Semi-Supervised End-To-End Contrastive Learning For Time Series Classification

Time series classification is a critical task in various domains, such as finance, healthcare, and sensor data analysis. Unsupervised contrastive learning has garnered significant interest in learning effective representations from time series data with limited labels. The prevalent approach in existing contrastive learning methods consists of two separate stages: pre-training the encoder on unlabeled datasets and fine-tuning the well-trained model on a small-scale labeled dataset. However, such two-stage approaches suffer from several shortcomings, such as the inability of unsupervised pre-training contrastive loss to directly affect downstream fine-tuning classifiers, and the lack of exploiting the classification loss which is guided by valuable ground truth. In this paper, we propose an end-to-end model called SLOTS (Semi-supervised Learning fOr Time clasSification). SLOTS receives semi-labeled datasets, comprising a large number of unlabeled samples and a small proportion of labeled samples, and maps them to an embedding space through an encoder. We calculate not only the unsupervised contrastive loss but also measure the supervised contrastive loss on the samples with ground truth. The learned embeddings are fed into a classifier, and the classification loss is calculated using the available true labels. The unsupervised, supervised contrastive losses and classification loss are jointly used to optimize the encoder and classifier. We evaluate SLOTS by comparing it with ten state-of-the-art methods across five datasets. The results demonstrate that SLOTS is a simple yet effective framework. When compared to the two-stage framework, our end-to-end SLOTS utilizes the same input data, consumes a similar computational cost, but delivers significantly improved performance. We release code and datasets at https://anonymous.4open.science/r/SLOTS-242E.Comment: Submitted to NeurIPS 202

Neurotoxicological effects induced by up-regulation of miR-137 following triclosan exposure to zebrafish (Danio rerio).

Triclosan (TCS) is a prevalent anthropogenic contaminant in aquatic environments and its chronic exposure can lead to a series of neurotoxic effects in zebrafish. Both qRT-PCR and W-ISH identified that TCS exposure resulted in significant up-regulation of miR-137, but downregulation of its regulatory genes (bcl11aa, MAPK6 and Runx1). These target genes are mainly associated with neurodevelopment and the MAPK signaling pathway, and showed especially high expression in the brain. After overexpression or knockdown treatments by manual intervention of miR-137, a series of abnormalities were induced, such as ventricular abnormality, bent spine, yolk cyst, closure of swim sac and venous sinus hemorrhage. The most sensitive larval toxicological endpoint from intervened miR-137 expression was impairment of the central nervous system (CNS), ventricular abnormalities and notochord curvature. Microinjection of microRNA mimics or inhibitors of miR-137 both caused zebrafish malformations. The posterior lateral line neuromasts became obscured and decreased in number in intervened miR-137 groups and TCS-exposure groups. Up-regulation of miR-137 led to more severe neurotoxic effects than its down-regulation. Behavioral observations demonstrated that both TCS exposure and miR-137 over-expression led to inhibited hearing or vision sensitivity. HE staining indicated that hearing and vision abnormalities induced by long-term TCS exposure originated from CNS injury, such as reduced glial cells and loose and hollow fiber structures. The findings of this study enhance our mechanistic understanding of neurotoxicity in aquatic animals in response to TCS exposure. These observations provide theoretical guidance for development of early intervention treatments for nervous system diseases

Regional metabolic patterns of abnormal postoperative behavioral performance in aged mice assessed by 1H-NMR dynamic mapping method

Abstract Abnormal postoperative neurobehavioral performance (APNP) is a common phenomenon in the early postoperative period. The disturbed homeostatic status of metabolites in the brain after anesthesia and surgery might make a significant contribution to APNP. The dynamic changes of metabolites in different brain regions after anesthesia and surgery, as well as their potential association with APNP are still not well understood. Here, we used a battery of behavioral tests to assess the effects of laparotomy under isoflurane anesthesia in aged mice, and investigated the metabolites in 12 different sub-regions of the brain at different time points using proton nuclear magnetic resonance (1H-NMR) spectroscopy. The abnormal neurobehavioral performance occurred at 6 h and/or 9 h, and recovered at 24 h after anesthesia/surgery. Compared with the control group, the altered metabolite of the model group at 6 h was aspartate (Asp), and the difference was mainly displayed in the cortex; while significant changes at 9 h occurred predominantly in the cortex and hippocampus, and the corresponding metabolites were Asp and glutamate (Glu). All changes returned to baseline at 24 h. The altered metabolic changes could have occurred as a result of the acute APNP, and the metabolites Asp and Glu in the cortex and hippocampus could provide preliminary evidence for understanding the APNP process

Breakdown Walkout in Polarization-Doped Vertical GaN Diodes

We demonstrate the avalanche capability and the existence of breakdown walkout in GaN-on-GaN vertical devices with polarization doping. By means of combined electrical and optical characterization, we demonstrate the following original results: 1) vertical p-n junctions with polarization doping have avalanche capability; 2) stress in avalanche regime induces an increase in breakdown voltage, referred to as breakdown walkout; 3) this process is fully-recoverable, thus being related to a trapping mechanism; 4) temperature-dependent measurements of the breakdown walkout identify $\text{C}_{N}$ defects responsible for this process; and 5) capacitance deep level transient spectroscopy (C-DLTS) and deep level optical spectroscopy (DLOS) confirm the presence of residual carbon in the devices under test. A possible model to explain the avalanche walkout is then proposed