Nanosecond anomaly detection with decision trees for high energy physics and real-time application to exotic Higgs decays

We present a novel implementation of the artificial intelligence autoencoding algorithm, used as an ultrafast and ultraefficient anomaly detector, built with a forest of deep decision trees on FPGA, field programmable gate arrays. Scenarios at the Large Hadron Collider at CERN are considered, for which the autoencoder is trained using known physical processes of the Standard Model. The design is then deployed in real-time trigger systems for anomaly detection of new unknown physical processes, such as the detection of exotic Higgs decays, on events that fail conventional threshold-based algorithms. The inference is made within a latency value of 25 ns, the time between successive collisions at the Large Hadron Collider, at percent-level resource usage. Our method offers anomaly detection at the lowest latency values for edge AI users with tight resource constraints.Comment: 26 pages, 9 figures, 1 tabl

Ridge preservation in the anterior maxilla after partial tooth extraction by retaining a buccal root segment

After tooth extraction in the esthetically challenging area of the anterior maxilla, dimensional stability of the buccal plate is a prerequisite for a successful functional and esthetic treatment result with implant-supported or conventional dentures. Many procedures and materials are currently available for socket and ridge preservation. With regard to the existing evidence of success criteria, there seems to be no significant clinical difference between different protocols and grafting materials. Furthermore, most of the grafting procedures are expensive and time-consuming. One predictable method to prevent resorption of the buccal plate and support the soft tissues in the anterior maxilla is a partial extraction of the respective tooth, leaving a buccal root segment in place. Possible applications for this particular method, also known as the socket-shield technique, will be presented in three clinical cases

Odds ratios of multivariate logistic regression for risk of unplanned 30-day readmission by social factors, health status and length of stay in hospital for all cancer (N total = 10,165/N readmissions = 516).

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Characteristics of patients with/without unplanned 30-day readmission: Demographic, social, health status and hospital stay.

Characteristics of patients with/without unplanned 30-day readmission: Demographic, social, health status and hospital stay.</p

Odds ratios of multivariate logistic regression for risk of unplanned 30-day readmission by social factors, health status and length of stay in hospital for back problems (N total = 12,691/N readmissions = 332).

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Odds ratios of multivariate logistic regression for risk of unplanned 30-day readmission by social factors, health status and length of stay in hospital for osteoarthritis (N total = 17,380/N readmissions = 229).

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Odds ratios of multivariate logistic regression for risk of unplanned 30-day readmission by social factors, health status and length of stay in hospital for breast cancer (N total = 4’547/N readmissions = 101).

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Odds ratios of multivariate logistic regression for risk of unplanned 30-day readmission by social factors, health status and length of stay in hospital for congestive heart failure (N total = 3,492°/N readmissions = 324).

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Odds ratios of multivariate logistic regression for risk of unplanned 30-day readmission by social factors, health status and length of stay in hospital for acute myocard infarctus (N total = 4,275/N readmissions = 166).

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