Traditional collision avoidance methods for unmanned underwater vehicles (UUVs) are highly dependent on the measurement accuracy of sensors and environmental maps and perform poorly in unknown dynamic environments. To improve the generalization ability of the UUV collision avoidance method when there is no prior knowledge of the underwater environment and sonar observation is uncertain, an improved soft actor-critic (SAC) method is proposed in this work. The improved SAC network architecture includes an encoder layer, a dynamic feature layer, and a merge layer. The long short-term memory (LSTM) network is integrated into the dynamic feature layer to capture historical state spaces and action spaces, and improve the collision policy’s memory capability. In this method, the state space is composed of forward-looking sonar perception, UUV motion state, and relative target position. In addition, to enhance the training efficiency and stability of the improved SAC, we design the continuous state space, the action space, and a composite reward function. We built a simulation platform and demonstrated that the improved SAC could effectively complete autonomous collision avoidance in environments with static and dynamic obstacles under external disturbances. The improved method enables the UUV to reach the target faster and reduces the path length and decision-making time of collision avoidance.

Palm vein recognition has gained traction in secure authentication due to its unique, stable, and inherently concealed biometric characteristics. However, the electromagnetic (EM) emissions from subcutaneous vein imaging sensors (SVIS) may unintentionally leak sensitive biometric information, fundamentally challenging the assumed security guarantees. In this paper, we propose VeinPhantom, a novel side-channel attack that reconstructs palm vein patterns from unintended EM emissions of SVIS, ultimately enabling spoofing attacks against biometric authentication systems. To overcome the challenge of low information entropy caused by weak EM signals and complex environmental interference, VeinPhantom first analyzes palm vein information from EM signals, then employs a cascaded enhancement strategy and incorporates a Dynamic Guidance Diffusion framework to progressively reconstruct high-fidelity palm vein patterns. Extensive experiments demonstrate that VeinPhantom achieves an average structure similarity index measure (SSIM) of 0.56 on commercial devices, along with a 56.96% spoofing success rate against state-of-the-art authentication systems. We further discuss potential mitigation strategies to defend against the attack.

Cloud service providers increasingly adopt SmartNICs to offload data-plane services (e.g., DPDK and SPDK) and control-plane tasks (such as disk and NIC initialization). Our analysis of production environments reveals that data-plane services statically provision CPUs for peak load, resulting in 67.5% idle CPU cycles during 99% of their runtime in IaaS clouds, leading to wasted CPU resources. On the other hand, control-plane tasks fail to meet critical Service Level Objectives (SLOs), such as virtual machine startup time. Unfortunately, achieving control-plane SLO improvements through co-scheduling with idle data-plane services remains highly challenging, due to the combined effects of intrinsic scheduling latency and the substantial architectural complexity inherent to control-plane ecosystems.

This paper proposes a novel deep reinforcement learning (DRL) based method for unmanned underwater vehicle (UUV) path-following and collision avoidance. An end-to-end architecture integrating guidance law, gradient-based condition for action policy smoothing method and soft actor-critic (Grad-CAPS-SAC) is proposed. Specifically, the Serret-Frenet frame and the guidance law generate the tracking error and the desired attitude, respectively. Since the oscillation of the control signal may cause wear and energy consumption of the physical system, an adaptive gradient-based action policy regularization method is proposed to smooth the control policy. In addition, the DRL controller controls the rudder and elevator, and Proportional-integral-derivative (PID) controller is adopted to maintain the desired cruise speed. Furthermore, a composite reward function with constraints is designed to avoid useless exploration and improve training efficiency. Simulation results show that Grad-CAPS-SAC has better path-following and collision avoidance performance than the mainstream DRL methods in different waypoint and obstacle configurations.

In this paper, we qualitatively and quantitatively discuss the design choices, production experience, and lessons in building the Elastic Block Storage ( EBS ) at Alibaba Cloud over the past decade. To cope with hardware advancement and users’ demands, we shift our focus from design simplicity in EBS1 to high performance and space efficiency in EBS2 , and finally reducing network traffic amplification in EBS3 . In addition to the architectural evolutions, we also summarize development lessons and experiences as four topics, including: (i) achieving high elasticity in latency, throughput, IOPS, and capacity; (ii) improving availability by minimizing the blast radius of individual, regional, and global failure events; (iii) identifying the motivations and key tradeoffs in various hardware offloading solutions; and (iv) identifying the pros/cons of alternative solutions and explaining why seemingly promising ideas would not work in practice.