Pattern Research Project: An Investigation of The Pattern And Printing Process - Acanthus

2017 Pattern Research Project Hongyi Zhu - Acanthus The Pattern Research Project involves research and analysis of contemporary patterns found in the textiles and wallcoverings of the built interior environment. Patterns use motif, repetition, color, geometry, craft, technology, and space to communicate place, time, and concept. Through this research and analysis, built environments - their designers, occupants, construction, and context - can be better understood. Hongyi Zhu, VCU Interior Design BFA 2020, selected the Acanthus pattern for the 2017 Pattern Research Project. The text below is excerpted from the student’s work: “Acanthus is a type of plant that [is] widespread in mediterranean region. There leaves were used as ornamentation for thousands of years. The most well-known example should be the corinthian order columns of Greek temples. Later, it was inherited by the Roman Empire and its successor, the Byzantine empire. The foliages symbolize immortality, healing and rebirth; it was commonly used as a decoration for Cathedral architectures. However, it had never became a significant religious symbol in Christianity. The pattern remained as a decorative element. The pattern was developed and transformed into numerous different forms through the age..”https://scholarscompass.vcu.edu/prp/1003/thumbnail.jp

Source-independent quantum random number generation

Quantum random number generators can provide genuine randomness by appealing to the fundamental principles of quantum mechanics. In general, a physical generator contains two parts---a randomness source and its readout. The source is essential to the quality of the resulting random numbers; hence, it needs to be carefully calibrated and modeled to achieve information-theoretical provable randomness. However, in practice, the source is a complicated physical system, such as a light source or an atomic ensemble, and any deviations in the real-life implementation from the theoretical model may affect the randomness of the output. To close this gap, we propose a source-independent scheme for quantum random number generation in which output randomness can be certified, even when the source is uncharacterized and untrusted. In our randomness analysis, we make no assumptions about the dimension of the source. For instance, multiphoton emissions are allowed in optical implementations. Our analysis takes into account the finite-key effect with the composable security definition. In the limit of large data size, the length of the input random seed is exponentially small compared to that of the output random bit. In addition, by modifying a quantum key distribution system, we experimentally demonstrate our scheme and achieve a randomness generation rate of over $5\times 10^3$ bit/s.Comment: 11 pages, 7 figure

Highly efficient schemes for time fractional Allen-Cahn equation using extended SAV approach

In this paper, we propose and analyze high order efficient schemes for the time fractional Allen-Cahn equation. The proposed schemes are based on the L1 discretization for the time fractional derivative and the extended scalar auxiliary variable (SAV) approach developed very recently to deal with the nonlinear terms in the equation. The main contributions of the paper consist in: 1) constructing first and higher order unconditionally stable schemes for different mesh types, and proving the unconditional stability of the constructed schemes for the uniform mesh; 2) carrying out numerical experiments to verify the efficiency of the schemes and to investigate the coarsening dynamics governed by the time fractional Allen-Cahn equation. Particularly, the influence of the fractional order on the coarsening behavior is carefully examined. Our numerical evidence shows that the proposed schemes are more robust than the existing methods, and their efficiency is less restricted to particular forms of the nonlinear potentials

Fear Appeals and Information Security Behaviors: An Empirical Study on Mechanical Turk

This study aims to conduct a methodological replication of the information security study conducted by Johnston and Warkentin (2010). This study leveraged the use of the fear appeals model (FAM) in the context of information security as they pertain to the individual use of anti-spyware software. We adopt all measures, instruments, statistical tests, theory, and models from the original study, but apply them to the Amazon Mechanical Turk population. The results from this replication study are not consistent with the original study, in that two of the five posited hypotheses have opposite effects than those originally found; threat severity is shown to have a positive effect on both response efficacy and self-efficacy, where in the original study, this is shown to have a negative effect on both. The results imply that there may be differences in which populations the study was conducted, thus requiring additional samples and statistical tests

Stein Variational Gradient Descent-based Detection For Random Access With Preambles In MTC

Traditional preamble detection algorithms have low accuracy in the grant-based random access scheme in massive machine-type communication (mMTC). We present a novel preamble detection algorithm based on Stein variational gradient descent (SVGD) at the second step of the random access procedure. It efficiently leverages deterministic updates of particles for continuous inference. To further enhance the performance of the SVGD detector, especially in a dense user scenario, we propose a normalized SVGD detector with momentum. It utilizes the momentum and a bias correction term to reduce the preamble estimation errors during the gradient descent process. Simulation results show that the proposed algorithm performs better than Markov Chain Monte Carlo-based approaches in terms of detection accuracy

A Hybrid Quantum-Classical Approach based on the Hadamard Transform for the Convolutional Layer

In this paper, we propose a novel Hadamard Transform (HT)-based neural network layer for hybrid quantum-classical computing. It implements the regular convolutional layers in the Hadamard transform domain. The idea is based on the HT convolution theorem which states that the dyadic convolution between two vectors is equivalent to the element-wise multiplication of their HT representation. Computing the HT is simply the application of a Hadamard gate to each qubit individually, so the HT computations of our proposed layer can be implemented on a quantum computer. Compared to the regular Conv2D layer, the proposed HT-perceptron layer is computationally more efficient. Compared to a CNN with the same number of trainable parameters and 99.26\% test accuracy, our HT network reaches 99.31\% test accuracy with 57.1\% MACs reduced in the MNIST dataset; and in our ImageNet-1K experiments, our HT-based ResNet-50 exceeds the accuracy of the baseline ResNet-50 by 0.59\% center-crop top-1 accuracy using 11.5\% fewer parameters with 12.6\% fewer MACs.Comment: To be presented at International Conference on Machine Learning (ICML), 202