Use of multiple singular value decompositions to analyze complex intracellular calcium ion signals

We compare calcium ion signaling ($\mathrm {Ca}^{2+}$) between two exposures; the data are present as movies, or, more prosaically, time series of images. This paper describes novel uses of singular value decompositions (SVD) and weighted versions of them (WSVD) to extract the signals from such movies, in a way that is semi-automatic and tuned closely to the actual data and their many complexities. These complexities include the following. First, the images themselves are of no interest: all interest focuses on the behavior of individual cells across time, and thus, the cells need to be segmented in an automated manner. Second, the cells themselves have 100$+$ pixels, so that they form 100$+$ curves measured over time, so that data compression is required to extract the features of these curves. Third, some of the pixels in some of the cells are subject to image saturation due to bit depth limits, and this saturation needs to be accounted for if one is to normalize the images in a reasonably unbiased manner. Finally, the $\mathrm {Ca}^{2+}$ signals have oscillations or waves that vary with time and these signals need to be extracted. Thus, our aim is to show how to use multiple weighted and standard singular value decompositions to detect, extract and clarify the $\mathrm {Ca}^{2+}$ signals. Our signal extraction methods then lead to simple although finely focused statistical methods to compare $\mathrm {Ca}^{2+}$ signals across experimental conditions.Comment: Published in at http://dx.doi.org/10.1214/09-AOAS253 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Coupling of Nitrogen-Vacancy Centers to Photonic Crystal Cavities in Monocrystalline Diamond

The zero-phonon transition rate of a nitrogen-vacancy center is enhanced by a factor of ~70 by coupling to a photonic crystal resonator fabricated in monocrystalline diamond using standard semiconductor fabrication techniques. Photon correlation measurements on the spectrally filtered zero-phonon line show antibunching, a signature that the collected photoluminescence is emitted primarily by a single nitrogen-vacancy center. The linewidth of the coupled nitrogen-vacancy center and the spectral diffusion are characterized using high-resolution photoluminescence and photoluminescence excitation spectroscopy

III/V-on-Si MQW lasers by using a novel photonic integration method of regrowth on a bonding template.

Silicon photonics is becoming a mainstream data-transmission solution for next-generation data centers, high-performance computers, and many emerging applications. The inefficiency of light emission in silicon still requires the integration of a III/V laser chip or optical gain materials onto a silicon substrate. A number of integration approaches, including flip-chip bonding, molecule or polymer wafer bonding, and monolithic III/V epitaxy, have been extensively explored in the past decade. Here, we demonstrate a novel photonic integration method of epitaxial regrowth of III/V on a III/V-on-SOI bonding template to realize heterogeneous lasers on silicon. This method decouples the correlated root causes, i.e., lattice, thermal, and domain mismatches, which are all responsible for a large number of detrimental dislocations in the heteroepitaxy process. The grown multi-quantum well vertical p-i-n diode laser structure shows a significantly low dislocation density of 9.5 × 104 cm-2, two orders of magnitude lower than the state-of-the-art conventional monolithic growth on Si. This low dislocation density would eliminate defect-induced laser lifetime concerns for practical applications. The fabricated lasers show room-temperature pulsed and continuous-wave lasing at 1.31 μm, with a minimal threshold current density of 813 A/cm2. This generic concept can be applied to other material systems to provide higher integration density, more functionalities and lower total cost for photonics as well as microelectronics, MEMS, and many other applications

Learning Data Quality Analytics for Financial Services

Financial institutions put tremendous efforts on the data analytics work associated with the risk data in recent years. Their analytical reports are yet to be accepted by regulators in financial services industry till early 2019. In particular, the enhancement needs to meet the regulatory requirement the APRA CPG 235. To improve data quality, we assist in the data quality analytics by developing a machine learning model to identify current issues and predict future issues. This helps to remediate data as early as possible for the mitigation of risk of re-occurrence. The analytical dimensions are customer related risks (market, credit, operational & liquidity risks) and business segments (private, wholesale & retail banks). The model is implemented with multiple Long Short-Term Memory ( LSTM ) Recurrent Neural Network ( RNNs ) to find the best one for the quality & prediction analytics. They are evaluated by divergent algorithms and cross-validation techniques