130 research outputs found
DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Indoor Semantic Segmentation
Deep learning approaches achieve prominent success in 3D semantic
segmentation. However, collecting densely annotated real-world 3D datasets is
extremely time-consuming and expensive. Training models on synthetic data and
generalizing on real-world scenarios becomes an appealing alternative, but
unfortunately suffers from notorious domain shifts. In this work, we propose a
Data-Oriented Domain Adaptation (DODA) framework to mitigate pattern and
context gaps caused by different sensing mechanisms and layout placements
across domains. Our DODA encompasses virtual scan simulation to imitate
real-world point cloud patterns and tail-aware cuboid mixing to alleviate the
interior context gap with a cuboid-based intermediate domain. The first
unsupervised sim-to-real adaptation benchmark on 3D indoor semantic
segmentation is also built on 3D-FRONT, ScanNet and S3DIS along with 7 popular
Unsupervised Domain Adaptation (UDA) methods. Our DODA surpasses existing UDA
approaches by over 13% on both 3D-FRONT ScanNet and 3D-FRONT
S3DIS. Code will be available
Progress and summary of reinforcement learning on energy management of MPS-EV
The high emission and low energy efficiency caused by internal combustion
engines (ICE) have become unacceptable under environmental regulations and the
energy crisis. As a promising alternative solution, multi-power source electric
vehicles (MPS-EVs) introduce different clean energy systems to improve
powertrain efficiency. The energy management strategy (EMS) is a critical
technology for MPS-EVs to maximize efficiency, fuel economy, and range.
Reinforcement learning (RL) has become an effective methodology for the
development of EMS. RL has received continuous attention and research, but
there is still a lack of systematic analysis of the design elements of RL-based
EMS. To this end, this paper presents an in-depth analysis of the current
research on RL-based EMS (RL-EMS) and summarizes the design elements of
RL-based EMS. This paper first summarizes the previous applications of RL in
EMS from five aspects: algorithm, perception scheme, decision scheme, reward
function, and innovative training method. The contribution of advanced
algorithms to the training effect is shown, the perception and control schemes
in the literature are analyzed in detail, different reward function settings
are classified, and innovative training methods with their roles are
elaborated. Finally, by comparing the development routes of RL and RL-EMS, this
paper identifies the gap between advanced RL solutions and existing RL-EMS.
Finally, this paper suggests potential development directions for implementing
advanced artificial intelligence (AI) solutions in EMS
Janus icosahedral particles: amorphization driven by three-dimensional atomic misfit and edge dislocation compensation
Icosahedral nanoparticles composed of fivefold twinned tetrahedra have broad
applications. The strain relief mechanism and angular deficiency in icosahedral
multiply twinned particles are poorly understood in three dimensions. Here, we
resolved the three-dimensional atomic structures of Janus icosahedral
nanoparticles using atomic resolution electron tomography. A geometrically
fivefold face consistently corresponds to a less ordered face like two
hemispheres. We quantify rich structural variety of icosahedra including bond
orientation order, bond length, strain tensor; and packing efficiency, atom
number, solid angle of each tetrahedron. These structural characteristics
exhibit two-sided distribution. Edge dislocations near the axial atoms and
small disordered domains fill the angular deficiency. Our findings provide new
insights how the fivefold symmetry can be compensated and the
geometrically-necessary internal strains relived in multiply twinned particles.Comment: 30 pages, 5 figure
Real-time Surveillance Application by Multiple Detectors and Compressive Trackers
A real-time surveillance system for IP network cameras is presented. Motion, part-body, and whole-body detectors are efficiently combined to generate robust and fast detections, which feed multiple compressive trackers. The generated trajectories are then improved using a reidentification strategy for long term operation
Alleviating Effect and Mechanism of Cold Shock Pretreatment on Browning of Fresh-Cut Pitaya Fruit
