Inducing Effect on the Percolation Transition in Complex Networks

Percolation theory concerns the emergence of connected clusters that percolate through a networked system. Previous studies ignored the effect that a node outside the percolating cluster may actively induce its inside neighbours to exit the percolating cluster. Here we study this inducing effect on the classical site percolation and K-core percolation, showing that the inducing effect always causes a discontinuous percolation transition. We precisely predict the percolation threshold and core size for uncorrelated random networks with arbitrary degree distributions. For low-dimensional lattices the percolation threshold fluctuates considerably over realizations, yet we can still predict the core size once the percolation occurs. The core sizes of real-world networks can also be well predicted using degree distribution as the only input. Our work therefore provides a theoretical framework for quantitatively understanding discontinuous breakdown phenomena in various complex systems.Comment: Main text and appendices. Title has been change

Comparison of capecitabine and tegafur/gimeracil/oteracil (S-1) in the treatment of advanced breast carcinoma in the elderly

Purpose: To analyse and compare the clinical effects and safety of capecitabine and tegafur/gimeracil/oteracil (S-1) in the treatment of advanced breast carcinoma.Methods: Eighty-four metastatic breast cancer elderly patients for whom first or second-line treatment had failed, were selected from among those admitted to the oncology ward of Binjiang People’s Hospital, China between January 2014 and June 2015. They were randomly divided into S-1 group (n =41) and capecitabine group (n = 41) and received varying doses of those drugs  according to body surface area. Clinical effects, progression-free survival, and incidence of adverse reactions were compared for the two groups following treatment.Results: Disease control rate (CR) in S-1 group was 55.6 %, much higher than 35.1 % observed for capecitabine group (p < 0.05). The disease control rate for the S-1 group was 93.7 %, also much higher than the 70.6 % found in capecitabine group. Survival analysis showed that the median survival times of the two groups did not differ significantly (p > 0.05). Furthermore, some adverse reactions such as myelosuppression and lack of strength, did not differ significantly between the two groups (p > 0.05), whereas others, including leukopenia, nausea and vomiting and hand-foot syndrome were more serious and frequent in capecitabine group than in S-1 group (p < 0.05).Conclusion: Monotherapy with S-1 is more effective than that with capecitabine. Adverse reactions are minimal for both drugs.Keywords: Breast carcinoma, Capecitabine, S-1, Adverse reactions,  Myelosuppression, Leukopenia, Hand-foot syndrom

Inc-part: incremental partitioning for load balancing in large-scale behavioral simulations

Large-scale behavioral simulations are widely used to study real-world multi-agent systems. Such programs normally run in discrete time-steps or ticks, with simulated space decomposed into domains that are distributed over a set of workers to achieve parallelism. A distinguishing feature of behavioral simulations is their frequent and high-volume group migration, the phenomenon in which simulated objects traverse domains in groups at massive scale in each tick. This results in continual and significant load imbalance among domains. To tackle this problem, traditional load balancing approaches either require excessive load re-profiling and redistribution, which lead to high computation/communication costs, or perform poorly because their statically partitioned data domains cannot reflect load changes brought by group migration. In this paper, we propose an effective and low-cost load balancing scheme, named Inc-part, based on a key observation that an object is unlikely to move a long distance (across many domains) within a single tick. This localized mobility property allows one to efficiently estimate the load of a dynamic domain incrementally, based on merely the load changes occurring in its neighborhood. The domains experiencing significant load changes are then partitioned or merged, and redistributed to redress load imbalance among the workers. Experiments on a 64-node (1,024-core) platform show that Inc-part can attain excellent load balance with dramatically lowered costs compared to state-of-the-art solutions

Predicting Transportation Carbon Emission with Urban Big Data

Transportation carbon emission is a significant contributor to the increase of greenhouse gases, which directly threatens the change of climate and human health. Under the pressure of the environment, it is very important to master the information of transportation carbon emission in real time. In the traditional way, we get the information of the transportation carbon emission by calculating the combustion of fossil fuel in the transportation sector. However, it is very difficult to obtain the real-time and accurate fossil fuel combustion in the transportation field. In this paper, we predict the real-time and fine-grained transportation carbon emission information in the whole city, based on the spatio-temporal datasets we observed in the city, that is taxi GPS data, transportation carbon emission data, road networks, points of interests (POIs), and meteorological data. We propose a three-layer perceptron neural network (3-layerPNN) to learn the characteristics of collected data and infer the transportation carbon emission. We evaluate our method with extensive experiments based on five real data sources obtained in Zhuhai, China. The results show that our method has advantages over the well-known three machine learning methods (Gaussian Naive Bayes, Linear Regression, and Logistic Regression) and two deep learning methods (Stacked Denoising Autoencoder and Deep Belief Networks)