浅议学习型党组织建设

建设学习型政党和学习型党组织,是中国共产党人将学习型组织理论运用于自身建设的又一重大理论和实践创新,是对建设学习型社会和学习型组织世界潮流的一种呼应。学习型党组织建设是党的建设改革创新的重要课题。深刻认识建设学习型党组织的时代背景,科学把握其基本特征,深入研究和探讨学习型党组织的科学内涵及建设途径,将有助于学习型党组织建设实践的推进。     

充分发挥党校在建设学习型政党中的重要作用

党的十七届四中全会提出了建设马克思主义学习型政党的战略任务。党校作为培训轮训党政领导干部的主阵地，在建设马克思主义学习型政党中，必须充分发挥理论教育作用、研究咨询作用、党性熔炉作用和学习平台作用。     

公安院校岗位实践教学中学员自主学习能力的培养

文章从自主学习理论出发,论述了在公安院校岗位实践教学中学生自主学习能力的培养问题。教师要帮助学生明确学习目标,增强自主意识,在实践中不断反思,掌握学习方法和技巧,从而养成良好的学习习惯,提高警察职业综合素质和实践创新能力。     

独立学院学习困难学生的成因及对策

独立学院学习困难学生的教育工作关系到学校的和谐稳定和发展,也关系到家庭和学生个人的前途。独立学院学习困难学生基本有5种类型,文章从主观和客观两个方面分析了此类学生学习困难的主要原因,提出了从点和面的层面所要采取的措施和对策。     

中西文明互鉴中的三种知识汇通模式

明末清初耶稣会士来华所引发的知识汇通,是中西文明互鉴的客观需求和重要表现。中西文化交流过程中形成了三种知识汇通模式:“中学为本”“西学为本”和“中西学并举”。它们分别以“西学东源”“中学西源”和“中西学异源”学说为理论依据,在不同历史时期都曾发挥了主流作用。在中西文化交流频繁的当下,如何将中西知识实现恰如其分的对接,仍是一个十分现实的问题。明末清初以来形成的三种中西知识汇通模式或可提供一些借鉴。     

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。