Self-Paced Multi-Task Learning

In this paper, we propose a novel multi-task learning (MTL) framework, called Self-Paced Multi-Task Learning (SPMTL). Different from previous works treating all tasks and instances equally when training, SPMTL attempts to jointly learn the tasks by taking into consideration the complexities of both tasks and instances. This is inspired by the cognitive process of human brain that often learns from the easy to the hard. We construct a compact SPMTL formulation by proposing a new task-oriented regularizer that can jointly prioritize the tasks and the instances. Thus it can be interpreted as a self-paced learner for MTL. A simple yet effective algorithm is designed for optimizing the proposed objective function. An error bound for a simplified formulation is also analyzed theoretically. Experimental results on toy and real-world datasets demonstrate the effectiveness of the proposed approach, compared to the state-of-the-art methods

SUR-Net: Predicting the Satisfied User Ratio Curve for Image Compression with Deep Learning

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The Satisfied User Ratio (SUR) curve for a lossy image compression scheme, e.g., JPEG, characterizes the probability distribution of the Just Noticeable Difference (JND) level, the smallest distortion level that can be perceived by a subject. We propose the first deep learning approach to predict such SUR curves. Instead of the direct approach of regressing the SUR curve itself for a given reference image, our model is trained on pairs of images, original and compressed. Relying on a Siamese Convolutional Neural Network (CNN), feature pooling, a fully connected regression-head, and transfer learning, we achieved a good prediction performance. Experiments on the MCL-JCI dataset showed a mean Bhattacharyya distance between the predicted and the original JND distributions of only 0.072

Global, regional, and national burden of chronic kidney disease attributable to high fasting plasma glucose from 1990 to 2019: a systematic analysis from the global burden of disease study 2019

PurposeGiven the rising prevalence of high fasting plasma glucose (HFPG) over the past three decades, it is crucial to assess its global, national, and regional impact on chronic kidney disease (CKD). This study aims to investigate the burden of CKD attributed to HFPG and its distribution across various levels.Methods and materialsThe data for this research was sourced from the Global Burden of Diseases Study 2019. To estimate the burden of CKD attributed to HFPG, we utilized DisMod-MR 2.1, a Bayesian meta-regression tool. The burden was measured using age-standardized mortality rate (ASMR) and age-standardized disability-adjusted life years (DALYs) rate. Correlation analysis was performed using the Spearman rank order correlation method. Temporal trends were analyzed by estimating the estimated annual percentage change (EAPC).ResultsGlobally in 2019, there were a total of 487.97 thousand deaths and 13,093.42 thousand DALYs attributed to CKD attributed to HFPG, which represent a substantial increase of 153.8% and 120%, respectively, compared to 1990. Over the period from 1990 to 2019, the burden of CKD attributable to HFPG increased across all regions, with the highest increases observed in regions with high socio-demographic index (SDI) and middle SDI. Regions with lower SDI exhibited higher ASMR and age-standardized DALYs (ASDR) compared to developed nations at the regional level. Additionally, the EAPC values, which indicate the rate of increase, were significantly higher in these regions compared to developed nations. Notably, high-income North America, belonging to the high SDI regions, experienced the greatest increase in both ASMR and ASDR over the past three decades. Furthermore, throughout the years from 1990 to 2019, males bore a greater burden of CKD attributable to HFPG.ConclusionWith an increasing population and changing dietary patterns, the burden of CKD attributed to HFPG is expected to worsen. From 1990 to 2019, males and developing regions have experienced a more significant burden. Notably, the EAPC values for both ASMR and ASDR were higher in males and regions with lower SDI (excluding high-income North America). This emphasizes the pressing requirement for effective interventions to reduce the burden of CKD attributable to HFPG

Altitude influences microbial diversity and herbage fermentation in the rumen of yaks

Publication history: Accepted - 27 November 2020; Published online - 4 December 2020Background: Rumen microbiota in ruminants are vital for sustaining good rumen ecology, health, and productivity. Currently, limited information is available regarding the response of yaks (Bos grunniens) to fluctuating environments, especially the rumen microbiome. To address this, we investigated the diet, rumen bacterial community, and volatile fatty acids (VFA) of rumen fluid of yaks raised in the great Qinghai-Tibet plateau (QTP) at 2800 (low altitude, L), 3700 (middle altitude, M), and 4700 m (high altitude, H) above sea level. Results: The results showed that despite a partial diet overlap, H yaks harbored higher fibrous fractious contents than the M and L grazing yaks. Bacteria including Christensenellaceae_R-7_group, Ruminococcus_1, Romboutsia, Alloprevotella, Eubacterium coprostanoligenes, Clostridium, Streptococcus, and Treponema were found to be enriched in the rumen of yaks grazing at H. They also showed higher rumen microbial diversity and total VFA concentrations than those shown by yaks at M and L. Principal coordinates analysis (PCoA) on weighted UniFrac distances revealed that the bacterial community structure of rumen differed between the three altitudes. Moreover, Tax4fun metagenome estimation revealed that microbial genes associated with energy requirement and carbohydrate metabolic fate were overexpressed in the rumen microbiota of H yaks. Conclusions: Collectively, our results revealed that H yaks had a stronger herbage fermenting ability via rumen microbial fermentation. Their enhanced ability of utilizing herbage may be partly owing to a microbiota adaptation for more energy requirements in the harsh H environment, such as lower temperature and the risk of hypoxia.This research was supported by grants from the Program for Strategic Priority Research Program of Chinese Academy of Sciences (XDA20100102), the Second Tibetan Plateau Scientific Expedition and Research: Grassland Ecosystem and Ecological Animal Husbandry (2019QZKK0302), and Innovative Research Team of Ministry of Education (IRT_17R50). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript

Learning-based Satisfied User Ratio Prediction for Symmetrically and Asymmetrically Compressed Stereoscopic Images

The file attached to this record is the author's final peer reviewed version.The Satisfied User Ratio (SUR) for a given distortion level is the fraction of subjects that cannot perceive a quality difference between the original image and its compressed version. By predicting the SUR, one can determine the highest distortion level which allows to save bit rate while guaranteeing a good visual quality. We propose the first method to predict the SUR for symmetrically and asymmetrically compressed stereoscopic images. Unlike SUR prediction techniques for 2D images and videos, our method exploits the properties of binocular vision. We first extract features that characterize image quality and image content. Then, we use gradient boosting decision trees to reduce the number of features and train a regression model that learns a mapping function from the features to the SUR values. Experimental results on the SIAT-JSSI and SIAT-JASI datasets show high SUR prediction accuracy for H.265 All-Intra and JPEG2000 symmetrically and asymmetrically compressed stereoscopic images

Satisfied user ratio prediction with support vector regression for compressed stereo images

We propose the first method to predict the Satisfied User Ratio (SUR) for compressed stereo images. The method consists of two main steps. First, considering binocular vision properties, we extract three types of features from stereo images: image quality features, monocular visual features, and binocular visual features. Then, we train a Support Vector Regression (SVR) model to learn a mapping function from the feature space to the SUR values. Experimental results on the SIAT-JSSI dataset show excellent prediction accuracy, with a mean absolute SUR error of only 0.08 for H.265 intra coding and only 0.13 for JPEG2000 compression

3D Single Object Tracking with Multi-View Unsupervised Center Uncertainty Learning

Center point localization is a major factor affecting the performance of 3D single object tracking. Point clouds themselves are a set of discrete points on the local surface of an object, and there is also a lot of noise in the labeling. Therefore, directly regressing the center coordinates is not very reasonable. Existing methods usually use volumetric-based, point-based, and view-based methods, with a relatively single modality. In addition, the sampling strategies commonly used usually result in the loss of object information, and holistic and detailed information is beneficial for object localization. To address these challenges, we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker (MUT). MUT models the potential uncertainty of center coordinates localization using an unsupervised manner, allowing the model to learn the true distribution. By projecting point clouds, MUT can obtain multi-view depth map features, realize efficient knowledge transfer from 2D to 3D, and provide another modality information for the tracker. We also propose a former attraction probability sampling strategy that preserves object information. By using both holistic and detailed descriptors of point clouds, the tracker can have a more comprehensive understanding of the tracking environment. Experimental results show that the proposed MUT network outperforms the baseline models on the KITTI dataset by 0.8% and 0.6% in precision and success rate, respectively, and on the NuScenes dataset by 1.4%, and 6.1% in precision and success rate, respectively. The code is made available at https://github.com/abchears/MUT.git

C1ql4 regulates breast cancer cell stemness and epithelial-mesenchymal transition through PI3K/AKT/NF-κB signaling pathway

BackgroundThe stemness characteristic of breast cancer (BC) is a crucial factor underlying cancer recurrence and metastasis after operative therapy and chemoradiotherapy. Understanding the potential mechanism of breast cancer stem cells (BCSCs) may ameliorate the prognosis of patients.MethodsWe collected clinical specimens of BC patients for staining and statistical analysis to verify the expression status and clinical significance of complement C1q-like 4 (C1ql4). Western blot and qRT-PCR were employed to detect the expression of molecules. Flow cytometry was used to examine cell cycle, cell apoptosis and the portion of BCSCs. Wound healing and Transwell assays were used to detect cell metastasis. The effect of C1ql4 on breast cancer progression in vivo was examined in a nude mouse tumor bearing model.ResultsOur clinical analysis showed that C1ql4 was highly expressed in BC tissues and cell lines, and the high expression of C1ql4 was significantly corelated with the malignancy of BC patients. Moreover, we also found that C1ql4 was overexpressed in BCSCs. C1ql4 knockdown suppressed the BCSC and EMT properties, promoted cell cycle progression, enhanced BC cell apoptosis, and inhibited cell migration and invasion, whereas the C1ql4 overexpression exhibited the opposite effects. Mechanistically, C1ql4 promoted the activation and nuclear location of NF-κB and the expression of downstream factors TNF-α and IL-1β. Moreover, inhibition of PI3K/AKT signaling suppressed the C1ql4-induced stemness and EMT.ConclusionsOur findings suggest that C1ql4 promotes the BC cell stemness and EMT via modulating the PI3K/AKT/NF-κB signaling, and provides a promising target for BC treatment