The Effects of Technology Import on High-Tech Industrial Structure Upgrading in China

Using panel data from 2004 to 2012 of five sub-industries in high technology industry, and the data of three economic regions of China, use factor analysis method to measure the degree of high technology industrial structure upgrading. Introducing cross terms of industrial and regional dummy variables to analyze the different effects of technology import, R&D capital stock on high technology industry sectors and regions of east, middle and west. Empirical Analysis shows that medical equipment and instrumentation manufacturing is most affected, but electronic and communication equipment, aviation spacecraft is little affected. From area: it significantly contributes to the middle region of high technology industry structural upgrading, while little impacts on the eastern and western regions

Interpreting CNN Knowledge via an Explanatory Graph

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-layer of a pre-trained CNN usually represents a mixture of object parts, we propose a simple yet efficient method to automatically disentangles different part patterns from each filter, and construct an explanatory graph. In the explanatory graph, each node represents a part pattern, and each edge encodes co-activation relationships and spatial relationships between patterns. More importantly, we learn the explanatory graph for a pre-trained CNN in an unsupervised manner, i.e., without a need of annotating object parts. Experiments show that each graph node consistently represents the same object part through different images. We transfer part patterns in the explanatory graph to the task of part localization, and our method significantly outperforms other approaches.Comment: in AAAI 201

Occurrence of cancer at multiple sites: Towards distinguishing multigenesis from metastasis

<p>Abstract</p> <p>Background</p> <p>Occurrence of tumors at multiple sites is a hallmark of malignant cancers and contributes to the high mortality of cancers. The formation of multi-site cancers (MSCs) has conventionally been regarded as a result of hematogenous metastasis. However, some MSCs may appear as unusual in the sense of vascular dissemination pattern and therefore be explained by alternative metastasis models or even by non-metastatic independent formation mechanisms.</p> <p>Results</p> <p>Through literature review and incorporation of recent advance in understanding aging and development, we identified two alternative mechanisms for the independent formation of MSCs: 1) formation of separate tumors from cancer-initiating cells (CICs) mutated at an early stage of development and then diverging as to their physical locations upon further development, 2) formation of separate tumors from different CICs that contain mutations in some convergent ways. Either of these processes does not require long-distance migration and/or vascular dissemination of cancer cells from a primary site to a secondary site. Thus, we classify the formation of these MSCs from indigenous CICs (iCICs) into a new mechanistic category of tumor formation – multigenesis.</p> <p>Conclusion</p> <p>A multigenesis view on multi-site cancer (MSCs) may offer explanations for some "unusual metastasis" and has important implications for designing expanded strategies for the diagnosis and treatment of cancers.</p> <p>Reviewers</p> <p>This article was reviewed by Carlo C. Maley nominated by Laura F. Landweber and Razvan T. Radulescu nominated by David R. Kaplan. For the full reviews, please go to the Reviewers' comments section.</p

Improved particle swarm optimization algorithm for multi-reservoir system operation

AbstractIn this paper, a hybrid improved particle swarm optimization (IPSO) algorithm is proposed for the optimization of hydroelectric power scheduling in multi-reservoir systems. The conventional particle swarm optimization (PSO) algorithm is improved in two ways: (1) The linearly decreasing inertia weight coefficient (LDIWC) is replaced by a self-adaptive exponential inertia weight coefficient (SEIWC), which could make the PSO algorithm more balanceable and more effective in both global and local searches. (2) The crossover and mutation idea inspired by the genetic algorithm (GA) is imported into the particle updating method to enhance the diversity of populations. The potential ability of IPSO in nonlinear numerical function optimization was first tested with three classical benchmark functions. Then, a long-term multi-reservoir system operation model based on IPSO was designed and a case study was carried out in the Minjiang Basin in China, where there is a power system consisting of 26 hydroelectric power plants. The scheduling results of the IPSO algorithm were found to outperform PSO and to be comparable with the results of the dynamic programming successive approximation (DPSA) algorithm