Photoacoustic (PA) imaging technology integrates the deep penetration depth of ultrasound imaging with the high resolution of optical imaging, demonstrating significant potential in biomedical applications. Many preclinical studies and clinical applications urgently require a portable, high-quality, low-cost, and fast imaging system. Thus, translating advanced image reconstruction algorithms into hardware implementations is highly desired. However, existing iterative PA image reconstructions, although exhibit higher accuracy than the delay-and-sum algorithm, suffer from high computational cost. In this paper, we introduce a novel model-based hardware architecture for palm-size PA tomography (palm-PAT), aiming at enhancing both the speed and performance of image reconstruction at a much lower system cost. To achieve this, we propose an innovative data reuse method that significantly reduces the consumption of hardware storage resources, achieving a reduction of approximately 75% in hardware storage requirements. We conducted experiments utilizing the FPGA implementation of the algorithm, using phantom, human finger data in vivo and ex vivo breast tumor data to verify the feasibility of the proposed method. The results demonstrate that our proposed architecture can substantially reduce system cost while maintaining high imaging performance. The novel hardware architecture design of the model-based algorithm achieves a speedup of up to approximately 270 times compared to the CPU, while the corresponding energy efficiency ratio is improved by more than 2700 times.

In cooperative multi-agent reinforcement learning (MARL), ensuring robustness against cooperative agents making unpredictable or worst-case adversarial actions is crucial for real-world deployment. In multi-agent settings, each agent may be perturbed or unperturbed, leading to an exponential increase in potential threat scenarios as the number of agents grows. Existing robust MARL methods either enumerate, or approximate all possible threat scenarios, leading to intense computation and insufficient robustness. In contrast, humans develop robust behaviors by maintaining a general level of caution rather than preparing for every possible threat. Inspired by human decision making, we frame robust MARL as a control-as-inference problem, and optimize worst-case robustness across all threat scenarios implicitly optimized through off-policy evaluation. Specifically, we introduce mutual information regularization as robust regularization (MIR3), which maximizes a lower bound on robustness during routine training, serving as a kind of caution for MARL without adversarial inputs. Further insights show that MIR3 acts as an information bottleneck, preventing agents from over-reacting to others and aligning policies with robust action priors. In the presence of worst-case adversaries, our MIR3 significantly surpasses baseline methods in robustness and training efficiency, and maintaining cooperative performance in StarCraft II, quadrotor swarm control, and robot swarm control. When deploying the robot swarm control algorithm in the real world, our method also outperforms the best baseline by 14.29% in reward. See code and demo videos at https://github.com/DIG-Beihang/MIR3.

WPA (Wi-Fi Protected Access) Enterprise is the de facto standard for safeguarding enterprise-level wireless networks. It relies on Transport Layer Security (TLS) to establish a secure tunnel during its authentication process, and thus the notoriously error-prone certificate validation may haunt it. Incorrect validation may lead to the SSL/TLS man-in-the-middle attack, or the evil twin attack in the context of wireless networking, where the supplicant connects and unwittingly sends authentication credentials to a fake access point.

By arranging multiple existing web services into workflows to create value-added services, automatic web service composition has received much attention in service-oriented computing. A large number of methods have been proposed for it although most of them are merely based on the matching of input-output parameters of services. Besides these parameters, some other elements can affect the execution of services and their composition, such as the preconditions and service execution results. In particular, the execution effects of some services are often uncertain because of the complex and dynamically changing application environments in the real world, and this can cause the emergence of nondeterministic choices in the workflows of composite services. However, the previous methods for automatic service composition mainly rely on sequential structures, which make them difficult to take into account uncertain effects during service composition. In this paper, Graphplan is employed and extended to tackle this problem. In order to model services with uncertain effects, we first extend the original form of Graphplan. Then, we propose a novel approach that can introduce branch structures into composite solutions to cope with such uncertainty in the service composition process. Extensive experiments are performed to evaluate and analyze the proposed methodology.