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关键词： 马克思   剥削观   “剥削”范畴  

摘要： “剥削”范畴本质上是一种经济关系。作为人类社会一定历史发展阶段特有的经济现象,它具有正当性和暂时性特征。社会主义市场经济下“剥削”范畴存在原因:(一)“剥削”范畴存在具有合理性;(二)“剥削”范畴存在是由“资社”并存格局所决定。“剥削”范畴存在的基础是同其赖以形成的生产关系一道构成社会的经济基础,并在上层建筑中获得存在的合法性和正当性,这是与我国现实生产力水平的客观性相适应,使得“剥削”范畴在中国特色社会主义社会发展的一个相当长的时期中,都将保留其存在的空间。但这并不意味着要抛弃马克思主义劳动价值论以及最终消灭剥削的信念,正因为劳动价值论构成了剥削行为的道德负价基础,剩余价值理论揭示了消灭剥削的实现途径和方法,更指明了消灭“剥削”范畴的条件和路径。

关键词： 言说动词   标句词   引语标记   去范畴化  

摘要： 标句词“说”是当代汉语口语中新兴的语法标记。既有的研究大多将位于动词及其宾语小句之间的“说”处理为标句词。在去范畴化的视角下重新审视这一问题,可以发现“说”作为标句词的解读,高度依赖于特定的语用条件,只有与认知动词或关系动词共现的“说”才具有标句词的性质。这样的“说”最大程度失去了言说动词的句法语义特征,并具备了小句连接的语法意义。此外,与言说动词或感官动词共现的“说”具有引语标记的性质。

关键词： categorical data   dynamic programming   high-dimensional regression   nonconvex penalty  

摘要： We propose a method for estimation in high-dimensional linear models with nominal categorical data. Our estimator, called SCOPE, fuses levels together by making their corresponding coefficients exactly equal. This is achieved using the minimax concave penalty on differences between the order statistics of the coefficients for a categorical variable, thereby clustering the coefficients. We provide an algorithm for exact and efficient computation of the global minimum of the resulting nonconvex objective in the case with a single variable with potentially many levels, and use this within a block coordinate descent procedure in the multivariate case. We show that an oracle least squares solution that exploits the unknown level fusions is a limit point of the coordinate descent with high probability, provided the true levels have a certain minimum separation;these conditions are known to be minimal in the univariate case. We demonstrate the favourable performance of SCOPE across a range of real and simulated datasets. An R package CatReg implementing SCOPE for linear models and also a version for logistic regression is available on CRAN.

关键词： Hypertension   Prevalence   2017 ACC/AHA   India   Epidemiology  

摘要： Introduction: In 2017, the American College of Cardiology/American Heart Association revised guidelines for diagnosis and management of hypertension in adults. The regional impact of the updated guidelines on the prevalence of hypertension in India is unknown. Methods: Data from nationally representative Indian households were analyzed to estimate the regional prevalence of hypertension according to the old and the new guidelines in men (age 18-54 years) and women (age 18-49 years). The old guidelines defined hypertension as a systolic blood pressure of >= 140 mmHg or diastolic blood pressure of >= 90 mmHg or treatment. The new guidelines define hypertension as a systolic blood pressure of >= 130 mmHg or diastolic blood pressure of >= 80 mmHg or treatment. We calculated the increase in the prevalence of hypertension among the states and union territories of India (hereafter "states"). Results: Among 679,712 participants (85.6% women), the median age was 31 years (interquartile range 24, 40) and was comparable among men and women (33 vs. 31 years, respectively). The overall weighted prevalence according to old and new guidelines was 18.5% (95% CI 18.2, 18.7) and 43.0% (95% CI 42.8, 43.3), respectively. There was a significant increase in hypertension prevalence, both among men and women, and across all regions. The northeast region of the country had the highest prevalence. Conclusion: The overall prevalence of hypertension significantly increases with the new compared to the old guidelines, however, the regional heterogeneity of prevalence of hypertension is maintained. (C) 2021 Cardiological Society of India. Published by Elsevier B.V.

