Existence Result for Impulsive Differential Equations with Integral Boundary Conditions

We investigate the following differential equations: -(y[1](x))'+q(x)y(x)=λf(x,y(x)), with impulsive and integral boundary conditions -Δ(y[1](xi))=Ii(y(xi)), i=1,2,…,m, y(0)-ay[1](0)=∫0ωg0(s)y(s)ds, y(ω)-by[1](ω)=∫0ωg1(s)y(s)ds, where y[1](x)=p(x)y'(x). The expression of Green's function and the existence of positive solution for the system are obtained. Upper and lower bounds for positive solutions are also given. When p(t), I(·), g0(s), and g1(s) take different values, the system can be simplified to some forms which has been studied in the works by Guo and LakshmiKantham (1988), Guo et al. (1995), Boucherif (2009), He et al. (2011), and Atici and Guseinov (2001). Our discussion is based on the fixed point index theory in cones

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Cross-Linguistic Perception and Learning of Mandarin Chinese Sounds by Japanese Adult Learners

This dissertation presents a cross-linguistic investigation of how nonnative sounds are perceived by second language (L2) learners in terms of their first language (L1) categories for an understudies language pair---Japanese and Mandarin Chinese. Category mapping experiment empirically measured the perceived phonetic distances between Chinese sounds and their most resembling Japanese categories, which generated testable predictions on discriminability of Chinese sound contrasts according to Perception Assimilation Model (PAM). Category discrimination experiment obtained data concerning L2 learners' actual performance on discrimination Chinese sounds. The discrepancy between PAM's predictions and actual performances revealed that PAM cannot be applied to L2 perceptual learning. It was suggested that the discriminability of L2 sound contrasts was not only determined by perceived phonetic distances but probably involved other factors, such as the distinctiveness of certain phonetic features, e.g. aspiration and retroflexion. The training experiment assessed the improvement of L2 learners' performance in identifying Chinese sound contrasts with exposure to high variability stimuli and feedback. The results not only proved the effectiveness of training in shaping L2 learners' perception but showed that the training effects were generalizable to new tokens spoken by unfamiliar talkers. In addition to perception, the production of Chinese sounds by Japanese learners was also examined from the phonetic perspective in terms of perceived foreign accentedness. Regression of L2 learners' and native speakers foreign accentedness ratings against acoustic measurements of their speech production revealed that although both segmental and suprasegmental variables contributed to the perception of foreign accent, suprasegmental variables such as total and intonation patterns were the most influential factor in predicting perceived foreign accent. To conclude, PAM failed to accurately predict learning difficulties of nonnative sounds faced by L2 learners solely based on perceived phonetic distances. As Speech Learning Model (SLM) hypothesizes, production was found to be driven by perception, since equivalence classification of L2 sounds to L1 categories prevented the establishment of a new phonological category, thus further resulted in divergence in L2 production. Although production was hypothesized to eventually resemble perception, asynchrony between production and perception was observed due to different mechanisms involved

Hanle detection for optical clocks

Considering the strong inhomogeneous spatial polarization and intensity distribution of spontaneous decay fluorescence due to the Hanle effect, we propose and demonstrate a universe Hanle detection configuration of electron-shelving method for optical clocks. Experimental results from Ca atomic beam optical frequency standard with 423 nm electron-shelving method show that a designed Hanle detection geometry with optimized magnetic field direction, detection laser beam propagation and polarization direction, and detector position can improve the fluorescence collection rate by more than one order of magnitude comparing with that of inefficient geometry. With the fixed 423 nm fluorescence, the improved 657 nm optical frequency standard signal intensity is presented. And the potential application of the Hanle detection geometry designed for facilitating the fluorescence collection for optical lattice clock with a limited solid angle of the fluorescence collection has been discussed. This Hanle detection configuration is also effective for ion detection in ion optical clock and quantum information experiments. Besides, a cylinder fluorescence collection structure is designed to increase the solid angle of the fluorescence collection in Ca atomic beam optical frequency standard.Comment: 5 pages, 6 figure

Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.Comment: 10 pages, 4 figures, 7 tables, accepted by AAAI 202

Vegetation Dynamics and Their Response to Climatic Variability in China

Based on SPOT VEGETATION data and meteorological data, NDVI (Normalized Difference Vegetation Index) and its response to temperature and precipitation in China and its different regions were investigated over the period 1998–2013 by using the maximum value composite and linear regression methods. The results showed that NDVI presented significant increase (0.0046/a) for all of China and all the regions over the last 16 years. Meanwhile, annual mean temperature of China presented a slightly increasing trend, while the annual precipitation showed a slightly decreasing trend over the last 16 years. Nevertheless, there were differences between temperature and precipitation in the subregions of China. The Annual NDVI had better relationships with precipitation (r=0.126) compared to temperature (r=-0.094), and NDVI also had a good correlation with precipitation rather than temperature in different subregions of China. Additionally, human activities also made a difference to the trends of NDVI in some regions. This study is conductive to the effects of climate change on vegetation activity in future research