Application of the improved dung beetle optimizer,muti-head attention and hybrid deep learning algorithms to groundwater depth prediction in the Ningxia area,China

Due to the lack of accurate data and complex parameterization,the prediction of groundwater depth is a chal-lenge for numerical models.Machine learning can effectively solve this issue and has been proven useful in the prediction of groundwater depth in many areas.In this study,two new models are applied to the prediction of groundwater depth in the Ningxia area,China.The two models combine the improved dung beetle optimizer(DBO)algorithm with two deep learning models:The Multi-head Attention-Convolution Neural Network-Long Short Term Memory networks(MH-CNN-LSTM)and the Multi-head Attention-Convolution Neural Network-Gated Recurrent Unit(MH-CNN-GRU).The models with DBO show better prediction performance,with larger R(correlation coefficient),RPD(residual prediction deviation),and lower RMSE(root-mean-square error).Com-pared with the models with the original DBO,the R and RPD of models with the improved DBO increase by over 1.5%,and the RMSE decreases by over 1.8%,indicating better prediction results.In addition,compared with the multiple linear regression model,a traditional statistical model,deep learning models have better prediction performance.

The Impairment Attention Capture by Topological Change in Children With Autism Spectrum Disorder

Objective Autism spectrum disorder(ASD)is a neurodevelopmental condition characterized by difficulties with communication and social interaction,restricted and repetitive behaviors.Previous studies have indicated that individuals with ASD exhibit early and lifelong attention deficits,which are closely related to the core symptoms of ASD.Basic visual attention processes may provide a critical foundation for their social communication and interaction abilities.Therefore,this study explores the behavior of children with ASD in capturing attention to changes in topological properties.Methods Our study recruited twenty-seven ASD children diagnosed by professional clinicians according to DSM-5 and twenty-eight typically developing(TD)age-matched controls.In an attention capture task,we recorded the saccadic behaviors of children with ASD and TD in response to topological change(TC)and non-topological change(nTC)stimuli.Saccadic reaction time(SRT),visual search time(VS),and first fixation dwell time(FFDT)were used as indicators of attentional bias.Pearson correlation tests between the clinical assessment scales and attentional bias were conducted.Results This study found that TD children had significantly faster SRT(P<0.05)and VS(P<0.05)for the TC stimuli compared to the nTC stimuli,while the children with ASD did not exhibit significant differences in either measure(P>0.05).Additionally,ASD children demonstrated significantly less attention towards the TC targets(measured by FFDT),in comparison to TD children(P<0.05).Furthermore,ASD children exhibited a significant negative linear correlation between their attentional bias(measured by VS)and their scores on the compulsive subscale(P<0.05).Conclusion The results suggest that children with ASD have difficulty shifting their attention to objects with topological changes during change detection.This atypical attention may affect the child’s cognitive and behavioral development,thereby impacting their social communication and interaction.In sum,our findings indicate that difficulties in attentional capture by TC may be a key feature of ASD.

TianXing:A Linear Complexity Transformer Model with Explicit Attention Decay for Global Weather Forecasting

In this paper,we introduce TianXing,a transformer-based data-driven model designed with physical augmentation for skillful and efficient global weather forecasting.Previous data-driven transformer models such as Pangu-Weather,FengWu,and FuXi have emerged as promising alternatives for numerical weather prediction in weather forecasting.However,these models have been characterized by their substantial computational resource consumption during training and limited incorporation of explicit physical guidance in their modeling frameworks.In contrast,TianXing applies a linear complexity mechanism that ensures proportional scalability with input data size while significantly diminishing GPU resource demands,with only a marginal compromise in accuracy.Furthermore,TianXing proposes an explicit attention decay mechanism in the linear attention derived from physical insights to enhance its forecasting skill.The mechanism can reweight attention based on Earth's spherical distances and learned sparse multivariate coupling relationships,promptingTianXing to prioritize dynamically relevant neighboring features.Finally,to enhance its performance in mediumrange forecasting,TianXing employs a stacked autoregressive forecast algorithm.Validation of the model's architecture is conducted using ERA5 reanalysis data at a 5.625°latitude-longitude resolution,while a high-resolution dataset at 0.25°is utilized for training the actual forecasting model.Notably,the TianXing exhibits excellent performance,particularly in the Z500(geopotential height)and T850(temperature)fields,surpassing previous data-driven models and operational fullresolution models such as NCEP GFS and ECMWF IFS,as evidenced by latitude-weighted RMSE and ACC metrics.Moreover,the TianXing has demonstrated remarkable capabilities in predicting extreme weather events,such as typhoons.

