Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs, is unsupervised, and does not require the number of events given a priori. Specifically, we incrementally explore the graph neighborhoods using 1-dimensional (1D) SE minimization to supplement the existing message graph with edges between semantically related messages. We then detect events from the message graph by hierarchically minimizing 2-dimensional (2D) SE. Our proposed 1D and 2D SE minimization algorithms are customized for social event detection and effectively tackle the efficiency problem of the existing SE minimization algorithms. Extensive experiments show that HISEvent consistently outperforms GNN-based methods and achieves the new SOTA for social event detection under both closed- and open-set settings while being efficient and robust.Comment: Accepted to AAAI 202

JAX-FEM: A differentiable GPU-accelerated 3D finite element solver for automatic inverse design and mechanistic data science

This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM) library. Constructed on top of Google JAX, a rising machine learning library focusing on high-performance numerical computing, JAX-FEM is implemented with pure Python while scalable to efficiently solve problems with moderate to large sizes. For example, in a 3D tensile loading problem with 7.7 million degrees of freedom, JAX-FEM with GPU achieves around 10$\times$ acceleration compared to a commercial FEM code depending on platform. Beyond efficiently solving forward problems, JAX-FEM employs the automatic differentiation technique so that inverse problems are solved in a fully automatic manner without the need to manually derive sensitivities. Examples of 3D topology optimization of nonlinear materials are shown to achieve optimal compliance. Finally, JAX-FEM is an integrated platform for machine learning-aided computational mechanics. We show an example of data-driven multi-scale computations of a composite material where JAX-FEM provides an all-in-one solution from microscopic data generation and model training to macroscopic FE computations. The source code of the library and these examples are shared with the community to facilitate computational mechanics research

Effective Dispersion of MgO Nanostructure on Biochar Support as a Basic Catalyst for Glucose Isomerization

Glucose isomerization to fructose is one of the most important reactions in the field of biomass valorization. We demonstrate wood waste valorization with MgCl2 salt to synthesize an environment-friendly catalyst (i.e., MgO-biochar), which exhibits effective glucose-to-fructose isomerization with over 30% fructose yield and 80% selectivity at only 100 °C for 30 min in water as a green medium. This study highlights that one-step synthesis can effectively disperse and tether MgO nanostructures to the biochar matrix, which displays a significant reduction of Mg leaching compared to MgO-biochars produced by two-step synthesis and pure MgO. The MgCl2 acts as a porogen that facilitates the formation of a porous biochar structure and dispersion of nanostructured MgO. We identify key parameters of impregnation media (ethylene glycol, ethanol, and water) and pyrolysis conditions (600/750 °C in N2/CO2 atmosphere) that are responsible for adjusting the reactivity and stability of MgO, which enable the design of more effective and recyclable biochar catalysts. Weak interactions between MgCl2 and biomass in the presence of aqueous miscible organic solvents as shape-directing agents are accountable for fast leaching of Mg from the MgO-biochar surface. The FTIR spectra confirm the existence of various coordinations on the hydroxylated surfaces of MgO-biochar surfaces. The mesoporous structures of the biochar support enhance the stability of MgO moieties as revealed by BET, XRD, and Raman analyses. Given the benefits of effective MgO dispersion on the biochar support, we can reduce the amount of MgO active species involved in each reaction run, which mitigates over-reaction compared to pure MgO catalysts and achieves high fructose yield and selectivity for three consecutive cycles

Effectiveness of respiratory protective equipment on source control of exhaled pollutants

Respiratory protective equipment (RPE) is used either to protect the wearer (protection effect during inhalation) or to protect others from potential transmission of pathogens from the wearer (source control effect during exhalation). Inhalation and exhalation are two processes that generate microenvironments with different characteristics inside the RPE. In order to quantitatively evaluate the source control effectiveness (SCE) of RPE, the gap size between the RPE and the face was measured using volunteers, and the tracer-gas-based method and aerosol-based method were applied to visualize and quantify the dispersion of exhaled pollutants through the face mask and the gap using a manikin. The tracer-gas-based method (using CO2) largely overestimated the concentration of exhaled pollutants in front of the RPE. The SCE of RPE was 76–99% for aerosols and 79–92% for tracer gas. The top gap of the face mask was the dominant leakage route of exhaled pollutants. When the size of the top gap increased from 8 mm to 20 mm, the SCE for aerosols smaller than 1 μm decreased by about 15%. The SCE of the combined RPE with an N95 mask and a face shield was about 7% higher than that for an N95 mask alone. Increasing the pulmonary ventilation rate from 6 to 13 L/min and the respiratory cycle from 3 to 5 s decreased the SCE of RPE by 16% and 9%, respectively, indicating the importance of considering the activity level. The data obtained provides a basis for evaluating and improving the performance of RPE

Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection

As a trending approach for social event detection, graph neural network (GNN)-based methods enable a fusion of natural language semantics and the complex social network structural information, thus showing SOTA performance. However, GNN-based methods can miss useful message correlations. Moreover, they require manual labeling for training and predetermining the number of events for prediction. In this work, we address social event detection via graph structural entropy (SE) minimization. While keeping the merits of the GNN-based methods, the proposed framework, HISEvent, constructs more informative message graphs, is unsupervised, and does not require the number of events given a priori. Specifically, we incrementally explore the graph neighborhoods using 1-dimensional (1D) SE minimization to supplement the existing message graph with edges between semantically related messages. We then detect events from the message graph by hierarchically minimizing 2-dimensional (2D) SE. Our proposed 1D and 2D SE minimization algorithms are customized for social event detection and effectively tackle the efficiency problem of the existing SE minimization algorithms. Extensive experiments show that HISEvent consistently outperforms GNN-based methods and achieves the new SOTA for social event detection under both closed- and open-set settings while being efficient and robust