Growth of laser damage on fused silica optical components depends on several key parameters including laser fluence, wavelength, pulse duration, and site size. Here we investigate the growth behavior of small damage sites on the exit surface of SiO₂ optics under exposure to tightly controlled laser pulses. Results demonstrate that the onset of damage growth is not governed by a threshold, but is probabilistic in nature and depends both on the current size of a damage site and the laser fluence to which it is exposed. We also develop models for use in growth prediction. In addition, we show that laser exposure history also influences the behavior of individual sites.

has been administrating a national cervical cancer screening program since 1992 by coordinating triennial cytology exam screenings for the female population between 25 and 69 years of age. Up to 80% of cancers are prevented through mass screening, but this comes at the expense of considerable screening activity and leads to overtreatment of clinically asymptomatic precancers. In this article, we present a continuous-time, time-inhomogeneous hidden Markov model which was developed to understand the screening process and cervical cancer carcinogenesis in detail. By leveraging 1.7 million individual's multivariate time-series of medical exams performed over a 25-year period, we simultaneously estimate all model parameters. We show that an age-dependent model reflects the Norwegian screening program by comparing empirical survival curves from observed registry data and data simulated from the proposed model. The model can be generalized to include more detailed individual-level covariates as well as new types of screening exams. By utilizing individual screening histories and covariate data, the proposed model shows potential for improving strategies for cancer screening programs by personalizing recommended screening intervals.

The 3.2 giga-pixel LSST camera will produce approximately half a petabyte of archive images every month. These data need to be reduced in under a minute to produce real-time transient alerts, and then added to the cumulative catalog for further analysis. The catalog is expected to grow about three hundred terabytes per year.The data volume, the real-time transient alerting requirements of the LSST, and its spatio-temporal aspects require innovative techniques to build an efficient data access system at reasonable cost. As currently envisioned, the system will rely on a database for catalogs and metadata. Several database systems are being evaluated to understand how they perform at these data rates, data volumes, and access patterns. This paper describes the LSST requirements, the challenges they impose, the data access philosophy, results to date from evaluating available database technologies against LSST requirements, and the proposed database architecture to meet the data challenges.

When a graph database is implemented on top of a relational database, queries in the graph query language are translated into relational SQL queries. Graph pattern queries are an important feature of a graph query language. Translating graph pattern queries into single SQL statements results in very poor query performance. By taking into account the pattern query structure and generating multiple SQL statements, pattern query performance can be dramatically improved. The performance problems encountered with the single SQL statements generated for pattern queries reflects a problem in the SQL query planner and optimizer. Addressing this problem would allow relational databases to better support semantic graph databases. Relational database systems that provide good support for graph databases may also be more flexible platforms for data warehouses. es_194 table is the vertex table that contains the attribute values for the JournalIssue vertices.

Empirical numerical descriptions of the growth of laser-induced damage have been previously developed. In this work, Monte-Carlo techniques use these descriptions to model the evolution of a population of damage sites. The accuracy of the model is compared against laser damage growth observations. In addition, a machine learning (classification) technique independently predicts site evolution from patterns extracted directly from the data. The results show that both the Monte-Carlo simulation and machine learning classification algorithm can accurately reproduce the growth of a population of damage sites for at least 10 shots, which is extremely valuable for modeling optics lifetime in operating high-energy laser systems. Furthermore, we have also found that machine learning can be used as an important tool to explore and increase our understanding of the growth process.