A Generative Adversarial Network with an Attention Spatiotemporal Mechanism for Tropical Cyclone Forecasts

Tropical cyclones(TCs)are complex and powerful weather systems,and accurately forecasting their path,structure,and intensity remains a critical focus and challenge in meteorological research.In this paper,we propose an Attention Spatio-Temporal predictive Generative Adversarial Network(AST-GAN)model for predicting the temporal and spatial distribution of TCs.The model forecasts the spatial distribution of TC wind speeds for the next 15 hours at 3-hour intervals,emphasizing the cyclone's center,high wind-speed areas,and its asymmetric structure.To effectively capture spatiotemporal feature transfer at different time steps,we employ a channel attention mechanism for feature selection,enhancing model performance and reducing parameter redundancy.We utilized High-Resolution Weather Research and Forecasting(HWRF)data to train our model,allowing it to assimilate a wide range of TC motion patterns.The model is versatile and can be applied to various complex scenarios,such as multiple TCs moving simultaneously or TCs approaching landfall.Our proposed model demonstrates superior forecasting performance,achieving a root-mean-square error(RMSE)of 0.71 m s^(-1)for overall wind speed and 2.74 m s^(-1)for maximum wind speed when benchmarked against ground truth data from HWRF.Furthermore,the model underwent optimization and independent testing using ERA5reanalysis data,showcasing its stability and scalability.After fine-tuning on the ERA5 dataset,the model achieved an RMSE of 1.33 m s^(-1)for wind speed and 1.75 m s^(-1)for maximum wind speed.The AST-GAN model outperforms other state-of-the-art models in RMSE on both the HWRF and ERA5 datasets,maintaining its superior performance and demonstrating its effectiveness for spatiotemporal prediction of TCs.

Enhancing Deep Learning Semantics:The Diffusion Sampling and Label-Driven Co-Attention Approach

The advent of self-attention mechanisms within Transformer models has significantly propelled the advancement of deep learning algorithms,yielding outstanding achievements across diverse domains.Nonetheless,self-attention mechanisms falter when applied to datasets with intricate semantic content and extensive dependency structures.In response,this paper introduces a Diffusion Sampling and Label-Driven Co-attention Neural Network(DSLD),which adopts a diffusion sampling method to capture more comprehensive semantic information of the data.Additionally,themodel leverages the joint correlation information of labels and data to introduce the computation of text representation,correcting semantic representationbiases in thedata,andincreasing the accuracyof semantic representation.Ultimately,the model computes the corresponding classification results by synthesizing these rich data semantic representations.Experiments on seven benchmark datasets show that our proposed model achieves competitive results compared to state-of-the-art methods.

引入上下文信息和Attention Gate的GUS-YOLO遥感目标检测算法

目前基于通用YOLO系列的遥感目标检测算法存在并未充分利用图像的全局上下文信息,在特征融合金字塔部分并未充分考虑缩小融合特征之间的语义鸿沟、抑制冗余信息干扰的缺点。在结合YOLO算法优点的基础上提出GUS-YOLO算法,其拥有一个能够充分利用全局上下文信息的骨干网络Global Backbone。除此之外,该算法在融合特征金字塔自顶向下的结构中引入Attention Gate模块,可以突出必要的特征信息,抑制冗余信息。另外,为Attention Gate模块设计了最佳的网络结构,提出了网络的特征融合结构U-Net。最后,为克服ReLU函数可能导致模型梯度不再更新的问题,该算法将Attention Gate模块的激活函数升级为可学习的SMU激活函数,提高模型鲁棒性。在NWPU VHR-10遥感数据集上,该算法相较于YOLOV7算法取得宽松指标mAP^(0.50)1.64个百分点和严格指标mAP^(0.75)9.39个百分点的性能提升。相较于目前主流的七种检测算法,该算法取得较好的检测性能。

