A Spacecraft Electrical Characteristics Multi-Label Classification Method Based on Off-Line FCM Clustering and On-Line WPSVM

This paper proposes a novel multi-label classification method for resolving the spacecraft electrical characteristics problems which involve many unlabeled test data processing, high-dimensional features, long computing time and identification of slow rate. Firstly, both the fuzzy c-means (FCM) offline clustering and the principal component feature extraction algorithms are applied for the feature selection process. Secondly, the approximate weighted proximal support vector machine (WPSVM) online classification algorithms is used to reduce the feature dimension and further improve the rate of recognition for electrical characteristics spacecraft. Finally, the data capture contribution method by using thresholds is proposed to guarantee the validity and consistency of the data selection. The experimental results indicate that the method proposed can obtain better data features of the spacecraft electrical characteristics, improve the accuracy of identification and shorten the computing time effectively.     

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.     

Feature Extraction and Classification of Heart Murmurs Based on Acoustic Qualities

Background and objectiveHeart murmur characterization is a crucial part of cardiac auscultation for determining the potential etiology and severity of heart diseases. One such helpful murmur characterization is the sonic qualities, which reflect both structural and hemodynamical states of the heart. Therefore, the objective is to develop a machine learning based solution for classifying murmur qualities.MethodsFour medically defined murmur qualities, namely the musical quality, blowing-like quality, coarse quality, and soft quality were examined. Feature was extracted from heart murmurs signals in their time domain, frequency domain, time-frequency domain, and phase space domain. Sequential forward floating selection (SFFS) was implemented along with three classifiers, including k-nearest neighbor (KNN), Naïve-Bayes (NB), and linear support vector machine (SVM).ResultsIt was found that multi-domain features are suited for better classification results and linear SVM was able to achieve a better balance between performance and the size of feature subsets among tested classifiers. Using the derived features, classification accuracies of 86%, 91%, 90%, and 84% were achieved for musical quality, blowing-like quality, coarse quality, and soft quality classifications respectively.ConclusionsThe study demonstrated that it is possible to effectively characterize heart murmur through its diagnostic characteristics instead of drawing direct conclusions, which is helpful for retaining versatility and generality found in the conventional cardiac auscultation.     

Integrating Social Science into the Long-Term Ecological Research (LTER) Network: Social Dimensions of Ecological Change and Ecological Dimensions of Social Change

The integration of the social sciences into long-term ecological research is an urgent priority. To address this need, a group of social, earth, and life scientists associated with the National Science Foundations (NSF) Long-Term Ecological Research (LTER) Network have articulated a conceptual framework for understanding the human dimensions of ecological change for the LTER Network. This framework explicitly advocates that what is often divided into natural and human systems be considered a single, complex social-ecological system (SES). In this paper, we propose a list of core social science research areas, concepts, and questions; identify the need for multiscale investigatory frameworks crucial for implementing integrated research; and suggest practical approaches for integration. In sum, this paper is a general outline for empirical and cross-site research projects where investigators agree that bringing together social, biological, and earth scientists can lead to synthetic approaches and a unified understanding of the mechanisms regulating SES. Although the motivation for this goal is specific to the LTER Network and similar projects, we believe that the issues and ideas presented here are widely applicable to other interdisciplinary SES studies.     

Analytical Dimensions in the Comparison of Social Organizations

The paper focuses on differences in the way social situations are defined in different cultures and societies. It develops and illustrates some dimensions for comparing social systems in this respect, viz: their inventory of statuses, the repertoires of persons, and the cultural ways in which occasions are distinguished. Some contrasting types of society are compared on these dimensions; and the problem is raised as to the epistemological character of such types, and the importance of the variable of scale. The dual nature of social organization as an organization both of people and of tasks is emphasized.     

The Social and Interactional Dimensions of Human-Computer Interfaces

The Social and Interactional Dimensions of Human- Computer Interfaces. Peter J. Thomas. ed. Cambridge: Cambridge University Press, 1995. 268 pp.     

Automated Characterization and Parameter-Free Classification of Cell Tracks Based on Local Migration Behavior

Cell migration is the driving force behind the dynamics of many diverse biological processes. Even though microscopy experiments are routinely performed today by which populations of cells are visualized in space and time, valuable information contained in image data is often disregarded because statistical analyses are performed at the level of cell populations rather than at the single-cell level. Image-based systems biology is a modern approach that aims at quantitatively analyzing and modeling biological processes by developing novel strategies and tools for the interpretation of image data. In this study, we take first steps towards a fully automated characterization and parameter-free classification of cell track data that can be generally applied to tracked objects as obtained from image data. The requirements to achieve this aim include: (i) combination of different measures for single cell tracks, such as the confinement ratio and the asphericity of the track volume, and (ii) computation of these measures in a staggered fashion to retrieve local information from all possible combinations of track segments. We demonstrate for a population of synthetic cell tracks as well as for in vitro neutrophil tracks obtained from microscopy experiment that the information contained in the track data is fully exploited in this way and does not require any prior knowledge, which keeps the analysis unbiased and general. The identification of cells that show the same type of migration behavior within the population of all cells is achieved via agglomerative hierarchical clustering of cell tracks in the parameter space of the staggered measures. The recognition of characteristic patterns is highly desired to advance our knowledge about the dynamics of biological processes.     

