Shanghai Symphony Orchestra in 'C' Major (1879 to 2010)

Shanghai Symphony Orchestra is a fascinating institution. It was first founded in 1879 under the name of Shanghai Public Band and was later, in 1907, developed into an orchestra with 33 members under the baton of German conductor Rudolf Buck. Since Mario Paci an Italian pianist became its conductor in 1919, the Orchestra developed swiftly and was crowned the best in the Far East 一 by a Japanese musician Tanabe Hisao in 1923. At that time, Shanghai was semi-colonized by the International Settlement and the French Concession controlled by the Shanghai Municipal Council and the French Council respectively. They were both exempt from local Chinese authority. The Orchestra was an affiliated organization of the former: the Shanghai Municipal Council. When the Chinese Communist Party took over mainland China in 1949, the Orchestra underwent dramatic transformations. It was applied as a political propaganda tool performing music by composers from the socialist camp and adapting folk Chinese songs to Western classical instruments in order to serve the masses. This egalitarian ideology went to extremes in the notorious 10-year Cultural Revolution. Surprisingly, the SSO was not disbanded; rather it was appropriated by the CCP to create background music for revolutionary modern operas such as Taking Tiger Mountain by Strategy. The end of Cultural Revolution after Mao's death in 1976 ushered in a brand new Reform-and-Opening-up era marked by Deng Xiaoping s public claim: Getting rich is glorious! Unlike previous decades when the Shanghai Symphony Orchestra together with music it performed was made to entertain the general masses, elitism came back under a social entourage characterized by Chinese-style socialism. The concept of elite, however, is worth a further thought. Shanghai is not only home to a large number of Chinese middle class but also constitutes a promising paradise for millions of nouveau riches which resembles, to a great extent, the venture land for those Shanghailanders a century ago. This thesis, as the title indicates, puts the historical development of the Shanghai Symphony Orchestra from 1879 to 2010 in C major applying Pierre Bourdieu's cultural capital theory so as to understand how this extraordinary musical currency is produced, represented, appropriated and received by different groups of people in Shanghai across five distinct historical stages. Cultural appropriation tactics and other relevant theories such as cultural imperialism and post colonialism are also combined to make sense of particular social environment in due course. To put the SSO in C major does not infer that this musical institution and music it performed through all these years are reduced to economic analysis. Nonetheless, the inner value of music itself is highlighted in each historical period. A psychological concept affordance, first applied by Tia DeNora in music sociology, is also integrated to help comprehend how and what Chinese people or the whole nation latched on to certain pieces of music performed at the SSO in different historical phases. Moreover, musicological analysis is carried out in due course to elaborate on the feasibility of, for example, adopting Chinese folk songs to Western classical instruments and creating a hybrid music type during Cultural Revolution. Aesthetic value of music is thus realized in the meantime. Archival research is mostly used in this thesis supplemented by one focus group and one in-depth interview with retired players at the SSO. Fieldwork of this research is mainly based in the Shanghai Symphony Orchestra Archive; although materials from Shanghai Library and Shanghai Municipal Archive are also collected and made use of

RNAi-based Gene Therapy for Blood Genetic Diseases

Therapies for blood genetic diseases can be divided into different categories, including chemotherapy, radiotherapy, gene therapy, and hematopoietic stem cell transplantation. Among these treatments, gene targeting is progressively becoming a therapeutic alternative that offers the possibility of a permanent cure for certain blood genetic diseases. In recent years, gene therapy has played a more important role in curing genetic blood disorders. RNA interference (RNAi) is one of the directions for gene therapy, which was intensively studied in the past decades for its potentials in the treatment of diseases. In order to provide useful references and prospective directions for further studies concerning RNAi-based gene therapy for blood genetic diseases, current RNAi-based gene therapies for several typical blood genetic diseases have been summarized and discussed in this chapter

Harvard Eye Fairness: A Large-Scale 3D Imaging Dataset for Equitable Eye Diseases Screening and Fair Identity Scaling

