Temporally Disentangled Representation Learning under Unknown Nonstationarity

In unsupervised causal representation learning for sequential data with time-delayed latent causal influences, strong identifiability results for the disentanglement of causally-related latent variables have been established in stationary settings by leveraging temporal structure. However, in nonstationary setting, existing work only partially addressed the problem by either utilizing observed auxiliary variables (e.g., class labels and/or domain indexes) as side information or assuming simplified latent causal dynamics. Both constrain the method to a limited range of scenarios. In this study, we further explored the Markov Assumption under time-delayed causally related process in nonstationary setting and showed that under mild conditions, the independent latent components can be recovered from their nonlinear mixture up to a permutation and a component-wise transformation, without the observation of auxiliary variables. We then introduce NCTRL, a principled estimation framework, to reconstruct time-delayed latent causal variables and identify their relations from measured sequential data only. Empirical evaluations demonstrated the reliable identification of time-delayed latent causal influences, with our methodology substantially outperforming existing baselines that fail to exploit the nonstationarity adequately and then, consequently, cannot distinguish distribution shifts.Comment: NeurIPS 202

PhenCards: a data resource linking human phenotype information to biomedical knowledge

We present PhenCards ( https://phencards.org ), a database and web server intended as a one-stop shop for previously disconnected biomedical knowledge related to human clinical phenotypes. Users can query human phenotype terms or clinical notes. PhenCards obtains relevant disease/phenotype prevalence and co-occurrence, drug, procedural, pathway, literature, grant, and collaborator data. PhenCards recommends the most probable genetic diseases and candidate genes based on phenotype terms from clinical notes. PhenCards facilitates exploration of phenotype, e.g., which drugs cause or are prescribed for patient symptoms, which genes likely cause specific symptoms, and which comorbidities co-occur with phenotypes

Isolation of a Pluripotent Neural Stem Cell from the Embryonic Bovine Brain

We recently isolated stem cells derived from the brain of a bovine fetus, utilizing a particular mechanical separation method. After improving our experimental conditions, we obtained neural stem cells using an optimized culture medium system. The cells were expanded, established in continuous cell culture and used for immunofluorescence cytochemistry. RT-PCR showed that embryonic neural stem cells (NSCs) not only expresses the protein Sox2, Nestin but also Pax6, Musashi proteins and were differentiated into the three classical neuronal phenotypes (neurons, astrocytes, and oligodendrocytes)

Efficient synthesis of dibenzyl carbonates from benzyl halides and Cs2CO3

A simple and efficient protocol for the synthesis of dibenzyl carbonates has been developed. The reaction was accomplished using benzyl halides and Cs2CO3 as the starting materials in the presence of atmospheric pressure of CO2, affording a variety of the dibenzyl carbonates in good to excellent yields under rather mild conditions

KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery

Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified

Investigation of two-phase flow mixing mechanism of a swirl burner using an electrostatic sensor array system

A novel electrostatic sensor array was designed to measure particle concentration downstream of a swirl burner. The fundamental mechanism and the primary constituent elements of the sensor array were described. The root-mean-square magnitude of the measured electrostatic voltage was determined as an indication of the particle concentration. The accuracy of the electrostatic sensor array was calibrated by the optical fluctuation method. Local particle concentrations at different cross-sections of the measuring chamber were measured to investigate the diffusion characteristic of the pulverized coal particles. Electrostatic sensor array showed its ability in the field measurement in this work. The measurements indicated that the velocity of the inner secondary air had a significant effect on the diffusion of the pulverized coal particles. The particles concentrated in the center of the cross-section after leaving the burner. With the development of the gas–solid two-phase flow, the particles distributed like a ring shape. The radius of the particle ring increased with the increase of the velocity of the inner secondary air. But the effect of the velocity of outer secondary air on the radius of the particle ring is very slight. The maximum radius occurred when the velocity of inner secondary air was 21 m/s, which was favorable for stable combustion