463 research outputs found
Deep Networks for Image Super-Resolution with Sparse Prior
Deep learning techniques have been successfully applied in many areas of
computer vision, including low-level image restoration problems. For image
super-resolution, several models based on deep neural networks have been
recently proposed and attained superior performance that overshadows all
previous handcrafted models. The question then arises whether large-capacity
and data-driven models have become the dominant solution to the ill-posed
super-resolution problem. In this paper, we argue that domain expertise
represented by the conventional sparse coding model is still valuable, and it
can be combined with the key ingredients of deep learning to achieve further
improved results. We show that a sparse coding model particularly designed for
super-resolution can be incarnated as a neural network, and trained in a
cascaded structure from end to end. The interpretation of the network based on
sparse coding leads to much more efficient and effective training, as well as a
reduced model size. Our model is evaluated on a wide range of images, and shows
clear advantage over existing state-of-the-art methods in terms of both
restoration accuracy and human subjective quality
Fractal analysis of the effect of particle aggregation distribution on thermal conductivity of nanofluids
This project was supported by the National Natural Science Foundation of China (No. 41572116), the Fundamental Research Funds for the Central Universities, China University of Geosciences, Wuhan) (No. CUG160602).Peer reviewedPostprin
Matching-CNN Meets KNN: Quasi-Parametric Human Parsing
Both parametric and non-parametric approaches have demonstrated encouraging
performances in the human parsing task, namely segmenting a human image into
several semantic regions (e.g., hat, bag, left arm, face). In this work, we aim
to develop a new solution with the advantages of both methodologies, namely
supervision from annotated data and the flexibility to use newly annotated
(possibly uncommon) images, and present a quasi-parametric human parsing model.
Under the classic K Nearest Neighbor (KNN)-based nonparametric framework, the
parametric Matching Convolutional Neural Network (M-CNN) is proposed to predict
the matching confidence and displacements of the best matched region in the
testing image for a particular semantic region in one KNN image. Given a
testing image, we first retrieve its KNN images from the
annotated/manually-parsed human image corpus. Then each semantic region in each
KNN image is matched with confidence to the testing image using M-CNN, and the
matched regions from all KNN images are further fused, followed by a superpixel
smoothing procedure to obtain the ultimate human parsing result. The M-CNN
differs from the classic CNN in that the tailored cross image matching filters
are introduced to characterize the matching between the testing image and the
semantic region of a KNN image. The cross image matching filters are defined at
different convolutional layers, each aiming to capture a particular range of
displacements. Comprehensive evaluations over a large dataset with 7,700
annotated human images well demonstrate the significant performance gain from
the quasi-parametric model over the state-of-the-arts, for the human parsing
task.Comment: This manuscript is the accepted version for CVPR 201
Unveiling the Roles of Binder in the Mechanical Integrity of Electrodes for Lithium-Ion Batteries
In lithium-ion secondary batteries research, binders have received the least attention, although the electrochemical performance of Li-ion batteries such as specific capacity and cycle life cannot be achieved if the adhesion strengths between electrode particles and between electrode films and current collectors are insufficient to endure charge-discharge cycling. In this paper, the roles of binders in the mechanical integrity of electrodes for lithium-ion batteries were studied by coupled microscratch and digital image correlation (DIC) techniques. A microscratch based composite model was developed to decouple the carbon particle/particle cohesion strength from the electrode-film/copper-current-collector adhesion strength. The dependences of microscratch coefficient of friction and the critical delamination load on the PVDF binder content suggest that the strength of different interfaces is ranked as follows: Cu/PVDF \u3c carbon-particle/PVDF \u3c PVDF/PVDF. The particle/particle cohesion strength increases while electrode-film/current-collector adhesion strength decreases with increasing PVDF binder content (up to 20% of binder). The electrolyte soaking-and-drying process leads to an increase in particle/particle cohesion but a decrease in electrode-film/copper-current-collector adhesion. Finally, the methodology developed here can provide new guidelines for binder selection and electrode design and lay a constitutive foundation for modeling the mechanical properties and performance of the porous electrodes in lithium-ion batteries
The total joint arthroplasty care patterns in China during the COVID-19 pandemic: a multicenter cohort study
BackgroundThe COVID-19 pandemic has profoundly affected the care practices of total joint arthroplasty (TJA) throughout the world. However, the impact of the pandemic on TJA care practices has not yet been studied in China.MethodsThis retrospective multicenter cohort included patients aged 18 years or older who underwent TJA between January 2019 and December 2019 (prepandemic period) and January 2020 to December 2021 (pandemic period). Data were obtained from the medical records of 17 Chinese hospitals. Interrupted time series (ITS) analysis was used to estimate differences in monthly TJA volume, hospitalization proportion of TJA, preoperative characteristics, postoperative complications, 30-day readmissions, length of stay (LOS), and costs in inpatients undergoing TJA between the prepandemic and pandemic periods. Multivariate regression and propensity score matching (PSM) analyses were used to assess the impact of the COVID-19 pandemic on hospital complications, readmissions at 30 days, LOS, and costs at the patient level.ResultsA total of 752,477 inpatients undergoing TJA in the prepandemic period, 1,291,248 in the pandemic period, with an average 13.1% yearly decrease in the volume of TJA during the pandemic. No significant changes were observed in the proportion of hospitalizations for TJA. ITS analyses showed increases in the proportion of comorbidities (8.5%, 95% CI: 3.4–14.0%) and the number of comorbidities (15.6%, 95% CI: 7.7–24.1%) in TJA cases during the pandemic, without increasing LOS, costs, complications, and readmission rates. Multivariate and PSM analyses showed 6% and 26% reductions in costs and readmission rates during the pandemic, respectively.ConclusionsThe COVID-19 pandemic was associated with more severe preoperative conditions and decreased volume, costs, and readmission rates in patients undergoing TJA in China. These findings demonstrate that the COVID-19 pandemic did not have a dramatic impact on the TJA care pattern in China, which may have resulted from active and strict strategies in combating COVID-19 as well as a rapid response in hospital management
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