2,040 research outputs found
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
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