GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression

Neural-network-based approaches recently emerged in the field of data compression and have already led to significant progress in image compression, especially in achieving a higher compression ratio. In the lossless image compression scenario, however, existing methods often struggle to learn a probability model of full-size high-resolution images due to the limitation of the computation source. The current strategy is to crop high-resolution images into multiple non-overlapping patches and process them independently. This strategy ignores long-term dependencies beyond patches, thus limiting modeling performance. To address this problem, we propose a hierarchical latent variable model with a global context to capture the long-term dependencies of high-resolution images. Besides the latent variable unique to each patch, we introduce shared latent variables between patches to construct the global context. The shared latent variables are extracted by a self-supervised clustering module inside the model's encoder. This clustering module assigns each patch the confidence that it belongs to any cluster. Later, shared latent variables are learned according to latent variables of patches and their confidence, which reflects the similarity of patches in the same cluster and benefits the global context modeling. Experimental results show that our global context model improves compression ratio compared to the engineered codecs and deep learning models on three benchmark high-resolution image datasets, DIV2K, CLIC.pro, and CLIC.mobile

Visual saliency guided textured model simplification

Mesh geometry can be used to model both object shape and details. If texture maps are involved, it is common to let mesh geometry mainly model object shapes and let the texture maps model the most object details, optimising data size and complexity of an object. To support efficient object rendering and transmission, model simplification can be applied to reduce the modelling data. However, existing methods do not well consider how object features are jointly represented by mesh geometry and texture maps, having problems in identifying and preserving important features for simplified objects. To address this, we propose a visual saliency detection method for simplifying textured 3D models. We produce good simplification results by jointly processing mesh geometry and texture map to produce a unified saliency map for identifying visually important object features. Results show that our method offers a better object rendering quality than existing methods

Associations between plasma metal mixture exposure and risk of hypertension: A cross-sectional study among adults in Shenzhen, China

BackgroundMetal exposure affects human health. Current studies mainly focus on the individual health effect of metal exposure on hypertension (HTN), and the results remain controversial. Moreover, the studies assessing overall effect of metal mixtures on hypertension risk are limited.MethodsA cross-sectional study was conducted by recruiting 1,546 Chinese adults who attended routine medical check-ups at the Eighth Affiliated Hospital of Sun Yat-sen University, Shenzhen. The plasma levels of 13 metals were measured using inductively coupled plasma mass spectrometry. Multivariate logistic regression model, restricted cubic spline (RCS) model and the Bayesian Kernel Machine Regression (BKMR) model were applied to explore the single and combined effect of metals on the risk of HTN.ResultsA total of 642 (41.5%) participants were diagnosed with HTN. In the logistic regression model, the adjusted odds ratios (ORs) were 0.71 (0.52, 0.97) for cobalt, 1.40 (1.04, 1.89) for calcium, 0.66 (0.48, 0.90), and 0.60 (0.43, 0.83) for aluminum in the second and third quartile, respectively. The RCS analysis showed a V-shaped or an inverse V-shaped dose-response relationship between metals (aluminum or calcium, respectively) and the risk of HTN (P for non-linearity was 0.017 or 0.009, respectively). However, no combined effect was found between metal mixture and the risk of hypertension.ConclusionsPlasma levels of cobalt, aluminum and calcium were found to be associated with the risk of HTN. Further studies are needed to confirm our findings and their potential mechanisms with prospective studies and experimental study designs

Scenario-based analysis for industrial project planning in the context of carbon peaking: Case study city, China

The Chinese government is actively pursuing a low-carbon development model with a clear target of reducing carbon dioxide (CO2) emissions by 2030. This study presents a project-based perspective on CO2 emissions from the industrial sector in the city. The article quantifies and analyzes the project planning by including the entire gross industrial production value, industrial structure, energy intensity, energy structure, and CO2 emission coefficient of the industrial sector's CO2 emissions decomposition model. Monte Carlo simulation and scenario analysis were coupled to evaluate how the city's industrial sector could achieve its peak carbon targets by industry. The expected range of CO2 emissions from the city's industrial sector in 2025, 2030, and 2035, based on the project plan and policies in place, is projected to be between 65.64 and 69.26 Mt, 72.13–78.48 Mt, and 69.32–76.62 Mt. However, the simulations show that there are still considerable uncertainties in reaching the peak carbon target in 2030, necessitating greater government efforts. To achieve the 2030 goal for the city's industrial sector, the paper recommends that the government increase the baseline criteria for industrial structure, energy intensity, and energy structure. This study provides scientific project planning guidance for Chinese cities to successfully achieve the 2030 goal