The alleviating effect and mechanism of cold shock pretreatment (−2 ℃ cold air for 3 h) on flesh browning in fresh-cut pitaya fruit was explored by evaluating its effect on the phenylpropanoid pathway and antioxidant system during storage. Results revealed that cold shock pretreatment efficiently inhibited the increase in electrical conductivity and malondialdehyde (MDA) content compared with the control. Cold shock pretreatment promoted the gene expression and activities of key enzymes (phenylalanine ammonia lyase, cinnamic acid 4-hydroxylase, and 4-coumarate-CoA ligase) related to phenylpropane biosynthesis, and improved the accumulation of total phenolics and flavonoids, as well as most individual phenolic compounds, which led to enhanced antioxidant capacity. The activities of superoxide dismutase (SOD), catalase (CAT) and ascorbate peroxidase (APX) were increased by cold shock pretreatment. In addition, cold shock pretreatment accelerated the production of superoxide anion and H2O2 at the early stage of storage, but had no effect on their peaks and led to lower levels of superoxide anion and H2O2 afterwards, alleviating flesh browning in fresh-cut pitaya fruit. These results indicated that cold shock pretreatment can effectively alleviate wound-induced oxidative stress by modulating the phenylpropanoid pathway and reactive oxygen species (ROS) metabolism, thus inhibiting flesh browning in fresh-cut pitaya fruit
Identifying microbial signatures for patients with postmenopausal osteoporosis using gut microbiota analyses and feature selection approaches
Osteoporosis (OP) is a metabolic bone disorder characterized by low bone mass and deterioration of micro-architectural bone tissue. The most common type of OP is postmenopausal osteoporosis (PMOP), with fragility fractures becoming a global burden for women. Recently, the gut microbiota has been connected to bone metabolism. The aim of this study was to characterize the gut microbiota signatures in PMOP patients and controls. Fecal samples from 21 PMOP patients and 37 controls were collected and analyzed using amplicon sequencing of the V3-V4 regions of the 16S rRNA gene. The bone mineral density (BMD) measurement and laboratory biochemical test were performed on all participants. Two feature selection algorithms, maximal information coefficient (MIC) and XGBoost, were employed to identify the PMOP-related microbial features. Results showed that the composition of gut microbiota changed in PMOP patients, and microbial abundances were more correlated with total hip BMD/T-score than lumbar spine BMD/T-score. Using the MIC and XGBoost methods, we identified a set of PMOP-related microbes; a logistic regression model revealed that two microbial markers (Fusobacteria and Lactobacillaceae) had significant abilities in disease classification between the PMOP and control groups. Taken together, the findings of this study provide new insights into the etiology of OP/PMOP, as well as modulating gut microbiota as a therapeutic target in the diseases. We also highlight the application of feature selection approaches in biological data mining and data analysis, which may improve the research in medical and life sciences
CD8(+) T Cells Involved in Metabolic Inflammation in Visceral Adipose Tissue and Liver of Transgenic Pigs
Anti-inflammatory therapies have the potential to become an effective treatment for obesity-related diseases. However, the huge gap of immune system between human and rodent leads to limitations of drug discovery. This work aims at constructing a transgenic pig model with higher risk of metabolic diseases and outlining the immune responses at the early stage of metaflammation by transcriptomic strategy. We used CRISPR/Cas9 techniques to targeted knock-in three humanized disease risk genes, GIPR(dn) , hIAPP and PNPLA3(I148M) . Transgenic effect increased the risk of metabolic disorders. Triple-transgenic pigs with short-term diet intervention showed early symptoms of type 2 diabetes, including glucose intolerance, pancreatic lipid infiltration, islet hypertrophy, hepatic lobular inflammation and adipose tissue inflammation. Molecular pathways related to CD8(+) T cell function were significantly activated in the liver and visceral adipose samples from triple-transgenic pigs, including antigen processing and presentation, T-cell receptor signaling, co-stimulation, cytotoxicity, and cytokine and chemokine secretion. The similar pro-inflammatory signaling in liver and visceral adipose tissue indicated that there might be a potential immune crosstalk between the two tissues. Moreover, genes that functionally related to liver antioxidant activity, mitochondrial function and extracellular matrix showed distinct expression between the two groups, indicating metabolic stress in transgenic pigs' liver samples. We confirmed that triple-transgenic pigs had high coincidence with human metabolic diseases, especially in the scope of inflammatory signaling at early stage metaflammation. Taken together, this study provides a valuable large animal model for the clinical study of metaflammation and metabolic diseases.Peer reviewe
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