Grid-Oscillator Beam-Steering Array

Recently Liao and York (see IEEE Trans. Microwave Theory Tech., vol.41, no.10, p.1810, 1993) showed that beam-steering can be achieved by detuning the end elements of a coupled-oscillator array. The advantage of this approach is that no phase shifters are required. Liao and York used a single line array of patch antennas. Here we report the results for a pair of 1×4 HEMT line-grid oscillators at 11 GHz. This array can scan from -6.5° to +5° by changing the bia

Robust Orthogonal Complement Principal Component Analysis

Recently, the robustification of principal component analysis has attracted lots of attention from statisticians, engineers and computer scientists. In this work we study the type of outliers that are not necessarily apparent in the original observation space but can seriously affect the principal subspace estimation. Based on a mathematical formulation of such transformed outliers, a novel robust orthogonal complement principal component analysis (ROC-PCA) is proposed. The framework combines the popular sparsity-enforcing and low rank regularization techniques to deal with row-wise outliers as well as element-wise outliers. A non-asymptotic oracle inequality guarantees the accuracy and high breakdown performance of ROC-PCA in finite samples. To tackle the computational challenges, an efficient algorithm is developed on the basis of Stiefel manifold optimization and iterative thresholding. Furthermore, a batch variant is proposed to significantly reduce the cost in ultra high dimensions. The paper also points out a pitfall of a common practice of SVD reduction in robust PCA. Experiments show the effectiveness and efficiency of ROC-PCA in both synthetic and real data

Multi-asset Spread Option Pricing and Hedging

We provide two new closed-form approximation methods for pricing spread options on a basket of risky assets: the extended Kirk approximation and the second-order boundary approximation. Numerical analysis shows that while the latter method is more accurate than the former, both methods are extremely fast and accurate. Approximations for important Greeks are also derived in closed form. Our approximation methods enable the accurate pricing of a bulk volume of spread options on a large number of assets in real time, which offers traders a potential edge in a dynamic market environment.multi-asset spread option, closed-form approximation

Temporary urbanism-spatial democracy in the temporary city

This thesis is committed to exploring and discussing the way people behave in the temporary urbanism, perceive and deploy their space arrangement rights and how this nourishes relationships between people, between people and society, and brings a greater sense of spiritual identity and belonging to people. The modern city is the result of the spatial distribution of material production, urban space is political and oriented to the distribution of power, and citizens are deprived of the subjective qualification and right to participate in the creation o f urban cultural space. Many factors have led to the monopolization of human participation in urban space, the weakening of the spiritual connection between people, the intense focus on the self, and the increasing loss of collective values and memories. The spaces in temporary urbanism are formed more intentionally and purposeful by every citizen with shared values, collective beliefs and memories. Fo r example, Burning Man, Kumbh Mela and Mecca, they are either temporary cities themselv es or temporary land use in permanent cities. By taking RISD strike 2023 as a base to further discuss spatial democracy as the continuation of the case study, and on the other hand, I could further sort out and compare the four cases deeply since I experienced the whole process. I could better appreciate how connections have been made between people, igniting new conversations. In the last part of the thesis, I got to comprehend and apply those principles on a small scale where I live daily

Comparison of Several New Energy Vehicles and Gasoline Vehicles During the Same Price Range

Consumers frequently consider the advantages between traditional gasoline vehicles and new energy vehicles. This experiment compared NEVs and traditional gasoline vehicles in aspects of carbon emissions, daily consumption, and value preservation rate. Traditional gasoline vehicles have a higher value preservation rate than new energy vehicles on average. However, NEVs have better environmental protection and money-saving properties. What is more, if the governments and companies use some new energy sources such as nuclear and wind as the electric supply, it will be more environmental-friendly, and help consumers save more money on daily driving. In summary, if consumers do not have plans to sell their cars, it is better for both daily use and environmental protection to buy a new energy vehicle. This experiment can help customers have a reference standard when choosing a car. However, many factors were not considered in this experiment, such as the life of the battery and engine and the impact of the customer's car habits on the car. The experimental results are only for reference

Semantic RGB-D Image Synthesis

Collecting diverse sets of training images for RGB-D semantic image segmentation is not always possible. In particular, when robots need to operate in privacy-sensitive areas like homes, the collection is often limited to a small set of locations. As a consequence, the annotated images lack diversity in appearance and approaches for RGB-D semantic image segmentation tend to overfit the training data. In this paper, we thus introduce semantic RGB-D image synthesis to address this problem. It requires synthesising a realistic-looking RGB-D image for a given semantic label map. Current approaches, however, are uni-modal and cannot cope with multi-modal data. Indeed, we show that extending uni-modal approaches to multi-modal data does not perform well. In this paper, we therefore propose a generator for multi-modal data that separates modal-independent information of the semantic layout from the modal-dependent information that is needed to generate an RGB and a depth image, respectively. Furthermore, we propose a discriminator that ensures semantic consistency between the label maps and the generated images and perceptual similarity between the real and generated images. Our comprehensive experiments demonstrate that the proposed method outperforms previous uni-modal methods by a large margin and that the accuracy of an approach for RGB-D semantic segmentation can be significantly improved by mixing real and generated images during training

Optimal batching plan of deoxidation alloying based on principal component analysis and linear programming

As the market competition of steel mills is severe, deoxidization alloying is an important link in the metallurgical process. To solve this problem, principal component regression analysis is adopted to reduce the dimension of influencing factors, and a reasonable and reliable prediction model of element yield is established. Based on the constraint conditions such as target cost function constraint, yield constraint and non-negative constraint, linear programming is adopted to design the lowest cost batting scheme that meets the national standards and production requirements. The research results provide a reliable optimization model for the deoxidization and alloying process of steel mills, which is of positive significance for improving the market competitiveness of steel mills, reducing waste discharge and protecting the environment

A Full Quantum Eigensolver for Quantum Chemistry Simulations

Quantum simulation of quantum chemistry is one of the most compelling applications of quantum computing. It is of particular importance in areas ranging from materials science, biochemistry and condensed matter physics. Here, we propose a full quantum eigensolver (FQE) algorithm to calculate the molecular ground energies and electronic structures using quantum gradient descent. Compared to existing classical-quantum hybrid methods such as variational quantum eigensolver (VQE), our method removes the classical optimizer and performs all the calculations on a quantum computer with faster convergence. The gradient descent iteration depth has a favorable complexity that is logarithmically dependent on the system size and inverse of the precision. Moreover, the FQE can be further simplified by exploiting perturbation theory for the calculations of intermediate matrix elements, and obtain results with a precision that satisfies the requirement of chemistry application. The full quantum eigensolver can be implemented on a near-term quantum computer. With the rapid development of quantum computing hardware, FQE provides an efficient and powerful tool to solve quantum chemistry problems