Online symptom checkers have been deployed by sites such as WebMD and Mayo Clinic to identify possible causes and treatments for diseases based on a patient’s symptoms. Symptom checking first assesses a patient by asking a series of questions about their symptoms, then attempts to predict potential diseases. The two design goals of a symptom checker are to achieve high accuracy and intuitive interactions. In this paper we present our context-aware hierarchical reinforcement learning scheme, which significantly improves accuracy of symptom checking over traditional systems while also making a limited number of inquiries.
To cope with last-minute design bugs and specification changes, engineering change order (ECO) is usually performed toward the end of the design process. This paper proposes an automatic ECO synthesis algorithm by interpolation. In particular, we tackle the problem by a series of partial rectifications. At each step, partial rectification can reduce the functional difference between an old implementation and a new specification. Our algorithm is especially effective for multiple error circuits. Experimental results show the proposed method is far superior to the most recent work and scales well on a set of large circuits.
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