers examined here. In both news outlets, however, the sources most frequently cited were those with vested interests in furthering cloning research. Both online and print news coverage of cloning tends to focus principally on the technical procedures and on the future benefits of cloning. More than 60% of the articles focused on the techniques and technologies of cloning. Less than 25% of the articles focused on social, ethical, or legal issues associated with cloning. Similarly, articles from all six sources (75%) tended to be both positive and future-oriented. Less than 5% of the total articles examined here had a strongly negative or critical tone. Moreover, both online and print news sources increasingly conveyed a strong sense of acceptance about the possibility of human cloning. Data from this study

Convolutional neural network architectures for predicting DNA–protein binding

Science.gov (United States)

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

Relation Classification via Recurrent Neural Network

OpenAIRE

Zhang, Dongxu; Wang, Dong

2015-01-01

Deep learning has gained much success in sentence-level relation classification. For example, convolutional neural networks (CNN) have delivered competitive performance without much effort on feature engineering as the conventional pattern-based methods. Thus a lot of works have been produced based on CNN structures. However, a key issue that has not been well addressed by the CNN-based method is the lack of capability to learn temporal features, especially long-distance dependency between no...

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

Science.gov (United States)

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.

Fusing Panchromatic and SWIR Bands Based on Cnn - a Preliminary Study Over WORLDVIEW-3 Datasets

Science.gov (United States)

Guo, M.; Ma, H.; Bao, Y.; Wang, L.

2018-04-01

The traditional fusion methods are based on the fact that the spectral ranges of the Panchromatic (PAN) and multispectral bands (MS) are almost overlapping. In this paper, we propose a new pan-sharpening method for the fusion of PAN and SWIR (short-wave infrared) bands, whose spectral coverages are not overlapping. This problem is addressed with a convolutional neural network (CNN), which is trained by WorldView-3 dataset. CNN can learn the complex relationship among bands, and thus alleviate spectral distortion. Consequently, in our network, we use the simple three-layer basic architecture with 16 × 16 kernels to conduct the experiment. Every layer use different receptive field. The first two layers compute 512 feature maps by using the 16 × 16 and 1 × 1 receptive field respectively and the third layer with a 8 × 8 receptive field. The fusion results are optimized by continuous training. As for assessment, four evaluation indexes including Entropy, CC, SAM and UIQI are selected built on subjective visual effect and quantitative evaluation. The preliminary experimental results demonstrate that the fusion algorithms can effectively enhance the spatial information. Unfortunately, the fusion image has spectral distortion, it cannot maintain the spectral information of the SWIR image.

Filtering and spectral processing of 1-D signals using cellular neural networks

NARCIS (Netherlands)

Moreira-Tamayo, O.; Pineda de Gyvez, J.

1996-01-01

This paper presents cellular neural networks (CNN) for one-dimensional discrete signal processing. Although CNN has been extensively used in image processing applications, little has been done for 1-dimensional signal processing. We propose a novel CNN architecture to carry out these tasks. This

News at Nine: The value of near-real time data for reaching mass media

Science.gov (United States)

Allen, J.; Ward, K.; Simmon, R. B.; Carlowicz, M. J.; Scott, M.; Przyborski, P. D.; Voiland, A. P.

2012-12-01

NASA's Earth Observatory (EO) is an online publication featuring NASA Earth science news and images. Since its inception in 1999, the EO team has relied heavily on near-real time satellite data to publish imagery of breaking news events, such as volcanoes, floods, fires, and dust storms. Major news outlets (Associated Press, The Weather Channel, CNN, etc.) have regularly republished Earth Observatory imagery in their coverage of events. Because of the nature of modern 24-hour news cycle, media almost always want near-real time coverage; providing it depends heavily on rapid data turnaround, user-friendly data systems, and fast data access. We will discuss how we use near-real time data and provide examples of how data systems have been transformed in the past 13 years. We will offer some thoughts on best practices (from the view of a user) in expedited data systems and the positive effect of those practices on public awareness of our content.. Finally, we will share how we work with science teams to see the potential stories in their data and the value of providing the data in a timely fashionAcquired October 9, 2010, this natural-color image shows the toxic sludge spill from an alumina plant in southern Hungary.

Boundary Spanners, Network Capital and the Rise of Edu-Businesses: The Case of News Corporation and Its Emerging Education Agenda

Science.gov (United States)

Hogan, Anna

2015-01-01

This paper provides a critical analysis of News Corporation and argues that through the acquisition of high profile policy actor, Joel Klein, News Corporation has been able to assemble significant "network capital" to position itself as an entity apparently responsible for the public good and with a role to play in public policymaking.…

Enhanced Cyberspace Defense through Covert Publish-Subscribe Broker Pattern Communications

Science.gov (United States)

2008-06-01

http://www.cnn.com/TECH/computing/9902/26/t_t/internet.time/, accessed June 2008. [3] C. V. Clausewitz, On War, 1st ed.. London, England: Kegan ...34 2003. [17] T. Greene . (2008 , Apr.) Network World. [Online]. http://www.networkworld.com/news/2008/040908-rsa-hack-power-grid.html, accessed

Brain tumor segmentation with Deep Neural Networks.

Science.gov (United States)

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.

Cellular Neural Network-Based Methods for Distributed Network Intrusion Detection

Directory of Open Access Journals (Sweden)

Kang Xie

2015-01-01

Full Text Available According to the problems of current distributed architecture intrusion detection systems (DIDS, a new online distributed intrusion detection model based on cellular neural network (CNN was proposed, in which discrete-time CNN (DTCNN was used as weak classifier in each local node and state-controlled CNN (SCCNN was used as global detection method, respectively. We further proposed a new method for design template parameters of SCCNN via solving Linear Matrix Inequality. Experimental results based on KDD CUP 99 dataset show its feasibility and effectiveness. Emerging evidence has indicated that this new approach is affordable to parallelism and analog very large scale integration (VLSI implementation which allows the distributed intrusion detection to be performed better.

Some Congruence Properties of a Restricted Bipartition Function cN(n

Directory of Open Access Journals (Sweden)

Nipen Saikia

2016-01-01

Full Text Available Let cN(n denote the number of bipartitions (λ,μ of a positive integer n subject to the restriction that each part of μ is divisible by N. In this paper, we prove some congruence properties of the function cN(n for N=7, 11, and 5l, for any integer l≥1, by employing Ramanujan’s theta-function identities.

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

Science.gov (United States)

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.

Classification of CT brain images based on deep learning networks.

Science.gov (United States)

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

Inspecting rapidly moving surfaces for small defects using CNN cameras

Science.gov (United States)

Blug, Andreas; Carl, Daniel; Höfler, Heinrich

2013-04-01

A continuous increase in production speed and manufacturing precision raises a demand for the automated detection of small image features on rapidly moving surfaces. An example are wire drawing processes where kilometers of cylindrical metal surfaces moving with 10 m/s have to be inspected for defects such as scratches, dents, grooves, or chatter marks with a lateral size of 100 μm in real time. Up to now, complex eddy current systems are used for quality control instead of line cameras, because the ratio between lateral feature size and surface speed is limited by the data transport between camera and computer. This bottleneck is avoided by "cellular neural network" (CNN) cameras which enable image processing directly on the camera chip. This article reports results achieved with a demonstrator based on this novel analogue camera - computer system. The results show that computational speed and accuracy of the analogue computer system are sufficient to detect and discriminate the different types of defects. Area images with 176 x 144 pixels are acquired and evaluated in real time with frame rates of 4 to 10 kHz - depending on the number of defects to be detected. These frame rates correspond to equivalent line rates on line cameras between 360 and 880 kHz, a number far beyond the available features. Using the relation between lateral feature size and surface speed as a figure of merit, the CNN based system outperforms conventional image processing systems by an order of magnitude.

S-CNN-BASED SHIP DETECTION FROM HIGH-RESOLUTION REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

R. Zhang

2016-06-01

Full Text Available Reliable ship detection plays an important role in both military and civil fields. However, it makes the task difficult with high-resolution remote sensing images with complex background and various types of ships with different poses, shapes and scales. Related works mostly used gray and shape features to detect ships, which obtain results with poor robustness and efficiency. To detect ships more automatically and robustly, we propose a novel ship detection method based on the convolutional neural networks (CNNs, called SCNN, fed with specifically designed proposals extracted from the ship model combined with an improved saliency detection method. Firstly we creatively propose two ship models, the “V” ship head model and the “||” ship body one, to localize the ship proposals from the line segments extracted from a test image. Next, for offshore ships with relatively small sizes, which cannot be efficiently picked out by the ship models due to the lack of reliable line segments, we propose an improved saliency detection method to find these proposals. Therefore, these two kinds of ship proposals are fed to the trained CNN for robust and efficient detection. Experimental results on a large amount of representative remote sensing images with different kinds of ships with varied poses, shapes and scales demonstrate the efficiency and robustness of our proposed S-CNN-Based ship detector.

The Effects of Market Structure on Television News Pricing.

Science.gov (United States)

Wirth, Michael O.; Wollert, James A.

Multiple regression techniques were used to examine the business side of local television news operations for November 1978. Research questions examined the effect of several variables on local television news prices (advertising rates), including type of ownership, network affiliation/signal type, market size, cable network penetration, market…

Anomaly Detection in Nanofibrous Materials by CNN-Based Self-Similarity

Directory of Open Access Journals (Sweden)

Paolo Napoletano

2018-01-01

Full Text Available Automatic detection and localization of anomalies in nanofibrous materials help to reduce the cost of the production process and the time of the post-production visual inspection process. Amongst all the monitoring methods, those exploiting Scanning Electron Microscope (SEM imaging are the most effective. In this paper, we propose a region-based method for the detection and localization of anomalies in SEM images, based on Convolutional Neural Networks (CNNs and self-similarity. The method evaluates the degree of abnormality of each subregion of an image under consideration by computing a CNN-based visual similarity with respect to a dictionary of anomaly-free subregions belonging to a training set. The proposed method outperforms the state of the art.

Learning-induced pattern classification in a chaotic neural network

International Nuclear Information System (INIS)

Li, Yang; Zhu, Ping; Xie, Xiaoping; He, Guoguang; Aihara, Kazuyuki

2012-01-01

In this Letter, we propose a Hebbian learning rule with passive forgetting (HLRPF) for use in a chaotic neural network (CNN). We then define the indices based on the Euclidean distance to investigate the evolution of the weights in a simplified way. Numerical simulations demonstrate that, under suitable external stimulations, the CNN with the proposed HLRPF acts as a fuzzy-like pattern classifier that performs much better than an ordinary CNN. The results imply relationship between learning and recognition. -- Highlights: ► Proposing a Hebbian learning rule with passive forgetting (HLRPF). ► Defining indices to investigate the evolution of the weights simply. ► The chaotic neural network with HLRPF acts as a fuzzy-like pattern classifier. ► The pattern classifier ability of the network is improved much.

Cellular neural networks for the stereo matching problem

International Nuclear Information System (INIS)

Taraglio, S.; Zanela, A.

1997-03-01

The applicability of the Cellular Neural Network (CNN) paradigm to the problem of recovering information on the tridimensional structure of the environment is investigated. The approach proposed is the stereo matching of video images. The starting point of this work is the Zhou-Chellappa neural network implementation for the same problem. The CNN based system we present here yields the same results as the previous approach, but without the many existing drawbacks

Tweeting News Articles

Directory of Open Access Journals (Sweden)

Marco Toledo Bastos

2013-09-01

Full Text Available In this article we investigate the impact of social media readership to the editorial profile of newspapers. We analyze tweets containing links to news articles from eight of the largest national newspapers in the United States, United Kingdom, Spain, Brazil, and Germany. The data collection follows the first two weeks of October 2012 and includes 2,842,699 tweets with links to news articles. Twitter-shortened links were resolved using a three-pass routine and assigned to 1 of the 21 newspaper sections. We found the concentration of links to news articles posted by top users to be lower than reported in the literature and the strategy of relaying headlines on Twitter via automatic news aggregators (feeds to be inefficient. The results of this investigation show which sections of a newspaper are the most and least read by readers in different parts of the world, with German readers placing greater emphasis on Politics and Economy; Brazilians on Sports and Arts; Spaniards on Local and National news; Britons and Americans on Opinion and World news. We also found that German and Spanish readers are more likely to read multiple national newspapers, while British readers more often resort to foreign sources of news. The results confirm that feedback to news items from a large user base is pivotal for the replication of content and that newspapers and news items can be clustered according to the editorial profile and principles of newsworthiness inherited from legacy media. The results of this investigation shed light onto the networked architecture of journalism that increasingly depends on readership agency.

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

Science.gov (United States)

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.

A CNN-Based Method of Vehicle Detection from Aerial Images Using Hard Example Mining

Directory of Open Access Journals (Sweden)

Yohei Koga

2018-01-01

Full Text Available Recently, deep learning techniques have had a practical role in vehicle detection. While much effort has been spent on applying deep learning to vehicle detection, the effective use of training data has not been thoroughly studied, although it has great potential for improving training results, especially in cases where the training data are sparse. In this paper, we proposed using hard example mining (HEM in the training process of a convolutional neural network (CNN for vehicle detection in aerial images. We applied HEM to stochastic gradient descent (SGD to choose the most informative training data by calculating the loss values in each batch and employing the examples with the largest losses. We picked 100 out of both 500 and 1000 examples for training in one iteration, and we tested different ratios of positive to negative examples in the training data to evaluate how the balance of positive and negative examples would affect the performance. In any case, our method always outperformed the plain SGD. The experimental results for images from New York showed improved performance over a CNN trained in plain SGD where the F1 score of our method was 0.02 higher.

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

National Research Council Canada - National Science Library

Greguss, Pal

2002-01-01

...) and the CNN chip for a few military applications. A polar beam splitter will be placed immediately after the relay lens to obtain two image planes, one will be used by the existing 64X64 CNN-UM focal plane array processor chip...

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

Science.gov (United States)

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.

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN.

Science.gov (United States)

Guo, Hao; Wu, Danni; An, Jubai

2017-08-09

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features.

Oil Exploration in Nigeria and its Socio-Economic Impact in Ogba ...

African Journals Online (AJOL)

Through its wide areas of operations, native of product demand and international network of operations, the industry affects almost every country in the world. As a result, a day hardly passes without oil being mentioned in the news. CNN is very handy in giving an update on world oil, prices on a daily basis. The industry has ...

Opinion mining on book review using CNN-L2-SVM algorithm

Science.gov (United States)

Rozi, M. F.; Mukhlash, I.; Soetrisno; Kimura, M.

2018-03-01

Review of a product can represent quality of a product itself. An extraction to that review can be used to know sentiment of that opinion. Process to extract useful information of user review is called Opinion Mining. Review extraction model that is enhancing nowadays is Deep Learning model. This Model has been used by many researchers to obtain excellent performance on Natural Language Processing. In this research, one of deep learning model, Convolutional Neural Network (CNN) is used for feature extraction and L2 Support Vector Machine (SVM) as classifier. These methods are implemented to know the sentiment of book review data. The result of this method shows state-of-the art performance in 83.23% for training phase and 64.6% for testing phase.

Drug-Drug Interaction Extraction via Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Shengyu Liu

2016-01-01

Full Text Available Drug-drug interaction (DDI extraction as a typical relation extraction task in natural language processing (NLP has always attracted great attention. Most state-of-the-art DDI extraction systems are based on support vector machines (SVM with a large number of manually defined features. Recently, convolutional neural networks (CNN, a robust machine learning method which almost does not need manually defined features, has exhibited great potential for many NLP tasks. It is worth employing CNN for DDI extraction, which has never been investigated. We proposed a CNN-based method for DDI extraction. Experiments conducted on the 2013 DDIExtraction challenge corpus demonstrate that CNN is a good choice for DDI extraction. The CNN-based DDI extraction method achieves an F-score of 69.75%, which outperforms the existing best performing method by 2.75%.

A ROUGH SET DECISION TREE BASED MLP-CNN FOR VERY HIGH RESOLUTION REMOTELY SENSED IMAGE CLASSIFICATION

Directory of Open Access Journals (Sweden)

C. Zhang

2017-09-01

Full Text Available 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.

Paediatric frontal chest radiograph screening with fine-tuned convolutional neural networks

CSIR Research Space (South Africa)

Gerrand, Jonathan D

2017-07-01

Full Text Available of fine-tuned convolutional neural networks (CNN). We use two popular CNN models that are pre-trained on a large natural image dataset and two distinct datasets containing paediatric and adult radiographs respectively. Evaluation is performed using a 5...

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

Science.gov (United States)

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

Cellular Neural Networks for NP-Hard Optimization

Directory of Open Access Journals (Sweden)

Mária Ercsey-Ravasz

2009-02-01

Full Text Available A cellular neural/nonlinear network (CNN is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson spin-glass system. Using the properties of CNN, we show that one single operation (template always yields a local minimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10×10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.

Detecting lung cancer symptoms with analogic CNN algorithms based on a constrained diffusion template

International Nuclear Information System (INIS)

Hirakawa, Satoshi; Nishio, Yoshifumi; Ushida, Akio; Ueno, Junji; Kasem, I.; Nishitani, Hiromu; Rekeczky, C.; Roska, T.

1997-01-01

In this article, a new type of diffusion template and an analogic CNN algorithm using this diffusion template for detecting some lung cancer symptoms in X-ray films are proposed. The performance of the diffusion template is investigated and our CNN algorithm is verified to detect some key lung cancer symptoms, successfully. (author)

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

International Nuclear Information System (INIS)

Sahiner, B.; Chan, H.P.; Petrick, N.; Helvie, M.A.; Adler, D.D.; Goodsitt, M.M.; Wei, D.

1996-01-01

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 subsequently 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

Deep hierarchical attention network for video description

Science.gov (United States)

Li, Shuohao; Tang, Min; Zhang, Jun

2018-03-01

Pairing video to natural language description remains a challenge in computer vision and machine translation. Inspired by image description, which uses an encoder-decoder model for reducing visual scene into a single sentence, we propose a deep hierarchical attention network for video description. The proposed model uses convolutional neural network (CNN) and bidirectional LSTM network as encoders while a hierarchical attention network is used as the decoder. Compared to encoder-decoder models used in video description, the bidirectional LSTM network can capture the temporal structure among video frames. Moreover, the hierarchical attention network has an advantage over single-layer attention network on global context modeling. To make a fair comparison with other methods, we evaluate the proposed architecture with different types of CNN structures and decoders. Experimental results on the standard datasets show that our model has a more superior performance than the state-of-the-art techniques.

Mining key elements for severe convection prediction based on CNN

Science.gov (United States)

Liu, Ming; Pan, Ning; Zhang, Changan; Sha, Hongzhou; Zhang, Bolei; Liu, Liang; Zhang, Meng

2017-04-01

Severe convective weather is a kind of weather disasters accompanied by heavy rainfall, gust wind, hail, etc. Along with recent developments on remote sensing and numerical modeling, there are high-volume and long-term observational and modeling data accumulated to capture massive severe convective events over particular areas and time periods. With those high-volume and high-variety weather data, most of the existing studies and methods carry out the dynamical laws, cause analysis, potential rule study, and prediction enhancement by utilizing the governing equations from fluid dynamics and thermodynamics. In this study, a key-element mining method is proposed for severe convection prediction based on convolution neural network (CNN). It aims to identify the key areas and key elements from huge amounts of historical weather data including conventional measurements, weather radar, satellite, so as numerical modeling and/or reanalysis data. Under this manner, the machine-learning based method could help the human forecasters on their decision-making on operational weather forecasts on severe convective weathers by extracting key information from the real-time and historical weather big data. In this paper, it first utilizes computer vision technology to complete the data preprocessing work of the meteorological variables. Then, it utilizes the information such as radar map and expert knowledge to annotate all images automatically. And finally, by using CNN model, it cloud analyze and evaluate each weather elements (e.g., particular variables, patterns, features, etc.), and identify key areas of those critical weather elements, then help forecasters quickly screen out the key elements from huge amounts of observation data by current weather conditions. Based on the rich weather measurement and model data (up to 10 years) over Fujian province in China, where the severe convective weathers are very active during the summer months, experimental tests are conducted with

The participation of Interactors in Social Network Sites as a News Event Dimension

Directory of Open Access Journals (Sweden)

Gabriela da Silva Zago

2013-07-01

Full Text Available Normal 0 21 false false false MicrosoftInternetExplorer4 The article discusses the possibility of considering the participation of interactors in social network sites as a news event dimension, to the extent that, by the recirculation, interactors can assign different and unexpected meanings to the event. We take as a starting point for the discussion, in an exploratory nature, the inauguration of the first stretch of the bike path from the Avenida Ipiranga, in Porto Alegre, Brazil, in May 2012.

Visibility of news items on Twitter and Facebook: comparative analysis of the most replicated news in Europe and the Americas

Directory of Open Access Journals (Sweden)

Gabriela da Silva Zago

2013-06-01

Full Text Available This paper identifies and compares the top replicated news items on Twitter and Facebook, on news websites from Brazil, Germany, Spain, the United States and United Kingdom. The data includes 8 of the newspapers with the largest circulation in the 5 countries, totaling 40 outlets. The volume of “retweets” and “likes” was used as a metric for representing the visibility of news items. Links posted on Twitter and messages posted by newspapers on Facebook were collected for a period of two weeks during October 2012. Content analysis was carried out for the top 25 replicated messages in each social network, which uncovered significant cultural traits and editorial differences across countries, based upon the content that circulated on social networks during the period. The results confirm users’ perceptions of social networking sites, with Facebook being used largely for entertainment and recreation, and Twitter for covering political, economic, and social events.

Chinese character recognition based on Gabor feature extraction and CNN

Science.gov (United States)

Xiong, Yudian; Lu, Tongwei; Jiang, Yongyuan

2018-03-01

As an important application in the field of text line recognition and office automation, Chinese character recognition has become an important subject of pattern recognition. However, due to the large number of Chinese characters and the complexity of its structure, there is a great difficulty in the Chinese character recognition. In order to solve this problem, this paper proposes a method of printed Chinese character recognition based on Gabor feature extraction and Convolution Neural Network(CNN). The main steps are preprocessing, feature extraction, training classification. First, the gray-scale Chinese character image is binarized and normalized to reduce the redundancy of the image data. Second, each image is convoluted with Gabor filter with different orientations, and the feature map of the eight orientations of Chinese characters is extracted. Third, the feature map through Gabor filters and the original image are convoluted with learning kernels, and the results of the convolution is the input of pooling layer. Finally, the feature vector is used to classify and recognition. In addition, the generalization capacity of the network is improved by Dropout technology. The experimental results show that this method can effectively extract the characteristics of Chinese characters and recognize Chinese characters.

Data Exchanges in Mobile News Apps

DEFF Research Database (Denmark)

Kammer, Aske; Wallberg, Filip

This paper maps the flows of data to and from third-party actors when users access content in mobile news apps. This way, it analyzes the strategic networks of collaboration and data exchange that news organizations engage in in the digital economy, expanding the traditional two-sided model...... of media economics (centered around sales to audiences and advertisers) to also account for what happens “behind the curtain”. Research already shows that the trade with data constitutes a central component of web-based business models (Evens & Van Damme, 2016; Gerlitz & Helmond, 2013; Lindskow, 2016......), but no systematic scrutiny of the same phenomenon exists with regards to mobile news use even though the use of mobile platforms for news accessing increases these years (Newman, Levy, & Nielsen, 2015). This study measures all calls made by the news apps upon accessing content, mapping which third-party actors get...

Tectonic modeling of Konya-Beysehir Region (Turkey using cellular neural networks

Directory of Open Access Journals (Sweden)

D. Aydogan

2007-06-01

Full Text Available In this paper, to separate regional-residual anomaly maps and to detect borders of buried geological bodies, we applied the Cellular Neural Network (CNN approach to gravity and magnetic anomaly maps. CNN is a stochastic image processing technique, based optimization of templates, which imply relationships of neighborhood pixels in 2-Dimensional (2D potential anomalies. Here, CNN performance in geophysics, tested by various synthetic examples and the results are compared to classical methods such as boundary analysis and second vertical derivatives. After we obtained satisfactory results in synthetic models, we applied CNN to Bouguer anomaly map of Konya-Beysehir Region, which has complex tectonic structure with various fault combinations. We evaluated CNN outputs and 2D/3D models, which are constructed using forward and inversion methods. Then we presented a new tectonic structure of Konya-Beysehir Region. We have denoted (F1, F2, …, F7 and (Konya1, Konya2 faults according to our evaluations of CNN outputs. Thus, we have concluded that CNN is a compromising stochastic image processing technique in geophysics.

Classification of crystal structure using a convolutional neural network.

Science.gov (United States)

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.

NetWorking News - A Method for Engaging Children Actively In Design

DEFF Research Database (Denmark)

Nørregaard, Peter; Dindler, Christian; Fritsch, Jonas

2003-01-01

For many years cooperative design was primarily concerned with the development of IT supported systems for professional users. However, the cooperative design approach can embrace other social practices such as children’s everyday life. At a methodological level there is no difference in designing...... the Networking News workshop, offers an opportunity to make first hand studies of children’s IT supported social activities in an informal classroom setting....... with adults or children. However there is a need for new methods to support communication and collaboration between designers and children. This article proposes a new method for understandings children’s appropriation of new technology in an interactive workshop setting. The method, which we call...

Cellular Neural Network Method for Critical Slab with Albedo Boundary Condition

International Nuclear Information System (INIS)

Pirouzmanda, A.; Hadada, K.; Suh, K. Y.

2010-01-01

The neutron transport problems have been studied theoretically and numerically for years. A number of researchers have studied the criticality problems of one-speed neutrons in homogeneous slabs and spheres using various methods. The Chebyshev polynomial approximation method (T N method) has lately been developed and improved for the neutron transport equation in slab geometry. The one-speed time-dependent neutron transport equation using the Cellular Neural Network (CNN) for the vacuum boundary condition has previously been solved. In this paper, we demonstrate the capacity of CNN in calculating the critical slab thickness for different boundary conditions and its variation with moments N. The architecture of the CNN has already been dealt with thoroughly. Essentially, the CNN is used to model a first-order system of the partial differential equations (PDEs). The original equations in the T N approximation are also a set of PDEs. The CNN approach lends itself to analog VLSI implementation. In this study, the CNN model is implemented using the HSpice software package

Cellular Neural Networks: A genetic algorithm for parameters optimization in artificial vision applications

International Nuclear Information System (INIS)

Taraglio, S.; Zanela, A.

1997-03-01

An optimization method for some of the CNN's (Cellular Neural Network) parameters, based on evolutionary strategies, is proposed. The new class of feedback template found is more effective in extracting features from the images that an autonomous vehicle acquires, than in the previous CNN's literature

Applying Gradient Descent in Convolutional Neural Networks

Science.gov (United States)

Cui, Nan

2018-04-01

With the development of the integrated circuit and computer science, people become caring more about solving practical issues via information technologies. Along with that, a new subject called Artificial Intelligent (AI) comes up. One popular research interest of AI is about recognition algorithm. In this paper, one of the most common algorithms, Convolutional Neural Networks (CNNs) will be introduced, for image recognition. Understanding its theory and structure is of great significance for every scholar who is interested in this field. Convolution Neural Network is an artificial neural network which combines the mathematical method of convolution and neural network. The hieratical structure of CNN provides it reliable computer speed and reasonable error rate. The most significant characteristics of CNNs are feature extraction, weight sharing and dimension reduction. Meanwhile, combining with the Back Propagation (BP) mechanism and the Gradient Descent (GD) method, CNNs has the ability to self-study and in-depth learning. Basically, BP provides an opportunity for backwardfeedback for enhancing reliability and GD is used for self-training process. This paper mainly discusses the CNN and the related BP and GD algorithms, including the basic structure and function of CNN, details of each layer, the principles and features of BP and GD, and some examples in practice with a summary in the end.

Determining Citizens’ Opinions About Stories in the News Media

Directory of Open Access Journals (Sweden)

Timo Wandhöfer

2012-12-01

Full Text Available We describe a method whereby a governmental policy maker can discover citizens’ reaction to news stories. This is particularly relevant in the political world, where governments’ policy statements are reported by the news media and discussed by citizens. The work here addresses two main questions: whereabouts are citizens discussing a news story, and what are they saying? Our strategy to answer the first question is to find news articles pertaining to the policy statements, then perform internet searches for references to the news articles’ headlines and URLs. We have created a software tool that schedules repeating Google searches for the news articles and collects the results in a database, enabling the user to aggregate and analyse them to produce ranked tables of sites that reference the news articles. Using data mining techniques we can analyse data so that resultant ranking reflects an overall aggregate score, taking into account multiple datasets, and this shows the most relevant places on the internet where the story is discussed. To answer the second question, we introduce the WeGov toolbox as a tool for analysing citizens’ comments and behaviour pertaining to news stories.  We first use the tool for identifying social network discussions, using different strategies for Facebook and Twitter. We apply different analysis components to analyse the data to distil the essence of the social network users’ comments, to determine influential users and identify important comments.

CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes

OpenAIRE

Li, Yuhong; Zhang, Xiaofan; Chen, Deming

2018-01-01

We propose a network for Congested Scene Recognition called CSRNet to provide a data-driven and deep learning method that can understand highly congested scenes and perform accurate count estimation as well as present high-quality density maps. The proposed CSRNet is composed of two major components: a convolutional neural network (CNN) as the front-end for 2D feature extraction and a dilated CNN for the back-end, which uses dilated kernels to deliver larger reception fields and to replace po...

Adaptive function projective synchronization of two-cell Quantum-CNN chaotic oscillators with uncertain parameters

International Nuclear Information System (INIS)

Sudheer, K. Sebastian; Sabir, M.

2009-01-01

This work investigates function projective synchronization of two-cell Quantum-CNN chaotic oscillators using adaptive method. Quantum-CNN oscillators produce nano scale chaotic oscillations under certain conditions. By Lyapunove stability theory, the adaptive control law and the parameter update law are derived to make the state of two chaotic systems function projective synchronized. Numerical simulations are presented to demonstrate the effectiveness of the proposed adaptive controllers.

Composability-Centered Convolutional Neural Network Pruning

Energy Technology Data Exchange (ETDEWEB)

Shen, Xipeng [North Carolina State University; Guan, Hui [North Carolina State University; Lim, Seung-Hwan [ORNL; Patton, Robert M. [ORNL

2018-02-01

This work studies the composability of the building blocks ofstructural CNN models (e.g., GoogleLeNet and Residual Networks) in thecontext of network pruning. We empirically validate that a networkcomposed of pre-trained building blocks (e.g. residual blocks andInception modules) not only gives a better initial setting fortraining, but also allows the training process to converge at asignificantly higher accuracy in much less time. Based on thatinsight, we propose a {\\em composability-centered} design for CNNnetwork pruning. Experiments show that this new scheme shortens theconfiguration process in CNN network pruning by up to 186.8X forResNet-50 and up to 30.2X for Inception-V3, and meanwhile, the modelsit finds that meet the accuracy requirement are significantly morecompact than those found by default schemes.

Cellular Neural Networks: A genetic algorithm for parameters optimization in artificial vision applications

Energy Technology Data Exchange (ETDEWEB)

Taraglio, S. [ENEA, Centro Ricerche Casaccia, Rome (Italy). Dipt. Innovazione; Zanela, A. [Rome Univ. `La Sapienza` (Italy). Dipt. di Fisica

1997-03-01

An optimization method for some of the CNN`s (Cellular Neural Network) parameters, based on evolutionary strategies, is proposed. The new class of feedback template found is more effective in extracting features from the images that an autonomous vehicle acquires, than in the previous CNN`s literature.

The Drosophila Pericentrin-like-protein (PLP cooperates with Cnn to maintain the integrity of the outer PCM

Directory of Open Access Journals (Sweden)

Jennifer H. Richens

2015-08-01

Full Text Available Centrosomes comprise a pair of centrioles surrounded by a matrix of pericentriolar material (PCM. In vertebrate cells, Pericentrin plays an important part in mitotic PCM assembly, but the Drosophila Pericentrin-like protein (PLP appears to have a more minor role in mitotic fly cells. Here we investigate the function of PLP during the rapid mitotic cycles of the early Drosophila embryo. Unexpectedly, we find that PLP is specifically enriched in the outer-most regions of the PCM, where it largely co-localizes with the PCM scaffold protein Cnn. In the absence of PLP the outer PCM appears to be structurally weakened, and it rapidly disperses along the centrosomal microtubules (MTs. As a result, centrosomal MTs are subtly disorganized in embryos lacking PLP, although mitosis is largely unperturbed and these embryos develop and hatch at near-normal rates. Y2H analysis reveals that PLP can potentially form multiple interactions with itself and with the PCM recruiting proteins Asl, Spd-2 and Cnn. A deletion analysis suggests that PLP participates in a complex network of interactions that ultimately help to strengthen the PCM.

Partial state feedback control of chaotic neural network and its application

International Nuclear Information System (INIS)

He Guoguang; Shrimali, Manish Dev; Aihara, Kazuyuki

2007-01-01

The chaos control in the chaotic neural network is studied using the partial state feedback with a control signal from a few control neurons. The controlled CNN converges to one of the stored patterns with a period which depends on the initial conditions, i.e., the set of control neurons and other control parameters. We show that the controlled CNN can distinguish between two initial patterns even if they have a small difference. This implies that such a controlled CNN can be feasibly applied to information processing such as pattern recognition

Principles of Multimedia News Systems for Business Applications

Directory of Open Access Journals (Sweden)

Ioan I. ANDONE

2009-01-01

Full Text Available In the past few years considerable demand for business oriented multimedia information systems has developed. A multimedia information system is one that can create, import, integrate, store, retrieve, edit, and delete two or more types of media materials in digital form, such as audio, image, full-motion video, and text information. Multimedia information systems play a central role in many business activities. They represent a very special class complex computing systems. This paper surveys a special type of multimedia information systems: multimedia news systems. Multimedia news systems deal with architectures to manage complex multimedia news databases, online presentation and distribution services or the integration of several existing services to meta-services using intelligent news retrieval engines. The leading presentation platform in multimedia news presentation is news networks providing television services and Internet content distribution. The primary focus is on advanced multimedia news systems infrastructure, document standards, application architecture and principles for multimedia news on the Web that suggest long-term trends in this increasingly important area.

Phylogenetic convolutional neural networks in metagenomics.

Science.gov (United States)

Fioravanti, Diego; Giarratano, Ylenia; Maggio, Valerio; Agostinelli, Claudio; Chierici, Marco; Jurman, Giuseppe; Furlanello, Cesare

2018-03-08

Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space. Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron. Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.

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

Science.gov (United States)

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.

Cellular neural network to the spherical harmonics approximation of neutron transport equation in x-y geometry. Part I: Modeling and verification for time-independent solution

International Nuclear Information System (INIS)

Pirouzmand, Ahmad; Hadad, Kamal

2011-01-01

Highlights: → This paper describes the solution of time-independent neutron transport equation. → Using a novel method based on cellular neural networks (CNNs) coupled with P N method. → Utilize the CNN model to simulate spatial scalar flux distribution in steady state. → The accuracy, stability, and capabilities of CNN model are examined in x-y geometry. - Abstract: This paper describes a novel method based on using cellular neural networks (CNN) coupled with spherical harmonics method (P N ) to solve the time-independent neutron transport equation in x-y geometry. To achieve this, an equivalent electrical circuit based on second-order form of neutron transport equation and relevant boundary conditions is obtained using CNN method. We use the CNN model to simulate spatial response of scalar flux distribution in the steady state condition for different order of spherical harmonics approximations. The accuracy, stability, and capabilities of CNN model are examined in 2D Cartesian geometry for fixed source and criticality problems.

GPU Boosted CNN Simulator Library for Graphical Flow-Based Programmability

Directory of Open Access Journals (Sweden)

Balázs Gergely Soós

2009-01-01

Full Text Available A graphical environment for CNN algorithm development is presented. The new generation of graphical cards with many general purpose processing units introduces the massively parallel computing into PC environment. Universal Machine on Flows- (UMF like notation, highlighting image flows and operations, is a useful tool to describe image processing algorithms. This documentation step can be turned into modeling using our framework backed with MATLAB Simulink and the power of a video card. This latter relatively cheap extension enables a convenient and fast analysis of CNN dynamics and complex algorithms. Comparison with other PC solutions is also presented. For single template execution, our approach yields run times 40x faster than that of the widely used Candy simulator. In the case of simpler algorithms, real-time execution is also possible.

Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo

Science.gov (United States)

Lu, Liang; Qi, Lin; Luo, Yisong; Jiao, Hengchao; Dong, Junyu

2018-01-01

Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN) instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects. We use the model to predict initial normal of real-world objects and iteratively optimize the fine-scale geometry in the multi-spectral photometric stereo framework. The experimental results illustrate the improvement of the proposed method compared with existing methods. PMID:29498703

Three-Dimensional Reconstruction from Single Image Base on Combination of CNN and Multi-Spectral Photometric Stereo

Directory of Open Access Journals (Sweden)

Liang Lu

2018-03-01

Full Text Available Multi-spectral photometric stereo can recover pixel-wise surface normal from a single RGB image. The difficulty lies in that the intensity in each channel is the tangle of illumination, albedo and camera response; thus, an initial estimate of the normal is required in optimization-based solutions. In this paper, we propose to make a rough depth estimation using the deep convolutional neural network (CNN instead of using depth sensors or binocular stereo devices. Since high-resolution ground-truth data is expensive to obtain, we designed a network and trained it with rendered images of synthetic 3D objects. We use the model to predict initial normal of real-world objects and iteratively optimize the fine-scale geometry in the multi-spectral photometric stereo framework. The experimental results illustrate the improvement of the proposed method compared with existing methods.

Convolutional Neural Networks with Batch Normalization for Classifying Hi-hat, Snare, and Bass Percussion Sound Samples

DEFF Research Database (Denmark)

Gajhede, Nicolai; Beck, Oliver; Purwins, Hendrik

2016-01-01

After having revolutionized image and speech processing, convolu- tional neural networks (CNN) are now starting to become more and more successful in music information retrieval as well. We compare four CNN types for classifying a dataset of more than 3000 acoustic and synthesized samples...

Static facial expression recognition with convolution neural networks

Science.gov (United States)

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.

Abnormality Detection in Mammography using Deep Convolutional Neural Networks

OpenAIRE

Xi, Pengcheng; Shu, Chang; Goubran, Rafik

2018-01-01

Breast cancer is the most common cancer in women worldwide. The most common screening technology is mammography. To reduce the cost and workload of radiologists, we propose a computer aided detection approach for classifying and localizing calcifications and masses in mammogram images. To improve on conventional approaches, we apply deep convolutional neural networks (CNN) for automatic feature learning and classifier building. In computer-aided mammography, deep CNN classifiers cannot be tra...

A convolutional neural network neutrino event classifier

International Nuclear Information System (INIS)

Aurisano, A.; Sousa, A.; Radovic, A.; Vahle, P.; Rocco, D.; Pawloski, G.; Himmel, A.; Niner, E.; Messier, M.D.; Psihas, F.

2016-01-01

Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying particle interactions in sampling calorimeters used commonly in high energy physics and high energy neutrino physics in particular. Following a discussion of the core concepts of CNNs and recent innovations in CNN architectures related to the field of deep learning, we outline a specific application to the NOvA neutrino detector. This algorithm, CVN (Convolutional Visual Network) identifies neutrino interactions based on their topology without the need for detailed reconstruction and outperforms algorithms currently in use by the NOvA collaboration.

3D Convolutional Neural Networks for Crop Classification with Multi-Temporal Remote Sensing Images

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Shunping Ji

2018-01-01

Full Text Available This study describes a novel three-dimensional (3D convolutional neural networks (CNN based method that automatically classifies crops from spatio-temporal remote sensing images. First, 3D kernel is designed according to the structure of multi-spectral multi-temporal remote sensing data. Secondly, the 3D CNN framework with fine-tuned parameters is designed for training 3D crop samples and learning spatio-temporal discriminative representations, with the full crop growth cycles being preserved. In addition, we introduce an active learning strategy to the CNN model to improve labelling accuracy up to a required threshold with the most efficiency. Finally, experiments are carried out to test the advantage of the 3D CNN, in comparison to the two-dimensional (2D CNN and other conventional methods. Our experiments show that the 3D CNN is especially suitable in characterizing the dynamics of crop growth and outperformed the other mainstream methods.

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

Science.gov (United States)

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.

The News Delivery Sequence: Bad News and Good News in Conversational Interaction.

Science.gov (United States)

Maynard, Douglas W.

1997-01-01

Explores the conditional nature of good and bad news while focusing on three topics: (1) the status of information as news according the participants in a conversation; (2) the valence of this information with regard to its perception as good or bad; and (3) the effect of news on individuals. Notes that good news is privileged over bad news in…

Convolutional neural network-based classification system design with compressed wireless sensor network images.

Science.gov (United States)

Ahn, Jungmo; Park, JaeYeon; Park, Donghwan; Paek, Jeongyeup; Ko, JeongGil

2018-01-01

With the introduction of various advanced deep learning algorithms, initiatives for image classification systems have transitioned over from traditional machine learning algorithms (e.g., SVM) to Convolutional Neural Networks (CNNs) using deep learning software tools. A prerequisite in applying CNN to real world applications is a system that collects meaningful and useful data. For such purposes, Wireless Image Sensor Networks (WISNs), that are capable of monitoring natural environment phenomena using tiny and low-power cameras on resource-limited embedded devices, can be considered as an effective means of data collection. However, with limited battery resources, sending high-resolution raw images to the backend server is a burdensome task that has direct impact on network lifetime. To address this problem, we propose an energy-efficient pre- and post- processing mechanism using image resizing and color quantization that can significantly reduce the amount of data transferred while maintaining the classification accuracy in the CNN at the backend server. We show that, if well designed, an image in its highly compressed form can be well-classified with a CNN model trained in advance using adequately compressed data. Our evaluation using a real image dataset shows that an embedded device can reduce the amount of transmitted data by ∼71% while maintaining a classification accuracy of ∼98%. Under the same conditions, this process naturally reduces energy consumption by ∼71% compared to a WISN that sends the original uncompressed images.

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

Science.gov (United States)

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.

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

Directory of Open Access Journals (Sweden)

Aichun Zhu

2018-03-01

Full Text Available 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.

Metaheuristic Algorithms for Convolution Neural Network.

Science.gov (United States)

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).

Metaheuristic Algorithms for Convolution Neural Network

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L. M. Rasdi Rere

2016-01-01

Full Text Available 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 deep 3D residual CNN for false-positive reduction in pulmonary nodule detection.

Science.gov (United States)

Jin, Hongsheng; Li, Zongyao; Tong, Ruofeng; Lin, Lanfen

2018-05-01

The automatic detection of pulmonary nodules using CT scans improves the efficiency of lung cancer diagnosis, and false-positive reduction plays a significant role in the detection. In this paper, we focus on the false-positive reduction task and propose an effective method for this task. We construct a deep 3D residual CNN (convolution neural network) to reduce false-positive nodules from candidate nodules. The proposed network is much deeper than the traditional 3D CNNs used in medical image processing. Specifically, in the network, we design a spatial pooling and cropping (SPC) layer to extract multilevel contextual information of CT data. Moreover, we employ an online hard sample selection strategy in the training process to make the network better fit hard samples (e.g., nodules with irregular shapes). Our method is evaluated on 888 CT scans from the dataset of the LUNA16 Challenge. The free-response receiver operating characteristic (FROC) curve shows that the proposed method achieves a high detection performance. Our experiments confirm that our method is robust and that the SPC layer helps increase the prediction accuracy. Additionally, the proposed method can easily be extended to other 3D object detection tasks in medical image processing. © 2018 American Association of Physicists in Medicine.

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

Science.gov (United States)

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.

Cellular neural network to the spherical harmonics approximation of neutron transport equation in x–y geometry

International Nuclear Information System (INIS)

Pirouzmand, Ahmad; Hadad, Kamal

2012-01-01

Highlights: ► This paper describes the solution of time-dependent neutron transport equation. ► We use a novel method based on cellular neural networks (CNNs) coupled with the spherical harmonics method. ► We apply the CNN model to simulate step and ramp perturbation transients in a core. ► The accuracy and capabilities of the CNN model are examined for x–y geometry. - Abstract: In an earlier paper we utilized a novel method using cellular neural networks (CNNs) coupled with spherical harmonics method to solve the steady state neutron transport equation in x–y geometry. Here, the previous work is extended to the study of time-dependent neutron transport equation. To achieve this goal, an equivalent electrical circuit based on a second-order form of time-dependent neutron transport equation and one equivalent group of neutron precursor density is obtained by the CNN method. The CNN model is used to simulate step and ramp perturbation transients in a typical 2D core.

Regional Journalism in Southeast Asia and ASEAN Identity in Making: Asia News Network as a Case for International and Intercultural Communication

Directory of Open Access Journals (Sweden)

Ulaş Başar Gezgin

2014-06-01

Full Text Available Somehow in analogy with the European Union, 10 Southeast Asian countries are in the process of forming a political and economic union which is (ASEAN Association of Southeast Asian Nations for decades. This process of regional integration goes in tandem with the regional media associations such as Asia News Network (ANN which is an Asian-German initiative that consists of daily newspapers from various Asian countries. As a regional association covering not only all ASEAN countries, but also ASEAN neighbors; ANN may serve a significant role for regional collaboration and integration. The network is instrumental to the promotion of freedom of speech which is not always easy to propound in some of these countries. This article analyzes news coverage and publication policy of ANN and discusses challenges and opportunities of regional journalism in SEA as a case for international and intercultural communication.

Transforming Musical Signals through a Genre Classifying Convolutional Neural Network

Science.gov (United States)

Geng, S.; Ren, G.; Ogihara, M.

2017-05-01

Convolutional neural networks (CNNs) have been successfully applied on both discriminative and generative modeling for music-related tasks. For a particular task, the trained CNN contains information representing the decision making or the abstracting process. One can hope to manipulate existing music based on this 'informed' network and create music with new features corresponding to the knowledge obtained by the network. In this paper, we propose a method to utilize the stored information from a CNN trained on musical genre classification task. The network was composed of three convolutional layers, and was trained to classify five-second song clips into five different genres. After training, randomly selected clips were modified by maximizing the sum of outputs from the network layers. In addition to the potential of such CNNs to produce interesting audio transformation, more information about the network and the original music could be obtained from the analysis of the generated features since these features indicate how the network 'understands' the music.

Accurate lithography simulation model based on convolutional neural networks

Science.gov (United States)

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.

Real-time vehicle detection and tracking in video based on faster R-CNN

Science.gov (United States)

Zhang, Yongjie; Wang, Jian; Yang, Xin

2017-08-01

Vehicle detection and tracking is a significant part in auxiliary vehicle driving system. Using the traditional detection method based on image information has encountered enormous difficulties, especially in complex background. To solve this problem, a detection method based on deep learning, Faster R-CNN, which has very high detection accuracy and flexibility, is introduced. An algorithm of target tracking with the combination of Camshift and Kalman filter is proposed for vehicle tracking. The computation time of Faster R-CNN cannot achieve realtime detection. We use multi-thread technique to detect and track vehicle by parallel computation for real-time application.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Multi-Input Convolutional Neural Network for Flower Grading

Directory of Open Access Journals (Sweden)

Yu Sun

2017-01-01

Full Text Available Flower grading is a significant task because it is extremely convenient for managing the flowers in greenhouse and market. With the development of computer vision, flower grading has become an interdisciplinary focus in both botany and computer vision. A new dataset named BjfuGloxinia contains three quality grades; each grade consists of 107 samples and 321 images. A multi-input convolutional neural network is designed for large scale flower grading. Multi-input CNN achieves a satisfactory accuracy of 89.6% on the BjfuGloxinia after data augmentation. Compared with a single-input CNN, the accuracy of multi-input CNN is increased by 5% on average, demonstrating that multi-input convolutional neural network is a promising model for flower grading. Although data augmentation contributes to the model, the accuracy is still limited by lack of samples diversity. Majority of misclassification is derived from the medium class. The image processing based bud detection is useful for reducing the misclassification, increasing the accuracy of flower grading to approximately 93.9%.

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

International Nuclear Information System (INIS)

Kostko, Oleg; Zhou, Jia; Sun, Bian Jian; Lie, Jie Shiuan; Chang, Agnes H.H.; Kaiser, Ralf I.; Ahmed, Musahid

2010-01-01

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-carbon 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.

Convolutional Neural Network for Histopathological Analysis of Osteosarcoma.

Science.gov (United States)

Mishra, Rashika; Daescu, Ovidiu; Leavey, Patrick; Rakheja, Dinesh; Sengupta, Anita

2018-03-01

Pathologists often deal with high complexity and sometimes disagreement over osteosarcoma tumor classification due to cellular heterogeneity in the dataset. Segmentation and classification of histology tissue in H&E stained tumor image datasets is a challenging task because of intra-class variations, inter-class similarity, crowded context, and noisy data. In recent years, deep learning approaches have led to encouraging results in breast cancer and prostate cancer analysis. In this article, we propose convolutional neural network (CNN) as a tool to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes (viable tumor, necrosis) versus nontumor. The proposed CNN architecture contains eight learned layers: three sets of stacked two convolutional layers interspersed with max pooling layers for feature extraction and two fully connected layers with data augmentation strategies to boost performance. The use of a neural network results in higher accuracy of average 92% for the classification. We compare the proposed architecture with three existing and proven CNN architectures for image classification: AlexNet, LeNet, and VGGNet. We also provide a pipeline to calculate percentage necrosis in a given whole slide image. We conclude that the use of neural networks can assure both high accuracy and efficiency in osteosarcoma classification.

Social Interaction and Market Reaction to Earnings News

OpenAIRE

Wang, Qiguang

2017-01-01

This study documents strong effects of social interaction on investors’ attention and interpretations of earnings news. I estimate the firm-level investor social network and find that higher connectedness increases the announcement price reaction, reduces post-announcement drift, and decreases the long-run impact of the news on return volatility. I also find that social interaction triggers persistent disagreement-driven volume during and after the announcement. The evidence combined highligh...

Financial News and Market Panics in the Age of Highfrequency Sentiment Trading Algorithms

DEFF Research Database (Denmark)

Kleinnijenhuis, Jan; Schultz, Friederike; Oegema, Dirk

2013-01-01

. As a case study of a market panic we show the impact of US news, UK news and Dutch news on three Dutch banks during the financial crisis of 2007–9. To avoid market panics, financial journalists may strive for greater transparency, not only on asset prices and corporate philosophies, but also on network...

A new method of machine vision reprocessing based on cellular neural networks

International Nuclear Information System (INIS)

Jianhua, W.; Liping, Z.; Fenfang, Z.; Guojian, H.

1996-01-01

This paper proposed a method of image preprocessing in machine vision based on Cellular Neural Network (CNN). CNN is introduced to design image smoothing, image recovering, image boundary detecting and other image preprocessing problems. The proposed methods are so simple that the speed of algorithms are increased greatly to suit the needs of real-time image processing. The experimental results show a satisfactory reply

Image quality assessment using deep convolutional networks

Science.gov (United States)

Li, Yezhou; Ye, Xiang; Li, Yong

2017-12-01

This paper proposes a method of accurately assessing image quality without a reference image by using a deep convolutional neural network. Existing training based methods usually utilize a compact set of linear filters for learning features of images captured by different sensors to assess their quality. These methods may not be able to learn the semantic features that are intimately related with the features used in human subject assessment. Observing this drawback, this work proposes training a deep convolutional neural network (CNN) with labelled images for image quality assessment. The ReLU in the CNN allows non-linear transformations for extracting high-level image features, providing a more reliable assessment of image quality than linear filters. To enable the neural network to take images of any arbitrary size as input, the spatial pyramid pooling (SPP) is introduced connecting the top convolutional layer and the fully-connected layer. In addition, the SPP makes the CNN robust to object deformations to a certain extent. The proposed method taking an image as input carries out an end-to-end learning process, and outputs the quality of the image. It is tested on public datasets. Experimental results show that it outperforms existing methods by a large margin and can accurately assess the image quality on images taken by different sensors of varying sizes.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Deep convolutional neural networks as strong gravitational lens detectors

Science.gov (United States)

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

Adaptive Steganalysis Based on Selection Region and Combined Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Donghui Hu

2017-01-01

Full Text Available Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

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

Science.gov (United States)

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

Directory of Open Access Journals (Sweden)

Bodo Rueckauer

2017-12-01

Full Text Available 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.

Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

Directory of Open Access Journals (Sweden)

Xiu Jin

2018-03-01

Full Text Available Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN. After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery.

Classification of time-series images using deep convolutional neural networks

Science.gov (United States)

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.

Robust Vehicle Detection in Aerial Images Based on Cascaded Convolutional Neural Networks.

Science.gov (United States)

Zhong, Jiandan; Lei, Tao; Yao, Guangle

2017-11-24

Vehicle detection in aerial images is an important and challenging task. Traditionally, many target detection models based on sliding-window fashion were developed and achieved acceptable performance, but these models are time-consuming in the detection phase. Recently, with the great success of convolutional neural networks (CNNs) in computer vision, many state-of-the-art detectors have been designed based on deep CNNs. However, these CNN-based detectors are inefficient when applied in aerial image data due to the fact that the existing CNN-based models struggle with small-size object detection and precise localization. To improve the detection accuracy without decreasing speed, we propose a CNN-based detection model combining two independent convolutional neural networks, where the first network is applied to generate a set of vehicle-like regions from multi-feature maps of different hierarchies and scales. Because the multi-feature maps combine the advantage of the deep and shallow convolutional layer, the first network performs well on locating the small targets in aerial image data. Then, the generated candidate regions are fed into the second network for feature extraction and decision making. Comprehensive experiments are conducted on the Vehicle Detection in Aerial Imagery (VEDAI) dataset and Munich vehicle dataset. The proposed cascaded detection model yields high performance, not only in detection accuracy but also in detection speed.

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

Science.gov (United States)

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.

Political science. Exposure to ideologically diverse news and opinion on Facebook.

Science.gov (United States)

Bakshy, Eytan; Messing, Solomon; Adamic, Lada A

2015-06-05

Exposure to news, opinion, and civic information increasingly occurs through social media. How do these online networks influence exposure to perspectives that cut across ideological lines? Using deidentified data, we examined how 10.1 million U.S. Facebook users interact with socially shared news. We directly measured ideological homophily in friend networks and examined the extent to which heterogeneous friends could potentially expose individuals to cross-cutting content. We then quantified the extent to which individuals encounter comparatively more or less diverse content while interacting via Facebook's algorithmically ranked News Feed and further studied users' choices to click through to ideologically discordant content. Compared with algorithmic ranking, individuals' choices played a stronger role in limiting exposure to cross-cutting content. Copyright © 2015, American Association for the Advancement of Science.

"I Have Good News and Bad News:" The Effects of Power Imbalances and Physical Distance on News-givers' Use of Blended News Delivery

OpenAIRE

Legg, Angela M.

2013-01-01

People dislike giving bad news, and one strategy they use to ease the process is to pair bad news with some good news, a phenomenon called blended news delivery. Often, blended news arrives from people in power positions such as physicians, managers, or teachers. But followers also find themselves needing to give bad news to those in higher power positions. Similarly, people can choose how they deliver bad news, such as in person or over email. The current study brings much needed empirical a...

No news is good news?

CERN Multimedia

Peter Schmid

I'm retired and living back home in Austria. But I am still excited about ATLAS and I try to follow the progress of the project as closely as I can. The ATLAS e-news are an excellent source of information. Appearing now every month they provide a broad, solid view of what is going on. But I'm greedy; I'd love to be "on-line". When the first End-Cap Toroid moved from hall 180 to the pit I was frustrated. I knew that it was happening but I could only get first pictures and reports a few days later. In the meantime the ECT was lowered into the cavern; no information on this available nowhere up to the this issue of the e-news. Here is my dream: an "ATLAS news ticker", i.e. a web page with the news appearing on the day they happen; just one line of information, possibly with a reference to a picture, a person or a report. My idea isn't new. On the ATLAS web-site for the public we have a window "latest news". But I was disappointed when, until a week ago, the latest news dated from December 2006 !!! Can't we do...

news-please

OpenAIRE

Hamborg, Felix; Meuschke, Norman; Breitinger, Corinna; Gipp, Bela

2017-01-01

The amount of news published and read online has increased tremendously in recent years, making news data an interesting resource for many research disciplines, such as the social sciences and linguistics. However, large scale collection of news data is cumbersome due to a lack of generic tools for crawling and extracting such data. We present news-please, a generic, multilanguage, open-source crawler and extractor for news that works out-of-thebox for a large variety of news websites. ...

