Media Culture 2020: collaborative teaching and blended learning using social media and cloud-based technologies

The Media Culture 2020 project was considered to be a great success by all the partners, academics and especially the students who took part. It is a true example of an intercultural, multidisciplinary, blended learning experience in higher education that achieved it goals of breaking down classroom walls and bridging geographical distance and cultural barriers. The students with different skills, coming from different countries and cultures, interacting with other enlarges the possibilities of creativity, collaboration and quality work. The blend of both synchronous and asynchronous teaching methods fostered an open, blended learning environment, one that extended the traditional boundaries of the classroom in time and space. The interactive and decentralized nature of digital tools enabled staff and students to communicate and strengthen social ties, alongside participation in the production of new knowledge and media content. For students and lecturers, the implementation of social media and cloud platforms offered an innovative solution to both teaching and learning in a collaborative manner. By leveraging the interactive and decentralised capabilities of a range of technologies in an educational context, this model of digital scholarship facilitates an open and dynamic working environment. Blended teaching methods allow for expansive collaboration, whereby information and knowledge can be accessed and disseminated across a number of networked devices

Positive Definite Solutions of the Nonlinear Matrix Equation X+AHXˉ−1A=IX+A^{\mathrm{H}}\bar{X}^{-1}A=I

This paper is concerned with the positive definite solutions to the matrix equation $X+A^{\mathrm{H}}\bar{X}^{-1}A=I$ where $X$ is the unknown and $A$ is a given complex matrix. By introducing and studying a matrix operator on complex matrices, it is shown that the existence of positive definite solutions of this class of nonlinear matrix equations is equivalent to the existence of positive definite solutions of the nonlinear matrix equation $W+B^{\mathrm{T}}W^{-1}B=I$ which has been extensively studied in the literature, where $B$ is a real matrix and is uniquely determined by $A.$ It is also shown that if the considered nonlinear matrix equation has a positive definite solution, then it has the maximal and minimal solutions. Bounds of the positive definite solutions are also established in terms of matrix $A$. Finally some sufficient conditions and necessary conditions for the existence of positive definite solutions of the equations are also proposed

Tourism's Forward and Backward Linkages

This paper proposes “linkage analysis” as a complement to the traditional “tourism impact analysis” to examine tourism’s economic imprints on a destination’s economy. Although related, the two methods are not the same. The starting point of tourism “impact analysis” is “final demand”; impact analysis measures the direct and indirect impacts of tourist spending on the local economy. By contrast, the starting point of “linkage analysis” is the tourism sector; the analysis examines the strengths of the inter-sectoral forward (FL) and backward (BL) relationships between the tourism sector and the non-tourism industries in the rest of the economy. The FL measures the relative importance of the tourism sector as supplier to the other (non-tourism) industries in the economy whereas the BL measures its relative importance as demander. Directly applying conventional linkage analysis to tourism is not straightforward because tourism is not a defined industry. Thus we develop a methodology to calculate tourism’s forward and backward linkages using information from national, regional, or local input-output tables and demonstrate its utility by applying it to Hawaii.

Warm DBI Inflation

We propose a warm inflationary model in the context of relativistic D-brane inflation in a warped throat, which has Dirac-Born-Infeld (DBI) kinetic term and is coupled to radiation through a dissipation term. The perturbation freezes at the sound horizon and the power spectrum is determined by a combination of the dissipative parameter and the sound speed parameter. The thermal dissipation ameliorates the {\it eta} problem and softens theoretical constraints from the extra-dimensional volume and from observational bounds on the tensor-to-scalar ratio. The warm DBI model can lead to appreciable non-Gaussianity of the equilateral type. As a phenomenological model, ignoring compactification constraints, we show that large-field warm inflation models do not necessarily yield a large tensor-to-scalar ratio.Comment: 5 pages, 1 figure, IPMU-10-019

Island Loss for Learning Discriminative Features in Facial Expression Recognition

Over the past few years, Convolutional Neural Networks (CNNs) have shown promise on facial expression recognition. However, the performance degrades dramatically under real-world settings due to variations introduced by subtle facial appearance changes, head pose variations, illumination changes, and occlusions. In this paper, a novel island loss is proposed to enhance the discriminative power of the deeply learned features. Specifically, the IL is designed to reduce the intra-class variations while enlarging the inter-class differences simultaneously. Experimental results on four benchmark expression databases have demonstrated that the CNN with the proposed island loss (IL-CNN) outperforms the baseline CNN models with either traditional softmax loss or the center loss and achieves comparable or better performance compared with the state-of-the-art methods for facial expression recognition.Comment: 8 pages, 3 figure

Panorama - Caring for the Palace Museum, Bejing, China

Shi Zhimin discusses his work as Director of the Ancient Building Management Office of The Palace Museum in Beijing, still also recognized by many visitors as the former Chinese imperial palace known as The Forbidden City, with Cai Bowen and Professor James Hagy, Director of The Rooftops Project.https://digitalcommons.nyls.edu/rooftops_project/1024/thumbnail.jp

Optimizing Filter Size in Convolutional Neural Networks for Facial Action Unit Recognition

Recognizing facial action units (AUs) during spontaneous facial displays is a challenging problem. Most recently, Convolutional Neural Networks (CNNs) have shown promise for facial AU recognition, where predefined and fixed convolution filter sizes are employed. In order to achieve the best performance, the optimal filter size is often empirically found by conducting extensive experimental validation. Such a training process suffers from expensive training cost, especially as the network becomes deeper. This paper proposes a novel Optimized Filter Size CNN (OFS-CNN), where the filter sizes and weights of all convolutional layers are learned simultaneously from the training data along with learning convolution filters. Specifically, the filter size is defined as a continuous variable, which is optimized by minimizing the training loss. Experimental results on two AU-coded spontaneous databases have shown that the proposed OFS-CNN is capable of estimating optimal filter size for varying image resolution and outperforms traditional CNNs with the best filter size obtained by exhaustive search. The OFS-CNN also beats the CNN using multiple filter sizes and more importantly, is much more efficient during testing with the proposed forward-backward propagation algorithm