Characterizing Intermittency of 4-Hz Quasi-periodic Oscillation in XTE J1550-564 using Hilbert-Huang Transform

We present the time-frequency analysis results based on the Hilbert-Huang transform (HHT) for the evolution of a 4-Hz low-frequency quasi-periodic oscillation (LFQPO) around the black hole X-ray binary XTE J1550-564. The origin of LFQPOs is still debated. To understand the cause of the peak broadening, we utilized a recently developed time-frequency analysis, HHT, for tracking the evolution of the 4-Hz LFQPO from XTE J1550 564. By adaptively decomposing the ~4-Hz oscillatory component from the light curve and acquiring its instantaneous frequency, the Hilbert spectrum illustrates that the LFQPO is composed of a series of intermittent oscillations appearing occasionally between 3 Hz and 5 Hz. We further characterized this intermittency by computing the confidence limits of the instantaneous amplitudes of the intermittent oscillations, and constructed both the distributions of the QPO's high and low amplitude durations, which are the time intervals with and without significant ~4-Hz oscillations, respectively. The mean high amplitude duration is 1.45 s and 90% of the oscillation segments have lifetimes below 3.1 s. The mean low amplitude duration is 0.42 s and 90% of these segments are shorter than 0.73 s. In addition, these intermittent oscillations exhibit a correlation between the oscillation's rms amplitude and mean count rate. This correlation could be analogous to the linear rms-flux relation found in the 4-Hz LFQPO through Fourier analysis. We conclude that the LFQPO peak in the power spectrum is broadened owing to intermittent oscillations with varying frequencies, which could be explained by using the Lense-Thirring precession model.Comment: 27 pages, 9 figures, accepted for publication in The Astrophysical Journa

Can the green bond market enter a new era under the fluctuation of oil price?

This paper investigates how oil price (OP) influences the prospects of green bonds by utilising the quantile-onquantile (QQ) method and researching the interactions between OP and green bond index (GBI) from 2011:M1 to 2021:M11. We find that impacts from OP on the GBI are positive in the short run. The positive effects indicate that high OP can promote the development of the green bond market, indicating that green bonds can be considered an asset to avoid OP shocks. However, in the medium and long term, there is a negative impact due to the oversupply of the oil market and the increase in green energy industry profits. These results are identical to the supply and demand-based correlation model of green bonds and oil price, which underlines a specific effect of OP on GBI. The GBI effect on OP is consistently positive across all quantiles. It indicates that green bonds cannot be considered efficient measures to alleviate the oil crisis due to the instability of the Middle East COVID-19 and the small scale of green bonds. The issuers of green bonds can make decisions based on OP. Understanding the relationship between OP and GBI is also beneficial for investors

A 10 km daily-level ultraviolet radiation predicting dataset based on machine learning models in China from 2005 to 2020

Ultraviolet (UV) radiation is closely related to health, but limited measurements hindered further investigation of its health effects in China. Machine learning algorithm has been widely used in predicting environmental factors with high accuracy, but limited studies have done for UV radiation. This study aimed to develop UV radiation prediction model based on random forest method, and predict UV radiation at daily level and 10 km resolution in mainland China in 2005–2020. A random forest model was employed to predict UV radiation by integrating ground UV radiation measurements from monitoring stations and multiple predictors, such as UV radiation data from satellite. Missing data of satellite-based UV radiation was filled by three-day moving average method. The model's performance was evaluated through multiple cross-validation (CV) methods. The overall R2 (root mean square error, RMSE) between measured and predicted UV radiation from model development and model 10-fold CV was 0.97 (15.64 W m-2) and 0.83 (37.44 W m-2) at daily level, respectively. The model with OMI EDD performed higher predicting accuracy than the one without it. Based on predictions of UV radiation at daily level and 10 km spatial resolution and nearly 100 % spatiotemporal coverage, we found UV radiation increased by 4.20 % while PM2.5 levels decreased by 48.51 % and O3 levels rose by 22.70 % in 2013–2020, suggesting a potential correlation among these environmental factors. Uneven spatial distribution of UV radiation was found to be associated with factors such as latitude, elevation, meteorological factors and seasons. The eastern areas of China posed higher risk with both high population density and UV radiation intensity. Based on machine learning algorithm, this study generated a gridded dataset characterized by relatively high precision and extensive spatiotemporal coverage of UV radiation, which demonstrates the spatiotemporal variability of UV radiation levels in China and can facilitate health-related research in the future. This dataset is currently freely available at https://doi.org/10.5281/zenodo.10884591 (Jiang et al., 2024)

BaLeNAS: Differentiable Architecture Search via the Bayesian Learning Rule

Differentiable Architecture Search (DARTS) has received massive attention in recent years, mainly because it significantly reduces the computational cost through weight sharing and continuous relaxation. However, more recent works find that existing differentiable NAS techniques struggle to outperform naive baselines, yielding deteriorative architectures as the search proceeds. Rather than directly optimizing the architecture parameters, this paper formulates the neural architecture search as a distribution learning problem through relaxing the architecture weights into Gaussian distributions. By leveraging the natural-gradient variational inference (NGVI), the architecture distribution can be easily optimized based on existing codebases without incurring more memory and computational consumption. We demonstrate how the differentiable NAS benefits from Bayesian principles, enhancing exploration and improving stability. The experimental results on NAS-Bench-201 and NAS-Bench-1shot1 benchmark datasets confirm the significant improvements the proposed framework can make. In addition, instead of simply applying the argmax on the learned parameters, we further leverage the recently-proposed training-free proxies in NAS to select the optimal architecture from a group architectures drawn from the optimized distribution, where we achieve state-of-the-art results on the NAS-Bench-201 and NAS-Bench-1shot1 benchmarks. Our best architecture in the DARTS search space also obtains competitive test errors with 2.37\%, 15.72\%, and 24.2\% on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively

Fault Feature Extraction of Bearings for the Petrochemical Industry and Diagnosis Based on High-Value Dimensionless Features

The time and frequency domain features of a petrochemical unit have a variety of effects on the fault type of bearings, and the signal exhibits nonlinearity, unpredictability, and ergodicity. The detection system\u27s important data are disrupted by noise, resulting in a huge number of invalid and partial records. To reduce the influence of these factors on feature extraction, this work presents a method for the fault feature extraction of bearings for the petrochemical industry and for diagnosis based on high-value dimensionless features. Effective data are extracted from the obtained data using a complex data preprocessing approach, and the dimensionless index is expressed. Then, based on the distribution rule of the dimensionless index, the high-value dimensionless features are retrieved. Finally, to ensure sample completeness, a high-value dimensionless feature augmented model is developed. This approach is applied to the bearing fault experiment platform of a petrochemical unit to effectively classify the bearing fault features, which benefits theoretical guidance for the feature extraction of bearings for a petrochemical unit

Detection of genetic and epigenetic DNA markers in urine for the early detection of primary and recurrent hepatocellular carcinoma

Poster presented at American Association of the Study of Liver Diseases (AASLD) meeting in San Francisco California. Objective: Develop a urine test using a panel of select genetic and epigenetic markers for the early detection of primary and recurrent HCC. Introduction: Hepatocellular carcinoma (HCC) or liver cancer is an aggressive disease and one of the fastest growing cancers by incidence in the United States. Early detection is the key for effective treatment of HCC as the 5-year survival rate is 26% in early stage HCC as compared to only 2% when found after spreading to distant organs. The current marker, alpha-feto protein (AFP) and its fucosylated glycoform, L3, are of limited value with only 40-60% sensitivity.https://jdc.jefferson.edu/gastrohepposters/1000/thumbnail.jp