Political interference and regulatory resilience in Brazil

Governments face a fundamental tradeoff between regulatory independence and control. Attempts of interference have the effect of reducing the system's level of commitment and credibility. On the other hand, an administration runs the risk that the autonomy delegated to regulators might be used to pursue outcomes that may harm their interests. This tradeoff is particularly relevant when there is an alternation of power with the arrival of a new political elite with different preferences. This paper uses data from a 2016 survey on regulatory governance applied to Brazilian regulatory agencies. This data is compared to a similar survey performed in 2005. The new survey results turn out to be surprisingly similar to those of a decade earlier, suggesting strong resilience of regulatory agencies despite significant attempts at political interference by powerful presidents. The factors explaining the resilience of regulatory governance in Brazil lie in its broader institutional endowment, which moderates the effects of executive interference.     

努力培养适应农村工作的大学生村官

实现大学生村官和农村工作的无缝对接,需要从源头上提高大学生对新农村建设的适应性,也就是要重视高校母体对大学生村官素质的培育工作。为此,要加强思想教育,培养大学生立志建设农村的情感;设置就业指导课,使大学生通过课堂了解党和国家的农业农村政策;通过暑期社会实践活动,使大学生实地感受农村生活;建立大学生与农村农民联系制度,促进大学生岗前转型;将农民代表请进课堂,使大学生不断加深对农村的了解。     

Performance of the supervised learning algorithms in sex estimation of the proximal femur: A comparative study in contemporary Egyptian and Turkish samples

Sex estimation standards are population specific however, we argue that machine learning techniques (ML) may enhance the biological sex determination on trans-population application. Linear discriminant analysis (LDA) versus nine ML including quadratic discriminant analysis (QDA), support vector machine (SVM), Decision Tree (DT), Gaussian process (GPC), Naïve Bayesian (NBC), K-Nearest Neighbor (KNN), Random Forest (RFM) and Adaptive boosting (Adaboost) were compared. The experiments involve two contemporary populations: Turkish (n = 300) and Egyptian populations (n = 100) for training and validation, respectively. Base models were calibrated using isotonic and sigmoid calibration schemes. Results were analyzed at posterior probabilities (pp) thresholds >0.95 and >0.80. At pp = 0.5, ML algorithms yielded comparable accuracies in the training (90% to 97%) and test sets (81% to 88%) which are not modified after employing the calibration techniques. At pp >0.95, the raw RFM, LDA, QDA, and SVM models have shown the best performance however, calibration techniques improved the performance of various classifier especially NBC and Adaboost. By contrast, the performance of GPC, KNN, QDA models worsened by calibration. RFM has shown the best performance among all models at both thresholds whereas LDA benefited the best from using both calibration methods at pp >0.80. Complex ML models are not necessarily achieving better performance metrics. LDA and QDA remain the fastest and simplest classifiers. We demonstrated the capability of enhancing sex estimation using ML on an independent population sample however, differences in the underlying probability distribution generated by models were detected which warranted more cautious application by forensic practitioners.     

Why are the affluent better represented around the world?

Scholars have discovered remarkable inequalities in who gets represented in electoral democracies. Around the world, the preferences of the rich tend to be better represented than those of the less well-off. In this paper, we use the most comprehensive comparative dataset of unequal representation available to answer why the poor are underrepresented. By leveraging variation over time and across countries, we study which factors explain why representation is more unequal in some places than in others. We compile a number of covariates examined in previous studies and use machine learning to describe which mechanisms best explain the data. Globally, we find that economic conditions and good governance are most important in determining the extent of unequal representation, and we find little support for hypotheses related to political institutions, interest groups or political behaviour, such as turnout. These results provide the first broadly comparative explanations for unequal representation.     