基于IGWO-Attention-GRU的短期电力负荷预测模型

为了提高短期电力负荷的预测精度,针对电力负荷序列波动性强、复杂性高的特点,综合考虑气象因素及日期类型的影响,文中提出一种基于改进灰狼优化算法(IGWO)优化Attention-GRU网络的短期电力负荷预测模型。首先,构建Attention-GRU网络;其次,对灰狼优化算法(GWO)进行改进,并利用IGWO寻找Attention-GRU网络的超参数;最后,使用IGWO-Attention-GRU模型在电力负荷数据集上进行实验,并与多种预测模型进行比较。实验结果表明,IGWO-Attention-GRU模型的MAPE、RMSE和MAE值均为各种预测模型中最低,验证了IGWO-Attention-GRU模型的优越性。

基于SABO-GRU-Attention的锂电池SOC估计

提出一种基于SABO-GRU-Attention(subtraction average based optimizer-gate recurrent unitattention)的锂电池SOC(state of charge)估计方法。采用基于平均减法优化算法自适应更新GRU神经网络的超参数,融合SE(squeeze and excitation)注意力机制自适应分配各通道权重,提高学习效率。对马里兰大学电池数据集进行预处理,输入电压、电流参数,进行锂电池充放电仿真实验,并搭建锂电池荷电状态实验平台进行储能锂电池充放电实验。结果表明,提出的SOC神经网络估计模型明显优于LSTM、GRU以及PSO-GRU等模型,具有较高的估计精度与应用价值。

基于XGBoost-WOA-BiLSTM-Attention的公共建筑暖通空调能耗预测研究

为在双碳目标下实现节能减排,降低能源成本,提出一种基于BiLSTM的公共建筑暖通空调能耗预测模型。在BiLSTM模型基础上,使用XGBoost算法对输入特征进行选择,剔除冗余特征,得到最佳模型输入特征;然后利用WOA优化算法对添加了Attention机制的BiLSTM模型中的6个超参数进行优化,将得到的最优参数代入BiLSTM-Attention神经网络中进行预测,并与BiLSTM模型、BiLSTM-Attention模型和WOA-BiLSTM-Attention模型进行对比。结果表明,所提出的XGBoost-WOA-BiLSTM-Attention模型的RMSE、MAE、R2分别为0.0106、0.006、0.9991,优于其他模型,且相对于持续模型在均方根误差RMSE上提升了98%,为降低公共建筑暖通空调能耗研究提供了参考。

Neurological Insights into Attentional Deficits in High Trait Anxiety: a Commentary on Hu et al.’s Paper in Cerebral Cortex (2023)

In a recent publication,Hu et al.(2023)have reported that individuals with high trait anxiety exhibit attentional deficits characterized by reduced inhibition of distractors and delayed attentional selection of targets,indicating impaired top-down attentional control.This commentary underscores their significant contributions to the cognitive theory of anxiety.Based on their findings,we propose a novel training approach called attentional inhibition training(AIT),aimed at improving top-down attentional control to alleviate symptoms of anxiety.Furthermore,we explore the potential application of non-invasive transcranial magnetic stimulation(TMS)for rapidly enhancing attentional control function.