Sharing and Discussing News in Private Social Media Groups

DEFF Research Database (Denmark)

Swart, Joëlle; Peters, Chris; Broersma, Marcel

2018-01-01

to their membership in a particular (1) location-based (2) work-related or (3) leisure-oriented community. It finds that communication within social media communities whose members consider their ties as weak generally tended to be more news-centred. Even more significant was perceived control over privacy......Social media platforms are an increasingly dominant medium through which people encounter news in everyday life. Yet while we know more-and-more about frequency of use and sharing, content preferences and network configurations around news use on social media, the social experiences associated...... with such practices remain relatively unexplored. This paper addresses this gap to consider if and how news facilitates conversations in everyday contexts where social media play a communicative role. It investigates how people engage with current affairs collectively in different social formations...

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

Science.gov (United States)

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.

The Spin Doctors of news sources

Directory of Open Access Journals (Sweden)

Aldo Antonio Schmitz

2013-06-01

Full Text Available This paper examines in a historical context, with reference to concrete cases, the phenomenon of spin doctors. What is it? How does it act? What is its purpose? Where does it operate? And what are its consequences? These questions are raised in order to help identify the actions and strategies benefitting news sources, as well as the impact on journalism: the accommodation of journalists, the reduction or elimination of investigative journalism, the transference of the news to digital social networks, and the expansion of media sources. The article draws on a survey conducted with 163 news sources and journalists, on the premise that spin doctors are professional communicators, who are able to forge public opinion using processes, procedures, journalist’s co-optation, and knowledge of journalism and public relations, in order to be successful in the media, or directly with the target audience.

THE SPIN DOCTORS OF NEWS SOURCES

Directory of Open Access Journals (Sweden)

Aldo Antonio Schmitz

2013-06-01

Full Text Available This paper examines in a historical context, with reference to concrete cases, the phenomenon of spin doctors. What is it? How does it act? What is its purpose? Where does it operate? And what are its consequences? These questions are raised in order to help identify the actions and strategies benefitting news sources, as well as the impact on journalism: the accommodation of journalists, the reduction or elimination of investigative journalism, the transference of the news to digital social networks, and the expansion of media sources. The article draws on a survey conducted with 163 news sources and journalists, on the premise that spin doctors are professional communicators, who are able to forge public opinion using processes, procedures, journalist’s co-optation, and knowledge of journalism and public relations, in order to be successful in the media, or directly with the target audience.

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

Science.gov (United States)

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.

Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images

Directory of Open Access Journals (Sweden)

Feimo Li

2017-05-01

Full Text Available Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required.

Change detection in multitemporal synthetic aperture radar images using dual-channel convolutional neural network

Science.gov (United States)

Liu, Tao; Li, Ying; Cao, Ying; Shen, Qiang

2017-10-01

This paper proposes a model of dual-channel convolutional neural network (CNN) that is designed for change detection in SAR images, in an effort to acquire higher detection accuracy and lower misclassification rate. This network model contains two parallel CNN channels, which can extract deep features from two multitemporal SAR images. For comparison and validation, the proposed method is tested along with other change detection algorithms on both simulated SAR images and real-world SAR images captured by different sensors. The experimental results demonstrate that the presented method outperforms the state-of-the-art techniques by a considerable margin.

Sensing the News: User Experiences when Reading Locative News

Directory of Open Access Journals (Sweden)

Kjetil Vaage Øie

2012-02-01

Full Text Available This article focuses on user experiences on reading location-aware news on the mobile platform and aims to explore what experiences this kind of locative journalism generates and how such experiences change the users’ social interaction with news. We produced a specially designed mobile application and tailored news stories specific to this project called LocaNews in order to explore participants’ relation to the content in this journalistic format. The result is generated through a field study and a questionnaire of 32 people to find out how they experience the news presented in this format. The user participants’ responses are analyzed based on their news experiences, contextualizing places and their social interaction with the news within this form of journalism. Results showed that the local, semi-local and non-local user approaches the locative news in a different manner, but that the average user found this kind of news more interesting and more informative than ordinary news. The participants also have a problem identifying this as journalism, rather than an information service.

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

Science.gov (United States)

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.

The 4C´s of Mobile News

DEFF Research Database (Denmark)

Westlund, Oscar; Nel, Francois

2012-01-01

Newspapers are in flux. Having seen their traditional businesses battered by forces that include structural changes fuelled by the rapid growth of networked digital technologies and cyclical shifts in the economy, mainstream news publishers have intensified efforts to adapt their journalism...

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

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Rotation Invariance Neural Network

OpenAIRE

Li, Shiyuan

2017-01-01

Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using thos...

Electricity price forecast using Combinatorial Neural Network trained by a new stochastic search method

International Nuclear Information System (INIS)

Abedinia, O.; Amjady, N.; Shafie-khah, M.; Catalão, J.P.S.

2015-01-01

Highlights: • Presenting a Combinatorial Neural Network. • Suggesting a new stochastic search method. • Adapting the suggested method as a training mechanism. • Proposing a new forecast strategy. • Testing the proposed strategy on real-world electricity markets. - Abstract: Electricity price forecast is key information for successful operation of electricity market participants. However, the time series of electricity price has nonlinear, non-stationary and volatile behaviour and so its forecast method should have high learning capability to extract the complex input/output mapping function of electricity price. In this paper, a Combinatorial Neural Network (CNN) based forecasting engine is proposed to predict the future values of price data. The CNN-based forecasting engine is equipped with a new training mechanism for optimizing the weights of the CNN. This training mechanism is based on an efficient stochastic search method, which is a modified version of chemical reaction optimization algorithm, giving high learning ability to the CNN. The proposed price forecast strategy is tested on the real-world electricity markets of Pennsylvania–New Jersey–Maryland (PJM) and mainland Spain and its obtained results are extensively compared with the results obtained from several other forecast methods. These comparisons illustrate effectiveness of the proposed strategy.

Types of Journalistic News Selection or Media Tracks

Directory of Open Access Journals (Sweden)

SILVIA BRANEA

2013-07-01

Full Text Available This article aims to answer to the following question: How do the TV news and the online media platforms reflect reality from Romania and from outside of Romania? The subjective response to this question will be given based on an audiovisual and online monitoring conducted in the week 2-8 May 2011. The main core of our analysis consists of data obtained through monitoring of programs at four local Romanian TV stations (TVR 2, B1TV, Realitatea TV and Antena 3 for one week at the beginning of May, 2011. We also used information provided by two news websites: hotnews.ro and realitatea.ro.The research starts from two assumptions: 1. The news presented by all four TV networks will focus on events in the proximity, on the one hand and on human interest, on the other hand. 2. Online news websites will be more interested in political and social news, both in the region and in more distant areas. From the methodological point of view, the analysis of documents (the audiovisual tracks and the online ones is based on the communicational approach and on hermeneutic analysis.

TYPES OF JOURNALISTIC NEWS SELECTION OR MEDIA TRACKS

Directory of Open Access Journals (Sweden)

SILVIA BRANEA

2013-01-01

Full Text Available This article aims to answer to the following question: How do the TV news and the online media platforms reflect reality from Romania and from outside of Romania? The subjective response to this question will be given based on an audiovisual and online monitoring conducted in the week 2−8 May 2011. The main core of our analysis consists of data obtained through monitoring of programs at four local Romanian TV stations (TVR 2, B1TV, Realitatea TV and Antena 3 for one week at the beginning of May, 2011. We also used information provided by two news websites: hotnews.ro and realitatea.ro. The research starts from two assumptions: 1. The news presented by all four TV networks will focus on events in the proximity, on the one hand and on human interest, on the other hand. 2. Online news websites will be more interested in political and social news, both in the region and in more distant areas. From the methodological point of view, the analysis of documents (the audiovisual tracks and the online ones is based on the communicational approach and on hermeneutic analysis

Multi-focus image fusion with the all convolutional neural network

Science.gov (United States)

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.

Editorial Tag Endogeneity for News Websites

OpenAIRE

Bruno Ribeiro; Ricardo Morla; Amílcar Correia

2013-01-01

Editors and journalists at some news websites label their articles with structure and content-related editorial tags. Each article can have more than one tag and each tag can be used in more than one article. A network of tags can be defined whose edges are all possible pairs of tags in each article. Because editorial tags relate to structure and content rather than individual articles, the analysis of a network of editorial tags could assist editorial decisions to prioritize types of content...

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

Science.gov (United States)

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

Hourglass-ShapeNetwork Based Semantic Segmentation for High Resolution Aerial Imagery

Directory of Open Access Journals (Sweden)

Yu Liu

2017-05-01

Full Text Available A new convolution neural network (CNN architecture for semantic segmentation of high resolution aerial imagery is proposed in this paper. The proposed architecture follows an hourglass-shaped network (HSN design being structured into encoding and decoding stages. By taking advantage of recent advances in CNN designs, we use the composed inception module to replace common convolutional layers, providing the network with multi-scale receptive areas with rich context. Additionally, in order to reduce spatial ambiguities in the up-sampling stage, skip connections with residual units are also employed to feed forward encoding-stage information directly to the decoder. Moreover, overlap inference is employed to alleviate boundary effects occurring when high resolution images are inferred from small-sized patches. Finally, we also propose a post-processing method based on weighted belief propagation to visually enhance the classification results. Extensive experiments based on the Vaihingen and Potsdam datasets demonstrate that the proposed architectures outperform three reference state-of-the-art network designs both numerically and visually.

Cross-Domain Shoe Retrieval with a Semantic Hierarchy of Attribute Classification Network.

Science.gov (United States)

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.

International News Flows in the Post-Cold War World: Mapping the News and the News Producers.

Science.gov (United States)

Sreberny-Mohammadi, Annabelle

1995-01-01

Reviews the global political environment, major global news providers, and technologies of global news production. Argues for a multinational comparative mapping of international news representation in the 1990s. Outlines a major international venture to update and elaborate the 1979 UNESCO/IAMCR study of foreign news in the media of 29 countries,…

Combining high-speed SVM learning with CNN feature encoding for real-time target recognition in high-definition video for ISR missions

Science.gov (United States)

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

Three-dimensional fingerprint recognition by using convolution neural network

Science.gov (United States)

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.

Epileptiform spike detection via convolutional neural networks

DEFF Research Database (Denmark)

Johansen, Alexander Rosenberg; Jin, Jing; Maszczyk, Tomasz

2016-01-01

The EEG of epileptic patients often contains sharp waveforms called "spikes", occurring between seizures. Detecting such spikes is crucial for diagnosing epilepsy. In this paper, we develop a convolutional neural network (CNN) for detecting spikes in EEG of epileptic patients in an automated...

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

OpenAIRE

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

2017-01-01

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 centr...

Pattern Recognition of Momentary Mental Workload Based on Multi-Channel Electrophysiological Data and Ensemble Convolutional Neural Networks.

Science.gov (United States)

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.

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

Science.gov (United States)

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.

Research News

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Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval

Directory of Open Access Journals (Sweden)

Weixun Zhou

2017-05-01

Full Text Available Learning powerful feature representations for image retrieval has always been a challenging task in the field of remote sensing. Traditional methods focus on extracting low-level hand-crafted features which are not only time-consuming but also tend to achieve unsatisfactory performance due to the complexity of remote sensing images. In this paper, we investigate how to extract deep feature representations based on convolutional neural networks (CNNs for high-resolution remote sensing image retrieval (HRRSIR. To this end, several effective schemes are proposed to generate powerful feature representations for HRRSIR. In the first scheme, a CNN pre-trained on a different problem is treated as a feature extractor since there are no sufficiently-sized remote sensing datasets to train a CNN from scratch. In the second scheme, we investigate learning features that are specific to our problem by first fine-tuning the pre-trained CNN on a remote sensing dataset and then proposing a novel CNN architecture based on convolutional layers and a three-layer perceptron. The novel CNN has fewer parameters than the pre-trained and fine-tuned CNNs and can learn low dimensional features from limited labelled images. The schemes are evaluated on several challenging, publicly available datasets. The results indicate that the proposed schemes, particularly the novel CNN, achieve state-of-the-art performance.

Deep 3D convolution neural network for CT brain hemorrhage classification

Science.gov (United States)

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

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

Science.gov (United States)

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.

Welcome to the Era of Fake News

Directory of Open Access Journals (Sweden)

Jonathan Albright

2017-06-01

Full Text Available For the news industry, information is used to tell stories, which have traditionally been organized around “facts”. A growing problem, however, is that fact-based evidence is not relevant to a growing segment of the populace. Journalists need facts to tell stories, but they need data to understand how to engage audiences with this accurate information. The implementation of data is part of the solution to countering the erosion of trust and the decay of social discourse across networked spaces. Rather than following “trends”, news organizations should establish the groundwork to make facts “matter” by shaping the narrative instead of following deceptive statements.

A locality aware convolutional neural networks accelerator

NARCIS (Netherlands)

Shi, R.; Xu, Z.; Sun, Z.; Peemen, M.C.J.; Li, A.; Corporaal, H.; Wu, D.

2015-01-01

The advantages of Convolutional Neural Networks (CNNs) with respect to traditional methods for visual pattern recognition have changed the field of machine vision. The main issue that hinders broad adoption of this technique is the massive computing workload in CNN that prevents real-time

News media coverage of trans fat: health risks and policy responses.

Science.gov (United States)

Jarlenski, Marian; Barry, Colleen L

2013-01-01

Prior research indicates that the news media play a critical role in transmitting information to the public about the most pressing public health problems, and framing attributions about who in society is responsible for solving these problems. In this article, we use content analysis methods to study the agenda-setting and framing functions of the news media in shaping perceptions about the health risks posed by trans fat in the U.S. diet. A census of news stories focusing on trans fat was collected from the two largest circulation U.S. newspapers and three major television networks from 1998 to 2008 (N = 156). The content of news coverage was analyzed using a 23-item instrument. Findings indicated that the news media served an important agenda-setting role in educating the public about the presence of trans fat in the U.S. diet and describing the health risks these foods pose. In addition, results indicate that news media coverage framed attributions of responsibility for solving the problem of trans fat in the food supply. News stories noting the heart disease risks of trans fat were significantly more likely to mention governmental responses aimed at curbing consumption than news coverage that did not note these health risks.

Covering Adoption: General Depictions in Broadcast News

Science.gov (United States)

Kline, Susan L.; Karel, Amanda I.; Chatterjee, Karishma

2006-01-01

Using theories of stigma (Goffman, 1963) and media frames (Iyengar, 1991), 292 news stories pertaining to adoption that appeared on major broadcast networks between 2001 and 2004 were analyzed. Media coverage of adoptees contained more problematic than positive depictions. Although birth parents were not always depicted, adoptive parent and…

A deep convolutional neural network for recognizing foods

Science.gov (United States)

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%.

Convolutional Neural Networks for Text Categorization: Shallow Word-level vs. Deep Character-level

OpenAIRE

Johnson, Rie; Zhang, Tong

2016-01-01

This paper reports the performances of shallow word-level convolutional neural networks (CNN), our earlier work (2015), on the eight datasets with relatively large training data that were used for testing the very deep character-level CNN in Conneau et al. (2016). Our findings are as follows. The shallow word-level CNNs achieve better error rates than the error rates reported in Conneau et al., though the results should be interpreted with some consideration due to the unique pre-processing o...

The wonders of CERN need your vote!

CERN Multimedia

2006-01-01

Two of CERN's creations were chosen by the US news group CNN as their 'seven wonders of the modern world'. The LHC and the World Wide Web are among the leading entries you can vote for to find the greatest wonder of all. Cast your vote at: http://edition.cnn.com/SPECIALS/2006/modern.wonders/

Good News in Bad News: How Negativity Enhances Economic Efficacy

OpenAIRE

Svensson, H.M.; Albæk, E.; van Dalen, A.; de Vreese, C.

2017-01-01

Negativity is a news ideology, and its negative effects on attitude formation are widely documented. Contrary to this view, the present study demonstrates that negative economic news can in fact be good news. Based on a two-wave national panel survey and a media content analysis, we show that individual exposure to negative economic news enhances internal economic efficacy, a sense of competence in and understanding of the economy. This is good news as internal economic efficacy may facilitat...

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Directory of Open Access Journals (Sweden)

Jingbo Chen

2018-02-01

Full Text Available Semantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs, have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable.

Pedestrian detection in video surveillance using fully convolutional YOLO neural network

Science.gov (United States)

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.

One-Dimensional Convolutional Neural Network Land-Cover Classification of Multi-Seasonal Hyperspectral Imagery in the San Francisco Bay Area, California

Directory of Open Access Journals (Sweden)

Daniel Guidici

2017-06-01

Full Text Available In this study, a 1-D Convolutional Neural Network (CNN architecture was developed, trained and utilized to classify single (summer and three seasons (spring, summer, fall of hyperspectral imagery over the San Francisco Bay Area, California for the year 2015. For comparison, the Random Forests (RF and Support Vector Machine (SVM classifiers were trained and tested with the same data. In order to support space-based hyperspectral applications, all analyses were performed with simulated Hyperspectral Infrared Imager (HyspIRI imagery. Three-season data improved classifier overall accuracy by 2.0% (SVM, 1.9% (CNN to 3.5% (RF over single-season data. The three-season CNN provided an overall classification accuracy of 89.9%, which was comparable to overall accuracy of 89.5% for SVM. Both three-season CNN and SVM outperformed RF by over 7% overall accuracy. Analysis and visualization of the inner products for the CNN provided insight to distinctive features within the spectral-temporal domain. A method for CNN kernel tuning was presented to assess the importance of learned features. We concluded that CNN is a promising candidate for hyperspectral remote sensing applications because of the high classification accuracy and interpretability of its inner products.

An adaptive deep convolutional neural network for rolling bearing fault diagnosis

International Nuclear Information System (INIS)

Fuan, Wang; Hongkai, Jiang; Haidong, Shao; Wenjing, Duan; Shuaipeng, Wu

2017-01-01

The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods. (paper)

Vehicle Color Recognition with Vehicle-Color Saliency Detection and Dual-Orientational Dimensionality Reduction of CNN Deep Features

Science.gov (United States)

Zhang, Qiang; Li, Jiafeng; Zhuo, Li; Zhang, Hui; Li, Xiaoguang

2017-12-01

Color is one of the most stable attributes of vehicles and often used as a valuable cue in some important applications. Various complex environmental factors, such as illumination, weather, noise and etc., result in the visual characteristics of the vehicle color being obvious diversity. Vehicle color recognition in complex environments has been a challenging task. The state-of-the-arts methods roughly take the whole image for color recognition, but many parts of the images such as car windows; wheels and background contain no color information, which will have negative impact on the recognition accuracy. In this paper, a novel vehicle color recognition method using local vehicle-color saliency detection and dual-orientational dimensionality reduction of convolutional neural network (CNN) deep features has been proposed. The novelty of the proposed method includes two parts: (1) a local vehicle-color saliency detection method has been proposed to determine the vehicle color region of the vehicle image and exclude the influence of non-color regions on the recognition accuracy; (2) dual-orientational dimensionality reduction strategy has been designed to greatly reduce the dimensionality of deep features that are learnt from CNN, which will greatly mitigate the storage and computational burden of the subsequent processing, while improving the recognition accuracy. Furthermore, linear support vector machine is adopted as the classifier to train the dimensionality reduced features to obtain the recognition model. The experimental results on public dataset demonstrate that the proposed method can achieve superior recognition performance over the state-of-the-arts methods.

BBC VERSUS EURO NEWS: DISCOURSE AND IDEOLOGY IN NEWS TRANSLATION

Directory of Open Access Journals (Sweden)

Hosseini, F.

2016-09-01

Full Text Available The present study aimed to compare discursive strategies employed by two international news agencies including Euro News and BBC. Van Dijk’s (2004 model of CDA was adopted. Thirty pieces of news about internal affairs of Iran together with their Persian translations were downloaded from the corresponding website, i.e. 30 pieces of English news and their corresponding Persian translations from the Euro News website and 30 pieces of English news with their corresponding translations from the BBC website. The frequency of lexical items was observed to not differ significantly. Two sets of translations were compared to their source texts based on four discursive strategies of hyperbole, polarization, vagueness and euphemism. An independent-samples t-test was conducted to compare the frequency of strategies applied by the two news agencies. Results revealed no significant difference between the two agencies except for the discursive strategy of vagueness.

TRACING DIGITAL NEWS NETWORKS. Towards an integrated framework of the dynamics of news production, circulation and use

NARCIS (Netherlands)

Costera Meijer, I.; Domingo, D.; Masip, P.

2015-01-01

Research on the evolution of journalism is still lacking appropriate theoretical tools to (re)conceptualise the blurring boundaries between professional news production in the media industry, the public actively engaged in using, circulating and producing information, and the diversity of social and

The good news about giving bad news to patients.

Science.gov (United States)

Farber, Neil J; Urban, Susan Y; Collier, Virginia U; Weiner, Joan; Polite, Ronald G; Davis, Elizabeth B; Boyer, E Gil

2002-12-01

There are few data available on how physicians inform patients about bad news. We surveyed internists about how they convey this information. We surveyed internists about their activities in giving bad news to patients. One set of questions was about activities for the emotional support of the patient (11 items), and the other was about activities for creating a supportive environment for delivering bad news (9 items). The impact of demographic factors on the performance of emotionally supportive items, environmentally supportive items, and on the number of minutes reportedly spent delivering news was analyzed by analysis of variance and multiple regression analysis. More than half of the internists reported that they always or frequently performed 10 of the 11 emotionally supportive items and 6 of the 9 environmentally supportive items while giving bad news to patients. The average time reportedly spent in giving bad news was 27 minutes. Although training in giving bad news had a significant impact on the number of emotionally supportive items reported (P woman, unmarried, and having a history of major illness were also associated with reporting a greater number of emotionally supportive activities. Internists report that they inform patients of bad news appropriately. Some deficiencies exist, specifically in discussing prognosis and referral of patients to support groups. Physician educational efforts should include discussion of prognosis with patients as well as the availability of support groups.

Adaptive Graph Convolutional Neural Networks

OpenAIRE

Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

2018-01-01

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

Learning text representation using recurrent convolutional neural network with highway layers

OpenAIRE

Wen, Ying; Zhang, Weinan; Luo, Rui; Wang, Jun

2016-01-01

Recently, the rapid development of word embedding and neural networks has brought new inspiration to various NLP and IR tasks. In this paper, we describe a staged hybrid model combining Recurrent Convolutional Neural Networks (RCNN) with highway layers. The highway network module is incorporated in the middle takes the output of the bi-directional Recurrent Neural Network (Bi-RNN) module in the first stage and provides the Convolutional Neural Network (CNN) module in the last stage with the i...

Causal Learning and Explanation of Deep Neural Networks via Autoencoded Activations

OpenAIRE

Harradon, Michael; Druce, Jeff; Ruttenberg, Brian

2018-01-01

Deep neural networks are complex and opaque. As they enter application in a variety of important and safety critical domains, users seek methods to explain their output predictions. We develop an approach to explaining deep neural networks by constructing causal models on salient concepts contained in a CNN. We develop methods to extract salient concepts throughout a target network by using autoencoders trained to extract human-understandable representations of network activations. We then bu...

A Content Analysis of News Media Coverage of the Accident at Three Mile Island.

Science.gov (United States)

Stephens, Mitchell; Edison, Nadyne G.

A study was conducted for the President's Commission on the Accident at Three Mile Island to analyze coverage of the accident by ten news organizations: two wire services, three commercial television networks, and five daily newspapers. Copies of all stories and transcripts of news programs during the first week of the accident were examined from…

Recurrent Neural Networks to Correct Satellite Image Classification Maps

Science.gov (United States)

Maggiori, Emmanuel; Charpiat, Guillaume; Tarabalka, Yuliya; Alliez, Pierre

2017-09-01

While initially devised for image categorization, convolutional neural networks (CNNs) are being increasingly used for the pixelwise semantic labeling of images. However, the proper nature of the most common CNN architectures makes them good at recognizing but poor at localizing objects precisely. This problem is magnified in the context of aerial and satellite image labeling, where a spatially fine object outlining is of paramount importance. Different iterative enhancement algorithms have been presented in the literature to progressively improve the coarse CNN outputs, seeking to sharpen object boundaries around real image edges. However, one must carefully design, choose and tune such algorithms. Instead, our goal is to directly learn the iterative process itself. For this, we formulate a generic iterative enhancement process inspired from partial differential equations, and observe that it can be expressed as a recurrent neural network (RNN). Consequently, we train such a network from manually labeled data for our enhancement task. In a series of experiments we show that our RNN effectively learns an iterative process that significantly improves the quality of satellite image classification maps.

Indian Classical Dance Action Identification and Classification with Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

P. V. V. Kishore

2018-01-01

Full Text Available Extracting and recognizing complex human movements from unconstrained online/offline video sequence is a challenging task in computer vision. This paper proposes the classification of Indian classical dance actions using a powerful artificial intelligence tool: convolutional neural networks (CNN. In this work, human action recognition on Indian classical dance videos is performed on recordings from both offline (controlled recording and online (live performances, YouTube data. The offline data is created with ten different subjects performing 200 familiar dance mudras/poses from different Indian classical dance forms under various background environments. The online dance data is collected from YouTube for ten different subjects. Each dance pose is occupied for 60 frames or images in a video in both the cases. CNN training is performed with 8 different sample sizes, each consisting of multiple sets of subjects. The remaining 2 samples are used for testing the trained CNN. Different CNN architectures were designed and tested with our data to obtain a better accuracy in recognition. We achieved a 93.33% recognition rate compared to other classifier models reported on the same dataset.

Classification of Microcalcifications for the Diagnosis of Breast Cancer Using Artificial Neural Networks

National Research Council Canada - National Science Library

Wu, Yuzheng

1997-01-01

.... A convolution neural network (CNN) was employed to classify benign and malignant microcalcifications in the radiographs of pathological specimen that were digitized at a high resolution of 21 microns x 21 microns...

Using Convolutional Neural Network Filters to Measure Left-Right Mirror Symmetry in Images

Directory of Open Access Journals (Sweden)

Anselm Brachmann

2016-12-01

Full Text Available We propose a method for measuring symmetry in images by using filter responses from Convolutional Neural Networks (CNNs. The aim of the method is to model human perception of left/right symmetry as closely as possible. Using the Convolutional Neural Network (CNN approach has two main advantages: First, CNN filter responses closely match the responses of neurons in the human visual system; they take information on color, edges and texture into account simultaneously. Second, we can measure higher-order symmetry, which relies not only on color, edges and texture, but also on the shapes and objects that are depicted in images. We validated our algorithm on a dataset of 300 music album covers, which were rated according to their symmetry by 20 human observers, and compared results with those from a previously proposed method. With our method, human perception of symmetry can be predicted with high accuracy. Moreover, we demonstrate that the inclusion of features from higher CNN layers, which encode more abstract image content, increases the performance further. In conclusion, we introduce a model of left/right symmetry that closely models human perception of symmetry in CD album covers.

Wrist sensor-based tremor severity quantification in Parkinson's disease using convolutional neural network.

Science.gov (United States)

Kim, Han Byul; Lee, Woong Woo; Kim, Aryun; Lee, Hong Ji; Park, Hye Young; Jeon, Hyo Seon; Kim, Sang Kyong; Jeon, Beomseok; Park, Kwang S

2018-04-01

Tremor is a commonly observed symptom in patients of Parkinson's disease (PD), and accurate measurement of tremor severity is essential in prescribing appropriate treatment to relieve its symptoms. We propose a tremor assessment system based on the use of a convolutional neural network (CNN) to differentiate the severity of symptoms as measured in data collected from a wearable device. Tremor signals were recorded from 92 PD patients using a custom-developed device (SNUMAP) equipped with an accelerometer and gyroscope mounted on a wrist module. Neurologists assessed the tremor symptoms on the Unified Parkinson's Disease Rating Scale (UPDRS) from simultaneously recorded video footages. The measured data were transformed into the frequency domain and used to construct a two-dimensional image for training the network, and the CNN model was trained by convolving tremor signal images with kernels. The proposed CNN architecture was compared to previously studied machine learning algorithms and found to outperform them (accuracy = 0.85, linear weighted kappa = 0.85). More precise monitoring of PD tremor symptoms in daily life could be possible using our proposed method. Copyright © 2018 Elsevier Ltd. All rights reserved.

