Sample Mixed-Based Data Augmentation for Domestic Audio Tagging

Audio tagging has attracted increasing attention since last decade and has various potential applications in many fields. The objective of audio tagging is to predict the labels of an audio clip. Recently deep learning methods have been applied to audio tagging and have achieved state-of-the-art performance, which provides a poor generalization ability on new data. However due to the limited size of audio tagging data such as DCASE data, the trained models tend to result in overfitting of the network. Previous data augmentation methods such as pitch shifting, time stretching and adding background noise do not show much improvement in audio tagging. In this paper, we explore the sample mixed data augmentation for the domestic audio tagging task, including mixup, SamplePairing and extrapolation. We apply a convolutional recurrent neural network (CRNN) with attention module with log-scaled mel spectrum as a baseline system. In our experiments, we achieve an state-of-the-art of equal error rate (EER) of 0.10 on DCASE 2016 task4 dataset with mixup approach, outperforming the baseline system without data augmentation.Comment: submitted to the workshop of Detection and Classification of Acoustic Scenes and Events 2018 (DCASE 2018), 19-20 November 2018, Surrey, U

DRAG: Divergence-based Adaptive Aggregation in Federated learning on Non-IID Data

Local stochastic gradient descent (SGD) is a fundamental approach in achieving communication efficiency in Federated Learning (FL) by allowing individual workers to perform local updates. However, the presence of heterogeneous data distributions across working nodes causes each worker to update its local model towards a local optimum, leading to the phenomenon known as ``client-drift" and resulting in slowed convergence. To address this issue, previous works have explored methods that either introduce communication overhead or suffer from unsteady performance. In this work, we introduce a novel metric called ``degree of divergence," quantifying the angle between the local gradient and the global reference direction. Leveraging this metric, we propose the divergence-based adaptive aggregation (DRAG) algorithm, which dynamically ``drags" the received local updates toward the reference direction in each round without requiring extra communication overhead. Furthermore, we establish a rigorous convergence analysis for DRAG, proving its ability to achieve a sublinear convergence rate. Compelling experimental results are presented to illustrate DRAG's superior performance compared to state-of-the-art algorithms in effectively managing the client-drift phenomenon. Additionally, DRAG exhibits remarkable resilience against certain Byzantine attacks. By securely sharing a small sample of the client's data with the FL server, DRAG effectively counters these attacks, as demonstrated through comprehensive experiments

Genetic approach to track neural cell fate decisions using human embryonic stem cells

With their capability to undergo unlimited self-renewal and to differentiate into all cell types in the body, human embryonic stem cells (hESCs) hold great promise in human cell therapy. However, there are limited tools for easily identifying and isolating live hESC-derived cells. To track hESC-derived neural progenitor cells (NPCs), we applied homologous recombination to knock-in the mCherry gene into the Nestin locus of hESCs. This facilitated the genetic labeling of Nestin positive neural progenitor cells with mCherry. Our reporter system enables the visualization of neural induction from hESCs both in vitro (embryoid bodies) and in vivo (teratomas). This system also permits the identification of different neural subpopulations based on the intensity of our fluorescent reporter. In this context, a high level of mCherry expression showed enrichment for neural progenitors, while lower mCherry corresponded with more committed neural states. Combination of mCherry high expression with cell surface antigen staining enabled further enrichment of hESC-derived NPCs. These mCherry(+) NPCs could be expanded in culture and their differentiation resulted in a down-regulation of mCherry consistent with the loss of Nestin expression. Therefore, we have developed a fluorescent reporter system that can be used to trace neural differentiation events of hESCs

Spatiotemporal Expression of MANF in the Developing Rat Brain

Mesencephalic astrocyte-derived neurotrophic factor (MANF) is an evolutionarily conserved neurotrophic factor which exhibited neuroprotective properties. Recent studies suggested that MANF may play a role in the neural development of Drosophila and zebra fishes. In this study, we investigated the spatiotemporal expression of MANF in the brain of postnatal and adult rats. MANF expression appeared wide spread and mainly localized in neurons. In the cerebral cortex, neurons in layer IV and VI displayed particularly strong MANF immunoreactivity. In the hippocampus, intensive MANF expression was observed throughout the subfields of Cornu Amonis (CA1, CA2, and CA3) and the granular layer of the dentate gyrus (DG). In the substantia nigra, high MANF expression was shown in the substantia nigra pars compacta (SNpc). In the thalamus, the anterodorsal thalamic nucleus (ADTN) exhibited the highest MANF immunoreactivity. In the hypothalamus, intensive MANF immunoreactivity was shown in the supraoptic nucleus (SON) and tuberomammillary nucleus (TMN). In the cerebellum, MANF was localized in the external germinal layer (EGL), Purkinje cell layer (PCL), internal granule layer (IGL) and the deep cerebellar nuclei (DCN). We examined the developmental expression of MANF on postnatal day (PD) 3, 5, 7, 9, 15, 21, 30 and adulthood. In general, the levels of MANF were high in the early PDs (PD3 and PD5), and declined gradually as the brain matured; MANF expression in the adult brain was the lowest among all time points examined. However, in some structures, such as PCL, IGL, SON, TMN and locus coeruleus (LC), high expression of MANF sustained throughout the postnatal period and persisted into adulthood. Our results indicated that MANF was developmentally regulated and may play a role in the maturation of the central nervous system (CNS)

