The creative industrial park : formation path and evolution mechanism

This paper has built a three-stage assumption of creative industrial park on the base of evolutionary economics, which are the gather of units, the construction of interface and the development of network. The gather of units is a reflection of resource search, the construction of interface is a need of identity, and the development of network is a result of multi-dimensional expansion.In the three-stage evolution, the creative industrial park increases constantly their evolution level from the simple geographic gathered to the division and cooperation of labor, until the formation of novel systems. Then this paper analyzes the 798 creative industrial park using the three-stage assumption. This paper finds the main problem of 798’s self-destructing after the low level development of the third stage is the exclusion of the commercial prosperity to the art production. Accordingly,the paper puts forword four modes of promoting the integration between art and commerce. At last, this paper argues the different characteristics of the creative industrial park from other industrial parks. On the angle of formation path, the essence of creative industries is integration of culture and economy, technology. On the angle of evolution mechanism, it reflects novel characteristic of unit, identity characteristic of interface, and co-creation characteristic of network.creative industrial park; formation path; evolution mechanism; integration of culture,economy,and technology

Closed-loop control of complex networks : A trade-off between time and energy

W. L. is supported by the National Science Foundation of China (NSFC) (Grants No. 11322111 and No. 61773125). Y.-Z. S. is supported by the NSFC (Grant No. 61403393). Y.-C. L. acknowledges support from the Vannevar Bush Faculty Fellowship program sponsored by the Basic Research Office of the Assistant Secretary of Defense for Research and Engineering and funded by the Office of Naval Research through Grant No. N00014-16-1-2828. Y.-Z. S. and S.-Y. L. contributed equally to this work.Peer reviewedPublisher PD

Deep Recurrent Survival Analysis

Survival analysis is a hotspot in statistical research for modeling time-to-event information with data censorship handling, which has been widely used in many applications such as clinical research, information system and other fields with survivorship bias. Many works have been proposed for survival analysis ranging from traditional statistic methods to machine learning models. However, the existing methodologies either utilize counting-based statistics on the segmented data, or have a pre-assumption on the event probability distribution w.r.t. time. Moreover, few works consider sequential patterns within the feature space. In this paper, we propose a Deep Recurrent Survival Analysis model which combines deep learning for conditional probability prediction at fine-grained level of the data, and survival analysis for tackling the censorship. By capturing the time dependency through modeling the conditional probability of the event for each sample, our method predicts the likelihood of the true event occurrence and estimates the survival rate over time, i.e., the probability of the non-occurrence of the event, for the censored data. Meanwhile, without assuming any specific form of the event probability distribution, our model shows great advantages over the previous works on fitting various sophisticated data distributions. In the experiments on the three real-world tasks from different fields, our model significantly outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code: https://github.com/rk2900/drs