On the surface helium abundance of B-type hot subdwarf stars from the WD+MS channel of Type Ia supernovae

The origin of intermediate helium (He)-rich hot subdwarfs are still unclear. Previous studies have suggested that some surviving Type Ia supernovae (SNe Ia) companions from the white dwarf~+~main-sequence (WD+MS) channel may contribute to the intermediate He-rich hot subdwarfs. However, previous studies ignored the impact of atomic diffusion on the post-explosion evolution of surviving companion stars of SNe Ia, leading to that they could not explain the observed surface He abundance of intermediate He-rich hot subdwarfs. In this work, by taking the atomic diffusion and stellar wind into account, we trace the surviving companions of SNe Ia from the WD+MS channel using the one-dimensional stellar evolution code \textsc{MESA} until they evolve into hot subdwarfs. We find that the surface He-abundances of our surviving companion models during their core He-burning phases are in a range of $-1 \lesssim {\rm log}(N_{\rm He}/N_{\rm H}) \lesssim 0$, which are consistent with those observed in intermediate He-rich hot subdwarfs. This seems to further support that surviving companions of SNe Ia in the WD+MS channel are possible to form some intermediate He-rich hot subdwarfs.Comment: 10 pages, 5 figure

Value of superb microvascular imaging ultrasonography in the diagnosis of carpal tunnel syndrome: Compared with color Doppler and power Doppler.

The aim of this study was to compare the value of superb microvascular imaging (SMI) in carpal tunnel syndrome (CTS) with that of color Doppler ultrasonography (CDUS) and power Doppler ultrasonography (PDUS).Fifty patients with symptomatic CTS and 25 healthy volunteers were enrolled. The cross-sectional area (CSA), CDUS score, PDUS score, and SMI score of the median nerve (MN) at the carpal tunnel were recorded. The value of different ultrasonography (US) diagnostic strategies was calculated.The blood flow display ratio in the MN of the healthy volunteers had no statistical difference between CDUS, PDUS, and SMI (20%, 32%, and 48%, respectively, P \u3e.05). The blood flow display ratio for SMI in patients was significantly higher than that of CDUS and PDUS (90%, 52%, and 60%, respectively,

Rethinking Dimensional Rationale in Graph Contrastive Learning from Causal Perspective

Graph contrastive learning is a general learning paradigm excelling at capturing invariant information from diverse perturbations in graphs. Recent works focus on exploring the structural rationale from graphs, thereby increasing the discriminability of the invariant information. However, such methods may incur in the mis-learning of graph models towards the interpretability of graphs, and thus the learned noisy and task-agnostic information interferes with the prediction of graphs. To this end, with the purpose of exploring the intrinsic rationale of graphs, we accordingly propose to capture the dimensional rationale from graphs, which has not received sufficient attention in the literature. The conducted exploratory experiments attest to the feasibility of the aforementioned roadmap. To elucidate the innate mechanism behind the performance improvement arising from the dimensional rationale, we rethink the dimensional rationale in graph contrastive learning from a causal perspective and further formalize the causality among the variables in the pre-training stage to build the corresponding structural causal model. On the basis of the understanding of the structural causal model, we propose the dimensional rationale-aware graph contrastive learning approach, which introduces a learnable dimensional rationale acquiring network and a redundancy reduction constraint. The learnable dimensional rationale acquiring network is updated by leveraging a bi-level meta-learning technique, and the redundancy reduction constraint disentangles the redundant features through a decorrelation process during learning. Empirically, compared with state-of-the-art methods, our method can yield significant performance boosts on various benchmarks with respect to discriminability and transferability. The code implementation of our method is available at https://github.com/ByronJi/DRGCL.Comment: Accepted by AAAI202

Effects of acupuncture on rheumatoid arthritis: a systematic review and meta-analysis

