上市公司自愿性信息披露动因机制的生态学角度研究

为了研究上市公司进行自愿性信息披露的原因和战略选择,将研究对象分为上市公司与公司股东及外部投资者的关系,从生态学的角度,利用Logistic模型建立互动模型,得到了最优模型及互惠共生模型,基于此对上市公司自愿性信息披露进行了进一步的分析,获得了虚假信息披露的生态学机理,并得到了上市公司进行自愿性信息披露的动因机制和策略.     

高校图书馆图书流通效用的建模设计

图书馆是文献信息传播中心。作为信息的重要载体之一的馆藏图书,在读者利用以后所产生的流通效用,是综合评价、检验藏书资源建设的数量、质量及结构体系的最终依据,也是预测未来潜在性需要,修改藏书发展计划的重要依据。本文将改变图书流通效用分析的定性分析方式,改定性分析为定量分析,通过图书流通效用指标分析,提出一种数学模型。     

企业信息生态系统的逻辑模型与运行机制

通过对信息生态学的分析,介绍了企业信息生态系统的内涵,并构建了企业信息生态系统逻辑模型,重点对企业信息生态系统的运行机制进行分析,最后构建了运行机制间的总体关系图。     

一个新的大样本ENSO集合预报系统的发展与检验

详细介绍了一个新的大样本集合预报系统. 为了减小ENSO(厄尔尼诺-南方涛动)预报中的预报不确定性,该集合预报系统首先基于一个中等复杂程度的耦合模式,利用集合卡尔曼滤波资料同化方法同化有效的海洋观测资料为集合预报系统提供集合初始场;同时,一个发展的用于12个月预报的一阶线性马尔可夫(Markov)随机误差模式被嵌套到集合预报系统中来模拟模式不确定性. 基于1992年11月~2008年10月100个样本的集合回报试验,从确定性预报技巧和概率预报技巧2个方面对集合预报系统的预报水平进行了检验. 该集合预报方法能够很有效地将传统的确定性预报扩展到概率预报领域,且检验结果表明,预报样本均值的预报水平要优于单一的确定性预报. 对于概率预报而言,集合预报样本能够很好地跟随观测的变化,并且能够提供单纯确定性预报所不能够提供的额外信息.     

中国碳超载下碳锁定与解锁路径实证研究

发展中国家在工业化进程中会出现碳锁定现象。中国处在工业化进程的关键时期,很有必要对碳锁定问题进行深入探讨,选用合适的指标来衡量碳锁定有助于理解碳锁定。本文通过测算1990-2014年中国碳汇量和碳排放量,用两者的差值作为碳超载量,用碳超载率（碳超载量/碳汇量）对碳锁定的程度以及变化趋势做出基本判断,然后利用ECM模型对碳锁定的影响因素进行分析。研究结果表明:①以2003年为界限,1990-2002年期间中国未出现碳超载,从2003年开始碳超载为正,以后逐年增加,碳锁定程度越来越严重;②降低化石能源消耗、增加技术投入在长期和短期内对碳锁定都有抑制作用,而第二产业和第三产业的发展速度对碳锁定的影响在长期和短期内是相反的;③提出在“十三五”期间实现中国碳汇量比2014年增加5%~15%的目标,以期在“十三五”结束时基本实现中国碳平衡。本研究以丰富中国碳锁定及碳解锁理论研究为侧重点,着重为中国碳锁定程度和碳锁定变化趋势研究提供科学依据,并对中国碳锁定解锁路径提出借鉴性建议。     

An end-to-end deep generative approach with meta-learning optimization for zero-shot object classification

Zero-shot object classification aims to recognize the object of unseen classes whose supervised data are unavailable in the training stage. Recent zero-shot learning (ZSL) methods usually propose to generate new supervised data for unseen classes by designing various deep generative networks. In this paper, we propose an end-to-end deep generative ZSL approach that trains the data generation module and object classification module jointly, rather than separately as in the majority of existing generation-based ZSL methods. Due to the ZSL assumption that unseen data are unavailable in the training stage, the distribution of generated unseen data will shift to the distribution of seen data, and subsequently causes the projection domain shift problem. Therefore, we further design a novel meta-learning optimization model to improve the proposed generation-based ZSL approach, where the parameters initialization and the parameters update algorithm are meta-learned to assist model convergence. We evaluate the proposed approach on five standard ZSL datasets. The average accuracy increased by the proposed jointly training strategy is 2.7% and 23.0% for the standard ZSL task and generalized ZSL task respectively, and the meta-learning optimization further improves the accuracy by 5.0% and 2.1% on two ZSL tasks respectively. Experimental results demonstrate that the proposed approach has significant superiority in various ZSL tasks.     