Evaluation of Chinese provincial ecological well-being performance based on the driving effect decomposition

The focus of this paper is three-fold. First, it recalculates the HDI and EFI, use the ratio of HDI and EF to build the EWP, then evaluate and analyze the EWP of China's provinces. Second, it develops a unique ecological well-being performance (EWP) model, which is divided into two driving effects: the well-being effect of economic growth and the ecological efficiency of economic growth. Third, using the Human Development Index (HDI), it measures the well-being effect and ecological efficiency of economic growth in 31 Chinese provinces. Based on the EWP results, it divides the Chinese provinces into five types from economic leading and upgrading to overall descending. The research results show that China's HDI has greatly improved from 2006 to 2016, displaying a trend of "Beijing, Tianjin and Shanghai take the lead in upgrading, and then the uprising trend expands from east to west". However, during the same period from 2006 to 2016, the growth rate of China's people’s well-being was significantly lower than that of per capita ecological footprint (EF), and the overall EWP declined. China's growth in people’s well-being is decoupled from economic growth, which indicates that China’s rapid economic growth was not followed by a similar progress in China’s people’s well-being. The above results suggest that China's total factor productivity (TFP) and green total factor productivity (GTFP) were improving but in different degrees during the above period. Other results show that, the carbon footprint has always been the largest component of China's EF, and the GTFP has always been lower than the TFP. According to the technology progress index and scale efficiency driving of change index, China’s provinces mainly focus on provincial TFP rather than GTFP. This paper suggests that the different types of provinces should adopt different strategies to improve their EWP in order to promote high-quality economic development

Non-High-Density Lipoprotein Cholesterol on the Risks of Stroke: A Result from the Kailuan Study

Aims To prospectively explore the association between non-high-density lipoprotein cholesterol (non-HDLC) and the risks of stroke and its subtypes. Methods: A total of 95,916 participants (18-98 years old; 76,354 men and 19,562 women) from a Chinese urban community who were free of myocardial infarction and stroke at baseline time point (2006-2007) were eligible and enrolled in the study. The serum non-HDLC levels of participants were determined by subtracting the high-density lipoprotein cholesterol (HDLC) from total serum cholesterol. The primary outcome was the first occurrence of stroke, which was diagnosed according to the World Health Organization criteria and classified into three subtypes: ischemic stroke, intracerebral hemorrhage, or subarachnoid hemorrhage. The Cox proportional hazards models were used to estimate risk of stroke and its subtypes. Results: During the four-year follow-up, we identified 1614 stroke events (1,156 ischemic, 416 intracerebral hemorrhagic and 42 subarachnoid hemorrhagic). Statistical analyses showed that hazard ratios (HR) (95% Confidence Interval: CI) of serum Non-HDLC level for total and subtypes of stroke were: 1.08 (1.03-1.12) (total), 1.10 (1.05-1.16) (ischemic), 1.03 (0.96-1.10) (intracerebral hemorrhage) and 0.83 (0.66-1.05) (subarachnoid hemorrhage). HR for non-HDLC refers to the increase per each 20 mg/dl. For total and ischemic stroke, the risks were significantly higher in the fourth and fifth quintiles of non-HDLC concentrations compared to the first quintile after adjusting the confounding factors (total stroke: 4th quintile HR=1.33 (1.12-1.59); 5th quintile HR = 1.36 (1.15-1.62); ischemic stroke: 4th quintile HR =1.34 (1.09-1.66); 5th quintile HR = 1.53 (1.24-1.88)). Conclusions: Our data suggest that serum non-HDLC level is an independent risk factor for total and ischemic stroke, and that higher serum non-HDLC concentrations are associated with increased risks for total stroke and ischemic stroke, but not for intracerebral and subarachnoid hemorrhage

The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region

Data and code availability The authors declare that the majority of the data supporting the findings of this study are available through the links given in the paper. The unpublished data are available from the corresponding author upon request. The new estimate of Tibetan soil carbon stock and R code are available in a persistent repository (https://figshare.com/s/4374f28d880f366eff6d). Acknowledgements This study was supported by the Strategic Priority Research Program (A) of the Chinese Academy of Sciences (XDA20050101), the National Natural Science Foundation of China (41871104), Key Research and Development Programs for Global Change and Adaptation (2017YFA0603604), International Partnership Program of the Chinese Academy of Sciences (131C11KYSB20160061) and the Thousand Youth Talents Plan project in China. Jinzhi Ding acknowledges the General (2017M620922) and the Special Grade (2018T110144) of the Financial Grant from the China Postdoctoral Science Foundation.Peer reviewedPublisher PD