Image Inpainting Technique Incorporating Edge Prior and Attention Mechanism

Recently,deep learning-based image inpainting methods have made great strides in reconstructing damaged regions.However,these methods often struggle to produce satisfactory results when dealing with missing images with large holes,leading to distortions in the structure and blurring of textures.To address these problems,we combine the advantages of transformers and convolutions to propose an image inpainting method that incorporates edge priors and attention mechanisms.The proposed method aims to improve the results of inpainting large holes in images by enhancing the accuracy of structure restoration and the ability to recover texture details.This method divides the inpainting task into two phases:edge prediction and image inpainting.Specifically,in the edge prediction phase,a transformer architecture is designed to combine axial attention with standard self-attention.This design enhances the extraction capability of global structural features and location awareness.It also balances the complexity of self-attention operations,resulting in accurate prediction of the edge structure in the defective region.In the image inpainting phase,a multi-scale fusion attention module is introduced.This module makes full use of multi-level distant features and enhances local pixel continuity,thereby significantly improving the quality of image inpainting.To evaluate the performance of our method.comparative experiments are conducted on several datasets,including CelebA,Places2,and Facade.Quantitative experiments show that our method outperforms the other mainstream methods.Specifically,it improves Peak Signal-to-Noise Ratio(PSNR)and Structure Similarity Index Measure(SSIM)by 1.141~3.234 db and 0.083~0.235,respectively.Moreover,it reduces Learning Perceptual Image Patch Similarity(LPIPS)and Mean Absolute Error(MAE)by 0.0347~0.1753 and 0.0104~0.0402,respectively.Qualitative experiments reveal that our method excels at reconstructing images with complete structural information and clear texture details.Furthermore,our model exhibits impressive performance in terms of the number of parameters,memory cost,and testing time.

Deep neural network based on multi-level wavelet and attention for structured illumination microscopy

Structured illumination microscopy(SIM)is a popular and powerful super-resolution(SR)technique in biomedical research.However,the conventional reconstruction algorithm for SIM heavily relies on the accurate prior knowledge of illumination patterns and signal-to-noise ratio(SNR)of raw images.To obtain high-quality SR images,several raw images need to be captured under high fluorescence level,which further restricts SIM’s temporal resolution and its applications.Deep learning(DL)is a data-driven technology that has been used to expand the limits of optical microscopy.In this study,we propose a deep neural network based on multi-level wavelet and attention mechanism(MWAM)for SIM.Our results show that the MWAM network can extract high-frequency information contained in SIM raw images and accurately integrate it into the output image,resulting in superior SR images compared to those generated using wide-field images as input data.We also demonstrate that the number of SIM raw images can be reduced to three,with one image in each illumination orientation,to achieve the optimal tradeoff between temporal and spatial resolution.Furthermore,our MWAM network exhibits superior reconstruction ability on low-SNR images compared to conventional SIM algorithms.We have also analyzed the adaptability of this network on other biological samples and successfully applied the pretrained model to other SIM systems.

MCBAN: A Small Object Detection Multi-Convolutional Block Attention Network

Object detection has made a significant leap forward in recent years.However,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susceptible to missed detection due to background noise.Additionally,small object information is affected due to the downsampling operations.Deep learning-based detection methods have been utilized to address the challenge posed by small objects.In this work,we propose a novel method,the Multi-Convolutional Block Attention Network(MCBAN),to increase the detection accuracy of minute objects aiming to overcome the challenge of information loss during the downsampling process.The multi-convolutional attention block(MCAB);channel attention and spatial attention module(SAM)that make up MCAB,have been crafted to accomplish small object detection with higher precision.We have carried out the experiments on the Karlsruhe Institute of Technology and Toyota Technological Institute(KITTI)and Pattern Analysis,Statical Modeling and Computational Learning(PASCAL)Visual Object Classes(VOC)datasets and have followed a step-wise process to analyze the results.These experiment results demonstrate that significant gains in performance are achieved,such as 97.75%for KITTI and 88.97%for PASCAL VOC.The findings of this study assert quite unequivocally the fact that MCBAN is much more efficient in the small object detection domain as compared to other existing approaches.

