Theoretical Analysis on the Optimization and Integration of College PE Curriculum Education and Teaching System

The purpose of this study was to promote the development of PE curriculum theory and practice. Literature review method and logical analysis were used in this study. The results showed that physical education curriculum and teaching related ideas are based on optimizing the teaching theory system of integrated physical education. At the macro theoretical guidance level, moral education is the theoretical basis for the construction of sports related curriculum education and teaching ideology. In terms of setting and construction, lifelong physical education is the theoretical basis of school physical education curriculum education and teaching system construction. In the overall construction, we should not only set up a reasonable framework, but also optimize and incorporate high-quality course teaching resources. On the practical level, physical education teaching refers to the relatively stable structure and procedure of physical education activities established under the guidance of certain teaching ideas or teaching theories, which is the theoretical basis for the practice of physical education teaching system. Physical education teaching practice system is the ultimate embodiment of physical education teaching implementation on the basis of physical education teaching ideology and construction system. Based on different groups and different types of physical education courses, the practice path and effect are optimized and integrated, and efficient and feasible physical education teaching practice system is constructed

Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement

Underwater images suffer from complex and diverse degradation, which inevitably affects the performance of underwater visual tasks. However, most existing learning-based Underwater image enhancement (UIE) methods mainly restore such degradations in the spatial domain, and rarely pay attention to the fourier frequency information. In this paper, we develop a novel UIE framework based on spatial-frequency interaction and gradient maps, namely SFGNet, which consists of two stages. Specifically, in the first stage, we propose a dense spatial-frequency fusion network (DSFFNet), mainly including our designed dense fourier fusion block and dense spatial fusion block, achieving sufficient spatial-frequency interaction by cross connections between these two blocks. In the second stage, we propose a gradient-aware corrector (GAC) to further enhance perceptual details and geometric structures of images by gradient map. Experimental results on two real-world underwater image datasets show that our approach can successfully enhance underwater images, and achieves competitive performance in visual quality improvement

EPIM: Efficient Processing-In-Memory Accelerators based on Epitome

The exploration of Processing-In-Memory (PIM) accelerators has garnered significant attention within the research community. However, the utilization of large-scale neural networks on Processing-In-Memory (PIM) accelerators encounters challenges due to constrained on-chip memory capacity. To tackle this issue, current works explore model compression algorithms to reduce the size of Convolutional Neural Networks (CNNs). Most of these algorithms either aim to represent neural operators with reduced-size parameters (e.g., quantization) or search for the best combinations of neural operators (e.g., neural architecture search). Designing neural operators to align with PIM accelerators' specifications is an area that warrants further study. In this paper, we introduce the Epitome, a lightweight neural operator offering convolution-like functionality, to craft memory-efficient CNN operators for PIM accelerators (EPIM). On the software side, we evaluate epitomes' latency and energy on PIM accelerators and introduce a PIM-aware layer-wise design method to enhance their hardware efficiency. We apply epitome-aware quantization to further reduce the size of epitomes. On the hardware side, we modify the datapath of current PIM accelerators to accommodate epitomes and implement a feature map reuse technique to reduce computation cost. Experimental results reveal that our 3-bit quantized EPIM-ResNet50 attains 71.59% top-1 accuracy on ImageNet, reducing crossbar areas by 30.65 times. EPIM surpasses the state-of-the-art pruning methods on PIM

Super-Resolution by Predicting Offsets: An Ultra-Efficient Super-Resolution Network for Rasterized Images

Rendering high-resolution (HR) graphics brings substantial computational costs. Efficient graphics super-resolution (SR) methods may achieve HR rendering with small computing resources and have attracted extensive research interests in industry and research communities. We present a new method for real-time SR for computer graphics, namely Super-Resolution by Predicting Offsets (SRPO). Our algorithm divides the image into two parts for processing, i.e., sharp edges and flatter areas. For edges, different from the previous SR methods that take the anti-aliased images as inputs, our proposed SRPO takes advantage of the characteristics of rasterized images to conduct SR on the rasterized images. To complement the residual between HR and low-resolution (LR) rasterized images, we train an ultra-efficient network to predict the offset maps to move the appropriate surrounding pixels to the new positions. For flat areas, we found simple interpolation methods can already generate reasonable output. We finally use a guided fusion operation to integrate the sharp edges generated by the network and flat areas by the interpolation method to get the final SR image. The proposed network only contains 8,434 parameters and can be accelerated by network quantization. Extensive experiments show that the proposed SRPO can achieve superior visual effects at a smaller computational cost than the existing state-of-the-art methods.Comment: This article has been accepted by ECCV202

Incremental Learning Meets Transfer Learning: Application to Multi-site Prostate MRI Segmentation

