An Optimized Rate Control Algorithm in Versatile Video Coding for 360° Videos

Today, 360° video has become an integral part of people’s lives. Despite the fact that the latest generation standard Versatile Video Coding (VVC) demonstrates a significant gain in encoding capacity over High Efficiency Video Coding (HEVC), it still has room for 360° video encoding improvements. To further enhance the applicability of 360° video coding, an optimized rate control (RC) algorithm in VVC for 360° video is proposed in this paper. We present an efficient extraction algorithm for obtaining the video’s saliency feature. Furthermore, for the characteristics of 360° video, a partitioning algorithm is also proposed to dividea frame into demand and non-demand regions. Additionally, to achieve precise and rational RC, a Coding Tree Unit (CTU)-level bit allocation strategy is proposed based on the saliency feature for the above-mentioned regions. The experimental results show that the proposed RC algorithm can achieve 11.77 % bitrate savings and more accurate allocation compared with the default algorithm of VVC. Also, performance enhancement has been observed in comparison to the most advanced algorithm

NTK-approximating MLP Fusion for Efficient Language Model Fine-tuning

Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. However, even fine-tuning the PLMs and doing inference are expensive, especially on edge devices with low computing power. Some general approaches (e.g. quantization and distillation) have been widely studied to reduce the compute/memory of PLM fine-tuning, while very few one-shot compression techniques are explored. In this paper, we investigate the neural tangent kernel (NTK)--which reveals the gradient descent dynamics of neural networks--of the multilayer perceptrons (MLP) modules in a PLM and propose to coin a lightweight PLM through NTK-approximating MLP fusion. To achieve this, we reconsider the MLP as a bundle of sub-MLPs, and cluster them into a given number of centroids, which can then be restored as a compressed MLP and surprisingly shown to well approximate the NTK of the original PLM. Extensive experiments of PLM fine-tuning on both natural language understanding (NLU) and generation (NLG) tasks are provided to verify the effectiveness of the proposed method MLP fusion. Our code is available at https://github.com/weitianxin/MLP_Fusion.Comment: ICML 202

On the Duality Between Sharpness-Aware Minimization and Adversarial Training

Adversarial Training (AT), which adversarially perturb the input samples during training, has been acknowledged as one of the most effective defenses against adversarial attacks, yet suffers from inevitably decreased clean accuracy. Instead of perturbing the samples, Sharpness-Aware Minimization (SAM) perturbs the model weights during training to find a more flat loss landscape and improve generalization. However, as SAM is designed for better clean accuracy, its effectiveness in enhancing adversarial robustness remains unexplored. In this work, considering the duality between SAM and AT, we investigate the adversarial robustness derived from SAM. Intriguingly, we find that using SAM alone can improve adversarial robustness. To understand this unexpected property of SAM, we first provide empirical and theoretical insights into how SAM can implicitly learn more robust features, and conduct comprehensive experiments to show that SAM can improve adversarial robustness notably without sacrificing any clean accuracy, shedding light on the potential of SAM to be a substitute for AT when accuracy comes at a higher priority. Code is available at https://github.com/weizeming/SAM_AT.Comment: ICML 202

Temperature Compensation Model for Monitoring Sensor in Steel Industry Load Management

The iron ore industry faces increasing electricity demand due to industrialization, making effective management of electricity demand crucial. This study proposes a temperature compensation model using Support Vector Regression (SVR), aiming to enhance the accuracy of sensors in monitoring electricity demand. An experiment is conducted to assess the impact of temperature on sensor measurements, and a modified Whale Optimization Algorithm is employed to correct the sensor outputs. The proposed model is compared with both PSO-SVR and unimproved WOA-SVR. Results show that the proposed model significantly improves accuracy, achieving a determination coefficient of 0.7882 and a relative standard deviation of the error square sum of 4.6412%. The results of this study not only enhance power demand management in iron mining but also hold potential applications across various industries

The signatures and crosstalk of gut microbiome, mycobiome, and metabolites in decompensated cirrhotic patients

BackgroundNumerous studies have confirmed that gut microbiota plays a crucial role in the progression of cirrhosis. However, the contribution of gut fungi in cirrhosis is often overlooked due to the relatively low abundance.MethodsWe employed 16S ribosomal RNA sequencing, internal transcribed spacer sequencing, and untargeted metabolomics techniques to investigate the composition and interaction of gut bacteria, fungi, and metabolites in cirrhotic patients.ResultsCirrhotic patients exhibited significant differences in the diversity and composition of gut microbiota and their metabolites in cirrhotic patients compared to healthy individuals. Increase in pathogenic microbial genera and a decrease in beneficial microbial genera including bacteria and fungi were observed. Various clinical indexes were closely connected with these increased metabolites, bacteria, fungi. Additionally, endoscopic treatment was found to impact the gut microbiota and metabolites in cirrhotic patients, although it did not significantly alter the gut ecology. Finally, we constructed a cirrhosis diagnostic model based on different features (bacteria, fungi, metabolites, clinical indexes) with an AUC of 0.938.ConclusionOur findings revealed the characteristics of gut microbial composition and their intricate internal crosstalk in cirrhotic patients, providing cutting-edge explorations of potential roles of gut microbes in cirrhosis

Association of oxidative balance score with hyperuricemia and gout: NHANES 2009-2018

IntroductionOxidative stress plays a crucial role in the development and progression of hyperuricemia/gout. This study aims to explore the relationship between the Oxidative Balance Score (OBS) and hyperuricemia/gout.MethodsThe study utilized complete data from adult participants in the National Health and Nutrition Examination Survey (NHANES) spanning from 2009 to 2018. OBS, composed of scores for 20 dietary and lifestyle factors, served as the exposure variable. Multivariable linear regression model was applied to evaluate the association between OBS and uric acid (UA). Multivariable logistic regression, subgroup analyses, and restricted cubic spline (RCS) regression were conducted to explore the relationship between OBS and hyperuricemia/gout.ResultsA total of 18,998 participants were included. In the fully adjusted model, compared to the lowest quartile, the highest quartiles of OBS, dietary OBS, and lifestyle OBS were negatively correlated with UA (β=-0.31 (-0.36,-0.25), β=-0.18 (-0.24,-0.12), and β=-0.64 (-0.69,-0.59), respectively) and hyperuricemia (OR=0.63 (0.55,0.71), OR=0.76 (0.67,0.86), OR=0.37 (0.33,0.42), respectively). Moreover, the highest quartiles of OBS and lifestyle OBS exhibited a negative correlation with gout (OR=0.72(0.58,0.91), OR=0.54 (0.43,0.67), respectively). Subgroup analyses revealed differences in the negative association between OBS and hyperuricemia concerning hypertension (p for interaction =0.002) and diabetes (p for interaction= 0.004), while gender-related disparities were observed in the negative association between OBS and gout (p for interaction =0.008). RCS analysis demonstrated a linear negative association between hyperuricemia and OBS (p for non-linearity >0.05), while gout exhibited a non-linear negative association (p for non-linearity<0.05).ConclusionThe study found that a higher OBS was associated with a decreased risk of developing hyperuricemia/gout, underscoring its potential in the prevention and management of these conditions