Attention Mechanism-Based Method for Intrusion Target Recognition in Railway

The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,the convolutional block attention module(CBAM),including spatial and channel attention modules,is seamlessly integrated into the YOLOv5 model,giving rise to the CBAM-YOLOv5 model.Furthermore,the distance intersection-over-union_non-maximum suppression(DIo U_NMS)algorithm is employed in lieu of the weighted nonmaximum suppression algorithm,resulting in improved detection performance for intrusive targets.To accelerate detection speed,the model undergoes pruning based on the batch normalization(BN)layer,and Tensor RT inference acceleration techniques are employed,culminating in the successful deployment of the algorithm on edge devices.The CBAM-YOLOv5 model exhibits a notable 2.1%enhancement in detection accuracy when evaluated on a selfconstructed railway dataset,achieving 95.0%for mean average precision(m AP).Furthermore,the inference speed on edge devices attains a commendable 15 frame/s.

Main focus of parents of children with attention deficit hyperactivity disorder and the effectiveness of early clinical screening

BACKGROUND Attention deficit hyperactivity disorder(ADHD)is a common mental and behavioral disorder among children.AIM To explore the focus of attention deficit hyperactivity disorder parents and the effectiveness of early clinical screening METHODS This study found that the main directions of parents seeking medical help were short attention time for children under 7 years old(16.6%)and poor academic performance for children over 7 years old(12.1%).We employed a two-stage experiment to diagnose ADHD.Among the 5683 children evaluated from 2018 to 2021,360 met the DSM-5 criteria.Those diagnosed with ADHD underwent assessments for letter,number,and figure attention.Following the exclusion of ADHD-H diagnoses,the detection rate rose to 96.0%,with 310 out of 323 cases identified.RESULTS This study yielded insights into the primary concerns of parents regarding their children's symptoms and validated the efficacy of a straightforward diagnostic test,offering valuable guidance for directing ADHD treatment,facilitating early detection,and enabling timely intervention.Our research delved into the predominant worries of parents across various age groups.Furthermore,we showcased the precision of the simple exclusion experiment in discerning between ADHD-I and ADHD-C in children.CONCLUSION Our study will help diagnose and guide future treatment directions for ADHD.

The Short-Term Prediction ofWind Power Based on the Convolutional Graph Attention Deep Neural Network

The fluctuation of wind power affects the operating safety and power consumption of the electric power grid and restricts the grid connection of wind power on a large scale.Therefore,wind power forecasting plays a key role in improving the safety and economic benefits of the power grid.This paper proposes a wind power predicting method based on a convolutional graph attention deep neural network with multi-wind farm data.Based on the graph attention network and attention mechanism,the method extracts spatial-temporal characteristics from the data of multiple wind farms.Then,combined with a deep neural network,a convolutional graph attention deep neural network model is constructed.Finally,the model is trained with the quantile regression loss function to achieve the wind power deterministic and probabilistic prediction based on multi-wind farm spatial-temporal data.A wind power dataset in the U.S.is taken as an example to demonstrate the efficacy of the proposed model.Compared with the selected baseline methods,the proposed model achieves the best prediction performance.The point prediction errors(i.e.,root mean square error(RMSE)and normalized mean absolute percentage error(NMAPE))are 0.304 MW and 1.177%,respectively.And the comprehensive performance of probabilistic prediction(i.e.,con-tinuously ranked probability score(CRPS))is 0.580.Thus,the significance of multi-wind farm data and spatial-temporal feature extraction module is self-evident.

New Fusion Approach of Spatial and Channel Attention for Semantic Segmentation of Very High Spatial Resolution Remote Sensing Images

The semantic segmentation of very high spatial resolution remote sensing images is difficult due to the complexity of interpreting the interactions between the objects in the scene. Indeed, effective segmentation requires considering spatial local context and long-term dependencies. To address this problem, the proposed approach is inspired by the MAC-UNet network which is an extension of U-Net, densely connected combined with channel attention. The advantages of this solution are as follows: 4) The new model introduces a new attention called propagate attention to build an attention-based encoder. 2) The fusion of multi-scale information is achieved by a weighted linear combination of the attentions whose coefficients are learned during the training phase. 3) Introducing in the decoder, the Spatial-Channel-Global-Local block which is an attention layer that uniquely combines channel attention and spatial attention locally and globally. The performances of the model are evaluated on 2 datasets WHDLD and DLRSD and show results of mean intersection over union (mIoU) index in progress between 1.54% and 10.47% for DLRSD and between 1.04% and 4.37% for WHDLD compared with the most efficient algorithms with attention mechanisms like MAU-Net and transformers like TMNet.

