Detecting the Maximum Common Subgraph (MCS) between two input graphs is fundamental for applications in biomedical analysis, malware detection, cloud computing, etc. This is especially important in the task of drug design, where the successful extraction of common substructures in compounds can reduce the number of experiments needed to be conducted by humans. However, MCS computation is NP-hard, and state-of-the-art exact MCS solvers do not have worst-case time complexity guarantee and cannot handle large graphs in practice. Designing learning based models to find the MCS between two graphs in an approximate yet accurate way while utilizing as few labeled MCS instances as possible remains to be a challenging task. Here we propose RLMCS, a Graph Neural Network based model for MCS detection through reinforcement learning. Our model uses an exploration tree to extract subgraphs in two graphs one node pair at a time, and is trained to optimize subgraph extraction rewards via Deep Q-Networks. A novel graph embedding method is proposed to generate state representations for nodes and extracted subgraphs jointly at each step. Experiments on real graph datasets demonstrate that our model performs favorably to exact MCS solvers and supervised neural graph matching network models in terms of accuracy and efficiency.

A novel artificial intelligence or AI tool, developed by researchers at Stanford University may assist radiologists for fast detection of brain aneurysms revealed the findings of a study published JAMA Network Open. The paper highlighted areas of a brain scan that are likely to contain an aneurysm. A brain aneurysm is characterized as bulges in blood vessels in the brain that can leak or burst open, potentially leading to stroke, brain damage or death. This tool, which is built around an algorithm called HeadXNet, improved clinicians' ability to correctly identify aneurysms at a level equivalent to finding six more aneurysms in 100 scans that contain aneurysms. It also improved consensus among the interpreting clinicians. While the success of HeadXNet in these experiments is promising, the team of researchers – who have expertise in machine learning, radiology, and neurosurgery – cautions that further investigation is needed to evaluate the generalizability of the AI tool prior to real-time clinical deployment given differences in scanner hardware and imaging protocols across different hospital centers.

There has been a tremendous advance in domain-independent planning over the past decades, and planners have become increasingly efficient at finding plans. However, this has not been paired by any corresponding improvement in detecting unsolvable instances. Such instances are obviously important but largely neglected in planning. In other areas, such as constraint solving and model checking, much effort has been spent on devising methods for detecting unsolvability. We introduce a method for detecting unsolvable planning instances that is loosely based on consistency checking in constraint programming. Our method balances completeness against efficiency through a parameter k: the algorithm identifies more unsolvable instances but takes more time for increasing values of k. We present empirical data for our algorithm and some standard planners on a number of unsolvable instances, demonstrating that our method can be very efficient where the planners fail to detect unsolvability within reasonable resource bounds. We observe that planners based on the h^m heuristic or pattern databases are better than other planners for detecting unsolvability. This is not a coincidence since there are similarities (but also significant differences) between our algorithm and these two heuristic methods.

We present a fast algorithm for the detection of multiple change-points when each is frequently shared by members of a set of co-occurring one-dimensional signals. We give conditions on consistency of the method when the number of signals increases, and provide empirical evidence to support the consistency results.