Ultrafast Broadband Terahertz Spectroscopy

This dissertation centers on broadband terahertz spectroscopy and is arranged in four main sections. In the first section, we describe terahertz generation from a two-color laser air breakdown plasma. This is modeled with a plasma current that includes plasma density and dispersion in propagation. The terahertz spatial profile has a ring-like structure with a frequency dependent radius. Parameters for optimal terahertz generation are also presented. The next two sections discuss broadband terahertz detection techniques: optically biased coherent detection and electro-absorption in a semiconductor. The subject of the fourth section is terahertz imaging with electroabsorption. Optically biased coherent detection is distinguished from air-breakdown coherent detection by replacing the electrical bias for an optical field. The importance of phase control in this technique is demonstrated. In addition, we found the terahertz-induced second harmonic to be spectrally delay-dependent. This is due to the phase matching condition and is discussed in detail. Terahertz-induced electro-absorption is performed in GaAs/AlGaAs multiple double quantum wells and an AlGaAs bulk semiconductor. To the best of the author\u27s knowledge, this is the first demonstration of large modulation induced by a single cycle terahertz pulse in such structures. The underlying mechanism is identified as the Franz-Keldysh effect that has been successfully modeled in both the temporal and spectral regimes. Terahertz imaging of the plasma profile is accomplished with electro-absorption in these structures. The observed ring pattern is reproduced with the model described in the first section of this dissertation. Terahertz raster scan imaging of a large object is also presented

Deep Learning-based Fall Detection Algorithm Using Ensemble Model of Coarse-fine CNN and GRU Networks

Falls are the public health issue for the elderly all over the world since the fall-induced injuries are associated with a large amount of healthcare cost. Falls can cause serious injuries, even leading to death if the elderly suffers a "long-lie". Hence, a reliable fall detection (FD) system is required to provide an emergency alarm for first aid. Due to the advances in wearable device technology and artificial intelligence, some fall detection systems have been developed using machine learning and deep learning methods to analyze the signal collected from accelerometer and gyroscopes. In order to achieve better fall detection performance, an ensemble model that combines a coarse-fine convolutional neural network and gated recurrent unit is proposed in this study. The parallel structure design used in this model restores the different grains of spatial characteristics and capture temporal dependencies for feature representation. This study applies the FallAllD public dataset to validate the reliability of the proposed model, which achieves a recall, precision, and F-score of 92.54%, 96.13%, and 94.26%, respectively. The results demonstrate the reliability of the proposed ensemble model in discriminating falls from daily living activities and its superior performance compared to the state-of-the-art convolutional neural network long short-term memory (CNN-LSTM) for FD

TinyKG: Memory-Efficient Training Framework for Knowledge Graph Neural Recommender Systems

There has been an explosion of interest in designing various Knowledge Graph Neural Networks (KGNNs), which achieve state-of-the-art performance and provide great explainability for recommendation. The promising performance is mainly resulting from their capability of capturing high-order proximity messages over the knowledge graphs. However, training KGNNs at scale is challenging due to the high memory usage. In the forward pass, the automatic differentiation engines (\textsl{e.g.}, TensorFlow/PyTorch) generally need to cache all intermediate activation maps in order to compute gradients in the backward pass, which leads to a large GPU memory footprint. Existing work solves this problem by utilizing multi-GPU distributed frameworks. Nonetheless, this poses a practical challenge when seeking to deploy KGNNs in memory-constrained environments, especially for industry-scale graphs. Here we present TinyKG, a memory-efficient GPU-based training framework for KGNNs for the tasks of recommendation. Specifically, TinyKG uses exact activations in the forward pass while storing a quantized version of activations in the GPU buffers. During the backward pass, these low-precision activations are dequantized back to full-precision tensors, in order to compute gradients. To reduce the quantization errors, TinyKG applies a simple yet effective quantization algorithm to compress the activations, which ensures unbiasedness with low variance. As such, the training memory footprint of KGNNs is largely reduced with negligible accuracy loss. To evaluate the performance of our TinyKG, we conduct comprehensive experiments on real-world datasets. We found that our TinyKG with INT2 quantization aggressively reduces the memory footprint of activation maps with $7 \times$, only with $2\%$ loss in accuracy, allowing us to deploy KGNNs on memory-constrained devices

Bis(4,4′-methyl­enedicyclo­hexyl­aminium) μ-benzene-1,4-dicarboxyl­ato-bis­[tri­chlorido­zinc(II)] tetra­hydrate

