Bertsekas recently proposed Asynchronous Policy Iteration (API) as an alternative algorithm of Policy Iteration (PI) for solving the problem of two-player zero-sum Markov games. To quantifying the benefits of API, besides its flexibility for parallel and asynchronous implementation, the focus of this paper is to derive the computational complexity of API. We show that to reach within ϵ error to the optimal value function, the computational complexity of API is at most O (poly (n, m1, m2, ln(1/(1 − γ))), where n is the number of states, m1, m2are the number of actions for player 1 and player 2 respectively, and γ is the discount factor.

Approximate computing is considered an innovative paradigm with wide applications to high performance and low power systems. These applications have relaxed requirements for accuracy, so they can tolerate errors in results and achieve high performance. In approximate computing, multipliers have been widely studied, but squarers (as similar schemes) have not received much attention. In this paper, an accurate squarer is designed based on a Radix-8 Booth-folding square algorithm to reduce the number of partial products and the depth of the partial product array. Several approximate squarers (R8AS1, R8AS2 and R8AS3) are proposed based on the exact squarer to reduce power and delay. Two approximate partial product generators are also designed to simplify the Radix-8 Booth square encoder in R8AS1 and R8AS2. In addition, approximate compressors with compensation are used in the partial product compression stage to reduce additional area and power consumption in R8AS3. Synthesis results for power, area, and delay at 28 nm CMOS technology are presented. Compared with designs in the technical literature with the same accuracy, the proposed 16-bit designs reduce the PDP by 37%; in general, the PDP is decreased by up to 51%. Finally, the proposed approximate squarers are implemented in a square-law detector as a communication application and achieve an SNR close to 30 dB. Also, the three proposed approximate squarers are applied to the k-means clustering algorithm for machine learning to accomplish high performance in classification.

Recent studies show that deep learning models perform well in many medical tasks such as medical imaging and automated diagnosis. With qualified training datasets, some models can achieve or even surpass expert-level performance on some tasks. However, as a typical black-box-style approach, deep learning lacks theoretical interpretability, which is especially important for medical tasks. On the other hand, there are many sources of domain knowledge for medical diagnosis from human experts, such as clinical guidelines. How to sufficiently integrate human knowledge in the model is crucial for explainable diagnosis. In this paper, we propose a novel framework for explainable automated diagnosis that leverages explicit medical knowledge. We automate the knowledge extraction from textual clinical guidelines with prompt-based learning, train a set of weighted first-order logical rules with constructed evidence database, and finally infer the diagnosis result with integrated knowledge and multi-sourced data. We instantiate the framework for pulmonary disease diagnosis, and our experiments on a real dataset show that our method outperforms the state-of-the-art baselines in accuracy and interpretability.

Knowledge graphs (KGs), either constructed automatically from texts or collected manually from crowdsourcing workers, may contain uncertainty. The uncertainty may propagate into the knowledge graph embedding and downstream tasks, which is potentially harmful, especially for those confidencesensitive applications such as medical diagnostic suggestion. Crowdsourcing workers with domain knowledge can help improve the data quality of knowledge graphs, by knowledge checking. However, due to the large scale of knowledge graphs and the limitation of adequate crowdsourcing workers, it is unrealistic to check all triplets in a knowledge graph to improve the data quality. Therefore, in this paper, we propose a crowdsourcing framework that efficiently improves the confidence of knowledge graphs with limited budget. We instantiate the framework in the medical domain and conduct a series of experiments with realworld medical data. We deploy the framework for knowledge graph embedding UKGE and corresponding downstream tasks. The experimental results show that the proposed method efficiently improves the quality of the knowledge graphs, and hence improves the performance of probabilistic knowledge graph embedding in the downstream tasks.

SIFT algorithm is one of the most significant and effective algorithms to describe the features of image in the field of image matching. To implement SIFT algorithm to hardware environment is apparently considerable and difficult. In this paper, we mainly discuss the realization of Key Point Description in SIFT algorithm, along with Matching process. In Key Point Description, we have proposed a new method of generating histograms, to avoid the rotation of adjacent regions and insure the rotational invariance. In Matching, we replace conventional Euclidean distance with Hamming distance. The results of the experiments fully prove that the structure we propose is real-time, accurate, and efficient. Future work is still needed to improve its performance in harsher conditions.