BTAN: Lightweight Super-Resolution Network with Target Transform and Attention

In the realm of single-image super-resolution (SISR), generating high-resolution (HR) images from a low-resolution (LR) input remains a challenging task. While deep neural networks have shown promising results, they often require significant computational resources. To address this issue, we introduce a lightweight convolutional neural network, named BTAN, that leverages the connection between LR and HR images to enhance performance without increasing the number of parameters. Our approach includes a target transform module that adjusts output features to match the target distribution and improve reconstruction quality, as well as a spatial and channel-wise attention module that modulates feature maps based on visual attention at multiple layers. We demonstrate the effectiveness of our approach on four benchmark datasets, showcasing superior accuracy, efficiency, and visual quality when compared to state-of-the-art methods.    

Neural Gaussian Similarity Modeling for Differential Graph Structure Learning

Graph Structure Learning (GSL) has demonstrated considerable potential in the analysis of graph-unknown non-Euclidean data across a wide range of domains. However, constructing an end-to-end graph structure learning model poses a challenge due to the impediment of gradient flow caused by the nearest neighbor sampling strategy. In this paper, we construct a differential graph structure learning model by replacing the non-differentiable nearest neighbor sampling with a differentiable sampling using the reparameterization trick. Under this framework, we argue that the act of sampling \mbox{nearest} neighbors may not invariably be essential, particularly in instances where node features exhibit a significant degree of similarity. To alleviate this issue, the bell-shaped Gaussian Similarity (GauSim) modeling is proposed to sample non-nearest neighbors. To adaptively model the similarity, we further propose Neural Gaussian Similarity (NeuralGauSim) with learnable parameters featuring flexible sampling behaviors. In addition, we develop a scalable method by transferring the large-scale graph to the transition graph to significantly reduce the complexity. Experimental results demonstrate the effectiveness of the proposed methods.Comment: Accepted by AAAI 202

SemiReward: A General Reward Model for Semi-supervised Learning

Semi-supervised learning (SSL) has witnessed great progress with various improvements in the self-training framework with pseudo labeling. The main challenge is how to distinguish high-quality pseudo labels against the confirmation bias. However, existing pseudo-label selection strategies are limited to pre-defined schemes or complex hand-crafted policies specially designed for classification, failing to achieve high-quality labels, fast convergence, and task versatility simultaneously. To these ends, we propose a Semi-supervised Reward framework (SemiReward) that predicts reward scores to evaluate and filter out high-quality pseudo labels, which is pluggable to mainstream SSL methods in wide task types and scenarios. To mitigate confirmation bias, SemiReward is trained online in two stages with a generator model and subsampling strategy. With classification and regression tasks on 13 standard SSL benchmarks across three modalities, extensive experiments verify that SemiReward achieves significant performance gains and faster convergence speeds upon Pseudo Label, FlexMatch, and Free/SoftMatch. Code and models are available at https://github.com/Westlake-AI/SemiReward.Comment: ICLR 2024 Camera Ready. Preprint V2 (25 pages) with the source code at https://github.com/Westlake-AI/SemiRewar

OpenSTL: A Comprehensive Benchmark of Spatio-Temporal Predictive Learning

Spatio-temporal predictive learning is a learning paradigm that enables models to learn spatial and temporal patterns by predicting future frames from given past frames in an unsupervised manner. Despite remarkable progress in recent years, a lack of systematic understanding persists due to the diverse settings, complex implementation, and difficult reproducibility. Without standardization, comparisons can be unfair and insights inconclusive. To address this dilemma, we propose OpenSTL, a comprehensive benchmark for spatio-temporal predictive learning that categorizes prevalent approaches into recurrent-based and recurrent-free models. OpenSTL provides a modular and extensible framework implementing various state-of-the-art methods. We conduct standard evaluations on datasets across various domains, including synthetic moving object trajectory, human motion, driving scenes, traffic flow and weather forecasting. Based on our observations, we provide a detailed analysis of how model architecture and dataset properties affect spatio-temporal predictive learning performance. Surprisingly, we find that recurrent-free models achieve a good balance between efficiency and performance than recurrent models. Thus, we further extend the common MetaFormers to boost recurrent-free spatial-temporal predictive learning. We open-source the code and models at https://github.com/chengtan9907/OpenSTL.Comment: Accepted by NeurIPS 2023. 33 pages, 17 figures, 19 tables. Under review. For more details, please refer to https://github.com/chengtan9907/OpenST