Fairness or equity in machine learning is profoundly important for societal well-being, but limited public datasets hinder its progress, especially in the area of medicine. It is undeniable that fairness in medicine is one of the most important areas for fairness learning's applications. Currently, no large-scale public medical datasets with 3D imaging data for fairness learning are available, while 3D imaging data in modern clinics are standard tests for disease diagnosis. In addition, existing medical fairness datasets are actually repurposed datasets, and therefore they typically have limited demographic identity attributes with at most three identity attributes of age, gender, and race for fairness modeling. To address this gap, we introduce our Eye Fairness dataset with 30,000 subjects (Harvard-EF) covering three major eye diseases including age-related macular degeneration, diabetic retinopathy, and glaucoma affecting 380 million patients globally. Our Harvard-EF dataset includes both 2D fundus photos and 3D optical coherence tomography scans with six demographic identity attributes including age, gender, race, ethnicity, preferred language, and marital status. We also propose a fair identity scaling (FIS) approach combining group and individual scaling together to improve model fairness. Our FIS approach is compared with various state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our Harvard-EF dataset for fairness learning. To facilitate fairness comparisons between different models, we propose performance-scaled disparity measures, which can be used to compare model fairness accounting for overall performance levels. The dataset and code are publicly accessible via https://ophai.hms.harvard.edu/datasets/harvard-ef30k

Needs Analysis Model Construction for the Advanced Level of HSK*

[ES] Los usuarios del Examen de Aptitud de Chino (Hanyu Shuiping Kaoshi, denominado HSK) son cada vez más diversos, y ya es hora de que las autoridades competentes investiguen las necesidades de los usuarios del examen y lancen un nuevo examen HSK avanzado. Por lo tanto, este artículo revisa en primer lugar los estudios relevantes de los modelos de análisis de necesidades de lenguas extranjeras, y presenta la importancia de establecer un modelo de análisis de necesidades para el HSK. Basándose en el modelo de análisis de necesidades de Dudley-Evans y St. John y en el modelo de análisis de necesidades de Knoch, U. y Macqueen, S., el artículo propone un marco teórico del modelo de análisis de necesidades del HSK (avanzado) que consta de siete perspectivas: información personal, información lingüística e información profesional, carencias de los estudiantes de chino, satisfacción con el HSK existente, expectativas para el HSK (avanzado), necesidades de recursos para el examen y el impacto de las políticas pertinentes en el examen. También presenta las fuentes de información, los métodos de recogida de información y las posibles preguntas, con el fin de proporcionar una referencia para el desarrollo del HSK (avanzado).[EN] The test users of Chinese Proficiency Test (Hanyu Shuiping Kaoshi, hereinafter referred to as HSK) have been becoming more and more diverse, and it is high time for the concerning authorities to investigate the test users’ needs and launch a new advanced HSK test. Therefore, this paper first reviews the relevant studies of foreign language needs analysis models, and presents the significance to establish a needs analysis model for HSK. Based on Dudley-Evans & St John's needs analysis model and Knoch, U. & Macqueen, S.'s needs analysis model, the paper proposes a theoretical framework of the needs analysis model of HSK (advanced) that consists seven perspectives: personal information, language information and professional information, gap of Chinese language learners, satisfaction with the existing HSK, expectations for HSK (advanced), needs for test resources, and the impact of relevant policies on the test. It also introduces the information sources, information collection methods and possible questions, in order to provide reference for the development of HSK (advanced).[ZH] 近年来，参加汉语水平考试（Hanyu Shuiping Kaoshi，简称HSK）的考生越来越 多样化，有关部门应调查考生需求并推出新的高级HSK考试。本文首先回顾了外语需求分析 模型的相关研究，并提出建立HSK需求分析模型的意义。在Dudley-Evans & St John的需 求分析模型和Knoch, U. & Macqueen, S.的需求分析模型的基础上，本文提出了HSK（高 级）需求分析模型的理论框架，包括七个方面：个人信息、语言信息和专业信息、汉语学 习者的差距、对现有HSK的满意度、对HSK（高级）的期望、对考试资源的需求、相关政策 对考试的影响。报告还介绍了信息来源、信息收集方法和可能出现的问题，为HSK（高级） 的发展提供参考

Harvard Glaucoma Fairness: A Retinal Nerve Disease Dataset for Fairness Learning and Fair Identity Normalization

Fairness (also known as equity interchangeably) in machine learning is important for societal well-being, but limited public datasets hinder its progress. Currently, no dedicated public medical datasets with imaging data for fairness learning are available, though minority groups suffer from more health issues. To address this gap, we introduce Harvard Glaucoma Fairness (Harvard-GF), a retinal nerve disease dataset with both 2D and 3D imaging data and balanced racial groups for glaucoma detection. Glaucoma is the leading cause of irreversible blindness globally with Blacks having doubled glaucoma prevalence than other races. We also propose a fair identity normalization (FIN) approach to equalize the feature importance between different identity groups. Our FIN approach is compared with various the-state-of-the-art fairness learning methods with superior performance in the racial, gender, and ethnicity fairness tasks with 2D and 3D imaging data, which demonstrate the utilities of our dataset Harvard-GF for fairness learning. To facilitate fairness comparisons between different models, we propose an equity-scaled performance measure, which can be flexibly used to compare all kinds of performance metrics in the context of fairness. The dataset and code are publicly accessible via \url{https://ophai.hms.harvard.edu/datasets/harvard-glaucoma-fairness-3300-samples/}

