On the Noise Scheduling for Generating Plausible Designs with Diffusion Models

Deep Generative Models (DGMs) are widely used to create innovative designs across multiple industries, ranging from fashion to the automotive sector. In addition to generating images of high visual quality, the task of structural design generation imposes more stringent constrains on the semantic expression, e.g., no floating material or missing part, which we refer to as plausibility in this work. We delve into the impact of noise schedules of diffusion models on the plausibility of the outcome: there exists a range of noise levels at which the model's performance decides the result plausibility. Also, we propose two techniques to determine such a range for a given image set and devise a novel parametric noise schedule for better plausibility. We apply this noise schedule to the training and sampling of the well-known diffusion model EDM and compare it to its default noise schedule. Compared to EDM, our schedule significantly improves the rate of plausible designs from 83.4% to 93.5% and Fr\'echet Inception Distance (FID) from 7.84 to 4.87. Further applications of advanced image editing tools demonstrate the model's solid understanding of structure

Efficient Deep Reinforcement Learning via Adaptive Policy Transfer

Transfer Learning (TL) has shown great potential to accelerate Reinforcement Learning (RL) by leveraging prior knowledge from past learned policies of relevant tasks. Existing transfer approaches either explicitly computes the similarity between tasks or select appropriate source policies to provide guided explorations for the target task. However, how to directly optimize the target policy by alternatively utilizing knowledge from appropriate source policies without explicitly measuring the similarity is currently missing. In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea. Our framework learns when and which source policy is the best to reuse for the target policy and when to terminate it by modeling multi-policy transfer as the option learning problem. PTF can be easily combined with existing deep RL approaches. Experimental results show it significantly accelerates the learning process and surpasses state-of-the-art policy transfer methods in terms of learning efficiency and final performance in both discrete and continuous action spaces.Comment: Accepted by IJCAI'202

N- and S-doped mesoporous carbon as metal-free cathode catalysts for direct biorenewable alcohol fuel cells

Nitrogen and sulfur were simultaneously doped into the framework of mesoporous CMK-3 as metal-free catalysts for direct biorenewable alcohol fuel cells. Glucose, NH3, and thiophene were used as carbon, nitrogen and sulfur precursors, respectively, to prepare mesoporous N-S-CMK-3 with uniform mesopores and extra macropores, resulting in good O2 diffusion both in half cell and alcohol fuel cell investigations. Among all investigated CMK-3 based catalysts, N-S-CMK-3 prepared at 800 °C exhibited the highest ORR activity with the onset potential of 0.92 V vs. RHE, Tafel slope of 68 mV dec−1, and 3.96 electron transfer number per oxygen molecule in 0.1 M KOH. The alkaline membrane-based direct alcohol fuel cell (DAFC) with N-S-CMK-3 cathode displayed 88.2 mW cm−2 peak power density without obvious O2 diffusion issue, reaching 84% initial performance of that with a Pt/C cathode. The high catalyst durability and fuel-crossover tolerance led to stable performance of the N-S-CMK-3 cathode DAFC with 90.6 mW cm−2 peak power density after 2 h operation, while the Pt/C cathode-based DAFC lost 36.9% of its peak power density. The high ORR activity of N-S-CMK-3 can be attributed to the synergistic effect between graphitic-N and S (C–S–C structure), suggesting great potential to use N-S-CMK-3 as an alternative to noble metal catalysts in the fuel cell cathode