Efficient Multi-order Gated Aggregation Network

Since the recent success of Vision Transformers (ViTs), explorations toward transformer-style architectures have triggered the resurgence of modern ConvNets. In this work, we explore the representation ability of DNNs through the lens of interaction complexities. We empirically show that interaction complexity is an overlooked but essential indicator for visual recognition. Accordingly, a new family of efficient ConvNets, named MogaNet, is presented to pursue informative context mining in pure ConvNet-based models, with preferable complexity-performance trade-offs. In MogaNet, interactions across multiple complexities are facilitated and contextualized by leveraging two specially designed aggregation blocks in both spatial and channel interaction spaces. Extensive studies are conducted on ImageNet classification, COCO object detection, and ADE20K semantic segmentation tasks. The results demonstrate that our MogaNet establishes new state-of-the-art over other popular methods in mainstream scenarios and all model scales. Typically, the lightweight MogaNet-T achieves 80.0\% top-1 accuracy with only 1.44G FLOPs using a refined training setup on ImageNet-1K, surpassing ParC-Net-S by 1.4\% accuracy but saving 59\% (2.04G) FLOPs.Comment: Preprint with 14 pages of main body and 5 pages of appendi

Monitoring power module solder degradation from heat dissipation in two opposite directions

Solder degradation is still a main failure mechanism for power semiconductor modules. This study proposes a monitoring method to detect the relative change in heat dissipation from a module in two opposing directions, affected by the degradation: upwards via the silicone gel and downwards via the solder layer to the heatsink. The method is based on external module package measurements, and a Condition Indicator is defined as the ratio of heat transfer rates in the two directions. The expected response of to the level of degradation is analysed for different module operating points and external environment conditions. The method is demonstrated by experiment

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

To fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.National Natural Science Foundation of China [32022078]; National Key R&D Program of China [2022YFF1000900]; Local Innovative and Research Teams Project of Guangdong Province [2019BT02N630]; China Agriculture Research System [CARS-35]. Funding for open access charge: National Natural Science Foundation of China [32022078].info:eu-repo/semantics/publishedVersio

PigBiobank: a valuable resource for understanding genetic and biological mechanisms of diverse complex traits in pigs

© The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] fully unlock the potential of pigs as both agricultural species for animal-based protein food and biomedical models for human biology and disease, a comprehensive understanding of molecular and cellular mechanisms underlying various complex phenotypes in pigs and how the findings can be translated to other species, especially humans, are urgently needed. Here, within the Farm animal Genotype-Tissue Expression (FarmGTEx) project, we build the PigBiobank (http://pigbiobank.farmgtex.org) to systematically investigate the relationships among genomic variants, regulatory elements, genes, molecular networks, tissues and complex traits in pigs. This first version of the PigBiobank curates 71 885 pigs with both genotypes and phenotypes from over 100 pig breeds worldwide, covering 264 distinct complex traits. The PigBiobank has the following functions: (i) imputed sequence-based genotype-phenotype associations via a standardized and uniform pipeline, (ii) molecular and cellular mechanisms underlying trait-associations via integrating multi-omics data, (iii) cross-species gene mapping of complex traits via transcriptome-wide association studies, and (iv) high-quality results display and visualization. The PigBiobank will be updated timely with the development of the FarmGTEx-PigGTEx project, serving as an open-access and easy-to-use resource for genetically and biologically dissecting complex traits in pigs and translating the findings to other species.National Natural Science Foundation of China [32022078]; National Key R&D Program of China [2022YFF1000900]; Local Innovative and Research Teams Project of Guangdong Province [2019BT02N630]; China Agriculture Research System [CARS-35]. Funding for open access charge: National Natural Science Foundation of China [32022078].Peer reviewe

Anti-blocking dynamic adjustment of communication data transmission based on blockchain technology

In order to improve the quality of network communication, it is necessary to transfer anti-blocking dynamic regulation of network communication data. A block chain wireless network data transmission anti-blocking dynamic regulation method based on block chain load equalization regulation technology is proposed. The block chain wireless communication network data transmission anti-blocking dynamic regulation transmission channel model is constructed, the anti-jamming design of block chain wireless communication is carried out by an adaptive multipath interference suppression algorithm, and the data transmission anti-blocking dynamic regulation state characteristic quantity of block chain wireless communication network data is extracted. The data allocation model of block chain wireless communication network is constructed, and the data allocation iteration model of block chain wireless communication network is obtained according to the different characteristics of data transmission impedance. The anti-blocking dynamic adjustment optimization of block chain wireless communication networks is realized, and the anti-jamming and security of data transmission anti-blocking dynamic regulation are improved. The simulation results show that the channel balance and output signal-to-noise ratio (SNR) of block-chain wireless communication networks are better, the anti-jamming ability is enhanced, the anti-blocking dynamic adjustment ability of communication data transmission is improved, the output bit error rate is reduced, and the security and load ability of data communication are increased