NONLINEAR OPTICS IN HYDROGENATED AMORPHOUS SILICON (A-SI:H) WAVEGUIDES

Silicon photonics combines wide-bandwidth capability afforded through optics with well-developed nano-fabrication technology, allowing for short-range communication at low cost, with low operating power and compact device footprints. In order to compete with traditional copper wiring, optical interconnects must be integrated vertically for maximum integration density. Crystalline silicon (c-Si) cannot be deposited; only epitaxially grown or bonded at high temperature thereby destroying the electronic devices and is consequently limited to single layer integration. Here we investigate a new silicon photonic material, hydrogenated amorphous silicon (a-Si:H). This material can be deposited at a low temperature 150 ~300 degree C within the CMOS thermal budget and is already available in the current fabrication process line. Nonlinear optical effects allow ultra-fast time scale all-optical signal processing. However, in c-Si the nonlinear coefficient is low; therefore high input power is required for operation. A-Si, due to its unique band structure, has an order of magnitude higher nonlinear coefficient than c-Si. This high nonlinearity enables all-optical nonlinear applications at very low powers. The first part of this dissertation will focus on the design and fabrication of the a-Si:H waveguide. The optical properties of the waveguide are measured and analyzed. Secondly, using the highly-nonlinear a-Si:H waveguide, I will discuss our demonstrations including: 1) broad-bandwidth wavelength conversion, 2) low power time-domain demultiplexing, 3) all optical signal regeneration, 4) short pulse characterization via frequency resolved optical gating (FROG), 5) broad-band optical parametric amplification and oscillation, and 6) correlated photon-pair generation

CPCP violation phase and decomposition relations in Higgs BSM amplitudes

We define a $CP$ violation phase angle $\xi$ to quantify the mixture of $CP$-even and $CP$-odd states for Higgs boson in new physics beyond Standard Model (BSM) firstly, and then show it explicitly in $H\to\gamma\gamma$, $H\to\gamma\ell\ell$ and $H\to 4\ell$ amplitudes. The analytical form gives a good explanation why the $CP$ violation phase could be observed and the interference between $CP$-even and $CP$-odd parts exist in $H\to 4\ell$ process, but not in $H\to\gamma\gamma$ and $H\to\gamma\ell\ell$ processes. To understand the analytical structure of these BSM amplitudes, we introduce a new method of decomposing $H\to\gamma\ell\ell$ and $H\to 4\ell$ amplitudes into $H\to\gamma\gamma$ amplitudes. For a comparison, by using the on-shell scattering amplitude approach we study the recursion relations of amplitudes and get a consistent result independently.Comment: 19 pages, 7 figure

Muon mass correction in partial wave analyses of charmed meson semi-leptonic decays

We derive the parameterization formula for partial wave analyses of charmed meson semi-leptonic decays with consideration of the effects caused by the lepton mass. As the proposed super-tau-charm factory will reach much enhanced luminosity and BESIII is taking $\psi(3770)\to D\bar{D}$ data, our results are helpful to improve the measurement precision of future partial wave analyses of charmed meson semi-muonic decays

Prognostic nomogram for bladder cancer with brain metastases: a National Cancer Database analysis.

BACKGROUND: This study aimed to establish and validate a nomogram for predicting brain metastasis in patients with bladder cancer (BCa) and assess various treatment modalities using a primary cohort comprising 234 patients with clinicopathologically-confirmed BCa from 2004 to 2015 in the National Cancer Database. METHODS: Machine learning method and Cox model were used for nomogram construction. For BCa patients with brain metastasis, surgery of the primary site, chemotherapy, radiation therapy, palliative care, brain confinement of metastatic sites, and the Charlson/Deyo Score were predictive features identified for building the nomogram. RESULTS: For the original 169 patients considered in the model, the areas under the receiver operating characteristic curve (AUC) were 0.823 (95% CI 0.758-0.889, P \u3c 0.001) and 0.854 (95% CI 0.785-0.924, P \u3c 0.001) for 0.5- and 1-year overall survival respectively. In the validation cohort, the nomogram displayed similar AUCs of 0.838 (95% CI 0.738-0.937, P \u3c 0.001) and 0.809 (95% CI 0.680-0.939, P \u3c 0.001), respectively. The high and low risk groups had median survivals of 1.91 and 5.09 months for the training cohort and 1.68 and 8.05 months for the validation set, respectively (both P \u3c 0.0001). CONCLUSIONS: Our prognostic nomogram provides a useful tool for overall survival prediction as well as assessing the risk and optimal treatment for BCa patients with brain metastasis

Analysis of interspecies adherence of oral bacteria using a membrane binding assay coupled with polymerase chain reaction-denaturing gradient gel electrophoresis profiling.

Information on co-adherence of different oral bacterial species is important for understanding interspecies interactions within oral microbial community. Current knowledge on this topic is heavily based on pariwise coaggregation of known, cultivable species. In this study, we employed a membrane binding assay coupled with polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE) to systematically analyze the co-adherence profiles of oral bacterial species, and achieved a more profound knowledge beyond pairwise coaggregation. Two oral bacterial species were selected to serve as "bait": Fusobacterium nucleatum (F. nucleatum) whose ability to adhere to a multitude of oral bacterial species has been extensively studied for pairwise interactions and Streptococcus mutans (S. mutans) whose interacting partners are largely unknown. To enable screening of interacting partner species within bacterial mixtures, cells of the "bait" oral bacterium were immobilized on nitrocellulose membranes which were washed and blocked to prevent unspecific binding. The "prey" bacterial mixtures (including known species or natural saliva samples) were added, unbound cells were washed off after the incubation period and the remaining cells were eluted using 0.2 mol x L(-1) glycine. Genomic DNA was extracted, subjected to 16S rRNA PCR amplification and separation of the resulting PCR products by DGGE. Selected bands were recovered from the gel, sequenced and identified via Nucleotide BLAST searches against different databases. While few bacterial species bound to S. mutans, consistent with previous findings F. nucleatum adhered to a variety of bacterial species including uncultivable and uncharacterized ones. This new approach can more effectively analyze the co-adherence profiles of oral bacteria, and could facilitate the systematic study of interbacterial binding of oral microbial species

i-Razor: A Differentiable Neural Input Razor for Feature Selection and Dimension Search in DNN-Based Recommender Systems

Input features play a crucial role in DNN-based recommender systems with thousands of categorical and continuous fields from users, items, contexts, and interactions. Noisy features and inappropriate embedding dimension assignments can deteriorate the performance of recommender systems and introduce unnecessary complexity in model training and online serving. Optimizing the input configuration of DNN models, including feature selection and embedding dimension assignment, has become one of the essential topics in feature engineering. However, in existing industrial practices, feature selection and dimension search are optimized sequentially, i.e., feature selection is performed first, followed by dimension search to determine the optimal dimension size for each selected feature. Such a sequential optimization mechanism increases training costs and risks generating suboptimal input configurations. To address this problem, we propose a differentiable neural input razor (i-Razor) that enables joint optimization of feature selection and dimension search. Concretely, we introduce an end-to-end differentiable model to learn the relative importance of different embedding regions of each feature. Furthermore, a flexible pruning algorithm is proposed to achieve feature filtering and dimension derivation simultaneously. Extensive experiments on two large-scale public datasets in the Click-Through-Rate (CTR) prediction task demonstrate the efficacy and superiority of i-Razor in balancing model complexity and performance.Comment: Accepted by IEEE Transactions on Knowledge and Data Engineering (TKDE