Low-complexity object detection with deep convolutional neural network for embedded systems

Science.gov (United States)

Tripathi, Subarna; Kang, Byeongkeun; Dane, Gokce; Nguyen, Truong

2017-09-01

We investigate low-complexity convolutional neural networks (CNNs) for object detection for embedded vision applications. It is well-known that consolidation of an embedded system for CNN-based object detection is more challenging due to computation and memory requirement comparing with problems like image classification. To achieve these requirements, we design and develop an end-to-end TensorFlow (TF)-based fully-convolutional deep neural network for generic object detection task inspired by one of the fastest framework, YOLO.1 The proposed network predicts the localization of every object by regressing the coordinates of the corresponding bounding box as in YOLO. Hence, the network is able to detect any objects without any limitations in the size of the objects. However, unlike YOLO, all the layers in the proposed network is fully-convolutional. Thus, it is able to take input images of any size. We pick face detection as an use case. We evaluate the proposed model for face detection on FDDB dataset and Widerface dataset. As another use case of generic object detection, we evaluate its performance on PASCAL VOC dataset. The experimental results demonstrate that the proposed network can predict object instances of different sizes and poses in a single frame. Moreover, the results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3× and memory-BW by 3 - 4× comparing with one of the best real-time CNN-based object detectors, YOLO. Our 8-bit fixed-point TF-model provides additional 4× memory reduction while keeping the accuracy nearly as good as the floating-point model. Moreover, the fixed- point model is capable of achieving 20× faster inference speed comparing with the floating-point model. Thus, the proposed method is promising for embedded implementations.

The Effects of Bad News and Good News on a Newspaper's Image.

Science.gov (United States)

Haskins, Jack B.; Miller, M. Mark

1984-01-01

Concludes that whether a newspaper carries mostly good news or mostly bad news affects the image of the paper, with bad news having negative effects and good news having positive effects on readers' perceptions of the newspaper. (FL)

Investigating people’s news diets: how online news users use offline news

NARCIS (Netherlands)

Trilling, D.; Schoenbach, K.

2015-01-01

The question how offline media use is related to online media use has been heavily debated in the last decades. If they are functionally equivalent, then advantages like low costs, rapid publication cycles, and easy access to online news could lead to them displacing offline news. Data from a

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

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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.

A patch-based convolutional neural network for remote sensing image classification.

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Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

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

Science.gov (United States)

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.

Implementation of news module for news client based on ApiCloud

OpenAIRE

Fu Xin; Liang Yu; Cao Sanxing; Gu Hongbo

2017-01-01

With the development of new media technology, news client has become the main battlefield of news browsing. Based on the ApiCloud hybrid development platform, this paper uses HTML, JavaScript and other technologies to develop the mobile client news module, and uses WAMP integrated development environment to build a news publishing system on the server side.

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

Science.gov (United States)

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.

Convolutional neural networks with balanced batches for facial expressions recognition

Science.gov (United States)

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.

New second-order difference algorithm for image segmentation based on cellular neural networks (CNNs)

Science.gov (United States)

Meng, Shukai; Mo, Yu L.

2001-09-01

Image segmentation is one of the most important operations in many image analysis problems, which is the process that subdivides an image into its constituents and extracts those parts of interest. In this paper, we present a new second order difference gray-scale image segmentation algorithm based on cellular neural networks. A 3x3 CNN cloning template is applied, which can make smooth processing and has a good ability to deal with the conflict between the capability of noise resistance and the edge detection of complex shapes. We use second order difference operator to calculate the coefficients of the control template, which are not constant but rather depend on the input gray-scale values. It is similar to Contour Extraction CNN in construction, but there are some different in algorithm. The result of experiment shows that the second order difference CNN has a good capability in edge detection. It is better than Contour Extraction CNN in detail detection and more effective than the Laplacian of Gauss (LOG) algorithm.

A deep convolutional neural network model to classify heartbeats.

Science.gov (United States)

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.

Implementation of news module for news client based on ApiCloud

Directory of Open Access Journals (Sweden)

Fu Xin

2017-01-01

Full Text Available With the development of new media technology, news client has become the main battlefield of news browsing. Based on the ApiCloud hybrid development platform, this paper uses HTML, JavaScript and other technologies to develop the mobile client news module, and uses WAMP integrated development environment to build a news publishing system on the server side.

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

Science.gov (United States)

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.

Application of deep learning in determining IR precipitation occurrence: a Convolutional Neural Network model

Science.gov (United States)

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.

Three-Dimensional Terahertz Coded-Aperture Imaging Based on Matched Filtering and Convolutional Neural Network.

Science.gov (United States)

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.

Assessment of Convolution Neural Networks for Surficial Geology Mapping in the South Rae Geological Region, Northwest Territories, Canada

Directory of Open Access Journals (Sweden)

Rasim Latifovic

2018-02-01

Full Text Available Mapping of surficial geology is an important requirement for broadening the geoscience database of northern Canada. Surficial geology maps are an integral data source for mineral and energy exploration. Moreover, they provide information such as the location of gravels and sands, which are important for infrastructure development. Currently, surficial geology maps are produced through expert interpretation of aerial photography and field data. However, interpretation is known to be subjective, labour-intensive and difficult to repeat. The expert knowledge required for interpretation can be challenging to maintain and transfer. In this research, we seek to assess the potential of deep neural networks to aid surficial geology mapping by providing an objective surficial materials initial layer that experts can modify to speed map development and improve consistency between mapped areas. Such an approach may also harness expert knowledge in a way that is transferable to unmapped areas. For this purpose, we assess the ability of convolution neural networks (CNN to predict surficial geology classes under two sampling scenarios. In the first scenario, a CNN uses samples collected over the area to be mapped. In the second, a CNN trained over one area is then applied to locations where the available samples were not used in training the network. The latter case is important, as a collection of in situ training data can be costly. The evaluation of the CNN was carried out using aerial photos, Landsat reflectance, and high-resolution digital elevation data over five areas within the South Rae geological region of Northwest Territories, Canada. The results are encouraging, with the CNN generating average accuracy of 76% when locally trained. For independent test areas (i.e., trained over one area and applied over other, accuracy dropped to 59–70% depending on the classes selected for mapping. In the South Rae region, significant confusion was found

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

Science.gov (United States)

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.

The Evaluations of Swine Flu Magnitudes in TV News: A Comparative Analysis of Paired Influenza Pandemics.

Science.gov (United States)

Pan, Po-Lin; Meng, Juan

2015-01-01

This study examined how major TV news networks covered two flu pandemics in 1976 and 2009 in terms of news frames, mortality exemplars, mortality subject attributes, vaccination, evaluation approaches, and news sources. Results showed that the first pandemic was frequently framed with the medical/scientific and political/legal issues, while the second pandemic was emphasized with the health risk issue in TV news. Both flu pandemics were regularly reported with mortality exemplars, but the focus in the first pandemic was on the flu virus threat and vaccination side effects, while the vaccination shortage was frequently revealed in the second outbreak.

Pushed news: when the news comes to the cellphone

Directory of Open Access Journals (Sweden)

Antonio Fidalgo

2009-12-01

Full Text Available Combining two findings of recent surveys on the Internet which state that 1 “the Internet will soon surpass all other media as a main source for national and international news” and 2 “the mobile device will be the primary connection tool to the Internet in 2020” leads us to the conclusion that smartphones will soon be the primary source for news access. But if so, how will news come to the Internetconnected cellphones? In accordance with the distinction, already drawn in 1997, between push and pull technologies as two different forms of how content is delivered to the end users, cellphones are characterized as push devices (passive reception, in opposition to computers, classified as pull devices (active reception. The news items that fit cellphones are pushed news. And they will be pushed as SMS, e-mails, tweets and through news aggregators.

PUSHED NEWS: When the news comes to the cellphone

Directory of Open Access Journals (Sweden)

Antonio Fidalgo

2009-12-01

Full Text Available Combining two findings of recent surveys on the Internet whichstate that 1 “the Internet will soon surpass all other media as a main source for national and international news” and 2 “the mobile device will be the primary connection tool to the Internet in 2020” leads us to the conclusion that smartphones will soon be the primary source for news access. But if so, how will news come to the Internetconnected cellphones? In accordance with the distinction, already drawn in 1997, between push and pull technologies as two different forms of how content is delivered to the end users, cellphones are characterized as push devices (passive reception, in opposition to computers, classified as pull devices (active reception. The news items that fit cellphones are pushed news. And they will be pushed as SMS, e-mails, tweets and through news aggregators.

Generalized synthetic aperture radar automatic target recognition by convolutional neural network with joint use of two-dimensional principal component analysis and support vector machine

Science.gov (United States)

Zheng, Ce; Jiang, Xue; Liu, Xingzhao

2017-10-01

Convolutional neural network (CNN), as a vital part of the deep learning research field, has shown powerful potential for automatic target recognition (ATR) of synthetic aperture radar (SAR). However, the high complexity caused by the deep structure of CNN makes it difficult to generalize. An improved form of CNN with higher generalization capability and less probability of overfitting, which further improves the efficiency and robustness of the SAR ATR system, is proposed. The convolution layers of CNN are combined with a two-dimensional principal component analysis algorithm. Correspondingly, the kernel support vector machine is utilized as the classifier layer instead of the multilayer perceptron. The verification experiments are implemented using the moving and stationary target acquisition and recognition database, and the results validate the efficiency of the proposed method.

Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network

Directory of Open Access Journals (Sweden)

Chunyong Ma

2018-01-01

Full Text Available Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction.

Good News in Bad News: How Negativity Enhances Economic Efficacy

NARCIS (Netherlands)

Svensson, H.M.; Albæk, E.; van Dalen, A.; de Vreese, C.

2017-01-01

Negativity is a news ideology, and its negative effects on attitude formation are widely documented. Contrary to this view, the present study demonstrates that negative economic news can in fact be good news. Based on a two-wave national panel survey and a media content analysis, we show that

Healthy depictions? Depicting adoption and adoption news events on broadcast news.

Science.gov (United States)

Kline, Susan L; Chatterjee, Karishma; Karel, Amanda I

2009-01-01

Given that the public uses the media to learn about adoption as a family form, this study analyzes U.S. television news coverage of adoption between 2001 and 2005 (N = 309 stories), to identify the types of news events covered about adoption. A majority of news stories covered fraud, crime, legal disputes, and negative international adoption cases. Adoptees as defective or unhealthy were depicted more in negative news event stories, birth parents appeared less overall, and adoptive parents were most likely to have healthy depictions in positively oriented adoption experience, big family, and reunion stories. Although three quarters of the stories used primary adoption participants as news sources, one-third of the negative event stories did not contain healthy depictions of adoption participants. The authors discuss ways journalists and researchers might improve adoption news coverage.

Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter

Energy Technology Data Exchange (ETDEWEB)

Volkova, Svitlana; Shaffer, Kyle J.; Jang, Jin Yea; Hodas, Nathan O.

2017-07-30

Pew research polls report 62 percent of U.S. adults get news on social media (Gottfried and Shearer, 2016). In a December poll, 64 percent of U.S. adults said that “made-up news” has caused a “great deal of confusion” about the facts of current events (Barthel et al., 2016). Fabricated stories spread in social media, ranging from deliberate propaganda to hoaxes and satire, contributes to this confusion in addition to having serious effects on global stability. In this work we build predictive models to classify 130 thousand news tweets as suspicious or verified, and predict four subtypes of suspicious news – satire, hoaxes, clickbait and propaganda. We demonstrate that neural network models trained on tweet content and social network interactions outperform lexical models. Unlike previous work on deception detection, we find that adding syntax and grammar features to our models decreases performance. Incorporating linguistic features, including bias and subjectivity, improves classification results, however social interaction features are most informative for finer-grained separation between our four types of suspicious news posts.

Similarity estimation for reference image retrieval in mammograms using convolutional neural network

Science.gov (United States)

Muramatsu, Chisako; Higuchi, Shunichi; Morita, Takako; Oiwa, Mikinao; Fujita, Hiroshi

2018-02-01

Periodic breast cancer screening with mammography is considered effective in decreasing breast cancer mortality. For screening programs to be successful, an intelligent image analytic system may support radiologists' efficient image interpretation. In our previous studies, we have investigated image retrieval schemes for diagnostic references of breast lesions on mammograms and ultrasound images. Using a machine learning method, reliable similarity measures that agree with radiologists' similarity were determined and relevant images could be retrieved. However, our previous method includes a feature extraction step, in which hand crafted features were determined based on manual outlines of the masses. Obtaining the manual outlines of masses is not practical in clinical practice and such data would be operator-dependent. In this study, we investigated a similarity estimation scheme using a convolutional neural network (CNN) to skip such procedure and to determine data-driven similarity scores. By using CNN as feature extractor, in which extracted features were employed in determination of similarity measures with a conventional 3-layered neural network, the determined similarity measures were correlated well with the subjective ratings and the precision of retrieving diagnostically relevant images was comparable with that of the conventional method using handcrafted features. By using CNN for determination of similarity measure directly, the result was also comparable. By optimizing the network parameters, results may be further improved. The proposed method has a potential usefulness in determination of similarity measure without precise lesion outlines for retrieval of similar mass images on mammograms.

Training Convolutional Neural Networks for Translational Invariance on SAR ATR

DEFF Research Database (Denmark)

Malmgren-Hansen, David; Engholm, Rasmus; Østergaard Pedersen, Morten

2016-01-01

In this paper we present a comparison of the robustness of Convolutional Neural Networks (CNN) to other classifiers in the presence of uncertainty of the objects localization in SAR image. We present a framework for simulating simple SAR images, translating the object of interest systematically...

Deep convolutional neural networks for dense non-uniform motion deblurring

CSIR Research Space (South Africa)

Cronje, J

2015-11-01

Full Text Available to form a dense non-uniform motion estimation map. Furthermore, a second CNN is trained to perform deblurring given a blurry image patch and the estimated motion vector. Combining the two trained networks result in a deep learning approach that can enhance...

THE EFFECT ON MOTIVATIONS OF NEWS MONITORING OF ADDICTION TO SOCIAL MEDIA

OpenAIRE

BECAN, Cihan

2018-01-01

The addiction to social media have inevitably increaseddue to the fact that people want to seek information instantly, make some senseout of events in their own environment, to interact with other social groupsfor any issue or meet their own social and psychological needs. Depending onthis addiction to social media, individuals tend to use social networks inorder to get news and much of the daily information. The main of the study isto identify the effects on news monitoring motivations throu...

Routinizing Breaking News

DEFF Research Database (Denmark)

Hartley, Jannie Møller

2011-01-01

This chapter revisits seminal theoretical categorizations of news proposed three decades earlier by US sociologist Gaye Tuchman. By exploring the definition of ”breaking news” in the contemporary online newsrooms of three Danish news organisations, the author offers us a long overdue re-theorizat......-theorization of journalistic practice in the online context and helpfully explores well-evidenced limitations to online news production, such as the relationship between original reporting and the use of ”shovelware.”......This chapter revisits seminal theoretical categorizations of news proposed three decades earlier by US sociologist Gaye Tuchman. By exploring the definition of ”breaking news” in the contemporary online newsrooms of three Danish news organisations, the author offers us a long overdue re...

Convolutional Neural Network Based on Extreme Learning Machine for Maritime Ships Recognition in Infrared Images.

Science.gov (United States)

Khellal, Atmane; Ma, Hongbin; Fei, Qing

2018-05-09

The success of Deep Learning models, notably convolutional neural networks (CNNs), makes them the favorable solution for object recognition systems in both visible and infrared domains. However, the lack of training data in the case of maritime ships research leads to poor performance due to the problem of overfitting. In addition, the back-propagation algorithm used to train CNN is very slow and requires tuning many hyperparameters. To overcome these weaknesses, we introduce a new approach fully based on Extreme Learning Machine (ELM) to learn useful CNN features and perform a fast and accurate classification, which is suitable for infrared-based recognition systems. The proposed approach combines an ELM based learning algorithm to train CNN for discriminative features extraction and an ELM based ensemble for classification. The experimental results on VAIS dataset, which is the largest dataset of maritime ships, confirm that the proposed approach outperforms the state-of-the-art models in term of generalization performance and training speed. For instance, the proposed model is up to 950 times faster than the traditional back-propagation based training of convolutional neural networks, primarily for low-level features extraction.

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

Science.gov (United States)

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.

The Dynamic Cross-Correlations between Mass Media News, New Media News, and Stock Returns

Directory of Open Access Journals (Sweden)

Zuochao Zhang

2018-01-01

Full Text Available We investigate the dynamic cross-correlations between mass media news, new media news, and stock returns for the SSE 50 Index in Chinese stock market by employing the MF-DCCA method. The empirical results show that (1 there exist power-law cross-correlations between two types of news as well as between news and its corresponding SSE 50 Index return; (2 the cross-correlations between mass media news and SSE 50 Index returns show larger multifractality and more complicated structures; (3 mass media news and new media news have both complementary and competitive relationships; (4 with the rolling window analysis, we further find that there is a general increasing trend for the cross-correlations between the two types of news as well as the cross-correlations between news and returns and this trend becomes more persistent over time.

Epidemics and agendas: the politics of nightly news coverage of AIDS.

Science.gov (United States)

Colby, D C; Cook, T E

1991-01-01

We examine why the exponential growth of AIDS cases or the wide-spread professional perception of a health crisis did not move the epidemic more quickly onto the agenda of public problems. One possible explanation focuses on how the national news media's construction of AIDS shaped the meaning of the epidemic for mass and elite audiences. An examination of nightly news coverage by the three major networks from 1982 to 1989 reveals considerable variability and volatility in their coverage. Topic-driven saturation coverage occurred only during three short periods in 1983, 1985, and 1987, when the epidemic seemed likely to affect the "general population". Only at such moments did public opinion shift and discussion and debate in government begin. Otherwise, the typical AIDS story tended less to sensationalize than to reassure, largely because journalists depended upon government officials and high-ranking doctors to present them with evidence of news. Such sources had interests either in avoiding coverage or in pointing toward breakthroughs; more critical sources, especially within the gay movement, had far less access to the news. In concluding, we considered the prospects and pitfalls of the news media's power to shape the public agenda.

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

Science.gov (United States)

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.

Micro Agenda Setters: The Effect of Social Media on Young Adults’ Exposure to and Attitude Toward News

Directory of Open Access Journals (Sweden)

Donghee Yvette Wohn

2016-02-01

Full Text Available Social media services like Facebook and Twitter are playing an increasingly large role as sources of news. This article investigates the ways the composition of social media networks affects people’s exposure to and attitude toward news. Focus groups (N = 31 and in-depth interviews (N = 15 with young adults of varying ethnicity and country of origin showed that people’s networks on social media function as micro agenda setters. The characteristics of people in one’s network can facilitate negative effects such as echo chambers and spirals of silence but can also unfold new perspectives and create awareness of topics not covered by legacy media.

Fake news propagate differently from real news even at early stages of spreading

OpenAIRE

Zhao, Zilong; Zhao, Jichang; Sano, Yukie; Levy, Orr; Takayasu, Hideki; Takayasu, Misako; Li, Daqing; Havlin, Shlomo

2018-01-01

Social media can be a double-edged sword for modern communications, either a convenient channel exchanging ideas or an unexpected conduit circulating fake news through a large population. Existing studies of fake news focus on efforts on theoretical modelling of propagation or identification methods based on black-box machine learning, neglecting the possibility of identifying fake news using only structural features of propagation of fake news compared to those of real news and in particular...

Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model

Directory of Open Access Journals (Sweden)

Dan Liu

2018-04-01

Full Text Available This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN and a continuous pairwise Conditional Random Field (CRF model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results.

Depth Reconstruction from Single Images Using a Convolutional Neural Network and a Condition Random Field Model.

Science.gov (United States)

Liu, Dan; Liu, Xuejun; Wu, Yiguang

2018-04-24

This paper presents an effective approach for depth reconstruction from a single image through the incorporation of semantic information and local details from the image. A unified framework for depth acquisition is constructed by joining a deep Convolutional Neural Network (CNN) and a continuous pairwise Conditional Random Field (CRF) model. Semantic information and relative depth trends of local regions inside the image are integrated into the framework. A deep CNN network is firstly used to automatically learn a hierarchical feature representation of the image. To get more local details in the image, the relative depth trends of local regions are incorporated into the network. Combined with semantic information of the image, a continuous pairwise CRF is then established and is used as the loss function of the unified model. Experiments on real scenes demonstrate that the proposed approach is effective and that the approach obtains satisfactory results.

News Media Industry

Science.gov (United States)

2007-01-01

radio news departments will operate on many technological platforms at the same time ( Papper , 2006, p.3). News radio stations will likely broadcast...Retrieved March 25, 2007, from http://firstlook.nytimes.com/index.php?cat=4 Papper , Bob. RTNDF’s 2006 Future of the News Survey, 25 Mar. 2006. 10 Mar

Prediction and Characterization of High-Activity Events in Social Media Triggered by Real-World News.

Science.gov (United States)

Kalyanam, Janani; Quezada, Mauricio; Poblete, Barbara; Lanckriet, Gert

2016-01-01

On-line social networks publish information on a high volume of real-world events almost instantly, becoming a primary source for breaking news. Some of these real-world events can end up having a very strong impact on on-line social networks. The effect of such events can be analyzed from several perspectives, one of them being the intensity and characteristics of the collective activity that it produces in the social platform. We research 5,234 real-world news events encompassing 43 million messages discussed on the Twitter microblogging service for approximately 1 year. We show empirically that exogenous news events naturally create collective patterns of bursty behavior in combination with long periods of inactivity in the network. This type of behavior agrees with other patterns previously observed in other types of natural collective phenomena, as well as in individual human communications. In addition, we propose a methodology to classify news events according to the different levels of intensity in activity that they produce. In particular, we analyze the most highly active events and observe a consistent and strikingly different collective reaction from users when they are exposed to such events. This reaction is independent of an event's reach and scope. We further observe that extremely high-activity events have characteristics that are quite distinguishable at the beginning stages of their outbreak. This allows us to predict with high precision, the top 8% of events that will have the most impact in the social network by just using the first 5% of the information of an event's lifetime evolution. This strongly implies that high-activity events are naturally prioritized collectively by the social network, engaging users early on, way before they are brought to the mainstream audience.

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

Science.gov (United States)

Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

2017-11-01

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection.

Science.gov (United States)

Kim, Jihun; Kim, Jonghong; Jang, Gil-Jin; Lee, Minho

2017-03-01

Deep learning has received significant attention recently as a promising solution to many problems in the area of artificial intelligence. Among several deep learning architectures, convolutional neural networks (CNNs) demonstrate superior performance when compared to other machine learning methods in the applications of object detection and recognition. We use a CNN for image enhancement and the detection of driving lanes on motorways. In general, the process of lane detection consists of edge extraction and line detection. A CNN can be used to enhance the input images before lane detection by excluding noise and obstacles that are irrelevant to the edge detection result. However, training conventional CNNs requires considerable computation and a big dataset. Therefore, we suggest a new learning algorithm for CNNs using an extreme learning machine (ELM). The ELM is a fast learning method used to calculate network weights between output and hidden layers in a single iteration and thus, can dramatically reduce learning time while producing accurate results with minimal training data. A conventional ELM can be applied to networks with a single hidden layer; as such, we propose a stacked ELM architecture in the CNN framework. Further, we modify the backpropagation algorithm to find the targets of hidden layers and effectively learn network weights while maintaining performance. Experimental results confirm that the proposed method is effective in reducing learning time and improving performance. Copyright © 2016 Elsevier Ltd. All rights reserved.

Network interventions - How citizens’ social media networks influence their political participation

DEFF Research Database (Denmark)

Ohme, Jakob; de Vreese, Claes Holger; Albæk, Erik

Social media platforms are special places of information exposure because they are structured around a user’s social network and not around content, like other news media. Studies could show that news exposure on social media can affect citizens’ political participation due to the personalized......, targeted, & inadvertent exposure. However, previous research did not strongly focus on how the characteristics of a citizens’ social media network might alter this relationship. We tests how political information exposure via three different media channels affects political participation among Danish...... citizens and examine possible moderation effects of users network size, network diversity and the newly introduced parameter of perceived network activity. To this end, a two-wave online survey (n=858) among the Danish population was conducted, applying a smartphone-based media diary study. We find strong...

A Real-Time Solution to the Image Segmentation Problem: CNN-Movels

OpenAIRE

Iannizzotto, Giancarlo; Lanzafame, Pietro; Rosa, Francesco La

2007-01-01

In this work we have described a re-formulation of a 2D still-image segmentation algorithm, implemented on a single-layer CNN, previously proposed (Iannizzotto, 2003). This algorithm is able to step-over limitation inherent to the class of active contours: sensitivity to insignificant false edges or "edge fragmentation". The approach features an iterative process of uniform shrinking and deformation of the active contour. Guided by statistical properties of edgeness of the image pixels, the c...

The End of Secrecy? Military Competitiveness in the Age of Transparency

National Research Council Canada - National Science Library

Kaspar, Beth

2000-01-01

.... These technologies are providing affordable, worldwide, near real-time, 24-hour, CNN-like news coverage, worldwide Internet access, and more importantly, access to commercial space systems, including...

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

Science.gov (United States)

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.

Measuring News Media Literacy

Science.gov (United States)

Maksl, Adam; Ashley, Seth; Craft, Stephanie

2015-01-01

News media literacy refers to the knowledge and motivations needed to identify and engage with journalism. This study measured levels of news media literacy among 500 teenagers using a new scale measure based on Potter's model of media literacy and adapted to news media specifically. The adapted model posits that news media literate individuals…

BRAZILIAN NEWS PORTALS CHARACTERISTICS

Directory of Open Access Journals (Sweden)

Heloiza G. Herckovitz

2011-02-01

Full Text Available A content analysis of four Brazilian news media portals found that economic news dominated the top headlines with little attention paid to education, the environment and welfare. Other trends included a focus on local events and national news sources, reliance on few sources, mostly official ones, and a low percentage of news that fitted the concept of newsworthiness (a combination of both social significance and deviance concepts. Other findings of a study of 432 top news stories published by UOL, Estadão, iG and Terra during a 15-day period between February and March 2008 indicate that the top portions of the portals’ front pages carry news that lacks story depth, editorial branding, and multimedia applications. The results suggest that online news portals are in their infancy although Brazil has the largest online population of Latin America. This study hopes to shed light on the gatekeeping process in Brazilian news portals. Brazilian media portals have yet to become a significant editorial force able to provide knowledge about social issues and public affairs in a socially responsible fashione.

Asymmetric News Effects on Volatility: Good vs. Bad News in Good vs. Bad Times

OpenAIRE

Laakkonen, Helinä; Lanne, Markku

2008-01-01

We study the impact of positive and negative macroeconomic US and European news announcements in different phases of the business cycle on the highfrequency volatility of the EUR/USD exchange rate. The results suggest that in general bad news increases volatility more than good news. The news effects also depend on the state of the economy: bad news increases volatility more in good times than in bad times, while there is no difference between the volatility effects of good news in bad and go...

Location Sensitive Deep Convolutional Neural Networks for Segmentation of White Matter Hyperintensities.

Science.gov (United States)

Ghafoorian, Mohsen; Karssemeijer, Nico; Heskes, Tom; van Uden, Inge W M; Sanchez, Clara I; Litjens, Geert; de Leeuw, Frank-Erik; van Ginneken, Bram; Marchiori, Elena; Platel, Bram

2017-07-11

The anatomical location of imaging features is of crucial importance for accurate diagnosis in many medical tasks. Convolutional neural networks (CNN) have had huge successes in computer vision, but they lack the natural ability to incorporate the anatomical location in their decision making process, hindering success in some medical image analysis tasks. In this paper, to integrate the anatomical location information into the network, we propose several deep CNN architectures that consider multi-scale patches or take explicit location features while training. We apply and compare the proposed architectures for segmentation of white matter hyperintensities in brain MR images on a large dataset. As a result, we observe that the CNNs that incorporate location information substantially outperform a conventional segmentation method with handcrafted features as well as CNNs that do not integrate location information. On a test set of 50 scans, the best configuration of our networks obtained a Dice score of 0.792, compared to 0.805 for an independent human observer. Performance levels of the machine and the independent human observer were not statistically significantly different (p-value = 0.06).

Effects of "Good News" and "Bad News" on Newscast Image and Community Image.

Science.gov (United States)

Galician, Mary-Lou; Vestre, Norris D.

1987-01-01

Investigates whether the relative amount of bad, neutral, and good news on television has corresponding effects on viewers' image of the community depicted and of the carrying newscast. Concludes that bad news creates a bad image for the community but that good news does not produce a more favorable image than neutral news. (MM)

Lagged correlation networks

Science.gov (United States)

Curme, Chester

structures in networks composed of market returns and news sentiment signals for 40 countries. We compare the degrees to which markets anticipate news, and news anticipate markets, and use the community structures to construct a recommender system for inputs to prediction models. Finally, we complement this work with novel investigations of the exogenous news items that may drive the financial system using topic models. This includes an analysis of how investors and the general public may interact with these news items using Internet search data, and how the diversity of stories in the news both responds to and influences market movements.

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

KAUST Repository

Umarov, Ramzan

2017-02-03

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.

Analysing the development of TV news programmes: from information to dramatization

Directory of Open Access Journals (Sweden)

Maribel Rodríguez Fidalgo, Ph.D.

