ExplainableFold: Understanding AlphaFold Prediction with Explainable AI

This paper presents ExplainableFold, an explainable AI framework for protein structure prediction. Despite the success of AI-based methods such as AlphaFold in this field, the underlying reasons for their predictions remain unclear due to the black-box nature of deep learning models. To address this, we propose a counterfactual learning framework inspired by biological principles to generate counterfactual explanations for protein structure prediction, enabling a dry-lab experimentation approach. Our experimental results demonstrate the ability of ExplainableFold to generate high-quality explanations for AlphaFold's predictions, providing near-experimental understanding of the effects of amino acids on 3D protein structure. This framework has the potential to facilitate a deeper understanding of protein structures.Comment: This work has been accepted for presentation at the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2023

Hydrogels with Self-Healing Attribute

Given increasing environmental issues and energy crisis, mimicking nature to confer materials with self-healing attribute to prolong their lifespan is highly imperative. As representative of soft matter with extensive applications, hydrogels have gained significant attention. In this chapter, a survey of the current strategies for synthesizing self-healing hydrogels based on inorganic-based, polymer and nanocomposite hydrogels is covered and highlighted. Several examples for non-autonomic and autonomic self-healing hydrogels, according to the trigger exerted, are presented. General mechanisms accounting for self-healing hydrogels are listed. Some typical instances to outline the emerging applications of self-healing hydrogels are also provided. Finally, a perspective on the current trends and challenges is briefly summarized

How does green finance affect the low-carbon economy? Capital allocation, green technology innovation and industry structure perspectives

The development of green finance and social low-carbon transformation is an essential concern for academia and industry. Based on Chinese provincial panel data spanning the period 2005–2019, we introduce the Cobb-Douglas production function and spatial Durbin and dynamic panel threshold models to deeply analyse the impact of green finance on the low-carbon economy. The mechanism test demonstrates that the scale, technique, and structural effects of green finance play a significant role in the low-carbon economy: they correct capital mismatch, promote green technology innovation, and optimise industrial structure. Meanwhile, green finance not only promotes the local low-carbon economy construction process, but also generates spatial spillover effects on neighbouring regions; however, there is regional heterogeneity in the impact of the transmission mechanism. Furthermore, only when capital mismatch is severe, and the low-end industrial structure poor is the positive impact of green finance on the low-carbon economy highlighted based on scale and structural effects; the ability of green finance to contribute to the low-carbon economy through the technique effect has been more stable and significant. This emphasises that green technology innovation is key to supporting low-carbon development in the long run

Conjugate Gradient Algorithm for the Symmetric Arrowhead Solution of Matrix Equation AXB=C

Based on the conjugate gradient (CG) algorithm, the constrained matrix equation AXB=C and the associate optimal approximation problem are considered for the symmetric arrowhead matrix solutions in the premise of consistency. The convergence results of the method are presented. At last, a numerical example is given to illustrate the efficiency of this method

Preparation of a low viscosity urethane-based composite for improved dental restoratives

Several new urethane-based dimethacrylates were synthesized, characterized and used to formulate the resin composites. Compressive strength (CS) was used as a screen tool to evaluate the mechanical property of the formed composites. Flexural strength, diametral tensile strength, water sorption, degree of conversion and shrinkage of the composites were also evaluated. The results show that most of the synthesized urethane-based dimethacrylates were solid, which are not suitable to dental filling restorations. However, it was found that liquid urethane-based dimethacrylates could be derivatized using asymmetrical methacrylate synthesis. Not only the newly synthesized urethane-based dimethacrylates showed lower viscosity values but also their constructed composites exhibited higher mechanical strengths. Without triethyleneglycol dimethacrylate (TEGDMA) addition, the new urethane-constructed composites showed significantly lower water sorption and shrinkage

Beyond Hard Samples: Robust and Effective Grammatical Error Correction with Cycle Self-Augmenting

Recent studies have revealed that grammatical error correction methods in the sequence-to-sequence paradigm are vulnerable to adversarial attack, and simply utilizing adversarial examples in the pre-training or post-training process can significantly enhance the robustness of GEC models to certain types of attack without suffering too much performance loss on clean data. In this paper, we further conduct a thorough robustness evaluation of cutting-edge GEC methods for four different types of adversarial attacks and propose a simple yet very effective Cycle Self-Augmenting (CSA) method accordingly. By leveraging the augmenting data from the GEC models themselves in the post-training process and introducing regularization data for cycle training, our proposed method can effectively improve the model robustness of well-trained GEC models with only a few more training epochs as an extra cost. More concretely, further training on the regularization data can prevent the GEC models from over-fitting on easy-to-learn samples and thus can improve the generalization capability and robustness towards unseen data (adversarial noise/samples). Meanwhile, the self-augmented data can provide more high-quality pseudo pairs to improve model performance on the original testing data. Experiments on four benchmark datasets and seven strong models indicate that our proposed training method can significantly enhance the robustness of four types of attacks without using purposely built adversarial examples in training. Evaluation results on clean data further confirm that our proposed CSA method significantly improves the performance of four baselines and yields nearly comparable results with other state-of-the-art models. Our code is available at https://github.com/ZetangForward/CSA-GEC

The Research of Car-Following Model Based on Real-Time Maximum Deceleration

This paper is concerned with the effect of real-time maximum deceleration in car-following. The real-time maximum acceleration is estimated with vehicle dynamics. It is known that an intelligent driver model (IDM) can control adaptive cruise control (ACC) well. The disadvantages of IDM at high and constant speed are analyzed. A new car-following model which is applied to ACC is established accordingly to modify the desired minimum gap and structure of the IDM. We simulated the new car-following model and IDM under two different kinds of road conditions. In the first, the vehicles drive on a single road, taking dry asphalt road as the example in this paper. In the second, vehicles drive onto a different road, and this paper analyzed the situation in which vehicles drive from a dry asphalt road onto an icy road. From the simulation, we found that the new car-following model can not only ensure driving security and comfort but also control the steady driving of the vehicle with a smaller time headway than IDM