Peak Criterion for Choosing Gaussian Kernel Bandwidth in Support Vector Data Description

Support Vector Data Description (SVDD) is a machine-learning technique used for single class classification and outlier detection. SVDD formulation with kernel function provides a flexible boundary around data. The value of kernel function parameters affects the nature of the data boundary. For example, it is observed that with a Gaussian kernel, as the value of kernel bandwidth is lowered, the data boundary changes from spherical to wiggly. The spherical data boundary leads to underfitting, and an extremely wiggly data boundary leads to overfitting. In this paper, we propose empirical criterion to obtain good values of the Gaussian kernel bandwidth parameter. This criterion provides a smooth boundary that captures the essential geometric features of the data

Honeycomb oxide heterostructure: a new platform for Kitaev quantum spin liquid

Kitaev quantum spin liquid, massively quantum entangled states, is so scarce in nature that searching for new candidate systems remains a great challenge. Honeycomb heterostructure could be a promising route to realize and utilize such an exotic quantum phase by providing additional controllability of Hamiltonian and device compatibility, respectively. Here, we provide epitaxial honeycomb oxide thin film Na3Co2SbO6, a candidate of Kitaev quantum spin liquid proposed recently. We found a spin glass and antiferromagnetic ground states depending on Na stoichiometry, signifying not only the importance of Na vacancy control but also strong frustration in Na3Co2SbO6. Despite its classical ground state, the field-dependent magnetic susceptibility shows remarkable scaling collapse with a single critical exponent, which can be interpreted as evidence of quantum criticality. Its electronic ground state and derived spin Hamiltonian from spectroscopies are consistent with the predicted Kitaev model. Our work provides a unique route to the realization and utilization of Kitaev quantum spin liquid

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Linear Programming Algorithms Using Least-Squares Method

This thesis is a computational study of recently developed algorithms which aim to overcome degeneracy in the simplex method. We study the following algorithms: the non-negative least squares algorithm, the least-squares primal-dual algorithm, the least-squares network flow algorithm, and the combined-objective least-squares algorithm. All of the four algorithms use least-squares measures to solve their subproblems, so they do not exhibit degeneracy. But they have never been efficiently implemented and thus their performance has also not been proved. In this research we implement these algorithms in an efficient manner and improve their performance compared to their preliminary results. For the non-negative least-squares algorithm, we develop the basis update technique and data structure that fit our purpose. In addition, we also develop a measure to help find a good ordering of columns and rows so that we have a sparse and concise representation of QR-factors. The least-squares primal-dual algorithm uses the non-negative least-squares problem as its subproblem, which minimizes infeasibility while satisfying dual feasibility and complementary slackness. The least-squares network flow algorithm is the least-squares primal-dual algorithm applied to min-cost network flow instances. The least-squares network flow algorithm can efficiently solve much bigger instances than the least-squares primal-dual algorithm. The combined-objective least-squares algorithm is the primal version of the least-squares primal-dual algorithm. Each subproblem tries to minimize true objective and infeasibility simultaneously so that optimality and primal feasibility can be obtained together. It uses a big-M to minimize the infeasibility. We developed the techniques to improve the convergence rates of each algorithm: the relaxation of complementary slackness condition, special pricing strategy, and dynamic-M value. Our computational results show that the least-squares primal-dual algorithm and the combined-objective least-squares algorithm perform better than the CPLEX Primal solver, but are slower than the CPLEX Dual solver. The least-squares network flow algorithm performs as fast as the CPLEX Network solver.Ph.D.Committee Chair: Ellis L. Johnson; Committee Co-Chair: Earl Barnes; Committee Member: Joel Sokol; Committee Member: Martin Savelsbergh; Committee Member: Prasad Tetal

Optimization Technique for High-Gain CMOS Power Amplifier for 5G Applications

In this study, a differential power amplifier (PA) with a high gain of over 30 dB by configuring a three-stage common source unit amplifier was designed. To ensure the stability of the high-gain differential PA, the analysis to apply the capacitive neutralization method to the differential common source PA was conducted. From the analysis, the required neutralized capacitance was quantitatively calculated from the estimated parasitic components of a power cell used in the PA. To verify the feasibility of the proposed optimization technique, a Ka-band PA was designed with a 65 nm RFCMOS process. The measurement results showed a gain of 30.7 dB. The saturated output power was measured as 16.1 dBm, maximum power-added efficiency (PAE) was 29.7%, and P1dB was 13.1 dBm

A Simple Printed Cross-Dipole Antenna with Modified Feeding Structure and Dual-Layer Printed Reflector for Direction Finding Systems

In this paper, a simple printed cross-dipole (PCD) antenna to achieve a right-hand circular polarization (RHCP) at the L/S-band for direction finding (DF) systems is presented. The radiating part of the antenna consists of two printed dipoles that interlock with each other and are mounted orthogonally on a dual-layer printed reflector. To connect the feedlines of the dipole elements to the antenna’s feed network, which is located on the backside of the reflector, a through-hole signal via (THSV) is employed as the signal interconnection instead of the mainstream approach of using coaxial bead conductor. This feeding technique provides a degree of freedom to control the impedance of the signal path between the feedlines and the feed network in the numerical simulation for improved matching conditions. The proposed THSV extending through the dual-layer printed reflector is more reliable, durable, and mechanically robust to stabilize the matching conditions of the fabricated antenna in contrast to the coaxial-based approach that is more susceptible to impedance mismatch due to solder fatigue. Thus, the proposed PCD antenna offers advantages of broadband, flexible impedance matching, and fabrication ease. The antenna exhibits an impedance bandwidth (IBW) of 59% (1.59–2.93 GHz), a 3-dB axial ratio bandwidth (ARBW) of 57% (1.5–2.7 GHz), and a peak of 7.5 dB within the operating frequency band