Ground-state Competition of Two-Component Bosons in Optical Lattice near a Feshbach Resonance

We investigate the ground state properties of an equal mixture of two species of bosons in its Mott-insulator phase at a filling factor two per site. We identify one type of spin triplet-singlet transition through the competition of ground state. When the on-site interaction is weak ($U<U_c$) the two particles prefer to stay in the lowest band and with weak tunnelling between neighboring sites the system is mapped into an effective spin-1 ferromagnetic exchange Hamiltonian. When the interaction is tuned by a Feshbach resonance to be large enough ($U>U_c$), higher band will be populated. Due to the orbital coupling term $S^+S^-$ in the Hamiltonian, the two atoms in different orbits on a site would form an on-site singlet. For a non-SU(2)-symmetric model, easy-axis or easy-plane ferromagnetic spin exchange models may be realized corresponding to phase separation or counter-flow superfluidity, respectively.Comment: Final version in PR

The lowest scattering state of one-dimensional Bose gas with attractive interactions

We investigate the lowest scattering state of one-dimensional Bose gas with attractive interactions trapped in a hard wall trap. By solving the Bethe ansatz equation numerically we determine the full energy spectrum and the exact wave function for different attractive interaction parameters. The resultant density distribution, momentum distribution, reduced one body density matrix and two body correlation show that the decreased attractive interaction induces rich density profiles and specific correlation properties in the weakly attractive Bose gas.Comment: 6 pages, 6figure

Sensitive, Label-free Biomolecular Binding Detection Using a One-dimensional Photonic Crystal Sensor.

Novel optical methods for performing label-free detection have attracted growing attention driven by increasing demands for better understanding of specific interactions between biomolecules, which provide a chemical foundation for all cellular processes. Although a number of label-free techniques for directly monitoring biomolecular binding exist, they are limited in their ability to measure the binding kinetics of very small molecules, to detect low concentrations of molecules, or to detect low affinity interactions. In this thesis, I develop a one-dimensional photonic crystal biosensor which enables highly-sensitive, label-free, real-time biomolecular binding analysis. This sensor uses a one-dimensional photonic crystal (PC) structure in a total-internal-reflection (TIR) geometry (PC-TIR), which forms a high-finesse Fabry-Pérot resonator with an open cavity. Detailed analysis on how to effectively design and fabricate suitable sensor structures is discussed. Experimentally, the sensor achieved a narrow resonance width (~ 1 nm) and large sensitivity (~ 1840 nm per refractive index unit (RIU)). By adopting normalized intensity modulation, this sensor demonstrates ultralow detection limits (i.e., high performances) in a series of experiments: 10^-8 RIU for bulk solvent refractive index, 2×10^-5 nm for molecular layer thickness, and 6 fg/mm2 for surface mass density. Moreover, its capability for label-free biomolecular detection is characterized with a standard streptavidin-biotin binding system. The specific binding of biotinylated molecules ranging over three orders of magnitude in molecular weight, including very small molecules ( 150,000 Da), to streptavidin covalently adsorbed on a sensing surface are detected in real time with high signal-to-noise ratios. Furthermore, it shows high efficiency for quantitative analysis on DNA studies, including strand length measurement, low concentration binding, and hybridization. Compared to the state-of-the-art surface-plasmon-resonance (SPR)-based biosensors whose performance is mainly restricted by broad resonance widths, the ultrahigh-Q resonant cavities such as whispering gallery modes (WGMs)-based biosensors which suffer from low sensitivity, thermal instability and nontrivial coupling, the PC-TIR sensor employing a simple geometry and a moderate Q, has achieved orders of magnitude higher detection sensitivity than other label-free optical biosensors reported to date, and thus is promising as a new sensing platform for biomolecular binding detection.Ph.D.Electrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/77939/1/guoybyw_1.pd

Polyp Segmentation in Colonoscopy Images with Convolutional Neural Networks

The thesis looks at approaches to segmentation of polyps in colonoscopy images. The aim was to investigate and develop methods that are robust, accurate and computationally efficient and which can compete with the current state-of-the-art in polyp segmentation. Colorectal cancer is one of the leading cause of cancer deaths worldwide. To decrease mortality, an assessment of polyp malignancy is performed during colonoscopy examination so polyps can be removed at an early stage. In current routine clinical practice, polyps are detected and delineated manually in colonoscopy images by highly trained clinicians. To automate these processes, machine learning and computer vision techniques have been utilised. They have been shown to improve polyp detectability and segmentation objectivity. However, polyp segmentation is a very challenging task due to inherent variability of polyp morphology and colonoscopy image appearance. This research considers a range of approaches to polyp segmentation – seeking out those that offer a best compromise between accuracy and computational complexity. Based on analysis of existing machine learning and polyp image segmentation techniques, a novel hybrid deep learning segmentation method is proposed to alleviate the impact of the above stated challenges on polyp segmentation. The method consists of two fully convolutional networks. The first proposed network is based on a compact architecture with large receptive fields and multiple classification paths. The method performs well on most images, accurately segmenting polyps of diverse morphology and appearance. However, this network is prone to misdetection of very small polyps. To solve this problem, a second network is proposed, which primarily aims to improve sensitivity to small polyp details by emphasising low-level image features. In order to fully utilise information contained in the available training dataset, comprehensive data augmentation techniques are adopted. To further improve the performance of the proposed segmentation methods, test-time data augmentation is also implemented. A comprehensive multi-criterion analysis of the proposed methods is provided. The result demonstrates that the new methodology has better accuracy and robustness than the current state-of-the-art, as proven by the outstanding performance at the 2017 and 2018 GIANA polyp segmentation challenges