Pharmacogenomics: Social, Ethical, and Clinical Dimensions

Books reviewed:
Pharmacogenomics: Social, Ethical, and Clinical Dimensions , edited by M.A. Rothstein. Hoboken, NJ: Wiley-Liss, A John Wiley & Sons, Inc. 368 pp. US$ 64.95 (hardback)     

Social Psychological Dimensions of Bioenergy Development and Public Acceptance

The best technology does no good unless people use it. Thus, the future of biofuels depends not only on development of effective and efficient technologies but also on the social, economic, and political climate within which people decide to use or avoid these new fuel sources and technologies. On a social psychological level, individual behaviors are often guided by people’s attitudes toward the behavior or objects involved and by the norms established by others within the social setting. Therefore, understanding technology adoption requires an understanding of how public attitudes and beliefs are formed or changed, as well as the implications of these changes for social norms. We provide an overview of basic social psychology research connecting attitudes and norms to individual behavior, and we describe implications of these social factors for biofuel development and use.     

基于贮食行为的鸟类社会认知研究进展

贮食是动物应对环境变化和不可预测性而进化出的有效生存对策,认知则是当前鸟类学研究的热点问题之一。目前鸟类贮食行为中的认知研究多集中在空间认知,而社会认知研究相对滞后。对于贮食物种而言,储藏食物被盗现象非常普遍,为了避免被盗食,贮食者不仅要有发达的空间认知能力去记忆贮食地点,同时还需要极强的社会认知能力处理与盗食者的关系,可见社会认知在鸟类的贮食行为中扮演着重要角色。本文将从鸟类贮食的社会关系认知以及社会地位认知两个方面,对鸟类贮食行为中的社会认知研究进行综述,以期为后续鸟类社会认知研究提供借鉴和参考。     

Artificial Immune Systems-Based Classification Model for Code-Mixed Social Media Data

The main focus of the paper is to propose an artificial immune systems-based classification model for code-mixed social media data. The artificial immune systems are computational models inspired by the biological immune system. In this paper, artificial immune systems are used to optimize the initial parameters of Long short-term memory (LSTM) model. The proposed artificial immune systems-based LSTM model is then used for the classification of code-mixed data. The classification of Hindi-English code-mixed data into Hindi, English, and ambiguous words is done. Popular word embedding features were used for the representation of each word. The word embedding features and character embedding features have been used. The proposed method helps in identifying the word context by extracting the intent of user for using the ambiguous word in code-mixed sentence. Extensive experiments reveal that the artificial immune systems-based classification model outperforms competitive models especially when there are some ambiguous words in the social media data.     

基于社会分工理论的分级诊疗服务体系研究

医疗卫生服务体系结构复杂,建立规划合理、分工明确的分级诊疗服务体系是保障医疗资源合理配置、提高医疗服务协调性和连续性的重要手段。通过探讨当前分级诊疗服务体系存在的问题,进而结合社会分工理论为形成适应我国国情的分级诊疗制度提出对策建议。     

General Practitioners Can Evaluate the Material,Social and Health Dimensions of Patient Social Status

Objective

To identify which physician and patient characteristics are associated with physicians'' estimation of their patient social status.

Design

Cross-sectional multicentric survey.

Setting

Fourty-seven primary care private offices in Western Switzerland.

Participants

Random sample of 2030 patients ≥16, who encountered a general practitioner (GP) between September 2010 and February 2011.

Main measures

Primary outcome: patient social status perceived by GPs, using the MacArthur Scale of Subjective Social Status, ranging from the bottom (0) to the top (10) of the social scale.Secondary outcome: Difference between GP''s evaluation and patient''s own evaluation of their social status. Potential patient correlates: material and social deprivation using the DiPCare-Q, health status using the EQ-5D, sources of income, and level of education. GP characteristics: opinion regarding patients'' deprivation and its influence on health and care.

Results

To evaluate patient social status, GPs considered the material, social, and health aspects of deprivation, along with education level, and amount and type of income. GPs declaring a frequent reflexive consideration of their own prejudice towards deprived patients, gave a higher estimation of patients'' social status (+1.0, p = 0.002). Choosing a less costly treatment for deprived patients was associated with a lower estimation (−0.7, p = 0.002). GP''s evaluation of patient social status was 0.5 point higher than the patient''s own estimate (p<0.0001).

Conclusions

GPs can perceive the various dimensions of patient social status, although heterogeneously, according partly to their own characteristics. Compared to patients'' own evaluation, GPs overestimate patient social status.