Design and Implementation of a Microscope Based on Magneto-Optic Effects

When light passes through a medium that is subjected to a strong magnetic field, its polarization state may change due to magneto-optic effects such as Faraday rotation. An imaging system based on this polarization change is designed and constructed. The imaging system is built around a magnetic pulse field generator and able to detect polarization change of the incident light due to magneto-optic effects. An automated scheme is implemented using LabView. The program is developed to integrate all hardware and conduct multiple measurements automatically to enhance sensitivity. Basic testing measurements are conducted to evaluate the performance of the system. A metal film made of 50nm thick nickel and aluminum layer is tested and preliminary results are presented. Apart from the final design and experimental results, problems about laser imaging, system vibration and an early design using simple concave lens are also discussed. While no system can be universally ideal for all kinds of samples, an attempt is made to discuss ideal samples for imaging and how the performance may be affected by other types of samples. Various possible future improvements are also discussed and prioritized

Imitation from Observation: Learning to Imitate Behaviors from Raw Video via Context Translation

Imitation learning is an effective approach for autonomous systems to acquire control policies when an explicit reward function is unavailable, using supervision provided as demonstrations from an expert, typically a human operator. However, standard imitation learning methods assume that the agent receives examples of observation-action tuples that could be provided, for instance, to a supervised learning algorithm. This stands in contrast to how humans and animals imitate: we observe another person performing some behavior and then figure out which actions will realize that behavior, compensating for changes in viewpoint, surroundings, object positions and types, and other factors. We term this kind of imitation learning "imitation-from-observation," and propose an imitation learning method based on video prediction with context translation and deep reinforcement learning. This lifts the assumption in imitation learning that the demonstration should consist of observations in the same environment configuration, and enables a variety of interesting applications, including learning robotic skills that involve tool use simply by observing videos of human tool use. Our experimental results show the effectiveness of our approach in learning a wide range of real-world robotic tasks modeled after common household chores from videos of a human demonstrator, including sweeping, ladling almonds, pushing objects as well as a number of tasks in simulation.Comment: Accepted at ICRA 2018, Brisbane. YuXuan Liu and Abhishek Gupta had equal contributio

A Full Core Resonance Self-shielding Method Accounting for Temperature-dependent Fuel Subregions and Resonance Interference.

This work presents a new resonance self-shielding method for deterministic neutron transport calculation. The new method is a fusion of two types of conventional methods, direct slowing-down equation and integral table based methods. The direct slowing-down method is essentially accurate in terms of using continuous-energy cross section data but is computationally expensive for the reactor assembly or whole core calculation. The integral table based methods use pre-calculated tables so that these methods are much more efficient than directly solving the slowing-down equation. However, the derivation of integral table based methods introduces a couple of approximations, leading to limitations of these methods to treat resonance interference, spatially distributed self-shielding, and non-uniform temperature profile within the fuel rod. To overcome these limitations, the new method incorporates a correction scheme. The conventional iteration of the embedded self-shielding method (ESSM) is still performed without subdivision of the fuel regions to capture the global inter-pin shielding effect. The resultant self-shielded cross sections are modified by correction factors incorporating the intra-pin effects due to radial variation of the shielded cross section, radial temperature distribution, and resonance interference. An efficient quasi-1D slowing-down equation is developed to calculate these correction factors. In essence, the assumption that underpins this new method is that the global Dancoff effect is treated satisfactorily with ESSM, while the effects of radial fuel regions and resonance interference are local phenomena that can be solved with the quasi-1D model. The new method yields substantially improved results for both radially dependent and energy-dependent reaction rates, which help to improve the within-pin physics for multi-region depletion and multiphysics calculations, as well as the overall eigenvalue estimation.PhDNuclear Engineering and Radiological SciencesUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111419/1/yuxuanl_1.pd

Implementable Quantum Classifier for Nonlinear Data

In this Letter, we propose a quantum machine learning scheme for the classification of classical nonlinear data. The main ingredients of our method are variational quantum perceptron (VQP) and a quantum generalization of classical ensemble learning. Our VQP employs parameterized quantum circuits to learn a Grover search (or amplitude amplification) operation with classical optimization, and can achieve quadratic speedup in query complexity compared to its classical counterparts. We show how the trained VQP can be used to predict future data with $O(1)$ {query} complexity. Ultimately, a stronger nonlinear classifier can be established, the so-called quantum ensemble learning (QEL), by combining a set of weak VQPs produced using a subsampling method. The subsampling method has two significant advantages. First, all $T$ weak VQPs employed in QEL can be trained in parallel, therefore, the query complexity of QEL is equal to that of each weak VQP multiplied by $T$. Second, it dramatically reduce the {runtime} complexity of encoding circuits that map classical data to a quantum state because this dataset can be significantly smaller than the original dataset given to QEL. This arguably provides a most satisfactory solution to one of the most criticized issues in quantum machine learning proposals. To conclude, we perform two numerical experiments for our VQP and QEL, implemented by Python and pyQuil library. Our experiments show that excellent performance can be achieved using a very small quantum circuit size that is implementable under current quantum hardware development. Specifically, given a nonlinear synthetic dataset with $4$ features for each example, the trained QEL can classify the test examples that are sampled away from the decision boundaries using $146$ single and two qubits quantum gates with $92\%$ accuracy.Comment: 9 page