Transition-metal chalcogenides (TMC) have been widely studied as active electrocatalysts toward the hydrogen evolution reaction due to their suitable d-electron configuration and relatively high electrical conductivity. Herein, we develop a feasible method to synthesize an orthorhombic phase of CoSe2 (o-CoSe2) from the regeneration of Co0.85Se, where the temperature plays a key role in controlling the structure transformation. To the best of our knowledge, this is the first report about this synthetic route for o-CoSe2. The resulting o-CoSe2 catalysts exhibit enhanced hydrogen evolution reaction performance with an overpotential of 220 mV to reach 10 mA cm(-2) in 1.0 M KOH. Density functional theory calculations further reveal that the change in the Gibbs free energy of hydrogen, water adsorption energy, and the downshifted d-band center make o-CoSe2 more suitable for accelerating the HER process.

In practical application, facial image recognition is vulnerable to be attacked by photos, videos, etc., while some currently used artificial feature extractors in machine learning, such as activity detection, texture descriptors, and distortion detection, are insufficient due to their weak detection ability in feature extraction from unknown attack. In order to deal with the aforementioned deficiency and improve the network security, this paper proposes directional difference convolution for the deep learning in gradient image information extraction, which analyzes pixel correlation within the convolution domain and calculates pixel gradients through difference calculation. Its combination with traditional convolution can be optimized by a parameter $\theta$. Its stronger ability in gradient extraction improves the learning and predicting ability of the network, whose performance testing on CASIA-MFSD, Replay-Attack, and MSU-MFSD for face anti-spoofing task shows that our method outperforms the current related methods.

Exploring procedurally-generated environments is a formidable challenge for model-free deep reinforcement learning (DRL). One of the state-of-the-art exploration methods, exploration via ranking the episodes (RAPID), assigns episode-level episodic exploration scores to past episodes and makes the DRL agent imitate exploration behaviors from the highly-scored episodes. However, in complex procedurally-generated environments, such continued imitation can hinder RAPID’s performance due to the emergence of solidified episodes, i.e., episodes that remain in the highly-scored episode set due to their high scores. These solidified episodes can lead the RAPID DRL agent to overfit, hindering its exploration and performance. To address this, we design an episode-decayed exploration score, which combines the episodic exploration score and an episodic decay factor, to avoid solidifying highly-scored episodes and aid in selecting good exploration episodes. Leveraging this score, we propose exploration via imitating highly-scored episode-decayed exploration episodes (EDEE), an effective and stable exploration method for procedurally-generated environments. EDEE assigns episode-decayed exploration scores to past episodes and stores the highly-scored episodes as good exploration episodes in a small ranking buffer. The DRL agent then imitates good exploration behaviors sampled from this ranking buffer through the exploration-based sampling to reproduce these good exploration behaviors from good exploration episodes. Extensive experiments on procedurally-generated environments, specifically MiniGrid and 3D maze from MiniWorld, and sparse MuJoCo environments show that EDEE significantly outperforms RAPID in terms of final performance and sample efficiency in complex procedurally-generated environments and sparse continuous environments. Moreover, even without extrinsic rewards, EDEE maintains excellent performance in procedurally-generated environments.

In dynamic and unstructured environments, the obstacle avoidance capabilities of Unmanned Aerial Vehicles (UAVs) are crucial for mission success. Traditional methods struggle with adaptability and effectiveness in unknown or changing scenes. In contrast, the commonly used deep reinforcement learning (DRL) ones suffer from slow convergence, reduced accuracy, and inadequate robustness due to the loss of sensitivity to outliers and parameter rigidity. To address these challenges, we propose an enhanced DRL framework that leverages a Dynamic Huber loss function tailored for UAV autonomous obstacle avoidance. By incorporating Soft updates for target network and dynamically tuning the Huber loss, the proposed method facilitates faster model convergence, superior control precision, and improved robustness. Both theoretical analysis and experimental simulation verify its effectiveness with superior planning success rate, shorter average path length, and faster model convergence over traditional approaches. Specifically, in static environments, the Dynamic Huber-loss-based DRL framework achieves a 98.85% success rate with an optimized average path length of 10.73; in dynamic environments, it attains a 74.20% success rate with an average path length of 37.04; adding wind disturbances in a dynamic environment, it attains a 70.95% success rate with an average path length of 40.40, highlighting its enhanced performance and adaptability.

Plant electrical signals have been shown to be generated in response to various environmental stresses, but the relationship between these signals and stress tolerance is not well understood. In this study, we used the Arabidopsis STTM165/166 mutant, which exhibits enhanced temperature tolerance, to examine this relationship. Surface recording techniques were utilized to compare the generation ratio and duration characteristics of electrical signals in the STTM165/166 mutant and wild type (WT). Patch-clamp recording was employed to assess ion channel currents, specifically those of calcium ions. The current intensity of the mutant was found to be lower than that of the WT. As calcium ions are involved in the generation of plant electrical signals, we hypothesized that the reduced calcium channel activity in the mutant increased its electrical signal threshold. RNA-Seq analysis revealed differential expression of AHA genes in the STTM165/166 mutant, which may contribute to the prolonged depolarization phenotype. Gene Ontology enrichment of differentially expressed genes (DEGs) identified associations between these DEGs and various stresses, including temperature, salt, and those related to the jasmonic acid and abscisic acid pathways. These findings provide experimental evidence for the use of plant electrical signals in characterizing stress tolerance and explore potential ion mechanisms through patch-clamp recording and DEG Gene Ontology analysis. They also emphasize the need for further research on the relationship between plant electrical signals and stress tolerance.

Exploring sparse reward environments remains a major challenge in model-free deep reinforcement learning. State-of-the-art exploration methods address this challenge by utilizing intrinsic rewards to guide exploration in uncertain environment dynamics or novel states. However, these methods fall short in procedurally-generated environments, where the agent is unlikely to visit a state more than once due to the different environments generated in each episode. Recently, imitation learning-based exploration methods have been proposed to guide exploration in different kinds of procedurally-generated environments by imitating high-quality exploration episodes. However, these methods have weaker exploration capabilities and lower sample efficiency in complex procedurally-generated environments. Motivated by the fact that demonstrations can guide exploration in sparse reward environments, we propose Improved Exploration with Demonstrations (IEWD), an improved imitation learning-based exploration method in procedurally-generated environments, which utilizes demonstrations from these environments. IEWD assigns different episode-level exploration scores to each demonstration episode and generated episode. IEWD then ranks these episodes based on their scores and stores highly-scored episodes into a small ranking buffer. IEWD treats these highly-scored episodes as good exploration episodes, and makes the deep RL agent imitate exploration behaviors from the ranking buffer to reproduce exploration behaviors from good exploration episodes. Additionally, IEWD adopts the experience replay buffer to store generated positive episodes and demonstrations, and employs self-imitating learning to utilize experiences from the experience replay buffer to optimize the policy of the deep RL agent. We evaluate our method IEWD on several procedurally-generated MiniGrid environments and 3D maze environments from MiniWorld. The results show that IEWD significantly outperforms existing learning from demonstration methods and exploration methods, including state-of-the-art imitation learning-based exploration methods, in terms of sample efficiency and final performance in complex procedurally-generated environments.