Shallow Triple Stream Three-dimensional CNN (STSTNet) for Micro-expression Recognition

In the recent year, state-of-the-art for facial micro-expression recognition have been significantly advanced by deep neural networks. The robustness of deep learning has yielded promising performance beyond that of traditional handcrafted approaches. Most works in literature emphasized on increasing the depth of networks and employing highly complex objective functions to learn more features. In this paper, we design a Shallow Triple Stream Three-dimensional CNN (STSTNet) that is computationally light whilst capable of extracting discriminative high level features and details of micro-expressions. The network learns from three optical flow features (i.e., optical strain, horizontal and vertical optical flow fields) computed based on the onset and apex frames of each video. Our experimental results demonstrate the effectiveness of the proposed STSTNet, which obtained an unweighted average recall rate of 0.7605 and unweighted F1-score of 0.7353 on the composite database consisting of 442 samples from the SMIC, CASME II and SAMM databases.Comment: 5 pages, 1 figure, Accepted and published in IEEE FG 201

SWAT use of gridded observations for simulating runoff – a Vietnam river basin study

Many research studies that focus on basin hydrology have applied the SWAT model using station data to simulate runoff. But over regions lacking robust station data, there is a problem of applying the model to study the hydrological responses. For some countries and remote areas, the rainfall data availability might be a constraint due to many different reasons such as lacking of technology, war time and financial limitation that lead to difficulty in constructing the runoff data. To overcome such a limitation, this research study uses some of the available globally gridded high resolution precipitation datasets to simulate runoff. Five popular gridded observation precipitation datasets: (1) Asian Precipitation Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE), (2) Tropical Rainfall Measuring Mission (TRMM), (3) Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN), (4) Global Precipitation Climatology Project (GPCP), (5) a modified version of Global Historical Climatology Network (GHCN2) and one reanalysis dataset, National Centers for Environment Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to simulate runoff over the Dak Bla river (a small tributary of the Mekong River) in Vietnam. Wherever possible, available station data are also used for comparison. Bilinear interpolation of these gridded datasets is used to input the precipitation data at the closest grid points to the station locations. Sensitivity Analysis and Auto-calibration are performed for the SWAT model. The Nash-Sutcliffe Efficiency (NSE) and Coefficient of Determination (<i>R</i><sup>2</sup>) indices are used to benchmark the model performance. Results indicate that the APHRODITE dataset performed very well on a daily scale simulation of discharge having a good NSE of 0.54 and <i>R</i><sup>2</sup> of 0.55, when compared to the discharge simulation using station data (0.68 and 0.71). The GPCP proved to be the next best dataset that was applied to the runoff modelling, with NSE and <i>R</i><sup>2</sup> of 0.46 and 0.51, respectively. The PERSIANN and TRMM rainfall data driven runoff did not show good agreement compared to the station data as both the NSE and <i>R</i><sup>2</sup> indices showed a low value of 0.3. GHCN2 and NCEP also did not show good correlations. The varied results by using these datasets indicate that although the gauge based and satellite-gauge merged products use some ground truth data, the different interpolation techniques and merging algorithms could also be a source of uncertainties. This entails a good understanding of the response of the hydrological model to different datasets and a quantification of the uncertainties in these datasets. Such a methodology is also useful for planning on Rainfall-runoff and even reservoir/river management both at rural and urban scales

Identifying factors that affected student enrolment in Additional Mathematics for urban areas of Kuantan district

The skilful and qualified Science, Technology, Engineering and Mathematics (STEM) workforces are expected to be in high demand in the 21st digital economy. In Malaysia public education system, the principal key to STEM education is Additional Mathematics. However, the statistics depicted that the number of upper secondary students enrolled in Additional Mathematics have been severely delineated. Furthermore, the prerequisite to enrol in STEM and Technical and Vocational Education and Training (TVET) courses in tertiary education is achieving a minimum grade C for Additional Mathematics. Therefore, the principal objective of this article is to identify the significant factors that affected the upper secondary students enrolled in Additional Mathematics using Pearson's chi-square test without Yates continuity correction and unadjusted odds ratio (OR). A validated questionnaire comprised nine potential factors that affected enrolment in additional mathematics was distributed to 389 Form Four students' cohort 2020 from four selected urban government schools to pursue this objective. Based on the finding of this article, several initiatives might be taken by the policymakers, such as the teachers may enhancing and throughout the broad of STEM and TVET careers in the 21st digital economy era, while the students' parents can participate in schools in building the communicating and coordinating mechanism. Consequently, the number of upper secondary enrolled in STEM education might be increased to pipeline the future STEM and TVET human capitals in sustaining and stabilising the national economy in the digital era

A hybrid, auto-adaptive, and rule-based multi-agent approach using evolutionary algorithms for improved searching

Selecting the most appropriate heuristic for solving a specific problem is not easy, for many reasons. This article focuses on one of these reasons: traditionally, the solution search process has operated in a given manner regardless of the specific problem being solved, and the process has been the same regardless of the size, complexity and domain of the problem. To cope with this situation, search processes should mould the search into areas of the search space that are meaningful for the problem. This article builds on previous work in the development of a multi-agent paradigm using techniques derived from knowledge discovery (data-mining techniques) on databases of so-far visited solutions. The aim is to improve the search mechanisms, increase computational efficiency and use rules to enrich the formulation of optimization problems, while reducing the search space and catering to realistic problems.Izquierdo Sebastián, J.; Montalvo Arango, I.; Campbell, E.; Pérez García, R. 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Bioresponsive Mesoporous Silica Nanoparticles for Triggered Drug Release

Mesoporous silica nanoparticles (MSNPs) have garnered a great deal of attention as potential carriers for therapeutic payloads. However, achieving triggered drug release from MSNPs in vivo has been challenging. Here, we describe the synthesis of stimulus-responsive polymer-coated MSNPs and the loading of therapeutics into both the core and shell domains. We characterize MSNP drug-eluting properties in vitro and demonstrate that the polymer-coated MSNPs release doxorubicin in response to proteases present at a tumor site in vivo, resulting in cellular apoptosis. These results demonstrate the utility of polymer-coated nanoparticles in specifically delivering an antitumor payload.National Science Foundation (U.S.) (grant R01-CA124427)National Science Foundation (U.S.) (grant U54-CA119349)National Science Foundation (U.S.) (grant U54-CA119335