Many medical datasets have recently been created for medical image segmentation tasks, and it is natural to question whether we can use them to sequentially train a single model that (1) performs better on all these datasets, and (2) generalizes well and transfers better to the unknown target site domain. Prior works have achieved this goal by jointly training one model on multi-site datasets, which achieve competitive performance on average but such methods rely on the assumption about the availability of all training data, thus limiting its effectiveness in practical deployment. In this paper, we propose a novel multi-site segmentation framework called incremental-transfer learning (ITL), which learns a model from multi-site datasets in an end-to-end sequential fashion. Specifically, "incremental" refers to training sequentially constructed datasets, and "transfer" is achieved by leveraging useful information from the linear combination of embedding features on each dataset. In addition, we introduce our ITL framework, where we train the network including a site-agnostic encoder with pre-trained weights and at most two segmentation decoder heads. We also design a novel site-level incremental loss in order to generalize well on the target domain. Second, we show for the first time that leveraging our ITL training scheme is able to alleviate challenging catastrophic forgetting problems in incremental learning. We conduct experiments using five challenging benchmark datasets to validate the effectiveness of our incremental-transfer learning approach. Our approach makes minimal assumptions on computation resources and domain-specific expertise, and hence constitutes a strong starting point in multi-site medical image segmentation

The effect of MWA protocols upon morphology and IVIM parameters of hepatic ablation zones—a preliminary in vivo animal study with an MRI-compatible microwave ablation device

PURPOSEWe aimed to explore the effect of microwave ablation (MWA) protocols upon morphology and instant changes in intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) parameters on MWA zones in porcine livers.METHODSAccording to the empirical protocol for MWA in tumors less than 3 cm in our hospital, the power and application duration were assigned as five groups: A, 60 W × 5 min (n = 6); B, 80 W × 3 min (n = 7); C, 80 W × 5 min (n = 10); D, 100 W × 3 min (n = 10); E, 100 W × 5 min (n = 9). Spearman correlation between MWA protocols, morphological metrics, and instant post-ablation IVIM parameters was performed.RESULTSThere was fair positive correlation between energy delivery and short axis (RSpearman = 0.426, P= .005) of the white zone. There was moderate-to-good positive correlation between wattage and short axis (RSpearman = 0.584, P < .001) of the white zone. For post-ablation IVIM parameters in the white zone, only wattage had moderate-to-good positive correlation with D value (RSpearman= 0.574, P < .001) or ADC value (RSpearman = 0.550, P < .001). No correlation between energy delivery, wattage, duration, and f value was observed (RSpearman = 0.185, P = .24; RSpearman= − 0.001, P = .99; RSpearman = 0.203, P = .20, respectively).CONCLUSIONThe increase in the short axis of the white zone is more likely to be affected by wattage than energy delivery. The instant post-ablation IVIM is feasible in monitoring the MWA zones since the f value in the white zones is not sensitive to changes in MWA protocols, which is promising in evaluating the instant effect of MWA

Probing the light harvesting and charge rectification of bismuth nanoparticles behind the promoted photoreactivity onto Bi/BiOCl catalyst by (in-situ) electron microscopy

State-of-the-art electron microscopy has enabled us to investigate microstructural details down to sub-subångström and milli-electron-volt resolution level. The enhanced photoreactivity over bismuth hybridized BiOCl catalyst (Bi/BiOCl) has been reported recently, however, the mechanistic understandings of this improved photoreactivity especially the optical behavior of bismuth nanoparticles (Bi NPs) are still obscured and in debate. The optical absorption features of Bi NPs and the charge transfer characteristic between bismuth and BiOCl have been considered as the major physicochemical origin for the promoted photoreactivity. Based on the advanced (in-situ) electron microscopy of monochromated electron energy loss spectroscopy in scanning transmission electron microscopy imaging mode (Mono-STEM-EELS) along with related theoretical investigations, in this work, we for the first time distinguished and explained the optical absorption originated from the localized surface plasmon resonances (LSPR) effect and direct band gap transition in an individual bismuth nanoparticle as well as transportation of photogenerated carriers at the interface of Bi/BiOCl. These findings could provide better understandings about the origin of the improved photoreactivity of various bismuth-hybridized photocatalysts

Genomes shed light on the evolution of Begonia, a mega‐diverse genus

Clarifying the evolutionary processes underlying species diversification and adaptation is a key focus of evolutionary biology. Begonia (Begoniaceae) is one of the most species-rich angiosperm genera with ~2,000 species, most of which are shade-adapted. Here, we present chromosome-scale genome assemblies for four species of Begonia (B. loranthoides, B. masoniana, B. darthvaderiana, and B. peltatifolia), and whole genome shot-gun data for an additional 74 Begonia representatives to investigate lineage evolution and shade adaptation of the genus. The four genome assemblies range in size from 331.75 Mb (B. peltatifolia) to 799.83 Mb (B. masoniana), and harbor 22,059 - 23,444 protein-coding genes. Synteny analysis revealed a lineage specific whole-genome duplication (WGD) that occurred just before the diversification of the Begonia. Functional enrichment of gene families retained after WGD highlight the significance of modified carbohydrate metabolism and photosynthesis possibly linked to shade-adaptation in the genus, which is further supported by expansions of gene families involved in light perception and harvesting. Phylogenomic reconstructions and genomics studies indicate that genomic introgression has also played a role in the evolution of Begonia. Overall, this study provides valuable genomic resources for Begonia and suggests potential drivers underlying the diversity and adaptive evolution of this mega-diverse clade