26 research outputs found
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 69.26Â Mt, 72.13â78.48Â Mt, and 69.32â76.62Â 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
HumidityâInduced SelfâOscillating and SelfâHealing Hypercrosslinked MetalâOrganic Polyhedra Membranes
Abstract Designing autonomously oscillating materials is highly desirable for emerging smart material fields but challenging. Herein, a type of hypercrosslinked metalâorganic polyhedra (HCMOPs) membranes formed by covalent crosslinking of boronic acidâmodified Zrâbased MOPs with polyvinyl alcohol (PVA) are rationally designed. In these membranes, MOPs serve as highâconnectivity 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 selfâoscillating and selfâhealing properties. HCMOP membranes can realize a selfâoscillating 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 MOPâpolymer hybrid materials but also expands the application scopes of MOPs for smart actuation devices
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