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关键词： 风景园林   境其地   意境   竹境   营造  

摘要： 随着风景园林学科相关理论的不断完善与发展,许多学者都对学科核心概念及理论框架的构建进行了深刻思考。清华大学景观学系杨锐教授在《论“境”与“境其地”》一文中,提出了以“境”为元概念及“境其地理论”的研究思想,其将“道、德、理、术、用、制、象、意”作为境其地理论中关于风景园林学科的核心范畴[1]。试图以境其地理论为参照,以竹及竹景观为载体,归纳与阐述境其地理论核心范畴在竹景观发展中的映射。通过对景园中竹元素的梳理,以“竹境”为例,试图理解和诠释“境其地”理论在景园中的运用。

关键词： 核心范畴   典型论   典型人物   个性化   普遍性   文艺理论  

摘要： 2021年是"典型"理论范畴进入中国的100周年。6月27日,由中国文艺评论家协会第二届理论委员会和北京大学艺术学院主办的"典型理论百年"学术研讨会在京召开。专家们从"典型"范畴的内在价值破题,思考它的本质规定性和在当下回归的必要性;同时延伸开去,重访中西文艺理论史,激活典型理论的新意涵;之后从文艺创作与文艺评论的实践出发,强调重启现实主义典型论的意义;接着,回到具体艺术门类和学科前沿理论研究,重构典型人物与典型图像,完成典型理论的当代转化;最后,王一川教授进行了总结,期待未来新百年核心理论范畴研究的新进展。

关键词： 顿范畴   强Gorenstein投射对象   Abel范畴  

摘要： 介绍了顿范畴(F,B),并依据A和B中的强Gorenstein投射对象,给出了(F,B)中强Gorenstein投射对象的等价刻画。

关键词： 中国共产党   人民范畴   长期执政   人民逻辑  

摘要： 人民是我们党长期执政的稳固基石。明确人民的范畴及其在不同发展阶段的具体表现,为我们党坚持人民主体地位,发挥人民主体作用提供了理论支撑和实践指引。准确把握人民范畴的演进,需要立足于唯物史观的基本观点,明确其在不同历史阶段的深刻内涵及演进的现实依据。在我们党长期执政的历史进程中,人民范畴演进的主导因素经历了由"政治导向"到"经济驱动"再到"价值引领"的转变,其范畴经历了由"劳动者"到"爱国者"再到"建设者",新时代又丰富了"奋斗者"的发展历程。我们党准确把握人民范畴的演进,遵循了在推动经济社会发展中拓展人民身份认同的历史逻辑,在推进马克思主义大众化中树立人民至上价值追求的理论逻辑,在社会主义现代化建设中激发人民主体创造伟力的实践逻辑。

关键词： 法   美学   字形   字义  

摘要： "法"字之美,美在结构,美在意蕴,文字中蕴养了中华民族的审美与思想,是中华民族内聚力的体现。法的起源与字形变化体现了我国文字变迁过程中由繁到简、由具体到抽象的复杂变化规律,法的语义的丰富与扩展,有其在中国传统文化演进过程中的独特性和深刻性,法的语义实现了早期古法的涵义到现代法的涵义的转变,不仅是中国古代的法制思想的变化过程的体现,同时也是中国古代法制文化风格美学的形成过程的体现。

摘要： Given a tensor category C over an algebraically closed field of characteristic zero, we may form the wreath product category Wn(C). It was shown in [10] that the Grothendieck rings of these wreath product categories stabilise in some sense as n oc. The resulting "limit" ring, go,,z (C), is isomorphic to the Grothendieck ring of the wreath product Deligne category St(C) as defined by [9] (although it is also related to FIG modules). This ring only depends on the Grothendieck ring g(c). Given a ring R that is free as a Z -module, we construct a ring g, z (R) that specialises to go,,z (C) when R = g(c). We give a description of g, z (R) using generators very similar to the basic hooks of [5]. We also show that go,,z (R) is a a, -ring wherever R is and that g, z (R) is (unconditionally) a Hopf algebra. Finally, we show that go,,z (R) is isomorphic to the Hopf algebra of distributions on the formal neighbourhood of the identity in (W oz R)x, where W is the ring of Big Witt Vectors.

关键词： Model structures   Category of finite graphs   x-homotopy equivalences  

摘要： In this article, we study model structures on the category of finite graphs with x-homotopy equivalences as the weak equivalences. We show that there does not exist an analogue of Strom-Hurewicz model structure on this category of graphs. More interestingly, we show that this category of graphs with x-homotopy equivalences does not have a model structure whenever the class of cofibrations is a subclass of graph inclusions.