СОВРЕМЕННЫЕ СИСТЕМЫ СОЦИАЛЬНОГО УПРАВЛЕНИЯ ДЛЯ ПОДРОСТКОВ

The most important aspect of studying adolescent socialization is the analysis of the system of factors that determine this process. The main components of the structure of factors of socialization are defined as:– Social community (the Chinese nation, consisting of 56 ethnic groups, large socio-demographic and status groups, small groups);– Social institutions (state, education, family, information processes, media and institutions in which these institutions organize their activities);– Culture and its individual elements (national culture, folk culture, youth subculture); subjects of management, the assignment of which to the individual com-ponents of the factor system is due to the task of this study.The end result will be meeting the individual needs of young people in access to education, as well as the objective social need to improve the quality of education, transfer cultural knowledge, preserve and develop the intellectual potential of the country.Наиболее важным аспектом изучения подростковой социализации является анализ системы факторов, определяющих этот процесс. Основные компоненты структуры факторов социализации определяются как– Социальная общность (китайская нация, состоящая из 56 этнических групп, большие социально-демографические и статусные группы, малые группы);– Социальные институты (государство, образование, семья, информационные процессы, СМИ и учреждения, в которых эти институты организуют свою деятельность);– Культура и ее отдельные элементы (национальная культура, народная культура, молодежная субкультура); субъекты управления, отнесение которых к отдельным компонентам факторной системы обусловлено задачей данного исследования.Конечным результатом станет удовлетворение индивидуальных потребностей молодежи в доступе к образованию, а также объективной социальной потребности в повышении качества образования, передаче культурных знаний, сохранении и развитии интеллектуального потенциала страны

Interactive Visualization of Geographic Vector Big Data Based on Viewport Generalization Model

The visualization of geographic vector data is an important premise for spatial analysis and spatial cognition. Traditional geographic vector data visualization methods are data-driven, and their computational costs have increased rapidly with the growth of the scale of data used. Even if the distributed parallel strategy is used, it is still difficult to achieve a real-time response when dealing with big geographic vector data (BGVD). To solve this problem, this paper proposes a viewport generalization model and a visualization method for the online interactive visualization of BGVD. The method takes the viewport display pixel as the analysis unit and synthesizes the existence or quantity results of geographic vector data in the corresponding spatial range of each viewport display pixel into the display value of this display pixel; thus, it converts traditional computational complexity, dependent on the data scale, into computational complexity dependent on the number of pixels in the viewport. When the number of pixels in the viewport is much smaller than that of the geographic vector data, the visualization efficiency is greatly improved. In order to realize the above conversion, the pixel quadtree index (VPQ) structure and the real-time visualization algorithm of geographic vector data based on VPQ are proposed. Experiments show that the proposed method can achieve the near-real-time interactive visualization of BGVD, and provides more than a tenfold performance improvement over the best existing methods

Analysis and Test of Internal Blowing Anti-Tangle Bag-Breaking Device for Domestic Waste

The mechanized resource utilization of domestic waste is the development trend in the field of waste treatment. The difficulty of bag breaking and the easy entanglement of domestic waste are the factors restricting the mechanization of waste separation and recycling. In response to the above problems, an internal blowing anti-tangle bag-breaking device for domestic waste was developed by combining the arc-type cutter and the internal flow field of the rotary. In addition, the motion trajectory of the cutters and the support rods were theoretically analyzed, as well as the force during the bag-breaking process of domestic waste. A three-factor, five-level orthogonal test was carried out to complete the regression ANOVA, and a relationship model was constructed between the test factors such as the cutting–support speed ratio, the center distance, the inlet flow rate and the response indicators such as the bag film length–perimeter ratio and bag film winding specific gravity. The key parameters and their significant interactions with the bag-breaking efficiency were analyzed to obtain the optimal combination of parameters for the device. Under the same conditions, the errors between the physical test and model predictions for the two response indicators were 5.46% and 7.90%, respectively, indicating that the verification test results were basically consistent with the model prediction results

HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time

Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data