A support vector machine (SVM) learns the decision surface from two distinct classes of the input points. In many applications, each input point may not be fully assigned to one of these two classes. In this paper, we apply a fuzzy membership to each input point and reformulate the SVMs such that different input points can make different contributions to the learning of decision surface. We call the proposed method fuzzy SVMs (FSVMs).

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While representation learning aims to derive interpretable features for describing visual data, representation disentanglement further results in such features so that particular image attributes can be identified and manipulated. However, one cannot easily address this task without observing ground truth annotation for the training data. To address this problem, we propose a novel deep learning model of Cross-Domain Representation Disentangler (CDRD). By observing fully annotated source-domain data and unlabeled target-domain data of interest, our model bridges the information across data domains and transfers the attribute information accordingly. Thus, cross-domain feature disentanglement and adaptation can be jointly performed. In the experiments, we provide qualitative results to verify our disentanglement capability. Moreover, we further confirm that our model can be applied for solving classification tasks of unsupervised domain adaptation, and performs favorably against state-of-the-art image disentanglement and translation methods.

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Background. Glioma is the most malignant tumor of the central nervous system. Efforts on the development of new chemotherapy are mandatory. Andrographolide (AND), a diterpenoid lactone isolated from the Andrographis paniculata, has been shown to have antitumor activities in several types of cancer cells. Whether AND can exert its antitumor activity in glioblastoma cells remains unknown. This study examined the anticancer effects of AND, both in vitro and in vivo. Methods. Cell apoptosis was assayed by flow cytometry and nuclear staining. The signaling pathway for AND was determined by western blotting. The effects of AND on tumor growth was evaluated in a mouse model. Results and Conclusion. In vitro, with application of specific inhibitors and siRNA, AND-induced apoptosis was proven through ROS-ERK-P53-caspase 7-PARP signaling pathway. In vivo, AND significantly retarded tumor growth and caused regression of well-formed tumors in vivo. Furthermore, AND did not induce apoptosis or activate ERK and p53 in primary cultured astrocyte cells, and it may serve as a potential therapeutic candidate for the treatment of glioma.

A string-matching engine capable of inspecting multiple characters in parallel can multiply the throughput. However, the space required for implementing a matching engine that can process multiple characters in parallel generally grows exponentially with respect to the characters to be processed in parallel. Based on the Aho-Corasick algorithm (AC-algorithm), this work presents a novel multicharacter transition Nondeterministic Finite Automaton (NFA) approach, called multicharacter AC-NFA , to allow for the inspection of multiple characters in parallel. This approach first converts an AC-trie to an AC-NFA by allowing for the simultaneous activation of multiple states and then converts the AC-NFA to a k -character AC-NFA by an algorithm with concatenation operations and assistant transitions. Additionally, the alignment problem, which occurs while multiple characters are being inspected in parallel, is solved using assistant transitions. Moreover, a corresponding output is provided for each inspected character by introducing priority multiplexers to determine the final matching outputs during implementation of the multicharacter AC-NFA. Consequently, the number of derived k -character transitions grows linearly with respect to the number k . Furthermore, the derived multicharacter AC-NFA is implemented on FPGAs for evaluation. The resulting throughput grows approximately 14 times and the hardware cost grows about 18 times for 16-character AC-NFA implementation, as compared with that for 1-character AC-NFA implementation. The achievable throughput is 21.4Gbps for the 16-character AC-NFA implementation operating at a 167.36MHz clock.

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