2010-01-01

Full Text Available Abstract: The TV news programme is the backbone of all television networks, be they national, regional or local. These types of programmes have been changing over the time. The essence of the information is being modified and has adopted a new format. Based on these principles, this research analyses the evolution of the language used on the Spanish TV news programmes, from their origins until today. This research has been carried out in three different phases: late 1980s, the 1990s and recent years. These stages were thoroughly examined through surveys applied to representative samples of the population. The results showed that TV news programmes have been acquiring a changing narrative style throughout time: they began using an “objective” narrative, followed by a mediated type, and finally dramatized narrative which uses shocking visual content and aims to achieve audience’s sensibilization and identification with news’ protagonists. The spectacularization of information has become the identitary feature of the current TV news programmes as a response to the need of reaching higher audience ratings.

News of the Year.

Science.gov (United States)

St. Lifer, Evan; Olson, Renee; Margolis, Rick; Glick, Andrea; Milliot, Jim

1999-01-01

Includes the following reports: "'LJ' (Library Journal) News Report: Libraries Success at Funding Books and Bytes"; "'SLJ' (School Library Journal) News Report: We're in the Money!"; and "'PW' (Publishers Weekly) News Reports". (AEF)

Hybrid case-neural network (CNN) diagnostic system

International Nuclear Information System (INIS)

Mohamed, A.H.

2010-01-01

recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

Arousing news characteristics in Dutch television news 1990-2004: an exploration of competitive strategies

NARCIS (Netherlands)

Hendriks Vettehen, P.; Beentjes, J.; Nuijten, K.; Peeters, A.

2011-01-01

This study investigates the processes by which competition in the television news market might promote the presence of arousing characteristics in television news. A total of 3,024 news stories from six Dutch television news programs over the period 1990 to 2004 were investigated through content

Arousing news characteristics in Dutch television news 1990-2004: An exploration of competitive strategies

NARCIS (Netherlands)

Hendriks Vettehen, P.G.J.; Beentjes, J.W.J.; Nuijten, C.M.; Peeters, A.L.

2011-01-01

This study investigates the processes by which competition in the television news market might promote the presence of arousing characteristics in television news. A total of 3,024 news stories from six Dutch television news programs over the period 1990 to 2004 were investigated through content

Statistical Hypothesis Testing using CNN Features for Synthesis of Adversarial Counterexamples to Human and Object Detection Vision Systems

Energy Technology Data Exchange (ETDEWEB)

Raj, Sunny [Univ. of Central Florida, Orlando, FL (United States); Jha, Sumit Kumar [Univ. of Central Florida, Orlando, FL (United States); Pullum, Laura L. [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Ramanathan, Arvind [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2017-05-01

Validating the correctness of human detection vision systems is crucial for safety applications such as pedestrian collision avoidance in autonomous vehicles. The enormous space of possible inputs to such an intelligent system makes it difficult to design test cases for such systems. In this report, we present our tool MAYA that uses an error model derived from a convolutional neural network (CNN) to explore the space of images similar to a given input image, and then tests the correctness of a given human or object detection system on such perturbed images. We demonstrate the capability of our tool on the pre-trained Histogram-of-Oriented-Gradients (HOG) human detection algorithm implemented in the popular OpenCV toolset and the Caffe object detection system pre-trained on the ImageNet benchmark. Our tool may serve as a testing resource for the designers of intelligent human and object detection systems.

Segmentation of histological images and fibrosis identification with a convolutional neural network.

Science.gov (United States)

Fu, Xiaohang; Liu, Tong; Xiong, Zhaohan; Smaill, Bruce H; Stiles, Martin K; Zhao, Jichao

2018-05-16

Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, such as fibrosis via Masson's trichrome staining. However, challenges are posed by the high variability and complexity of structural features in such images, in addition to imaging artifacts. Further, the conventional approach of manual thresholding is labor-intensive, and highly sensitive to inter- and intra-image intensity variations. An accurate and robust automated segmentation method is of high interest. We propose and evaluate an elegant convolutional neural network (CNN) designed for segmentation of histological images, particularly those with Masson's trichrome stain. The network comprises 11 successive convolutional - rectified linear unit - batch normalization layers. It outperformed state-of-the-art CNNs on a dataset of cardiac histological images (labeling fibrosis, myocytes, and background) with a Dice similarity coefficient of 0.947. With 100 times fewer (only 300,000) trainable parameters than the state-of-the-art, our CNN is less susceptible to overfitting, and is efficient. Additionally, it retains image resolution from input to output, captures fine-grained details, and can be trained end-to-end smoothly. To the best of our knowledge, this is the first deep CNN tailored to the problem of concern, and may potentially be extended to solve similar segmentation tasks to facilitate investigations into pathology and clinical treatment. Copyright © 2018. Published by Elsevier Ltd.

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

Science.gov (United States)

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.

Comparing the Hierarchy of Keywords in On-Line News Portals.

Science.gov (United States)

Tibély, Gergely; Sousa-Rodrigues, David; Pollner, Péter; Palla, Gergely

2016-01-01

Hierarchical organization is prevalent in networks representing a wide range of systems in nature and society. An important example is given by the tag hierarchies extracted from large on-line data repositories such as scientific publication archives, file sharing portals, blogs, on-line news portals, etc. The tagging of the stored objects with informative keywords in such repositories has become very common, and in most cases the tags on a given item are free words chosen by the authors independently. Therefore, the relations among keywords appearing in an on-line data repository are unknown in general. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialized ones at the bottom. There are several algorithms available for deducing this hierarchy from the statistical features of the keywords. In the present work we apply a recent, co-occurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy) or of less interest (categorized low in the hierarchy), but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals.

Comparing the Hierarchy of Keywords in On-Line News Portals.

Directory of Open Access Journals (Sweden)

Gergely Tibély

Full Text Available Hierarchical organization is prevalent in networks representing a wide range of systems in nature and society. An important example is given by the tag hierarchies extracted from large on-line data repositories such as scientific publication archives, file sharing portals, blogs, on-line news portals, etc. The tagging of the stored objects with informative keywords in such repositories has become very common, and in most cases the tags on a given item are free words chosen by the authors independently. Therefore, the relations among keywords appearing in an on-line data repository are unknown in general. However, in most cases the topics and concepts described by these keywords are forming a latent hierarchy, with the more general topics and categories at the top, and more specialized ones at the bottom. There are several algorithms available for deducing this hierarchy from the statistical features of the keywords. In the present work we apply a recent, co-occurrence-based tag hierarchy extraction method to sets of keywords obtained from four different on-line news portals. The resulting hierarchies show substantial differences not just in the topics rendered as important (being at the top of the hierarchy or of less interest (categorized low in the hierarchy, but also in the underlying network structure. This reveals discrepancies between the plausible keyword association frameworks in the studied news portals.

Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models.

Science.gov (United States)

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.

The U.S. Online News Coverage of Mammography Based on a Google News Search.

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Young Lin, Leng Leng; Rosenkrantz, Andrew B

2017-12-01

To characterize online news coverage relating to mammography, including articles' stance toward screening mammography. Google News was used to search U.S. news sites over a 9-year period (2006-2015) based on the search terms "mammography" and "mammogram." The top 100 search results were recorded. Identified articles were manually reviewed. The top 100 news articles were from the following sources: local news outlet (50%), national news outlet (24%), nonimaging medical source (13%), entertainment or culture news outlet (6%), business news outlet (4%), peer-reviewed journal (1%), and radiology news outlet (1%). Most common major themes were the screening mammography controversy (29%), description of a new breast imaging technology (23%), dense breasts (11%), and promotion of a public screening initiative (11%). For the most recent year, article stance toward screening mammography was 59%, favorable; 16%, unfavorable; and 25%, neutral. After 2010, there was an abrupt shift in articles' stances from neutral to both favorable and unfavorable. A wide range of online news sources addressed a range of issues related to mammography. National, rather than local, news sites were more likely to focus on the screening controversy and more likely to take an unfavorable view. The controversial United States Preventive Services Task Force guidelines may have influenced articles to take a stance on screening mammography. As such online news may impact public perception of the topic and thus potentially impact guideline adherence, radiologists are encouraged to maintain awareness of this online coverage and to support the online dissemination of reliable and accurate information. Copyright © 2017 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Thrilling News Revisited: The Role of Suspense for the Enjoyment of News Stories.

Science.gov (United States)

Kaspar, Kai; Zimmermann, Daniel; Wilbers, Anne-Kathrin

2016-01-01

Previous research on news perception has been dominated by a cognitively oriented perspective on reception processes, whereas emotions have been widely neglected. Consequently, it has remained open which features of a news story might elicit affective responses and hence modulate news perception, shifting the focus to the emotional potential of the narrative. According to the affective-disposition theory, the experience of suspense is the striving force of immersion in fictional dramas. Thereby, a positive affective disposition toward the protagonist of a story and a high likelihood of a bad ending should increase suspense that, in turn, should positively influence reading appreciation and lingering interest in the story. We investigated whether suspense and its determinants also play such a key role in the context of news stories. Study 1 ( n = 263) successfully replicated results of an earlier study, whereas Studies 2 ( n = 255) and 3 ( n = 599) challenged the generalizability of some effects related to manipulated characteristics of a news story. In contrast, correlational relationships between perceived news characteristics and news evaluation were relatively stable. In particular, participants' liking of the protagonist and the perceived likelihood of a good ending were positively associated with suspense, reading appreciation, and lingering interest. This result indicates a preference for happy endings and contradicts the notion that likely negative outcomes are beneficial for suspense and the enjoyment of news stories, as postulated by the affective-disposition theory in the context of fictional dramas. Moreover, experienced suspense reliably mediated the correlations between, on the one hand, participants' liking of the protagonist and the perceived likelihood of a good ending and, on the other hand, reading appreciation and lingering interest. The news story's personal relevance was less influential than expected. Further, we observed a large absence of

Super-resolution reconstruction of MR image with a novel residual learning network algorithm

Science.gov (United States)

Shi, Jun; Liu, Qingping; Wang, Chaofeng; Zhang, Qi; Ying, Shihui; Xu, Haoyu

2018-04-01

Spatial resolution is one of the key parameters of magnetic resonance imaging (MRI). The image super-resolution (SR) technique offers an alternative approach to improve the spatial resolution of MRI due to its simplicity. Convolutional neural networks (CNN)-based SR algorithms have achieved state-of-the-art performance, in which the global residual learning (GRL) strategy is now commonly used due to its effectiveness for learning image details for SR. However, the partial loss of image details usually happens in a very deep network due to the degradation problem. In this work, we propose a novel residual learning-based SR algorithm for MRI, which combines both multi-scale GRL and shallow network block-based local residual learning (LRL). The proposed LRL module works effectively in capturing high-frequency details by learning local residuals. One simulated MRI dataset and two real MRI datasets have been used to evaluate our algorithm. The experimental results show that the proposed SR algorithm achieves superior performance to all of the other compared CNN-based SR algorithms in this work.

How Television News Programs Use Video News Releases.

Science.gov (United States)

Harmon, Mark D.; White, Candace

2001-01-01

Examines actual use in television news broadcasts of video news releases (VNRs). Finds that all sizes of markets were likely to use VNRs. Finds that the most common use was as a voice-over story in an early evening newscast, and that VNRs associated with children and their safety or health got the greatest number of uses. (SR)

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

Science.gov (United States)

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

How the Networks Cover Education: Schools Are not the Media's Pet.

Science.gov (United States)

Robinson, Michael J.

1984-01-01

An analysis of network news coverage of public education from April 1982 to April 1983 showed that such coverage was overwhelmingly negative, with almost four times as much news space devoted to "bad news" about public education as to "good news." (CMG)

Village Building Identification Based on Ensemble Convolutional Neural Networks

Science.gov (United States)

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

Classification of polycystic ovary based on ultrasound images using competitive neural network

Science.gov (United States)

Dewi, R. M.; Adiwijaya; Wisesty, U. N.; Jondri

2018-03-01

Infertility in the women reproduction system due to inhibition of follicles maturation process causing the number of follicles which is called polycystic ovaries (PCO). PCO detection is still operated manually by a gynecologist by counting the number and size of follicles in the ovaries, so it takes a long time and needs high accuracy. In general, PCO can be detected by calculating stereology or feature extraction and classification. In this paper, we designed a system to classify PCO by using the feature extraction (Gabor Wavelet method) and Competitive Neural Network (CNN). CNN was selected because this method is the combination between Hemming Net and The Max Net so that the data classification can be performed based on the specific characteristics of ultrasound data. Based on the result of system testing, Competitive Neural Network obtained the highest accuracy is 80.84% and the time process is 60.64 seconds (when using 32 feature vectors as well as weight and bias values respectively of 0.03 and 0.002).

NERSC News

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NERSC Powering Scientific Discovery Since 1974 Login Site Map | My NERSC search... Go Home About Scheduled Outages Login Node Status My NERSC Now Computing Highlights Timeline News & Publications News Coming to NERSC Login Page May 21, 2018 NERSC rolls out a redesigned login page on June 11. Read More Â

Automated Detection of Fronts using a Deep Learning Convolutional Neural Network

Science.gov (United States)

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

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks

Directory of Open Access Journals (Sweden)

Shalin Savalia

2018-05-01

Full Text Available The electrocardiogram (ECG plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP and convolution neural network (CNN. The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Cardiac Arrhythmia Classification by Multi-Layer Perceptron and Convolution Neural Networks.

Science.gov (United States)

Savalia, Shalin; Emamian, Vahid

2018-05-04

The electrocardiogram (ECG) plays an imperative role in the medical field, as it records heart signal over time and is used to discover numerous cardiovascular diseases. If a documented ECG signal has a certain irregularity in its predefined features, this is called arrhythmia, the types of which include tachycardia, bradycardia, supraventricular arrhythmias, and ventricular, etc. This has encouraged us to do research that consists of distinguishing between several arrhythmias by using deep neural network algorithms such as multi-layer perceptron (MLP) and convolution neural network (CNN). The TensorFlow library that was established by Google for deep learning and machine learning is used in python to acquire the algorithms proposed here. The ECG databases accessible at PhysioBank.com and kaggle.com were used for training, testing, and validation of the MLP and CNN algorithms. The proposed algorithm consists of four hidden layers with weights, biases in MLP, and four-layer convolution neural networks which map ECG samples to the different classes of arrhythmia. The accuracy of the algorithm surpasses the performance of the current algorithms that have been developed by other cardiologists in both sensitivity and precision.

Tissue classification and segmentation of pressure injuries using convolutional neural networks.

Science.gov (United States)

Zahia, Sofia; Sierra-Sosa, Daniel; Garcia-Zapirain, Begonya; Elmaghraby, Adel

2018-06-01

This paper presents a new approach for automatic tissue classification in pressure injuries. These wounds are localized skin damages which need frequent diagnosis and treatment. Therefore, a reliable and accurate systems for segmentation and tissue type identification are needed in order to achieve better treatment results. Our proposed system is based on a Convolutional Neural Network (CNN) devoted to performing optimized segmentation of the different tissue types present in pressure injuries (granulation, slough, and necrotic tissues). A preprocessing step removes the flash light and creates a set of 5x5 sub-images which are used as input for the CNN network. The network output will classify every sub-image of the validation set into one of the three classes studied. The metrics used to evaluate our approach show an overall average classification accuracy of 92.01%, an average total weighted Dice Similarity Coefficient of 91.38%, and an average precision per class of 97.31% for granulation tissue, 96.59% for necrotic tissue, and 77.90% for slough tissue. Our system has been proven to make recognition of complicated structures in biomedical images feasible. Copyright © 2018 Elsevier B.V. All rights reserved.

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

Science.gov (United States)

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.

Deep vector-based convolutional neural network approach for automatic recognition of colonies of induced pluripotent stem cells.

Directory of Open Access Journals (Sweden)

Muthu Subash Kavitha

Full Text Available Pluripotent stem cells can potentially be used in clinical applications as a model for studying disease progress. This tracking of disease-causing events in cells requires constant assessment of the quality of stem cells. Existing approaches are inadequate for robust and automated differentiation of stem cell colonies. In this study, we developed a new model of vector-based convolutional neural network (V-CNN with respect to extracted features of the induced pluripotent stem cell (iPSC colony for distinguishing colony characteristics. A transfer function from the feature vectors to the virtual image was generated at the front of the CNN in order for classification of feature vectors of healthy and unhealthy colonies. The robustness of the proposed V-CNN model in distinguishing colonies was compared with that of the competitive support vector machine (SVM classifier based on morphological, textural, and combined features. Additionally, five-fold cross-validation was used to investigate the performance of the V-CNN model. The precision, recall, and F-measure values of the V-CNN model were comparatively higher than those of the SVM classifier, with a range of 87-93%, indicating fewer false positives and false negative rates. Furthermore, for determining the quality of colonies, the V-CNN model showed higher accuracy values based on morphological (95.5%, textural (91.0%, and combined (93.2% features than those estimated with the SVM classifier (86.7, 83.3, and 83.4%, respectively. Similarly, the accuracy of the feature sets using five-fold cross-validation was above 90% for the V-CNN model, whereas that yielded by the SVM model was in the range of 75-77%. We thus concluded that the proposed V-CNN model outperforms the conventional SVM classifier, which strongly suggests that it as a reliable framework for robust colony classification of iPSCs. It can also serve as a cost-effective quality recognition tool during culture and other experimental

High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

Science.gov (United States)

Hisano, Ryohei; Sornette, Didier; Mizuno, Takayuki; Ohnishi, Takaaki; Watanabe, Tsutomu

2013-01-01

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information. PMID:23762258

High quality topic extraction from business news explains abnormal financial market volatility.

Directory of Open Access Journals (Sweden)

Ryohei Hisano

Full Text Available Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc. affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their "thematic" features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be "abnormally large," can be partially explained by the flow of news. In this sense, our results prove that there is no "excess trading," when restricting to times when news is genuinely novel and provides relevant financial information.

Thrilling news revisited: The role of suspense for the enjoyment of news stories

Directory of Open Access Journals (Sweden)

Kai Kaspar

2016-12-01

Full Text Available Previous research on news perception has been dominated by a cognitively oriented perspective on reception processes, whereas emotions have been widely neglected. Consequently, it has remained open which features of a news story might elicit affective responses and hence modulate news perception, shifting the focus to the emotional potential of the narrative. According to the affective-disposition theory, the experience of suspense is the striving force of immersion in fictional dramas. Thereby, a positive affective disposition toward the protagonist of a story and a high likelihood of a bad ending should increase suspense that, in turn, should positively influence reading appreciation and lingering interest in the story. We investigated whether suspense and its determinants also play such a key role in the context of news stories. Study 1 (n = 263 successfully replicated results of an earlier study, whereas Studies 2 (n = 255 and 3 (n = 599 challenged the generalizability of some effects related to manipulated characteristics of a news story. In contrast, correlational relationships between perceived news characteristics were relatively stable. In particular, a higher liking of the protagonist and a higher perceived likelihood of a good versus bad ending were positively associated with suspense, reading appreciation, and lingering interest. This result indicates a preference for happy endings and it contradicts the notion that likely negative outcomes are beneficial for suspense and the enjoyment of news stories, as postulated by the affective-disposition theory in the context of fictional dramas. Moreover, experienced suspense reliably mediated the correlations between, on the one hand, participants’ liking of the protagonist and the perceived likelihood of a good ending and, on the other hand, reading appreciation and lingering interest. The news story’s personal relevance was less influential than expected. Further, we observed a large

Convolutional neural networks for segmentation and object detection of human semen

DEFF Research Database (Denmark)

Nissen, Malte Stær; Krause, Oswin; Almstrup, Kristian

2017-01-01

We compare a set of convolutional neural network (CNN) architectures for the task of segmenting and detecting human sperm cells in an image taken from a semen sample. In contrast to previous work, samples are not stained or washed to allow for full sperm quality analysis, making analysis harder due...

News/Press Releases

Data.gov (United States)

Office of Personnel Management — A press release, news release, media release, press statement is written communication directed at members of the news media for the purpose of announcing programs...

Who Makes The News?

DEFF Research Database (Denmark)

Jørndrup, Hanne; Bentsen, Martine

As newsroom staff around the world went about their day on 25 March 2015, hundreds of volunteers located in over 100 countries gathered to monitor their news media as part of the Fifth Global Media Monitoring Project (GMMP). The Global Media Monitoring Project (GMMP) is the world’s longest......-running and most extensive research on gender in the news media. It began in 1995 when volunteers in 71 countries around the world monitored women’s presence in their national radio, television and print news. The research revealed that only 17% of news subjects – the people who are interviewed or whom the news...... is about – were women. It found that gender parity was ‘a distant prospect in any region of the world. News [was] more often being presented by women but it [was] still rarely about women. Denmark participates in GMMP for the second time and both times we can recognize the global inequality in the Danish...

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

Science.gov (United States)

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.

Navigating cross-media news use

DEFF Research Database (Denmark)

Swart, Joëlle; Peters, Chris; Broersma, Marcel

2017-01-01

distinctive cross-media repertoires, and what makes these compositions meaningful. This article analyzes the value of different platforms, genres and practices in everyday life by mapping patterns of cross-media news use. Combining Q methodology with think-aloud protocols and day-in-the-life-interviews, five...... distinct news media repertoires are identified: 1) regionally-oriented 2) background-oriented 3) digital 4) laid-back and 5) nationally-oriented news use. Our findings indicate that users do not always use what they prefer, nor do they prefer what they use. Moreover, the boundaries they draw between news......The current news media landscape is characterized by an abundance of digital outlets and increased opportunities for users to navigate news themselves. Yet, it is still unclear how people negotiate this fluctuating environment to decide which news media to select or ignore, how they assemble...

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

Science.gov (United States)

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

Artificial neural network for bubbles pattern recognition on the images

International Nuclear Information System (INIS)

Poletaev, I E; Pervunin, K S; Tokarev, M P

2016-01-01

Two-phase bubble flows have been used in many technological and energy processes as processing oil, chemical and nuclear reactors. This explains large interest to experimental and numerical studies of such flows last several decades. Exploiting of optical diagnostics for analysis of the bubble flows allows researchers obtaining of instantaneous velocity fields and gaseous phase distribution with the high spatial resolution non-intrusively. Behavior of light rays exhibits an intricate manner when they cross interphase boundaries of gaseous bubbles hence the identification of the bubbles images is a complicated problem. This work presents a method of bubbles images identification based on a modern technology of deep learning called convolutional neural networks (CNN). Neural networks are able to determine overlapping, blurred, and non-spherical bubble images. They can increase accuracy of the bubble image recognition, reduce the number of outliers, lower data processing time, and significantly decrease the number of settings for the identification in comparison with standard recognition methods developed before. In addition, usage of GPUs speeds up the learning process of CNN owning to the modern adaptive subgradient optimization techniques. (paper)

Extended LaSalle's Invariance Principle for Full-Range Cellular Neural Networks

Directory of Open Access Journals (Sweden)

Mauro Di Marco

2009-01-01

Full Text Available In several relevant applications to the solution of signal processing tasks in real time, a cellular neural network (CNN is required to be convergent, that is, each solution should tend toward some equilibrium point. The paper develops a Lyapunov method, which is based on a generalized version of LaSalle's invariance principle, for studying convergence and stability of the differential inclusions modeling the dynamics of the full-range (FR model of CNNs. The applicability of the method is demonstrated by obtaining a rigorous proof of convergence for symmetric FR-CNNs. The proof, which is a direct consequence of the fact that a symmetric FR-CNN admits a strict Lyapunov function, is much more simple than the corresponding proof of convergence for symmetric standard CNNs.

News coverage of climate change in Nature News and ScienceNOW during 2007

DEFF Research Database (Denmark)

Nielsen, Kristian Hvidtfelt; Kjærgaard, Rikke Schmidt

2011-01-01

of appreciating climate change, its severe consequences, and its anthropogenic causes. During that year the two journals’ online news services Nature News and ScienceNOW framed climate change to fit particular agendas resulting in markedly different narratives. This article demonstrates that Nature News reported...... more critically on political decisions, scientific results, and social matters of climate change compared to ScienceNOW. Operating under different institutional constraints ScienceNOW generally took a more cautious line. The evidence drawn from both textual and visual analyses shows that news sections...

Aggregated channels network for real-time pedestrian detection

Science.gov (United States)

Ghorban, Farzin; Marín, Javier; Su, Yu; Colombo, Alessandro; Kummert, Anton

2018-04-01

Convolutional neural networks (CNNs) have demonstrated their superiority in numerous computer vision tasks, yet their computational cost results prohibitive for many real-time applications such as pedestrian detection which is usually performed on low-consumption hardware. In order to alleviate this drawback, most strategies focus on using a two-stage cascade approach. Essentially, in the first stage a fast method generates a significant but reduced amount of high quality proposals that later, in the second stage, are evaluated by the CNN. In this work, we propose a novel detection pipeline that further benefits from the two-stage cascade strategy. More concretely, the enriched and subsequently compressed features used in the first stage are reused as the CNN input. As a consequence, a simpler network architecture, adapted for such small input sizes, allows to achieve real-time performance and obtain results close to the state-of-the-art while running significantly faster without the use of GPU. In particular, considering that the proposed pipeline runs in frame rate, the achieved performance is highly competitive. We furthermore demonstrate that the proposed pipeline on itself can serve as an effective proposal generator.

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

Science.gov (United States)

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

A CNN-Based Fusion Method for Feature Extraction from Sentinel Data

Directory of Open Access Journals (Sweden)

Giuseppe Scarpa

2018-02-01

Full Text Available Sensitivity to weather conditions, and specially to clouds, is a severe limiting factor to the use of optical remote sensing for Earth monitoring applications. A possible alternative is to benefit from weather-insensitive synthetic aperture radar (SAR images. In many real-world applications, critical decisions are made based on some informative optical or radar features related to items such as water, vegetation or soil. Under cloudy conditions, however, optical-based features are not available, and they are commonly reconstructed through linear interpolation between data available at temporally-close time instants. In this work, we propose to estimate missing optical features through data fusion and deep-learning. Several sources of information are taken into account—optical sequences, SAR sequences, digital elevation model—so as to exploit both temporal and cross-sensor dependencies. Based on these data and a tiny cloud-free fraction of the target image, a compact convolutional neural network (CNN is trained to perform the desired estimation. To validate the proposed approach, we focus on the estimation of the normalized difference vegetation index (NDVI, using coupled Sentinel-1 and Sentinel-2 time-series acquired over an agricultural region of Burkina Faso from May–November 2016. Several fusion schemes are considered, causal and non-causal, single-sensor or joint-sensor, corresponding to different operating conditions. Experimental results are very promising, showing a significant gain over baseline methods according to all performance indicators.

A fast button surface defects detection method based on convolutional neural network

Science.gov (United States)

Liu, Lizhe; Cao, Danhua; Wu, Songlin; Wu, Yubin; Wei, Taoran

2018-01-01

Considering the complexity of the button surface texture and the variety of buttons and defects, we propose a fast visual method for button surface defect detection, based on convolutional neural network (CNN). CNN has the ability to extract the essential features by training, avoiding designing complex feature operators adapted to different kinds of buttons, textures and defects. Firstly, we obtain the normalized button region and then use HOG-SVM method to identify the front and back side of the button. Finally, a convolutional neural network is developed to recognize the defects. Aiming at detecting the subtle defects, we propose a network structure with multiple feature channels input. To deal with the defects of different scales, we take a strategy of multi-scale image block detection. The experimental results show that our method is valid for a variety of buttons and able to recognize all kinds of defects that have occurred, including dent, crack, stain, hole, wrong paint and uneven. The detection rate exceeds 96%, which is much better than traditional methods based on SVM and methods based on template match. Our method can reach the speed of 5 fps on DSP based smart camera with 600 MHz frequency.

When Information from Public Health Officials is Untrustworthy: The Use of Online News, Interpersonal Networks, and Social Media during the MERS Outbreak in South Korea.

Science.gov (United States)

Jang, Kyungeun; Baek, Young Min

2018-03-20

Public health officials (PHOs) are responsible for providing trustworthy information during a public health crisis; however, there is little research on how the public behaves when their expectations for such information are violated. Drawing on media dependency theory and source credibility research as our primary theoretical framework, we tested how credibility of information from PHOs is associated with people's reliance on a particular communication channel in the context of the 2015 Middle East Respiratory Syndrome (MERS) outbreak in South Korea. Using nationally representative data (N = 1036) collected during the MERS outbreak, we found that less credible information from PHOs led to more frequent use of online news, interpersonal networks, and social media for acquiring MERS-related information. However, credibility of information from PHOs was not associated with the use of television news or print newspapers. The theoretical and practical implications of our results on communication channels usage are discussed.