基于CNN-LSTM-Attention和自回归的混合水位预测模型

【目的】水位预测对交通运输、农业以及防洪措施具有重要影响。精确的水位值可用于提升水道运输的安全及效率、降低洪水风险,同时也是保障区域可持续发展的必要条件。【方法】提出一种CRANet的混合水位预测模型,以卷积神经网络(CNN)、长短期记忆网络(LSTM)、注意力机制以及自回归(AR)组件为基础,旨在应对时间序列数据中存在的线性与非线性问题,缓解自回归及ARIMA模型的缺陷。其应用不仅在于为航运调度提供决策支撑,加强导航安全效率,同样能提升防洪减灾的能力。其中,CNN和LSTM组件有效地针对数据集内的局部和全局关系进行捕捉,AR组件则能充分考虑数据的时间序列特性。同时,通过注意力机制,模型能够优先考虑相关特性,提高预测效果。【结果】研究成果所提出的模型已成功应用于中国西江梧州站的水位预测,在测试集上预测未来3 h级别水位的MAE、RMSE和R^(2)分别为0.086、0.114 5和0.950 8。【结论】结果表明所提出的CRANet模型在水位预测方面的高可用性、准确度与稳健性,相较于AR、SVR、CNN、LSTM等模型具有更优的MAE、RMSE和R^(2)。

基于Coordinate Attention和空洞卷积的异物识别

在我国工厂的工业化生产中,带式运输机占有重要的地位,但是在其运输物料的过程中,常有木板、金属管、大型金属片等混入物料中,从而对带式运输机的传送带造成损毁,引起巨大的经济损失.为了检测出传送带上的不规则异物,设计了一种新的异物检测方法.针对传统异物检测方法中存在的对于图像特征提取能力不足以及网络感受野相对较小的问题,我们提出了一种基于coordinate attention和空洞卷积的单阶段异物识别方法.首先,网络利用coordinate attention机制,使网络更加关注图像的空间信息,并对图像中的重要特征进行了增强,增强了网络的性能;其次,在网络提取多尺度特征的部分,将原网络的静态卷积变为空洞卷积,有效减少了常规卷积造成的信息损失;除此之外,我们还使用了新的损失函数,进一步提高了网络的性能.实验结果证明,我们提出的网络能有效识别出传送带上的异物,较好地完成异物检测任务.

融合MacBERT和Talking⁃Heads Attention实体关系联合抽取模型

针对现有的医学文本关系抽取任务模型在训练过程中存在语义理解能力不足,可能导致关系抽取的效果不尽人意的问题,文中提出一种融合MacBERT和Talking⁃Heads Attention的实体关系联合抽取模型。该模型首先利用MacBERT语言模型来获取动态字向量表达,MacBERT作为改进的BERT模型,能够减少预训练和微调阶段之间的差异,从而提高模型的泛化能力;然后,将这些动态字向量表达输入到双向门控循环单元(BiGRU)中,以便提取文本的上下文特征。BiGRU是一种改进的循环神经网络(RNN),具有更好的长期依赖捕获能力。在获取文本上下文特征之后,使用Talking⁃Heads Attention来获取全局特征。Talking⁃Heads Attention是一种自注意力机制,可以捕获文本中不同位置之间的关系,从而提高关系抽取的准确性。实验结果表明,与实体关系联合抽取模型GRTE相比,该模型F1值提升1%,precision值提升0.4%,recall值提升1.5%。