Waveguides, bends and Y-junctions with improved transmission and bandwidth in hexagon-type SOI photonic crystal slabs

This paper presents novel ways of implementing waveguide components in photonic crystal slabs based on silicon-on-insulator (SOI). The integration platform we consider consists of hexa¬gonal holes arranged in a triangular lattice (‘hexagon-type’ photonic crystal). The waveguides are made of one missing row of holes (W1) with triangular air inclusions symmetrically added on each side of the waveguide. \ud Size and position of these inclusions are tuning parameters for the band diagram and can be used for minimizing the distributed Bragg reflection (DBR) effect. The waveguides show single-mode behavior with reasonably high group velocity and large transmission window, inside the gap between H-like modes**. These waveguides, closely resembling conventional ridge waveguides, can be combined to form efficient bends and Y-junctions. The bends and Y-junctions include intermediate short waveguide sections at half the bend angle playing the role of corner ‘mirrors’. Qualitative design rules were obtained from 2D calculations based on effective index approximation.\u

State of the States 2005

Summarizes major state policy developments in 2004 and projects likely trends for 2005. Includes health care, education, homeland security, tax and budget policy, the same-sex marriage controversy, and profiles of governors elected in November 2004

Response shifts in mental health interventions: An illustration of longitudinal measurement invariance.

The efficacy of treatments for depression is often measured by comparing observed total scores on self-report inventories, in both clinical practice and research. However, the occurrence of response shifts (changes in subjects' values, or their standards for measurement) may limit the validity of such comparisons. As most psychological treatments for depression are aimed at changing patients' values and frame of reference, response shifts are likely to occur over the course of such treatments. In this article, we tested whether response shifts occurred over the course of treatment in an influential randomized clinical trial. Using confirmatory factor analysis, measurement models underlying item scores on the Beck Depression Inventory (Beck & Beamesderfer, 1974) of the National Institute of Mental Health Treatment of Depression Collaborative Research Program (Elkin, Parloff, Hadley, & Autry, 1985) were analyzed. Compared with before treatment, after-treatment item scores appeared to overestimate depressive symptomatology, measurement errors were smaller, and correlations between constructs were stronger. These findings indicate a response shift, in the sense that participants seem to get better at assessing their level of depressive symptomatology. Comparing measurement models of patients receiving psychotherapy and medication suggested that the aforementioned effects were more apparent in the psychotherapy groups. Consequently, comparisons of observed total scores on self-report inventories may yield confounded measures of treatment efficacy. © 2013 American Psychological Association

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Article / Letter to editorInstituut Psychologi

Fast prototyping of planar photonic crystal components using a combination of optical lithography and focused ion beam etching

A combination of conventional optical lithography and focused ion beam etching provides a novel method for fast and precise (10 nm accuracy) prototyping of planar photonic crystal structures having submicron features, in silicon on insulator wafers

25 kHz narrow spectral bandwidth of a wavelength tunable diode laser with a short waveguide-based external cavity

We report on the spectral properties of a diode laser with a tunable external cavity in integrated optics. Even though the external cavity is short compared to other small-bandwidth external cavity lasers, the spectral bandwidth of this tunable laser is as small as 25 kHz (FWHM), at a side-mode suppression ratio (SMSR) of 50 dB. Our laser is also able to access preset wavelengths in as little as 200 us and able to tune over the full telecom C-band (1530 nm - 1565 nm).Comment: 8 pages, 7 figure

Detecting treatment-subgroup interactions in clustered data with generalized linear mixed-effects model trees

Identification of subgroups of patients for whom treatment A is more effective than treatment B, and vice versa, is of key importance to the development of personalized medicine. Tree-based algorithms are helpful tools for the detection of such interactions, but none of the available algorithms allow for taking into account clustered or nested dataset structures, which are particularly common in psychological research. Therefore, we propose the generalized linear mixed-effects model tree (GLMM tree) algorithm, which allows for the detection of treatment-subgroup interactions, while accounting for the clustered structure of a dataset. The algorithm uses model-based recursive partitioning to detect treatment-subgroup interactions, and a GLMM to estimate the random-effects parameters. In a simulation study, GLMM trees show higher accuracy in recovering treatment-subgroup interactions, higher predictive accuracy, and lower type II error rates than linear-model-based recursive partitioning and mixed-effects regression trees. Also, GLMM trees show somewhat higher predictive accuracy than linear mixed-effects models with pre-specified interaction effects, on average. We illustrate the application of GLMM trees on an individual patient-level data meta-analysis on treatments for depression. We conclude that GLMM trees are a promising exploratory tool for the detection of treatment-subgroup interactions in clustered datasets.Multivariate analysis of psychological dat

SEM-based out-of-sample predictions

Multivariate analysis of psychological dat

Time based radar signal analysis revealing nature and properties of surface scans

To clarify subsurface properties, it is necessary to investigate the time base of the signal. However, it is often necessary to solve the problem of determining the structure of only the surface layer. Our method addresses this problem. Advantages of the method: 1. Highlights homogeneous areas in terms of surface conditions 2. It can process large data on profile measurements in an almost continuous mode, using any one (or a small amount) information signal attribute. 3. There is no need for desk data processing, interpretation. 4. This method uses a signal of any nature. Here we explore if it is possible to obtain more information about the quality and state of the object by not only looking at a single time-based measurement, but instead looking at consecutive measurements as from a stream. By studying the structure of the stream and the changes in it, properties like moisture content can be revealed. A method is proposed for fingerprinting radar signals and detecting the boundaries of homogeneous media during a scan along changing objects properties