Fast-MoCo: Boost Momentum-based Contrastive Learning with Combinatorial Patches

Contrastive-based self-supervised learning methods achieved great success in recent years. However, self-supervision requires extremely long training epochs (e.g., 800 epochs for MoCo v3) to achieve promising results, which is unacceptable for the general academic community and hinders the development of this topic. This work revisits the momentum-based contrastive learning frameworks and identifies the inefficiency in which two augmented views generate only one positive pair. We propose Fast-MoCo - a novel framework that utilizes combinatorial patches to construct multiple positive pairs from two augmented views, which provides abundant supervision signals that bring significant acceleration with neglectable extra computational cost. Fast-MoCo trained with 100 epochs achieves 73.5% linear evaluation accuracy, similar to MoCo v3 (ResNet-50 backbone) trained with 800 epochs. Extra training (200 epochs) further improves the result to 75.1%, which is on par with state-of-the-art methods. Experiments on several downstream tasks also confirm the effectiveness of Fast-MoCo.Comment: Accepted for publication at the 2022 European Conference on Computer Vision (ECCV 2022

Photocatalytic hydrogen evolution over nickel cobalt bimetallic phosphate anchored graphitic carbon nitrides by regulation of the d-band electronic structure

Non-precious metal co-catalysts with high activity and stability are extremely desirable for economically viable photocatalytic molecular hydrogen (H₂) evolution. Herein, nickel cobalt phosphate (NiCo–Pi) was introduced into graphitic carbon nitride layers (g-C₃N₄) via a sonication-assisted ion intercalation method as a substitute for noble metal co-catalysts. Under visible light irradiation, NiCo–Pi/g-C₃N₄ (Ni/Co molar ratio of 4 : 5) exhibited the highest photocatalytic activity (ca. 10 184 μmol h⁻¹ g⁻¹) and stability for H₂ evolution. Synchrotron radiation X-ray absorption spectroscopy (XAS) indicated that NiCo–Pi is closely bound to g-C₃N₄ via covalent binding, which accelerates electron transport. Moreover, the unoccupied d-orbital in NiCo–Pi causes the surface to strongly adsorb atomic hydrogen (*H). Theoretically, density functional theory (DFT) calculations demonstrated that the d-band center position of NiCo–Pi is relocated upon adjusting the Ni/Co molar ratio, which changes the adsorption energy of NiCo–Pi toward intermediate state *H. This work provides new insights for exploring the role of the bimetallic composition in non-noble co-catalysts for highly efficient H₂ evolution

Photocatalytic hydrogen evolution over nickel cobalt bimetallic phosphate anchored graphitic carbon nitrides by regulation of the d-band electronic structure

Non-precious metal co-catalysts with high activity and stability are extremely desirable for economically viable photocatalytic molecular hydrogen (H₂) evolution. Herein, nickel cobalt phosphate (NiCo–Pi) was introduced into graphitic carbon nitride layers (g-C₃N₄) via a sonication-assisted ion intercalation method as a substitute for noble metal co-catalysts. Under visible light irradiation, NiCo–Pi/g-C₃N₄ (Ni/Co molar ratio of 4 : 5) exhibited the highest photocatalytic activity (ca. 10 184 μmol h⁻¹ g⁻¹) and stability for H₂ evolution. Synchrotron radiation X-ray absorption spectroscopy (XAS) indicated that NiCo–Pi is closely bound to g-C₃N₄ via covalent binding, which accelerates electron transport. Moreover, the unoccupied d-orbital in NiCo–Pi causes the surface to strongly adsorb atomic hydrogen (*H). Theoretically, density functional theory (DFT) calculations demonstrated that the d-band center position of NiCo–Pi is relocated upon adjusting the Ni/Co molar ratio, which changes the adsorption energy of NiCo–Pi toward intermediate state *H. This work provides new insights for exploring the role of the bimetallic composition in non-noble co-catalysts for highly efficient H₂ evolution

Advancing Attack-Resilient Scheduling of Integrated Energy Systems with Demand Response via Deep Reinforcement Learning

Optimally scheduling multi-energy flow is an effective method to utilize renewable energy sources (RES) and improve the stability and economy of integrated energy systems (IES). However, the stable demand-supply of IES faces challenges from uncertainties that arise from RES and loads, as well as the increasing impact of cyber-attacks with advanced information and communication technologies adoption. To address these challenges, this paper proposes an innovative model-free resilience scheduling method based on state-adversarial deep reinforcement learning (DRL) for integrated demand response (IDR)-enabled IES. The proposed method designs an IDR program to explore the interaction ability of electricity-gas-heat flexible loads. Additionally, a state-adversarial Markov decision process (SA-MDP) model characterizes the energy scheduling problem of IES under cyber-attack. The state-adversarial soft actor-critic (SA-SAC) algorithm is proposed to mitigate the impact of cyber-attacks on the scheduling strategy. Simulation results demonstrate that our method is capable of adequately addressing the uncertainties resulting from RES and loads, mitigating the impact of cyber-attacks on the scheduling strategy, and ensuring a stable demand supply for various energy sources. Moreover, the proposed method demonstrates resilience against cyber-attacks. Compared to the original soft actor-critic (SAC) algorithm, it achieves a 10\% improvement in economic performance under cyber-attack scenarios