Plant multiscale networks:charting plant connectivity by multi-level analysis and imaging techniques

In multicellular and even single-celled organisms,individual components are interconnected at multiscale levels to produce enormously complex biological networks that help these systems maintain homeostasis for development and environmental adaptation.Systems biology studies initially adopted network analysis to explore how relationships between individual components give rise to complex biological processes.Network analysis has been applied to dissect the complex connectivity of mammalian brains across different scales in time and space in The Human Brain Project.In plant science,network analysis has similarly been applied to study the connectivity of plant components at the molecular,subcellular,cellular,organic,and organism levels.Analysis of these multiscale networks contributes to our understanding of how genotype determines phenotype.In this review,we summarized the theoretical framework of plant multiscale networks and introduced studies investigating plant networks by various experimental and computational modalities.We next discussed the currently available analytic methodologies and multi-level imaging techniques used to map multiscale networks in plants.Finally,we highlighted some of the technical challenges and key questions remaining to be addressed in this emerging field.

Multi-Feature Fusion-Guided Multiscale Bidirectional Attention Networks for Logistics Pallet Segmentation

In the smart logistics industry,unmanned forklifts that intelligently identify logistics pallets can improve work efficiency in warehousing and transportation and are better than traditional manual forklifts driven by humans.Therefore,they play a critical role in smart warehousing,and semantics segmentation is an effective method to realize the intelligent identification of logistics pallets.However,most current recognition algorithms are ineffective due to the diverse types of pallets,their complex shapes,frequent blockades in production environments,and changing lighting conditions.This paper proposes a novel multi-feature fusion-guided multiscale bidirectional attention(MFMBA)neural network for logistics pallet segmentation.To better predict the foreground category(the pallet)and the background category(the cargo)of a pallet image,our approach extracts three types of features(grayscale,texture,and Hue,Saturation,Value features)and fuses them.The multiscale architecture deals with the problem that the size and shape of the pallet may appear different in the image in the actual,complex environment,which usually makes feature extraction difficult.Our study proposes a multiscale architecture that can extract additional semantic features.Also,since a traditional attention mechanism only assigns attention rights from a single direction,we designed a bidirectional attention mechanism that assigns cross-attention weights to each feature from two directions,horizontally and vertically,significantly improving segmentation.Finally,comparative experimental results show that the precision of the proposed algorithm is 0.53%–8.77%better than that of other methods we compared.

Multiscale and Auto-Tuned Semi-Supervised Deep Subspace Clustering and Its Application in Brain Tumor Clustering

In this paper,we introduce a novel Multi-scale and Auto-tuned Semi-supervised Deep Subspace Clustering(MAS-DSC)algorithm,aimed at addressing the challenges of deep subspace clustering in high-dimensional real-world data,particularly in the field of medical imaging.Traditional deep subspace clustering algorithms,which are mostly unsupervised,are limited in their ability to effectively utilize the inherent prior knowledge in medical images.Our MAS-DSC algorithm incorporates a semi-supervised learning framework that uses a small amount of labeled data to guide the clustering process,thereby enhancing the discriminative power of the feature representations.Additionally,the multi-scale feature extraction mechanism is designed to adapt to the complexity of medical imaging data,resulting in more accurate clustering performance.To address the difficulty of hyperparameter selection in deep subspace clustering,this paper employs a Bayesian optimization algorithm for adaptive tuning of hyperparameters related to subspace clustering,prior knowledge constraints,and model loss weights.Extensive experiments on standard clustering datasets,including ORL,Coil20,and Coil100,validate the effectiveness of the MAS-DSC algorithm.The results show that with its multi-scale network structure and Bayesian hyperparameter optimization,MAS-DSC achieves excellent clustering results on these datasets.Furthermore,tests on a brain tumor dataset demonstrate the robustness of the algorithm and its ability to leverage prior knowledge for efficient feature extraction and enhanced clustering performance within a semi-supervised learning framework.

