Limited biomarkers have been identified as prognostic predictors for stage III colon cancer. To combat this shortfall, we developed a computer-aided approach which combing convolutional neural network with machine classifier to predict the prognosis of stage III colon cancer from routinely haematoxylin and eosin (H&E) stained tissue slides. We trained the model by using 101 cancers from West China Hospital (WCH). The predictive effectivity of the model was validated by using 67 cancers from WCH and 47 cancers from The Cancer Genome Atlas Colon Adenocarcinoma database. The selected model (Gradient Boosting-Colon) provided a hazard ratio (HR) for high- vs. low-risk recurrence of 8.976 (95% confidence interval (CI), 2.824–28.528; P, 0.000), and 10.273 (95% CI, 2.177–48.472; P, 0.003) in the two test groups, from the multivariate Cox proportional hazards analysis. It gave a HR value of 10.687(95% CI, 2.908–39.272; P, 0.001) and 5.033 (95% CI,1.792–14.132; P, 0.002) for the poor vs. good prognosis groups. Gradient Boosting-Colon is an independent machine prognostic predictor which allows stratification of stage III colon cancer into high- and low-risk recurrence groups, and poor and good prognosis groups directly from the H&E tissue slides. Our findings could provide crucial information to aid treatment planning during stage III colon cancer.

Abstract. Since a decade, text categorization has become an active field of research in the machine learning community. Most of the approaches are based on the term occurrence frequency. The performance of such surface-based methods can decrease when the texts are too complex, i.e., ambiguous. One alternative is to use the semantic-based approaches to process textual documents according to their meaning. In this paper, we propose a Concept-based Vector Space Model which reflects the more abstract version of the semantic information instead of the Vector Space Model for the text. This model adjusts the weight of the Vector Space by importing the hypernymy-hyponymy relation between synonymy sets and the Concept Chain in the WordNet. Experimental results on several data sets show that the proposed approach, conception built from Wordnet, can achieve significant improvements with respect to the baseline algorithm.

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Videos have become the new preference comparing with images in recent years. However, during the recording of videos, the cameras are inevitably occluded by some objects or persons that pass through the cameras, which would highly increase the workload of video editors for searching out such occlusions. In this paper, for releasing the burden of video editors, a frame-level video occlusion detection method is proposed, which is a fundamental component of automatic video editing. The proposed method enhances the extraction of spatial-temporal information based on C3D yet only using around half amount of parameters, with an occlusion correction algorithm for correcting the prediction results. In addition, a novel loss function is proposed to better extract the characterization of occlusion and improve the detection performance. For performance evaluation, this paper builds a new large scale dataset, containing 1,000 video segments from seven different real-world scenarios, which could be available at: https://junhua-liao.github.io/Occlusion-Detection/. All occlusions in video segments are annotated frame by frame with bounding-boxes so that the dataset could be utilized in both frame-level occlusion detection and precise occlusion location. The experimental results illustrate that the proposed method could achieve good performance on video occlusion detection compared with the state-of-the-art approaches. To the best of our knowledge, * Both authors contributed equally to this research.

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A safe charging algorithm in wireless rechargeable sensor network ensures the charging efficiency and the electromagnetic radiation below the threshold. Compared with the current charging algorithms, the safe charging algorithm is more complicated due to the radiation constraint and the mobility of the chargers. A safe charging algorithm based on multiple mobile chargers is proposed in this paper to charge the sensor nodes with mobile chargers, in order to ensure the premise of radiation safety, multiple mobile chargers can effectively complete the network charging task. Firstly, this algorithm narrows the possible location of the sensor nodes by utilizing the charging time and antenna waveform. Secondly, the performance of non-partition charging algorithm which algorithm allow chargers to charge different sensors sets in a different cycle is evaluated against the one of partition charging which does not allow for charging different ones. The moving distance of the charger node will be reduced by 18%. It not only improves the safety level which is inversely proportional to electromagnetic radiation but also expands the application scope of the wireless sensor nodes.

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