Automatic emotional state detection using facial expression dynamic in videos

In this paper, an automatic emotion detection system is built for a computer or machine to detect the emotional state from facial expressions in human computer communication. Firstly, dynamic motion features are extracted from facial expression videos and then advanced machine learning methods for classification and regression are used to predict the emotional states. The system is evaluated on two publicly available datasets, i.e. GEMEP_FERA and AVEC2013, and satisfied performances are achieved in comparison with the baseline results provided. With this emotional state detection capability, a machine can read the facial expression of its user automatically. This technique can be integrated into applications such as smart robots, interactive games and smart surveillance systems

Capital and macroeconomic instability in a discrete-time model with forward-looking interest rate rules

The authors establish the necessary and sufficient conditions for local real determinacy in a discrete-time production economy with monopolistic competition and a quadratic price adjustment cost under forward-looking policy rules, for the case where capital is in exogenously fixed supply and the case with endogenous capital accumulation. Using these conditions, they show that (i) indeterminacy is more likely to occur with a greater share of payment to capital in value-added production cost; (ii) indeterminacy can be more or less likely to occur with constant capital than with variable capital; (iii) indeterminacy is more likely to occur when prices are modelled as jump variables than as predetermined variables; (iv) indeterminacy is less likely to occur with a greater degree of steady-state monopolistic distortions; and (v) indeterminacy is less likely to occur with a greater degree of price stickiness or with a higher steady-state inflation rate. In contrast to some existing research, the authors' analysis indicates that capital tends to lead to macroeconomic instability by affecting firms' pricing behavior in product markets rather than households' arbitrage activity in asset markets even under forward-looking policy rules.Capital ; Interest rates

Silencing E3 Ubiqutin ligase ITCH as a potential therapy to enhance chemotherapy efficacy in p53 mutant neuroblastoma cells

P53 mutations are responsible for drug-resistance of tumour cells which impacts on the efficacy of treatment. Alternative tumour suppressor pathways need to be explored to treat p53- deficient tumours. The E3 ubiquitin ligase, ITCH, negatively regulates the tumour suppressor protein TP73, providing a therapeutic target to enhance the sensitivity of the tumour cells to the treatment. In the present study, two p53-mutant neuroblastoma cell lines were used as in vitro models. Using immunostaining, western blot and qPCR methods, we firstly identified that ITCH was expressed on p53-mutant neuroblastoma cell lines. Transfection of these cell lines with ITCH siRNA could effectively silence the ITCH expression, and result in the stabilization of TP73 protein, which mediated the apoptosis of the neuroblastoma cells upon irradiation treatment. Finally, in vivo delivery of the ITCH siRNA using nanoparticles to the neuroblastoma xenograft mouse model showed around 15–20% ITCH silencing 48 hours after transfection. Our data suggest that ITCH could be silenced both in vitro and in vivo using nanoparticles, and silencing of ITCH sensitizes the tumour cells to irradiation treatment. This strategy could be further explored to combine the chemotherapy/radiotherapy treatment to enhance the therapeutic effects on p53-deficient neuroblastoma

Automatic Classification of Text Databases through Query Probing

Many text databases on the web are "hidden" behind search interfaces, and their documents are only accessible through querying. Search engines typically ignore the contents of such search-only databases. Recently, Yahoo-like directories have started to manually organize these databases into categories that users can browse to find these valuable resources. We propose a novel strategy to automate the classification of search-only text databases. Our technique starts by training a rule-based document classifier, and then uses the classifier's rules to generate probing queries. The queries are sent to the text databases, which are then classified based on the number of matches that they produce for each query. We report some initial exploratory experiments that show that our approach is promising to automatically characterize the contents of text databases accessible on the web.Comment: 7 pages, 1 figur