Background: The aim of this study was to evaluate the efficacy of acupuncture for treating rheumatoid arthritis (RA).Materials and Methods: The literature were searched using 6 databases, including Pubmed, Embase, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, VIP and Wanfang database up to December 2013, without language restrictions. All randomized clinical trials (RCTs) comparing acupuncture treatment with non-acupuncture treatment of RA was considered. Methodological quality was assessed using the Jadad score.Results: After strict screening, a total of 21 studies containing 1772 participants were included. The meta-analysis indicated that a significant benefit of acupuncture compared with non-acupuncture on improving the symptoms of RA (pooled RR = 1.19, 95% CI 1.08–1.31, Z = 3.47, P = 0.001). In the subgroup analysis, 9 RCTs showed significant effects of acupuncture for response rate compared with western medicine (RR = 1.26, 95% CI 1.02–1.55, Z = 2.19, P = 0.028); 5 RCTs showed significant effects of acupuncture plus traditional Chinese drug compared with traditional Chinese drug (RR = 1.17, 95% CI 1.07–1.29, Z = 3.31, P = 0.001); 5 RCTs showed beneficial effects of acupuncture plus western medicine compared with western medicine (RR = 1.27, 95% CI 1.06–1.53, Z = 2.56 P = 0.01).Conclusion: This meta-analysis demonstrates that acupuncture may have a favorable effect on treating RA. However, the evidence was limited by the small sample size and the low methodological quality. Considering the potential of acupuncture, more researches and well-designed, rigorous and large clinical trials are needed.Key words: Traditional Chinese Medicine, Acupuncture, Rheumatoid arthritis, Meta-analysi

Experimental Study and CFD Modelling of Down-Reaching Flame Behaviors of Tank Fires with Large Ullage Heights

This paper is aimed at studying the down-reaching flame behaviors of tank fires with large ullage heights. Experiments were first conducted using a gas burner in a transparent quartz glass cylinder to simulate the large ullage and the experimental data was used to validate the computational fluid dynamics (CFD) model. Subsequently the effects of ullage height, fuel velocity and burner diameter on the flame behaviors were examined systematically. Both experimental and numerical results showed that, for lower fuel velocities, the down-reaching flame height (hdown) is restricted by the ullage height. As the fuel velocity continues to increase exceeding a critical value, independent of the ullage height, hdown starts to decrease. For a given fuel velocity, hdown increases with an increase of the burner diameter owing to enhanced air entrainment. A detailed analysis of the flow field and oxygen concentration inside the tank at the steady burning stage was also carried out. Based on the numerical results and dimensionless analysis, a piecewise function was proposed to predict the down-reaching flame height and validated against the experimental data

A WINNER+ Based 3-D Non-Stationary Wideband MIMO Channel Model

In this paper, a three-dimensional (3-D) non-stationary wideband multiple-input multiple-output (MIMO) channel model based on the WINNER+ channel model is proposed. The angular distributions of clusters in both the horizontal and vertical planes are jointly considered. The receiver and clusters can be moving, which makes the model more general. Parameters including number of clusters, powers, delays, azimuth angles of departure (AAoDs), azimuth angles of arrival (AAoAs), elevation angles of departure (EAoDs), and elevation angles of arrival (EAoAs) are time-variant. The cluster time evolution is modeled using a birth-death process. Statistical properties, including spatial cross-correlation function (CCF), temporal autocorrelation function (ACF), Doppler power spectrum density (PSD), level-crossing rate (LCR), average fading duration (AFD), and stationary interval are investigated and analyzed. The LCR, AFD, and stationary interval of the proposed channel model are validated against the measurement data. Numerical and simulation results show that the proposed channel model has the ability to reproduce the main properties of real non-stationary channels. Furthermore, the proposed channel model can be adapted to various communication scenarios by adjusting different parameter values

DLIP: Distilling Language-Image Pre-training

Vision-Language Pre-training (VLP) shows remarkable progress with the assistance of extremely heavy parameters, which challenges deployment in real applications. Knowledge distillation is well recognized as the essential procedure in model compression. However, existing knowledge distillation techniques lack an in-depth investigation and analysis of VLP, and practical guidelines for VLP-oriented distillation are still not yet explored. In this paper, we present DLIP, a simple yet efficient Distilling Language-Image Pre-training framework, through which we investigate how to distill a light VLP model. Specifically, we dissect the model distillation from multiple dimensions, such as the architecture characteristics of different modules and the information transfer of different modalities. We conduct comprehensive experiments and provide insights on distilling a light but performant VLP model. Experimental results reveal that DLIP can achieve a state-of-the-art accuracy/efficiency trade-off across diverse cross-modal tasks, e.g., image-text retrieval, image captioning and visual question answering. For example, DLIP compresses BLIP by 1.9x, from 213M to 108M parameters, while achieving comparable or better performance. Furthermore, DLIP succeeds in retaining more than 95% of the performance with 22.4% parameters and 24.8% FLOPs compared to the teacher model and accelerates inference speed by 2.7x