Establishing and validating a spotted tongue recognition and extraction model based on multiscale convolutional neural network

Objective In tongue diagnosis,the location,color,and distribution of spots can be used to speculate on the viscera and severity of the heat evil.This work focuses on the image analysis method of artificial intelligence(AI)to study the spotted tongue recognition of traditional Chinese medicine(TCM).Methods A model of spotted tongue recognition and extraction is designed,which is based on the principle of image deep learning and instance segmentation.This model includes multiscale feature map generation,region proposal searching,and target region recognition.Firstly,deep convolution network is used to build multiscale low-and high-abstraction feature maps after which,target candidate box generation algorithm and selection strategy are used to select high-quality target candidate regions.Finally,classification network is used for classifying target regions and calculating target region pixels.As a result,the region segmentation of spotted tongue is obtained.Under non-standard illumination conditions,various tongue images were taken by mobile phones,and experiments were conducted.Results The spotted tongue recognition achieved an area under curve(AUC)of 92.40%,an accuracy of 84.30%with a sensitivity of 88.20%,a specificity of 94.19%,a recall of 88.20%,a regional pixel accuracy index pixel accuracy(PA)of 73.00%,a mean pixel accuracy(m PA)of73.00%,an intersection over union(Io U)of 60.00%,and a mean intersection over union(mIo U)of 56.00%.Conclusion The results of the study verify that the model is suitable for the application of the TCM tongue diagnosis system.Spotted tongue recognition via multiscale convolutional neural network(CNN)would help to improve spot classification and the accurate extraction of pixels of spot area as well as provide a practical method for intelligent tongue diagnosis of TCM.

Grid Side Distributed Energy Storage Cloud Group End Region Hierarchical Time-Sharing Configuration Algorithm Based onMulti-Scale and Multi Feature Convolution Neural Network

There is instability in the distributed energy storage cloud group end region on the power grid side.In order to avoid large-scale fluctuating charging and discharging in the power grid environment and make the capacitor components showa continuous and stable charging and discharging state,a hierarchical time-sharing configuration algorithm of distributed energy storage cloud group end region on the power grid side based on multi-scale and multi feature convolution neural network is proposed.Firstly,a voltage stability analysis model based onmulti-scale and multi feature convolution neural network is constructed,and the multi-scale and multi feature convolution neural network is optimized based on Self-OrganizingMaps(SOM)algorithm to analyze the voltage stability of the cloud group end region of distributed energy storage on the grid side under the framework of credibility.According to the optimal scheduling objectives and network size,the distributed robust optimal configuration control model is solved under the framework of coordinated optimal scheduling at multiple time scales;Finally,the time series characteristics of regional power grid load and distributed generation are analyzed.According to the regional hierarchical time-sharing configuration model of“cloud”,“group”and“end”layer,the grid side distributed energy storage cloud group end regional hierarchical time-sharing configuration algorithm is realized.The experimental results show that after applying this algorithm,the best grid side distributed energy storage configuration scheme can be determined,and the stability of grid side distributed energy storage cloud group end region layered timesharing configuration can be improved.

A MULTISCALE MODELING APPROACH INCORPORATING ARIMA AND ANNS FOR FINANCIAL MARKET VOLATILITY FORECASTING

The financial market volatility forecasting is regarded as a challenging task because of irreg ularity, high fluctuation, and noise. In this study, a multiscale ensemble forecasting model is proposed. The original financial series are decomposed firstly different scale components （i.e., approximation and details） using the maximum overlap discrete wavelet transform （MODWT）. The approximation is pre- dicted by a hybrid forecasting model that combines autoregressive integrated moving average （ARIMA） with feedforward neural network （FNN）. ARIMA model is used to generate a linear forecast, and then FNN is developed as a tool for nonlinear pattern recognition to correct the estimation error in ARIMA forecast. Moreover, details are predicted by Elman neural networks. Three weekly exchange rates data are collected to establish and validate the forecasting model. Empirical results demonstrate consistent better performance of the proposed approach.

Domain adaptive semantic segmentation by optimal transport

Scene segmentation is widely used in autonomous driving for environmental perception.Semantic scene segmentation has gained considerable attention owing to its rich semantic information.It assigns labels to the pixels in an image,thereby enabling automatic image labeling.Current approaches are based mainly on convolutional neural networks(CNN),however,they rely on numerous labels.Therefore,the use of a small amount of labeled data to achieve semantic segmentation has become increasingly important.In this study,we developed a domain adaptation framework based on optimal transport(OT)and an attention mechanism to address this issue.Specifically,we first generated the output space via a CNN owing to its superior of feature representation.Second,we utilized OT to achieve a more robust alignment of the source and target domains in the output space,where the OT plan defined a well attention mechanism to improve the adaptation of the model.In particular,the OT reduced the number of network parameters and made the network more interpretable.Third,to better describe the multiscale properties of the features,we constructed a multiscale segmentation network to perform domain adaptation.Finally,to verify the performance of the proposed method,we conducted an experiment to compare the proposed method with three benchmark and four SOTA methods using three scene datasets.The mean intersection-over-union(mIOU)was significantly improved,and visualization results under multiple domain adaptation scenarios also show that the proposed method performed better than semantic segmentation methods.