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This report is of a retrospective study of data from 258 patients who received spinal cord stimulation (SCS) for the treatment of peripheral vascular disease as a result of arteriosclerosis. The patients' clinical outcomes were monitored over a period of 18 months. In patients with a low baseline transcutaneous oxygen pressure (TcPO(2)) value of <10 mm Hg, limb survival at 18 months of follow-up (estimated by use of Kaplan-Meier survival analysis) was 77.8%, and this was even higher, at 89.5%, in patients with a medium baseline TcPO(2) value of 10-30 mm Hg. This successful treatment was accompanied by a sustained increase in TcPO(2) values to approximately 30 mm Hg in both of these groups. In looking at diabetic and nondiabetic patients, there is no difference in limb survival as a result of the treatment. It is concluded that SCS is an effective therapy in improving limb survival in patients with peripheral vascular disease. In addition, TcPO(2) values at baseline may be a useful predictor of treatment outcome.

When it comes to clustering nonconvex shapes, two paradigms are used to find the most suitable clustering: minimum cut and maximum density. The most popular algorithms incorporating these paradigms are Spectral Clustering and DBSCAN. Both paradigms have their pros and cons. While minimum cut clusterings are sensitive to noise, density-based clusterings have trouble handling clusters with varying densities. In this paper, we propose SPECTACL: a method combining the advantages of both approaches, while solving the two mentioned drawbacks. Our method is easy to implement, such as Spectral Clustering, and theoretically founded to optimize a proposed density criterion of clusterings. Through experiments on synthetic and real-world data, we demonstrate that our approach provides robust and reliable clusterings. 1 the scikit-learn clustering documentation (cf. https: //scikit-learn.org/stable/modules/clustering. html) shows how SC and DBSCAN can succeed on this dataset, but that version contains barely any noise (scikit's noise parameter set to 0.05, instead of our still benign 0.1).

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Mining and exploring databases should provide users with knowledge and new insights. Tiles of data strive to unveil true underlying structure and distinguish valuable information from various kinds of noise. We propose a novel Boolean matrix factorization algorithm to solve the tiling problem, based on recent results from optimization theory. In contrast to existing work, the new algorithm minimizes the description length of the resulting factorization. This approach is well known for model selection and data compression, but not for finding suitable factorizations via numerical optimization. We demonstrate the superior robustness of the new approach in the presence of several kinds of noise and types of underlying structure. Moreover, our general framework can work with any cost measure having a suitable real-valued relaxation. Thereby, no convexity assumptions have to be met. The experimental results on synthetic data and image data show that the new method identifies interpretable patterns which explain the data almost always better than the competing algorithms.