A Survey of Selected Classical Chinese Art Songs for Solo Voice and Piano from 1920 to 1950

This study aims to provide a repertoire guide of Chinese art songs for the pedagogical and research needs of musicians and scholars. Chinese art songs are derived from Western art songs. This work provides a valuable resource outside the scope of the standard art song repertoire. There are very limited studies with annotated lists of Chinese art song repertoire in English. Therefore, it is difficult for teachers to search for specific Chinese art songs to utilize with students who have different voice types and learning levels. This study will provide a practical tool that will meet these pedagogical needs. All the Chinese art songs selected for use in this study were composed between 1920 and 1950. Most of these songs were original works; some were arranged with piano accompaniment at a later point. Most of the works included in this study were originally composed for solo voice and piano. Several chamber pieces are also included, but there are no songs included that have orchestral accompaniments. With each entry, there is an annotation providing information about the song. This information typically includes the work’s composer, title, publisher/year, poet, poem style, voice type (gender/classifications), tessitura, difficulty level (beginning/intermediate/advanced), duration, and brief comments. Learning Chinese art songs is beneficial for singers, teachers, and music scholars. This study provides a new resource which aims to make this invaluable vocal repertoire more accessible to teachers and musicians. The unique tunes and musical styles of Chinese art songs also bring fresh varieties of sound for audiences. Moreover, Chinese art songs are mostly based on both Classical Chinese poetry and modern Chinese poetry written by renowned Chinese poets. The Chinese art songs analyzed in this study also reflect important historical and cultural revolutions in China. This study provides a channel for western scholars and musicologists to study Chinese art and culture through music and poems, making a new field for scholarly exploration more readily available

An Association Test Based on Kernel-Based Neural Networks for Complex Genetic Association Analysis

The advent of artificial intelligence, especially the progress of deep neural networks, is expected to revolutionize genetic research and offer unprecedented potential to decode the complex relationships between genetic variants and disease phenotypes, which could mark a significant step toward improving our understanding of the disease etiology. While deep neural networks hold great promise for genetic association analysis, limited research has been focused on developing neural-network-based tests to dissect complex genotype-phenotype associations. This complexity arises from the opaque nature of neural networks and the absence of defined limiting distributions. We have previously developed a kernel-based neural network model (KNN) that synergizes the strengths of linear mixed models with conventional neural networks. KNN adopts a computationally efficient minimum norm quadratic unbiased estimator (MINQUE) algorithm and uses KNN structure to capture the complex relationship between large-scale sequencing data and a disease phenotype of interest. In the KNN framework, we introduce a MINQUE-based test to assess the joint association of genetic variants with the phenotype, which considers non-linear and non-additive effects and follows a mixture of chi-square distributions. We also construct two additional tests to evaluate and interpret linear and non-linear/non-additive genetic effects, including interaction effects. Our simulations show that our method consistently controls the type I error rate under various conditions and achieves greater power than a commonly used sequence kernel association test (SKAT), especially when involving non-linear and interaction effects. When applied to real data from the UK Biobank, our approach identified genes associated with hippocampal volume, which can be further replicated and evaluated for their role in the pathogenesis of Alzheimer's disease.Comment: 34 pages, 4 figures, 3 table

Investigations in improving image visualization and quality in positron emission tomography/computed tomography (PET/CT) imaging

Positron Emission Tomography/Computed Tomography (PET/CT) is a widely used imaging modality for managing patients with cancer. The combination of PET and CT can provide both functional and anatomic information of disease distribution. However, despite its widespread use, it also has some limitations. One drawback is that current PET/CT scanners cannot acquire whole-body scan in a single acquisition but rather has to divide it into multiple sections due to a limitation in the extent of bed travel. The first part of this thesis focuses on developing a software tool that can display multiple PET segments as a single scan to improve the interpretation of these studies. The second part of the thesis focuses on another limitation of PET/CT imaging, namely its low image quality. In this section, an investigation of the correlation between injected dose, patient BMI and scanner design on PET image quality is performed. The objective of this investigation is to determine the significance and extent by which these factors can impact PET image quality. The results of this work can be used as a guide to improve protocol design in an effort to generate an optimal PET image quality

