Multiresolution Equivariant Graph Variational Autoencoder

In this paper, we propose Multiresolution Equivariant Graph Variational Autoencoders (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner. At each resolution level, MGVAE employs higher order message passing to encode the graph while learning to partition it into mutually exclusive clusters and coarsening into a lower resolution that eventually creates a hierarchy of latent distributions. MGVAE then constructs a hierarchical generative model to variationally decode into a hierarchy of coarsened graphs. Importantly, our proposed framework is end-to-end permutation equivariant with respect to node ordering. MGVAE achieves competitive results with several generative tasks including general graph generation, molecular generation, unsupervised molecular representation learning to predict molecular properties, link prediction on citation graphs, and graph-based image generation

Factors Affecting the Creativity of Young Lecturers

Purpose: The study was conducted to determine the factors affecting the creative capacity of young lecturers in the Vietnamese higher education system.   Theoretical framework: Creativity is the creation of new and helpful ideas in the field of science, art, business, and everyday activities (Amabile, 1996; Amabile, 1997). According to Woodman et al. (1993), creativity is the creation of new products, services, ideas, procedures, or processes that are useful and valuable. Kreitner & Kinicki (2004) argued that creativity is defined as the process of using imagination and skills to develop a new, unique idea, product, or process. Creativity is a difficult concept to define, researchers do not fully agree with any single definition (DiLiello & Houghton, 2006).   Design/methodology/approach: An official survey was conducted from March to April 2022. The selected subjects are young lecturers (under 40 years old) working at 15 universities in the higher education system in Vietnam. The number of survey questionnaires achieved was 328, and applying structural equation modeling (SEM) to test the research hypotheses.   Findings: The research has pointed out four factors that have positive impacts on the creative capacity of young lecturers, including intrinsic motivation, creative self-efficacy, thinking style, and the support environment. Among these, intrinsic motivation is the factor that has the most influence on the creative ability of young lecturers.   Research, Practical & Social implications: Several managerial implications are proposed to promote the creativity of young lecturers. Firstly, universities should have policies to encourage young lecturers to accept challenges and come up with new ideas. Secondly, universities should build a system to receive, evaluate, support, and provide practical suggestions for creative ideas from young lecturers. Thirdly, universities should develop a policy of recognizing and rewarding their efforts.   Originality/value: In general, the study has achieved the set goals. The study has demonstrated four factors affecting the creative capacity of young lecturers in the higher education system in Vietnam. All these factors have a positive impact on creative capacity

Standard gradient models and crack simulation

The standard gradient models have been intensively studied in the literature, cf. Fremond (1985) or Gurtin (1991) for various applications in plasticity, damage mechanics and phase change analysis. The governing equations for a solid have been introduced essentially from an extended version of the virtual equation. It is shown here first that these equations can also be derived from the formalism of energy and dissipation potentials and appear as a generalized Biot equation for the solid. In this spirit, the governing equations for higher gradient models can be straightforwardly given. The interest of gradient models is then discussed in the context of damage mechanics and crack simulation. The phenomenon of strain localization in a time-dependent or time-independent process of damage is explored as a convenient numerical method to simulate the propagation of cracks, in relation with some recent works of theliterature, cf. Bourdin Marigo [3], Lorentz al  [5], Henry al [12]

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators

Knowledge Creation And Green Entrepreneurship: A Study Of Two Vietnamese Green Firms

This paper aims to advance the understanding and practice of knowledge-based management in Vietnam by studying two Vietnamese agricultural companies. It provides illustrative examples of how knowledge-based management, pursuing a vision that fosters creativity and innovation by employees, could ultimately fulfil the profitability objective of the business and at the same time add value to the community’s quality of life. Using the SECI model as the parameter for analysis, we found that knowledge creation processes were affected by a combination of leadership, teamwork and Ba, corporate culture, and human resource management. Our conclusion emphasises the need for future research to further examine the practice of knowledge-based management in cross-industry segments in Vietnam and in other countries with similar conditions

THE DIVERSITY OF YELLOW CAMELLIAS IN THE CENTRAL HIGHLANDS, VIETNAM

The Central Highlands (Tây Nguyên) is a center of yellow camellia diversity in Vietnam and the world. The Central Highlands contains 18 of Vietnam’s yellow camellia species, accounting for 37% of yellow camellia species in Vietnam and 28% of yellow camellia species worldwide. Moreover, all 18 yellow camellia species in the Central Highlands are endemic to Vietnam. The camellias of the Central Highlands belong to nine sections, accounting for 75% of the world. The yellow colors occur in three groups: pale yellow, yellow, and yellow with compound colors. The yellow camellia distribution is dispersed at 500–1600 m elevation in evergreen broadleaf forests and mixed wood-bamboo forests

Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures

Contemporary graph learning algorithms are not well-defined for large molecules since they do not consider the hierarchical interactions among the atoms, which are essential to determine the molecular properties of macromolecules. In this work, we propose Multiresolution Graph Transformers (MGT), the first graph transformer architecture that can learn to represent large molecules at multiple scales. MGT can learn to produce representations for the atoms and group them into meaningful functional groups or repeating units. We also introduce Wavelet Positional Encoding (WavePE), a new positional encoding method that can guarantee localization in both spectral and spatial domains. Our proposed model achieves competitive results on two macromolecule datasets consisting of polymers and peptides, and one drug-like molecule dataset. Importantly, our model outperforms other state-of-the-art methods and achieves chemical accuracy in estimating molecular properties (e.g., GAP, HOMO and LUMO) calculated by Density Functional Theory (DFT) in the polymers dataset. Furthermore, the visualizations, including clustering results on macromolecules and low-dimensional spaces of their representations, demonstrate the capability of our methodology in learning to represent long-range and hierarchical structures. Our PyTorch implementation is publicly available at https://github.com/HySonLab/Multires-Graph-Transforme