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

Science.gov (United States)

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.

Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.

Science.gov (United States)

Acharya, U Rajendra; Oh, Shu Lih; Hagiwara, Yuki; Tan, Jen Hong; Adeli, Hojjat

2017-09-27

An encephalogram (EEG) is a commonly used ancillary test to aide in the diagnosis of epilepsy. The EEG signal contains information about the electrical activity of the brain. Traditionally, neurologists employ direct visual inspection to identify epileptiform abnormalities. This technique can be time-consuming, limited by technical artifact, provides variable results secondary to reader expertise level, and is limited in identifying abnormalities. Therefore, it is essential to develop a computer-aided diagnosis (CAD) system to automatically distinguish the class of these EEG signals using machine learning techniques. This is the first study to employ the convolutional neural network (CNN) for analysis of EEG signals. In this work, a 13-layer deep convolutional neural network (CNN) algorithm is implemented to detect normal, preictal, and seizure classes. The proposed technique achieved an accuracy, specificity, and sensitivity of 88.67%, 90.00% and 95.00%, respectively. Copyright © 2017 Elsevier Ltd. All rights reserved.

Cascading Corruption News

DEFF Research Database (Denmark)

Damgaard, Mads

2018-01-01

Through a content analysis of 8,800 news items and six months of front pages in three Brazilian newspapers, all dealing with corruption and political transgression, this article documents the remarkable skew of media attention to corruption scandals. The bias is examined as an information...... phenomenon, arising from systemic and commercial factors of Brazil’s news media: An information cascade of news on corruption formed, destabilizing the governing coalition and legitimizing the impeachment process of Dilma Rousseff. As this process gained momentum, questions of accountability were disregarded...

A test of rivaling approaches to explain news effects: A multi-wave panel study of agenda setting, social and economic conditions, the tone of the news, and horse race news

NARCIS (Netherlands)

Kleinnijenhuis, J.; van Hoof, A.M.J.; Oegema, D.; de Ridder, J.A.

2007-01-01

Different "paradigmatic" approaches to explain news effects on voting may supplement each other, because their starting points are based on different news types in political campaign news: news on issue positions of parties, news on real-world developments, news on support or criticism for parties,

Extraction of temporal networks from term co-occurrences in online textual sources.

Directory of Open Access Journals (Sweden)

Marko Popović

Full Text Available A stream of unstructured news can be a valuable source of hidden relations between different entities, such as financial institutions, countries, or persons. We present an approach to continuously collect online news, recognize relevant entities in them, and extract time-varying networks. The nodes of the network are the entities, and the links are their co-occurrences. We present a method to estimate the significance of co-occurrences, and a benchmark model against which their robustness is evaluated. The approach is applied to a large set of financial news, collected over a period of two years. The entities we consider are 50 countries which issue sovereign bonds, and which are insured by Credit Default Swaps (CDS in turn. We compare the country co-occurrence networks to the CDS networks constructed from the correlations between the CDS. The results show relatively small, but significant overlap between the networks extracted from the news and those from the CDS correlations.

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

Science.gov (United States)

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.

Strong convective storm nowcasting using a hybrid approach of convolutional neural network and hidden Markov model

Science.gov (United States)

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.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices.

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Science.gov (United States)

Gokmen, Tayfun; Onen, Murat; Haensch, Wilfried

2017-01-01

In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN) in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU) devices to convolutional neural networks (CNNs). We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures. PMID:29066942

Deep multi-scale location-aware 3D convolutional neural networks for automated detection of lacunes of presumed vascular origin

Directory of Open Access Journals (Sweden)

Mohsen Ghafoorian

2017-01-01

In this paper, we propose an automated two-stage method using deep convolutional neural networks (CNN. We show that this method has good performance and can considerably benefit readers. We first use a fully convolutional neural network to detect initial candidates. In the second step, we employ a 3D CNN as a false positive reduction tool. As the location information is important to the analysis of candidate structures, we further equip the network with contextual information using multi-scale analysis and integration of explicit location features. We trained, validated and tested our networks on a large dataset of 1075 cases obtained from two different studies. Subsequently, we conducted an observer study with four trained observers and compared our method with them using a free-response operating characteristic analysis. Shown on a test set of 111 cases, the resulting CAD system exhibits performance similar to the trained human observers and achieves a sensitivity of 0.974 with 0.13 false positives per slice. A feasibility study also showed that a trained human observer would considerably benefit once aided by the CAD system.

Lane marking detection based on waveform analysis and CNN

Science.gov (United States)

Ye, Yang Yang; Chen, Hou Jin; Hao, Xiao Li

2017-06-01

Lane markings detection is a very important part of the ADAS to avoid traffic accidents. In order to obtain accurate lane markings, in this work, a novel and efficient algorithm is proposed, which analyses the waveform generated from the road image after inverse perspective mapping (IPM). The algorithm includes two main stages: the first stage uses an image preprocessing including a CNN to reduce the background and enhance the lane markings. The second stage obtains the waveform of the road image and analyzes the waveform to get lanes. The contribution of this work is that we introduce local and global features of the waveform to detect the lane markings. The results indicate the proposed method is robust in detecting and fitting the lane markings.

Good Friends, Bad News

DEFF Research Database (Denmark)

Hansen, Lars Kai; Arvidsson, Adam; Nielsen, Finn Årup

. In this paper we explore the apparent paradox in a quantitative analysis of information diffusion on Twitter. Twitter is interesting in this context as it has been shown to present both the characteristics social and news media. The basic measure of virality in Twitter is the probability of retweet. Twitter...... is different from email in that retweeting does not depend on pre-existing social relations, but often occur among strangers, thus in this respect Twitter may be more similar to traditional news media. We therefore hypothesize that negative news content is more likely to be retweeted, while for non-news tweets...

Exploiting Tri-Relationship for Fake News Detection

OpenAIRE

Shu, Kai; Wang, Suhang; Liu, Huan

2017-01-01

Social media for news consumption is becoming popular nowadays. The low cost, easy access and rapid information dissemination of social media bring benefits for people to seek out news timely. However, it also causes the widespread of fake news, i.e., low-quality news pieces that are intentionally fabricated. The fake news brings about several negative effects on individual consumers, news ecosystem, and even society trust. Previous fake news detection methods mainly focus on news contents fo...

There is no news like bad news: women are more remembering and stress reactive after reading real negative news than men.

Science.gov (United States)

Marin, Marie-France; Morin-Major, Julie-Katia; Schramek, Tania E; Beaupré, Annick; Perna, Andrea; Juster, Robert-Paul; Lupien, Sonia J

2012-01-01

With the advent of specialized television channels offering 24-hour coverage, Internet and smart phones, the possibility to be constantly in contact with the media has increased dramatically in the last decades. Despite this higher access to knowledge, the impact media exposure has on healthy individuals remains poorly studied. Given that most information conveyed in the media is negative and that upon perception of threat, the brain activates the stress system, which leads to cortisol secretion, we decided to determine how healthy individuals react to media information. Accordingly, we investigated whether reading real negative news (1) is physiologically stressful, (2) modulates one's propensity to be stress reactive to a subsequent stressor and (3) modulates remembrance for these news. Sixty participants (30 women, 30 men) were randomly assigned to either twenty-four real neutral news excerpts or to twenty-four real negative excerpts for 10 minutes. They were then all exposed to a well-validated psychosocial stressor, the Trier Social Stress Test (TSST), which consists of an anticipation phase of 10 minutes and a test phase of 10 minutes. A total of eight salivary cortisol samples were collected, at 10-minutes intervals, throughout the experimental procedure. One day later, a free recall of the news was performed. Results showed that although reading negative news did not lead to change in cortisol levels (p>0.05), it led to a significant increase in cortisol to a subsequent stressor in women only (pnegative news condition experienced better memory for these news excerpts compared to men (pmedia exposure could increase stress reactivity and memory for negative news in women.

Tailor-Made News: Meeting the demands of news users on mobile and social media

NARCIS (Netherlands)

Groot Kormelink, T.; Costera Meijer, I.

2014-01-01

Despite the technological possibilities for portable, personalized, and participatory news use, the public has not turned en masse from passive receivers who consume news on the producers' terms, into active users who tailor news to fit their personal preferences and practices. Unmistakably, some

Cascading Corruption News

DEFF Research Database (Denmark)

Damgaard, Mads

2018-01-01

Through a content analysis of 8,800 news items and six months of front pages in three Brazilian newspapers, all dealing with corruption and political transgression, this article documents the remarkable skew of media attention to corruption scandals. The bias is examined as an information...... phenomenon, arising from systemic and commercial factors of Brazil’s news media: An information cascade of news on corruption formed, destabilizing the governing coalition and legitimizing the impeachment process of Dilma Rousseff. As this process gained momentum, questions of accountability were disregarded...... by the media, with harmful effects on democracy....

Synthesis of technetium-99m labeled clinafloxacin (99mTc-CNN) complex and biological evaluation as a potential Staphylococcus aureus infection imaging agent

International Nuclear Information System (INIS)

Syed Qaiser Shah; Muhammad Rafiullah Khan

2011-01-01

In the present study synthesis of the 99m Tc-CNN complex and its efficacy as a prospective Staphylococcus aureus (S. aureus) infection imaging agent was assessed. The 99m Tc-CNN complex was characterized in terms of stability in saline, serum, in vitro binding with S. aureus and in vivo percent absorption in male Wister rats (MWR) infected with live and heat killed S. aureus. Radiochemically the 99m Tc-CNN complex showed stable behavior in saline and serum at different intervals. At 30 min after reconstitution the complex showed maximum radiochemical purity (RCP) yield of 97.55 ± 0.22%. The RCP yield decreased to 90.50 ± 0.18% within 240 min. In serum, 18.15% unwanted side product was appeared within 16 h of the incubation. In vitro saturated binding with S. aureus was observed at different intervals with a 62.00% maximum at 90 min. Normal percent in vivo uptake was observed in MWR artificially infected with live S. aureus with a five times higher in the infected muscle as compared to the inflamed and normal muscles. No difference in the percent uptake of the complex in MWR infected with heat killed S. aureus in the infected, inflamed and normal muscles were observed. Based on the promising in vitro and in vivo radiochemical and biological characteristics, we recommend the 99m Tc-CNN complex for in vivo localization of the S. aureus infectious foci. (author)

Target recognition based on convolutional neural network

Science.gov (United States)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

News Authorship and News Sources: Impacts on Environmental Coverage in The Nigerian Press

Directory of Open Access Journals (Sweden)

Ogadimma C. Emenyeonu

2017-07-01

Full Text Available This study examines the impacts of news authorship and news sources on environmental coverage in the Nigerian press to shed light on the roles they play in news construction. The study finds that journalists in conjunction with policy makers are the catalyst for environmental information, whereas citizens who are pivotal in creating relevant public opinion on environmental issues are left behind. The study reveals that investigative reporting lacks in environmental coverage because most coverage are events driven which explains why environmental news is reported as straight news and as such journalists rely heavily on official sources rather than subsidiary sources. The study opines that for proper environmental coverage, journalists must choose sources from both main and subsidiary actors and revert to proactive, investigative and interpretive reporting so as to make environmental stories relatable to the intended audiences.

Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

Science.gov (United States)

Cabrera-Vives, Guillermo; Reyes, Ignacio; Förster, Francisco; Estévez, Pablo A.; Maureira, Juan-Carlos

2017-02-01

We introduce Deep-HiTS, a rotation-invariant convolutional neural network (CNN) model for classifying images of transient candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RFs). We show that our CNN significantly outperforms the RF model, reducing the error by almost half. Furthermore, for a fixed number of approximately 2000 allowed false transient candidates per night, we are able to reduce the misclassified real transients by approximately one-fifth. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope. We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS. Deep-HiTS is licensed under the terms of the GNU General Public License v3.0.

Young people’s news orientations and uses of traditional and new media for news

NARCIS (Netherlands)

van Cauwenberge, A.; d'Haenens, L.; Beentjes, H.

2013-01-01

This article reports on Flemish college students’ news orientations and their uses of traditional and new media for news within a public service media environment. We used five homogeneous focus groups that covered variation in news media use. The analysis of the focus groups revealed major

The news Engine

DEFF Research Database (Denmark)

Andersson, Ralf

for ideas etc. Reporters have limitedpossibilities for making own stories and less time for research - each live reportercovers 5-7 stories during a day. People : In a survey, most reporters replied that the new workflow was afundamental change of their work and had major impact on their identity......The News Engine How a new experiment in newsrooms can change process, product and people.   By Ralf Andersson   In fall 2012, the news department of the Danish Broadcasting Corporation,decided to implement a new workflow called ”The News Engine” - in order to workfaster, more freely, flexible...... and with fewer resources. This was done to raisethe productivity. The fundamental principle was that all stories should fit all platforms(content sharing) - and that no one did their own story anymore. DR News introduced 8-10 mobile live teams who are responsible for doinginterviews, record pictures and sound...

There is no news like bad news: women are more remembering and stress reactive after reading real negative news than men.

Directory of Open Access Journals (Sweden)

Marie-France Marin

Full Text Available With the advent of specialized television channels offering 24-hour coverage, Internet and smart phones, the possibility to be constantly in contact with the media has increased dramatically in the last decades. Despite this higher access to knowledge, the impact media exposure has on healthy individuals remains poorly studied. Given that most information conveyed in the media is negative and that upon perception of threat, the brain activates the stress system, which leads to cortisol secretion, we decided to determine how healthy individuals react to media information. Accordingly, we investigated whether reading real negative news (1 is physiologically stressful, (2 modulates one's propensity to be stress reactive to a subsequent stressor and (3 modulates remembrance for these news. Sixty participants (30 women, 30 men were randomly assigned to either twenty-four real neutral news excerpts or to twenty-four real negative excerpts for 10 minutes. They were then all exposed to a well-validated psychosocial stressor, the Trier Social Stress Test (TSST, which consists of an anticipation phase of 10 minutes and a test phase of 10 minutes. A total of eight salivary cortisol samples were collected, at 10-minutes intervals, throughout the experimental procedure. One day later, a free recall of the news was performed. Results showed that although reading negative news did not lead to change in cortisol levels (p>0.05, it led to a significant increase in cortisol to a subsequent stressor in women only (p<0.001. Also, women in the negative news condition experienced better memory for these news excerpts compared to men (p<0.01. These results suggest a potential mechanism by which media exposure could increase stress reactivity and memory for negative news in women.

Pneumothorax detection in chest radiographs using convolutional neural networks

Science.gov (United States)

Blumenfeld, Aviel; Konen, Eli; Greenspan, Hayit

2018-02-01

This study presents a computer assisted diagnosis system for the detection of pneumothorax (PTX) in chest radiographs based on a convolutional neural network (CNN) for pixel classification. Using a pixel classification approach allows utilization of the texture information in the local environment of each pixel while training a CNN model on millions of training patches extracted from a relatively small dataset. The proposed system uses a pre-processing step of lung field segmentation to overcome the large variability in the input images coming from a variety of imaging sources and protocols. Using a CNN classification, suspected pixel candidates are extracted within each lung segment. A postprocessing step follows to remove non-physiological suspected regions and noisy connected components. The overall percentage of suspected PTX area was used as a robust global decision for the presence of PTX in each lung. The system was trained on a set of 117 chest x-ray images with ground truth segmentations of the PTX regions. The system was tested on a set of 86 images and reached diagnosis accuracy of AUC=0.95. Overall preliminary results are promising and indicate the growing ability of CAD based systems to detect findings in medical imaging on a clinical level accuracy.

Pattern recognition based on time-frequency analysis and convolutional neural networks for vibrational events in φ-OTDR

Science.gov (United States)

Xu, Chengjin; Guan, Junjun; Bao, Ming; Lu, Jiangang; Ye, Wei

2018-01-01

Based on vibration signals detected by a phase-sensitive optical time-domain reflectometer distributed optical fiber sensing system, this paper presents an implement of time-frequency analysis and convolutional neural network (CNN), used to classify different types of vibrational events. First, spectral subtraction and the short-time Fourier transform are used to enhance time-frequency features of vibration signals and transform different types of vibration signals into spectrograms, which are input to the CNN for automatic feature extraction and classification. Finally, by replacing the soft-max layer in the CNN with a multiclass support vector machine, the performance of the classifier is enhanced. Experiments show that after using this method to process 4000 vibration signal samples generated by four different vibration events, namely, digging, walking, vehicles passing, and damaging, the recognition rates of vibration events are over 90%. The experimental results prove that this method can automatically make an effective feature selection and greatly improve the classification accuracy of vibrational events in distributed optical fiber sensing systems.

Bearings Fault Diagnosis Based on Convolutional Neural Networks with 2-D Representation of Vibration Signals as Input

Directory of Open Access Journals (Sweden)

Zhang Wei

2017-01-01

Full Text Available Periodic vibration signals captured by the accelerometers carry rich information for bearing fault diagnosis. Existing methods mostly rely on hand-crafted time-consuming preprocessing of data to acquire suitable features. In this paper, we use an easy and effective method to transform the 1-D temporal vibration signal into a 2-D image. With the signal image, convolutional Neural Network (CNN is used to train the raw vibration data. As powerful feature extractor and classifier for image recognition, CNN can learn to acquire features most suitable for the classification task by being trained. With the image format of vibration signals, the neuron in fully-connected layer of CNN can see farther and capture the periodic feature of signals. According to the results of the experiments, when fed in enough training samples, the proposed method outperforms other common methods. The proposed method can also be applied to solve intelligent diagnosis problems of other machine systems.

News media old and new

DEFF Research Database (Denmark)

Schrøder, Kim Christian

2014-01-01

This article presents and discusses three different approaches to the exploration of the cross-media challenges facing news audiences, as they seek access to, navigate in and make sense of the multitude of news sources across print, broadcasting, online and mobile media platforms. From a modernized...... uses and gratifications perspective, based on the notion of “worthwhileness” as the determinant of people's everyday selections from the “supermarket of news”, the article first reports from a longitudinal survey study in Denmark in which the author's foundational mapping of cross-media news...... consumption in pre-mobile 2008 is compared with replicating mappings carried out in 2011 and 2012, in a collaborative project between academics and news publishers. The analytical interest here focuses on the fluctuations between traditional news media and the surging digital news outlets of the internet...

Something fishy? News media presentation of complex health issues related to fish consumption guidelines.

Science.gov (United States)

Greiner, Amelia; Clegg Smith, Katherine; Guallar, Eliseo

2010-11-01

The news media are an important source of dietary information. Understanding news content, particularly the portrayal of risks and benefits of certain foods, is relevant for effective public health communication. Fish consumption may reduce risk for CVD and aid neonatal development, but recent work shows public confusion about the benefits of fish, challenged by the evidence of mercury and other contaminants in fish. We present an analysis of the messages about fish in US news media over 15 years, identifying trends in coverage and highlighting implications of current messaging. We conducted a descriptive text analysis and coded for manifest content: locality of focus, story frame, reference to studies, inclusion of government guidelines and portrayal of uncertainty. We identified chronological patterns and analysed the data for statistically significant relationships between media source and content. News stories were selected from five daily newspapers and five television networks (1993-2007). We analysed 310 health-related news stories on fish. Risk messages outweighed benefit messages four to one, and health benefits only became prominent after 2002. No difference existed in coverage topic by news source. Fish consumption has increasingly become a national issue. With the bulk of messages about fish consumption focused on risk, the benefits may be lost to consumers. This gap creates a need for public health to work with news media to more effectively communicate benefits and risks around fish consumption and health and to consider options for communicating tailored information where it can be more readily utilised.

Googling the news

DEFF Research Database (Denmark)

Ørmen, Jacob

2016-01-01

Search engines provide a window into the changing association between websites and keywords across cultures and countries and over time. As such, they offer journalism and news researchers an opportunity to study how search engines, in this case Google, mediate news events and stories online...

APPLICATION OF CONVOLUTIONAL NEURAL NETWORK IN CLASSIFICATION OF HIGH RESOLUTION AGRICULTURAL REMOTE SENSING IMAGES

Directory of Open Access Journals (Sweden)

C. Yao

2017-09-01

Full Text Available 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.

They Came, They Liked, They Commented: Social Influence on Facebook News Channels.

Science.gov (United States)

Winter, Stephan; Brückner, Caroline; Krämer, Nicole C

2015-08-01

Due to the increasing importance of social networking sites as sources of information, news media organizations have set up Facebook channels in which they publish news stories or links to articles. This research investigated how journalistic texts are perceived in this new context and how reactions of other users change the influence of the main articles. In an online experiment (N=197), a Facebook posting of a reputable news site and the corresponding article were shown. The type of user comments and the number of likes were systematically varied. Negative comments diminished the persuasive influence of the article, while there were no strengthening effects of positive comments. When readers perceived the topic as personally relevant, comments including relevant arguments were more influential than comments with subjective opinions, which can be explained by higher levels of elaboration. However, against expectations of bandwagon perceptions, a high number of likes did not lead to conformity effects, which suggests that exemplifying comments are more influential than statistical user representations. Results are discussed with regard to effects of news media content and the mechanisms of social influence in Web 2.0.

Outsourcing the news? An empirical assessment of the role of sources and news agencies in the contemporary news landscape

NARCIS (Netherlands)

Boumans, J.W.

2016-01-01

Journalists are increasingly accused of uncritically recycling subsidized material in the form of press releases and news agency copy. This practice has been labeled churnalism and is believed to compromise journalism’s autonomy and threaten news quality. While the context - rampant competition,

Automatic Detection of Fake News

OpenAIRE

Pérez-Rosas, Verónica; Kleinberg, Bennett; Lefevre, Alexandra; Mihalcea, Rada

2017-01-01

The proliferation of misleading information in everyday access media outlets such as social media feeds, news blogs, and online newspapers have made it challenging to identify trustworthy news sources, thus increasing the need for computational tools able to provide insights into the reliability of online content. In this paper, we focus on the automatic identification of fake content in online news. Our contribution is twofold. First, we introduce two novel datasets for the task of fake news...

The Stock Market's Reaction to Unemployment News: Why Bad News is Usually Good for Stocks

OpenAIRE

John H. Boyd; Ravi Jagannathan; Jian Hu

2001-01-01

We find that on average an announcement of rising unemployment is 'good news' for stocks during economic expansions and 'bad news' during economic contractions. Thus stock prices usually increase on news of rising unemployment, since the economy is usually in an expansion phase. We provide an explanation for this phenomenon. Unemployment news bundles two primitive types of information relevant for valuing stocks: information about future interest rates and future corporate earnings and divide...

When Sound Bites Become the News: a Case Study on Manufacturing News in Croatia

Directory of Open Access Journals (Sweden)

Tena Perišin

2017-06-01

Full Text Available Media environment is rapidly changing and facing a widespread crisis in journalism. It is followed by the decline of audience trust and increasing market pressures. The main goal is to win the audience’s attention, very often by creating drama and producing ‘conflict’. The news is not based on something that really happened and that is relevant, but it is more often manufactured or artificially produced. In this case study we explore the curious life cycle of a sound bite from a passing remark by the then Croatian Prime Minister Zoran Milanović’s to the headlines, discussions and extensive reports which developed over the course of several days. This example shows how news could be manufactured and content blurred when it is built around a fragment without providing the context, in this case a political quote. For several days, politicians, experts, war veterans, but also ordinary citizens were involved in the manufactured news story without making a reference to the context. Consequently, the democratic debate was avoided. Drawing on a discussion of news fragmentation as isolation from context, we show that in this case, news values (what news is are increasingly blurred, preventing the news from becoming the source of information and discussion of the country’s key issues.

Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer

OpenAIRE

Zagoruyko, Sergey; Komodakis, Nikos

2016-01-01

Attention plays a critical role in human visual experience. Furthermore, it has recently been demonstrated that attention can also play an important role in the context of applying artificial neural networks to a variety of tasks from fields such as computer vision and NLP. In this work we show that, by properly defining attention for convolutional neural networks, we can actually use this type of information in order to significantly improve the performance of a student CNN network by forcin...

A Study of a Sample of Facebook Users Finds They Do Not Seek Political News through Facebook But Are Exposed to Political News through This Medium

Directory of Open Access Journals (Sweden)

Elizabeth Margaret Stovold

2018-03-01

Full Text Available A Review of: Schaferm, S., Sulflow, M., & Muller, P. (2017. The special taste of snack news: an application of niche theory to understand the appeal of Facebook as a source for political news. First Monday, 22(4-3. http://dx.doi.org/10.5210/fm.v22i4.7431 Abstract Objective – To investigate Facebook as a source of exposure to political news stories and to compare the reasons for using Facebook as a news source and the gratifications obtained, compared with other news sources. Design – Survey questionnaire. Setting – Facebook. Subjects – 422 German Facebook users. Methods – An online survey was developed to investigate the use of Facebook as a news source compared with other sources. Specific research questions were informed by the ‘theory of niche’ (Dimmick, 2003 which examines the coexistence and competition between different media outlets by examining the breadth, overlap and superiority of one platform over another. The survey was distributed using a ‘snowball’ technique between July and August 2015. The survey was shared by 52 student research assistants on their Facebook profiles. They asked their friends to complete the survey and share it with their own networks. Main results – The mean (M age of the 422 respondents was 23.5 years (SD=8.25. The majority were female (61% with a high school degree (89%. TV news and news websites were the most frequently used sources of political news. Facebook ranked third, ahead of newspapers, search engines, magazines, email provider websites, and Twitter. The mean score for the importance of Facebook as a news sources was 2.46 (SD=1.13 on a scale of 1 to 5, where 1 is low and 5 is high. This fell in the middle of the range when compared with the top ranked source assessed by importance (TV news, M 4.40, SD=0.88 and the lowest (email providers, M 1.92, SD=0.97. Users rarely visited Facebook with the purpose of finding news (M 1.59, SD=0.73. However, they estimated around 24% of the

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

Science.gov (United States)

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.

7 CFR 28.904 - Market news.

Science.gov (United States)

2010-01-01

... 7 Agriculture 2 2010-01-01 2010-01-01 false Market news. 28.904 Section 28.904 Agriculture..., TESTING, AND STANDARDS Cotton Classification and Market News Service for Producers Classification and Market News Services § 28.904 Market news. The Director shall cause to be distributed to producers of...

Diverse artikelen in Gay Amsterdam News

NARCIS (Netherlands)

Hekma, G.

2002-01-01

Vernedering, in: Gay Amsterdam News 125 (jan 2002), pp. 20-21; Webseks, zaad, zweetseks, in: Gay Amsterdam News 126 (feb 2002), pp. 30-31; Wurgseks, in: Gay Amsterdam News 127 (mrt 2002), pp. 30-31; Wijnandus Johannes Sengers (1927-2002), in: Gay Amsterdam News 133 (sept 2002), pp. 49.

Fake News

OpenAIRE

Grunewald, Andreas; Kräkel, Matthias

2017-01-01

In the last decade, social media and the Internet have amplified the possibility to spread false information, a.k.a. fake news, which has become a serious threat to the credibility of politicians, organizations, and other decision makers. This paper proposes a framework for investigating the incentives to strategically spread fake news under different institutional configurations and payoff structures. In particular, we show under what conditions institutions that foster transparency in the m...

The New News Media: Democratic implications of undergraduate education and news consumption over social and traditional media

OpenAIRE

Miller, Jennifer

2013-01-01

Communication students at Simon Fraser University were surveyed and interviewed to deduce perceptions and behaviour of news consumption over social and traditional media. Both social media and traditional media are used to consume news with traditional media acting as the primary news source and as more accessible and reliable than social media. News stories considered important or having various perspectives were verified the most, especially world news. Extent of accessibility of sources an...

Indonesian News Harvester and Recommender System

Directory of Open Access Journals (Sweden)

Adi Wibowo

2017-09-01

Full Text Available To provide convenience for the user that frequently read the news, a system to gather, classify, and provide news from several news websites in one place was needed. This system utilized a recommender system to provide only relevant news to the user. This research proposed a system architecture that used vector space model, and Rocchio relevance feedback to provide specific news recommendation to user’s feedback. The results are that the proposed system architecture can achieve the goal by using five levels of feedback from the user. However, the time needed to gather news is increasing exponentially in line with the number of terms gathered from articles.