Automated detection and classification of counterfeit banknotes using quantitative features captured by spectral-domain optical coherence tomography

Counterfeiting of banknotes is still a severe crime problem in many countries. One of the most significant issue for solving the crime is to classify the counterfeit types and identify the sources. Most of the current methods to classify counterfeit banknotes rely on manual examination that is time-consuming and labor-intensive. Moreover, these methods only detect surface features which can be easily imitated through advanced printing technology. In this study, an automated method based on optical coherence tomography (OCT) and machine-learning algorithms was proposed to classify different types of banknotes based on the internal features. A spectral-domain OCT (SD-OCT) system was employed for sub-surface imaging and quantitative assessment of banknotes. A total of 29 Chinese 100-Yuan banknotes were collected, in which 4 of them were real and 25 of them were counterfeiting by three different printing processes. Each banknote was imaged 10 times in 3 distinct regions, which resulted in a dataset of 290 samples. Each sample was characterized by extracting 2 A-scan (OCT signal intensity along depth) based features and 14B-scan (cross-sectional OCT images) based features. Several machine-learning models, including logistic regression (LR), support vector machines (SVM), K-nearest neighbor (KNN) and random forest (RF), were built and optimized as the classifiers that were trained using 203 samples and applied to predict 87 testing samples. The best performance was achieved by SVM classifier in which the sensitivity of 96.55% and specificity of 98.85% were obtained in discriminating between authentic and counterfeit banknotes, and the sensitivity of 94.67% and specificity of 98.22% were obtained in predicting the types of counterfeit banknotes. These classifiers were also evaluated using the receiver operating characteristic (ROC) curves. To the best of our knowledge, this is the first study where A-scan and B-scan derived features from OCT images have been used for the detection and classification of different types of counterfeit banknotes.     

建设马克思主义学习型政党的历史透视

中国共产党始终注重学习马克思主义理论,并不断将其与中国实际相结合,努力实现马克思主义中国化。党学习马克思主义理论,在党创立后的前30年,着重从思想上建设党,确立了正确的思想和政治路线,实现了马克思主义中国化第一次历史性飞跃,取得了新民主主义革命伟大胜利;在第二个＂30年＂,对适合中国情况的社会主义建设道路进行了艰苦的探索,教训十分深刻;在最近的＂30年＂,党从号召学习马克思主义理论走向建设马克思主义学习型政党,期间现实了马克思主义中国化第二次历史性飞跃,从而取得了中国特色社会主义事业的伟大成就。党建设马克思主义学习型政党的历史给予我们许多重要启示。     

活动理论与当代教育

活动理论对当代教育的启发在于:启发了学习环境设计和教学课堂设计,提倡小组学习;为网络学习提供了理论指导。     

刍议加强党校培训制度建设

党校培训制度是否健全完善,直接关系着党校的培训质量,最终关系党和国家事业的兴衰成败.必须从关系党和国家生死存亡的高度充分认识加强党校培训制度建设的重要性和紧迫性,并找出加强党校培训制度建设的重要途径.应从创新党校培训内容、丰富党校培训形式、加强党校培训的考核和监督制度建设等方面入手.     

趋同还是存异——世界经济一体化下各国劳动关系的比较研究

在1945 -1975年期间的所谓资本主义的黄金时代,典型资本主义社会的劳动关系各具特色,而且总体是协调的,劳资双方在妥协基础上达到了平衡,形成了劳资两利的双赢格局.随着世界经济一体化的不断深入,各个国家的劳动关系产生了柔性化的趋同倾向.趋同确实发生,但模式多样性持续存在并且在现代全球经济中依然重要.对于中国而言,在尊重市场原则的基础上,不迷信市场,充分重视制度作用和历史传承才是解决中国劳动关系问题的关键.     

论科学发展观的学习与实践

学习实践科学发展观是整个胡锦涛学习思想的重要内容,要充分认识科学发展观的范式意义,做好学习调研、分析检查、整改落实的工作。明确学习调研是学习实践科学发展观的前提,分析检查是学习实践科学发展观的难点,整改落实是学习实践科学发展观的关键,学习活动与实践活动相结合,为解决现实问题提供理论指导,在理论与实践的结合中实现社会的全面协调可持续发展,使人民群众得到实惠。