Focused Ion Beam Milling Strategies of Photonic Crystal Structures in Silicon

We report on optimisation of the side wall angle of focused ion beam (FIB) fabricated submicron diameter holes in silicon. Two optimisation steps were performed. First, we compare two different FIB scanning procedures and show the advantages of using a spiral scanning method for the definition of holes in photonic crystal slab structures. Secondly, we investigate the effect on the geometry, of parameters for reducing the tapering effect. Furthermore, we report on the initial results regarding effects of $Ga^{+}$ ion implantation during FIB milling on optical losses, both before and after an annealing step, showing over a decade reduction of optical loss

Realization of 2-dimensional air-bridge silicon photonic crystals by focused ion beam milling and nanopolishing

We report the design and fabrication of small photonic crystal structures which are combined with conventional dielectric ridge waveguides. We describe in details the fabrication of both rough and smooth membranes, which are used as host for photonic crystals. Two Focused Ion Beam milling experiments are highlighted: the first one shows how photonic crystals can be fast and accurate milled into a Si membrane, whereas the second experiment demonstrates how focused ion beam milling can turn a rough surface into a well-patterned nano-smooth surface. The previously ultra rough surface showed no detectable roughness after milling due to the nanopolishing effect of the focused ion beam milling

Performance of the Straumann Bone Level Implant system for anterior single-tooth replacements in augmented and nonaugmented sites:A prospective cohort study with 60 consecutive patients

<p>Aim The purpose of this prospective study was to evaluate radiographic, clinical and aesthetic outcomes and patient satisfaction of cases treated with platform-switched single implant restorations in the aesthetic region of the maxilla. Furthermore, the influence of an augmentation procedure 3months before implant placement and the type of restoration (screw-retained vs. cement-retained) was evaluated.</p><p>Material and methods Sixty patients with a missing anterior tooth in the maxilla were treated with a Straumann Bone Level Implant. Bone augmentation was performed in 29 patients at 3months before implant placement. Implants were loaded after 3months of submerged healing. Follow-up was conducted at 7 and 18months after implant placement. Peri-implant mucosa and implant crown aesthetic outcomes were determined using the Implant Crown Aesthetic Index (ICAI) and the Pink Esthetic Score-White Esthetic Score (PES-WES).</p><p>Results No implants were lost. At 18months after implant placement, mean bone level change was -0.10 +/- 0.27mm and mean probing pocket depth was 2.57mm. No differences were found between augmented and nonaugmented sites (P=0.28). The ICAI indicated satisfactory mucosa and crown aesthetics in 67% and 75% of the cases, respectively, while the PES score was 14.4. ICAI mucosa (P=0.004) and PES (P=0.02) scores were significantly less favourable for augmented sites compared with nonaugmented sites. Patient satisfaction was high (8.9 +/- 1.1 on VAS-score).</p><p>Conclusions From the present prospective, clinical study, it can be concluded that the Straumann Bone Level Implant shows an excellent survival rate, marginal bone stability and good clinical and aesthetic results. Bone augmentation before implant placement does not lead to more marginal bone loss. However, less favourable pink aesthetic outcomes were found in augmented sites compared with nonaugmented sites, while no differences were found between cement-retained and screw-retained restorations.</p>

Surface state charge dynamics of a high-mobility three dimensional topological insulator

We present a magneto-optical study of the three-dimensional topological insulator, strained HgTe using a technique which capitalizes on advantages of time-domain spectroscopy to amplify the signal from the surface states. This measurement delivers valuable and precise information regarding the surface state dispersion within <1 meV of the Fermi level. The technique is highly suitable for the pursuit of the topological magnetoelectric effect and axion electrodynamics.Comment: Published version, online Sept 23, 201

Multivariate neuroanatomical classification of cognitive subtypes in schizophrenia: A support vector machine learning approach