An effective convolutional neural network model for Chinese sentiment analysis

Science.gov (United States)

Zhang, Yu; Chen, Mengdong; Liu, Lianzhong; Wang, Yadong

2017-06-01

Nowadays microblog is getting more and more popular. People are increasingly accustomed to expressing their opinions on Twitter, Facebook and Sina Weibo. Sentiment analysis of microblog has received significant attention, both in academia and in industry. So far, Chinese microblog exploration still needs lots of further work. In recent years CNN has also been used to deal with NLP tasks, and already achieved good results. However, these methods ignore the effective use of a large number of existing sentimental resources. For this purpose, we propose a Lexicon-based Sentiment Convolutional Neural Networks (LSCNN) model focus on Weibo's sentiment analysis, which combines two CNNs, trained individually base on sentiment features and word embedding, at the fully connected hidden layer. The experimental results show that our model outperforms the CNN model only with word embedding features on microblog sentiment analysis task.

The Usefulness of a News Media Literacy Measure in Evaluating a News Literacy Curriculum

Science.gov (United States)

Maksl, Adam; Craft, Stephanie; Ashley, Seth; Miller, Dean

2017-01-01

A survey of college students showed those who had taken a news literacy course had significantly higher levels of news media literacy, greater knowledge of current events, and higher motivation to consume news, compared with students who had not taken the course. The effect of taking the course did not diminish over time. Results validate the News…

The shifting cross-media news landscape

DEFF Research Database (Denmark)

Schrøder, Kim Christian; Steeg Larsen, Bent

2010-01-01

and lifestyles. Theoretically the study is anchored in Habermas’s notion of the public sphere, and its recent reconceptualizations in theories of ‘cultural citizenship’, 'civic agency' and 'public connection'. The project operationalizes these theories through the concept of users' perceived “worthwhileness......The article offers new insights for democracy and for news producers by mapping the use and users of today’s cross-media news landscape, as the everyday consumption of news across the range of available news media and formats is shifting as a result of transformations of technology, culture......” of news media, a user-anchored concept which incorporates the different functionalities of the situational cross-media use of news by citizen/consumers in everyday life. Empirically the article presents the findings of a large-scale survey that traces the imminent challenges facing players in the news...

Navigating Cross-Media News Use : Media repertoires and the value of news in everyday life.

NARCIS (Netherlands)

Swart, Joelle; Peters, Chris; Broersma, Marcel

2017-01-01

The current news media landscape is characterized by an abundance of digital outlets and increased opportunities for users to navigate news themselves. Yet, it is still unclear how people negotiate this fluctuating environment to decide which news media to select or ignore, how they assemble

News Sources, Gender and Majority-Minority in Danish TV News Programs

DEFF Research Database (Denmark)

Fiig, Christina

In this paper, I am going to identify some central methodological challenges in relation to a project on TV-news and news sources in a perspective of intersectionality and to sketch a research design. In what follows, I will present some methodological issues and research designs of previous...

Convolutional neural network features based change detection in satellite images

Science.gov (United States)

Mohammed El Amin, Arabi; Liu, Qingjie; Wang, Yunhong

2016-07-01

With the popular use of high resolution remote sensing (HRRS) satellite images, a huge research efforts have been placed on change detection (CD) problem. An effective feature selection method can significantly boost the final result. While hand-designed features have proven difficulties to design features that effectively capture high and mid-level representations, the recent developments in machine learning (Deep Learning) omit this problem by learning hierarchical representation in an unsupervised manner directly from data without human intervention. In this letter, we propose approaching the change detection problem from a feature learning perspective. A novel deep Convolutional Neural Networks (CNN) features based HR satellite images change detection method is proposed. The main guideline is to produce a change detection map directly from two images using a pretrained CNN. This method can omit the limited performance of hand-crafted features. Firstly, CNN features are extracted through different convolutional layers. Then, a concatenation step is evaluated after an normalization step, resulting in a unique higher dimensional feature map. Finally, a change map was computed using pixel-wise Euclidean distance. Our method has been validated on real bitemporal HRRS satellite images according to qualitative and quantitative analyses. The results obtained confirm the interest of the proposed method.

Understanding the Convolutional Neural Networks with Gradient Descent and Backpropagation

Science.gov (United States)

Zhou, XueFei

2018-04-01

With the development of computer technology, the applications of machine learning are more and more extensive. And machine learning is providing endless opportunities to develop new applications. One of those applications is image recognition by using Convolutional Neural Networks (CNNs). CNN is one of the most common algorithms in image recognition. It is significant to understand its theory and structure for every scholar who is interested in this field. CNN is mainly used in computer identification, especially in voice, text recognition and other aspects of the application. It utilizes hierarchical structure with different layers to accelerate computing speed. In addition, the greatest features of CNNs are the weight sharing and dimension reduction. And all of these consolidate the high effectiveness and efficiency of CNNs with idea computing speed and error rate. With the help of other learning altruisms, CNNs could be used in several scenarios for machine learning, especially for deep learning. Based on the general introduction to the background and the core solution CNN, this paper is going to focus on summarizing how Gradient Descent and Backpropagation work, and how they contribute to the high performances of CNNs. Also, some practical applications will be discussed in the following parts. The last section exhibits the conclusion and some perspectives of future work.

Multi-Task Convolutional Neural Network for Pose-Invariant Face Recognition

Science.gov (United States)

Yin, Xi; Liu, Xiaoming

2018-02-01

This paper explores multi-task learning (MTL) for face recognition. We answer the questions of how and why MTL can improve the face recognition performance. First, we propose a multi-task Convolutional Neural Network (CNN) for face recognition where identity classification is the main task and pose, illumination, and expression estimations are the side tasks. Second, we develop a dynamic-weighting scheme to automatically assign the loss weight to each side task, which is a crucial problem in MTL. Third, we propose a pose-directed multi-task CNN by grouping different poses to learn pose-specific identity features, simultaneously across all poses. Last but not least, we propose an energy-based weight analysis method to explore how CNN-based MTL works. We observe that the side tasks serve as regularizations to disentangle the variations from the learnt identity features. Extensive experiments on the entire Multi-PIE dataset demonstrate the effectiveness of the proposed approach. To the best of our knowledge, this is the first work using all data in Multi-PIE for face recognition. Our approach is also applicable to in-the-wild datasets for pose-invariant face recognition and achieves comparable or better performance than state of the art on LFW, CFP, and IJB-A datasets.

Strabismus Recognition Using Eye-Tracking Data and Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Zenghai Chen

2018-01-01

Full Text Available Strabismus is one of the most common vision diseases that would cause amblyopia and even permanent vision loss. Timely diagnosis is crucial for well treating strabismus. In contrast to manual diagnosis, automatic recognition can significantly reduce labor cost and increase diagnosis efficiency. In this paper, we propose to recognize strabismus using eye-tracking data and convolutional neural networks. In particular, an eye tracker is first exploited to record a subject’s eye movements. A gaze deviation (GaDe image is then proposed to characterize the subject’s eye-tracking data according to the accuracies of gaze points. The GaDe image is fed to a convolutional neural network (CNN that has been trained on a large image database called ImageNet. The outputs of the full connection layers of the CNN are used as the GaDe image’s features for strabismus recognition. A dataset containing eye-tracking data of both strabismic subjects and normal subjects is established for experiments. Experimental results demonstrate that the natural image features can be well transferred to represent eye-tracking data, and strabismus can be effectively recognized by our proposed method.

Yarn-dyed fabric defect classification based on convolutional neural network

Science.gov (United States)

Jing, Junfeng; Dong, Amei; Li, Pengfei; Zhang, Kaibing

2017-09-01

Considering that manual inspection of the yarn-dyed fabric can be time consuming and inefficient, we propose a yarn-dyed fabric defect classification method by using a convolutional neural network (CNN) based on a modified AlexNet. CNN shows powerful ability in performing feature extraction and fusion by simulating the learning mechanism of human brain. The local response normalization layers in AlexNet are replaced by the batch normalization layers, which can enhance both the computational efficiency and classification accuracy. In the training process of the network, the characteristics of the defect are extracted step by step and the essential features of the image can be obtained from the fusion of the edge details with several convolution operations. Then the max-pooling layers, the dropout layers, and the fully connected layers are employed in the classification model to reduce the computation cost and extract more precise features of the defective fabric. Finally, the results of the defect classification are predicted by the softmax function. The experimental results show promising performance with an acceptable average classification rate and strong robustness on yarn-dyed fabric defect classification.

Political News and Political Consciousness

Science.gov (United States)

Schertges, Claudia

2007-01-01

This article deals with mass media in modern democratic societies, using the example of Israeli news reports in German television (TV) news. Central to this interest are processes of mediating politics: political socialisation and education; that is to say, empowering citizens via TV news to participate in democratic processes. The article…

Cross-validation of the factorial structure of the Neighborhood Environment Walkability Scale (NEWS and its abbreviated form (NEWS-A

Directory of Open Access Journals (Sweden)

Cerin Ester

2009-06-01

Full Text Available Abstract Background The Neighborhood Environment Walkability Scale (NEWS and its abbreviated form (NEWS-A assess perceived environmental attributes believed to influence physical activity. A multilevel confirmatory factor analysis (MCFA conducted on a sample from Seattle, WA showed that, at the respondent level, the factor-analyzable items of the NEWS and NEWS-A measured 11 and 10 constructs of perceived neighborhood environment, respectively. At the census blockgroup (used by the US Census Bureau as a subunit of census tracts level, the MCFA yielded five factors for both NEWS and NEWS-A. The aim of this study was to cross-validate the individual- and blockgroup-level measurement models of the NEWS and NEWS-A in a geographical location and population different from those used in the original validation study. Methods A sample of 912 adults was recruited from 16 selected neighborhoods (116 census blockgroups in the Baltimore, MD region. Neighborhoods were stratified according to their socio-economic status and transport-related walkability level measured using Geographic Information Systems. Participants self-completed the NEWS. MCFA was used to cross-validate the individual- and blockgroup-level measurement models of the NEWS and NEWS-A. Results The data provided sufficient support for the factorial validity of the original individual-level measurement models, which consisted of 11 (NEWS and 10 (NEWS-A correlated factors. The original blockgroup-level measurement model of the NEWS and NEWS-A showed poor fit to the data and required substantial modifications. These included the combining of aspects of building aesthetics with safety from crime into one factor; the separation of natural aesthetics and building aesthetics into two factors; and for the NEWS-A, the separation of presence of sidewalks/walking routes from other infrastructure for walking. Conclusion This study provided support for the generalizability of the individual

Finger vein recognition based on convolutional neural network

Directory of Open Access Journals (Sweden)

Meng Gesi

2017-01-01

Full Text Available Biometric Authentication Technology has been widely used in this information age. As one of the most important technology of authentication, finger vein recognition attracts our attention because of its high security, reliable accuracy and excellent performance. However, the current finger vein recognition system is difficult to be applied widely because its complicated image pre-processing and not representative feature vectors. To solve this problem, a finger vein recognition method based on the convolution neural network (CNN is proposed in the paper. The image samples are directly input into the CNN model to extract its feature vector so that we can make authentication by comparing the Euclidean distance between these vectors. Finally, the Deep Learning Framework Caffe is adopted to verify this method. The result shows that there are great improvements in both speed and accuracy rate compared to the previous research. And the model has nice robustness in illumination and rotation.

Climate News Across Media Platforms

DEFF Research Database (Denmark)

Eskjær, Mikkel Fugl

2015-01-01

In a changing media landscape marked by technological, institutional and cultural convergence, comparative and cross-media content analysis represents a valuable analytical tool in mapping the diverse channels of climate change communication. This paper presents a comparative study of climate...... quantitative and qualitative content analysis the paper documents and explores the extent and character of climate change news across different media platforms. The study aims at contributing to the on-going assessment of how news media are addressing climate change at a time when old and new media...... change news on five different media platforms: newspapers, television, radio, web-news and mobile news. It investigates the themes and actors represented in public climate change communication as well as the diverse possibilities of participating in public debates and information sharing. By combining...

CERN television news

CERN Multimedia

2002-01-01

CERN events brought right to your desktop by the new video bulletin.   CERN now has its very own news broadcast , or rather 'webcast', with a host of special reports and even a star presenter. From today onwards, just go to the Bulletin's web page, click on the 'video news' link and sit back and enjoy the latest news about CERN, presented in images by Wendy Korda. The ten-minute newscast in both French and English, the Organization's two official languages, presents interviews, pictures of experiments and computer-generated graphics, bringing you right up to date with some of the Laboratory's latest stories. The show concludes with a selection of the best snapshots taken by the CERN Photo Lab. So every one or two months CERN's Audio-Video Service (ETT/DH) will be putting together a video news report that you can watch on your own desktop computer. Daniel Boileau, Patrick Gilbert de Vautibault and Jacques Fichet, the Service's three technicians, came up with the idea of producing this regular feat...

Training Deep Convolutional Neural Networks with Resistive Cross-Point Devices

Directory of Open Access Journals (Sweden)

Tayfun Gokmen

2017-10-01

Full Text Available In a previous work we have detailed the requirements for obtaining maximal deep learning performance benefit by implementing fully connected deep neural networks (DNN in the form of arrays of resistive devices. Here we extend the concept of Resistive Processing Unit (RPU devices to convolutional neural networks (CNNs. We show how to map the convolutional layers to fully connected RPU arrays such that the parallelism of the hardware can be fully utilized in all three cycles of the backpropagation algorithm. We find that the noise and bound limitations imposed by the analog nature of the computations performed on the arrays significantly affect the training accuracy of the CNNs. Noise and bound management techniques are presented that mitigate these problems without introducing any additional complexity in the analog circuits and that can be addressed by the digital circuits. In addition, we discuss digitally programmable update management and device variability reduction techniques that can be used selectively for some of the layers in a CNN. We show that a combination of all those techniques enables a successful application of the RPU concept for training CNNs. The techniques discussed here are more general and can be applied beyond CNN architectures and therefore enables applicability of the RPU approach to a large class of neural network architectures.

Co-trained convolutional neural networks for automated detection of prostate cancer in multi-parametric MRI.

Science.gov (United States)

Yang, Xin; Liu, Chaoyue; Wang, Zhiwei; Yang, Jun; Min, Hung Le; Wang, Liang; Cheng, Kwang-Ting Tim

2017-12-01

Multi-parameter magnetic resonance imaging (mp-MRI) is increasingly popular for prostate cancer (PCa) detection and diagnosis. However, interpreting mp-MRI data which typically contains multiple unregistered 3D sequences, e.g. apparent diffusion coefficient (ADC) and T2-weighted (T2w) images, is time-consuming and demands special expertise, limiting its usage for large-scale PCa screening. Therefore, solutions to computer-aided detection of PCa in mp-MRI images are highly desirable. Most recent advances in automated methods for PCa detection employ a handcrafted feature based two-stage classification flow, i.e. voxel-level classification followed by a region-level classification. This work presents an automated PCa detection system which can concurrently identify the presence of PCa in an image and localize lesions based on deep convolutional neural network (CNN) features and a single-stage SVM classifier. Specifically, the developed co-trained CNNs consist of two parallel convolutional networks for ADC and T2w images respectively. Each network is trained using images of a single modality in a weakly-supervised manner by providing a set of prostate images with image-level labels indicating only the presence of PCa without priors of lesions' locations. Discriminative visual patterns of lesions can be learned effectively from clutters of prostate and surrounding tissues. A cancer response map with each pixel indicating the likelihood to be cancerous is explicitly generated at the last convolutional layer of the network for each modality. A new back-propagated error E is defined to enforce both optimized classification results and consistent cancer response maps for different modalities, which help capture highly representative PCa-relevant features during the CNN feature learning process. The CNN features of each modality are concatenated and fed into a SVM classifier. For images which are classified to contain cancers, non-maximum suppression and adaptive

The interaction of financial news between mass media and new media: Evidence from news on Chinese stock market

Science.gov (United States)

Zhang, Yongjie; Zhang, Zuochao; Liu, Lanbiao; Shen, Dehua

2017-11-01

In this paper, we investigate both the contemporaneous and the lead-lag relationships between the mass media news and the new media news of the financial news on the constitute stocks of the CSI 300. The empirical results show that: (1) there exists a strong correlation between these two types of news; (2) the granger causality direction from new media news to mass media news is increasingly obvious, while the reverse direction has a downward trend; (3) new media is playing a increasingly important role in the stock market and exhibits a trend to substitutes the mass media.

Online News Sites and Journalism 2.0: Reader Comments on Al Jazeera Arabic

Directory of Open Access Journals (Sweden)

Muhammad M. Abdul-Mageed

2008-12-01

Full Text Available The current paper investigates reader commenting on news sites as one facet of journalism 2.0. Specifically, the themes, frequency, and regional coverage of readers’ comments—and in general, their activity levels and distribution—are considered, with a goal to increase knowledge of convergent media and computer-mediated communication (CMC, as well as shed light on the interactivity strategies adopted by influential news producers. The corpus is collected from the Arabic news site of the controversial Middle East-based, bilingual network Al Jazeera. Reader commenting was found to be a regular occurrence on the site but distributed unevenly across stories. The stories focused mostly on themes related to military and political violence, politics, and foreign relations, and covered events related to the Arab world more than other regions. Also, patterns of commenting varied according to day of the week and position of the story on the web page. Overall, these findings suggest that citizen journalism—journalism is performed by lay persons—on Al Jazeera tends to be shaped by the coverage and layout of the news site. Moreover, citizen participation in online news sites such as Al Jazeera is still far from ideal, in that commenters are given neither the access nor the facilitation to use modalities other than written text. These limitations are critiqued in light of contemporary discourses about media convergence and journalism 2.0.

Market News Price Dataset

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — Real-time price data collected by the Boston Market News Reporter. The NOAA Fisheries' "Fishery Market News" began operations in New York City on February 14, 1938....

Using convolutional neural networks to estimate time-of-flight from PET detector waveforms

Science.gov (United States)

Berg, Eric; Cherry, Simon R.

2018-01-01

Although there have been impressive strides in detector development for time-of-flight positron emission tomography, most detectors still make use of simple signal processing methods to extract the time-of-flight information from the detector signals. In most cases, the timing pick-off for each waveform is computed using leading edge discrimination or constant fraction discrimination, as these were historically easily implemented with analog pulse processing electronics. However, now with the availability of fast waveform digitizers, there is opportunity to make use of more of the timing information contained in the coincident detector waveforms with advanced signal processing techniques. Here we describe the application of deep convolutional neural networks (CNNs), a type of machine learning, to estimate time-of-flight directly from the pair of digitized detector waveforms for a coincident event. One of the key features of this approach is the simplicity in obtaining ground-truth-labeled data needed to train the CNN: the true time-of-flight is determined from the difference in path length between the positron emission and each of the coincident detectors, which can be easily controlled experimentally. The experimental setup used here made use of two photomultiplier tube-based scintillation detectors, and a point source, stepped in 5 mm increments over a 15 cm range between the two detectors. The detector waveforms were digitized at 10 GS s-1 using a bench-top oscilloscope. The results shown here demonstrate that CNN-based time-of-flight estimation improves timing resolution by 20% compared to leading edge discrimination (231 ps versus 185 ps), and 23% compared to constant fraction discrimination (242 ps versus 185 ps). By comparing several different CNN architectures, we also showed that CNN depth (number of convolutional and fully connected layers) had the largest impact on timing resolution, while the exact network parameters, such as convolutional

Age, Gender, and Fine-Grained Ethnicity Prediction using Convolutional Neural Networks for the East Asian Face Dataset

Energy Technology Data Exchange (ETDEWEB)

Srinivas, Nisha [ORNL; Rose, Derek C [ORNL; Bolme, David S [ORNL; Mahalingam, Gayathri [ORNL; Atwal, Harleen [ORNL; Ricanek, Karl [ORNL

2017-01-01

This paper examines the difficulty associated with performing machine-based automatic demographic prediction on a sub-population of Asian faces. We introduce the Wild East Asian Face dataset (WEAFD), a new and unique dataset to the research community. This dataset consists primarily of labeled face images of individuals from East Asian countries, including Vietnam, Burma, Thailand, China, Korea, Japan, Indonesia, and Malaysia. East Asian turk annotators were uniquely used to judge the age and fine grain ethnicity attributes to reduce the impact of the other race effect and improve quality of annotations. We focus on predicting age, gender and fine-grained ethnicity of an individual by providing baseline results with a convolutional neural network (CNN). Finegrained ethnicity prediction refers to predicting ethnicity of an individual by country or sub-region (Chinese, Japanese, Korean, etc.) of the East Asian continent. Performance for two CNN architectures is presented, highlighting the difficulty of these tasks and showcasing potential design considerations that ease network optimization by promoting region based feature extraction.

The Changing Landscape of Science News

Science.gov (United States)

Riordon, James

2011-03-01

Social media are revolutionizing the ways that people communicate and the ways they get their news. Traditional news outlets are in decline, and no subject area is declining faster than science news. Every day there are fewer professional science journalists working in traditional media. On the other hand, ever greater numbers of scientists, science enthusiasts, and online journalists are turning to blogs, podcasts, eBooks, twitter feeds, and social media sites like Facebook and Tumbler to spread news about science. I will present an overview of the state of science journalism and speculate on the likely directions it seems to be heading. I will also offer some general guidelines to help scientists understand what makes a good science news story, as well as suggesting ways that they can get their work in the news.

News with an attitude: assessing the mechanisms underlying the effects of opinionated news

NARCIS (Netherlands)

Boukes, M.; Boomgaarden, H.G.; Moorman, M.; de Vreese, C.H.

2014-01-01

Opinionated news targets communities of likeminded viewers, relies on dramaturgical storytelling techniques, and shares characteristics with political satire. Accordingly, opinionated news should be understood as a specific form of political entertainment. We have investigated the mechanisms

The Use of Convolutional Neural Network in Relating Precipitation to Circulation

Science.gov (United States)

Pan, B.; Hsu, K. L.; AghaKouchak, A.; Sorooshian, S.

2017-12-01

Precipitation prediction in dynamical weather and climate models depends on 1) the predictability of pressure or geopotential height for the forecasting period and 2) the successive work of interpreting the pressure field in terms of precipitation events. The later task is represented as parameterization schemes in numerical models, where detailed computing inevitably blurs the hidden cause-and-effect relationship in precipitation generation. The "big data" provided by numerical simulation, reanalysis and observation networks requires better causation analysis for people to digest and realize their use. While classic synoptical analysis methods are very-often insufficient for spatially distributed high dimensional data, a Convolutional Neural Network(CNN) is developed here to directly relate precipitation with circulation. Case study carried over west coast United States during boreal winter showed that CNN can locate and capture key pressure zones of different structures to project precipitation spatial distribution with high accuracy across hourly to monthly scales. This direct connection between atmospheric circulation and precipitation offers a probe for attributing precipitation to the coverage, location, intensity and spatial structure of characteristic pressure zones, which can be used for model diagnosis and improvement.

Displaying fairness while delivering bad news: Testing the effectiveness of organizational bad news training in the layoff context.

Science.gov (United States)

Richter, Manuela; König, Cornelius J; Koppermann, Christopher; Schilling, Michael

2016-06-01

Although giving bad news at work is a stressful experience, managers are often underprepared for this challenging task. As a solution, we introduce organizational bad news training that integrates (a) principles of delivering bad news from the context of health care (i.e., bad news delivery component), and (b) principles of organizational justice theory (i.e., fairness component). We argue that both the formal and fair delivery of bad news at work can be enhanced with the help of training to mitigate distress both for the messenger and the recipient. We tested the effectiveness of training for the delivery of a layoff as a typical bad news event at work. In 2 studies, we compared the performance of a training group (receiving both components of training) with that of a control group (Study 1, Study 2) and a basics group (receiving the bad news delivery component only; Study 2) during a simulated dismissal notification meeting. In general, the results supported our hypotheses: Training improved the formal delivery of bad news and predicted indicators of procedural fairness during the conversation in both studies. In Study 2, we also considered layoff victims' negativity after the layoff and found that training significantly reduced negative responses. This relationship was fully mediated by layoff victims' fairness perceptions. Despite preparation, however, giving bad news remained a challenging task in both studies. In summary, we recommend that organizations provide managers with organizational bad news training in order to promote professional and fair bad news conversations at work. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Breaking bad news in cancer patients.

Science.gov (United States)

Konstantis, Apostolos; Exiara, Triada

2015-01-01

In a regional hospital, many patients are newly diagnosed with cancer. Breaking the bad news in these patients and their relatives is a tough task. Many doctors are not experienced in talking to patients about death or death-related diseases. In recent years, there have been great efforts to change the current situation. The aim of this study was to investigate the experience and education of medical personnel in breaking bad news in a secondary hospital. 59 doctors from General Hospital of Komotini, Greece were included in the study. All the doctors were in clinical specialties that treated cancer patients. A brief questionnaire was developed based on current guidelines such as Baile/SPIKES framework and the ABCDE mnemonic. Residents are involved in delivering bad news less frequently than specialists. Only 21 doctors (35.59%) had specific training on breaking bad news. 20 doctors (33.90%) were aware of the available techniques and protocols on breaking bad news. 47 doctors (79.66%) had a consistent plan for breaking bad news. 57 (96.61%) delivered bad news in a quiet place, 53 (89.83%) ensured no interruptions and enough time, 53 (89.83%) used simple words and 54 (91.53%) checked for understanding and did not rush through the news. 46 doctors (77.97%) allowed relatives to determine patient's knowledge about the disease. There were low rates of specific training in breaking bad news. However, the selected location, the physician's speech and their plan were according to current guidelines.

The Cognitive Information Effect of Televised News.

Science.gov (United States)

Lăzăroiu, George; Pera, Aurel; Ştefănescu-Mihăilă, Ramona O; Bratu, Sofia; Mircică, Nela

2017-01-01

The purpose of this review is to summarize the key findings which prove that the biased perceptions of viewers may provide an inaccurate image of the informational validity of televised news. The news may generate distorted recollections of what occurred in particular reported events if displayed routines influence viewers not to pay attention to the essential features of a narrative. Elaborating on Fiske and Hartley (2010), Zelizer (2010), and Gunter (2015), we indicate that the character of the news setting has altered and individuals' news consumption routines have changed in adapting to media advancements. The news may be undergone at various psychological stages by news publics. Televised news may transmit information undeviatingly to publics that may (not) be committed successfully to memory. Our paper shows that individuals' skills to handle information that is displayed in a linguistic configuration are influenced by their abilities in the utilization of certain symbol systems that are employed to represent notions and meanings. Televised news may shape what individuals grasp, influence their perceptions, convictions, and views regarding prevailing events and matters, and transmit knowledge and interpretation. If news stories can be jotted down in a linguistic style that sidesteps making needless processing demands and captivate news users by facilitating them to make connections with former knowledge, they may be more worthy of note and more edifying. We conclude that news narratives present a cognitive demanding task to individuals, displaying novel information regarding evolving events in a multifarious format. Broadcast news exhibits intricate contents, displaying configurations that employ excessively the cognitive abilities for information processing of viewers.

Assessing News Contagion in Finance

Directory of Open Access Journals (Sweden)

Paola Cerchiello

2018-02-01

Full Text Available The analysis of news in the financial context has gained a prominent interest in the last years. This is because of the possible predictive power of such content especially in terms of associated sentiment/mood. In this paper, we focus on a specific aspect of financial news analysis: how the covered topics modify according to space and time dimensions. To this purpose, we employ a modified version of topic model LDA, the so-called Structural Topic Model (STM, that takes into account covariates as well. Our aim is to study the possible evolution of topics extracted from two well known news archive—Reuters and Bloomberg—and to investigate a causal effect in the diffusion of the news by means of a Granger causality test. Our results show that both the temporal dynamics and the spatial differentiation matter in the news contagion.

From everyday communicative figurations to rigorous audience news repertoires: A mixed method approach to cross-media news consumption

Directory of Open Access Journals (Sweden)

Christian Kobbernagel

2016-06-01

Full Text Available In the last couple of decades there has been an unprecedented explosion of news media platforms and formats, as a succession of digital and social media have joined the ranks of legacy media. We live in a ‘hybrid media system’ (Chadwick, 2013, in which people build their cross-media news repertoires from the ensemble of old and new media available. This article presents an innovative mixed-method approach with considerable explanatory power to the exploration of patterns of news media consumption. This approach tailors Q-methodology in the direction of a qualitative study of news consumption, in which a card sorting exercise serves to translate the participants’ news media preferences into a form that enables the researcher to undertake a rigorous factor-analytical construction of their news consumption repertoires. This interpretive, factor-analytical procedure, which results in the building of six audience news repertoires in Denmark, also preserves the qualitative thickness of the participants’ verbal accounts of the communicative figurations of their day-in-the-life with the news media.