关键词： Outlier detection   Feature selection   Non-IID learning   Categorical data   Homophily relation   Random walk   Coupling learning  

摘要： Most of existing outlier detection methods assume that the outlier factors (i.e., outlierness scoring measures) of data entities (e.g., feature values and data objects) are Independent and Identically Distributed (IID). This assumption does not hold in real-world applications where the outlierness of different entities is dependent on each other and/or taken from different probability distributions (non-IID). This may lead to the failure of detecting important outliers that are too subtle to be identified without considering the non-IID nature. The issue is even intensified in more challenging contexts, e.g., high-dimensional data with many noisy features. This work introduces a novel outlier detection framework and its two instances to identify outliers in categorical data by capturing non-IID outlier factors. Our approach first defines and incorporates distribution-sensitive outlier factors and their interdependence into a value-value graph-based representation. It then models an outlierness propagation process in the value graph to learn the outlierness of feature values. The learned value outlierness allows for either direct outlier detection or outlying feature selection. The graph representation and mining approach is employed here to well capture the rich non-IID characteristics. Our empirical results on 15 real-world data sets with different levels of data complexities show that (i) the proposed outlier detection methods significantly outperform five state-of-the-art methods at the 95%/99% confidence level, achieving 10-28% AUC improvement on the 10 most complex data sets;and (ii) the proposed feature selection methods significantly outperform three competing methods in enabling subsequent outlier detection of two different existing detectors.

摘要： An algebra is said to be tau-tilting finite provided it has only a finite number of tau-rigid objects up to isomorphism. To each such algebra, we associate a category whose objects are the wide subcategories of its category of finite dimensional modules, and whose morphisms are indexed by support tau-tilting pairs.

关键词： Unsupervised learning   Categorical clustering   Rough set theory   K-modes   Uncertainty  

摘要： The categorical clustering problem has attracted much attention especially in the last decades since many real world applications produce categorical data. The k-mode algorithm, proposed since 1998, and its multiple variants were widely used in this context. However, they suffer from a great limitation related to the update of the modes in each iteration. The mode in the last step of these algorithms is randomly selected although it is possible to identify many candidate ones. In this paper, a rough density mode selection method is proposed to identify the adequate modes among a list of candidate ones in each iteration of the k-modes. The proposed method, called Density Rough k-Modes (DRk-M) was experimented using real world datasets extracted from the UCI Machine Learning Repository, the Global Terrorism Database (GTD) and a set of collected Tweets. The DRk-M was also compared to many states of the art clustering methods and has shown great efficiency.

关键词： Category-based fractal modelling   Monte Carlo simulation   Geochemical anomalous area   Factor analysis   Centred log-ratio   Till  

摘要： Parent lithology exerts a major influence on the associated regolith geochemistry and affects the setting of population or domain boundaries. In regional geochemical mapping and anomaly detection, a lack of samples pertaining to one or more key lithologies may results in interpolation errors. Such errors can be addressed via category-based fractal modelling to characterise different background and anomalous populations by taking both error propagation and geochemical data values into account. Such geochemical anomaly classification models have been developed using Monte Carlo simulation (MCSIM) within a given lithological domain to provide estimates of population breaks. Two types of category-based fractal models used are (i) related lithological groups representative samples where there is an adequate number of samples derived from a given lithological or geological domain, and (ii) related lithological groups whole samples and a group of Gaussian simulated samples regenerated based on the mean and standard deviation of the available samples per lithological group. Both models were applied to VMS-related element concentrations in the low-density till geochemical mapping of Sweden following centred log-ratio (clr) transformation. Compared with the concentration-area fractal model the category-based fractal models provided more reliable estimates for population thresholds and identification of anomalous classes.

关键词： Image classification   Novel visual categories   Self-supervision   Pairwise similarity   Local data structure  

摘要： Semi-supervised learning has largely alleviated the strong demand for large amount of annotations in deep learning. However, most of the methods have adopted a common assumption that there is always labeled data from the same class of unlabeled data, which is impractical and restricted for real-world applications. In this research work, our focus is on semi-supervised learning when the categories of unlabeled data and labeled data are disjoint from each other. The main challenge is how to effectively leverage knowledge in labeled data to unlabeled data when they are independent from each other, and not belonging to the same categories. Previous state-of-the-art methods have proposed to construct pairwise similarity pseudo labels as supervising signals. However, two issues are commonly inherent in these methods: (1) All of previous methods are comprised of multiple training phases, which makes it difficult to train the model in an end-to-end fashion. (2) Strong dependence on the quality of pairwise similarity pseudo labels limits the performance as pseudo labels are vulnerable to noise and bias. Therefore, we propose to exploit the use of self-supervision as auxiliary task during model training such that labeled data and unlabeled data will share the same set of surrogate labels and overall supervising signals can have strong regularization. By doing so, all modules in the proposed algorithm can be trained simultaneously, which will boost the learning capability as end-to-end learning can be achieved. Moreover, we propose to utilize local structure information in feature space during pairwise pseudo label construction, as local properties are more robust to noise. Extensive experiments have been conducted on three frequently used visual datasets, i.e., CIFAR-10, CIFAR-100 and SVHN, in this paper. Experiment results have indicated the effectiveness of our proposed algorithm as we have achieved new state-of-the-art performance for novel visual categories learning for t