EFFECTS OF ACUPUNCTURE ON RHEUMATOID ARTHRITIS: A SYSTEMATIC REVIEW AND META-ANALYSIS

Background: The aim of this study was to evaluate the efficacy of acupuncture for treating rheumatoid arthritis (RA). Materials and Methods: The literature were searched using 6 databases, including Pubmed, Embase, Chinese Biomedical Literature Database, China National Knowledge Infrastructure, VIP and Wanfang database up to December 2013, without language restrictions. All randomized clinical trials (RCTs) comparing acupuncture treatment with non-acupuncture treatment of RA was considered. Methodological quality was assessed using the Jadad score. Results: After strict screening, a total of 21 studies containing 1772 participants were included. The meta-analysis indicated that a significant benefit of acupuncture compared with non-acupuncture on improving the symptoms of RA (pooled RR = 1.19, 95% CI 1.08–1.31, Z = 3.47, P = 0.001). In the subgroup analysis, 9 RCTs showed significant effects of acupuncture for response rate compared with western medicine (RR = 1.26, 95% CI 1.02–1.55, Z = 2.19, P = 0.028); 5 RCTs showed significant effects of acupuncture plus traditional Chinese drug compared with traditional Chinese drug (RR = 1.17, 95% CI 1.07–1.29, Z = 3.31, P = 0.001); 5 RCTs showed beneficial effects of acupuncture plus western medicine compared with western medicine (RR = 1.27, 95% CI 1.06–1.53, Z = 2.56 P = 0.01). Conclusion: This meta-analysis demonstrates that acupuncture may have a favorable effect on treating RA. However, the evidence was limited by the small sample size and the low methodological quality. Considering the potential of acupuncture, more researches and well-designed, rigorous and large clinical trials are needed

Antioxidant Assay Based on Quenching of Photocatalytically Generated Reactive Oxygen Species

A method based on photogeneration of OH radicals in water from TiO2 nanoparticles was developed to study the kinetics of oxidation of organic molecules which were used as biological antioxidants. The kinetics of oxidation of terephthalic acid as a reference probe was monitored by fluorescence measurements of the concentration of its oxidized form, 2-hydroterephthalic acid (lambda(ex) = 315 nm, lambda(em) = 425 nm). The kinetics of oxidation of other antioxidant molecules was then deduced from the radical scavenging competition. The antioxidant properties of normal antioxidants were compared based on this kinetic model. And the antioxidant kinetic decreased in the order: lipoic acid, gallic acid, glutathione, uric acid, vitamin C, vitamin E, trolox and bilirubin

AutoDiffusion: Training-Free Optimization of Time Steps and Architectures for Automated Diffusion Model Acceleration

Diffusion models are emerging expressive generative models, in which a large number of time steps (inference steps) are required for a single image generation. To accelerate such tedious process, reducing steps uniformly is considered as an undisputed principle of diffusion models. We consider that such a uniform assumption is not the optimal solution in practice; i.e., we can find different optimal time steps for different models. Therefore, we propose to search the optimal time steps sequence and compressed model architecture in a unified framework to achieve effective image generation for diffusion models without any further training. Specifically, we first design a unified search space that consists of all possible time steps and various architectures. Then, a two stage evolutionary algorithm is introduced to find the optimal solution in the designed search space. To further accelerate the search process, we employ FID score between generated and real samples to estimate the performance of the sampled examples. As a result, the proposed method is (i).training-free, obtaining the optimal time steps and model architecture without any training process; (ii). orthogonal to most advanced diffusion samplers and can be integrated to gain better sample quality. (iii). generalized, where the searched time steps and architectures can be directly applied on different diffusion models with the same guidance scale. Experimental results show that our method achieves excellent performance by using only a few time steps, e.g. 17.86 FID score on ImageNet 64 $\times$ 64 with only four steps, compared to 138.66 with DDIM. The code is available at https://github.com/lilijiangg/AutoDiffusion