The title compound, (C13H28N2)2[Zn2(C8H4O4)Cl6]·4H2O, was prepared by the reaction of ZnCl2·6H2O, benzene-1,4-dicarboxylic acid and 4,4′-diamino­dicyclo­hexyl­methane in methanol. The [Zn2Cl6(C8H4O4)]4− anions lie on centres of inversion and comprise two ZnCl3 groups bridged by benzene-1,4-dicarboxyl­ate. In addition to N—H⋯Cl and N—H⋯O hydrogen bonds between the cations and anions, solvent water mol­ecules form O—H⋯O and O—H⋯Cl hydrogen bonds to give a three-dimensional network

Outcome and prognostic factors in critically ill patients with systemic lupus erythematosus: a retrospective study

INTRODUCTION: Systemic lupus erythematosus (SLE) is an archetypal autoimmune disease, involving multiple organ systems with varying course and prognosis. However, there is a paucity of clinical data regarding prognostic factors in SLE patients admitted to the intensive care unit (ICU). METHODS: From January 1992 to December 2000, all patients admitted to the ICU with a diagnosis of SLE were included. Patients were excluded if the diagnosis of SLE was established at or after ICU admission. A multivariate logistic regression model was applied using Acute Physiology and Chronic Health Evaluation II scores and variables that were at least moderately associated (P < 0.2) with survival in the univariate analysis. RESULTS: A total of 51 patients meeting the criteria were included. The mortality rate was 47%. The most common cause of admission was pneumonia with acute respiratory distress syndrome. Multivariate logistic regression analysis showed that intracranial haemorrhage occurring while the patient was in the ICU (relative risk = 18.68), complicating gastrointestinal bleeding (relative risk = 6.97) and concurrent septic shock (relative risk = 77.06) were associated with greater risk of dying, whereas causes of ICU admission and Acute Physiology and Chronic Health Evaluation II score were not significantly associated with death. CONCLUSION: The mortality rate in critically ill SLE patients was high. Gastrointestinal bleeding, intracranial haemorrhage and septic shock were significant prognostic factors in SLE patients admitted to the ICU

Identification of the genetic determinants of Salmonella enterica serotype Typhimurium that may regulate the expression of the type 1 fimbriae in response to solid agar and static broth culture conditions

<p>Abstract</p> <p>Background</p> <p>Type 1 fimbriae are the most commonly found fimbrial appendages on the outer membrane of <it>Salmonella enterica </it>serotype Typhimurium. Previous investigations indicate that static broth culture favours <it>S</it>. Typhimurium to produce type 1 fimbriae, while non-fimbriate bacteria are obtained by growth on solid agar media. The phenotypic expression of type 1 fimbriae in <it>S</it>. Typhimurium is the result of the interaction and cooperation of several genes in the <it>fim </it>gene cluster. Other gene products that may also participate in the regulation of type 1 fimbrial expression remain uncharacterized.</p> <p>Results</p> <p>In the present study, transposon insertion mutagenesis was performed on <it>S</it>. Typhimurium to generate a library to screen for those mutants that would exhibit different type 1 fimbrial phenotypes than the parental strain. Eight-two mutants were obtained from 7,239 clones screened using the yeast agglutination test. Forty-four mutants produced type 1 fimbriae on both solid agar and static broth media, while none of the other 38 mutants formed type 1 fimbriae in either culture condition. The flanking sequences of the transposons from 54 mutants were cloned and sequenced. These mutants can be classified according to the functions or putative functions of the open reading frames disrupted by the transposon. Our current results indicate that the genetic determinants such as those involved in the fimbrial biogenesis and regulation, global regulators, transporter proteins, prophage-derived proteins, and enzymes of different functions, to name a few, may play a role in the regulation of type 1 fimbrial expression in response to solid agar and static broth culture conditions. A complementation test revealed that transforming a recombinant plasmid possessing the coding sequence of a NAD(P)H-flavin reductase gene <it>ubiB </it>restored an <it>ubiB </it>mutant to exhibit the type 1 fimbrial phenotype as its parental strain.</p> <p>Conclusion</p> <p>Genetic determinants other than the <it>fim </it>genes may involve in the regulation of type 1 fimbrial expression in <it>S</it>. Typhimurium. How each gene product may influence type 1 fimbrial expression is an interesting research topic which warrants further investigation.</p