151 research outputs found

    Graduate Recital Yuxiao Sun, Piano

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    Yuxiao Sun presented a solo piano recital on November 2, 2023 at 8:00 p.m. in Davis Hall of the Gallagher-Bluedorn Performing Arts Center at the University of Northern Iowa. This recital marked a partial completion of her Master of Music degree in Piano Performance. The program included the following works: Piano Sonata No. 23 in F minor, Op. 57 “Appassionata” by Ludwig van Beethoven, Partita No.1 in B flat Major, BWV 825 by Johann Sebastian Bach, and Pour le Piano, L. 95 by Claude Debussy. Piano Sonata No. 23 in F minor, Op. 57 “Appassionata” by Ludwig van Beethoven Ludwig van Beethoven was born in Bonn, Germany, in 1770 and died in Vienna, Austria, in 1827. He is regarded as a leading musical figure of the 19th century. The piano sonata Appassionata, composed between 1804 and 1806, is one of the significant keyboard works of Beethoven\u27s middle period (roughly 1802-1812).2 At this time, his style became bolder and more innovative, markedly different from the classical traditions, especially those of Haydn and Mozart. He unfortunately began to lose his hearing during this phase and his compositional approach and musical philosophy were profoundly affected. He began to gravitate towards more introspective and philosophical themes such as fate, heroism, and personal struggle. This sonata continually conveys a spirit of restrained heroism, which perhaps can be interpreted as Beethoven\u27s personal struggles between resisting the arrangement of fate and accepting the harsh reality

    Three dimensional vibrational spectroscopy and shock initiation of hmx

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    3D spectroscopy was employed to probe and visualize energy flows through molecules from excited parent states. Studies with IR-Raman pump probe spectroscopy show clearly that the characteristics of the vibrational relaxation pathway depend strongly upon the initial excited states. By employing 3D spectroscopy, it is possible to track and visualize relaxation pathways for an entire range of parent states, providing a better understanding of intramolecular energy transfer. Laser driven shocks and sum frequency generation spectroscopy were used to attempt to observe the mechanisms of initiation for HMX. Those first bond breaking events are poorly understood, and much could be learned from experimental observation. Much refinement of the sample design was done, but there was still no events observed by SFG that were clearly attributable to HMX initiation. No broadening or new peaks were observed at all, and no significant loss of SFG intensity occurred besides the expected loss from the destruction of the aluminum substrate. It is possible that a more uniform and better made sample, perhaps along with significantly more laser power, could successfully observe initiation. Flyer driven shocks and emission spectroscopy were used to determine the relative shock sensitivities of delta and beta HMX, as well as investigate the first nanoseconds after shock compression for HMX. While previous studies by the Dlott group have observed interesting phenomena in those first nanoseconds to microseconds after shock, those studies were done on beta HMX. Similar experiments were done on delta HMX, and it exhibits similar behavior initially with a very high temperature spike, though later behavior is not identical

    DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction

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    In spatial statistics, a common objective is to predict the values of a spatial process at unobserved locations by exploiting spatial dependence. In geostatistics, Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes. However, when considering non-linear prediction for non-Gaussian and categorical data, the Kriging prediction is not necessarily optimal, and the associated variance is often overly optimistic. We propose to use deep neural networks (DNNs) for spatial prediction. Although DNNs are widely used for general classification and prediction, they have not been studied thoroughly for data with spatial dependence. In this work, we propose a novel neural network structure for spatial prediction by adding an embedding layer of spatial coordinates with basis functions. We show in theory that the proposed DeepKriging method has multiple advantages over Kriging and classical DNNs only with spatial coordinates as features. We also provide density prediction for uncertainty quantification without any distributional assumption and apply the method to PM2.5_{2.5} concentrations across the continental United States

    Improving Cross-Domain Chinese Word Segmentation with Word Embeddings

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    Cross-domain Chinese Word Segmentation (CWS) remains a challenge despite recent progress in neural-based CWS. The limited amount of annotated data in the target domain has been the key obstacle to a satisfactory performance. In this paper, we propose a semi-supervised word-based approach to improving cross-domain CWS given a baseline segmenter. Particularly, our model only deploys word embeddings trained on raw text in the target domain, discarding complex hand-crafted features and domain-specific dictionaries. Innovative subsampling and negative sampling methods are proposed to derive word embeddings optimized for CWS. We conduct experiments on five datasets in special domains, covering domains in novels, medicine, and patent. Results show that our model can obviously improve cross-domain CWS, especially in the segmentation of domain-specific noun entities. The word F-measure increases by over 3.0% on four datasets, outperforming state-of-the-art semi-supervised and unsupervised cross-domain CWS approaches with a large margin. We make our code and data available on Github

    The impact of disability-related deprivation on employment opportunity at the neighborhood level: does family socioeconomic status matter?

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    IntroductionDifficulties in attaining employment significantly contribute to socioeconomic poverty among individuals with disabilities. However, our understanding of how socioeconomic deprivation experienced by individuals and families with disabilities influences employment opportunities remains incomplete. This study aims to explore the relationship between index of disability-related multiple deprivation (IDMD) and employment opportunities (EMPO), while also investigating the role of family socioeconomic status (FSES) in shaping this relation.MethodsThis study explores the heterogeneous effects of IDMD, FSES, and the interaction between IDMD*FSES on EMPO among four disabled population groups categorized by IDMD and FSES.ResultsResults reveal that IDMD has a significant negative impact on EMPO, suggesting that persons with disabilities are confronted with a poverty trap resulting from the relationship between IDMD and EMPO. Furthermore, FSES demonstrates an effective moderating role in the IDMD-EMPO relationship, with the greatest impact observed among disabled population groups characterized by high IDMD and low FSES.DiscussionThe findings suggest that family-level support is crucial for vulnerable groups of disabled individuals to overcome the poverty trap, surpassing the reliance on individual-level assistance alone. This study supports a paradigm shift in comprehending disability-related deprivation by acknowledging its association with families, thereby presenting opportunities to enhance the welfare of people with disabilities

    Case report: Multidisciplinary collaboration in diagnosis and treatment of child gaucher disease

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    Gaucher disease (GD) is an inherited lysosomal storage disease caused by mutations in the glucocerebrosidase gene. The decrease of glucocerebrosidase activity in lysosomes results in the accumulation of its substrate glucocerebroside in the lysosomes of macrophages in organs such as the liver, spleen, bones, lungs, brain and eyes, and the formation of typical storage cells, namely “Gaucher cells”, leading to lesions in the affected tissues and organs. Hepatosplenomegaly, bone pain, cytopenia, neurological symptoms, and other systemic manifestations are common in clinical practice. Most pediatric patients have severe symptoms. Early diagnosis and treatment are crucial to improve the curative effect and prognosis. However, due to the low incidence of this disease, multi-system involvement in patients, and diverse clinical manifestations, multidisciplinary teamwork is needed for comprehensive evaluation, diagnosis and treatment. In this study, we reported 2 cases of different types of GD who were diagnosed, treated and followed up by multidisciplinary collaboration in infancy
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