Tongue Images Classification Based on Constrained High Dispersal Network

Directory of Open Access Journals (Sweden)

Dan Meng

2017-01-01

Full Text Available Computer aided tongue diagnosis has a great potential to play important roles in traditional Chinese medicine (TCM. However, the majority of the existing tongue image analyses and classification methods are based on the low-level features, which may not provide a holistic view of the tongue. Inspired by deep convolutional neural network (CNN, we propose a novel feature extraction framework called constrained high dispersal neural networks (CHDNet to extract unbiased features and reduce human labor for tongue diagnosis in TCM. Previous CNN models have mostly focused on learning convolutional filters and adapting weights between them, but these models have two major issues: redundancy and insufficient capability in handling unbalanced sample distribution. We introduce high dispersal and local response normalization operation to address the issue of redundancy. We also add multiscale feature analysis to avoid the problem of sensitivity to deformation. Our proposed CHDNet learns high-level features and provides more classification information during training time, which may result in higher accuracy when predicting testing samples. We tested the proposed method on a set of 267 gastritis patients and a control group of 48 healthy volunteers. Test results show that CHDNet is a promising method in tongue image classification for the TCM study.

When Reporters get Hands-on with Robo-writing: Professionals Consider Automated Journalism’s Capabilities and Consequences

OpenAIRE

Thurman, Neil; Doerr, Konstantin; Kunert, Jessica

2017-01-01

The availability of data feeds, the demand for news on digital devices, and advances in algorithms are helping to make automated journalism more prevalent. This article extends the literature on the subject by analysing professional journalists’ experiences with, and opinions about, the technology. Uniquely, the participants were drawn from a range of news organizations—including the BBC, CNN, and Thomson Reuters—and had first-hand experience working with robo-writing software provided by one...

Terra News: sensationalism and fait-divers on the Internet

Directory of Open Access Journals (Sweden)

Carlos Golembiewski

2009-12-01

Full Text Available This article presents an analysis of the news program Jornal do Terra (Terra News shown on the Terra website. The study involved two aspects: forms of news presentations on TV, based on studies by Pedro Maciel, and criteria of news value, based on Mário Erbolatto’s view. In addition, we used Luis Arthur Ferraretto’s studies of the news formats used specifically on the radio. The objective of this work was to verify what kind of news is transmitted by the news program Terra News, and to compare it with the traditional news program we watch on TV. The study confirmed that Terra News utilizes conventional formats of news presentation and makes a selection of sensationalist news about fait-divers.

TERRA NEWS: Sensationalism and Fait-divers on the Internet

Directory of Open Access Journals (Sweden)

Carlos Golembiewski

2011-02-01

Full Text Available This article presents an analysis of the news program Jornal do Terra (Terra News shown on the Terra website. The study involved two aspects: forms of news presentations on TV, based on studies by Pedro Maciel, and criteria of news value, based on Mário Erbolatto’s view. In addition, we used Luis Arthur Ferraretto’s studies of the news formats used specifically on the radio. The objective of this work was to verify what kind of news is transmitted by the news program Terra News, and to compare it with the traditional news program we watch on TV. The study confirmed that Terra News utilizes conventional formats of news presentation and makes a selection of sensationalist news about fait-divers.

A Novel Fault Diagnosis Method for Rotating Machinery Based on a Convolutional Neural Network.

Science.gov (United States)

Guo, Sheng; Yang, Tao; Gao, Wei; Zhang, Chen

2018-05-04

Fault diagnosis is critical to ensure the safety and reliable operation of rotating machinery. Most methods used in fault diagnosis of rotating machinery extract a few feature values from vibration signals for fault diagnosis, which is a dimensionality reduction from the original signal and may omit some important fault messages in the original signal. Thus, a novel diagnosis method is proposed involving the use of a convolutional neural network (CNN) to directly classify the continuous wavelet transform scalogram (CWTS), which is a time-frequency domain transform of the original signal and can contain most of the information of the vibration signals. In this method, CWTS is formed by discomposing vibration signals of rotating machinery in different scales using wavelet transform. Then the CNN is trained to diagnose faults, with CWTS as the input. A series of experiments is conducted on the rotor experiment platform using this method. The results indicate that the proposed method can diagnose the faults accurately. To verify the universality of this method, the trained CNN was also used to perform fault diagnosis for another piece of rotor equipment, and a good result was achieved.

Press problem related to nuclear energy news reporting

International Nuclear Information System (INIS)

Arai, Mitsuo

2008-01-01

Since the event of Niigataken Chuetsu-oki Earthquake in 2007 and the subsequent press reports on damage of nuclear power station after it, a stance of media is being questioned. In order to clear this problem, basic organizational structure of the press related to nuclear energy news was analyzed. Local news department, social news department, science news department and economical news department involve in nuclear energy news the accordance with their own situations and concerns. This structure makes problem of nuclear energy news reporting complicated. Changing this system is required but very difficult. It is concluded that the press problem around nuclear energy news is strange. (author)

Research and Practice of the News Map Compilation Service

Science.gov (United States)

Zhao, T.; Liu, W.; Ma, W.

2018-04-01

Based on the needs of the news media on the map, this paper researches on the news map compilation service, conducts demand research on the service of compiling news maps, designs and compiles the public authority base map suitable for media publication, and constructs the news base map material library. It studies the compilation of domestic and international news maps with timeliness and strong pertinence and cross-regional characteristics, constructs the hot news thematic gallery and news map customization services, conducts research on types of news maps, establish closer liaison and cooperation methods with news media, and guides news media to use correct maps. Through the practice of the news map compilation service, this paper lists two cases of news map preparation services used by different media, compares and analyses cases, summarizes the research situation of news map compilation service, and at the same time puts forward outstanding problems and development suggestions in the service of news map compilation service.

RESEARCH AND PRACTICE OF THE NEWS MAP COMPILATION SERVICE

Directory of Open Access Journals (Sweden)

T. Zhao

2018-04-01

Full Text Available Based on the needs of the news media on the map, this paper researches on the news map compilation service, conducts demand research on the service of compiling news maps, designs and compiles the public authority base map suitable for media publication, and constructs the news base map material library. It studies the compilation of domestic and international news maps with timeliness and strong pertinence and cross-regional characteristics, constructs the hot news thematic gallery and news map customization services, conducts research on types of news maps, establish closer liaison and cooperation methods with news media, and guides news media to use correct maps. Through the practice of the news map compilation service, this paper lists two cases of news map preparation services used by different media, compares and analyses cases, summarizes the research situation of news map compilation service, and at the same time puts forward outstanding problems and development suggestions in the service of news map compilation service.

A Blind Angle? News Sources, Gender and Ethnicity in Danish TV News

DEFF Research Database (Denmark)

Fiig, Christina

The paper will present and discuss a framework for grasping some of the democratic consequences of biased TV news programs. In line with Jürgen Habermas, one can ask what consequences it has for a democratic public sphere that the national TV news landscape is biased in term of source diversity (...

Breaking News as Radicalisation

DEFF Research Database (Denmark)

Hartley, Jannie Møller

The aim of the paper is to make explicit how the different categories are applied in the online newsroom and thus how new categories can be seen as positioning strategies in the form of radicalisations of already existing categories. Thus field theory provides us with tools to analyse how online...... journalists are using the categorisations to create hierarchies within the journalistic field in order to position themselves as specialists in what Tuchman has called developing news, aiming and striving for what today is know as breaking news and the “exclusive scoop,” as the trademark of online journalism...... in a media environment where immediacy rules (Domingo 2008a). Following this research the primary focus of this paper is the category breaking news and Tuchmans developing news, but as they are all connected the analysis will also draw upon the other categories in Tuchmans typology. The theoretical framework...

The Cognitive Information Effect of Televised News

Directory of Open Access Journals (Sweden)

George Lăzăroiu

2017-07-01

Full Text Available The purpose of this review is to summarize the key findings which prove that the biased perceptions of viewers may provide an inaccurate image of the informational validity of televised news. The news may generate distorted recollections of what occurred in particular reported events if displayed routines influence viewers not to pay attention to the essential features of a narrative. Elaborating on Fiske and Hartley (2010, Zelizer (2010, and Gunter (2015, we indicate that the character of the news setting has altered and individuals’ news consumption routines have changed in adapting to media advancements. The news may be undergone at various psychological stages by news publics. Televised news may transmit information undeviatingly to publics that may (not be committed successfully to memory. Our paper shows that individuals’ skills to handle information that is displayed in a linguistic configuration are influenced by their abilities in the utilization of certain symbol systems that are employed to represent notions and meanings. Televised news may shape what individuals grasp, influence their perceptions, convictions, and views regarding prevailing events and matters, and transmit knowledge and interpretation. If news stories can be jotted down in a linguistic style that sidesteps making needless processing demands and captivate news users by facilitating them to make connections with former knowledge, they may be more worthy of note and more edifying. We conclude that news narratives present a cognitive demanding task to individuals, displaying novel information regarding evolving events in a multifarious format. Broadcast news exhibits intricate contents, displaying configurations that employ excessively the cognitive abilities for information processing of viewers.

Hyperspectral Image Classification With Markov Random Fields and a Convolutional Neural Network

Science.gov (United States)

Cao, Xiangyong; Zhou, Feng; Xu, Lin; Meng, Deyu; Xu, Zongben; Paisley, John

2018-05-01

This paper presents a new supervised classification algorithm for remotely sensed hyperspectral image (HSI) which integrates spectral and spatial information in a unified Bayesian framework. First, we formulate the HSI classification problem from a Bayesian perspective. Then, we adopt a convolutional neural network (CNN) to learn the posterior class distributions using a patch-wise training strategy to better use the spatial information. Next, spatial information is further considered by placing a spatial smoothness prior on the labels. Finally, we iteratively update the CNN parameters using stochastic gradient decent (SGD) and update the class labels of all pixel vectors using an alpha-expansion min-cut-based algorithm. Compared with other state-of-the-art methods, the proposed classification method achieves better performance on one synthetic dataset and two benchmark HSI datasets in a number of experimental settings.

Breaking bad news in cancer patients

Directory of Open Access Journals (Sweden)

Apostolos Konstantis

2015-01-01

Full Text Available Objective: In a regional hospital, many patients are newly diagnosed with cancer. Breaking the bad news in these patients and their relatives is a tough task. Many doctors are not experienced in talking to patients about death or death-related diseases. In recent years, there have been great efforts to change the current situation. The aim of this study was to investigate the experience and education of medical personnel in breaking bad news in a secondary hospital. Materials and Methods: 59 doctors from General Hospital of Komotini, Greece were included in the study. All the doctors were in clinical specialties that treated cancer patients. A brief questionnaire was developed based on current guidelines such as Baile/SPIKES framework and the ABCDE mnemonic. Results: Residents are involved in delivering bad news less frequently than specialists. Only 21 doctors (35.59% had specific training on breaking bad news. 20 doctors (33.90% were aware of the available techniques and protocols on breaking bad news. 47 doctors (79.66% had a consistent plan for breaking bad news. 57 (96.61% delivered bad news in a quiet place, 53 (89.83% ensured no interruptions and enough time, 53 (89.83% used simple words and 54 (91.53% checked for understanding and did not rush through the news. 46 doctors (77.97% allowed relatives to determine patient′s knowledge about the disease. Conclusions: There were low rates of specific training in breaking bad news. However, the selected location, the physician′s speech and their plan were according to current guidelines.

Incorporating popularity in a personalized news recommender system

Directory of Open Access Journals (Sweden)

Nirmal Jonnalagedda

2016-06-01

Full Text Available Online news reading has become a widely popular way to read news articles from news sources around the globe. With the enormous amount of news articles available, users are easily overwhelmed by information of little interest to them. News recommender systems help users manage this flood by recommending articles based on user interests rather than presenting articles in order of their occurrence. We present our research on developing personalized news recommendation system with the help of a popular micro-blogging service, “Twitter.” News articles are ranked based on the popularity of the article identified from Twitter’s public timeline. In addition, users construct profiles based on their interests and news articles are also ranked based on their match to the user profile. By integrating these two approaches, we present a hybrid news recommendation model that recommends interesting news articles to the user based on their popularity as well as their relevance to the user profile.

A multi-scale convolutional neural network for phenotyping high-content cellular images.

Science.gov (United States)

Godinez, William J; Hossain, Imtiaz; Lazic, Stanley E; Davies, John W; Zhang, Xian

2017-07-01

Identifying phenotypes based on high-content cellular images is challenging. Conventional image analysis pipelines for phenotype identification comprise multiple independent steps, with each step requiring method customization and adjustment of multiple parameters. Here, we present an approach based on a multi-scale convolutional neural network (M-CNN) that classifies, in a single cohesive step, cellular images into phenotypes by using directly and solely the images' pixel intensity values. The only parameters in the approach are the weights of the neural network, which are automatically optimized based on training images. The approach requires no a priori knowledge or manual customization, and is applicable to single- or multi-channel images displaying single or multiple cells. We evaluated the classification performance of the approach on eight diverse benchmark datasets. The approach yielded overall a higher classification accuracy compared with state-of-the-art results, including those of other deep CNN architectures. In addition to using the network to simply obtain a yes-or-no prediction for a given phenotype, we use the probability outputs calculated by the network to quantitatively describe the phenotypes. This study shows that these probability values correlate with chemical treatment concentrations. This finding validates further our approach and enables chemical treatment potency estimation via CNNs. The network specifications and solver definitions are provided in Supplementary Software 1. william_jose.godinez_navarro@novartis.com or xian-1.zhang@novartis.com. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

The History of the Decline and Fall of News of the World : How Legitimacy Upholds the License to Operate

OpenAIRE

Elfgren, Kaj; Meharena, Million

2013-01-01

This thesis provides a biographical case study of the crisis faced by News of the World, after revelations of unethical journalistic practices within the newspaper. A scandal that rapidly moved up the organizational hierarchy and spread globally, affecting stakeholders amongst all its networks from local British communities to its parent company News Corporation and its main owners, the Murdoch family. Corporate social responsibility and stakeholder management is presented as a key factor in ...

Delivering bad news in emergency care medicine.

Science.gov (United States)

Maynard, Douglas W

2017-01-01

Forecasting is a strategy for delivering bad news and is compared to two other strategies, stalling and being blunt. Forecasting provides some warning that bad news is forthcoming without keeping the recipient in a state of indefinite suspense (stalling) or conveying the news abruptly (being blunt). Forecasting appears to be more effective than stalling or being blunt in helping a recipient to "realize" the bad news because it involves the deliverer and recipient in a particular social relation. The deliverer of bad news initiates the telling by giving an advance indication of the bad news to come; this allows the recipient to calculate the news in advance of its final presentation, when the deliverer confirms what the recipient has been led to anticipate. Thus, realization of bad news emerges from intimate collaboration, whereas stalling and being blunt require recipients to apprehend the news in a social vacuum. Exacerbating disruption to recipients' everyday world, stalling and being blunt increase the probability of misapprehension (denying, blaming, taking the situation as a joke, etc.) and thereby inhibit rather than facilitate realization. Particular attention is paid to the "perspective display sequence", a particular forecasting strategy that enables both confirming the recipient's perspective and using that perspective to affirm the clinical news. An example from acute or emergency medicine is examined at the close of the paper.

Esophagus segmentation in CT via 3D fully convolutional neural network and random walk.

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Fechter, Tobias; Adebahr, Sonja; Baltas, Dimos; Ben Ayed, Ismail; Desrosiers, Christian; Dolz, Jose

2017-12-01

Precise delineation of organs at risk is a crucial task in radiotherapy treatment planning for delivering high doses to the tumor while sparing healthy tissues. In recent years, automated segmentation methods have shown an increasingly high performance for the delineation of various anatomical structures. However, this task remains challenging for organs like the esophagus, which have a versatile shape and poor contrast to neighboring tissues. For human experts, segmenting the esophagus from CT images is a time-consuming and error-prone process. To tackle these issues, we propose a random walker approach driven by a 3D fully convolutional neural network (CNN) to automatically segment the esophagus from CT images. First, a soft probability map is generated by the CNN. Then, an active contour model (ACM) is fitted to the CNN soft probability map to get a first estimation of the esophagus location. The outputs of the CNN and ACM are then used in conjunction with a probability model based on CT Hounsfield (HU) values to drive the random walker. Training and evaluation were done on 50 CTs from two different datasets, with clinically used peer-reviewed esophagus contours. Results were assessed regarding spatial overlap and shape similarity. The esophagus contours generated by the proposed algorithm showed a mean Dice coefficient of 0.76 ± 0.11, an average symmetric square distance of 1.36 ± 0.90 mm, and an average Hausdorff distance of 11.68 ± 6.80, compared to the reference contours. These results translate to a very good agreement with reference contours and an increase in accuracy compared to existing methods. Furthermore, when considering the results reported in the literature for the publicly available Synapse dataset, our method outperformed all existing approaches, which suggests that the proposed method represents the current state-of-the-art for automatic esophagus segmentation. We show that a CNN can yield accurate estimations of esophagus location, and that

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

Science.gov (United States)

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.

A novel image block cryptosystem based on a spatiotemporal chaotic system and a chaotic neural network

International Nuclear Information System (INIS)

Wang Xing-Yuan; Bao Xue-Mei

2013-01-01

In this paper, we propose a novel block cryptographic scheme based on a spatiotemporal chaotic system and a chaotic neural network (CNN). The employed CNN comprises a 4-neuron layer called a chaotic neuron layer (CNL), where the spatiotemporal chaotic system participates in generating its weight matrix and other parameters. The spatiotemporal chaotic system used in our scheme is the typical coupled map lattice (CML), which can be easily implemented in parallel by hardware. A 160-bit-long binary sequence is used to generate the initial conditions of the CML. The decryption process is symmetric relative to the encryption process. Theoretical analysis and experimental results prove that the block cryptosystem is secure and practical, and suitable for image encryption. (general)

Convolutional neural networks for vibrational spectroscopic data analysis.

Science.gov (United States)

Acquarelli, Jacopo; van Laarhoven, Twan; Gerretzen, Jan; Tran, Thanh N; Buydens, Lutgarde M C; Marchiori, Elena

2017-02-15

In this work we show that convolutional neural networks (CNNs) can be efficiently used to classify vibrational spectroscopic data and identify important spectral regions. CNNs are the current state-of-the-art in image classification and speech recognition and can learn interpretable representations of the data. These characteristics make CNNs a good candidate for reducing the need for preprocessing and for highlighting important spectral regions, both of which are crucial steps in the analysis of vibrational spectroscopic data. Chemometric analysis of vibrational spectroscopic data often relies on preprocessing methods involving baseline correction, scatter correction and noise removal, which are applied to the spectra prior to model building. Preprocessing is a critical step because even in simple problems using 'reasonable' preprocessing methods may decrease the performance of the final model. We develop a new CNN based method and provide an accompanying publicly available software. It is based on a simple CNN architecture with a single convolutional layer (a so-called shallow CNN). Our method outperforms standard classification algorithms used in chemometrics (e.g. PLS) in terms of accuracy when applied to non-preprocessed test data (86% average accuracy compared to the 62% achieved by PLS), and it achieves better performance even on preprocessed test data (96% average accuracy compared to the 89% achieved by PLS). For interpretability purposes, our method includes a procedure for finding important spectral regions, thereby facilitating qualitative interpretation of results. Copyright © 2016 Elsevier B.V. All rights reserved.

Bad news: The influence of news coverage and Google searches on Gardasil adverse event reporting.

Science.gov (United States)

Faasse, Kate; Porsius, Jarry T; Faasse, Jonathan; Martin, Leslie R

2017-12-14

Human papilloma virus vaccines are a safe and effective tool for reducing HPV infections that can cause cervical cancer. However, uptake of these vaccines has been suboptimal, with many people holding negative beliefs and misconceptions. Such beliefs have been linked with the experience of unpleasant side effects following medical treatment, and media coverage may heighten such concerns. The present study sought to assess the influence of news coverage (number of news articles per month) on adverse event reporting in response to Gardasil vaccination in New Zealand over a 7.5-year period, and whether the influence of news coverage was mediated by internet search activity (Google search volumes). Multiple linear regression analyses and simple mediation analyses were used, controlling for year and number of vaccinations delivered. News coverage in the previous month, and Google search volumes in the same month, were significant predictors of adverse event reporting, after accounting for vaccination rates and year. Concurrent Google search volumes partially mediated the effect of prior news coverage. The results suggest that some of the adverse events reported were not related to the vaccination itself, but to news coverage and internet search volumes, which may have contributed to public concerns about potentially unpleasant or harmful outcomes. These findings have implications for the importance of psychological and social factors in adverse event reporting, and the role of the news media in disseminating health information. Copyright © 2017 Elsevier Ltd. All rights reserved.

Segment convolutional neural networks (Seg-CNNs) for classifying relations in clinical notes.

Science.gov (United States)

Luo, Yuan; Cheng, Yu; Uzuner, Özlem; Szolovits, Peter; Starren, Justin

2018-01-01

We propose Segment Convolutional Neural Networks (Seg-CNNs) for classifying relations from clinical notes. Seg-CNNs use only word-embedding features without manual feature engineering. Unlike typical CNN models, relations between 2 concepts are identified by simultaneously learning separate representations for text segments in a sentence: preceding, concept1, middle, concept2, and succeeding. We evaluate Seg-CNN on the i2b2/VA relation classification challenge dataset. We show that Seg-CNN achieves a state-of-the-art micro-average F-measure of 0.742 for overall evaluation, 0.686 for classifying medical problem-treatment relations, 0.820 for medical problem-test relations, and 0.702 for medical problem-medical problem relations. We demonstrate the benefits of learning segment-level representations. We show that medical domain word embeddings help improve relation classification. Seg-CNNs can be trained quickly for the i2b2/VA dataset on a graphics processing unit (GPU) platform. These results support the use of CNNs computed over segments of text for classifying medical relations, as they show state-of-the-art performance while requiring no manual feature engineering. © The Author 2017. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Visualizing news: obstacles, challenges, and solutions

NARCIS (Netherlands)

Dr. Piet Bakker; Gerard Smit; Yael de Haan; Laura Buijs

2013-01-01

Depicting news graphically is considered an apt way to deal with challenges of modern journalism: to disclose big data, and present news attractively, visually, and fast to grasp. This study delves into reported obstacles and challenges for the production of news visualizations. It focuses on the

A pre-trained convolutional neural network based method for thyroid nodule diagnosis.

Science.gov (United States)

Ma, Jinlian; Wu, Fa; Zhu, Jiang; Xu, Dong; Kong, Dexing

2017-01-01

In ultrasound images, most thyroid nodules are in heterogeneous appearances with various internal components and also have vague boundaries, so it is difficult for physicians to discriminate malignant thyroid nodules from benign ones. In this study, we propose a hybrid method for thyroid nodule diagnosis, which is a fusion of two pre-trained convolutional neural networks (CNNs) with different convolutional layers and fully-connected layers. Firstly, the two networks pre-trained with ImageNet database are separately trained. Secondly, we fuse feature maps learned by trained convolutional filters, pooling and normalization operations of the two CNNs. Finally, with the fused feature maps, a softmax classifier is used to diagnose thyroid nodules. The proposed method is validated on 15,000 ultrasound images collected from two local hospitals. Experiment results show that the proposed CNN based methods can accurately and effectively diagnose thyroid nodules. In addition, the fusion of the two CNN based models lead to significant performance improvement, with an accuracy of 83.02%±0.72%. These demonstrate the potential clinical applications of this method. Copyright © 2016 Elsevier B.V. All rights reserved.

Squeezeposenet: Image Based Pose Regression with Small Convolutional Neural Networks for Real Time Uas Navigation

Science.gov (United States)

Müller, M. S.; Urban, S.; Jutzi, B.

2017-08-01

The number of unmanned aerial vehicles (UAVs) is increasing since low-cost airborne systems are available for a wide range of users. The outdoor navigation of such vehicles is mostly based on global navigation satellite system (GNSS) methods to gain the vehicles trajectory. The drawback of satellite-based navigation are failures caused by occlusions and multi-path interferences. Beside this, local image-based solutions like Simultaneous Localization and Mapping (SLAM) and Visual Odometry (VO) can e.g. be used to support the GNSS solution by closing trajectory gaps but are computationally expensive. However, if the trajectory estimation is interrupted or not available a re-localization is mandatory. In this paper we will provide a novel method for a GNSS-free and fast image-based pose regression in a known area by utilizing a small convolutional neural network (CNN). With on-board processing in mind, we employ a lightweight CNN called SqueezeNet and use transfer learning to adapt the network to pose regression. Our experiments show promising results for GNSS-free and fast localization.

Qualification of contemporary French TV news

Directory of Open Access Journals (Sweden)

Sylvie LELEU-MERVIEL

2011-01-01

Full Text Available The News is one of the main programs on TV. In this regard, many investigations are concerned with TV News tackling the problem of the specificity of the audiovisual media. So, descriptive methods were designed to investigate conception and writing processes. Today, the emerging forms (non-stop news TV channels, no comment images TV, revitalize the information processing at television.Carrying on with the analyses of (Compte & Mouchon, 1990, this paper develops a method to examine the news productions in a relevant way. To do so, this article advocates the use of MCR, Méthode générale de Conceptualisation Relativisée based upon knowledge weaving theory (Mugur-Schächter, 2006. This study analysed a few TV news programs by using MCR tools. The method was applied to a corpus of news programs which were broadcasted the same day by two French channels. The study revealed the specificities of the present TV innovations.

A content-based news video retrieval system: NVRS

Science.gov (United States)

Liu, Huayong; He, Tingting

2009-10-01

This paper focus on TV news programs and design a content-based news video browsing and retrieval system, NVRS, which is convenient for users to fast browsing and retrieving news video by different categories such as political, finance, amusement, etc. Combining audiovisual features and caption text information, the system automatically segments a complete news program into separate news stories. NVRS supports keyword-based news story retrieval, category-based news story browsing and generates key-frame-based video abstract for each story. Experiments show that the method of story segmentation is effective and the retrieval is also efficient.

A Construction System for CALL Materials from TV News with Captions

Science.gov (United States)

Kobayashi, Satoshi; Tanaka, Takashi; Mori, Kazumasa; Nakagawa, Seiichi

Many language learning materials have been published. In language learning, although repetition training is obviously necessary, it is difficult to maintain the learner's interest/motivation using existing learning materials, because those materials are limited in their scope and contents. In addition, we doubt whether the speech sounds used in most materials are natural in various situations. Nowadays, some TV news programs (CNN, ABC, PBS, NHK, etc.) have closed/open captions corresponding to the announcer's speech. We have developed a system that makes Computer Assisted Language Learning (CALL) materials for both English learning by Japanese and Japanese learning by foreign students from such captioned newscasts. This system computes the synchronization between captions and speech by using HMMs and a forced alignment algorithm. Materials made by the system have following functions: full/partial text caption display, repetition listening, consulting an electronic dictionary, display of the user's/announcer's sound waveform and pitch contour, and automatic construction of a dictation test. Materials have following advantages: materials present polite and natural speech, various and timely topics. Furthermore, the materials have the following possibility: automatic creation of listening/understanding tests, and storage/retrieval of the many materials. In this paper, firstly, we present the organization of the system. Then, we describe results of questionnaires on trial use of the materials. As the result, we got enough accuracy on the synchronization between captions and speech. Speaking totally, we encouraged to research this system.

Spectral-spatial classification of hyperspectral image using three-dimensional convolution network

Science.gov (United States)

Liu, Bing; Yu, Xuchu; Zhang, Pengqiang; Tan, Xiong; Wang, Ruirui; Zhi, Lu

2018-01-01

Recently, hyperspectral image (HSI) classification has become a focus of research. However, the complex structure of an HSI makes feature extraction difficult to achieve. Most current methods build classifiers based on complex handcrafted features computed from the raw inputs. The design of an improved 3-D convolutional neural network (3D-CNN) model for HSI classification is described. This model extracts features from both the spectral and spatial dimensions through the application of 3-D convolutions, thereby capturing the important discrimination information encoded in multiple adjacent bands. The designed model views the HSI cube data altogether without relying on any pre- or postprocessing. In addition, the model is trained in an end-to-end fashion without any handcrafted features. The designed model was applied to three widely used HSI datasets. The experimental results demonstrate that the 3D-CNN-based method outperforms conventional methods even with limited labeled training samples.

Spread the News: How the Crisis Affected the Impact of News on the European Sovereign Bond Markets

OpenAIRE

Beetsma, Roel; de Jong, Frank; Giuliodori, Massimo; Widijanto, Daniel

2012-01-01

We investigate how "news" affected domestic interest spreads vis-à-vis Germany and how it propagated to other countries during the recent crisis period, thereby distinguishing between the so-called GIIPS countries and other European countries. We make original use of the Eurointelligence newsflash to construct news variables based on the amount of news that is released on a country on a given date. We find that more news on average raises the domestic interest spread of GIIPS countries since ...