Optical design of split-beam photonic crystal nanocavities

We design high quality factor photonic crystal nanobeam cavities formed by two mechanically isolated cantilevers. These "split-beam" cavities have a physical gap at the center, allowing mechanical excitations of one or both of the cavity halves. They are designed by analyzing the optical band structures and mode profiles of waveguides perforated by elliptical holes and rectangular gaps, and are predicted to support optical resonances with quality factors exceeding 1E6 at wavelengths of ~ 1.6 um.Comment: To appear in Optics Letter

Cavity optomechanics in gallium phosphide microdisks

We demonstrate gallium phosphide (GaP) microdisk optical cavities with intrinsic quality factors $> 2.8\times10^{5}$ and mode volumes $< 10 (\lambda/n)^3$, and study their nonlinear and optomechanical properties. For optical intensities up to $8.0\times10^4$ intracavity photons, we observe optical loss in the microcavity to decrease with increasing intensity, indicating that saturable absorption sites are present in the GaP material, and that two-photon absorption is not significant. We observe optomechanical coupling between optical modes of the microdisk around 1.5 $\mu$m and several mechanical resonances, and measure an optical spring effect consistent with a theoretically predicted optomechanical coupling rate $g_0/2\pi \sim 30$ kHz for the fundamental mechanical radial breathing mode at 488 MHz.Comment: Published Versio

Conversion of neutral nitrogen-vacancy centers to negatively-charged nitrogen-vacancy centers through selective oxidation

The conversion of neutral nitrogen-vacancy centers to negatively charged nitrogen-vacancy centers is demonstrated for centers created by ion implantation and annealing in high-purity diamond. Conversion occurs with surface exposure to an oxygen atmosphere at 465 C. The spectral properties of the charge-converted centers are investigated. Charge state control of nitrogen-vacancy centers close to the diamond surface is an important step toward the integration of these centers into devices for quantum information and magnetic sensing applications.Comment: 4 pages, 3 figure

Artificial neural network prediction of weld distortion rectification using a travelling induction coil

An experimental investigation has been carried out to determine the applicability of an induction heating process with a travelling induction coil for the rectification of angular welding distortion. The results obtained from experimentation have been used to create artificial neural network models with the ability to predict the welding induced distortion and the distortion rectification achieved using a travelling induction coil. The experimental results have shown the ability to reduce the angular distortion for 8 mm and 10 mm thick DH36 steel plate and effectively eliminate the distortion on 6 mm thick plate. Results for 6 mm plate also show the existence of a critical induction coil travel speed at which maximum corrective bending occurs. Artificial neural networks have demonstrated the ability to predict the final distortion of the plate after both welding and induction heating. The models have also been used as a tool to determine the optimum speed to minimise the resulting distortion of steel plate after being subjected to both welding and induction heating processes

On the Ground Validation of Online Diagnosis with Twitter and Medical Records

Social media has been considered as a data source for tracking disease. However, most analyses are based on models that prioritize strong correlation with population-level disease rates over determining whether or not specific individual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detection at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individual's publicly available Twitter data. We find that about half (17/35 = 48.57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By developing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diagnose an individual with greater than 99% accuracy even if she does not discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul, Kore

On the Ground Validation of Online Diagnosis with Twitter and Medical Records

Social media has been considered as a data source for tracking disease. However, most analyses are based on models that prioritize strong correlation with population-level disease rates over determining whether or not specific individual users are actually sick. Taking a different approach, we develop a novel system for social-media based disease detection at the individual level using a sample of professionally diagnosed individuals. Specifically, we develop a system for making an accurate influenza diagnosis based on an individual's publicly available Twitter data. We find that about half (17/35 = 48.57%) of the users in our sample that were sick explicitly discuss their disease on Twitter. By developing a meta classifier that combines text analysis, anomaly detection, and social network analysis, we are able to diagnose an individual with greater than 99% accuracy even if she does not discuss her health.Comment: Presented at of WWW2014. WWW'14 Companion, April 7-11, 2014, Seoul, Kore

Development of Prognosis in Palliative care Study (PiPS) predictor models to improve prognostication in advanced cancer: prospective cohort study

OBJECTIVE: To develop a novel prognostic indicator for use in patients with advanced cancer that is significantly better than clinicians' estimates of survival. DESIGN: Prospective multicentre observational cohort study. SETTING: 18 palliative care services in the UK (including hospices, hospital support teams, and community teams). PARTICIPANTS: 1018 patients with locally advanced or metastatic cancer, no longer being treated for cancer, and recently referred to palliative care services. MAIN OUTCOME MEASURES: Performance of a composite model to predict whether patients were likely to survive for "days" (0-13 days), "weeks" (14-55 days), or "months+" (>55 days), compared with actual survival and clinicians' predictions. RESULTS: On multivariate analysis, 11 core variables (pulse rate, general health status, mental test score, performance status, presence of anorexia, presence of any site of metastatic disease, presence of liver metastases, C reactive protein, white blood count, platelet count, and urea) independently predicted both two week and two month survival. Four variables had prognostic significance only for two week survival (dyspnoea, dysphagia, bone metastases, and alanine transaminase), and eight variables had prognostic significance only for two month survival (primary breast cancer, male genital cancer, tiredness, loss of weight, lymphocyte count, neutrophil count, alkaline phosphatase, and albumin). Separate prognostic models were created for patients without (PiPS-A) or with (PiPS-B) blood results. The area under the curve for all models varied between 0.79 and 0.86. Absolute agreement between actual survival and PiPS predictions was 57.3% (after correction for over-optimism). The median survival across the PiPS-A categories was 5, 33, and 92 days and survival across PiPS-B categories was 7, 32, and 100.5 days. All models performed as well as, or better than, clinicians' estimates of survival. CONCLUSIONS: In patients with advanced cancer no longer being treated, a combination of clinical and laboratory variables can reliably predict two week and two month survival

Chip-based microcavities coupled to NV centers in single crystal diamond

Optical coupling of nitrogen vacancy centers in single-crystal diamond to an on-chip microcavity is demonstrated. The microcavity is fabricated from a hybrid gallium phosphide and diamond material system, and supports whispering gallery mode resonances with spectrometer resolution limited Q > 25000