A novel learning procedure, called FloatBoost, is proposed for learning a boosted classifier for achieving the minimum error rate. FloatBoost learning uses a backtrack mechanism after each iteration of AdaBoost learning to minimize the error rate directly, rather than minimizing an exponential function of the margin as in the traditional AdaBoost algorithms. A second contribution of the paper is a novel statistical model for learning best weak classifiers using a stagewise approximation of the posterior probability. These novel techniques lead to a classifier which requires fewer weak classifiers than AdaBoost yet achieves lower error rates in both training and testing, as demonstrated by extensive experiments. Applied to face detection, the FloatBoost learning method, together with a proposed detector pyramid architecture, leads to the first real-time multiview face detection system reported.

Perhaps the most fundamental application of affective computing would be Human-Computer Interaction (HCI) in which the computer is able to detect and track the user's affective states, and make corresponding feedback. The human multi-sensor affect system defines the expectation of multimodal affect analyzer. In this paper, we present our efforts toward audio-visual HCI-related affect recognition. With HCI applications in mind, we take into account some special affective states which indicate users' cognitive/motivational states. Facing the fact that a facial expression is influenced by both an affective state and speech content, we apply a smoothing method to extract the information of the affective state from facial features. In our fusion stage, a voting method is applied to combine audio and visual modalities so that the final affect recognition accuracy is greatly improved. We test our bimodal affect recognition approach on 38 subjects with 11 HCI-related affect states. The extensive experimental results show that the average person-dependent affect recognition accuracy is almost 90% for our bimodal fusion.

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Trichosanthis Pericarpium (TP) is a traditional Chinese medicine for treating cardiovascular diseases. In this study, we investigated the effects of TP aqueous extract (TPAE) on hypoxia/reoxygenation (H/R) induced injury in H9c2 cardiomyocytes and explored the underlying mechanisms. H9c2 cells were cultured under the hypoxia condition induced by sodium hydrosulfite for 30 min and reoxygenated for 4 h. Cell viability was measured by MTT assay. The amounts of LDH, NO, eNOS, and iNOS were tested by ELISA kits. Apoptotic rate was detected by Annexin V-FITC/PI staining. QRT-PCR was performed to analyze the relative mRNA expression of Akt, Bcl-2, Bax, eNOS, and iNOS. Western blotting was used to detect the expression of key members in the PI3K/Akt pathway. Results showed that the pretreatment of TPAE remarkably enhanced cell viability and decreased apoptosis induced by H/R. Moreover, TPAE decreased the release of LDH and expression of iNOS. In addition, TPAE increased NO production and Bcl-2/Bax ratio. Furthermore, the mRNA and protein expression of p-Akt and eNOS were activated by TPAE pretreatment. On the contrary, a specific inhibitor of PI3K, LY294002 not only inhibited TPAE-induced p-Akt/eNOS upregulation but alleviated its anti-apoptotic effects. In conclusion, results indicated that TPAE protected against H/R injury in cardiomyocytes, which consequently activated the PI3K/Akt/NO signaling pathway.

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