A Dual-Pointer guided transition system for end-to-end structured sentiment analysis with global graph reasoning

Structured sentiment analysis is a newly proposed task, which aims to summarize the overall sentiment and opinion status on given texts, i.e., the opinion expression, the sentiment polarity of the opinion, the holder of the opinion, and the target the opinion towards. In this work, we investigate a transition-based model for end-to-end structured sentiment analysis task. We design a transition architecture which supports the recognition of all the possible opinion quadruples in one shot. Based on the transition backbone, we then propose a Dual-Pointer module for more accurate term boundary detection. Besides, we further introduce a global graph reasoning mechanism, which helps to learn the global-level interactions between the overlapped quadruples. The high-order features are navigated into the transition system to enhance the final predictions. Extensive experimental results on five benchmarks demonstrate both the prominent efficacy and efficiency of our system. Our model outperforms all baselines in terms of all metrics, especially achieving a 10.5% point gain over the current best-performing system only detecting the holder-target-opinion triplets. Further analyses reveal that our framework is also effective in solving the overlapping structure and long-range dependency issues.     

Fake news detection via knowledgeable prompt learning

The spread of fake news has become a significant social problem, drawing great concern for fake news detection (FND). Pretrained language models (PLMs), such as BERT and RoBERTa can benefit this task much, leading to state-of-the-art performance. The common paradigm of utilizing these PLMs is fine-tuning, in which a linear classification layer is built upon the well-initialized PLM network, resulting in an FND mode, and then the full model is tuned on a training corpus. Although great successes have been achieved, this paradigm still involves a significant gap between the language model pretraining and target task fine-tuning processes. Fortunately, prompt learning, a new alternative to PLM exploration, can handle the issue naturally, showing the potential for further performance improvements. To this end, we propose knowledgeable prompt learning (KPL) for this task. First, we apply prompt learning to FND, through designing one sophisticated prompt template and the corresponding verbal words carefully for the task. Second, we incorporate external knowledge into the prompt representation, making the representation more expressive to predict the verbal words. Experimental results on two benchmark datasets demonstrate that prompt learning is better than the baseline fine-tuning PLM utilization for FND and can outperform all previous representative methods. Our final knowledgeable model (i.e, KPL) can provide further improvements. In particular, it achieves an average increase of 3.28% in F1 score under low-resource conditions compared with fine-tuning.     

The social as the heart of social innovation and social entrepreneurship: An emerging area or an old crossroads?

This paper aims to contribute to the conceptual debate on the connection between social innovation and social entrepreneurship, considering the role of the ‘social’ within this connection. Supported by a systematic literature review (SLR) with an in-depth analysis of 34 articles from Scopus-indexed and Web of Science databases journals, this paper identifies, analyzes and describes the difficulties and opportunities in the social innovation and social entrepreneurship literature. Little is known about the link between both concepts and the influence of the ‘social’ in their development and implementation. This SLR was conducted to identify and describe definitions and patterns. Results show that the connection between social innovation and social entrepreneurship is in its take-off phase, but it still is a fragmented field with a huge lack of consensus. Thus, it will be important to see where the field will head, as this paper aims to be a first step in the understanding of social innovation and social entrepreneurship through a collective and integrated perspective, providing an elucidation of the different perspectives of the literature.     

土地经营对中国城市空间扩张的驱动机制——基于273个地级市的实证分析

分税制改革后,地方政府采取"以地生财、以财养地"的城市土地经营模式,凭借土地出让获取土地收益,并用于城市基础设施等城市建设投资,以推动城市经济发展,但土地经营对城市规模的空间扩张机制仍有待理清.基于地方政府土地收益和城市建设投资两个维度,本文构建了中国地方政府土地经营驱动的城市空间扩张理论逻辑,选择2003-2017年...