1,525 research outputs found
Classification of breast mass abnormalities using denseness and architectural distortion
This paper presents an electronic second opinion system for the classification of mass abnormalities in mammograms into benign and malignant categories. This system is designed to help radiologists to reduce the number of benign breast cancer biopsies. Once a mass abnormality is detected and marked on a mammogram by a radiologist, two textural features, named denseness and architectural distortion, are extracted from the marked area. The denseness feature provides a measure of radiographic denseness of the marked area, whereas the architectural distortion feature provides a measure of its irregularity. These features are then fed into a neural network classifier. Receiver operating characteristic (ROC) analysis was conducted to evaluate the system performance. The area under the ROC curve reached 0.90 for the DDSM database consisting of 404 biopsy proven masses. A sensitivity analysis was also performed to examine the robustness of the introduced texture features to variations in sizes of abnormality markings
The analysis of journals topics and trend: text mining and word cloud
The objective of this study is to identify the major topics over time of 4 selected journals in the field of Information Science and Library Science by analyzing word usage and the frequency with which certain words are used in the journal. A further objective is to determine the future direction of research in related subject areas. The basis of the study uses the program R to collect, text mine, and analyze a published article’s word usage and concepts that are represented in a word cloud. Four journals were selected and collected from Web of Science (Thomson Reuters). The journal selection considered the 5-Year Impact Factor, the journal’s aims and scope, and subject uniqueness. The journals that were selected are: Journal of the Association for Information Science and Technology, Journal of the American Medical Informatics Association, Journal of Documentation, and Scientometrics. A total 8,148 articles were collected and analyzed
Community Currency in Korea
Community currency schemes were first introduced in Korea in 1998. Since then, there have been many efforts to use them but no report or academic research on the topic in Korea. Thus, we conducted a field investigation to identify the scope of community currency schemes in Korea and as of 2012 we found 43 groups which use them. The design elements were also investigated but most groups were in an under-developed state, therefore design elements were unidentifiable. Furthermore, we investigate how the community currency coordinators in Korea envision the system using Q-methodology, a method to find the subjective views on the topic. The result shows that the perception on community currency can be divided into four types: ‘Neighborhood as a community’ in which coordinators agree with mainstream economic values and view community currencies as a tool to revitalize the community and to empower local residents; ‘Alternative community’ in which coordinators view currencies as the means to resist the dominant neoliberal ideology; ‘Community through eco-friendly affinity groups’, in which the scheme is a tool to promote an ecologically-friendly lifestyle, and ‘Ecological community’, which represents coordinators who believe that it is an alternative to capitalism and a way to maintain an ecological community
Data Analysis for Solar Energy Generation in a University Microgrid
This paper presents a data acquisition process for solar energy generation and then analyzes the dynamics of its data stream, mainly employing open software solutions such as Python, MySQL, and R. For the sequence of hourly power generations during the period from January 2016 to March 2017, a variety of queries are issued to obtain the number of valid reports as well as the average, maximum, and total amount of electricity generation in 7 solar panels. The query result on all-time, monthly, and daily basis has found that the panel-by panel difference is not so significant in a university-scale microgrid, the maximum gap being 7.1% even in the exceptional case. In addition, for the time series of daily energy generations, we develop a neural network-based trace and prediction model. Due to the time lagging effect in forecasting, the average prediction error for the next hours or days reaches 27.6%. The data stream is still being accumulated and the accuracy will be enhanced by more intensive machine learning
The Regulation of Germline Stem Cells and Their Neighbouring Somatic Cells in the Fruit Fly (Drosophila melanogaster)
The Drosophila germline stem cells (GSCs) remain as one of the most well-understood adult stem cells. The number of stem cells that self-renews and differentiates must be tightly controlled to maintain tissue homeostasis. The Drosophila GSCs are maintained by local signals emanated from the niche, which is composed of the surrounding somatic cells. Notably, GSC homeostasis is also known to be influenced by systemic signals and external stimuli. The Drosophila hormone ecdysone and its signalling cascade were found to regulate GSC homeostasis. The insulin signalling pathway as well as nutrient availability can also regulate GSC number. Furthermore, neuronal sex peptide signalling induced in female flies after mating was shown to increase GSC number. Hence, the Drosophila GSC system serves as a useful model towards understanding the mammalian stem cells. Compared with the mammalian stem cell models, the Drosophila GSC system is anatomically simpler where stem cells can be easily identified, imaged and manipulated genetically. Nevertheless, recent findings have facilitated our understanding into how GSCs and their neighbouring somatic cells sense and respond to changes in a variety of local, systemic and external stimuli
Leveraging Speaker Embeddings with Adversarial Multi-task Learning for Age Group Classification
Recently, researchers have utilized neural network-based speaker embedding
techniques in speaker-recognition tasks to identify speakers accurately.
However, speaker-discriminative embeddings do not always represent speech
features such as age group well. In an embedding model that has been highly
trained to capture speaker traits, the task of age group classification is
closer to speech information leakage. Hence, to improve age group
classification performance, we consider the use of speaker-discriminative
embeddings derived from adversarial multi-task learning to align features and
reduce the domain discrepancy in age subgroups. In addition, we investigated
different types of speaker embeddings to learn and generalize the
domain-invariant representations for age groups. Experimental results on the
VoxCeleb Enrichment dataset verify the effectiveness of our proposed adaptive
adversarial network in multi-objective scenarios and leveraging speaker
embeddings for the domain adaptation task
転写後遺伝子サイレンシングにおけるRNA依存性RNAポリメラーゼ6の生化学的解析
学位の種別: 課程博士審査委員会委員 : (主査)東京大学教授 泊 幸秀, 東京大学教授 上田 卓也, 東京大学教授 富田 耕造, 東京大学教授 渡邊 雄一郎, 東京大学准教授 深井 周也University of Tokyo(東京大学
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