Image Reconstructions of Compressed Sensing MRI with Multichannel Data

Magnetic resonance imaging (MRI) provides high spatial resolution, high-quality of soft-tissue contrast, and multi-dimensional images. However, the speed of data acquisition limits potential applications. Compressed sensing (CS) theory allowing data being sampled at sub-Nyquist rate provides a possibility to accelerate the MRI scan time. Since most MRI scanners are currently equipped with multi-channel receiver systems, integrating CS with multi-channel systems can further shorten the scan time and also provide a better image quality. In this dissertation, we develop several techniques for integrating CS with parallel MRI. First, we propose a method which extends the reweighted l1 minimization to the CS-MRI with multi-channel data. The individual channel images are recovered according to the reweighted l1 minimization algorithm. Then, the final image is combined by the sum-of-squares method. Computer simulations show that the new method can improve the reconstruction quality at a slightly increased computation cost. Second, we propose a reconstruction approach using the ubiquitously available multi-core CPU to accelerate CS reconstructions of multiple channel data. CS reconstructions for phase array system using iterative l1 minimization are significantly time-consuming, where the computation complexity scales with the number of channels. The experimental results show that the reconstruction efficiency benefits significantly from parallelizing the CS reconstructions, and pipelining multi-channel data on multi-core processors. In our experiments, an additional speedup factor of 1.6 to 2.0 was achieved using the proposed method on a quad-core CPU. Finally, we present an efficient reconstruction method for high-dimensional CS MRI with a GPU platform to shorten the time of iterative computations. Data managements as well as the iterative algorithm are properly designed to meet the way of SIMD (single instruction/multiple data) parallelizations. For three-dimension multi-channel data, all slices along frequency encoding direction and multiple channels are highly parallelized and simultaneously processed within GPU. Generally, the runtime on GPU only requires 2.3 seconds for reconstructing a simulated 4-channel data with a volume size of 256×256×32. Comparing to 67 seconds using CPU, it achieves 28 faster with the proposed method. The rapid reconstruction algorithms demonstrated in this work are expected to help bring high dimensional, multichannel parallel CS MRI closer to clinical applications

Interest Rate Rules, Target Policies, and Endogenous Economic Growth in an Open Economy

This paper sets up an endogenous growth model of an open economy in which the monetary authority implements a gradualist interest-rate rule with targets for inflation and economic growth. We show that, under a passive rule, a monetary equilibrium exists and is unique; moreover, the equilibrium is locally determinate. Under an active rule, the open economy either generates multiple equilibria or does not have any equilibrium. If equilibria exist, the high-growth equilibrium is locally determinate while the low-growth equilibrium is a source. Besides these, the stabilization and growth effects of alternative target policies are also explored in this study.Nominal interest rate rules, gradualism, endogenous economic growth

BLIP-Adapter: Parameter-Efficient Transfer Learning for Mobile Screenshot Captioning

This study aims to explore efficient tuning methods for the screenshot captioning task. Recently, image captioning has seen significant advancements, but research in captioning tasks for mobile screens remains relatively scarce. Current datasets and use cases describing user behaviors within product screenshots are notably limited. Consequently, we sought to fine-tune pre-existing models for the screenshot captioning task. However, fine-tuning large pre-trained models can be resource-intensive, requiring considerable time, computational power, and storage due to the vast number of parameters in image captioning models. To tackle this challenge, this study proposes a combination of adapter methods, which necessitates tuning only the additional modules on the model. These methods are originally designed for vision or language tasks, and our intention is to apply them to address similar challenges in screenshot captioning. By freezing the parameters of the image caption models and training only the weights associated with the methods, performance comparable to fine-tuning the entire model can be achieved, while significantly reducing the number of parameters. This study represents the first comprehensive investigation into the effectiveness of combining adapters within the context of the screenshot captioning task. Through our experiments and analyses, this study aims to provide valuable insights into the application of adapters in vision-language models and contribute to the development of efficient tuning techniques for the screenshot captioning task. Our study is available at https://github.com/RainYuGG/BLIP-Adapte

Piglets Comfort with Hot Water by Biogas Combustion under Controllable Ventilation

The purpose of this study is to develop a hot-water heating system for pig farms which use biogas as the energy source while the air quality is regulated using an inverter-controlled fan. The biogas is a by-product from the 3-stage wastewater treatment process in regular pig farms. The biogas is burned for hot water which is circulated to warm piglet compartments with regulated, forced ventilation. The hot water is connected to a heat exchanger and hot air is hence blown into the pigsty. To maintain the pigsty at a comfort atmosphere, ventilation is regulated using an inverter-controlled fan. The mechanical ventilation is to be optimized as a compromise between indoor air quality and ventilation rate. The temperature uniformity and air quality in the pigsty is to be secured for comfortability. Experimental results show that hot water circulating at 0.043 m3/min and 60°C could keep the pigsty at 28°C for a stocking density of 1.77 pig/m2. Forced ventilation of 1.7 ACH (air change rate per hour) at 28°C could keep the pigsty comfort in terms of indoor temperature, relative humidity, and carbon-dioxide concentration

Genome-wide analysis of the cis-regulatory modules of divergent gene pairs in yeast

AbstractIn budding yeast, approximately a quarter of adjacent genes are divergently transcribed (divergent gene pairs). Whether genes in a divergent pair share the same regulatory system is still unknown. By examining transcription factor (TF) knockout experiments, we found that most TF knockout only altered the expression of one gene in a divergent pair. This prompted us to conduct a comprehensive analysis in silico to estimate how many divergent pairs are regulated by common sets of TFs (cis-regulatory modules, CRMs) using TF binding sites and expression data. Analyses of ten expression datasets show that only a limited number of divergent gene pairs share CRMs in any single dataset. However, around half of divergent pairs do share a regulatory system in at least one dataset. Our analysis suggests that genes in a divergent pair tend to be co-regulated in at least one condition; however, in most conditions, they may not be co-regulated

Alcohol Use, Abuse, and Dependency in Shanghai

The use of alcohol for social and ceremonial occasions was recorded in Chinese history as early as 1760 B.C. during the Yin Dynasty (Ci-Hai Encyclopedia, 1979:936). The cultural tradition of ancient China placed alcoholic beverages at the center of social occasions, which presumably was the origin of the adage: Without wine, there is no li (or etiquette). Thus, the use of alcoholic beverages has always been accompanied by the concept of propriety and the discharging of one\u27s role obligations m social functions, rather than that of personal indulgence