3D Mesh Compression and Transmission for Mobile Robotic Applications

Mobile robots are useful for environment exploration and rescue operations. In such applications, it is crucial to accurately analyse and represent an environment, providing appropriate inputs for motion planning in order to support robot navigation and operations. 2D mapping methods are simple but cannot handle multilevel or multistory environments. To address this problem, 3D mapping methods generate structural 3D representations of the robot operating environment and its objects by 3D mesh reconstruction. However, they face the challenge of efficiently transmitting those 3D representations to system modules for 3D mapping, motion planning, and robot operation visualization. This paper proposes a quality-driven mesh compression and transmission method to address this. Our method is efficient, as it compresses a mesh by quantizing its transformed vertices without the need to spend time constructing an a-priori structure over the mesh. A visual distortion function is developed to govern the level of quantization, allowing mesh transmission to be controlled under different network conditions or time constraints. Our experiments demonstrate how the visual quality of a mesh can be manipulated by the visual distortion function

Humidity‐Induced Self‐Oscillating and Self‐Healing Hypercrosslinked Metal–Organic Polyhedra Membranes

Abstract Designing autonomously oscillating materials is highly desirable for emerging smart material fields but challenging. Herein, a type of hypercrosslinked metal–organic polyhedra (HCMOPs) membranes formed by covalent crosslinking of boronic acid‐modified Zr‐based MOPs with polyvinyl alcohol (PVA) are rationally designed. In these membranes, MOPs serve as high‐connectivity nodes and provide dynamic borate bonds with PVA in hypercrosslinked networks, which can be broken/formed reversibly upon the stimulus of water vapor. The humidity response characteristic of HCMOPs promotes their self‐oscillating and self‐healing properties. HCMOP membranes can realize a self‐oscillating property above the water surface even after loading a cargo that is 1.5 times the weight of the membrane due to the fast adsorption and desorption kinetics. Finally, the HCMOP actuator can realize energy conversion from mechanical energy into electricity when coupled with a piezoelectric membrane. This work not only paves a new avenue to construct MOP‐polymer hybrid materials but also expands the application scopes of MOPs for smart actuation devices

Additional file 2 of Downregulation of exosomal miR-7-5p promotes breast cancer migration and invasion by targeting RYK and participating in the atypical WNT signalling pathway

Additional file 2

Additional file 1 of Downregulation of exosomal miR-7-5p promotes breast cancer migration and invasion by targeting RYK and participating in the atypical WNT signalling pathway

Additional file 1: Table S1. Sequences of oligonucleotide fragment. Table S2 Sequences of primers required for the experiment. Table S3 Relative expression of several miRNAs screened from the GSE114329 dataset

Scene Text Recognition from Two-Dimensional Perspective

Inspired by speech recognition, recent state-of-the-art algorithms mostly consider scene text recognition as a sequence prediction problem. Though achieving excellent performance, these methods usually neglect an important fact that text in images are actually distributed in two-dimensional space. It is a nature quite different from that of speech, which is essentially a one-dimensional signal. In principle, directly compressing features of text into a one-dimensional form may lose useful information and introduce extra noise. In this paper, we approach scene text recognition from a two-dimensional perspective. A simple yet effective model, called Character Attention Fully Convolutional Network (CA-FCN), is devised for recognizing the text of arbitrary shapes. Scene text recognition is realized with a semantic segmentation network, where an attention mechanism for characters is adopted. Combined with a word formation module, CA-FCN can simultaneously recognize the script and predict the position of each character. Experiments demonstrate that the proposed algorithm outperforms previous methods on both regular and irregular text datasets. Moreover, it is proven to be more robust to imprecise localizations in the text detection phase, which are very common in practice.Comment: To appear in AAAI 201