关键词： Latent category mining   Latent categorical representation   Sequential representation   Prediction fusion   Session-based recommendation  

摘要： Recommendation systems have been becoming ubiquitous in most of online e-commerce platforms, news portals and etc. [1-5], which help users to easily discover their interested items in this era of information explosion. In some application scenarios, users' personal information may not be available beforehand, for example, the click behaviors of a user when browsing websites without logging-in. In such cases, we only have many anonymous users' browsing sequences. Recently, sessionbased recommendation (SBR) has been proposed to predict the next behavior of a user (e.g. the next item to click) for an ongoing anonymous session [6]. According to [7-10], the SBR task can be formally defined as follows. An anonymous session is defined as an item sequence s = { v1, v2, ... , vt} ordered by timestamps, where vi E V represents Session-based recommendation is to predict the next item for an anonymous item sequence. Most of recent neural models have focused on how to learn sessions' sequential representations based on the assumption that items can be projected into a single latent embedding space to describe their latent attributes. In this paper, we argue that an item can also be described by some latent categorical abstractions. To examine our argument, we first mine items' latent categorical distributions via random walk on an item graph constructed from sessions. We design a new neural model which consists of two prediction modules: One is to learn a session's latent categorical representation;The other is to learn a session's sequential representation. Each module independently makes a next item prediction, and their predictions are fused as the final recommendation result. Experiments on three public datasets validate that our model achieves performance improvements over the recent state-of-the-art algorithms. (c) 2021 Elsevier Inc. All rights reserved.

关键词： Land use classification   Block-level mapping   Geospatial big data   Ensemble learning   NAIP   Sentinel-1/2  

摘要： Urban land-use maps outlining the distribution, pattern, and composition of various land use types are critically important for urban planning, environmental management, disaster control, health protection, and biodiversity conservation. Recent advances in remote sensing and social sensing data and methods have shown great potentials in mapping urban land use categories, but they are still constrained by mixed land uses, limited predictors, non-localized models, and often relatively low accuracies. To inform these issues, we proposed a robust and cost-effective framework for mapping urban land use categories using openly available multi-source geo-spatial "big data". With street blocks generated from OpenStreetMap (OSM) data as the minimum classification unit, we integrated an expansive set of multi-scale spatially explicit information on land surface, vertical height, socio-economic attributes, social media, demography, and topography. We further proposed to apply the automatic ensemble learning that leverages a bunch of machine learning algorithms in deriving optimal urban land use classification maps. Results of block-level urban land use classification in five metropolitan areas of the United States found the overall accuracies of major-class (Level-I) and minor-class (Level-II) classification could be high as 91% and 86%, respectively. A multi-model comparison revealed that for urban land use classification with high-dimensional features, the multi-layer stacking ensemble models achieved better performance than base models such as random forest, extremely randomized trees, LightGBM, CatBoost, and neural networks. We found without very-high-resolution National Agriculture Imagery Program imagery, the classification results derived from Sentinel-1, Sentinel-2, and other open big data based features could achieve plausible overall accuracies of Level-I and Level-II classification at 88% and 81%, respectively. We also found that model transferability depended highl

关键词： basket trial   Bayes   frequentist   overview   precision medicine  

摘要： Basket trials have become a virulent topic in medical and statistical research during the last decade. The core idea of them is to treat patients, who express the same genetic predisposition-either personally or their disease-with the same treatment irrespective of the location of the disease. The location of the disease defines each basket and the pathway of the treatment uses the common genetic predisposition among the baskets. This opens the opportunity to share information among baskets, which can consequently increase the information of the basket-wise response with respect to the investigated treatment. This further allows dynamic decisions regarding futility and efficacy of individual baskets during the ongoing trial. Several statistical designs have been proposed on how a basket trial can be conducted and this has left an unclear situation with many options. The different designs propose different mathematical and statistical techniques, different decision rules, and also different trial purposes. This paper presents a broad overview of existing designs, categorizes them, and elaborates their similarities and differences. A uniform and consistent notation facilitates the first contact, introduction, and understanding of the statistical methodologies and techniques used in basket trials. Finally, this paper presents a modular approach for the construction of basket trials in applied medical science and forms a base for further research of basket trial designs and their techniques.