Optimization of novel developments in Positron Emission Tomography (PET) imaging

Positron Emission Tomography (PET) is a widely used imaging modality for diagnosing patients with cancer. Recently, there have been three novel developments in PET imaging aiming to increase PET image quality and quantification. This thesis focuses on the optimization of PET image quality on these three developments. The first development is the fully 3D PET data acquisition and reconstruction. 3D Acquisitions are not constrained in collecting events in single 2D planes and can span across different planes. 3D acquisition provides better detection since it can accept more events. Also it can result in lower radiation dose to the patient and shorter imaging times. With the application of 3D acquisition, a fully 3D iterative reconstruction algorithm was also developed. The aim of the first project in this thesis is to evaluate the PET image and raw data quality when this fully 3D iterative reconstruction algorithm is applied. The second development in PET imaging is the time-of-flight (TOF) PET data acquisition and reconstruction. TOF imaging has the ability to measure the difference between the detection times, thus localize the event location more accurately to increase the image quality. The second project in this thesis focuses on optimizing the TOF reconstruction parameters on a newly developed TOF PET scanner. Then the improvement of TOF information on image quality is assessed using the derived optimal parameters. Finally the effect of scan duration is evaluated to determine whether similar image quality could be obtained between TOF and non-TOF while using less scan time for TOF. The third development is the interest in building PET / magnetic resonance (MR) multi-modality scanner. MR imaging has the ability to show high soft tissue contrast and can assess physiological processes, which cannot be achieved on PET images. One problem in developing PET/MR system is that it is not possible with current MR acquisition schemes to translate the MR image into an attenuation map to correct for PET attenuations. The third project in this thesis proposed and assessed an approach for the attenuation correction of PET data in potential PET/MR systems to improve PET image quality and quantification

Statistical distribution of HI 21cm intervening absorbers as potential cosmic acceleration probes

Damped Lyman-$\alpha$ Absorber (DLA), or HI 21cm Absorber (H21A), is an important probe to model-independently measure the acceleration of spectroscopic velocity ($v_\mathrm{S}$) via the Sandage-Loeb (SL) effect. Confined by the shortage of DLAs and Background Radio Sources (BRSs) with adequate information, the detectable amount of DLAs is ambiguous in the bulk of previous work. After differing the acceleration of scale factor ($\ddot{a}$) from the first order time derivative of spectroscopic velocity ($\dot{v}_\mathrm{S}$), we make a statistical investigation of the amount of potential DLAs in the most of this paper. Using Kernel Density Estimation (KDE) to depict general redshift distributions of BRSs, observed DLAs and a DLA detection rate with different limitations (1.4GHz flux, HI column density and spin temperature), we provide fitted multi-Gaussian expressions of the three components and their 1$\sigma$ regions by bootstrap, with a proportional constant of H21As in detected DLAs, leading to the measurable number predictions of H21As for FAST, ASKAP and SKA1-Mid in HI absorption blind survey. In our most optimistic condition ($F_\mathrm{1.4GHz}$>10mJy, $N_\mathrm{HI}>2\times10^{20}\mathrm{cm^{-2}}$ and $T_\mathrm{S}$>500K), the FAST, AKSAP and SKA1-Mid would probe about 80, 500 and 600 H21As respectively.Comment: Accepted by MNRAS, 11 pages(without references), 20 figures, 6 table

Target of Rapamycin Regulates Photosynthesis and Cell Growth in Auxenochlorella pyrenoidosa

Auxenochlorella pyrenoidosa is an efficient photosynthetic microalga with autotrophic growth and reproduction, which has the advantages of rich nutrition and high protein content. Target of rapamycin (TOR) is a conserved protein kinase in eukaryotes both structurally and functionally, but little is known about the TOR signalling in Auxenochlorella pyrenoidosa. Here, we found a conserved ApTOR protein in Auxenochlorella pyrenoidosa, and the key components of TOR complex 1 (TORC1) were present, while the components RICTOR and SIN1 of the TORC2 were absent in Auxenochlorella pyrenoidosa. Drug sensitivity experiments showed that AZD8055 could effectively inhibit the growth of Auxenochlorella pyrenoidosa, whereas rapamycin, Torin1 and KU0063794 had no obvious effect on the growth of Auxenochlorella pyrenoidosa a. Transcriptome data results indicated that Auxenochlorella pyrenoidosa TOR (ApTOR) regulates various intracellular metabolism and signaling pathways in Auxenochlorella pyrenoidosa. Most genes related to chloroplast development and photosynthesis were significantly down-regulated under ApTOR inhibition by AZD8055. In addition, ApTOR was involved in regulating protein synthesis and catabolism by multiple metabolic pathways in Auxenochlorella pyrenoidosa. Importantly, the inhibition of ApTOR by AZD8055 disrupted the normal carbon and nitrogen metabolism, protein and fatty acid metabolism, and TCA cycle of Auxenochlorella pyrenoidosa cells, thus inhibiting the growth of Auxenochlorella pyrenoidosa. These RNA-seq results indicated that ApTOR plays important roles in photosynthesis, intracellular metabolism and cell growth, and provided some insights into the function of ApTOR in Auxenochlorella pyrenoidosa

Diffusion Action Segmentation

Temporal action segmentation is crucial for understanding long-form videos. Previous works on this task commonly adopt an iterative refinement paradigm by using multi-stage models. Our paper proposes an essentially different framework via denoising diffusion models, which nonetheless shares the same inherent spirit of such iterative refinement. In this framework, action predictions are progressively generated from random noise with input video features as conditions. To enhance the modeling of three striking characteristics of human actions, including the position prior, the boundary ambiguity, and the relational dependency, we devise a unified masking strategy for the conditioning inputs in our framework. Extensive experiments on three benchmark datasets, i.e., GTEA, 50Salads, and Breakfast, are performed and the proposed method achieves superior or comparable results to state-of-the-art methods, showing the effectiveness of a generative approach for action segmentation. Our codes will be made available