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Existing adversarial algorithms for Deep Reinforcement Learning (DRL) have largely focused on identifying an optimal time to attack a DRL agent. However, little work has been explored in injecting efficient adversarial perturbations in DRL environments. We propose a suite of novel DRL adversarial attacks, called ACADIA, representing AttaCks Against Deep reInforcement leArning. ACADIA provides a set of efficient and robust perturbation-based adversarial attacks to disturb the DRL agent's decision-making based on novel combinations of techniques utilizing momentum, ADAM optimizer (i.e., Root Mean Square Propagation, or RMSProp), and initial randomization. These kinds of DRL attacks with novel integration of such techniques have not been studied in the existing Deep Neural Networks (DNNs) and DRL research. We consider two well-known DRL algorithms, Deep-Q Learning Network (DQN) and Proximal Policy Optimization (PPO), under Atari games and MuJoCo where both targeted and non-targeted attacks are considered with or without the state-of-the-art defenses in DRL (i.e., RADIAL and ATLA). Our results demonstrate that the proposed ACADIA outperforms existing gradient-based counterparts under a wide range of experimental settings. ACADIA is nine times faster than the state-of-the-art Carlini & Wagner (CW) method with better performance under defenses of DRL. 

We report a new neural backdoor attack, named Hibernated Backdoor, which is stealthy, aggressive and devastating. The backdoor is planted in a hibernated mode to avoid being detected. Once deployed and fine-tuned on end-devices, the hibernated backdoor turns into the active state that can be exploited by the attacker. To the best of our knowledge, this is the first hibernated neural backdoor attack. It is achieved by maximizing the mutual information (MI) between the gradients of regular and malicious data on the model. We introduce a practical algorithm to achieve MI maximization to effectively plant the hibernated backdoor. To evade adaptive defenses, we further develop a targeted hibernated backdoor, which can only be activated by specific data samples and thus achieves a higher degree of stealthiness. We show the hibernated backdoor is robust and cannot be removed by existing backdoor removal schemes. It has been fully tested on four datasets with two neural network architectures, compared to five existing backdoor attacks, and evaluated using seven backdoor detection schemes. The experiments demonstrate the effectiveness of the hibernated backdoor attack under various settings. 

Electrochemical two-electron water oxidation reaction (2e-WOR) has drawn significant attention as a promising process to achieve the continuous on-site production of hydrogen peroxide (H₂O₂). However, compared to the cathodic H₂O₂generation, the anodic 2e-WOR is more challenging to establish catalysts due to the severe oxidizing environment. In this study, we combine density functional theory (DFT) calculations with experiments to discover a stable and efficient perovskite catalyst for the anodic 2e-WOR. Our theoretical screening efforts identify LaAlO₃perovskite as a stable, active, and selective candidate for catalyzing 2e-WOR. Our experimental results verify that LaAlO₃achieves an overpotential of 510 mV at 10 mA cm⁻²in 4 M K₂CO₃/KHCO₃, lower than those of many reported metal oxide catalysts. In addition, LaAlO₃maintains a stable H₂O₂Faradaic efficiency with only a 3% decrease after 3 h at 2.7 V vs. RHE. This computation-experiment synergistic approach introduces another effective direction to discover promising catalysts for the harsh anodic 2e-WOR towards H₂O₂.

 

Thermoelectrics operating at high temperature can cost-effectively convert waste heat and compete with other zero-carbon technologies. Among different high-temperature thermoelectrics materials, silicon nanowires possess the combined attributes of cost effectiveness and mature manufacturing infrastructures. Despite significant breakthroughs in silicon nanowires based thermoelectrics for waste heat conversion, the figure of merit (ZT) or operating temperature has remained low. Here, we report the synthesis of large-area, wafer-scale arrays of porous silicon nanowires with ultra-thin Si crystallite size of ~4 nm. Concurrent measurements of thermal conductivity (κ), electrical conductivity (σ), and Seebeck coefficient (S) on the same nanowire show aZTof 0.71 at 700 K, which is more than ~18 times higher than bulk Si. ThisZTvalue is more than two times higher than any nanostructured Si-based thermoelectrics reported in the literature at 700 K. Experimental data and theoretical modeling demonstrate that this work has the potential to achieve aZTof ~1 at 1000 K.

 

Carbon materials with high specific surface areas are ideal support materials for many applications. However, high specific surface area and large pore volume usually render them with low bulk density, which is undesirable for applications aiming at high volumetric performance. Low bulk density stems from large interparticle‐free volume caused by inefficient random packing within the materials. Herein, a simple synthesis and assembly method is reported to afford dense carbon pellets with both high specific surface area and high bulk density, obtained from the ordered packing of low polydispersity carbon flower particles. The densely packed carbon flower particles exhibit similar specific surface area to their pressed powder analogs, while exhibiting a 66–84% increase in bulk density (0.815 g cm⁻³), and an ultrahigh volumetric surface area (1081 m² cm⁻³). The advantages of our materials are demonstrated by supercapacitors, which achieve a high volumetric capacitance of up to 153 F cm⁻³. The results reinforce the importance of controlling particle size and shape for porous materials to reduce their bulk volume. The developed materials possessing high volumetric surface area will be useful for many applications, such as gas storage, supercapacitors, and batteries.