AbstractHeterogeneity in the structural brain abnormalities associated with schizophrenia has made identification of reliable neuroanatomical markers of the disease difficult. The use of more homogenous clinical phenotypes may improve the accuracy of predicting psychotic disorder/s on the basis of observable brain disturbances. Here we investigate the utility of cognitive subtypes of schizophrenia – ‘cognitive deficit’ and ‘cognitively spared’ – in determining whether multivariate patterns of volumetric brain differences can accurately discriminate these clinical subtypes from healthy controls, and from each other. We applied support vector machine classification to grey- and white-matter volume data from 126 schizophrenia patients previously allocated to the cognitive spared subtype, 74 cognitive deficit schizophrenia patients, and 134 healthy controls. Using this method, cognitive subtypes were distinguished from healthy controls with up to 72% accuracy. Cross-validation analyses between subtypes achieved an accuracy of 71%, suggesting that some common neuroanatomical patterns distinguish both subtypes from healthy controls. Notably, cognitive subtypes were best distinguished from one another when the sample was stratified by sex prior to classification analysis: cognitive subtype classification accuracy was relatively low (<60%) without stratification, and increased to 83% for females with sex stratification. Distinct neuroanatomical patterns predicted cognitive subtype status in each sex: sex-specific multivariate patterns did not predict cognitive subtype status in the other sex above chance, and weight map analyses demonstrated negative correlations between the spatial patterns of weights underlying classification for each sex. These results suggest that in typical mixed-sex samples of schizophrenia patients, the volumetric brain differences between cognitive subtypes are relatively minor in contrast to the large common disease-associated changes. Volumetric differences that distinguish between cognitive subtypes on a case-by-case basis appear to occur in a sex-specific manner that is consistent with previous evidence of disrupted relationships between brain structure and cognition in male, but not female, schizophrenia patients. Consideration of sex-specific differences in brain organization is thus likely to assist future attempts to distinguish subgroups of schizophrenia patients on the basis of neuroanatomical features

Feature-level domain adaptation

Domain adaptation is the supervised learning setting in which the training and test data are sampled from different distributions: training data is sampled from a source domain, whilst test data is sampled from a target domain. This paper proposes and studies an approach, called feature-level domain adaptation (FLDA), that models the dependence between the two domains by means of a feature-level transfer model that is trained to describe the transfer from source to target domain. Subsequently, we train a domain-adapted classifier by minimizing the expected loss under the resulting transfer model. For linear classifiers and a large family of loss functions and transfer models, this expected loss can be computed or approximated analytically, and minimized efficiently. Our empirical evaluation of FLDA focuses on problems comprising binary and count data in which the transfer can be naturally modeled via a dropout distribution, which allows the classifier to adapt to differences in the marginal probability of features in the source and the target domain. Our experiments on several real-world problems show that FLDA performs on par with state-of-the-art domain-adaptation techniques.Comment: 32 pages, 13 figures, 9 table

Drag of suction cup tags on swimming animals : modeling and measurement

© The Author(s), 2013. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Marine Mammal Science 30 (2014): 726–746, doi:10.1111/mms.12083.Bio-logging tags are widely used to study the behavior and movements of marine mammals with the tacit assumption of little impact to the animal. However, tags on fast-swimming animals generate substantial hydrodynamic forces potentially affecting behavior and energetics adversely, or promoting early removal of the tag. In this work, hydrodynamic loading of three novel tag housing designs are compared over a range of swimming speeds using computational fluid dynamics (CFD). Results from CFD simulation were verified using tag models in a water flume with close agreement. Drag forces were reduced by minimizing geometric disruptions to the flow around the housing, while lift forces were reduced by minimizing the frontal cross-sectional area of the housing and holding the tag close to the attachment surface. Hydrodynamic tag design resulted in an experimentally measured 60% drag force reduction in 5.6 m/s flow. For all housing designs, off-axis flow increased the magnitude of the force on the tag. Experimental work with a common dolphin (Delphinus delphis) cadaver indicates that the suction cups used to attach the types of tags described here provide sufficient attachment force to resist failure to predicted forces at swimming speeds of up to 10 m/s.This work was supported by NOPP with NSF funds through ONR Grant N00014-11-1- 0113. MJ was supported by NOPP and the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland)