Efficacy of co-administration of oxiracetam and butylphthalide in the treatment of elderly patients with hypertensive intracerebral hemorrhage, and its effect on NIHSS and ADL scores

Purpose: To investigate the effect of combined use of oxiracetam and butylphthalide on hypertensive intracerebral hemorrhage (HICH) in elderly patients, and its influence on NIHSS and activities of daily living (ADL) scores of patients.Methods: Ninety (90) elderly patients with HICH who were admitted to Renmin Hospital of Wuhan University, Wuhan, China served as study subjects, and were randomly assigned to control and study groups, with 45 patients per group. The patients in the control group were treated with oxiracetam alone, while patients in the study group received a combination of oxiracetam and butylphthalide. Clinical efficacy, undesirable side effects and serum indices were determined. The NIHSS and ADL rating scales were used to evaluate cerebral nerve function and ADL score before and after treatment.Results: There were significantly higher total treatment effectiveness and lower incidence of adverse reactions in the study group than in control group, while tissue inhibitor of metalloproteinase-1 (TIMP-1) index, matrix metalloproteinase-9 (MMP-9) index and NIHSS score were reduced in study patients, relative to controls (p < 0.001). However, ADL score in the study group was higher than that in the control group (p < 0.001).Conclusion: Treatment of elderly patients with HICH using a combination of oxiracetam and butylphthalide improves various serum indices, cerebral nerve function and ADL, as well as clinical efficacy. Further research on the combined medication will help to establish a reliable treatment plan for these patients

Volatility forecasting using deep neural network with time-series feature embedding

Volatility is usually a proxy indicator for market variation or tendency, containing essential information for investors and policymakers. This paper proposes a novel hybrid deep neural network model (HDNN) with temporal embedding for volatility forecasting. The main idea of our HDNN is that it encodes one-dimensional time-series data as two-dimensional GAF images, which enables the follow-up convolution neural network (CNN) to learn volatility- related feature mappings automatically. Specifically, HDNN adopts an elegant end-to-end learning paradigm for volatility forecasting, which consists of feature embedding and regression components. The feature embedding component explores the volatility-related temporal information from GAF images via the elaborate CNN in an underlying temporal embedding space. Then, the regression component takes these embedding vectors as input for volatility forecasting tasks. Finally, we examine the feasibility of HDNN on four synthetic GBM datasets and five realworld Stock Index datasets in terms of five regression metrics. The results demonstrate that HDNN has better performance in most cases than the baseline forecasting models of GARCH, EGACH, SVR, and NN. It confirms that the volatility-related temporal features extracted by HDNN indeed improve the forecasting ability. Furthermore, the Friedman test verifies that HDNN is statistically superior to the compared forecasting models

Exploring the total Galactic extinction with SDSS BHB stars

Aims: We used 12,530 photometrically-selected blue horizontal branch (BHB) stars from the Sloan Digital Sky Survey (SDSS) to estimate the total extinction of the Milky Way at the high Galactic latitudes, $R_V$ and $A_V$ in each line of sight. Methods: A Bayesian method was developed to estimate the reddening values in the given lines of sight. Based on the most likely values of reddening in multiple colors, we were able to derive the values of $R_V$ and $A_V$. Results: We selected 94 zero-reddened BHB stars from seven globular clusters as the template. The reddening in the four SDSS colors for the northern Galactic cap were estimated by comparing the field BHB stars with the template stars. The accuracy of this estimation is around 0.01\,mag for most lines of sight. We also obtained $$ to be around 2.40$\pm1.05$ and $A_V$ map within an uncertainty of 0.1\,mag. The results, including reddening values in the four SDSS colors, $A_V$, and $R_V$ in each line of sight, are released on line. In this work, we employ an up-to-date parallel technique on GPU card to overcome time-consuming computations. We plan to release online the C++ CUDA code used for this analysis. Conclusions: The extinction map derived from BHB stars is highly consistent with that from Schlegel, Finkbeiner & Davis(1998). The derived $R_V$ is around 2.40$\pm1.05$. The contamination probably makes the $R_V$ be larger.Comment: 16 pages, 13 figures, 4 tables, accepted for publication in A&

ReConTab: Regularized Contrastive Representation Learning for Tabular Data

Representation learning stands as one of the critical machine learning techniques across various domains. Through the acquisition of high-quality features, pre-trained embeddings significantly reduce input space redundancy, benefiting downstream pattern recognition tasks such as classification, regression, or detection. Nonetheless, in the domain of tabular data, feature engineering and selection still heavily rely on manual intervention, leading to time-consuming processes and necessitating domain expertise. In response to this challenge, we introduce ReConTab, a deep automatic representation learning framework with regularized contrastive learning. Agnostic to any type of modeling task, ReConTab constructs an asymmetric autoencoder based on the same raw features from model inputs, producing low-dimensional representative embeddings. Specifically, regularization techniques are applied for raw feature selection. Meanwhile, ReConTab leverages contrastive learning to distill the most pertinent information for downstream tasks. Experiments conducted on extensive real-world datasets substantiate the framework's capacity to yield substantial and robust performance improvements. Furthermore, we empirically demonstrate that pre-trained embeddings can seamlessly integrate as easily adaptable features, enhancing the performance of various traditional methods such as XGBoost and Random Forest