XGBoostPP:Tree-based Estimation of Point Process Intensity Functions

We propose a novel tree-based ensemble method, named XGBoostPP, to nonparametrically estimate the intensity of a point process as a function of covariates. It extends the use of gradient-boosted regression trees (Chen & Guestrin, 2016) to the point process literature via two carefully designed loss functions. The first loss is based on the Poisson likelihood, working for general point processes. The second loss is based on the weighted Poisson likelihood, where spatially dependent weights are introduced to further improve the estimation efficiency for clustered processes. An efficient greedy search algorithm is developed for model estimation, and the effectiveness of the proposed method is demonstrated through extensive simulation studies and two real data analyses. In particular, we report that XGBoostPP achieves superior performance to existing approaches when the dimension of the covariate space is high, revealing the advantages of tree-based ensemble methods in estimating complex intensity function

STQS:Interpretable multi-modal Spatial-Temporal-seQuential model for automatic Sleep scoring

Sleep scoring is an important step for the detection of sleep disorders and usually performed by visual analysis. Since manual sleep scoring is time consuming, machine-learning based approaches have been proposed. Though efficient, these algorithms are black-box in nature and difficult to interpret by clinicians. In this paper, we propose a deep learning architecture for multi-modal sleep scoring, investigate the model's decision making process, and compare the model's reasoning with the annotation guidelines in the AASM manual. Our architecture, called STQS, uses convolutional neural networks (CNN) to automatically extract spatio-temporal features from 3 modalities (EEG, EOG and EMG), a bidirectional long short-term memory (Bi-LSTM) to extract sequential information, and residual connections to combine spatio-temporal and sequential features. We evaluated our model on two large datasets, obtaining an accuracy of 85% and 77% and a macro F1 score of 79% and 73% on SHHS and an in-house dataset, respectively. We further quantify the contribution of various architectural components and conclude that adding LSTM layers improves performance over a spatio-temporal CNN, while adding residual connections does not. Our interpretability results show that the output of the model is well aligned with AASM guidelines, and therefore, the model's decisions correspond to domain knowledge. We also compare multi-modal models and single-channel models and suggest that future research should focus on improving multi-modal models

The synthesis and characterization of 1111-type diluted magnetic semiconductors (La1-xSrx)(Zn1-xTMx)AsO (TM = Mn, Fe, Co)

The doping effect of Sr and transition metals Mn, Fe, Co into the direct-gap semiconductor LaZnAsO has been investigated. Our results indicate that the single phase ZrCuSiAs-type tetragonal crystal structure is preserved in (La1-xSrx)(Zn1-xTMx)AsO (TM = Mn, Fe, Co) with the doping level up to x = 0.1. While the system remains semiconducting, doping with Sr and Mn results in ferromagnetic order with TC ~ 30K, and doping with Sr and Fe results in a spin glass like state below ~6K with a saturation moment of ~0.02 muB/Fe, an order of magnitude smaller than the ~0.4 muB/Mn of Sr and Mn doped samples. The same type of magnetic state is observed neither for (Zn,Fe) substitution without carrier doping, nor for Sr and Co doped specimens.Comment: Accepted for publication in EP

Theoretical Investigation of the Formation Mechanism of NH3 and HCN during Pyrrole Pyrolysis: The Effect of H2O

Coal is a major contributor to the global emission of nitrogen oxides (NOx). The NOx formation during coal utilization typically derives from the thermal decomposition of N-containing compounds (e.g., pyrrolic groups). NH3 and HCN are common precursors of NOx from the decomposition of N-containing compounds. The existence of H2O has significant influences on the pyrrole decomposition and NOx formation. In this study, the effects of H2O on pyrrole pyrolysis to form NOx precursors HCN and NH3 are investigated using the density functional theory (DFT) method. The calculation results indicate that the presence of H2O can lead to the formation of both NH3 and HCN during pyrrole pyrolysis, while only HCN is formed in the absence of H2O. The initial interaction between pyrrole and H2O determines the N products. NH3 will be formed when H2O attacks the C2 position of pyrrole with its hydroxyl group. On the contrary, HCN will be generated instead of NH3 when H2O attacks the C3 position of pyrrole with its hydroxyl group. In addition, the DFT calculations clearly indicate that the formation of NH3 will be promoted by H2O, whereas the formation of HCN is inhibite

Infill asymptotics for logistic regression estimators for spatio-temporal point processes

This paper discusses infill asymptotics for logistic regression estimators for spatio-temporal point processes whose intensity functions are of log-linear form. We establish strong consistency and asymptotic normality for the parameters of a Poisson point process model and demonstrate how these results can be extended to general point process models. Additionally, under proper conditions, we also extend our central limit theorem to other unbiased estimating equations that are based on the Campbell--Mecke theorem

Effect of acupotomy on nitric oxide synthase and beta-endorphin in third lumbar vertebrae transverse process syndrome model rats

AbstractObjectiveTo explore the long-term effects and pain relief mechanism of acupotomy by observing changes in nitric oxide synthase (NOS) and beta-endorphin (β-EP) in the hypothalamus, spinal cord, and peripheral blood of rats with third lumbar vertebrae (L3) transverse process syndrome.MethodsTwenty-eight SD rats were randomly assigned to normal, model, electroacupuncture (EA), and acupotomy group. The last three groups were put through an operation to emulate L3 transverse process syndrome. Fourteen days after the simulation operation, EA and acupotomy treatments were applied to the respective groups. Fifty-six days after the simulation operation, biochemistry tests and enzyme-linked immunosorbent assay were used to measure NOS and β-EP in the hypothalamus, spinal cord, and peripheral blood.ResultsRats with the simulation operation showed significantly higher levels of NOS and β-EP in the hypothalamus, spinal cord, and peripheral blood than those in the normal group. The EA and acupotomy groups had significantly lower levels of NOS and β-EP than those in the model group. There was no statistical difference between the EA and acupotomy groups.ConclusionEA and acupotomy treatments significantly lowered NOS and β-EP levels in the hypothalamus, spinal cord, and peripheral blood and alleviated L3 transverse process syndrome